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$Avg_Canopy_Cover)
## [1] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia" &
!Name %in% c("Dasypus_novemcinctus", "Odocoileus_virginianus",
"Procyon_lotor", "Sciurus_niger")) %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 2) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$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 7
# 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, 1:7] 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 [1:7] "Canis_latrans" "Lynx_rufus" "Didelphis_virginiana" "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 [1:7, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:7] "Canis_latrans" "Lynx_rufus" "Didelphis_virginiana" "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 <- 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3325
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8849 0.4124 -1.7099 -0.8775 -0.0259 1.0078 1889
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0073 1.1217 0.1354 0.6969 3.7072 1.0118 1517
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.844 0.2841 -3.4195 -2.8425 -2.2893 1.006 1737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4426 0.7014 0.0614 0.2805 1.5963 1.0916 923
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.0973 0.3924 -0.6427 0.0921 0.8759 1.0050
## (Intercept)-Lynx_rufus -0.2475 0.5274 -1.1274 -0.3010 0.9112 1.0244
## (Intercept)-Didelphis_virginiana -1.3120 0.4191 -2.1891 -1.2909 -0.5358 1.0028
## (Intercept)-Sylvilagus_floridanus -0.5386 0.4201 -1.3331 -0.5421 0.3200 1.0128
## (Intercept)-Sciurus_carolinensis -1.2764 0.4119 -2.1472 -1.2561 -0.5260 1.0055
## (Intercept)-Vulpes_vulpes -1.4783 0.5882 -2.6421 -1.4747 -0.3469 1.0287
## (Intercept)-Sus_scrofa -1.6958 0.5424 -2.8131 -1.6711 -0.7308 1.0027
## ESS
## (Intercept)-Canis_latrans 1956
## (Intercept)-Lynx_rufus 761
## (Intercept)-Didelphis_virginiana 2291
## (Intercept)-Sylvilagus_floridanus 1182
## (Intercept)-Sciurus_carolinensis 2017
## (Intercept)-Vulpes_vulpes 645
## (Intercept)-Sus_scrofa 1259
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6220 0.1627 -2.9575 -2.6147 -2.3179 1.0006
## (Intercept)-Lynx_rufus -3.3256 0.2973 -3.9512 -3.3098 -2.8072 1.0254
## (Intercept)-Didelphis_virginiana -2.4057 0.2453 -2.9120 -2.3999 -1.9357 1.0203
## (Intercept)-Sylvilagus_floridanus -3.0680 0.2551 -3.5975 -3.0623 -2.5930 1.0438
## (Intercept)-Sciurus_carolinensis -2.5261 0.2576 -3.0559 -2.5179 -2.0273 1.0012
## (Intercept)-Vulpes_vulpes -3.4522 0.5281 -4.6711 -3.3953 -2.5995 1.0353
## (Intercept)-Sus_scrofa -2.9434 0.4042 -3.8870 -2.9072 -2.2461 1.0027
## ESS
## (Intercept)-Canis_latrans 1183
## (Intercept)-Lynx_rufus 438
## (Intercept)-Didelphis_virginiana 1282
## (Intercept)-Sylvilagus_floridanus 687
## (Intercept)-Sciurus_carolinensis 1183
## (Intercept)-Vulpes_vulpes 291
## (Intercept)-Sus_scrofa 702
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5755
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4420 1.1121 -3.5017 -1.5474 1.1789 1.0006 269
## Cogon_Patch_Size -0.6035 1.0578 -2.6941 -0.6151 1.6193 1.0051 392
## Veg_shannon_index 0.8855 0.8069 -0.8149 0.8846 2.4601 1.0378 385
## total_shrub_cover -0.6453 0.9215 -2.7197 -0.5451 0.9911 1.0468 191
## Avg_Cogongrass_Cover 2.0157 0.9608 0.0028 1.9987 3.9495 1.0717 196
## Tree_Density -1.5356 1.1079 -3.6218 -1.5239 0.7215 1.0294 268
## Avg_Canopy_Cover 1.8136 1.0889 -0.4985 1.8332 3.9290 1.0634 874
## avg_veg_height -0.4970 0.7366 -1.9113 -0.5268 1.0517 1.0800 204
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.8059 26.1417 0.1334 4.3209 79.9163 1.0528 60
## Cogon_Patch_Size 11.2748 24.7130 0.1319 4.2085 64.1611 1.3658 82
## Veg_shannon_index 4.0592 8.9569 0.0673 1.2266 27.1423 1.4401 123
## total_shrub_cover 8.6511 32.6747 0.0604 1.3253 67.5229 2.5137 44
## Avg_Cogongrass_Cover 2.6398 7.5258 0.0572 0.7085 18.9693 1.2624 140
## Tree_Density 10.9990 30.1959 0.0888 2.6195 80.7094 1.7158 54
## Avg_Canopy_Cover 21.7229 49.8994 0.5151 6.1911 152.8273 2.4978 46
## avg_veg_height 1.0853 2.6905 0.0477 0.3911 6.5678 1.4530 278
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.1169 12.3783 0.2226 3.2647 40.2182 2.1876 62
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1756 0.3174 -3.8190 -3.1675 -2.5951 1.0277 372
## shrub_cover 0.5438 0.4283 -0.2752 0.5282 1.4443 1.0449 253
## veg_height -0.0302 0.2434 -0.5240 -0.0248 0.4486 1.0080 1670
## week -0.0248 0.1725 -0.3875 -0.0222 0.3008 1.0118 1228
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5241 0.6796 0.0552 0.3353 2.1166 1.0657 557
## shrub_cover 0.9589 1.1878 0.1261 0.6169 3.6496 1.0118 496
## veg_height 0.3442 0.3262 0.0680 0.2532 1.2037 1.0328 1349
## week 0.1415 0.1568 0.0266 0.0983 0.5121 1.0024 1390
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5893 1.9738 -2.3838 0.3000
## (Intercept)-Lynx_rufus 0.3709 3.5335 -3.6793 -0.4787
## (Intercept)-Didelphis_virginiana -2.7583 1.7692 -7.3115 -2.5424
## (Intercept)-Sylvilagus_floridanus -1.7095 1.7516 -5.8345 -1.6507
## (Intercept)-Sciurus_carolinensis -3.1575 2.0537 -8.6540 -2.7423
## (Intercept)-Vulpes_vulpes -2.9391 2.1577 -8.0059 -2.7077
## (Intercept)-Sus_scrofa -4.0707 2.6518 -10.9288 -3.6106
## Cogon_Patch_Size-Canis_latrans 1.3254 2.0043 -1.1959 0.8360
## Cogon_Patch_Size-Lynx_rufus -1.0103 2.3762 -5.2144 -1.1910
## Cogon_Patch_Size-Didelphis_virginiana 1.5371 1.6209 -0.6781 1.2309
## Cogon_Patch_Size-Sylvilagus_floridanus -3.0469 3.0216 -10.9570 -2.3085
## Cogon_Patch_Size-Sciurus_carolinensis -2.0118 2.2361 -7.4710 -1.6192
## Cogon_Patch_Size-Vulpes_vulpes -1.5359 2.7498 -7.7422 -1.3352
## Cogon_Patch_Size-Sus_scrofa -1.4802 2.3769 -6.9597 -1.1961
## Veg_shannon_index-Canis_latrans 1.6250 1.0840 -0.1486 1.4832
## Veg_shannon_index-Lynx_rufus 0.9837 1.4409 -1.7340 0.8773
## Veg_shannon_index-Didelphis_virginiana 1.5749 1.4305 -0.3852 1.3012
## Veg_shannon_index-Sylvilagus_floridanus 1.4618 1.4049 -0.4525 1.2292
## Veg_shannon_index-Sciurus_carolinensis -0.3535 1.7303 -4.3659 -0.0441
## Veg_shannon_index-Vulpes_vulpes -0.2163 1.7770 -5.2494 0.1352
## Veg_shannon_index-Sus_scrofa 2.0984 1.5994 -0.2384 1.7281
## total_shrub_cover-Canis_latrans 1.1591 1.8834 -1.2344 0.6178
## total_shrub_cover-Lynx_rufus -2.0288 3.4573 -12.1345 -1.1524
## total_shrub_cover-Didelphis_virginiana -1.6986 2.5224 -8.5421 -1.0419
## total_shrub_cover-Sylvilagus_floridanus -1.0341 2.1343 -7.0305 -0.5670
## total_shrub_cover-Sciurus_carolinensis -1.1360 1.8834 -6.2716 -0.6501
## total_shrub_cover-Vulpes_vulpes -1.6006 2.4467 -8.1933 -0.9565
## total_shrub_cover-Sus_scrofa -0.6091 1.9310 -5.4363 -0.3745
## Avg_Cogongrass_Cover-Canis_latrans 2.5333 1.2665 0.4202 2.3984
## Avg_Cogongrass_Cover-Lynx_rufus 2.5577 1.5178 0.0376 2.3870
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1645 1.2229 -0.1020 2.1062
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4651 1.3433 -1.4346 1.5325
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4382 1.3889 0.0639 2.3142
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6929 1.6247 0.0698 2.5099
## Avg_Cogongrass_Cover-Sus_scrofa 1.6611 1.7030 -2.2271 1.7533
## Tree_Density-Canis_latrans -3.3699 2.2993 -9.7230 -2.8761
## Tree_Density-Lynx_rufus 0.3064 2.3229 -3.0329 -0.0234
## Tree_Density-Didelphis_virginiana -2.0593 2.1194 -6.5630 -2.0415
## Tree_Density-Sylvilagus_floridanus -3.0892 2.7363 -10.6945 -2.5479
## Tree_Density-Sciurus_carolinensis -2.6127 2.7895 -10.0450 -2.1791
## Tree_Density-Vulpes_vulpes -1.4904 2.2586 -5.5373 -1.5006
## Tree_Density-Sus_scrofa -2.6419 3.2874 -10.1441 -2.0144
## Avg_Canopy_Cover-Canis_latrans -0.1895 0.9107 -2.4220 -0.1047
## Avg_Canopy_Cover-Lynx_rufus 0.4085 2.3893 -5.2782 0.4299
## Avg_Canopy_Cover-Didelphis_virginiana 5.0939 3.5375 1.5849 4.0204
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.8232 3.7723 1.4755 4.8189
## Avg_Canopy_Cover-Sciurus_carolinensis 4.3454 2.7836 1.2556 3.5624
## Avg_Canopy_Cover-Vulpes_vulpes 3.8667 3.3722 0.4342 2.8249
## Avg_Canopy_Cover-Sus_scrofa 3.1122 2.6401 0.3072 2.4701
## avg_veg_height-Canis_latrans -0.4846 0.8274 -2.0111 -0.5205
## avg_veg_height-Lynx_rufus -0.6439 1.0831 -2.7808 -0.6472
## avg_veg_height-Didelphis_virginiana -0.7001 0.9913 -2.6914 -0.6776
## avg_veg_height-Sylvilagus_floridanus -0.7243 1.0306 -2.7628 -0.7140
## avg_veg_height-Sciurus_carolinensis 0.0163 1.0816 -1.6667 -0.1222
## avg_veg_height-Vulpes_vulpes -0.4707 1.1370 -2.5521 -0.5432
## avg_veg_height-Sus_scrofa -0.5677 1.0292 -2.6699 -0.5443
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.7576 1.0214 98
## (Intercept)-Lynx_rufus 10.9995 1.0687 48
## (Intercept)-Didelphis_virginiana 0.2868 1.0893 163
## (Intercept)-Sylvilagus_floridanus 1.5724 1.0098 172
## (Intercept)-Sciurus_carolinensis -0.1585 1.0315 90
## (Intercept)-Vulpes_vulpes 0.7807 1.0957 113
## (Intercept)-Sus_scrofa -0.2594 1.0918 96
## Cogon_Patch_Size-Canis_latrans 6.7912 1.0810 114
## Cogon_Patch_Size-Lynx_rufus 4.3817 1.2829 90
## Cogon_Patch_Size-Didelphis_virginiana 5.5516 1.1936 84
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6402 1.2045 104
## Cogon_Patch_Size-Sciurus_carolinensis 1.5290 1.0458 255
## Cogon_Patch_Size-Vulpes_vulpes 4.0573 1.1335 131
## Cogon_Patch_Size-Sus_scrofa 2.4875 1.1087 227
## Veg_shannon_index-Canis_latrans 4.2522 1.0580 298
## Veg_shannon_index-Lynx_rufus 4.3027 1.0935 249
## Veg_shannon_index-Didelphis_virginiana 5.5541 1.2582 86
## Veg_shannon_index-Sylvilagus_floridanus 4.8257 1.2567 158
## Veg_shannon_index-Sciurus_carolinensis 1.9004 1.1134 133
## Veg_shannon_index-Vulpes_vulpes 2.1807 1.0485 128
## Veg_shannon_index-Sus_scrofa 6.1326 1.0268 250
## total_shrub_cover-Canis_latrans 6.0741 1.4722 39
## total_shrub_cover-Lynx_rufus 1.4184 2.3671 23
## total_shrub_cover-Didelphis_virginiana 0.7316 1.8012 33
## total_shrub_cover-Sylvilagus_floridanus 1.8658 1.2105 76
## total_shrub_cover-Sciurus_carolinensis 1.2739 1.2723 61
## total_shrub_cover-Vulpes_vulpes 1.4670 1.3530 63
## total_shrub_cover-Sus_scrofa 2.7116 1.3112 98
## Avg_Cogongrass_Cover-Canis_latrans 5.5452 1.0579 236
## Avg_Cogongrass_Cover-Lynx_rufus 6.0564 1.0868 226
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.7753 1.0678 320
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.0407 1.0409 327
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.4701 1.1568 227
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.3845 1.2383 179
## Avg_Cogongrass_Cover-Sus_scrofa 4.6435 1.0894 215
## Tree_Density-Canis_latrans -0.4382 1.0596 150
## Tree_Density-Lynx_rufus 6.7402 1.2701 58
## Tree_Density-Didelphis_virginiana 2.2819 1.0407 181
## Tree_Density-Sylvilagus_floridanus 0.7453 1.0834 130
## Tree_Density-Sciurus_carolinensis 1.2416 1.1961 75
## Tree_Density-Vulpes_vulpes 2.8618 1.0465 198
## Tree_Density-Sus_scrofa 1.1361 1.2372 66
## Avg_Canopy_Cover-Canis_latrans 1.3173 1.0866 161
## Avg_Canopy_Cover-Lynx_rufus 5.6957 1.3529 56
## Avg_Canopy_Cover-Didelphis_virginiana 15.9244 1.8054 23
## Avg_Canopy_Cover-Sylvilagus_floridanus 16.6898 1.5667 67
## Avg_Canopy_Cover-Sciurus_carolinensis 12.3805 1.5684 65
## Avg_Canopy_Cover-Vulpes_vulpes 14.2748 1.7226 38
## Avg_Canopy_Cover-Sus_scrofa 11.3357 1.8880 33
## avg_veg_height-Canis_latrans 1.2024 1.0374 285
## avg_veg_height-Lynx_rufus 1.4701 1.0388 298
## avg_veg_height-Didelphis_virginiana 1.2151 1.0391 328
## avg_veg_height-Sylvilagus_floridanus 1.3674 1.0504 175
## avg_veg_height-Sciurus_carolinensis 2.6523 1.2171 239
## avg_veg_height-Vulpes_vulpes 1.9952 1.0855 146
## avg_veg_height-Sus_scrofa 1.4431 1.0328 316
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8344 0.1907 -3.2113 -2.8290 -2.4779 1.0284
## (Intercept)-Lynx_rufus -3.5808 0.3447 -4.3206 -3.5640 -2.9806 1.0238
## (Intercept)-Didelphis_virginiana -2.7913 0.3164 -3.4081 -2.7906 -2.1883 1.0522
## (Intercept)-Sylvilagus_floridanus -3.2253 0.2564 -3.7507 -3.2142 -2.7480 1.0404
## (Intercept)-Sciurus_carolinensis -2.9339 0.3070 -3.5583 -2.9326 -2.3235 1.0065
## (Intercept)-Vulpes_vulpes -3.8992 0.5682 -5.1984 -3.8444 -2.9282 1.0771
## (Intercept)-Sus_scrofa -3.4987 0.5649 -4.7461 -3.4629 -2.4964 1.0949
## shrub_cover-Canis_latrans -0.3856 0.2442 -0.8619 -0.3936 0.1048 1.0384
## shrub_cover-Lynx_rufus 0.0329 0.3955 -0.8097 0.0661 0.7410 1.1085
## shrub_cover-Didelphis_virginiana 1.1917 0.4338 0.4174 1.1721 2.0894 1.0586
## shrub_cover-Sylvilagus_floridanus 0.5582 0.4193 -0.2375 0.5434 1.3939 1.0562
## shrub_cover-Sciurus_carolinensis 1.1809 0.4530 0.3015 1.1772 2.0754 1.0249
## shrub_cover-Vulpes_vulpes 0.4223 0.6284 -0.9517 0.4465 1.5998 1.0064
## shrub_cover-Sus_scrofa 1.0193 0.9067 -0.7004 0.9702 2.9646 1.0654
## veg_height-Canis_latrans -0.6478 0.1885 -1.0292 -0.6473 -0.2921 1.0103
## veg_height-Lynx_rufus 0.0715 0.2597 -0.4567 0.0797 0.5453 1.0014
## veg_height-Didelphis_virginiana 0.5103 0.2709 0.0277 0.4953 1.0926 1.0118
## veg_height-Sylvilagus_floridanus 0.1538 0.2531 -0.3420 0.1542 0.6608 1.0051
## veg_height-Sciurus_carolinensis 0.1620 0.2333 -0.2896 0.1553 0.6169 1.0015
## veg_height-Vulpes_vulpes -0.2420 0.3491 -0.9751 -0.2273 0.3966 1.0850
## veg_height-Sus_scrofa -0.2031 0.3436 -0.8892 -0.1945 0.4627 1.0257
## week-Canis_latrans 0.0858 0.1379 -0.1931 0.0885 0.3465 1.0127
## week-Lynx_rufus -0.0183 0.1976 -0.4292 -0.0140 0.3398 1.0058
## week-Didelphis_virginiana -0.2229 0.2381 -0.7356 -0.2130 0.1991 1.0091
## week-Sylvilagus_floridanus -0.1471 0.2164 -0.6055 -0.1369 0.2464 1.0020
## week-Sciurus_carolinensis 0.1740 0.1887 -0.2073 0.1800 0.5336 1.0006
## week-Vulpes_vulpes -0.1151 0.2877 -0.7512 -0.0965 0.4062 1.0069
## week-Sus_scrofa 0.1216 0.2538 -0.3843 0.1207 0.6120 1.0206
## ESS
## (Intercept)-Canis_latrans 419
## (Intercept)-Lynx_rufus 171
## (Intercept)-Didelphis_virginiana 304
## (Intercept)-Sylvilagus_floridanus 478
## (Intercept)-Sciurus_carolinensis 214
## (Intercept)-Vulpes_vulpes 153
## (Intercept)-Sus_scrofa 124
## shrub_cover-Canis_latrans 249
## shrub_cover-Lynx_rufus 273
## shrub_cover-Didelphis_virginiana 147
## shrub_cover-Sylvilagus_floridanus 267
## shrub_cover-Sciurus_carolinensis 305
## shrub_cover-Vulpes_vulpes 279
## shrub_cover-Sus_scrofa 153
## veg_height-Canis_latrans 669
## veg_height-Lynx_rufus 538
## veg_height-Didelphis_virginiana 663
## veg_height-Sylvilagus_floridanus 690
## veg_height-Sciurus_carolinensis 894
## veg_height-Vulpes_vulpes 521
## veg_height-Sus_scrofa 792
## week-Canis_latrans 1423
## week-Lynx_rufus 929
## week-Didelphis_virginiana 1133
## week-Sylvilagus_floridanus 1029
## week-Sciurus_carolinensis 1478
## week-Vulpes_vulpes 791
## week-Sus_scrofa 1053
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4875
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7394 0.4244 -1.573 -0.7446 0.1224 1.0045 1408
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0358 1.5216 0.1278 0.6775 3.8427 1.1288 1466
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0518 0.2910 -3.6312 -3.0535 -2.4613 1.0258 1118
## shrub_cover 0.3097 0.3800 -0.3958 0.2996 1.0753 1.0321 882
## veg_height -0.0443 0.2383 -0.5212 -0.0401 0.3992 1.0051 1500
## week -0.0194 0.1671 -0.3654 -0.0191 0.2987 1.0074 1394
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4482 0.5783 0.0494 0.2761 1.9119 1.0140 489
## shrub_cover 0.7786 0.9651 0.1039 0.5296 2.9419 1.0922 1336
## veg_height 0.3020 0.3181 0.0596 0.2202 1.0290 1.0143 1755
## week 0.1369 0.1413 0.0269 0.0963 0.4865 1.0165 1792
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2017 0.4221 -0.5880 0.1900 1.0702 1.0043
## (Intercept)-Lynx_rufus -0.1145 0.5438 -1.0465 -0.1577 1.0964 1.0098
## (Intercept)-Didelphis_virginiana -1.1333 0.4440 -2.0610 -1.1083 -0.3215 1.0110
## (Intercept)-Sylvilagus_floridanus -0.4621 0.4301 -1.2708 -0.4812 0.3899 1.0000
## (Intercept)-Sciurus_carolinensis -1.1120 0.4405 -1.9805 -1.0997 -0.2980 1.0103
## (Intercept)-Vulpes_vulpes -1.3732 0.6499 -2.6670 -1.3539 -0.0643 1.0158
## (Intercept)-Sus_scrofa -1.5088 0.5695 -2.6825 -1.4818 -0.4658 1.0354
## ESS
## (Intercept)-Canis_latrans 1807
## (Intercept)-Lynx_rufus 715
## (Intercept)-Didelphis_virginiana 1538
## (Intercept)-Sylvilagus_floridanus 1409
## (Intercept)-Sciurus_carolinensis 1451
## (Intercept)-Vulpes_vulpes 484
## (Intercept)-Sus_scrofa 903
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7930 0.1788 -3.1416 -2.7916 -2.4464 1.0005
## (Intercept)-Lynx_rufus -3.5023 0.3315 -4.2129 -3.4894 -2.9006 1.0332
## (Intercept)-Didelphis_virginiana -2.6902 0.2880 -3.2716 -2.6882 -2.1286 1.0133
## (Intercept)-Sylvilagus_floridanus -3.1522 0.2673 -3.6908 -3.1489 -2.6377 1.0495
## (Intercept)-Sciurus_carolinensis -2.7575 0.3058 -3.3825 -2.7556 -2.1659 1.0029
## (Intercept)-Vulpes_vulpes -3.6536 0.5678 -4.9616 -3.5747 -2.7590 1.0221
## (Intercept)-Sus_scrofa -3.2959 0.4876 -4.3188 -3.2736 -2.3695 1.0774
## shrub_cover-Canis_latrans -0.3096 0.2296 -0.7465 -0.3110 0.1360 1.0037
## shrub_cover-Lynx_rufus -0.2194 0.3658 -0.9285 -0.2197 0.4797 1.0146
## shrub_cover-Didelphis_virginiana 0.9899 0.3999 0.2522 0.9645 1.8122 1.0412
## shrub_cover-Sylvilagus_floridanus 0.2773 0.4392 -0.5518 0.2574 1.1704 1.0146
## shrub_cover-Sciurus_carolinensis 0.8719 0.4296 0.0694 0.8608 1.7629 1.0474
## shrub_cover-Vulpes_vulpes -0.0161 0.6266 -1.2758 -0.0153 1.2205 1.0033
## shrub_cover-Sus_scrofa 0.6749 0.7721 -0.7964 0.6779 2.2108 1.0590
## veg_height-Canis_latrans -0.6244 0.1927 -1.0203 -0.6181 -0.2630 1.0022
## veg_height-Lynx_rufus 0.0098 0.2550 -0.4957 0.0198 0.4778 1.0114
## veg_height-Didelphis_virginiana 0.4580 0.2583 -0.0185 0.4561 0.9753 1.0050
## veg_height-Sylvilagus_floridanus 0.1068 0.2521 -0.3784 0.1026 0.6087 1.0016
## veg_height-Sciurus_carolinensis 0.1031 0.2289 -0.3419 0.1012 0.5494 1.0052
## veg_height-Vulpes_vulpes -0.1383 0.3369 -0.8548 -0.1175 0.4613 1.0231
## veg_height-Sus_scrofa -0.1852 0.3507 -0.8950 -0.1829 0.5146 1.0078
## week-Canis_latrans 0.0853 0.1332 -0.1908 0.0885 0.3393 1.0044
## week-Lynx_rufus -0.0258 0.1970 -0.4312 -0.0244 0.3554 1.0202
## week-Didelphis_virginiana -0.2159 0.2313 -0.7238 -0.2033 0.2030 1.0076
## week-Sylvilagus_floridanus -0.1496 0.2193 -0.6355 -0.1341 0.2254 1.0012
## week-Sciurus_carolinensis 0.1709 0.1877 -0.2041 0.1772 0.5185 1.0043
## week-Vulpes_vulpes -0.1075 0.2908 -0.7189 -0.0887 0.4201 1.0042
## week-Sus_scrofa 0.1205 0.2379 -0.3575 0.1180 0.5691 0.9996
## ESS
## (Intercept)-Canis_latrans 661
## (Intercept)-Lynx_rufus 297
## (Intercept)-Didelphis_virginiana 672
## (Intercept)-Sylvilagus_floridanus 582
## (Intercept)-Sciurus_carolinensis 570
## (Intercept)-Vulpes_vulpes 203
## (Intercept)-Sus_scrofa 512
## shrub_cover-Canis_latrans 823
## shrub_cover-Lynx_rufus 430
## shrub_cover-Didelphis_virginiana 557
## shrub_cover-Sylvilagus_floridanus 485
## shrub_cover-Sciurus_carolinensis 564
## shrub_cover-Vulpes_vulpes 573
## shrub_cover-Sus_scrofa 551
## veg_height-Canis_latrans 651
## veg_height-Lynx_rufus 681
## veg_height-Didelphis_virginiana 1059
## veg_height-Sylvilagus_floridanus 844
## veg_height-Sciurus_carolinensis 898
## veg_height-Vulpes_vulpes 553
## veg_height-Sus_scrofa 1104
## week-Canis_latrans 1570
## week-Lynx_rufus 983
## week-Didelphis_virginiana 1294
## week-Sylvilagus_floridanus 958
## week-Sciurus_carolinensis 1794
## week-Vulpes_vulpes 1168
## week-Sus_scrofa 1328
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5885
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5163 0.7020 -1.7746 -0.5488 1.0552 1.1076 151
## Avg_Cogongrass_Cover 0.2125 0.5077 -0.8007 0.2177 1.1927 1.0048 531
## total_shrub_cover -0.9576 0.7053 -2.5033 -0.8981 0.2450 1.0080 187
## avg_veg_height 0.1227 0.4838 -0.8014 0.1093 1.0688 1.0271 349
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2395 2.5278 0.0530 0.5307 6.2200 1.2554 153
## Avg_Cogongrass_Cover 0.7759 1.5237 0.0488 0.3362 4.2500 1.0425 196
## total_shrub_cover 3.1027 8.3615 0.0679 1.0011 22.3603 1.5618 30
## avg_veg_height 0.4974 1.1152 0.0380 0.2387 2.3158 1.1362 319
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.0384 7.2856 0.3606 2.8748 25.7332 1.1272 35
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2287 0.3007 -3.8424 -3.2204 -2.6557 1.0066 278
## shrub_cover 0.7197 0.4204 -0.1157 0.7208 1.5712 1.0175 495
## veg_height -0.0549 0.2406 -0.5419 -0.0526 0.4325 1.0002 1144
## week -0.0129 0.1770 -0.3643 -0.0145 0.3327 1.0000 1264
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4131 0.5481 0.0464 0.2424 1.8605 1.0155 466
## shrub_cover 0.9692 1.0431 0.1372 0.6865 3.4100 1.0159 287
## veg_height 0.3255 0.3556 0.0588 0.2331 1.1791 1.0250 1754
## week 0.1380 0.1690 0.0269 0.0946 0.5290 1.0038 1594
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0321 0.9898 -1.7398 -0.0569
## (Intercept)-Lynx_rufus -0.4251 0.9555 -2.1850 -0.4733
## (Intercept)-Didelphis_virginiana -0.7516 0.9536 -2.6510 -0.7666
## (Intercept)-Sylvilagus_floridanus -0.1840 0.9657 -1.8337 -0.2859
## (Intercept)-Sciurus_carolinensis -0.6573 1.0212 -2.3837 -0.7166
## (Intercept)-Vulpes_vulpes -0.8374 1.1267 -2.9907 -0.8814
## (Intercept)-Sus_scrofa -0.9634 1.0324 -3.1076 -0.9254
## Avg_Cogongrass_Cover-Canis_latrans 0.5357 0.6656 -0.5976 0.4939
## Avg_Cogongrass_Cover-Lynx_rufus 0.5994 0.7053 -0.5913 0.5385
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3748 0.6652 -0.7926 0.3434
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3752 0.7916 -2.2384 -0.2893
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1275 0.6154 -1.1613 0.1504
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3705 0.6749 -0.9255 0.3582
## Avg_Cogongrass_Cover-Sus_scrofa -0.0791 0.8664 -2.0753 0.0030
## total_shrub_cover-Canis_latrans 0.4093 1.0587 -1.3708 0.2545
## total_shrub_cover-Lynx_rufus -1.7134 1.2878 -4.6453 -1.5160
## total_shrub_cover-Didelphis_virginiana -1.3174 1.4802 -5.4106 -0.9976
## total_shrub_cover-Sylvilagus_floridanus -1.8754 1.5368 -6.0248 -1.5214
## total_shrub_cover-Sciurus_carolinensis -1.3485 1.3901 -4.7491 -1.0869
## total_shrub_cover-Vulpes_vulpes -1.3081 1.5688 -5.2170 -1.0570
## total_shrub_cover-Sus_scrofa -0.8315 1.1960 -3.4807 -0.7486
## avg_veg_height-Canis_latrans 0.1365 0.5598 -0.8817 0.1087
## avg_veg_height-Lynx_rufus 0.0541 0.7024 -1.4036 0.0522
## avg_veg_height-Didelphis_virginiana -0.0206 0.6356 -1.3064 0.0069
## avg_veg_height-Sylvilagus_floridanus 0.0801 0.6798 -1.1799 0.0670
## avg_veg_height-Sciurus_carolinensis 0.5115 0.6717 -0.5978 0.4388
## avg_veg_height-Vulpes_vulpes 0.0475 0.6340 -1.2028 0.0586
## avg_veg_height-Sus_scrofa 0.1720 0.6745 -1.0690 0.1566
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2947 1.1511 248
## (Intercept)-Lynx_rufus 1.5307 1.1197 248
## (Intercept)-Didelphis_virginiana 1.1522 1.0519 197
## (Intercept)-Sylvilagus_floridanus 2.0065 1.0875 260
## (Intercept)-Sciurus_carolinensis 1.3320 1.1067 117
## (Intercept)-Vulpes_vulpes 1.4542 1.0829 154
## (Intercept)-Sus_scrofa 1.0764 1.0519 210
## Avg_Cogongrass_Cover-Canis_latrans 2.0348 1.0199 481
## Avg_Cogongrass_Cover-Lynx_rufus 2.2904 1.0124 601
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7709 1.0197 437
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8624 1.0162 314
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2290 1.0120 718
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7704 1.0081 679
## Avg_Cogongrass_Cover-Sus_scrofa 1.3661 1.0054 474
## total_shrub_cover-Canis_latrans 2.8786 1.0613 84
## total_shrub_cover-Lynx_rufus 0.3146 1.0783 58
## total_shrub_cover-Didelphis_virginiana 0.2428 1.4769 33
## total_shrub_cover-Sylvilagus_floridanus 0.0372 1.1013 75
## total_shrub_cover-Sciurus_carolinensis 0.3397 1.2525 70
## total_shrub_cover-Vulpes_vulpes 1.1002 1.0450 92
## total_shrub_cover-Sus_scrofa 1.3022 1.0246 229
## avg_veg_height-Canis_latrans 1.3100 1.0179 524
## avg_veg_height-Lynx_rufus 1.4601 1.0099 466
## avg_veg_height-Didelphis_virginiana 1.1430 1.0139 493
## avg_veg_height-Sylvilagus_floridanus 1.3634 1.0223 414
## avg_veg_height-Sciurus_carolinensis 1.9569 1.0336 278
## avg_veg_height-Vulpes_vulpes 1.2453 1.0109 474
## avg_veg_height-Sus_scrofa 1.5191 1.0378 369
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8785 0.1995 -3.2844 -2.8734 -2.4901 1.0538
## (Intercept)-Lynx_rufus -3.4756 0.3015 -4.0966 -3.4545 -2.9174 1.0128
## (Intercept)-Didelphis_virginiana -2.9588 0.3307 -3.6385 -2.9462 -2.3276 1.0090
## (Intercept)-Sylvilagus_floridanus -3.3117 0.2521 -3.8333 -3.3071 -2.8272 1.0400
## (Intercept)-Sciurus_carolinensis -3.0256 0.3342 -3.7027 -3.0162 -2.3886 1.0144
## (Intercept)-Vulpes_vulpes -3.8107 0.5954 -5.1885 -3.7164 -2.8483 1.0155
## (Intercept)-Sus_scrofa -3.6077 0.4858 -4.6772 -3.5645 -2.7563 1.0256
## shrub_cover-Canis_latrans -0.2960 0.2817 -0.8328 -0.3136 0.2858 1.0126
## shrub_cover-Lynx_rufus 0.1730 0.3958 -0.6860 0.2063 0.8615 1.0257
## shrub_cover-Didelphis_virginiana 1.4232 0.4653 0.5468 1.3969 2.4042 1.0125
## shrub_cover-Sylvilagus_floridanus 0.8682 0.4198 0.0326 0.8756 1.6668 1.0299
## shrub_cover-Sciurus_carolinensis 1.3387 0.4273 0.4811 1.3370 2.1492 1.0401
## shrub_cover-Vulpes_vulpes 0.5302 0.7112 -0.9964 0.5747 1.8122 1.0118
## shrub_cover-Sus_scrofa 1.2840 0.7776 -0.2326 1.2568 2.9344 1.0134
## veg_height-Canis_latrans -0.6503 0.1952 -1.0566 -0.6442 -0.2838 1.0132
## veg_height-Lynx_rufus 0.0278 0.2537 -0.4860 0.0398 0.5154 1.0073
## veg_height-Didelphis_virginiana 0.4486 0.2787 -0.0723 0.4358 1.0047 1.0045
## veg_height-Sylvilagus_floridanus 0.0200 0.2505 -0.4633 0.0215 0.5113 1.0019
## veg_height-Sciurus_carolinensis 0.1695 0.2479 -0.2984 0.1679 0.6768 1.0225
## veg_height-Vulpes_vulpes -0.1597 0.3438 -0.8777 -0.1481 0.4772 1.0340
## veg_height-Sus_scrofa -0.2320 0.3409 -0.9283 -0.2218 0.4127 1.0017
## week-Canis_latrans 0.0817 0.1380 -0.2066 0.0866 0.3398 1.0028
## week-Lynx_rufus -0.0212 0.2009 -0.4414 -0.0124 0.3481 1.0056
## week-Didelphis_virginiana -0.2180 0.2390 -0.7090 -0.1987 0.2099 1.0075
## week-Sylvilagus_floridanus -0.1281 0.2085 -0.5780 -0.1134 0.2478 1.0109
## week-Sciurus_carolinensis 0.1765 0.1887 -0.1893 0.1744 0.5451 1.0038
## week-Vulpes_vulpes -0.0829 0.2802 -0.6718 -0.0697 0.4343 1.0121
## week-Sus_scrofa 0.1218 0.2347 -0.3623 0.1277 0.5586 1.0014
## ESS
## (Intercept)-Canis_latrans 450
## (Intercept)-Lynx_rufus 370
## (Intercept)-Didelphis_virginiana 176
## (Intercept)-Sylvilagus_floridanus 562
## (Intercept)-Sciurus_carolinensis 270
## (Intercept)-Vulpes_vulpes 167
## (Intercept)-Sus_scrofa 147
## shrub_cover-Canis_latrans 200
## shrub_cover-Lynx_rufus 342
## shrub_cover-Didelphis_virginiana 157
## shrub_cover-Sylvilagus_floridanus 330
## shrub_cover-Sciurus_carolinensis 223
## shrub_cover-Vulpes_vulpes 148
## shrub_cover-Sus_scrofa 180
## veg_height-Canis_latrans 645
## veg_height-Lynx_rufus 650
## veg_height-Didelphis_virginiana 596
## veg_height-Sylvilagus_floridanus 488
## veg_height-Sciurus_carolinensis 636
## veg_height-Vulpes_vulpes 486
## veg_height-Sus_scrofa 931
## week-Canis_latrans 1442
## week-Lynx_rufus 931
## week-Didelphis_virginiana 896
## week-Sylvilagus_floridanus 861
## week-Sciurus_carolinensis 1392
## week-Vulpes_vulpes 861
## week-Sus_scrofa 1273
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5083
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1060 0.6354 -2.2942 -1.1272 0.1952 1.0039 591
## Tree_Density -0.7520 0.5827 -1.9629 -0.7197 0.3591 1.0172 455
## Avg_Canopy_Cover 1.1659 0.5653 0.0977 1.1356 2.3279 1.0028 511
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2615 3.6464 0.0877 1.2396 10.7447 1.0322 341
## Tree_Density 1.5283 2.6726 0.0650 0.7269 8.7466 1.0972 606
## Avg_Canopy_Cover 1.8391 2.5649 0.1456 1.1052 8.0108 1.0410 711
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.037 1.3919 0.0604 0.5669 4.9193 1.1782 104
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0898 0.2785 -3.6348 -3.0951 -2.5264 1.0077 1219
## shrub_cover 0.3810 0.3704 -0.3728 0.3791 1.1361 1.0243 1171
## veg_height -0.0113 0.2287 -0.4690 -0.0195 0.4620 1.0289 1211
## week -0.0120 0.1769 -0.3728 -0.0110 0.3331 1.0033 1460
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4313 0.5846 0.0503 0.2712 1.7499 1.0378 475
## shrub_cover 0.7591 0.8198 0.1184 0.5431 2.6641 1.0052 1400
## veg_height 0.3200 0.3407 0.0610 0.2300 1.1475 1.0279 2029
## week 0.1507 0.2047 0.0268 0.1006 0.5526 1.0312 1701
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0301 0.7824 -1.4998 -0.0348 1.5212
## (Intercept)-Lynx_rufus -0.1003 1.2323 -1.9580 -0.3215 2.9832
## (Intercept)-Didelphis_virginiana -1.6486 0.7768 -3.3473 -1.5966 -0.2991
## (Intercept)-Sylvilagus_floridanus -0.8687 0.7382 -2.2409 -0.8842 0.5841
## (Intercept)-Sciurus_carolinensis -1.7487 0.8244 -3.4856 -1.6844 -0.3192
## (Intercept)-Vulpes_vulpes -1.9140 0.8896 -3.8382 -1.8467 -0.2882
## (Intercept)-Sus_scrofa -2.2247 0.9756 -4.4311 -2.1454 -0.5518
## Tree_Density-Canis_latrans -1.0694 0.6857 -2.6740 -0.9786 0.0478
## Tree_Density-Lynx_rufus 0.4217 0.8935 -0.9771 0.2964 2.6833
## Tree_Density-Didelphis_virginiana -1.1075 0.9247 -3.3517 -0.9486 0.3205
## Tree_Density-Sylvilagus_floridanus -1.2495 1.0654 -3.8776 -1.0638 0.2850
## Tree_Density-Sciurus_carolinensis -0.9802 0.9388 -3.2144 -0.8536 0.4786
## Tree_Density-Vulpes_vulpes -0.6044 0.8839 -2.5607 -0.5490 0.9204
## Tree_Density-Sus_scrofa -1.0311 1.0112 -3.5352 -0.8813 0.5760
## Avg_Canopy_Cover-Canis_latrans -0.1850 0.5166 -1.2843 -0.1730 0.7829
## Avg_Canopy_Cover-Lynx_rufus 0.7291 0.8493 -0.6799 0.6433 2.6232
## Avg_Canopy_Cover-Didelphis_virginiana 1.7993 0.8333 0.5308 1.6675 3.8507
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4305 1.2103 0.7799 2.1556 5.4181
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6771 0.7884 0.4130 1.5546 3.5297
## Avg_Canopy_Cover-Vulpes_vulpes 1.1247 0.7440 -0.1442 1.0528 2.8323
## Avg_Canopy_Cover-Sus_scrofa 1.4217 0.7252 0.2298 1.3470 3.0415
## Rhat ESS
## (Intercept)-Canis_latrans 1.0153 360
## (Intercept)-Lynx_rufus 1.0479 145
## (Intercept)-Didelphis_virginiana 1.0109 683
## (Intercept)-Sylvilagus_floridanus 1.0068 770
## (Intercept)-Sciurus_carolinensis 1.0111 455
## (Intercept)-Vulpes_vulpes 1.0244 493
## (Intercept)-Sus_scrofa 1.0121 433
## Tree_Density-Canis_latrans 1.0026 1005
## Tree_Density-Lynx_rufus 1.0509 298
## Tree_Density-Didelphis_virginiana 1.0295 646
## Tree_Density-Sylvilagus_floridanus 1.0319 443
## Tree_Density-Sciurus_carolinensis 1.0187 715
## Tree_Density-Vulpes_vulpes 1.0097 645
## Tree_Density-Sus_scrofa 1.0072 517
## Avg_Canopy_Cover-Canis_latrans 1.0133 950
## Avg_Canopy_Cover-Lynx_rufus 1.0527 498
## Avg_Canopy_Cover-Didelphis_virginiana 1.0196 523
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0649 322
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0436 631
## Avg_Canopy_Cover-Vulpes_vulpes 1.0066 626
## Avg_Canopy_Cover-Sus_scrofa 1.0051 788
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8176 0.1878 -3.2025 -2.8120 -2.4693 1.0017
## (Intercept)-Lynx_rufus -3.6004 0.3487 -4.3071 -3.5813 -2.9598 1.0843
## (Intercept)-Didelphis_virginiana -2.7481 0.2869 -3.3140 -2.7491 -2.2056 1.0212
## (Intercept)-Sylvilagus_floridanus -3.1398 0.2474 -3.6666 -3.1281 -2.6740 1.0033
## (Intercept)-Sciurus_carolinensis -2.8203 0.3071 -3.4139 -2.8252 -2.2212 1.0715
## (Intercept)-Vulpes_vulpes -3.6519 0.5313 -4.8752 -3.5933 -2.7812 1.0216
## (Intercept)-Sus_scrofa -3.2875 0.4564 -4.2363 -3.2646 -2.4220 1.0044
## shrub_cover-Canis_latrans -0.3028 0.2260 -0.7535 -0.3005 0.1259 1.0042
## shrub_cover-Lynx_rufus -0.2286 0.3445 -0.9106 -0.2292 0.4403 1.0619
## shrub_cover-Didelphis_virginiana 1.0643 0.3769 0.3708 1.0424 1.8296 1.0395
## shrub_cover-Sylvilagus_floridanus 0.4732 0.3914 -0.2891 0.4754 1.2236 1.0037
## shrub_cover-Sciurus_carolinensis 0.9984 0.4078 0.1837 0.9962 1.8232 1.0237
## shrub_cover-Vulpes_vulpes 0.0620 0.6208 -1.2272 0.1031 1.2257 1.0264
## shrub_cover-Sus_scrofa 0.7261 0.7296 -0.6345 0.7023 2.2405 1.0021
## veg_height-Canis_latrans -0.6289 0.1872 -0.9970 -0.6275 -0.2738 1.0229
## veg_height-Lynx_rufus 0.0537 0.2391 -0.4372 0.0606 0.4927 1.0172
## veg_height-Didelphis_virginiana 0.5173 0.2630 0.0049 0.5199 1.0431 1.0023
## veg_height-Sylvilagus_floridanus 0.1467 0.2433 -0.3289 0.1419 0.6426 1.0055
## veg_height-Sciurus_carolinensis 0.1355 0.2241 -0.2962 0.1326 0.5748 1.0372
## veg_height-Vulpes_vulpes -0.1320 0.3101 -0.7675 -0.1245 0.4402 1.0000
## veg_height-Sus_scrofa -0.1400 0.3393 -0.8478 -0.1258 0.4910 1.0131
## week-Canis_latrans 0.0919 0.1348 -0.1774 0.0927 0.3509 1.0099
## week-Lynx_rufus -0.0094 0.2013 -0.4447 -0.0054 0.3644 1.0015
## week-Didelphis_virginiana -0.2139 0.2366 -0.7127 -0.1993 0.1984 1.0029
## week-Sylvilagus_floridanus -0.1542 0.2219 -0.6397 -0.1352 0.2309 1.0122
## week-Sciurus_carolinensis 0.1702 0.1902 -0.2242 0.1793 0.5185 1.0033
## week-Vulpes_vulpes -0.0836 0.2944 -0.6924 -0.0728 0.4695 1.0069
## week-Sus_scrofa 0.1326 0.2471 -0.3568 0.1295 0.6118 1.0035
## ESS
## (Intercept)-Canis_latrans 750
## (Intercept)-Lynx_rufus 171
## (Intercept)-Didelphis_virginiana 555
## (Intercept)-Sylvilagus_floridanus 760
## (Intercept)-Sciurus_carolinensis 654
## (Intercept)-Vulpes_vulpes 293
## (Intercept)-Sus_scrofa 658
## shrub_cover-Canis_latrans 582
## shrub_cover-Lynx_rufus 400
## shrub_cover-Didelphis_virginiana 647
## shrub_cover-Sylvilagus_floridanus 618
## shrub_cover-Sciurus_carolinensis 632
## shrub_cover-Vulpes_vulpes 539
## shrub_cover-Sus_scrofa 784
## veg_height-Canis_latrans 751
## veg_height-Lynx_rufus 690
## veg_height-Didelphis_virginiana 895
## veg_height-Sylvilagus_floridanus 852
## veg_height-Sciurus_carolinensis 869
## veg_height-Vulpes_vulpes 756
## veg_height-Sus_scrofa 1110
## week-Canis_latrans 1531
## week-Lynx_rufus 845
## week-Didelphis_virginiana 1258
## week-Sylvilagus_floridanus 948
## week-Sciurus_carolinensis 1485
## week-Vulpes_vulpes 1155
## week-Sus_scrofa 1515
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5468
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8133 0.6608 -2.0186 -0.8633 0.6943 1.0363 191
## Cogon_Patch_Size -0.3799 0.6785 -1.8497 -0.3399 0.8441 1.0014 720
## Avg_Cogongrass_Cover 0.4080 0.4266 -0.4359 0.4119 1.2360 1.0226 590
## total_shrub_cover -0.6925 0.6875 -2.1898 -0.6091 0.4802 1.0617 141
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2452 2.1323 0.0629 0.6520 5.7173 1.0147 716
## Cogon_Patch_Size 2.6997 4.3975 0.1145 1.4156 13.0532 1.0083 450
## Avg_Cogongrass_Cover 0.5746 1.0073 0.0409 0.2795 2.9866 1.0183 910
## total_shrub_cover 1.4010 2.5797 0.0564 0.6588 7.1599 1.0321 160
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.4467 3.2237 0.2171 2.5532 12.0227 1.178 88
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1614 0.2935 -3.7382 -3.1592 -2.5775 1.0083 440
## shrub_cover 0.6346 0.4010 -0.1584 0.6342 1.4533 1.0301 360
## veg_height -0.0388 0.2299 -0.4958 -0.0397 0.4210 1.0020 1639
## week -0.0120 0.1724 -0.3877 -0.0054 0.3157 1.0070 1326
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4257 0.5636 0.0489 0.2566 1.9162 1.0394 439
## shrub_cover 0.8415 0.8742 0.1346 0.5806 3.3626 1.0101 690
## veg_height 0.2932 0.2943 0.0567 0.2071 1.0686 1.0044 2053
## week 0.1371 0.1421 0.0281 0.0973 0.4778 1.0171 1407
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0919 0.9252 -1.7119 -0.1679
## (Intercept)-Lynx_rufus -0.6730 0.9163 -2.4262 -0.7192
## (Intercept)-Didelphis_virginiana -1.0068 0.8437 -2.6989 -1.0274
## (Intercept)-Sylvilagus_floridanus -0.5713 0.9545 -2.3248 -0.6324
## (Intercept)-Sciurus_carolinensis -1.1602 0.8735 -3.0209 -1.1429
## (Intercept)-Vulpes_vulpes -1.2966 0.9459 -3.2498 -1.2598
## (Intercept)-Sus_scrofa -1.4233 0.9970 -3.5498 -1.3317
## Cogon_Patch_Size-Canis_latrans 0.8593 0.9409 -0.4465 0.6792
## Cogon_Patch_Size-Lynx_rufus -0.3508 0.9820 -2.2009 -0.4137
## Cogon_Patch_Size-Didelphis_virginiana 0.7217 0.6340 -0.3952 0.6825
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6273 1.4800 -5.7143 -1.2891
## Cogon_Patch_Size-Sciurus_carolinensis -1.1878 1.0882 -3.9633 -0.9689
## Cogon_Patch_Size-Vulpes_vulpes -0.8929 1.2358 -3.6810 -0.7350
## Cogon_Patch_Size-Sus_scrofa -0.7867 1.2014 -3.7637 -0.5988
## Avg_Cogongrass_Cover-Canis_latrans 0.4783 0.5112 -0.4324 0.4412
## Avg_Cogongrass_Cover-Lynx_rufus 0.7594 0.6586 -0.3227 0.6805
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2804 0.5353 -0.8061 0.2922
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0549 0.6638 -1.3651 0.0922
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6045 0.5457 -0.4146 0.5794
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5768 0.5775 -0.5099 0.5479
## Avg_Cogongrass_Cover-Sus_scrofa 0.1656 0.7563 -1.5798 0.2254
## total_shrub_cover-Canis_latrans 0.2339 0.8070 -1.1800 0.1919
## total_shrub_cover-Lynx_rufus -1.3054 1.0480 -3.8676 -1.1603
## total_shrub_cover-Didelphis_virginiana -0.9611 0.8469 -3.0849 -0.8214
## total_shrub_cover-Sylvilagus_floridanus -1.1934 0.9928 -3.5266 -1.0281
## total_shrub_cover-Sciurus_carolinensis -0.7265 0.8649 -2.7887 -0.6057
## total_shrub_cover-Vulpes_vulpes -0.8205 1.2558 -3.8012 -0.6635
## total_shrub_cover-Sus_scrofa -0.4982 0.9759 -2.7140 -0.4254
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9092 1.0057 323
## (Intercept)-Lynx_rufus 1.2591 1.0149 255
## (Intercept)-Didelphis_virginiana 0.6889 1.0481 325
## (Intercept)-Sylvilagus_floridanus 1.5870 1.0397 231
## (Intercept)-Sciurus_carolinensis 0.5725 1.0107 313
## (Intercept)-Vulpes_vulpes 0.5300 1.0103 312
## (Intercept)-Sus_scrofa 0.3821 1.0158 345
## Cogon_Patch_Size-Canis_latrans 3.2128 1.0099 687
## Cogon_Patch_Size-Lynx_rufus 1.8150 1.0077 400
## Cogon_Patch_Size-Didelphis_virginiana 2.0527 1.0060 726
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2941 1.0202 318
## Cogon_Patch_Size-Sciurus_carolinensis 0.3513 1.0117 532
## Cogon_Patch_Size-Vulpes_vulpes 1.1301 1.0139 292
## Cogon_Patch_Size-Sus_scrofa 1.0255 1.0014 481
## Avg_Cogongrass_Cover-Canis_latrans 1.5762 1.0127 798
## Avg_Cogongrass_Cover-Lynx_rufus 2.2429 1.0111 793
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2750 1.0203 577
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2361 1.0288 553
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7772 1.0037 708
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8509 1.0136 860
## Avg_Cogongrass_Cover-Sus_scrofa 1.5221 1.0460 601
## total_shrub_cover-Canis_latrans 2.1091 1.0410 205
## total_shrub_cover-Lynx_rufus 0.3304 1.0295 146
## total_shrub_cover-Didelphis_virginiana 0.3190 1.1344 198
## total_shrub_cover-Sylvilagus_floridanus 0.3699 1.0666 117
## total_shrub_cover-Sciurus_carolinensis 0.6655 1.0311 237
## total_shrub_cover-Vulpes_vulpes 1.3736 1.0797 140
## total_shrub_cover-Sus_scrofa 1.3157 1.0257 184
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8202 0.1977 -3.2283 -2.8153 -2.4472 1.0191
## (Intercept)-Lynx_rufus -3.4310 0.3075 -4.0868 -3.4112 -2.8785 1.0051
## (Intercept)-Didelphis_virginiana -2.8404 0.3082 -3.4751 -2.8311 -2.2500 1.0728
## (Intercept)-Sylvilagus_floridanus -3.2943 0.2598 -3.8395 -3.2880 -2.8144 1.0030
## (Intercept)-Sciurus_carolinensis -2.9380 0.3314 -3.6149 -2.9352 -2.3001 1.0109
## (Intercept)-Vulpes_vulpes -3.7571 0.5914 -5.1099 -3.6780 -2.8160 1.0311
## (Intercept)-Sus_scrofa -3.5146 0.4745 -4.5052 -3.4992 -2.6473 1.0071
## shrub_cover-Canis_latrans -0.3081 0.2476 -0.7729 -0.3189 0.2010 1.0047
## shrub_cover-Lynx_rufus 0.1907 0.3542 -0.5340 0.2015 0.8537 1.0102
## shrub_cover-Didelphis_virginiana 1.2778 0.4213 0.4881 1.2699 2.1194 1.0362
## shrub_cover-Sylvilagus_floridanus 0.7833 0.4323 -0.1096 0.7933 1.6191 1.0249
## shrub_cover-Sciurus_carolinensis 1.1870 0.4530 0.2708 1.1857 2.0552 1.0358
## shrub_cover-Vulpes_vulpes 0.3992 0.7283 -1.1816 0.4494 1.6814 1.0240
## shrub_cover-Sus_scrofa 1.0889 0.7426 -0.3706 1.0932 2.5176 1.0415
## veg_height-Canis_latrans -0.6215 0.1954 -1.0166 -0.6170 -0.2601 1.0032
## veg_height-Lynx_rufus 0.0325 0.2433 -0.4490 0.0345 0.5031 1.0101
## veg_height-Didelphis_virginiana 0.4409 0.2575 -0.0441 0.4398 0.9500 1.0253
## veg_height-Sylvilagus_floridanus 0.0359 0.2549 -0.4517 0.0295 0.5388 1.0216
## veg_height-Sciurus_carolinensis 0.1513 0.2468 -0.3135 0.1443 0.6652 1.0106
## veg_height-Vulpes_vulpes -0.1608 0.3274 -0.8755 -0.1456 0.4459 1.0166
## veg_height-Sus_scrofa -0.1922 0.3237 -0.8380 -0.1813 0.4473 1.0188
## week-Canis_latrans 0.0832 0.1354 -0.1905 0.0858 0.3402 1.0079
## week-Lynx_rufus -0.0198 0.2022 -0.4412 -0.0092 0.3473 1.0095
## week-Didelphis_virginiana -0.2117 0.2340 -0.7174 -0.1906 0.2012 1.0041
## week-Sylvilagus_floridanus -0.1346 0.2212 -0.6216 -0.1183 0.2581 1.0622
## week-Sciurus_carolinensis 0.1728 0.1876 -0.2191 0.1753 0.5302 1.0116
## week-Vulpes_vulpes -0.1172 0.2986 -0.7732 -0.0939 0.4015 1.0066
## week-Sus_scrofa 0.1336 0.2338 -0.3196 0.1316 0.5850 1.0044
## ESS
## (Intercept)-Canis_latrans 555
## (Intercept)-Lynx_rufus 359
## (Intercept)-Didelphis_virginiana 258
## (Intercept)-Sylvilagus_floridanus 331
## (Intercept)-Sciurus_carolinensis 342
## (Intercept)-Vulpes_vulpes 192
## (Intercept)-Sus_scrofa 231
## shrub_cover-Canis_latrans 365
## shrub_cover-Lynx_rufus 483
## shrub_cover-Didelphis_virginiana 297
## shrub_cover-Sylvilagus_floridanus 335
## shrub_cover-Sciurus_carolinensis 328
## shrub_cover-Vulpes_vulpes 284
## shrub_cover-Sus_scrofa 269
## veg_height-Canis_latrans 640
## veg_height-Lynx_rufus 787
## veg_height-Didelphis_virginiana 1041
## veg_height-Sylvilagus_floridanus 609
## veg_height-Sciurus_carolinensis 579
## veg_height-Vulpes_vulpes 635
## veg_height-Sus_scrofa 1038
## week-Canis_latrans 1587
## week-Lynx_rufus 883
## week-Didelphis_virginiana 1184
## week-Sylvilagus_floridanus 692
## week-Sciurus_carolinensis 1584
## week-Vulpes_vulpes 840
## week-Sus_scrofa 1372
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4913
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0063 0.5338 -2.0745 -1.0037 0.1093 1.0425 461
## Veg_shannon_index 0.3569 0.3912 -0.4254 0.3553 1.1150 1.0018 1045
## Avg_Cogongrass_Cover 0.3842 0.3521 -0.2938 0.3765 1.0996 1.0086 745
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0924 1.7279 0.0613 0.5976 4.9776 1.0176 660
## Veg_shannon_index 0.5488 0.7701 0.0448 0.3116 2.3876 1.0137 1003
## Avg_Cogongrass_Cover 0.4634 0.7442 0.0424 0.2574 2.3358 1.0093 1119
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3532 1.3688 0.072 0.9376 4.8922 1.0244 171
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0297 0.2835 -3.5875 -3.0235 -2.4573 1.0029 1045
## shrub_cover 0.3298 0.3609 -0.3995 0.3275 1.0722 1.0130 1076
## veg_height -0.0536 0.2447 -0.5698 -0.0529 0.4314 1.0014 1356
## week -0.0096 0.1701 -0.3787 -0.0032 0.2962 0.9997 1506
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4285 0.5437 0.0497 0.2699 1.7742 1.0179 541
## shrub_cover 0.7231 0.8988 0.0922 0.4733 2.8188 1.0027 1391
## veg_height 0.2958 0.3114 0.0591 0.2084 1.0468 1.0114 1741
## week 0.1451 0.1537 0.0290 0.0994 0.5684 1.0027 1526
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2293 0.7474 -1.7208 -0.2132
## (Intercept)-Lynx_rufus -0.6183 0.7788 -2.0681 -0.6391
## (Intercept)-Didelphis_virginiana -1.3162 0.6422 -2.6472 -1.2792
## (Intercept)-Sylvilagus_floridanus -0.7847 0.6672 -2.1286 -0.7884
## (Intercept)-Sciurus_carolinensis -1.3217 0.6284 -2.6552 -1.2969
## (Intercept)-Vulpes_vulpes -1.4433 0.8088 -3.1268 -1.4012
## (Intercept)-Sus_scrofa -1.6986 0.8287 -3.5820 -1.6096
## Veg_shannon_index-Canis_latrans 0.7392 0.4588 -0.0868 0.7113
## Veg_shannon_index-Lynx_rufus 0.1740 0.5550 -1.0440 0.2007
## Veg_shannon_index-Didelphis_virginiana 0.5660 0.4579 -0.2659 0.5449
## Veg_shannon_index-Sylvilagus_floridanus 0.4545 0.4992 -0.4705 0.4313
## Veg_shannon_index-Sciurus_carolinensis -0.1070 0.4837 -1.1242 -0.0689
## Veg_shannon_index-Vulpes_vulpes -0.0109 0.5436 -1.1798 0.0296
## Veg_shannon_index-Sus_scrofa 0.7874 0.6320 -0.2454 0.7194
## Avg_Cogongrass_Cover-Canis_latrans 0.6748 0.4632 -0.0817 0.6245
## Avg_Cogongrass_Cover-Lynx_rufus 0.6723 0.4869 -0.1268 0.6261
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5090 0.4191 -0.2807 0.4959
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1049 0.5070 -1.2652 -0.0695
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4478 0.4044 -0.3363 0.4452
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4640 0.5230 -0.5167 0.4351
## Avg_Cogongrass_Cover-Sus_scrofa 0.0754 0.6322 -1.2747 0.1249
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2663 1.0138 279
## (Intercept)-Lynx_rufus 1.0226 1.0421 279
## (Intercept)-Didelphis_virginiana -0.1568 1.0520 567
## (Intercept)-Sylvilagus_floridanus 0.5248 1.0220 494
## (Intercept)-Sciurus_carolinensis -0.1867 1.0288 504
## (Intercept)-Vulpes_vulpes 0.1306 1.0836 439
## (Intercept)-Sus_scrofa -0.3030 1.0139 467
## Veg_shannon_index-Canis_latrans 1.6884 1.0013 1324
## Veg_shannon_index-Lynx_rufus 1.2256 1.0113 910
## Veg_shannon_index-Didelphis_virginiana 1.5338 1.0012 1456
## Veg_shannon_index-Sylvilagus_floridanus 1.5012 1.0013 1128
## Veg_shannon_index-Sciurus_carolinensis 0.7332 1.0092 1190
## Veg_shannon_index-Vulpes_vulpes 0.9350 1.0022 897
## Veg_shannon_index-Sus_scrofa 2.2415 1.0206 834
## Avg_Cogongrass_Cover-Canis_latrans 1.7424 1.0066 1032
## Avg_Cogongrass_Cover-Lynx_rufus 1.7906 1.0066 673
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3642 1.0077 1147
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8041 1.0087 1009
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2623 1.0022 1335
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5508 1.0092 877
## Avg_Cogongrass_Cover-Sus_scrofa 1.1824 1.0014 661
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7829 0.1792 -3.1429 -2.7757 -2.4388 1.0055
## (Intercept)-Lynx_rufus -3.4711 0.3054 -4.0942 -3.4580 -2.8835 1.0294
## (Intercept)-Didelphis_virginiana -2.6829 0.2744 -3.2150 -2.6807 -2.1472 1.0088
## (Intercept)-Sylvilagus_floridanus -3.1607 0.2641 -3.7302 -3.1435 -2.6774 1.0012
## (Intercept)-Sciurus_carolinensis -2.7338 0.2979 -3.3479 -2.7248 -2.1699 1.0047
## (Intercept)-Vulpes_vulpes -3.6262 0.5658 -4.9734 -3.5543 -2.7265 1.0053
## (Intercept)-Sus_scrofa -3.2854 0.4655 -4.2836 -3.2575 -2.4201 1.0009
## shrub_cover-Canis_latrans -0.2770 0.2163 -0.6965 -0.2779 0.1267 1.0017
## shrub_cover-Lynx_rufus -0.1676 0.3680 -0.9450 -0.1419 0.5157 1.0064
## shrub_cover-Didelphis_virginiana 0.9832 0.3833 0.2919 0.9628 1.8166 1.0383
## shrub_cover-Sylvilagus_floridanus 0.3086 0.4170 -0.4993 0.2980 1.1921 1.0032
## shrub_cover-Sciurus_carolinensis 0.8714 0.4213 0.0822 0.8662 1.7193 1.0007
## shrub_cover-Vulpes_vulpes 0.0236 0.5933 -1.1486 0.0396 1.1429 1.0246
## shrub_cover-Sus_scrofa 0.6504 0.7137 -0.6749 0.6206 2.1463 1.0165
## veg_height-Canis_latrans -0.6152 0.1796 -0.9758 -0.6108 -0.2717 1.0060
## veg_height-Lynx_rufus -0.0564 0.2559 -0.5614 -0.0487 0.4329 1.0144
## veg_height-Didelphis_virginiana 0.4502 0.2525 -0.0268 0.4479 0.9528 1.0112
## veg_height-Sylvilagus_floridanus 0.1283 0.2538 -0.3699 0.1297 0.6249 1.0118
## veg_height-Sciurus_carolinensis 0.0830 0.2213 -0.3301 0.0759 0.5209 1.0049
## veg_height-Vulpes_vulpes -0.1680 0.3207 -0.8423 -0.1514 0.4273 1.0143
## veg_height-Sus_scrofa -0.1741 0.3418 -0.8514 -0.1729 0.5113 1.0368
## week-Canis_latrans 0.0828 0.1338 -0.1849 0.0895 0.3299 1.0104
## week-Lynx_rufus -0.0125 0.1953 -0.4135 -0.0051 0.3408 1.0055
## week-Didelphis_virginiana -0.2188 0.2373 -0.7135 -0.2043 0.2037 1.0056
## week-Sylvilagus_floridanus -0.1511 0.2217 -0.6402 -0.1368 0.2419 1.0013
## week-Sciurus_carolinensis 0.1748 0.1887 -0.2146 0.1812 0.5363 1.0028
## week-Vulpes_vulpes -0.1052 0.3079 -0.8033 -0.0835 0.4230 1.0029
## week-Sus_scrofa 0.1321 0.2477 -0.3798 0.1398 0.6049 1.0007
## ESS
## (Intercept)-Canis_latrans 772
## (Intercept)-Lynx_rufus 399
## (Intercept)-Didelphis_virginiana 608
## (Intercept)-Sylvilagus_floridanus 577
## (Intercept)-Sciurus_carolinensis 823
## (Intercept)-Vulpes_vulpes 172
## (Intercept)-Sus_scrofa 475
## shrub_cover-Canis_latrans 958
## shrub_cover-Lynx_rufus 438
## shrub_cover-Didelphis_virginiana 446
## shrub_cover-Sylvilagus_floridanus 503
## shrub_cover-Sciurus_carolinensis 651
## shrub_cover-Vulpes_vulpes 656
## shrub_cover-Sus_scrofa 640
## veg_height-Canis_latrans 782
## veg_height-Lynx_rufus 684
## veg_height-Didelphis_virginiana 1051
## veg_height-Sylvilagus_floridanus 845
## veg_height-Sciurus_carolinensis 1095
## veg_height-Vulpes_vulpes 616
## veg_height-Sus_scrofa 1275
## week-Canis_latrans 1626
## week-Lynx_rufus 1043
## week-Didelphis_virginiana 1296
## week-Sylvilagus_floridanus 880
## week-Sciurus_carolinensis 1693
## week-Vulpes_vulpes 932
## week-Sus_scrofa 1479
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.481
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9087 0.5374 -3.0310 -1.9007 -0.8688 1.0022 536
## Avg_Cogongrass_Cover -0.9327 0.5054 -1.9492 -0.9239 0.0614 1.0028 419
## I(Avg_Cogongrass_Cover^2) 1.0893 0.4835 0.2471 1.0665 2.0637 1.0105 455
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9524 1.2594 0.0600 0.5449 4.3589 1.0704 910
## Avg_Cogongrass_Cover 0.6867 0.9990 0.0531 0.3546 3.3967 1.0025 687
## I(Avg_Cogongrass_Cover^2) 0.8115 1.4989 0.0450 0.3325 4.4607 1.0911 453
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8967 0.8172 0.0689 0.6686 3.1094 1.0075 291
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0303 0.2716 -3.5522 -3.0293 -2.4786 1.0127 1123
## shrub_cover 0.3701 0.3555 -0.3330 0.3664 1.0678 1.0016 1108
## veg_height -0.0181 0.2251 -0.4613 -0.0193 0.4146 1.0018 1342
## week -0.0161 0.1809 -0.3864 -0.0085 0.3280 1.0116 1255
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3721 0.4855 0.0453 0.2280 1.5039 1.0046 843
## shrub_cover 0.6978 0.9812 0.0939 0.4730 2.6007 1.0449 1651
## veg_height 0.2901 0.3307 0.0535 0.2053 1.0118 1.0397 2106
## week 0.1458 0.1564 0.0283 0.0976 0.5655 1.0697 1290
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.2110 0.7475 -2.6100 -1.2419
## (Intercept)-Lynx_rufus -1.8042 0.7206 -3.2845 -1.8032
## (Intercept)-Didelphis_virginiana -2.0269 0.6609 -3.4101 -2.0037
## (Intercept)-Sylvilagus_floridanus -1.6592 0.6723 -3.0299 -1.6674
## (Intercept)-Sciurus_carolinensis -2.3793 0.7521 -3.9813 -2.3181
## (Intercept)-Vulpes_vulpes -2.5116 0.8202 -4.3790 -2.4266
## (Intercept)-Sus_scrofa -2.4227 0.7857 -4.1724 -2.3475
## Avg_Cogongrass_Cover-Canis_latrans -0.5126 0.6023 -1.6081 -0.5311
## Avg_Cogongrass_Cover-Lynx_rufus -0.8550 0.6452 -2.1406 -0.8581
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.6333 0.6330 -1.8141 -0.6639
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5448 0.7806 -3.4058 -1.4531
## Avg_Cogongrass_Cover-Sciurus_carolinensis -1.0372 0.6434 -2.4263 -1.0139
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9641 0.6915 -2.4371 -0.9419
## Avg_Cogongrass_Cover-Sus_scrofa -1.2071 0.7740 -2.8914 -1.1505
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8140 1.0175 0.4763 1.5800
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5117 0.6307 0.5150 1.4262
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8303 0.5228 -0.0666 0.7861
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0297 0.5483 0.1058 0.9784
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1659 0.4598 0.3404 1.1328
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0726 0.5471 0.1753 1.0168
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5550 0.7762 -1.2647 0.6454
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2991 1.0117 566
## (Intercept)-Lynx_rufus -0.3581 1.0001 537
## (Intercept)-Didelphis_virginiana -0.7194 1.0014 685
## (Intercept)-Sylvilagus_floridanus -0.2658 1.0113 751
## (Intercept)-Sciurus_carolinensis -1.0394 1.0030 650
## (Intercept)-Vulpes_vulpes -1.1113 1.0131 475
## (Intercept)-Sus_scrofa -1.0544 1.0063 599
## Avg_Cogongrass_Cover-Canis_latrans 0.7917 1.0012 838
## Avg_Cogongrass_Cover-Lynx_rufus 0.4290 1.0121 577
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6959 1.0005 887
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2766 1.0036 427
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1196 1.0031 559
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3336 1.0151 758
## Avg_Cogongrass_Cover-Sus_scrofa 0.1878 1.0099 492
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3594 1.0240 245
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9647 1.0270 447
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.9893 1.0135 472
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.2521 1.0072 451
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1293 1.0058 584
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3366 1.0068 442
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.8486 1.0296 388
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7966 0.1839 -3.1657 -2.7957 -2.4410 1.0012
## (Intercept)-Lynx_rufus -3.3826 0.3086 -4.0342 -3.3664 -2.8156 1.0071
## (Intercept)-Didelphis_virginiana -2.7418 0.2832 -3.2962 -2.7470 -2.1795 1.0160
## (Intercept)-Sylvilagus_floridanus -3.1648 0.2542 -3.6911 -3.1524 -2.7047 1.0021
## (Intercept)-Sciurus_carolinensis -2.7547 0.2805 -3.3114 -2.7535 -2.2222 1.0112
## (Intercept)-Vulpes_vulpes -3.5402 0.5213 -4.7590 -3.4539 -2.7272 1.0110
## (Intercept)-Sus_scrofa -3.2352 0.4282 -4.1175 -3.2235 -2.4222 1.0161
## shrub_cover-Canis_latrans -0.2423 0.2217 -0.6845 -0.2340 0.1604 1.0099
## shrub_cover-Lynx_rufus -0.0911 0.3614 -0.8225 -0.0878 0.5809 1.0303
## shrub_cover-Didelphis_virginiana 1.0607 0.4051 0.3045 1.0479 1.8920 1.0086
## shrub_cover-Sylvilagus_floridanus 0.3192 0.4018 -0.4119 0.3010 1.1338 1.0254
## shrub_cover-Sciurus_carolinensis 0.8623 0.4135 0.0946 0.8570 1.7161 1.0126
## shrub_cover-Vulpes_vulpes 0.0860 0.6090 -1.1167 0.0980 1.2843 0.9999
## shrub_cover-Sus_scrofa 0.6271 0.6965 -0.7997 0.6302 1.9839 1.0121
## veg_height-Canis_latrans -0.6015 0.1888 -0.9843 -0.5926 -0.2519 1.0055
## veg_height-Lynx_rufus 0.0633 0.2490 -0.4279 0.0665 0.5487 1.0007
## veg_height-Didelphis_virginiana 0.4192 0.2765 -0.0881 0.4034 0.9857 1.0448
## veg_height-Sylvilagus_floridanus 0.1430 0.2548 -0.3422 0.1389 0.6485 1.0229
## veg_height-Sciurus_carolinensis 0.1087 0.2209 -0.3111 0.1038 0.5442 1.0085
## veg_height-Vulpes_vulpes -0.1185 0.3050 -0.7290 -0.1110 0.4606 1.0008
## veg_height-Sus_scrofa -0.1558 0.3385 -0.8215 -0.1506 0.5123 1.0058
## week-Canis_latrans 0.0831 0.1382 -0.1973 0.0848 0.3527 1.0043
## week-Lynx_rufus -0.0217 0.1957 -0.4259 -0.0117 0.3287 1.0126
## week-Didelphis_virginiana -0.2165 0.2365 -0.7293 -0.1999 0.1906 1.0206
## week-Sylvilagus_floridanus -0.1457 0.2256 -0.6167 -0.1346 0.2557 1.0244
## week-Sciurus_carolinensis 0.1732 0.1857 -0.1925 0.1720 0.5201 1.0011
## week-Vulpes_vulpes -0.1118 0.3100 -0.8079 -0.0840 0.4138 1.0373
## week-Sus_scrofa 0.1311 0.2455 -0.3546 0.1338 0.6140 1.0008
## ESS
## (Intercept)-Canis_latrans 723
## (Intercept)-Lynx_rufus 397
## (Intercept)-Didelphis_virginiana 587
## (Intercept)-Sylvilagus_floridanus 657
## (Intercept)-Sciurus_carolinensis 801
## (Intercept)-Vulpes_vulpes 263
## (Intercept)-Sus_scrofa 736
## shrub_cover-Canis_latrans 818
## shrub_cover-Lynx_rufus 485
## shrub_cover-Didelphis_virginiana 502
## shrub_cover-Sylvilagus_floridanus 624
## shrub_cover-Sciurus_carolinensis 709
## shrub_cover-Vulpes_vulpes 663
## shrub_cover-Sus_scrofa 648
## veg_height-Canis_latrans 770
## veg_height-Lynx_rufus 621
## veg_height-Didelphis_virginiana 600
## veg_height-Sylvilagus_floridanus 571
## veg_height-Sciurus_carolinensis 1066
## veg_height-Vulpes_vulpes 737
## veg_height-Sus_scrofa 1085
## week-Canis_latrans 1630
## week-Lynx_rufus 1210
## week-Didelphis_virginiana 1127
## week-Sylvilagus_floridanus 751
## week-Sciurus_carolinensis 1829
## week-Vulpes_vulpes 854
## week-Sus_scrofa 1672
## 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5558
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5542 1.1939 -4.6575 -2.6843 0.1937 1.0519 223
## Cogon_Patch_Size 0.0070 0.9957 -2.0807 0.0196 1.8590 1.0329 408
## Veg_shannon_index 1.0107 0.7135 -0.3973 1.0094 2.4155 1.0138 426
## total_shrub_cover -0.8749 0.8557 -2.7490 -0.8224 0.6799 1.0198 230
## Avg_Cogongrass_Cover -0.5028 1.2010 -2.8574 -0.4902 1.7946 1.2021 137
## Tree_Density -1.7064 1.0314 -3.7394 -1.6955 0.2500 1.0453 254
## Avg_Canopy_Cover 1.9682 1.0995 -0.3742 1.9911 4.0736 1.0467 719
## I(Avg_Cogongrass_Cover^2) 1.7835 0.7972 0.2553 1.7549 3.4059 1.1153 202
## avg_veg_height -0.2840 0.7081 -1.7177 -0.2760 1.0874 1.0693 222
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.3748 22.0711 0.1148 4.1633 67.4385 1.0076 83
## Cogon_Patch_Size 8.1829 13.1091 0.1969 4.3946 39.5297 1.0250 340
## Veg_shannon_index 2.1632 4.3445 0.0637 0.8579 12.3583 1.1225 191
## total_shrub_cover 3.2082 6.5185 0.0629 1.0770 20.1066 1.0244 139
## Avg_Cogongrass_Cover 1.8171 3.5750 0.0538 0.6486 10.7398 1.0255 522
## Tree_Density 5.0121 12.4580 0.0696 1.6687 29.4548 1.1506 251
## Avg_Canopy_Cover 14.6155 24.1596 0.7367 7.4017 72.6191 1.0446 151
## I(Avg_Cogongrass_Cover^2) 2.1286 4.2883 0.0569 0.7017 13.8502 1.0045 179
## avg_veg_height 0.9454 1.8509 0.0420 0.3903 5.2698 1.0624 452
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.6828 5.6863 0.0776 1.5031 20.2723 1.0145 61
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1722 0.3122 -3.8348 -3.1651 -2.5400 1.0352 491
## shrub_cover 0.5868 0.4023 -0.1918 0.5785 1.4008 1.0022 470
## veg_height 0.0039 0.2444 -0.4885 0.0042 0.4864 1.0080 1411
## week -0.0135 0.1754 -0.3801 -0.0088 0.3117 1.0016 1367
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4780 0.5483 0.0570 0.3152 1.8274 1.0430 306
## shrub_cover 0.8950 1.1081 0.1333 0.6103 3.3649 1.0330 1152
## veg_height 0.3378 0.4527 0.0636 0.2359 1.2094 1.0622 2158
## week 0.1386 0.1566 0.0267 0.0960 0.4919 1.0089 1976
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3302 1.7543 -4.1163 -1.5345
## (Intercept)-Lynx_rufus -1.7197 2.1010 -5.5613 -1.9255
## (Intercept)-Didelphis_virginiana -4.1120 1.7085 -7.9747 -3.9107
## (Intercept)-Sylvilagus_floridanus -2.9161 1.7055 -6.5217 -2.8695
## (Intercept)-Sciurus_carolinensis -4.5079 1.9448 -9.4130 -4.1637
## (Intercept)-Vulpes_vulpes -5.0280 2.4690 -11.0780 -4.5607
## (Intercept)-Sus_scrofa -5.1620 2.6216 -11.6137 -4.6568
## Cogon_Patch_Size-Canis_latrans 2.3088 1.7893 -0.1790 1.9579
## Cogon_Patch_Size-Lynx_rufus -0.3287 1.9262 -3.6632 -0.5203
## Cogon_Patch_Size-Didelphis_virginiana 2.2058 1.3495 -0.0246 2.0350
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9057 2.3179 -7.6168 -1.4572
## Cogon_Patch_Size-Sciurus_carolinensis -1.2719 1.8609 -5.8360 -0.9776
## Cogon_Patch_Size-Vulpes_vulpes -0.7743 1.9481 -5.2034 -0.5885
## Cogon_Patch_Size-Sus_scrofa -0.7991 2.1141 -5.6973 -0.5539
## Veg_shannon_index-Canis_latrans 1.5433 0.9149 -0.0365 1.4769
## Veg_shannon_index-Lynx_rufus 1.1069 1.2087 -1.2009 1.0925
## Veg_shannon_index-Didelphis_virginiana 1.3723 0.9881 -0.3255 1.2962
## Veg_shannon_index-Sylvilagus_floridanus 1.2969 0.9976 -0.4447 1.1906
## Veg_shannon_index-Sciurus_carolinensis 0.1111 1.2628 -2.8408 0.2875
## Veg_shannon_index-Vulpes_vulpes 0.4138 1.2919 -2.5301 0.5805
## Veg_shannon_index-Sus_scrofa 1.9173 1.4984 -0.1093 1.6451
## total_shrub_cover-Canis_latrans 0.2897 1.1399 -1.4451 0.1183
## total_shrub_cover-Lynx_rufus -1.7925 1.6598 -6.2217 -1.4622
## total_shrub_cover-Didelphis_virginiana -1.4799 1.4399 -5.2592 -1.2336
## total_shrub_cover-Sylvilagus_floridanus -1.0943 1.5062 -4.8298 -0.9295
## total_shrub_cover-Sciurus_carolinensis -1.0240 1.3477 -4.3281 -0.8591
## total_shrub_cover-Vulpes_vulpes -1.4603 1.8733 -6.4691 -1.0901
## total_shrub_cover-Sus_scrofa -0.8275 1.5547 -4.6177 -0.6941
## Avg_Cogongrass_Cover-Canis_latrans -0.4467 1.4347 -3.2657 -0.4567
## Avg_Cogongrass_Cover-Lynx_rufus -0.3208 1.5955 -3.2668 -0.3648
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4812 1.5215 -3.3897 -0.5271
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1842 1.5842 -4.6024 -1.1452
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3880 1.4887 -3.1582 -0.4447
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.1862 1.5806 -3.1309 -0.2458
## Avg_Cogongrass_Cover-Sus_scrofa -0.7132 1.6734 -4.1801 -0.7006
## Tree_Density-Canis_latrans -2.9424 1.7835 -7.1524 -2.6602
## Tree_Density-Lynx_rufus -0.4573 1.8260 -3.4133 -0.6816
## Tree_Density-Didelphis_virginiana -2.2431 1.6246 -5.8641 -2.0743
## Tree_Density-Sylvilagus_floridanus -2.6160 1.9049 -7.3666 -2.3384
## Tree_Density-Sciurus_carolinensis -2.2416 1.7540 -6.3337 -2.0925
## Tree_Density-Vulpes_vulpes -1.6282 1.7791 -5.2854 -1.6436
## Tree_Density-Sus_scrofa -2.3443 2.0894 -7.5100 -2.0462
## Avg_Canopy_Cover-Canis_latrans -0.1955 0.8100 -1.9392 -0.1384
## Avg_Canopy_Cover-Lynx_rufus 1.0207 2.0042 -2.3931 0.7264
## Avg_Canopy_Cover-Didelphis_virginiana 4.8278 2.3576 1.6944 4.3417
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9385 2.9487 1.7130 5.4255
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2134 2.1555 1.3093 3.8171
## Avg_Canopy_Cover-Vulpes_vulpes 3.2754 2.2371 0.4370 2.8098
## Avg_Canopy_Cover-Sus_scrofa 2.6802 1.5782 0.2651 2.4526
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6118 1.2610 0.8178 2.3736
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7149 1.3359 0.7310 2.4712
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4396 0.8816 -0.3119 1.4331
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5823 0.9966 -0.2564 1.5380
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0543 0.9870 0.3843 1.9630
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1848 1.0437 0.4445 2.0608
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1478 1.6522 -3.1725 1.3549
## avg_veg_height-Canis_latrans -0.3203 0.7672 -1.9668 -0.3370
## avg_veg_height-Lynx_rufus -0.5144 1.0502 -2.8842 -0.4292
## avg_veg_height-Didelphis_virginiana -0.5002 0.9830 -2.6341 -0.4162
## avg_veg_height-Sylvilagus_floridanus -0.3824 0.9263 -2.2455 -0.3403
## avg_veg_height-Sciurus_carolinensis 0.2411 0.9754 -1.4067 0.1401
## avg_veg_height-Vulpes_vulpes -0.4169 1.0684 -2.7482 -0.3668
## avg_veg_height-Sus_scrofa -0.2521 0.9699 -2.2555 -0.2328
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.8698 1.1065 129
## (Intercept)-Lynx_rufus 3.1299 1.0075 132
## (Intercept)-Didelphis_virginiana -1.2311 1.0473 187
## (Intercept)-Sylvilagus_floridanus 0.5784 1.0525 219
## (Intercept)-Sciurus_carolinensis -1.4961 1.0052 101
## (Intercept)-Vulpes_vulpes -1.3088 1.0032 118
## (Intercept)-Sus_scrofa -1.3464 1.0083 110
## Cogon_Patch_Size-Canis_latrans 6.8199 1.0121 329
## Cogon_Patch_Size-Lynx_rufus 3.6554 1.0293 170
## Cogon_Patch_Size-Didelphis_virginiana 5.2821 1.0080 223
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3661 1.0130 280
## Cogon_Patch_Size-Sciurus_carolinensis 1.6061 1.0590 246
## Cogon_Patch_Size-Vulpes_vulpes 2.6786 1.0263 281
## Cogon_Patch_Size-Sus_scrofa 2.6901 1.0705 240
## Veg_shannon_index-Canis_latrans 3.5698 1.0517 531
## Veg_shannon_index-Lynx_rufus 3.6350 1.0096 357
## Veg_shannon_index-Didelphis_virginiana 3.6036 1.0106 580
## Veg_shannon_index-Sylvilagus_floridanus 3.4739 1.0093 458
## Veg_shannon_index-Sciurus_carolinensis 2.0933 1.0153 283
## Veg_shannon_index-Vulpes_vulpes 2.4421 1.0286 313
## Veg_shannon_index-Sus_scrofa 5.6044 1.0392 286
## total_shrub_cover-Canis_latrans 3.2299 1.0394 161
## total_shrub_cover-Lynx_rufus 0.5782 1.0707 145
## total_shrub_cover-Didelphis_virginiana 0.6693 1.0980 195
## total_shrub_cover-Sylvilagus_floridanus 1.2892 1.0595 172
## total_shrub_cover-Sciurus_carolinensis 1.1346 1.0498 181
## total_shrub_cover-Vulpes_vulpes 1.0370 1.0501 136
## total_shrub_cover-Sus_scrofa 1.9243 1.0128 156
## Avg_Cogongrass_Cover-Canis_latrans 2.4344 1.1258 231
## Avg_Cogongrass_Cover-Lynx_rufus 3.0303 1.1342 189
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.5398 1.1222 193
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6736 1.1635 239
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7246 1.1279 209
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2639 1.1329 220
## Avg_Cogongrass_Cover-Sus_scrofa 2.4266 1.1167 202
## Tree_Density-Canis_latrans -0.4225 1.0437 237
## Tree_Density-Lynx_rufus 4.0529 1.0456 167
## Tree_Density-Didelphis_virginiana 0.4818 1.0160 245
## Tree_Density-Sylvilagus_floridanus 0.3478 1.0458 266
## Tree_Density-Sciurus_carolinensis 0.6100 1.0223 186
## Tree_Density-Vulpes_vulpes 2.0104 1.0278 304
## Tree_Density-Sus_scrofa 0.8008 1.0067 295
## Avg_Canopy_Cover-Canis_latrans 1.2761 1.0076 420
## Avg_Canopy_Cover-Lynx_rufus 5.5983 1.0688 116
## Avg_Canopy_Cover-Didelphis_virginiana 10.7396 1.0159 140
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.2569 1.0058 167
## Avg_Canopy_Cover-Sciurus_carolinensis 9.5834 1.0390 113
## Avg_Canopy_Cover-Vulpes_vulpes 8.9882 1.0545 159
## Avg_Canopy_Cover-Sus_scrofa 6.7052 1.0124 299
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.9109 1.0372 205
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1575 1.0816 184
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2701 1.0697 228
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7032 1.1215 292
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.3770 1.0841 273
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.6340 1.0877 284
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.9076 1.0705 112
## avg_veg_height-Canis_latrans 1.2152 1.0383 262
## avg_veg_height-Lynx_rufus 1.3810 1.0301 258
## avg_veg_height-Didelphis_virginiana 1.2252 1.0637 305
## avg_veg_height-Sylvilagus_floridanus 1.3449 1.0210 326
## avg_veg_height-Sciurus_carolinensis 2.5005 1.0327 307
## avg_veg_height-Vulpes_vulpes 1.5503 1.0644 313
## avg_veg_height-Sus_scrofa 1.7097 1.0347 343
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7883 0.1931 -3.1931 -2.7833 -2.4304 1.0070
## (Intercept)-Lynx_rufus -3.6104 0.3401 -4.2657 -3.6028 -2.9654 1.0442
## (Intercept)-Didelphis_virginiana -2.8228 0.3002 -3.4127 -2.8138 -2.2584 1.0183
## (Intercept)-Sylvilagus_floridanus -3.2120 0.2440 -3.7068 -3.2027 -2.7647 1.0083
## (Intercept)-Sciurus_carolinensis -2.9391 0.2934 -3.5137 -2.9391 -2.3742 1.0215
## (Intercept)-Vulpes_vulpes -3.8475 0.5429 -5.0507 -3.7963 -2.9273 1.0399
## (Intercept)-Sus_scrofa -3.5225 0.5202 -4.6357 -3.4838 -2.5684 1.0580
## shrub_cover-Canis_latrans -0.2869 0.2498 -0.7802 -0.2879 0.2065 1.0149
## shrub_cover-Lynx_rufus 0.0329 0.3849 -0.7116 0.0383 0.7813 1.0207
## shrub_cover-Didelphis_virginiana 1.2291 0.4097 0.4696 1.2128 2.1007 1.0113
## shrub_cover-Sylvilagus_floridanus 0.6157 0.4079 -0.1711 0.6233 1.4044 1.0196
## shrub_cover-Sciurus_carolinensis 1.2031 0.4126 0.3738 1.1943 2.0222 1.0044
## shrub_cover-Vulpes_vulpes 0.4605 0.6048 -0.7442 0.4526 1.6762 1.0027
## shrub_cover-Sus_scrofa 1.0978 0.8195 -0.3975 1.0600 2.8563 1.0223
## veg_height-Canis_latrans -0.5841 0.1899 -0.9719 -0.5797 -0.2371 1.0166
## veg_height-Lynx_rufus 0.1368 0.2471 -0.3424 0.1385 0.6127 1.0120
## veg_height-Didelphis_virginiana 0.5257 0.2691 0.0280 0.5156 1.0831 1.0280
## veg_height-Sylvilagus_floridanus 0.1422 0.2517 -0.3472 0.1514 0.6458 1.0079
## veg_height-Sciurus_carolinensis 0.1844 0.2266 -0.2511 0.1858 0.6283 1.0176
## veg_height-Vulpes_vulpes -0.2246 0.3536 -0.9858 -0.2124 0.4190 1.0184
## veg_height-Sus_scrofa -0.1985 0.3507 -0.9519 -0.1829 0.4652 0.9999
## week-Canis_latrans 0.0847 0.1333 -0.1830 0.0882 0.3410 1.0003
## week-Lynx_rufus -0.0134 0.1997 -0.4253 0.0009 0.3509 1.0059
## week-Didelphis_virginiana -0.2174 0.2388 -0.7140 -0.1979 0.1977 1.0126
## week-Sylvilagus_floridanus -0.1375 0.2119 -0.5772 -0.1228 0.2549 1.0015
## week-Sciurus_carolinensis 0.1655 0.1869 -0.2090 0.1673 0.5220 1.0005
## week-Vulpes_vulpes -0.0917 0.2742 -0.6794 -0.0856 0.4228 1.0066
## week-Sus_scrofa 0.1200 0.2373 -0.3445 0.1211 0.5892 0.9998
## ESS
## (Intercept)-Canis_latrans 454
## (Intercept)-Lynx_rufus 259
## (Intercept)-Didelphis_virginiana 339
## (Intercept)-Sylvilagus_floridanus 628
## (Intercept)-Sciurus_carolinensis 581
## (Intercept)-Vulpes_vulpes 164
## (Intercept)-Sus_scrofa 112
## shrub_cover-Canis_latrans 418
## shrub_cover-Lynx_rufus 319
## shrub_cover-Didelphis_virginiana 308
## shrub_cover-Sylvilagus_floridanus 379
## shrub_cover-Sciurus_carolinensis 387
## shrub_cover-Vulpes_vulpes 477
## shrub_cover-Sus_scrofa 153
## veg_height-Canis_latrans 729
## veg_height-Lynx_rufus 482
## veg_height-Didelphis_virginiana 780
## veg_height-Sylvilagus_floridanus 710
## veg_height-Sciurus_carolinensis 1142
## veg_height-Vulpes_vulpes 513
## veg_height-Sus_scrofa 811
## week-Canis_latrans 1667
## week-Lynx_rufus 886
## week-Didelphis_virginiana 1018
## week-Sylvilagus_floridanus 781
## week-Sciurus_carolinensis 1504
## week-Vulpes_vulpes 1012
## week-Sus_scrofa 1503
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3398
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5404 1.0532 -3.4075 -1.6430 0.8502 1.0008 287
## Cogon_Patch_Size -0.6421 0.9146 -2.5530 -0.6213 1.1289 1.0056 1267
## Veg_shannon_index 0.7003 0.7277 -0.7547 0.7031 2.1457 1.0874 352
## total_shrub_cover -0.0880 0.5466 -1.1832 -0.0869 0.9691 1.0017 520
## Avg_Cogongrass_Cover 1.9799 0.7589 0.5609 1.9588 3.5467 1.1043 307
## Tree_Density -1.7685 0.8685 -3.4471 -1.7598 0.0264 1.0280 195
## Avg_Canopy_Cover 1.7726 0.8531 0.0259 1.7677 3.5023 1.0160 780
## avg_veg_height -0.6753 0.5760 -1.8208 -0.6653 0.4734 1.0666 301
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.8438 25.4364 0.2646 5.4860 68.2347 1.0634 95
## Cogon_Patch_Size 10.6434 22.9695 0.2390 4.2383 62.0391 1.0545 83
## Veg_shannon_index 2.8526 5.2913 0.0704 1.1305 15.9892 1.1472 102
## total_shrub_cover 0.8534 1.6781 0.0471 0.3601 4.7090 1.0574 685
## Avg_Cogongrass_Cover 1.2209 2.5256 0.0482 0.4744 7.4933 1.1204 788
## Tree_Density 6.5347 48.5423 0.0689 1.1844 28.6417 1.6706 188
## Avg_Canopy_Cover 5.2230 8.5217 0.2479 2.8562 24.7689 1.0583 203
## avg_veg_height 0.5948 0.8664 0.0410 0.3100 3.1087 1.0206 539
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7392 3.685 0.0856 1.4491 12.8206 1.309 97
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.894 0.3101 -3.5139 -2.8955 -2.2324 1.019 1510
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5972 0.7305 0.0815 0.4111 2.2111 1.1821 700
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4772 1.4827 -2.1150 0.3709
## (Intercept)-Lynx_rufus 0.8006 3.3009 -2.9270 -0.1364
## (Intercept)-Didelphis_virginiana -3.0719 1.3539 -5.9654 -2.9687
## (Intercept)-Sylvilagus_floridanus -1.9830 1.3671 -5.1690 -1.8853
## (Intercept)-Sciurus_carolinensis -3.6056 1.6837 -7.6828 -3.3263
## (Intercept)-Vulpes_vulpes -3.1799 1.9378 -7.0317 -3.0569
## (Intercept)-Sus_scrofa -4.8973 2.3434 -10.8800 -4.4705
## Cogon_Patch_Size-Canis_latrans 1.3404 1.7423 -0.8009 0.9434
## Cogon_Patch_Size-Lynx_rufus -0.7635 2.4360 -4.2540 -1.0614
## Cogon_Patch_Size-Didelphis_virginiana 1.4496 1.1009 -0.2993 1.3143
## Cogon_Patch_Size-Sylvilagus_floridanus -3.2010 2.9775 -12.1565 -2.4166
## Cogon_Patch_Size-Sciurus_carolinensis -2.3088 2.1520 -7.9482 -1.8579
## Cogon_Patch_Size-Vulpes_vulpes -1.6958 2.2152 -6.6864 -1.4184
## Cogon_Patch_Size-Sus_scrofa -1.8400 2.5182 -8.4060 -1.2906
## Veg_shannon_index-Canis_latrans 1.3290 0.8298 -0.0120 1.2214
## Veg_shannon_index-Lynx_rufus 0.4880 1.5699 -3.1064 0.6517
## Veg_shannon_index-Didelphis_virginiana 1.0629 0.9020 -0.5050 0.9964
## Veg_shannon_index-Sylvilagus_floridanus 1.1397 0.9392 -0.4229 1.0334
## Veg_shannon_index-Sciurus_carolinensis -0.2859 1.0331 -2.6733 -0.1603
## Veg_shannon_index-Vulpes_vulpes -0.3250 1.4081 -3.5237 -0.0925
## Veg_shannon_index-Sus_scrofa 2.0460 1.5760 -0.0314 1.7028
## total_shrub_cover-Canis_latrans 0.2220 0.7023 -0.9308 0.1485
## total_shrub_cover-Lynx_rufus -0.5960 0.9712 -2.9108 -0.4853
## total_shrub_cover-Didelphis_virginiana -0.3277 0.7174 -1.9473 -0.2795
## total_shrub_cover-Sylvilagus_floridanus -0.0171 0.8263 -1.5220 -0.0157
## total_shrub_cover-Sciurus_carolinensis 0.1104 0.6659 -1.1618 0.0851
## total_shrub_cover-Vulpes_vulpes -0.2759 0.8259 -2.0784 -0.2359
## total_shrub_cover-Sus_scrofa 0.1712 0.8204 -1.3702 0.1313
## Avg_Cogongrass_Cover-Canis_latrans 2.2419 0.9058 0.6902 2.1614
## Avg_Cogongrass_Cover-Lynx_rufus 2.4255 1.0873 0.6189 2.3030
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1203 0.9021 0.5188 2.0608
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5646 1.0027 -0.5271 1.5897
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3182 0.9667 0.6002 2.2336
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3832 1.1194 0.4804 2.2514
## Avg_Cogongrass_Cover-Sus_scrofa 1.5818 1.1307 -0.8662 1.6239
## Tree_Density-Canis_latrans -2.5828 1.3928 -6.1718 -2.3253
## Tree_Density-Lynx_rufus -0.3422 1.8963 -3.1157 -0.6030
## Tree_Density-Didelphis_virginiana -2.3044 1.2172 -5.1955 -2.1289
## Tree_Density-Sylvilagus_floridanus -2.5682 1.5266 -6.2640 -2.3281
## Tree_Density-Sciurus_carolinensis -2.4756 1.5644 -6.1222 -2.1772
## Tree_Density-Vulpes_vulpes -1.4359 3.4677 -4.6264 -1.7530
## Tree_Density-Sus_scrofa -2.0716 1.5023 -5.6653 -1.9240
## Avg_Canopy_Cover-Canis_latrans 0.0610 0.7523 -1.5420 0.1070
## Avg_Canopy_Cover-Lynx_rufus 0.8527 1.6310 -2.0628 0.7049
## Avg_Canopy_Cover-Didelphis_virginiana 3.1536 1.1793 1.3889 2.9831
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0228 1.9127 1.2623 3.6838
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5769 1.0798 0.9254 2.4211
## Avg_Canopy_Cover-Vulpes_vulpes 2.4143 1.5216 0.2496 2.1351
## Avg_Canopy_Cover-Sus_scrofa 2.2000 1.0630 0.5130 2.0544
## avg_veg_height-Canis_latrans -0.8292 0.6447 -2.1271 -0.8145
## avg_veg_height-Lynx_rufus -0.6575 0.8278 -2.3327 -0.6753
## avg_veg_height-Didelphis_virginiana -0.7651 0.7187 -2.3151 -0.7398
## avg_veg_height-Sylvilagus_floridanus -0.8261 0.7307 -2.3345 -0.7978
## avg_veg_height-Sciurus_carolinensis -0.3017 0.7180 -1.6701 -0.3409
## avg_veg_height-Vulpes_vulpes -0.7979 0.8221 -2.5000 -0.7593
## avg_veg_height-Sus_scrofa -0.7466 0.7763 -2.3394 -0.7208
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.1886 1.0360 153
## (Intercept)-Lynx_rufus 10.2289 1.0884 45
## (Intercept)-Didelphis_virginiana -0.7434 1.0573 174
## (Intercept)-Sylvilagus_floridanus 0.3937 1.0157 262
## (Intercept)-Sciurus_carolinensis -1.1670 1.0711 120
## (Intercept)-Vulpes_vulpes 0.2989 1.0720 133
## (Intercept)-Sus_scrofa -1.6846 1.0159 75
## Cogon_Patch_Size-Canis_latrans 5.8338 1.0044 273
## Cogon_Patch_Size-Lynx_rufus 5.8917 1.1924 70
## Cogon_Patch_Size-Didelphis_virginiana 4.0976 1.0407 214
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1151 1.0259 104
## Cogon_Patch_Size-Sciurus_carolinensis 0.2576 1.0281 185
## Cogon_Patch_Size-Vulpes_vulpes 2.0440 1.0793 195
## Cogon_Patch_Size-Sus_scrofa 1.1582 1.0149 149
## Veg_shannon_index-Canis_latrans 3.3524 1.0575 171
## Veg_shannon_index-Lynx_rufus 3.0987 1.1597 104
## Veg_shannon_index-Didelphis_virginiana 3.0195 1.0218 792
## Veg_shannon_index-Sylvilagus_floridanus 3.2690 1.0675 520
## Veg_shannon_index-Sciurus_carolinensis 1.4161 1.0085 395
## Veg_shannon_index-Vulpes_vulpes 1.6762 1.1791 171
## Veg_shannon_index-Sus_scrofa 6.4107 1.0364 148
## total_shrub_cover-Canis_latrans 1.9017 1.0199 574
## total_shrub_cover-Lynx_rufus 1.0285 1.0147 375
## total_shrub_cover-Didelphis_virginiana 0.9898 1.0189 810
## total_shrub_cover-Sylvilagus_floridanus 1.6137 1.0092 558
## total_shrub_cover-Sciurus_carolinensis 1.5546 1.0009 897
## total_shrub_cover-Vulpes_vulpes 1.2279 1.0160 475
## total_shrub_cover-Sus_scrofa 2.0105 1.0115 713
## Avg_Cogongrass_Cover-Canis_latrans 4.2208 1.1063 297
## Avg_Cogongrass_Cover-Lynx_rufus 4.8574 1.2095 367
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0544 1.1095 334
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4765 1.0444 379
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4545 1.1587 278
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9310 1.1583 252
## Avg_Cogongrass_Cover-Sus_scrofa 3.6160 1.0377 430
## Tree_Density-Canis_latrans -0.6321 1.1627 292
## Tree_Density-Lynx_rufus 4.6586 1.0355 100
## Tree_Density-Didelphis_virginiana -0.4659 1.1217 497
## Tree_Density-Sylvilagus_floridanus -0.3102 1.0900 293
## Tree_Density-Sciurus_carolinensis -0.3741 1.1125 147
## Tree_Density-Vulpes_vulpes 1.9042 1.6811 98
## Tree_Density-Sus_scrofa 0.5795 1.0256 344
## Avg_Canopy_Cover-Canis_latrans 1.4310 1.0626 373
## Avg_Canopy_Cover-Lynx_rufus 4.6323 1.0238 155
## Avg_Canopy_Cover-Didelphis_virginiana 5.9724 1.0900 223
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.6004 1.1194 253
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1666 1.0670 254
## Avg_Canopy_Cover-Vulpes_vulpes 6.1867 1.0883 191
## Avg_Canopy_Cover-Sus_scrofa 4.5927 1.0400 394
## avg_veg_height-Canis_latrans 0.4232 1.0349 393
## avg_veg_height-Lynx_rufus 1.0532 1.0288 483
## avg_veg_height-Didelphis_virginiana 0.5821 1.0319 479
## avg_veg_height-Sylvilagus_floridanus 0.5832 1.0082 560
## avg_veg_height-Sciurus_carolinensis 1.2174 1.0826 439
## avg_veg_height-Vulpes_vulpes 0.7589 1.0229 396
## avg_veg_height-Sus_scrofa 0.7425 1.0307 599
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6607 0.1732 -3.0072 -2.6541 -2.3260 1.0007
## (Intercept)-Lynx_rufus -3.5743 0.3571 -4.3272 -3.5502 -2.9307 1.0028
## (Intercept)-Didelphis_virginiana -2.3779 0.2470 -2.8690 -2.3747 -1.9080 1.0030
## (Intercept)-Sylvilagus_floridanus -3.1447 0.2459 -3.6480 -3.1399 -2.6814 1.0426
## (Intercept)-Sciurus_carolinensis -2.5168 0.2670 -3.0736 -2.5063 -2.0218 1.0045
## (Intercept)-Vulpes_vulpes -3.6934 0.5634 -4.8877 -3.6676 -2.6984 1.2176
## (Intercept)-Sus_scrofa -2.9039 0.4015 -3.7964 -2.8771 -2.1884 1.0453
## ESS
## (Intercept)-Canis_latrans 854
## (Intercept)-Lynx_rufus 80
## (Intercept)-Didelphis_virginiana 1156
## (Intercept)-Sylvilagus_floridanus 553
## (Intercept)-Sciurus_carolinensis 947
## (Intercept)-Vulpes_vulpes 182
## (Intercept)-Sus_scrofa 770
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.326
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0870 0.5198 -2.0600 -1.1038 0.0038 1.0058 579
## Avg_Cogongrass_Cover 0.3893 0.3965 -0.3810 0.3808 1.1575 1.0043 491
## total_shrub_cover -0.0987 0.3531 -0.7927 -0.0943 0.5827 1.0199 821
## avg_veg_height -0.1170 0.3689 -0.8486 -0.1117 0.5801 1.0023 561
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0801 1.5780 0.0617 0.5975 5.0802 1.0098 605
## Avg_Cogongrass_Cover 0.4840 0.7710 0.0448 0.2556 2.1531 1.0057 1180
## total_shrub_cover 0.4701 0.7695 0.0415 0.2440 2.1806 1.0213 611
## avg_veg_height 0.3044 0.4360 0.0374 0.1872 1.2848 1.0263 1835
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8624 1.6671 0.1759 1.3915 6.3212 1.0415 168
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8763 0.2874 -3.4622 -2.8718 -2.3155 1.0119 1139
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.464 0.5765 0.0614 0.3115 1.7365 1.0156 779
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3449 0.7244 -1.6778 -0.3607
## (Intercept)-Lynx_rufus -0.7509 0.7607 -2.0741 -0.8005
## (Intercept)-Didelphis_virginiana -1.3731 0.6642 -2.7348 -1.3501
## (Intercept)-Sylvilagus_floridanus -0.8308 0.7075 -2.1523 -0.8671
## (Intercept)-Sciurus_carolinensis -1.4880 0.6980 -2.9907 -1.4489
## (Intercept)-Vulpes_vulpes -1.5152 0.8020 -3.2548 -1.4762
## (Intercept)-Sus_scrofa -1.7379 0.7941 -3.5122 -1.6530
## Avg_Cogongrass_Cover-Canis_latrans 0.6216 0.5089 -0.2731 0.5960
## Avg_Cogongrass_Cover-Lynx_rufus 0.6699 0.5322 -0.2379 0.6225
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5472 0.4713 -0.3241 0.5349
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0719 0.5623 -1.2577 -0.0397
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4228 0.4588 -0.4641 0.4133
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5116 0.5398 -0.4765 0.4900
## Avg_Cogongrass_Cover-Sus_scrofa 0.0446 0.6434 -1.4144 0.1020
## total_shrub_cover-Canis_latrans 0.2023 0.4402 -0.5868 0.1681
## total_shrub_cover-Lynx_rufus -0.5753 0.6283 -2.1033 -0.4775
## total_shrub_cover-Didelphis_virginiana -0.1236 0.4342 -1.0305 -0.1067
## total_shrub_cover-Sylvilagus_floridanus -0.2183 0.4797 -1.3107 -0.1865
## total_shrub_cover-Sciurus_carolinensis -0.0149 0.4175 -0.8301 -0.0147
## total_shrub_cover-Vulpes_vulpes -0.1722 0.5807 -1.3413 -0.1636
## total_shrub_cover-Sus_scrofa 0.1616 0.5334 -0.8361 0.1320
## avg_veg_height-Canis_latrans -0.2059 0.4515 -1.1086 -0.1957
## avg_veg_height-Lynx_rufus -0.1100 0.5154 -1.1567 -0.1080
## avg_veg_height-Didelphis_virginiana -0.1373 0.4642 -1.0919 -0.1289
## avg_veg_height-Sylvilagus_floridanus -0.2535 0.4771 -1.2257 -0.2369
## avg_veg_height-Sciurus_carolinensis 0.1871 0.4497 -0.6596 0.1651
## avg_veg_height-Vulpes_vulpes -0.2056 0.5103 -1.2737 -0.1820
## avg_veg_height-Sus_scrofa -0.1280 0.5012 -1.1272 -0.1253
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1455 1.0215 477
## (Intercept)-Lynx_rufus 0.9465 1.0129 406
## (Intercept)-Didelphis_virginiana -0.1181 1.0025 812
## (Intercept)-Sylvilagus_floridanus 0.7242 1.0023 542
## (Intercept)-Sciurus_carolinensis -0.2539 1.0080 611
## (Intercept)-Vulpes_vulpes 0.0324 1.0097 539
## (Intercept)-Sus_scrofa -0.3546 1.0388 621
## Avg_Cogongrass_Cover-Canis_latrans 1.6880 1.0015 891
## Avg_Cogongrass_Cover-Lynx_rufus 1.8644 1.0069 733
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5308 1.0088 873
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9432 1.0016 799
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3238 1.0080 679
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6126 1.0099 957
## Avg_Cogongrass_Cover-Sus_scrofa 1.2066 1.0043 902
## total_shrub_cover-Canis_latrans 1.1844 1.0035 1119
## total_shrub_cover-Lynx_rufus 0.4006 1.0106 543
## total_shrub_cover-Didelphis_virginiana 0.6954 1.0134 1218
## total_shrub_cover-Sylvilagus_floridanus 0.7005 1.0139 937
## total_shrub_cover-Sciurus_carolinensis 0.7853 1.0034 1960
## total_shrub_cover-Vulpes_vulpes 0.9384 1.0264 779
## total_shrub_cover-Sus_scrofa 1.2964 1.0018 1225
## avg_veg_height-Canis_latrans 0.6887 1.0062 889
## avg_veg_height-Lynx_rufus 0.9299 1.0080 949
## avg_veg_height-Didelphis_virginiana 0.7845 0.9998 899
## avg_veg_height-Sylvilagus_floridanus 0.6677 0.9996 820
## avg_veg_height-Sciurus_carolinensis 1.1212 1.0011 1015
## avg_veg_height-Vulpes_vulpes 0.7671 1.0014 746
## avg_veg_height-Sus_scrofa 0.8527 1.0008 965
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6497 0.1729 -3.0008 -2.6439 -2.3281 1.0009
## (Intercept)-Lynx_rufus -3.3895 0.2911 -3.9960 -3.3786 -2.8535 1.0645
## (Intercept)-Didelphis_virginiana -2.4176 0.2499 -2.9187 -2.4171 -1.9489 1.0084
## (Intercept)-Sylvilagus_floridanus -3.1170 0.2732 -3.6755 -3.1028 -2.6159 1.0090
## (Intercept)-Sciurus_carolinensis -2.5310 0.2648 -3.0889 -2.5201 -2.0514 1.0088
## (Intercept)-Vulpes_vulpes -3.5202 0.5369 -4.7314 -3.4560 -2.6321 1.0060
## (Intercept)-Sus_scrofa -2.9407 0.3853 -3.7523 -2.9127 -2.2422 1.0124
## ESS
## (Intercept)-Canis_latrans 942
## (Intercept)-Lynx_rufus 303
## (Intercept)-Didelphis_virginiana 1251
## (Intercept)-Sylvilagus_floridanus 510
## (Intercept)-Sciurus_carolinensis 1106
## (Intercept)-Vulpes_vulpes 289
## (Intercept)-Sus_scrofa 804
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3158
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2978 0.5874 -2.4835 -1.3048 -0.0561 1.0401 944
## Tree_Density -0.7294 0.5730 -1.9941 -0.6856 0.3402 1.0139 553
## Avg_Canopy_Cover 1.0007 0.4482 0.1542 0.9730 1.9303 1.0025 1253
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8813 2.7162 0.1362 1.2159 7.3223 1.0128 1206
## Tree_Density 1.4284 3.1399 0.0682 0.6669 7.1704 1.0985 1058
## Avg_Canopy_Cover 1.0918 1.7388 0.0809 0.6600 4.8238 1.0334 1019
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7325 0.8489 0.0504 0.4484 2.9574 1.0213 169
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8474 0.2831 -3.3935 -2.8458 -2.2849 1.0041 1733
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4657 0.7139 0.0601 0.2916 1.7889 1.064 817
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1926 0.6965 -1.5936 -0.1763 1.1626
## (Intercept)-Lynx_rufus -0.4580 0.9206 -2.0443 -0.5277 1.6962
## (Intercept)-Didelphis_virginiana -1.8720 0.6680 -3.3338 -1.8300 -0.6462
## (Intercept)-Sylvilagus_floridanus -1.0519 0.6711 -2.3956 -1.0445 0.2793
## (Intercept)-Sciurus_carolinensis -1.9496 0.7162 -3.4807 -1.8973 -0.6871
## (Intercept)-Vulpes_vulpes -1.9790 0.8772 -3.7467 -1.9723 -0.1593
## (Intercept)-Sus_scrofa -2.4443 0.8680 -4.3310 -2.3809 -0.9594
## Tree_Density-Canis_latrans -0.9450 0.6369 -2.4343 -0.8604 0.0769
## Tree_Density-Lynx_rufus 0.4002 0.8048 -0.8146 0.2688 2.3765
## Tree_Density-Didelphis_virginiana -1.1202 0.9069 -3.2963 -0.9434 0.1986
## Tree_Density-Sylvilagus_floridanus -1.2488 0.9346 -3.5332 -1.0698 0.0983
## Tree_Density-Sciurus_carolinensis -1.0274 0.8774 -3.1843 -0.8884 0.2927
## Tree_Density-Vulpes_vulpes -0.6341 0.8406 -2.5598 -0.5799 0.8747
## Tree_Density-Sus_scrofa -1.0150 0.9794 -3.3290 -0.8868 0.5507
## Avg_Canopy_Cover-Canis_latrans -0.0169 0.4794 -0.9769 -0.0170 0.9357
## Avg_Canopy_Cover-Lynx_rufus 0.5769 0.6588 -0.6110 0.5439 1.9633
## Avg_Canopy_Cover-Didelphis_virginiana 1.3386 0.5516 0.4228 1.2846 2.5725
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8876 0.8893 0.6181 1.7282 4.0750
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3037 0.5539 0.3527 1.2530 2.5648
## Avg_Canopy_Cover-Vulpes_vulpes 0.9929 0.6207 -0.0927 0.9493 2.3874
## Avg_Canopy_Cover-Sus_scrofa 1.2323 0.5905 0.2005 1.1844 2.5192
## Rhat ESS
## (Intercept)-Canis_latrans 1.0160 564
## (Intercept)-Lynx_rufus 1.0408 332
## (Intercept)-Didelphis_virginiana 1.0070 1070
## (Intercept)-Sylvilagus_floridanus 1.0030 871
## (Intercept)-Sciurus_carolinensis 1.0072 858
## (Intercept)-Vulpes_vulpes 1.0447 469
## (Intercept)-Sus_scrofa 1.0458 620
## Tree_Density-Canis_latrans 1.0103 829
## Tree_Density-Lynx_rufus 1.0440 553
## Tree_Density-Didelphis_virginiana 1.0184 559
## Tree_Density-Sylvilagus_floridanus 1.0890 488
## Tree_Density-Sciurus_carolinensis 1.0066 593
## Tree_Density-Vulpes_vulpes 1.0074 746
## Tree_Density-Sus_scrofa 1.0303 591
## Avg_Canopy_Cover-Canis_latrans 1.0027 1271
## Avg_Canopy_Cover-Lynx_rufus 1.0016 714
## Avg_Canopy_Cover-Didelphis_virginiana 1.0024 931
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0096 472
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0004 1193
## Avg_Canopy_Cover-Vulpes_vulpes 1.0051 1031
## Avg_Canopy_Cover-Sus_scrofa 1.0042 1080
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6467 0.1739 -2.9943 -2.6436 -2.3235 1.0182
## (Intercept)-Lynx_rufus -3.3818 0.3029 -3.9997 -3.3741 -2.8304 1.0696
## (Intercept)-Didelphis_virginiana -2.4076 0.2458 -2.9066 -2.4036 -1.9465 1.0016
## (Intercept)-Sylvilagus_floridanus -3.0590 0.2540 -3.5800 -3.0482 -2.5948 1.0081
## (Intercept)-Sciurus_carolinensis -2.5201 0.2673 -3.0661 -2.5165 -2.0235 1.0160
## (Intercept)-Vulpes_vulpes -3.4844 0.5637 -4.8086 -3.4183 -2.6148 1.0338
## (Intercept)-Sus_scrofa -2.8985 0.3862 -3.6945 -2.8789 -2.1858 1.0149
## ESS
## (Intercept)-Canis_latrans 917
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 1369
## (Intercept)-Sylvilagus_floridanus 665
## (Intercept)-Sciurus_carolinensis 1036
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 728
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3247
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2387 0.5767 -2.4084 -1.2392 -0.1053 1.0307 431
## Cogon_Patch_Size -0.4304 0.6901 -1.9169 -0.3711 0.8628 1.0104 1006
## Avg_Cogongrass_Cover 0.3948 0.3578 -0.2704 0.3802 1.1379 1.0203 718
## total_shrub_cover -0.0286 0.3510 -0.7243 -0.0288 0.6795 1.0041 810
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3012 1.9269 0.0707 0.6770 6.1917 1.0156 573
## Cogon_Patch_Size 2.8897 5.0432 0.1363 1.4671 14.2957 1.0114 450
## Avg_Cogongrass_Cover 0.4205 0.6095 0.0410 0.2346 1.9172 1.0252 1191
## total_shrub_cover 0.4040 0.6173 0.0397 0.2212 1.9277 1.0350 802
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1961 1.8153 0.1742 1.7461 7.1393 1.0134 185
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.862 0.2907 -3.4509 -2.8654 -2.2696 0.9997 912
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4899 0.6227 0.0639 0.3064 1.9138 1.0211 409
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3389 0.8494 -1.9531 -0.3898
## (Intercept)-Lynx_rufus -0.9560 0.8209 -2.5128 -0.9999
## (Intercept)-Didelphis_virginiana -1.5104 0.6922 -3.0206 -1.4713
## (Intercept)-Sylvilagus_floridanus -1.0473 0.8067 -2.6264 -1.0628
## (Intercept)-Sciurus_carolinensis -1.6892 0.7902 -3.5374 -1.6205
## (Intercept)-Vulpes_vulpes -1.7453 0.9270 -3.7781 -1.6863
## (Intercept)-Sus_scrofa -1.9202 0.8789 -4.0172 -1.8306
## Cogon_Patch_Size-Canis_latrans 0.8651 0.8681 -0.3078 0.6968
## Cogon_Patch_Size-Lynx_rufus -0.6275 0.9577 -2.3463 -0.6782
## Cogon_Patch_Size-Didelphis_virginiana 0.8177 0.7796 -0.2539 0.7453
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6087 1.4280 -5.0282 -1.3207
## Cogon_Patch_Size-Sciurus_carolinensis -1.1688 0.9643 -3.5129 -0.9934
## Cogon_Patch_Size-Vulpes_vulpes -1.1104 1.2413 -4.1702 -0.8801
## Cogon_Patch_Size-Sus_scrofa -0.7251 1.1320 -3.4656 -0.5595
## Avg_Cogongrass_Cover-Canis_latrans 0.3818 0.4144 -0.4010 0.3838
## Avg_Cogongrass_Cover-Lynx_rufus 0.7536 0.5485 -0.1178 0.6862
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3426 0.4505 -0.6018 0.3519
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0732 0.5056 -0.9715 0.0880
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6548 0.4499 -0.1618 0.6348
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4981 0.4863 -0.4280 0.4820
## Avg_Cogongrass_Cover-Sus_scrofa 0.1251 0.6167 -1.2458 0.1683
## total_shrub_cover-Canis_latrans 0.2180 0.4388 -0.5623 0.1948
## total_shrub_cover-Lynx_rufus -0.4038 0.5788 -1.7449 -0.3249
## total_shrub_cover-Didelphis_virginiana -0.1625 0.4461 -1.1313 -0.1511
## total_shrub_cover-Sylvilagus_floridanus -0.0742 0.5206 -1.2077 -0.0544
## total_shrub_cover-Sciurus_carolinensis 0.0957 0.4286 -0.7219 0.0885
## total_shrub_cover-Vulpes_vulpes -0.0768 0.5207 -1.1670 -0.0670
## total_shrub_cover-Sus_scrofa 0.2227 0.5437 -0.7681 0.1799
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4373 1.0307 289
## (Intercept)-Lynx_rufus 0.7862 1.0139 391
## (Intercept)-Didelphis_virginiana -0.2352 1.0175 559
## (Intercept)-Sylvilagus_floridanus 0.6408 1.0112 439
## (Intercept)-Sciurus_carolinensis -0.3414 1.0059 472
## (Intercept)-Vulpes_vulpes -0.0930 1.0317 370
## (Intercept)-Sus_scrofa -0.4351 1.0252 457
## Cogon_Patch_Size-Canis_latrans 3.1223 1.0063 743
## Cogon_Patch_Size-Lynx_rufus 1.5875 1.0151 508
## Cogon_Patch_Size-Didelphis_virginiana 2.2424 1.0774 353
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1638 1.0098 286
## Cogon_Patch_Size-Sciurus_carolinensis 0.2214 1.0072 511
## Cogon_Patch_Size-Vulpes_vulpes 0.6192 1.0456 306
## Cogon_Patch_Size-Sus_scrofa 0.9998 1.0578 508
## Avg_Cogongrass_Cover-Canis_latrans 1.2438 1.0081 1383
## Avg_Cogongrass_Cover-Lynx_rufus 2.0268 1.0087 966
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1921 1.0222 1255
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0559 1.0182 888
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6043 1.0058 1115
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4944 1.0106 991
## Avg_Cogongrass_Cover-Sus_scrofa 1.2318 1.0245 849
## total_shrub_cover-Canis_latrans 1.2139 1.0093 979
## total_shrub_cover-Lynx_rufus 0.5265 1.0028 586
## total_shrub_cover-Didelphis_virginiana 0.6798 1.0062 1098
## total_shrub_cover-Sylvilagus_floridanus 0.9078 1.0072 1074
## total_shrub_cover-Sciurus_carolinensis 0.9820 1.0099 1301
## total_shrub_cover-Vulpes_vulpes 0.9019 1.0024 1013
## total_shrub_cover-Sus_scrofa 1.3916 1.0025 990
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6188 0.1640 -2.9524 -2.6153 -2.3131 1.0121
## (Intercept)-Lynx_rufus -3.3565 0.2819 -3.9686 -3.3518 -2.8255 1.0055
## (Intercept)-Didelphis_virginiana -2.4080 0.2525 -2.9160 -2.4008 -1.9335 1.0000
## (Intercept)-Sylvilagus_floridanus -3.1589 0.2797 -3.7392 -3.1468 -2.6566 1.0019
## (Intercept)-Sciurus_carolinensis -2.5258 0.2588 -3.0508 -2.5188 -2.0315 1.0027
## (Intercept)-Vulpes_vulpes -3.5300 0.6193 -5.0279 -3.4285 -2.6027 1.0423
## (Intercept)-Sus_scrofa -2.9627 0.3985 -3.8261 -2.9262 -2.2723 1.0046
## ESS
## (Intercept)-Canis_latrans 1067
## (Intercept)-Lynx_rufus 424
## (Intercept)-Didelphis_virginiana 1111
## (Intercept)-Sylvilagus_floridanus 358
## (Intercept)-Sciurus_carolinensis 1165
## (Intercept)-Vulpes_vulpes 150
## (Intercept)-Sus_scrofa 641
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3117
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1263 0.4692 -2.0514 -1.1248 -0.1595 1.0012 814
## Veg_shannon_index 0.3734 0.3722 -0.3210 0.3653 1.1149 1.0051 1027
## Avg_Cogongrass_Cover 0.3442 0.3292 -0.3259 0.3505 0.9770 0.9998 1196
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9745 1.3567 0.0564 0.5689 4.5676 1.0133 858
## Veg_shannon_index 0.5681 0.7752 0.0481 0.3350 2.5253 1.0415 922
## Avg_Cogongrass_Cover 0.4121 0.5759 0.0388 0.2398 1.7598 1.0073 1451
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2291 1.1834 0.1069 0.9142 4.5361 1.0065 187
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8445 0.2745 -3.38 -2.8458 -2.2765 1.0021 1651
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4278 0.4883 0.0613 0.2926 1.7132 1.0129 860
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3794 0.6868 -1.6717 -0.4030
## (Intercept)-Lynx_rufus -0.7590 0.6685 -2.0130 -0.7947
## (Intercept)-Didelphis_virginiana -1.4321 0.5998 -2.6797 -1.4064
## (Intercept)-Sylvilagus_floridanus -0.8448 0.6166 -2.0353 -0.8534
## (Intercept)-Sciurus_carolinensis -1.4421 0.6085 -2.7774 -1.4039
## (Intercept)-Vulpes_vulpes -1.5430 0.7173 -3.0277 -1.5245
## (Intercept)-Sus_scrofa -1.8654 0.7505 -3.4984 -1.7834
## Veg_shannon_index-Canis_latrans 0.7715 0.4441 -0.0097 0.7370
## Veg_shannon_index-Lynx_rufus 0.1548 0.5810 -1.0507 0.1733
## Veg_shannon_index-Didelphis_virginiana 0.5610 0.4528 -0.2647 0.5291
## Veg_shannon_index-Sylvilagus_floridanus 0.5186 0.5040 -0.3320 0.4756
## Veg_shannon_index-Sciurus_carolinensis -0.0633 0.4481 -0.9850 -0.0392
## Veg_shannon_index-Vulpes_vulpes -0.0427 0.5610 -1.2847 0.0003
## Veg_shannon_index-Sus_scrofa 0.8084 0.6012 -0.1878 0.7359
## Avg_Cogongrass_Cover-Canis_latrans 0.6014 0.3976 -0.1099 0.5785
## Avg_Cogongrass_Cover-Lynx_rufus 0.6435 0.4590 -0.1582 0.6002
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5086 0.3967 -0.2498 0.4998
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0838 0.4808 -1.1670 -0.0361
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4359 0.3860 -0.2743 0.4306
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3539 0.4707 -0.5609 0.3422
## Avg_Cogongrass_Cover-Sus_scrofa 0.0261 0.5639 -1.2719 0.0818
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0142 1.0175 382
## (Intercept)-Lynx_rufus 0.6728 1.0298 522
## (Intercept)-Didelphis_virginiana -0.3053 1.0033 931
## (Intercept)-Sylvilagus_floridanus 0.4050 1.0161 670
## (Intercept)-Sciurus_carolinensis -0.3288 1.0087 895
## (Intercept)-Vulpes_vulpes -0.1976 1.0051 721
## (Intercept)-Sus_scrofa -0.6242 1.0466 597
## Veg_shannon_index-Canis_latrans 1.7183 1.0097 1066
## Veg_shannon_index-Lynx_rufus 1.2250 1.0255 715
## Veg_shannon_index-Didelphis_virginiana 1.5324 1.0095 1561
## Veg_shannon_index-Sylvilagus_floridanus 1.6077 1.0058 867
## Veg_shannon_index-Sciurus_carolinensis 0.7314 1.0079 1408
## Veg_shannon_index-Vulpes_vulpes 0.9702 1.0016 956
## Veg_shannon_index-Sus_scrofa 2.1760 1.0150 1014
## Avg_Cogongrass_Cover-Canis_latrans 1.4554 1.0030 1650
## Avg_Cogongrass_Cover-Lynx_rufus 1.6815 1.0035 1373
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3322 1.0012 1766
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7524 1.0012 1189
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2585 1.0096 1394
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3429 1.0004 1220
## Avg_Cogongrass_Cover-Sus_scrofa 1.0049 1.0031 1103
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6133 0.1670 -2.9550 -2.6072 -2.3013 1.0127
## (Intercept)-Lynx_rufus -3.3517 0.2814 -3.9367 -3.3510 -2.8168 1.0262
## (Intercept)-Didelphis_virginiana -2.4248 0.2468 -2.9245 -2.4197 -1.9608 1.0088
## (Intercept)-Sylvilagus_floridanus -3.1145 0.2838 -3.7146 -3.0970 -2.6089 1.0364
## (Intercept)-Sciurus_carolinensis -2.5181 0.2605 -3.0655 -2.5106 -2.0391 1.0057
## (Intercept)-Vulpes_vulpes -3.4529 0.5261 -4.5995 -3.3904 -2.6007 1.0170
## (Intercept)-Sus_scrofa -2.9125 0.3800 -3.7193 -2.8949 -2.2201 1.0041
## ESS
## (Intercept)-Canis_latrans 1088
## (Intercept)-Lynx_rufus 419
## (Intercept)-Didelphis_virginiana 1217
## (Intercept)-Sylvilagus_floridanus 495
## (Intercept)-Sciurus_carolinensis 1165
## (Intercept)-Vulpes_vulpes 307
## (Intercept)-Sus_scrofa 836
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9788 0.5472 -3.0672 -1.9829 -0.9270 1.0075 483
## Avg_Cogongrass_Cover -0.9014 0.5010 -1.8912 -0.8970 0.0360 1.0096 472
## I(Avg_Cogongrass_Cover^2) 1.0261 0.4697 0.1663 0.9992 2.0709 1.0020 593
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8935 1.4439 0.0557 0.4799 4.0984 1.0070 1040
## Avg_Cogongrass_Cover 0.6155 1.1208 0.0446 0.2998 3.0503 1.0730 335
## I(Avg_Cogongrass_Cover^2) 1.0669 2.2841 0.0450 0.4164 6.1304 1.0201 285
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9089 0.8337 0.0765 0.6658 3.3079 1.0051 237
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.847 0.2584 -3.3671 -2.8434 -2.3411 1.0073 1414
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4001 0.5556 0.0501 0.2526 1.5958 1.0099 776
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3650 0.7230 -2.7471 -1.3774
## (Intercept)-Lynx_rufus -1.9349 0.6955 -3.3023 -1.9273
## (Intercept)-Didelphis_virginiana -2.1195 0.6370 -3.3759 -2.1009
## (Intercept)-Sylvilagus_floridanus -1.7146 0.6764 -2.9957 -1.7355
## (Intercept)-Sciurus_carolinensis -2.4528 0.7122 -4.0101 -2.3852
## (Intercept)-Vulpes_vulpes -2.5180 0.8097 -4.3448 -2.4468
## (Intercept)-Sus_scrofa -2.4375 0.7926 -4.2328 -2.3697
## Avg_Cogongrass_Cover-Canis_latrans -0.6200 0.5881 -1.7574 -0.6433
## Avg_Cogongrass_Cover-Lynx_rufus -0.8117 0.6211 -2.0550 -0.8025
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5365 0.6409 -1.6629 -0.5743
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4460 0.7315 -3.1141 -1.3816
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.9813 0.6253 -2.3034 -0.9411
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9635 0.6647 -2.3479 -0.9350
## Avg_Cogongrass_Cover-Sus_scrofa -1.1962 0.8140 -3.0183 -1.1084
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8974 1.1567 0.4250 1.5859
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5195 0.6804 0.4780 1.4234
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7253 0.5469 -0.1951 0.6847
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0491 0.6494 0.1081 0.9683
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1198 0.4612 0.3399 1.0777
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0175 0.5395 0.1350 0.9597
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3087 0.8264 -1.5755 0.4320
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.1236 1.0042 440
## (Intercept)-Lynx_rufus -0.5810 1.0022 440
## (Intercept)-Didelphis_virginiana -0.8706 1.0039 859
## (Intercept)-Sylvilagus_floridanus -0.3508 1.0022 571
## (Intercept)-Sciurus_carolinensis -1.2249 1.0119 611
## (Intercept)-Vulpes_vulpes -1.1098 1.0201 506
## (Intercept)-Sus_scrofa -1.0642 1.0072 661
## Avg_Cogongrass_Cover-Canis_latrans 0.6215 1.0026 914
## Avg_Cogongrass_Cover-Lynx_rufus 0.4186 1.0130 614
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8267 1.0077 714
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2236 1.0089 386
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1678 1.0190 459
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3127 1.0024 615
## Avg_Cogongrass_Cover-Sus_scrofa 0.1224 1.0399 478
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.8052 1.0023 195
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.1802 1.0146 343
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.8756 1.0267 403
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.5367 1.0342 255
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1493 1.0151 470
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2561 1.0158 408
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.4298 1.0556 304
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6607 0.1734 -3.0048 -2.6571 -2.3318 1.0026
## (Intercept)-Lynx_rufus -3.2603 0.2764 -3.8314 -3.2449 -2.7567 1.0430
## (Intercept)-Didelphis_virginiana -2.4558 0.2633 -2.9755 -2.4412 -1.9715 1.0149
## (Intercept)-Sylvilagus_floridanus -3.1158 0.2732 -3.7035 -3.0962 -2.6323 1.0077
## (Intercept)-Sciurus_carolinensis -2.5385 0.2553 -3.0594 -2.5339 -2.0433 1.0051
## (Intercept)-Vulpes_vulpes -3.4152 0.5236 -4.5675 -3.3476 -2.5818 1.0225
## (Intercept)-Sus_scrofa -2.9255 0.3771 -3.7449 -2.9061 -2.2355 1.0024
## ESS
## (Intercept)-Canis_latrans 856
## (Intercept)-Lynx_rufus 504
## (Intercept)-Didelphis_virginiana 629
## (Intercept)-Sylvilagus_floridanus 466
## (Intercept)-Sciurus_carolinensis 951
## (Intercept)-Vulpes_vulpes 274
## (Intercept)-Sus_scrofa 694
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3522
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5324 1.2552 -4.7283 -2.6703 0.2897 1.0545 334
## Cogon_Patch_Size -0.1075 1.0314 -2.1802 -0.0949 1.8723 1.0129 662
## Veg_shannon_index 0.8747 0.6843 -0.4857 0.8730 2.2488 1.0209 593
## total_shrub_cover -0.2049 0.5726 -1.3826 -0.1773 0.8752 1.0245 341
## Avg_Cogongrass_Cover -0.1975 1.1859 -2.7124 -0.1192 1.9010 1.1744 132
## Tree_Density -1.9866 0.9912 -3.8746 -1.9976 0.0894 1.0502 250
## Avg_Canopy_Cover 1.8482 0.8583 0.1920 1.8455 3.6042 1.0018 665
## I(Avg_Cogongrass_Cover^2) 1.6363 0.8326 -0.0895 1.6196 3.3045 1.1149 244
## avg_veg_height -0.5350 0.6615 -1.8187 -0.5319 0.7173 1.0630 176
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.1187 35.6961 0.1262 6.2258 98.0870 2.2183 70
## Cogon_Patch_Size 15.5138 42.8288 0.3430 5.3501 99.0523 2.1592 70
## Veg_shannon_index 2.3276 4.1789 0.0600 0.9588 13.3260 1.0800 345
## total_shrub_cover 0.9879 1.7397 0.0500 0.4276 5.5014 1.0322 444
## Avg_Cogongrass_Cover 1.4775 3.3905 0.0487 0.5070 8.9867 1.0169 788
## Tree_Density 5.9816 19.9638 0.0637 1.1294 43.9448 1.2311 91
## Avg_Canopy_Cover 6.0393 11.5439 0.2154 2.7169 34.2645 1.2068 140
## I(Avg_Cogongrass_Cover^2) 4.0597 11.3382 0.0620 0.9579 27.8464 1.2348 150
## avg_veg_height 0.6353 1.0326 0.0425 0.2980 3.4017 1.0710 615
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7375 5.3717 0.0657 0.9508 18.7565 1.8396 22
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9292 0.3378 -3.5719 -2.9422 -2.2389 1.0009 2008
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6939 0.7748 0.0994 0.4756 2.6771 1.0058 589
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3657 1.7921 -4.3028 -1.5049
## (Intercept)-Lynx_rufus -0.9327 2.5324 -4.8127 -1.3910
## (Intercept)-Didelphis_virginiana -4.5787 1.7409 -8.8301 -4.3084
## (Intercept)-Sylvilagus_floridanus -3.2543 1.8247 -7.3170 -3.0928
## (Intercept)-Sciurus_carolinensis -5.4351 2.4361 -11.9884 -4.9446
## (Intercept)-Vulpes_vulpes -5.1306 2.5905 -11.6016 -4.7510
## (Intercept)-Sus_scrofa -6.1595 2.9840 -13.5599 -5.5285
## Cogon_Patch_Size-Canis_latrans 2.9985 3.4301 0.0006 2.2122
## Cogon_Patch_Size-Lynx_rufus -0.6689 2.2657 -5.1298 -0.7277
## Cogon_Patch_Size-Didelphis_virginiana 2.4289 1.5720 0.2851 2.1475
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4672 2.9141 -9.7721 -1.7822
## Cogon_Patch_Size-Sciurus_carolinensis -1.7022 2.2429 -7.3481 -1.1932
## Cogon_Patch_Size-Vulpes_vulpes -1.1842 2.7818 -7.7301 -0.8920
## Cogon_Patch_Size-Sus_scrofa -1.2615 2.9111 -8.1887 -0.6884
## Veg_shannon_index-Canis_latrans 1.5392 0.9845 0.0043 1.3982
## Veg_shannon_index-Lynx_rufus 0.9842 1.2425 -1.5040 0.9546
## Veg_shannon_index-Didelphis_virginiana 1.0282 0.9176 -0.6246 0.9532
## Veg_shannon_index-Sylvilagus_floridanus 1.0901 0.9796 -0.6234 1.0230
## Veg_shannon_index-Sciurus_carolinensis -0.1239 1.1482 -2.9294 0.0548
## Veg_shannon_index-Vulpes_vulpes 0.2786 1.2242 -2.4688 0.4291
## Veg_shannon_index-Sus_scrofa 1.9727 1.4103 -0.0052 1.6600
## total_shrub_cover-Canis_latrans 0.0560 0.6789 -1.2392 0.0370
## total_shrub_cover-Lynx_rufus -0.8591 1.1064 -3.5760 -0.6919
## total_shrub_cover-Didelphis_virginiana -0.4362 0.7652 -2.1619 -0.3625
## total_shrub_cover-Sylvilagus_floridanus -0.1361 0.8062 -1.7414 -0.1350
## total_shrub_cover-Sciurus_carolinensis 0.0920 0.7211 -1.2703 0.0749
## total_shrub_cover-Vulpes_vulpes -0.3619 0.9119 -2.4737 -0.2667
## total_shrub_cover-Sus_scrofa 0.1038 0.8407 -1.5200 0.0800
## Avg_Cogongrass_Cover-Canis_latrans -0.2935 1.4136 -3.2531 -0.2125
## Avg_Cogongrass_Cover-Lynx_rufus -0.0132 1.5112 -3.0347 -0.0073
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0087 1.3795 -2.7888 0.0228
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6728 1.5304 -4.0308 -0.5284
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0404 1.4530 -3.0872 -0.0032
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0156 1.5013 -3.0562 -0.0083
## Avg_Cogongrass_Cover-Sus_scrofa -0.4666 1.5293 -3.7735 -0.3660
## Tree_Density-Canis_latrans -3.0467 1.7054 -7.3023 -2.7584
## Tree_Density-Lynx_rufus -0.7291 2.4258 -3.6610 -1.1421
## Tree_Density-Didelphis_virginiana -2.4493 1.4187 -5.6074 -2.3049
## Tree_Density-Sylvilagus_floridanus -2.8367 1.6439 -6.9383 -2.5694
## Tree_Density-Sciurus_carolinensis -2.8997 1.6880 -7.5715 -2.6094
## Tree_Density-Vulpes_vulpes -1.8462 2.2427 -5.5489 -2.0119
## Tree_Density-Sus_scrofa -2.6250 2.5312 -8.4200 -2.2732
## Avg_Canopy_Cover-Canis_latrans 0.0723 0.8599 -1.6921 0.0891
## Avg_Canopy_Cover-Lynx_rufus 1.3838 1.7939 -1.7891 1.2546
## Avg_Canopy_Cover-Didelphis_virginiana 3.1618 1.3780 1.2364 2.9122
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2853 2.4533 1.2647 3.7753
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6346 1.3595 0.8684 2.4217
## Avg_Canopy_Cover-Vulpes_vulpes 2.6655 1.7649 0.3611 2.2792
## Avg_Canopy_Cover-Sus_scrofa 2.2095 1.1120 0.4812 2.0617
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9472 1.9347 0.8993 2.4808
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0841 2.4638 0.8532 2.5341
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3023 0.8689 -0.2877 1.2455
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5004 1.0100 -0.2157 1.3827
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8316 0.9335 0.2406 1.7342
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1992 1.1458 0.4474 2.0379
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4441 1.7909 -4.0157 0.8059
## avg_veg_height-Canis_latrans -0.7146 0.7607 -2.3477 -0.6840
## avg_veg_height-Lynx_rufus -0.5407 0.9478 -2.5460 -0.5356
## avg_veg_height-Didelphis_virginiana -0.6145 0.8197 -2.3619 -0.5753
## avg_veg_height-Sylvilagus_floridanus -0.5623 0.7989 -2.1894 -0.5460
## avg_veg_height-Sciurus_carolinensis -0.1873 0.7906 -1.6843 -0.1948
## avg_veg_height-Vulpes_vulpes -0.7146 0.9595 -2.7426 -0.6674
## avg_veg_height-Sus_scrofa -0.5642 0.8610 -2.3597 -0.5397
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.2928 1.2308 122
## (Intercept)-Lynx_rufus 5.1212 1.0897 93
## (Intercept)-Didelphis_virginiana -1.9457 1.1869 79
## (Intercept)-Sylvilagus_floridanus -0.2922 1.1973 139
## (Intercept)-Sciurus_carolinensis -2.4100 1.5483 65
## (Intercept)-Vulpes_vulpes -1.0420 1.2951 101
## (Intercept)-Sus_scrofa -2.3440 1.4397 73
## Cogon_Patch_Size-Canis_latrans 12.5557 1.9904 52
## Cogon_Patch_Size-Lynx_rufus 4.2819 1.0201 212
## Cogon_Patch_Size-Didelphis_virginiana 6.5187 1.1796 100
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0415 1.2221 121
## Cogon_Patch_Size-Sciurus_carolinensis 1.0145 1.1947 201
## Cogon_Patch_Size-Vulpes_vulpes 4.1017 1.1820 142
## Cogon_Patch_Size-Sus_scrofa 2.0738 1.2693 131
## Veg_shannon_index-Canis_latrans 3.8194 1.0127 456
## Veg_shannon_index-Lynx_rufus 3.5864 1.0314 409
## Veg_shannon_index-Didelphis_virginiana 3.0414 1.0118 558
## Veg_shannon_index-Sylvilagus_floridanus 3.2917 1.0144 579
## Veg_shannon_index-Sciurus_carolinensis 1.6214 1.0607 231
## Veg_shannon_index-Vulpes_vulpes 2.4636 1.0667 357
## Veg_shannon_index-Sus_scrofa 5.4348 1.0260 263
## total_shrub_cover-Canis_latrans 1.4766 1.0091 549
## total_shrub_cover-Lynx_rufus 0.9060 1.0194 249
## total_shrub_cover-Didelphis_virginiana 0.9167 1.0110 437
## total_shrub_cover-Sylvilagus_floridanus 1.5281 1.0273 507
## total_shrub_cover-Sciurus_carolinensis 1.6231 1.0186 658
## total_shrub_cover-Vulpes_vulpes 1.2884 1.0064 469
## total_shrub_cover-Sus_scrofa 1.8694 1.0103 660
## Avg_Cogongrass_Cover-Canis_latrans 2.2549 1.1364 169
## Avg_Cogongrass_Cover-Lynx_rufus 3.0329 1.1303 202
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6042 1.1373 151
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9008 1.0626 236
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6138 1.1403 185
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8600 1.1419 153
## Avg_Cogongrass_Cover-Sus_scrofa 2.2831 1.0810 222
## Tree_Density-Canis_latrans -0.7098 1.0228 143
## Tree_Density-Lynx_rufus 5.8619 1.1197 106
## Tree_Density-Didelphis_virginiana -0.2743 1.0302 165
## Tree_Density-Sylvilagus_floridanus -0.3934 1.0140 277
## Tree_Density-Sciurus_carolinensis -0.5779 1.0205 182
## Tree_Density-Vulpes_vulpes 2.4925 1.2694 105
## Tree_Density-Sus_scrofa 0.3571 1.1747 85
## Avg_Canopy_Cover-Canis_latrans 1.8176 1.0580 222
## Avg_Canopy_Cover-Lynx_rufus 5.2486 1.0477 180
## Avg_Canopy_Cover-Didelphis_virginiana 6.6692 1.0674 90
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.7705 1.0878 133
## Avg_Canopy_Cover-Sciurus_carolinensis 5.5987 1.1460 140
## Avg_Canopy_Cover-Vulpes_vulpes 7.6894 1.0124 253
## Avg_Canopy_Cover-Sus_scrofa 4.8296 1.0734 303
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.2580 1.0965 117
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.4293 1.2172 67
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2376 1.1947 92
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.6862 1.0775 241
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.8915 1.1119 236
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.8460 1.1171 235
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0703 1.1095 126
## avg_veg_height-Canis_latrans 0.6996 1.0685 196
## avg_veg_height-Lynx_rufus 1.3337 1.0659 301
## avg_veg_height-Didelphis_virginiana 0.8982 1.0387 216
## avg_veg_height-Sylvilagus_floridanus 0.9901 1.0210 318
## avg_veg_height-Sciurus_carolinensis 1.4455 1.0203 327
## avg_veg_height-Vulpes_vulpes 1.0259 1.0490 315
## avg_veg_height-Sus_scrofa 1.0780 1.0304 269
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6419 0.1730 -2.9939 -2.6402 -2.3131 1.0369
## (Intercept)-Lynx_rufus -3.6297 0.3153 -4.2620 -3.6176 -3.0468 1.0268
## (Intercept)-Didelphis_virginiana -2.3904 0.2480 -2.8982 -2.3818 -1.9300 1.0032
## (Intercept)-Sylvilagus_floridanus -3.1800 0.2557 -3.7037 -3.1627 -2.7334 1.0188
## (Intercept)-Sciurus_carolinensis -2.5179 0.2684 -3.0847 -2.5027 -2.0277 1.0121
## (Intercept)-Vulpes_vulpes -3.8501 0.5971 -5.1518 -3.8145 -2.8108 1.0077
## (Intercept)-Sus_scrofa -2.9118 0.4162 -3.7742 -2.8829 -2.1666 1.0567
## ESS
## (Intercept)-Canis_latrans 971
## (Intercept)-Lynx_rufus 207
## (Intercept)-Didelphis_virginiana 1070
## (Intercept)-Sylvilagus_floridanus 572
## (Intercept)-Sciurus_carolinensis 963
## (Intercept)-Vulpes_vulpes 166
## (Intercept)-Sus_scrofa 398
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9548 0.4511 -1.8781 -0.9495 -0.0384 1.0088 656
## Avg_Cogongrass_Cover 0.2823 0.3218 -0.3762 0.2887 0.9254 1.0111 928
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8393 1.1753 0.0597 0.4870 3.7550 1.0808 929
## Avg_Cogongrass_Cover 0.3702 0.4968 0.0399 0.2162 1.5958 1.0030 1242
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4118 1.297 0.1441 1.0508 4.7912 1.0127 219
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0393 0.2718 -3.5579 -3.0393 -2.4963 1.0016 1014
## shrub_cover 0.3633 0.3737 -0.3934 0.3559 1.1322 1.0007 1251
## veg_height -0.0479 0.2321 -0.4987 -0.0471 0.4175 1.0077 1820
## week -0.0124 0.1766 -0.3723 -0.0078 0.3193 1.0072 1447
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3813 0.5208 0.0478 0.2365 1.5898 1.0153 784
## shrub_cover 0.7082 0.7480 0.0962 0.4975 2.7180 1.0129 819
## veg_height 0.2899 0.2951 0.0539 0.2105 1.0206 1.0317 1840
## week 0.1446 0.1604 0.0274 0.0966 0.5670 1.0095 1702
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2185 0.6781 -1.4477 -0.2476
## (Intercept)-Lynx_rufus -0.7130 0.6521 -1.9739 -0.7524
## (Intercept)-Didelphis_virginiana -1.1820 0.5947 -2.3944 -1.1514
## (Intercept)-Sylvilagus_floridanus -0.7267 0.6150 -1.8580 -0.7509
## (Intercept)-Sciurus_carolinensis -1.2294 0.5975 -2.5012 -1.1960
## (Intercept)-Vulpes_vulpes -1.3890 0.7185 -2.8670 -1.3443
## (Intercept)-Sus_scrofa -1.4635 0.7126 -3.0893 -1.4006
## Avg_Cogongrass_Cover-Canis_latrans 0.4879 0.4076 -0.2524 0.4597
## Avg_Cogongrass_Cover-Lynx_rufus 0.5309 0.4294 -0.2106 0.5057
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3940 0.3883 -0.3465 0.3923
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1668 0.4810 -1.2306 -0.1243
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4107 0.3791 -0.2987 0.3965
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3666 0.4464 -0.4796 0.3619
## Avg_Cogongrass_Cover-Sus_scrofa 0.0104 0.5673 -1.2584 0.0714
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1864 1.0055 567
## (Intercept)-Lynx_rufus 0.6893 1.0063 534
## (Intercept)-Didelphis_virginiana -0.0641 1.0135 813
## (Intercept)-Sylvilagus_floridanus 0.5180 1.0012 614
## (Intercept)-Sciurus_carolinensis -0.1286 1.0253 744
## (Intercept)-Vulpes_vulpes -0.0673 1.0639 556
## (Intercept)-Sus_scrofa -0.1938 1.0272 634
## Avg_Cogongrass_Cover-Canis_latrans 1.3926 1.0087 1441
## Avg_Cogongrass_Cover-Lynx_rufus 1.4768 1.0164 1295
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1872 1.0063 1418
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6713 1.0102 817
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2196 0.9999 1690
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2901 1.0036 1285
## Avg_Cogongrass_Cover-Sus_scrofa 1.0036 1.0298 888
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7973 0.1808 -3.1637 -2.7952 -2.4576 1.0056
## (Intercept)-Lynx_rufus -3.4374 0.3117 -4.0509 -3.4189 -2.8711 1.0103
## (Intercept)-Didelphis_virginiana -2.7112 0.2733 -3.2557 -2.7050 -2.1865 1.0006
## (Intercept)-Sylvilagus_floridanus -3.1432 0.2602 -3.6829 -3.1306 -2.6635 1.0095
## (Intercept)-Sciurus_carolinensis -2.7521 0.3011 -3.3622 -2.7457 -2.1726 1.0005
## (Intercept)-Vulpes_vulpes -3.5711 0.5224 -4.7755 -3.5187 -2.7037 1.0331
## (Intercept)-Sus_scrofa -3.2557 0.4324 -4.1266 -3.2386 -2.3963 1.0025
## shrub_cover-Canis_latrans -0.2776 0.2272 -0.7163 -0.2771 0.1637 1.0051
## shrub_cover-Lynx_rufus -0.1274 0.3516 -0.8271 -0.1300 0.5725 1.0102
## shrub_cover-Didelphis_virginiana 1.0236 0.4002 0.3029 1.0038 1.8638 1.0116
## shrub_cover-Sylvilagus_floridanus 0.3389 0.3938 -0.4347 0.3358 1.1027 1.0124
## shrub_cover-Sciurus_carolinensis 0.9073 0.4296 0.1093 0.8941 1.8156 1.0034
## shrub_cover-Vulpes_vulpes 0.0553 0.6048 -1.1597 0.0530 1.2163 1.0031
## shrub_cover-Sus_scrofa 0.6715 0.7315 -0.7249 0.6444 2.1978 0.9998
## veg_height-Canis_latrans -0.6201 0.1857 -0.9968 -0.6170 -0.2663 1.0095
## veg_height-Lynx_rufus -0.0284 0.2488 -0.5304 -0.0198 0.4400 1.0202
## veg_height-Didelphis_virginiana 0.4441 0.2621 -0.0572 0.4384 0.9859 1.0034
## veg_height-Sylvilagus_floridanus 0.1193 0.2543 -0.3687 0.1153 0.5991 1.0102
## veg_height-Sciurus_carolinensis 0.0944 0.2189 -0.3325 0.0905 0.5400 1.0033
## veg_height-Vulpes_vulpes -0.1408 0.3099 -0.8012 -0.1281 0.4233 1.0040
## veg_height-Sus_scrofa -0.1668 0.3443 -0.8718 -0.1613 0.5021 1.0005
## week-Canis_latrans 0.0847 0.1320 -0.1865 0.0879 0.3307 1.0002
## week-Lynx_rufus -0.0205 0.1982 -0.4422 -0.0163 0.3411 1.0089
## week-Didelphis_virginiana -0.2085 0.2408 -0.6978 -0.1921 0.2106 1.0065
## week-Sylvilagus_floridanus -0.1474 0.2167 -0.6260 -0.1359 0.2383 1.0063
## week-Sciurus_carolinensis 0.1775 0.1866 -0.1843 0.1798 0.5498 1.0013
## week-Vulpes_vulpes -0.0903 0.2851 -0.6908 -0.0698 0.4255 1.0197
## week-Sus_scrofa 0.1213 0.2470 -0.3937 0.1258 0.6016 1.0001
## ESS
## (Intercept)-Canis_latrans 821
## (Intercept)-Lynx_rufus 439
## (Intercept)-Didelphis_virginiana 784
## (Intercept)-Sylvilagus_floridanus 621
## (Intercept)-Sciurus_carolinensis 798
## (Intercept)-Vulpes_vulpes 339
## (Intercept)-Sus_scrofa 680
## shrub_cover-Canis_latrans 830
## shrub_cover-Lynx_rufus 538
## shrub_cover-Didelphis_virginiana 471
## shrub_cover-Sylvilagus_floridanus 647
## shrub_cover-Sciurus_carolinensis 657
## shrub_cover-Vulpes_vulpes 616
## shrub_cover-Sus_scrofa 555
## veg_height-Canis_latrans 782
## veg_height-Lynx_rufus 825
## veg_height-Didelphis_virginiana 1032
## veg_height-Sylvilagus_floridanus 843
## veg_height-Sciurus_carolinensis 947
## veg_height-Vulpes_vulpes 905
## veg_height-Sus_scrofa 1149
## week-Canis_latrans 1574
## week-Lynx_rufus 873
## week-Didelphis_virginiana 1245
## week-Sylvilagus_floridanus 907
## week-Sciurus_carolinensis 1867
## week-Vulpes_vulpes 1288
## week-Sus_scrofa 1604
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0463 0.4771 -2.0097 -1.0599 -0.0363 1.0038 581
## Avg_Cogongrass_Cover 0.2685 0.3005 -0.3445 0.2634 0.8774 1.0514 1172
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9620 1.3208 0.0586 0.5716 4.1454 1.0423 694
## Avg_Cogongrass_Cover 0.3837 0.7338 0.0391 0.2122 1.6864 1.0583 1368
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2235 1.1471 0.0985 0.8761 4.3082 1.0108 239
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8536 0.2913 -3.4273 -2.85 -2.256 1.0113 1325
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4484 0.547 0.0625 0.2897 1.8465 1.037 385
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2885 0.6741 -1.5110 -0.2916
## (Intercept)-Lynx_rufus -0.7046 0.6973 -1.9239 -0.7428
## (Intercept)-Didelphis_virginiana -1.3522 0.5851 -2.6383 -1.3118
## (Intercept)-Sylvilagus_floridanus -0.7889 0.6369 -1.9156 -0.8181
## (Intercept)-Sciurus_carolinensis -1.3910 0.6248 -2.6976 -1.3551
## (Intercept)-Vulpes_vulpes -1.4875 0.7376 -2.9585 -1.4565
## (Intercept)-Sus_scrofa -1.6959 0.7138 -3.2625 -1.6343
## Avg_Cogongrass_Cover-Canis_latrans 0.4003 0.3651 -0.2616 0.3791
## Avg_Cogongrass_Cover-Lynx_rufus 0.5159 0.3984 -0.1845 0.4982
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3991 0.3690 -0.3291 0.3878
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1531 0.4639 -1.1786 -0.1109
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4086 0.3641 -0.2738 0.3996
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3345 0.4262 -0.4780 0.3249
## Avg_Cogongrass_Cover-Sus_scrofa -0.0575 0.5306 -1.3102 -0.0057
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0811 1.0078 461
## (Intercept)-Lynx_rufus 0.8385 1.0162 495
## (Intercept)-Didelphis_virginiana -0.2858 1.0067 756
## (Intercept)-Sylvilagus_floridanus 0.5840 1.0050 541
## (Intercept)-Sciurus_carolinensis -0.2831 1.0054 730
## (Intercept)-Vulpes_vulpes -0.0047 1.0193 530
## (Intercept)-Sus_scrofa -0.4360 1.0008 571
## Avg_Cogongrass_Cover-Canis_latrans 1.1541 1.0226 1950
## Avg_Cogongrass_Cover-Lynx_rufus 1.3561 1.0134 1352
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1247 1.0169 1579
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6649 1.0062 1084
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1428 1.0343 1586
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2251 1.0135 1440
## Avg_Cogongrass_Cover-Sus_scrofa 0.8861 1.0100 833
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6343 0.1638 -2.9698 -2.6322 -2.3283 1.0035
## (Intercept)-Lynx_rufus -3.3353 0.2927 -3.9606 -3.3242 -2.8038 1.0136
## (Intercept)-Didelphis_virginiana -2.4202 0.2477 -2.9043 -2.4180 -1.9472 1.0086
## (Intercept)-Sylvilagus_floridanus -3.0898 0.2738 -3.6660 -3.0775 -2.6014 1.0088
## (Intercept)-Sciurus_carolinensis -2.5337 0.2564 -3.0761 -2.5274 -2.0600 1.0084
## (Intercept)-Vulpes_vulpes -3.4681 0.5736 -4.8795 -3.3875 -2.5909 1.0253
## (Intercept)-Sus_scrofa -2.9262 0.3937 -3.7754 -2.8973 -2.2191 1.0063
## ESS
## (Intercept)-Canis_latrans 1156
## (Intercept)-Lynx_rufus 321
## (Intercept)-Didelphis_virginiana 1178
## (Intercept)-Sylvilagus_floridanus 510
## (Intercept)-Sciurus_carolinensis 1161
## (Intercept)-Vulpes_vulpes 224
## (Intercept)-Sus_scrofa 744
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4312
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0618 0.4481 -1.9613 -1.0621 -0.1502 1.0008 861
## Avg_Cogongrass_Cover 0.2554 0.3005 -0.3585 0.2578 0.8438 1.0037 1134
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9196 1.5174 0.0592 0.5224 4.0142 1.0348 1169
## Avg_Cogongrass_Cover 0.3546 0.4799 0.0415 0.2144 1.4997 1.0031 1092
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.291 1.2725 0.1084 0.9592 4.246 1.0082 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8781 0.2827 -3.4695 -2.8734 -2.3352 1.0171 1484
## week -0.0170 0.1715 -0.3643 -0.0144 0.3253 1.0166 1461
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4352 0.5650 0.0555 0.2714 1.9002 1.0249 637
## week 0.1372 0.1517 0.0262 0.0970 0.4859 1.0346 1933
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3198 0.6542 -1.5456 -0.3284
## (Intercept)-Lynx_rufus -0.7791 0.6425 -1.9622 -0.7991
## (Intercept)-Didelphis_virginiana -1.3445 0.5619 -2.5424 -1.3145
## (Intercept)-Sylvilagus_floridanus -0.8075 0.6023 -1.9972 -0.8198
## (Intercept)-Sciurus_carolinensis -1.3912 0.5941 -2.6627 -1.3751
## (Intercept)-Vulpes_vulpes -1.4831 0.7404 -3.0773 -1.4369
## (Intercept)-Sus_scrofa -1.6795 0.7314 -3.2863 -1.5943
## Avg_Cogongrass_Cover-Canis_latrans 0.3907 0.3691 -0.3068 0.3742
## Avg_Cogongrass_Cover-Lynx_rufus 0.5179 0.4114 -0.1816 0.4706
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4012 0.3687 -0.2990 0.3939
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1564 0.4357 -1.0991 -0.1269
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4077 0.3663 -0.3024 0.4021
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3110 0.4346 -0.5268 0.3028
## Avg_Cogongrass_Cover-Sus_scrofa -0.0568 0.5672 -1.3727 0.0085
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0129 1.0060 646
## (Intercept)-Lynx_rufus 0.5149 1.0028 628
## (Intercept)-Didelphis_virginiana -0.3358 1.0117 906
## (Intercept)-Sylvilagus_floridanus 0.4337 1.0046 867
## (Intercept)-Sciurus_carolinensis -0.2836 1.0068 787
## (Intercept)-Vulpes_vulpes -0.1094 1.0011 717
## (Intercept)-Sus_scrofa -0.4771 1.0044 683
## Avg_Cogongrass_Cover-Canis_latrans 1.1715 1.0011 1772
## Avg_Cogongrass_Cover-Lynx_rufus 1.4825 1.0061 1470
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1694 1.0103 1669
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5960 1.0030 1226
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2023 1.0027 1792
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1710 1.0040 1541
## Avg_Cogongrass_Cover-Sus_scrofa 0.8649 1.0036 867
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6461 0.1694 -2.9982 -2.6413 -2.3341 1.0062
## (Intercept)-Lynx_rufus -3.3319 0.2816 -3.9273 -3.3174 -2.8221 1.0584
## (Intercept)-Didelphis_virginiana -2.4507 0.2538 -2.9787 -2.4485 -1.9706 1.0063
## (Intercept)-Sylvilagus_floridanus -3.1120 0.2766 -3.6866 -3.0928 -2.6178 1.0280
## (Intercept)-Sciurus_carolinensis -2.5693 0.2558 -3.0869 -2.5684 -2.0824 1.0020
## (Intercept)-Vulpes_vulpes -3.4840 0.5467 -4.7139 -3.4200 -2.5844 1.0312
## (Intercept)-Sus_scrofa -2.9760 0.3904 -3.8348 -2.9427 -2.2708 1.0101
## week-Canis_latrans 0.0797 0.1343 -0.1897 0.0829 0.3327 1.0041
## week-Lynx_rufus -0.0156 0.2001 -0.4418 -0.0086 0.3373 1.0050
## week-Didelphis_virginiana -0.2118 0.2380 -0.7489 -0.1943 0.2034 1.0136
## week-Sylvilagus_floridanus -0.1418 0.2150 -0.6041 -0.1294 0.2286 1.0117
## week-Sciurus_carolinensis 0.1562 0.1896 -0.2130 0.1600 0.5161 1.0100
## week-Vulpes_vulpes -0.1040 0.2908 -0.7114 -0.0903 0.4243 1.0011
## week-Sus_scrofa 0.1338 0.2416 -0.3424 0.1359 0.5934 1.0065
## ESS
## (Intercept)-Canis_latrans 1064
## (Intercept)-Lynx_rufus 453
## (Intercept)-Didelphis_virginiana 1412
## (Intercept)-Sylvilagus_floridanus 431
## (Intercept)-Sciurus_carolinensis 1212
## (Intercept)-Vulpes_vulpes 234
## (Intercept)-Sus_scrofa 794
## week-Canis_latrans 1646
## week-Lynx_rufus 976
## week-Didelphis_virginiana 1171
## week-Sylvilagus_floridanus 1038
## week-Sciurus_carolinensis 2066
## week-Vulpes_vulpes 990
## week-Sus_scrofa 1678
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4608
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6069 0.9906 -3.5056 -1.6542 0.5481 1.0376 433
## Cogon_Patch_Size -0.6519 0.8655 -2.4353 -0.6187 0.9775 1.0101 555
## Veg_shannon_index 0.6761 0.6135 -0.5962 0.6973 1.8576 1.0135 773
## total_shrub_cover -0.1255 0.5348 -1.2385 -0.1139 0.8785 1.0393 539
## Avg_Cogongrass_Cover 1.9686 0.7463 0.5731 1.9460 3.4045 1.0271 242
## Tree_Density -1.7558 0.8446 -3.4713 -1.7517 -0.1537 1.0545 273
## Avg_Canopy_Cover 1.7447 0.7270 0.2909 1.7182 3.2587 1.0104 688
## avg_veg_height -0.7287 0.5539 -1.8582 -0.7197 0.3881 1.0265 304
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.1091 11.5501 0.3529 4.5251 37.2373 1.0203 168
## Cogon_Patch_Size 5.4903 7.7982 0.2144 3.1289 25.6683 1.0309 394
## Veg_shannon_index 1.9353 3.8131 0.0674 0.7522 10.9084 1.0517 280
## total_shrub_cover 0.9968 2.5956 0.0489 0.3797 5.4981 1.0944 229
## Avg_Cogongrass_Cover 1.0326 1.6860 0.0480 0.4616 5.5782 1.0675 474
## Tree_Density 2.6703 5.4790 0.0710 1.0897 15.0640 1.0748 545
## Avg_Canopy_Cover 3.3614 5.2541 0.1734 1.8406 15.9069 1.0124 495
## avg_veg_height 0.5344 0.8957 0.0421 0.2758 2.6195 1.0126 1175
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7841 2.1143 0.0639 1.1148 7.2814 1.0616 140
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9056 0.3207 -3.5315 -2.9063 -2.2292 1.0065 1443
## week -0.0099 0.1721 -0.3725 -0.0040 0.3158 1.0130 1305
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5866 0.7585 0.0847 0.3839 2.1664 1.170 309
## week 0.1393 0.1401 0.0264 0.0983 0.5080 1.017 1411
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2868 1.1022 -1.9704 0.2989
## (Intercept)-Lynx_rufus 0.3524 2.3235 -2.8079 -0.0703
## (Intercept)-Didelphis_virginiana -2.9888 1.1601 -5.6426 -2.8654
## (Intercept)-Sylvilagus_floridanus -1.7172 1.1927 -4.3525 -1.6520
## (Intercept)-Sciurus_carolinensis -3.2147 1.2125 -5.9994 -3.0711
## (Intercept)-Vulpes_vulpes -3.0901 1.6076 -6.4378 -3.0127
## (Intercept)-Sus_scrofa -4.4627 1.8475 -9.1098 -4.2373
## Cogon_Patch_Size-Canis_latrans 0.9780 1.3080 -0.9079 0.7363
## Cogon_Patch_Size-Lynx_rufus -0.7951 1.4867 -3.3651 -0.9411
## Cogon_Patch_Size-Didelphis_virginiana 1.1597 0.9459 -0.4003 1.0390
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4708 1.9585 -7.4492 -2.0570
## Cogon_Patch_Size-Sciurus_carolinensis -1.8356 1.4078 -5.1679 -1.6277
## Cogon_Patch_Size-Vulpes_vulpes -1.5268 1.6611 -5.4434 -1.3039
## Cogon_Patch_Size-Sus_scrofa -1.4150 1.7218 -5.5612 -1.1211
## Veg_shannon_index-Canis_latrans 1.1711 0.6621 0.0031 1.1117
## Veg_shannon_index-Lynx_rufus 0.5015 1.1522 -2.3148 0.6216
## Veg_shannon_index-Didelphis_virginiana 0.9605 0.7678 -0.4243 0.9171
## Veg_shannon_index-Sylvilagus_floridanus 0.9859 0.7737 -0.4202 0.9434
## Veg_shannon_index-Sciurus_carolinensis -0.1306 0.8721 -2.0462 -0.0251
## Veg_shannon_index-Vulpes_vulpes -0.1471 1.0815 -2.6376 0.0117
## Veg_shannon_index-Sus_scrofa 1.7253 1.3159 -0.0589 1.4397
## total_shrub_cover-Canis_latrans 0.2167 0.6326 -0.8574 0.1690
## total_shrub_cover-Lynx_rufus -0.6902 1.0952 -3.3295 -0.5231
## total_shrub_cover-Didelphis_virginiana -0.3517 0.7033 -1.9714 -0.3007
## total_shrub_cover-Sylvilagus_floridanus -0.0821 0.7709 -1.6699 -0.0853
## total_shrub_cover-Sciurus_carolinensis 0.0822 0.6354 -1.1489 0.0767
## total_shrub_cover-Vulpes_vulpes -0.3775 1.0865 -2.8013 -0.2430
## total_shrub_cover-Sus_scrofa 0.1776 0.7758 -1.2379 0.1297
## Avg_Cogongrass_Cover-Canis_latrans 2.2415 0.9448 0.5738 2.1738
## Avg_Cogongrass_Cover-Lynx_rufus 2.3868 1.0297 0.6208 2.2988
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1002 0.8692 0.5184 2.0572
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5430 0.9667 -0.4357 1.5578
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2664 0.9314 0.6158 2.1994
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3617 1.0578 0.4856 2.2857
## Avg_Cogongrass_Cover-Sus_scrofa 1.5615 1.0900 -0.8219 1.6074
## Tree_Density-Canis_latrans -2.3935 1.1774 -5.1740 -2.2162
## Tree_Density-Lynx_rufus -0.5274 1.3677 -2.9240 -0.6518
## Tree_Density-Didelphis_virginiana -2.1867 1.1085 -4.8038 -2.0544
## Tree_Density-Sylvilagus_floridanus -2.4571 1.3408 -5.7852 -2.2745
## Tree_Density-Sciurus_carolinensis -2.3104 1.2573 -5.3688 -2.1713
## Tree_Density-Vulpes_vulpes -1.7323 1.3457 -4.5505 -1.7309
## Tree_Density-Sus_scrofa -2.1198 1.4434 -5.7824 -1.9377
## Avg_Canopy_Cover-Canis_latrans 0.2728 0.6712 -1.0616 0.2785
## Avg_Canopy_Cover-Lynx_rufus 0.9785 1.2179 -1.2233 0.8944
## Avg_Canopy_Cover-Didelphis_virginiana 2.8116 1.0471 1.1624 2.6722
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4277 1.6359 1.1539 3.1045
## Avg_Canopy_Cover-Sciurus_carolinensis 2.3579 0.8795 0.9063 2.2498
## Avg_Canopy_Cover-Vulpes_vulpes 2.1592 1.1782 0.3196 1.9829
## Avg_Canopy_Cover-Sus_scrofa 2.0766 0.9200 0.5365 1.9838
## avg_veg_height-Canis_latrans -0.8625 0.6433 -2.1655 -0.8589
## avg_veg_height-Lynx_rufus -0.7601 0.8040 -2.4174 -0.7464
## avg_veg_height-Didelphis_virginiana -0.7934 0.7125 -2.1883 -0.7790
## avg_veg_height-Sylvilagus_floridanus -0.8728 0.6988 -2.3470 -0.8352
## avg_veg_height-Sciurus_carolinensis -0.3594 0.7108 -1.6878 -0.3972
## avg_veg_height-Vulpes_vulpes -0.7779 0.7790 -2.4035 -0.7657
## avg_veg_height-Sus_scrofa -0.7695 0.7336 -2.3060 -0.7360
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5612 1.0548 321
## (Intercept)-Lynx_rufus 7.0966 1.0789 77
## (Intercept)-Didelphis_virginiana -1.0332 1.0188 278
## (Intercept)-Sylvilagus_floridanus 0.5805 1.0189 316
## (Intercept)-Sciurus_carolinensis -1.2301 1.0304 284
## (Intercept)-Vulpes_vulpes -0.1091 1.0509 172
## (Intercept)-Sus_scrofa -1.6555 1.0145 183
## Cogon_Patch_Size-Canis_latrans 4.1819 1.0056 519
## Cogon_Patch_Size-Lynx_rufus 2.5671 1.0210 283
## Cogon_Patch_Size-Didelphis_virginiana 3.2646 1.0037 367
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2549 1.0095 271
## Cogon_Patch_Size-Sciurus_carolinensis 0.1820 1.0022 437
## Cogon_Patch_Size-Vulpes_vulpes 1.3023 1.0072 228
## Cogon_Patch_Size-Sus_scrofa 1.0571 1.0146 324
## Veg_shannon_index-Canis_latrans 2.6735 1.0031 746
## Veg_shannon_index-Lynx_rufus 2.3512 1.0635 195
## Veg_shannon_index-Didelphis_virginiana 2.6868 1.0204 628
## Veg_shannon_index-Sylvilagus_floridanus 2.7176 1.0191 708
## Veg_shannon_index-Sciurus_carolinensis 1.2478 1.0094 392
## Veg_shannon_index-Vulpes_vulpes 1.5201 1.0285 327
## Veg_shannon_index-Sus_scrofa 5.0388 1.0305 266
## total_shrub_cover-Canis_latrans 1.6860 1.0103 573
## total_shrub_cover-Lynx_rufus 0.9195 1.0636 241
## total_shrub_cover-Didelphis_virginiana 0.8943 1.0515 736
## total_shrub_cover-Sylvilagus_floridanus 1.4911 1.0275 661
## total_shrub_cover-Sciurus_carolinensis 1.3460 1.0258 792
## total_shrub_cover-Vulpes_vulpes 1.1351 1.0908 185
## total_shrub_cover-Sus_scrofa 1.8739 1.0095 733
## Avg_Cogongrass_Cover-Canis_latrans 4.2351 1.0226 320
## Avg_Cogongrass_Cover-Lynx_rufus 4.7017 1.0409 305
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9121 1.0230 368
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3449 1.0074 372
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2766 1.0148 317
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7037 1.0434 291
## Avg_Cogongrass_Cover-Sus_scrofa 3.5408 1.0151 301
## Tree_Density-Canis_latrans -0.5257 1.0080 407
## Tree_Density-Lynx_rufus 2.6797 1.1522 222
## Tree_Density-Didelphis_virginiana -0.4355 1.0187 427
## Tree_Density-Sylvilagus_floridanus -0.3891 1.0072 289
## Tree_Density-Sciurus_carolinensis -0.2686 1.0144 297
## Tree_Density-Vulpes_vulpes 1.0708 1.0348 385
## Tree_Density-Sus_scrofa 0.2437 1.0077 407
## Avg_Canopy_Cover-Canis_latrans 1.6052 1.0144 685
## Avg_Canopy_Cover-Lynx_rufus 3.7041 1.0022 236
## Avg_Canopy_Cover-Didelphis_virginiana 5.2684 1.0094 289
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.4851 1.0797 252
## Avg_Canopy_Cover-Sciurus_carolinensis 4.4027 1.0191 495
## Avg_Canopy_Cover-Vulpes_vulpes 4.9976 1.0243 235
## Avg_Canopy_Cover-Sus_scrofa 4.1643 1.0167 614
## avg_veg_height-Canis_latrans 0.3642 1.0209 397
## avg_veg_height-Lynx_rufus 0.8039 1.0276 392
## avg_veg_height-Didelphis_virginiana 0.6011 1.0133 430
## avg_veg_height-Sylvilagus_floridanus 0.4371 1.0054 478
## avg_veg_height-Sciurus_carolinensis 1.1776 1.0108 481
## avg_veg_height-Vulpes_vulpes 0.7935 1.0224 393
## avg_veg_height-Sus_scrofa 0.5915 1.0350 514
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6581 0.1744 -3.0120 -2.6552 -2.3325 1.0000
## (Intercept)-Lynx_rufus -3.5673 0.3462 -4.2437 -3.5484 -2.9377 1.0449
## (Intercept)-Didelphis_virginiana -2.4125 0.2468 -2.9072 -2.4093 -1.9287 1.0155
## (Intercept)-Sylvilagus_floridanus -3.1554 0.2527 -3.6681 -3.1535 -2.6734 1.0356
## (Intercept)-Sciurus_carolinensis -2.5518 0.2685 -3.1243 -2.5401 -2.0390 1.0014
## (Intercept)-Vulpes_vulpes -3.6963 0.5994 -5.0760 -3.6062 -2.7363 1.1217
## (Intercept)-Sus_scrofa -2.9339 0.3985 -3.7830 -2.9138 -2.1941 1.0070
## week-Canis_latrans 0.0816 0.1345 -0.1958 0.0874 0.3293 1.0022
## week-Lynx_rufus -0.0116 0.1981 -0.4408 -0.0010 0.3502 1.0104
## week-Didelphis_virginiana -0.2108 0.2344 -0.7083 -0.1948 0.2098 1.0324
## week-Sylvilagus_floridanus -0.1528 0.2205 -0.6341 -0.1364 0.2443 1.0456
## week-Sciurus_carolinensis 0.1664 0.1839 -0.1865 0.1669 0.5237 1.0001
## week-Vulpes_vulpes -0.0946 0.2879 -0.6948 -0.0802 0.4268 1.0412
## week-Sus_scrofa 0.1348 0.2467 -0.3524 0.1344 0.6272 1.0144
## ESS
## (Intercept)-Canis_latrans 894
## (Intercept)-Lynx_rufus 210
## (Intercept)-Didelphis_virginiana 1286
## (Intercept)-Sylvilagus_floridanus 607
## (Intercept)-Sciurus_carolinensis 1011
## (Intercept)-Vulpes_vulpes 181
## (Intercept)-Sus_scrofa 841
## week-Canis_latrans 1620
## week-Lynx_rufus 852
## week-Didelphis_virginiana 1314
## week-Sylvilagus_floridanus 819
## week-Sciurus_carolinensis 1815
## week-Vulpes_vulpes 956
## week-Sus_scrofa 1730
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4485
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0803 0.5169 -2.0485 -1.1005 0.0477 1.0230 346
## Avg_Cogongrass_Cover 0.3371 0.3984 -0.4344 0.3329 1.1146 1.0522 611
## total_shrub_cover -0.1067 0.3535 -0.8172 -0.1025 0.5561 1.0312 687
## avg_veg_height -0.1010 0.3707 -0.8796 -0.0941 0.5928 1.0158 580
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9704 1.3646 0.0609 0.5331 4.3809 1.0449 664
## Avg_Cogongrass_Cover 0.4467 0.6403 0.0410 0.2427 2.0544 1.0015 1020
## total_shrub_cover 0.4357 0.6795 0.0392 0.2286 2.1805 1.0909 554
## avg_veg_height 0.3058 0.4240 0.0358 0.1808 1.3527 1.0394 1185
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7804 1.7188 0.1255 1.3437 5.9383 1.1444 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8939 0.3108 -3.5065 -2.8949 -2.2838 1.0181 1282
## week -0.0133 0.1751 -0.3688 -0.0098 0.3262 1.0003 1326
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5344 0.7265 0.0657 0.3288 2.1588 1.0603 601
## week 0.1405 0.1390 0.0272 0.0976 0.5169 1.0056 2130
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3595 0.7627 -1.7682 -0.3985
## (Intercept)-Lynx_rufus -0.7911 0.7369 -2.1473 -0.8197
## (Intercept)-Didelphis_virginiana -1.3502 0.6052 -2.6194 -1.3305
## (Intercept)-Sylvilagus_floridanus -0.7844 0.7179 -2.0742 -0.8261
## (Intercept)-Sciurus_carolinensis -1.4299 0.6156 -2.7152 -1.4112
## (Intercept)-Vulpes_vulpes -1.4533 0.8498 -3.0859 -1.4552
## (Intercept)-Sus_scrofa -1.6881 0.7409 -3.3302 -1.6459
## Avg_Cogongrass_Cover-Canis_latrans 0.5547 0.4856 -0.3251 0.5293
## Avg_Cogongrass_Cover-Lynx_rufus 0.6107 0.5260 -0.3484 0.5824
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5074 0.4630 -0.3910 0.4898
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1127 0.5813 -1.3826 -0.0692
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3968 0.4614 -0.5323 0.3948
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4450 0.5514 -0.6115 0.4374
## Avg_Cogongrass_Cover-Sus_scrofa 0.0156 0.6694 -1.5199 0.0773
## total_shrub_cover-Canis_latrans 0.2102 0.4355 -0.5690 0.1772
## total_shrub_cover-Lynx_rufus -0.5677 0.6147 -1.9952 -0.4848
## total_shrub_cover-Didelphis_virginiana -0.1314 0.4173 -0.9840 -0.1148
## total_shrub_cover-Sylvilagus_floridanus -0.2000 0.5031 -1.1959 -0.1865
## total_shrub_cover-Sciurus_carolinensis -0.0061 0.4271 -0.8512 -0.0033
## total_shrub_cover-Vulpes_vulpes -0.1830 0.5482 -1.3530 -0.1633
## total_shrub_cover-Sus_scrofa 0.1273 0.5055 -0.7950 0.1070
## avg_veg_height-Canis_latrans -0.1716 0.4377 -1.0452 -0.1567
## avg_veg_height-Lynx_rufus -0.0955 0.5285 -1.1431 -0.0840
## avg_veg_height-Didelphis_virginiana -0.1362 0.4554 -1.0528 -0.1270
## avg_veg_height-Sylvilagus_floridanus -0.2514 0.4815 -1.2506 -0.2351
## avg_veg_height-Sciurus_carolinensis 0.2018 0.4779 -0.6859 0.1855
## avg_veg_height-Vulpes_vulpes -0.1894 0.5323 -1.2926 -0.1750
## avg_veg_height-Sus_scrofa -0.1230 0.4983 -1.1291 -0.1062
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2278 1.0475 374
## (Intercept)-Lynx_rufus 0.7372 1.0159 449
## (Intercept)-Didelphis_virginiana -0.2402 1.0149 1045
## (Intercept)-Sylvilagus_floridanus 0.7072 1.0182 415
## (Intercept)-Sciurus_carolinensis -0.2881 1.0284 703
## (Intercept)-Vulpes_vulpes 0.3309 1.0684 248
## (Intercept)-Sus_scrofa -0.3533 1.0242 519
## Avg_Cogongrass_Cover-Canis_latrans 1.5529 1.0277 979
## Avg_Cogongrass_Cover-Lynx_rufus 1.6953 1.0080 917
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4638 1.0557 725
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9349 1.0242 849
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3328 1.0245 946
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5543 1.0214 925
## Avg_Cogongrass_Cover-Sus_scrofa 1.1484 1.0173 720
## total_shrub_cover-Canis_latrans 1.1710 1.0224 1148
## total_shrub_cover-Lynx_rufus 0.4076 1.0556 463
## total_shrub_cover-Didelphis_virginiana 0.6650 1.0161 1360
## total_shrub_cover-Sylvilagus_floridanus 0.7628 1.0358 853
## total_shrub_cover-Sciurus_carolinensis 0.8502 1.0130 1467
## total_shrub_cover-Vulpes_vulpes 0.8323 1.0175 772
## total_shrub_cover-Sus_scrofa 1.1875 1.0166 1176
## avg_veg_height-Canis_latrans 0.6897 1.0096 940
## avg_veg_height-Lynx_rufus 0.9393 1.0147 774
## avg_veg_height-Didelphis_virginiana 0.7211 1.0223 805
## avg_veg_height-Sylvilagus_floridanus 0.6445 1.0157 771
## avg_veg_height-Sciurus_carolinensis 1.1901 1.0116 899
## avg_veg_height-Vulpes_vulpes 0.8412 1.0179 878
## avg_veg_height-Sus_scrofa 0.8792 1.0032 894
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6620 0.1740 -3.0343 -2.6538 -2.3389 1.0025
## (Intercept)-Lynx_rufus -3.3967 0.2790 -3.9444 -3.3924 -2.8806 1.0412
## (Intercept)-Didelphis_virginiana -2.4514 0.2570 -2.9847 -2.4443 -1.9811 1.0019
## (Intercept)-Sylvilagus_floridanus -3.1801 0.2878 -3.7792 -3.1613 -2.6595 1.0833
## (Intercept)-Sciurus_carolinensis -2.5490 0.2651 -3.0966 -2.5428 -2.0518 1.0033
## (Intercept)-Vulpes_vulpes -3.6190 0.6392 -5.1961 -3.4940 -2.6701 1.1288
## (Intercept)-Sus_scrofa -3.0011 0.4152 -3.9124 -2.9684 -2.2572 1.0274
## week-Canis_latrans 0.0799 0.1388 -0.1990 0.0839 0.3446 1.0059
## week-Lynx_rufus -0.0277 0.2012 -0.4360 -0.0205 0.3482 1.0021
## week-Didelphis_virginiana -0.2105 0.2347 -0.7116 -0.1916 0.1886 1.0083
## week-Sylvilagus_floridanus -0.1371 0.2190 -0.5975 -0.1237 0.2506 1.0035
## week-Sciurus_carolinensis 0.1652 0.1868 -0.2080 0.1689 0.5186 1.0011
## week-Vulpes_vulpes -0.1009 0.2873 -0.7234 -0.0851 0.4053 1.0063
## week-Sus_scrofa 0.1400 0.2529 -0.3740 0.1442 0.6222 1.0142
## ESS
## (Intercept)-Canis_latrans 920
## (Intercept)-Lynx_rufus 442
## (Intercept)-Didelphis_virginiana 1012
## (Intercept)-Sylvilagus_floridanus 439
## (Intercept)-Sciurus_carolinensis 1215
## (Intercept)-Vulpes_vulpes 161
## (Intercept)-Sus_scrofa 478
## week-Canis_latrans 1320
## week-Lynx_rufus 943
## week-Didelphis_virginiana 1291
## week-Sylvilagus_floridanus 939
## week-Sciurus_carolinensis 1957
## week-Vulpes_vulpes 1015
## week-Sus_scrofa 1725
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8576 0.4353 -1.6886 -0.8691 0.0441 1.0017 1826
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1008 1.3827 0.1469 0.7328 4.2563 1.0106 1373
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8699 0.2915 -3.4528 -2.8602 -2.2929 1.0033 1265
## week -0.0142 0.1745 -0.3797 -0.0055 0.3131 1.0080 1491
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4467 0.5484 0.0600 0.2884 1.8338 1.0281 551
## week 0.1451 0.1471 0.0289 0.1013 0.5290 1.0091 1631
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.0971 0.4158 -0.6756 0.0776 0.9270 1.0017
## (Intercept)-Lynx_rufus -0.2122 0.5345 -1.0981 -0.2759 1.0286 1.0066
## (Intercept)-Didelphis_virginiana -1.2974 0.4243 -2.1793 -1.2862 -0.5030 1.0113
## (Intercept)-Sylvilagus_floridanus -0.5245 0.4420 -1.3262 -0.5455 0.3770 1.0052
## (Intercept)-Sciurus_carolinensis -1.2818 0.4212 -2.1680 -1.2577 -0.5075 1.0006
## (Intercept)-Vulpes_vulpes -1.4727 0.6201 -2.6747 -1.4872 -0.2366 1.0084
## (Intercept)-Sus_scrofa -1.6967 0.5332 -2.7882 -1.6675 -0.7167 1.0097
## ESS
## (Intercept)-Canis_latrans 1666
## (Intercept)-Lynx_rufus 702
## (Intercept)-Didelphis_virginiana 2162
## (Intercept)-Sylvilagus_floridanus 1211
## (Intercept)-Sciurus_carolinensis 2385
## (Intercept)-Vulpes_vulpes 650
## (Intercept)-Sus_scrofa 1050
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6366 0.1669 -2.9805 -2.6319 -2.3246 1.0011
## (Intercept)-Lynx_rufus -3.3630 0.2976 -3.9884 -3.3408 -2.8281 1.0219
## (Intercept)-Didelphis_virginiana -2.4490 0.2555 -2.9750 -2.4421 -1.9570 1.0031
## (Intercept)-Sylvilagus_floridanus -3.1019 0.2703 -3.6674 -3.0886 -2.6128 1.0126
## (Intercept)-Sciurus_carolinensis -2.5465 0.2587 -3.0875 -2.5405 -2.0538 1.0015
## (Intercept)-Vulpes_vulpes -3.4818 0.5509 -4.7574 -3.4067 -2.5880 1.0367
## (Intercept)-Sus_scrofa -2.9667 0.4112 -3.8356 -2.9418 -2.2125 1.0070
## week-Canis_latrans 0.0792 0.1347 -0.1973 0.0828 0.3307 1.0039
## week-Lynx_rufus -0.0145 0.1981 -0.4366 -0.0031 0.3419 1.0168
## week-Didelphis_virginiana -0.2194 0.2370 -0.7452 -0.2023 0.1959 1.0032
## week-Sylvilagus_floridanus -0.1382 0.2172 -0.6167 -0.1264 0.2421 1.0055
## week-Sciurus_carolinensis 0.1714 0.1869 -0.1987 0.1740 0.5239 1.0022
## week-Vulpes_vulpes -0.1147 0.2989 -0.7548 -0.0941 0.4234 1.0150
## week-Sus_scrofa 0.1315 0.2534 -0.3636 0.1380 0.6195 1.0111
## ESS
## (Intercept)-Canis_latrans 1040
## (Intercept)-Lynx_rufus 414
## (Intercept)-Didelphis_virginiana 1252
## (Intercept)-Sylvilagus_floridanus 575
## (Intercept)-Sciurus_carolinensis 1185
## (Intercept)-Vulpes_vulpes 264
## (Intercept)-Sus_scrofa 750
## week-Canis_latrans 1568
## week-Lynx_rufus 1051
## week-Didelphis_virginiana 1321
## week-Sylvilagus_floridanus 912
## week-Sciurus_carolinensis 2019
## week-Vulpes_vulpes 1134
## week-Sus_scrofa 1733
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.437
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1473 0.5186 -2.1866 -1.1518 -0.1049 1.0419 690
## Veg_shannon_index 0.3782 0.3556 -0.2950 0.3675 1.0870 1.0109 960
## Avg_Cogongrass_Cover 0.3493 0.3394 -0.3131 0.3460 1.0079 1.0169 976
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1089 2.1204 0.0650 0.5862 5.1092 1.0602 1014
## Veg_shannon_index 0.5332 0.7870 0.0488 0.3008 2.3872 1.0029 966
## Avg_Cogongrass_Cover 0.4454 0.6926 0.0416 0.2444 2.0987 1.0167 1155
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2922 1.232 0.0869 0.9544 4.6384 1.0035 244
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8799 0.2780 -3.4245 -2.8800 -2.3344 1.0004 1651
## week -0.0217 0.1751 -0.3914 -0.0154 0.3101 1.0023 1245
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4445 0.5820 0.0621 0.2735 1.7799 1.0060 533
## week 0.1420 0.1406 0.0268 0.0998 0.5152 1.0125 1804
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3968 0.7139 -1.7687 -0.3959
## (Intercept)-Lynx_rufus -0.8100 0.7138 -2.1934 -0.8275
## (Intercept)-Didelphis_virginiana -1.4721 0.6227 -2.8583 -1.4285
## (Intercept)-Sylvilagus_floridanus -0.8317 0.6877 -2.0675 -0.8797
## (Intercept)-Sciurus_carolinensis -1.4669 0.5988 -2.7633 -1.4370
## (Intercept)-Vulpes_vulpes -1.5987 0.7640 -3.1461 -1.5621
## (Intercept)-Sus_scrofa -1.9297 0.8369 -3.8598 -1.8435
## Veg_shannon_index-Canis_latrans 0.7402 0.4384 -0.0339 0.6969
## Veg_shannon_index-Lynx_rufus 0.1520 0.5661 -1.0562 0.1711
## Veg_shannon_index-Didelphis_virginiana 0.5547 0.4463 -0.2737 0.5280
## Veg_shannon_index-Sylvilagus_floridanus 0.5219 0.5180 -0.3762 0.4824
## Veg_shannon_index-Sciurus_carolinensis -0.0399 0.4413 -0.9702 -0.0139
## Veg_shannon_index-Vulpes_vulpes -0.0018 0.5308 -1.1244 0.0365
## Veg_shannon_index-Sus_scrofa 0.7867 0.6340 -0.2121 0.7047
## Avg_Cogongrass_Cover-Canis_latrans 0.5876 0.4115 -0.1726 0.5593
## Avg_Cogongrass_Cover-Lynx_rufus 0.6495 0.4738 -0.1442 0.6039
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5128 0.4054 -0.2507 0.5046
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0996 0.4998 -1.2180 -0.0614
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4518 0.3992 -0.3004 0.4409
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3912 0.4963 -0.5262 0.3735
## Avg_Cogongrass_Cover-Sus_scrofa 0.0151 0.5899 -1.4134 0.0783
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0001 1.0153 390
## (Intercept)-Lynx_rufus 0.6222 1.0392 545
## (Intercept)-Didelphis_virginiana -0.3613 1.0232 815
## (Intercept)-Sylvilagus_floridanus 0.6358 1.0076 440
## (Intercept)-Sciurus_carolinensis -0.3597 1.0187 714
## (Intercept)-Vulpes_vulpes -0.1548 1.0055 505
## (Intercept)-Sus_scrofa -0.5762 1.0266 471
## Veg_shannon_index-Canis_latrans 1.7006 1.0065 1180
## Veg_shannon_index-Lynx_rufus 1.1812 1.0201 995
## Veg_shannon_index-Didelphis_virginiana 1.5127 1.0054 1776
## Veg_shannon_index-Sylvilagus_floridanus 1.6878 1.0052 967
## Veg_shannon_index-Sciurus_carolinensis 0.7720 1.0023 1528
## Veg_shannon_index-Vulpes_vulpes 0.9247 1.0023 919
## Veg_shannon_index-Sus_scrofa 2.2416 1.0093 752
## Avg_Cogongrass_Cover-Canis_latrans 1.4671 1.0214 1436
## Avg_Cogongrass_Cover-Lynx_rufus 1.7151 1.0107 1017
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3479 1.0133 1660
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7943 1.0090 1054
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2659 1.0096 1702
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4507 1.0006 1229
## Avg_Cogongrass_Cover-Sus_scrofa 1.0259 1.0109 896
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6283 0.1638 -2.9674 -2.6248 -2.3187 1.0040
## (Intercept)-Lynx_rufus -3.3458 0.2889 -3.9277 -3.3325 -2.8233 1.0167
## (Intercept)-Didelphis_virginiana -2.4533 0.2592 -2.9873 -2.4519 -1.9418 1.0130
## (Intercept)-Sylvilagus_floridanus -3.1401 0.2764 -3.7178 -3.1259 -2.6432 1.0284
## (Intercept)-Sciurus_carolinensis -2.5635 0.2553 -3.0892 -2.5568 -2.0891 1.0025
## (Intercept)-Vulpes_vulpes -3.4844 0.5521 -4.7779 -3.3975 -2.6484 1.0165
## (Intercept)-Sus_scrofa -2.9699 0.3823 -3.8020 -2.9415 -2.2971 1.0031
## week-Canis_latrans 0.0804 0.1361 -0.2005 0.0820 0.3379 1.0009
## week-Lynx_rufus -0.0191 0.2017 -0.4447 -0.0114 0.3595 1.0028
## week-Didelphis_virginiana -0.2156 0.2395 -0.7361 -0.1972 0.1943 1.0057
## week-Sylvilagus_floridanus -0.1434 0.2057 -0.5867 -0.1314 0.2181 1.0081
## week-Sciurus_carolinensis 0.1597 0.1905 -0.2253 0.1634 0.5221 1.0029
## week-Vulpes_vulpes -0.1192 0.2886 -0.7151 -0.0949 0.4074 1.0148
## week-Sus_scrofa 0.1209 0.2579 -0.3962 0.1253 0.6094 1.0014
## ESS
## (Intercept)-Canis_latrans 1015
## (Intercept)-Lynx_rufus 396
## (Intercept)-Didelphis_virginiana 1076
## (Intercept)-Sylvilagus_floridanus 387
## (Intercept)-Sciurus_carolinensis 1197
## (Intercept)-Vulpes_vulpes 214
## (Intercept)-Sus_scrofa 772
## week-Canis_latrans 1653
## week-Lynx_rufus 930
## week-Didelphis_virginiana 1380
## week-Sylvilagus_floridanus 1172
## week-Sciurus_carolinensis 1705
## week-Vulpes_vulpes 1244
## week-Sus_scrofa 1572
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.444
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2117 0.5791 -2.3737 -1.2066 -0.0854 1.0145 426
## Cogon_Patch_Size -0.4531 0.6858 -1.9126 -0.4052 0.8448 1.0138 820
## Avg_Cogongrass_Cover 0.4082 0.3705 -0.3222 0.4094 1.1266 1.0100 634
## total_shrub_cover -0.0471 0.3696 -0.7450 -0.0426 0.6933 1.0128 674
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3636 2.1462 0.0683 0.7084 6.6038 1.0174 471
## Cogon_Patch_Size 2.9894 4.4392 0.1440 1.5929 14.8800 1.0748 451
## Avg_Cogongrass_Cover 0.4312 0.6338 0.0408 0.2329 2.2013 1.0040 887
## total_shrub_cover 0.4444 0.7347 0.0379 0.2278 2.3222 1.1064 853
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2322 2.002 0.1884 1.6787 7.8876 1.0127 176
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9074 0.3227 -3.5306 -2.9094 -2.2867 1.0088 1299
## week -0.0162 0.1786 -0.3819 -0.0105 0.3246 1.0010 1221
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5726 0.7445 0.0717 0.3483 2.4581 1.0194 363
## week 0.1479 0.1664 0.0279 0.1010 0.5376 1.0084 1628
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3252 0.8862 -1.9978 -0.3611
## (Intercept)-Lynx_rufus -0.9162 0.8188 -2.4774 -0.9562
## (Intercept)-Didelphis_virginiana -1.4624 0.6733 -2.9149 -1.4386
## (Intercept)-Sylvilagus_floridanus -0.9827 0.8105 -2.5728 -0.9925
## (Intercept)-Sciurus_carolinensis -1.7084 0.7436 -3.3537 -1.6500
## (Intercept)-Vulpes_vulpes -1.7125 1.0117 -3.9628 -1.6446
## (Intercept)-Sus_scrofa -1.9092 0.8911 -3.8264 -1.8043
## Cogon_Patch_Size-Canis_latrans 0.9220 0.8764 -0.3503 0.7664
## Cogon_Patch_Size-Lynx_rufus -0.5762 0.9908 -2.3303 -0.6573
## Cogon_Patch_Size-Didelphis_virginiana 0.7941 0.6398 -0.2380 0.7344
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7494 1.4559 -5.5836 -1.4434
## Cogon_Patch_Size-Sciurus_carolinensis -1.2528 1.0303 -3.7559 -1.0708
## Cogon_Patch_Size-Vulpes_vulpes -1.1455 1.3523 -4.3931 -0.9011
## Cogon_Patch_Size-Sus_scrofa -0.8191 1.2339 -3.6975 -0.6672
## Avg_Cogongrass_Cover-Canis_latrans 0.3941 0.4230 -0.3941 0.3855
## Avg_Cogongrass_Cover-Lynx_rufus 0.7663 0.5650 -0.1710 0.7015
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3527 0.4563 -0.5577 0.3619
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0935 0.5404 -1.0544 0.1298
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6865 0.4697 -0.1692 0.6537
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5267 0.4856 -0.4003 0.5158
## Avg_Cogongrass_Cover-Sus_scrofa 0.1098 0.6181 -1.3595 0.1829
## total_shrub_cover-Canis_latrans 0.2096 0.4486 -0.6010 0.1763
## total_shrub_cover-Lynx_rufus -0.4335 0.6457 -2.0028 -0.3431
## total_shrub_cover-Didelphis_virginiana -0.1750 0.4459 -1.0730 -0.1595
## total_shrub_cover-Sylvilagus_floridanus -0.1205 0.5419 -1.2592 -0.0886
## total_shrub_cover-Sciurus_carolinensis 0.0908 0.4457 -0.7506 0.0779
## total_shrub_cover-Vulpes_vulpes -0.1066 0.5884 -1.2963 -0.0790
## total_shrub_cover-Sus_scrofa 0.2120 0.5388 -0.7264 0.1833
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5228 1.0080 324
## (Intercept)-Lynx_rufus 0.8814 1.0066 391
## (Intercept)-Didelphis_virginiana -0.2083 1.0328 649
## (Intercept)-Sylvilagus_floridanus 0.6299 1.0129 366
## (Intercept)-Sciurus_carolinensis -0.4185 1.0099 602
## (Intercept)-Vulpes_vulpes 0.1037 1.0095 300
## (Intercept)-Sus_scrofa -0.3983 1.0238 347
## Cogon_Patch_Size-Canis_latrans 3.0160 1.0203 698
## Cogon_Patch_Size-Lynx_rufus 1.8919 1.0237 429
## Cogon_Patch_Size-Didelphis_virginiana 2.1977 1.0281 711
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2107 1.1279 334
## Cogon_Patch_Size-Sciurus_carolinensis 0.2059 1.0029 525
## Cogon_Patch_Size-Vulpes_vulpes 0.7717 1.0035 438
## Cogon_Patch_Size-Sus_scrofa 1.0166 1.0271 518
## Avg_Cogongrass_Cover-Canis_latrans 1.2791 1.0014 1274
## Avg_Cogongrass_Cover-Lynx_rufus 2.1082 1.0023 796
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2204 1.0049 962
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0858 1.0160 1060
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7352 0.9998 817
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5606 1.0036 1179
## Avg_Cogongrass_Cover-Sus_scrofa 1.1573 1.0129 782
## total_shrub_cover-Canis_latrans 1.1592 1.0062 1288
## total_shrub_cover-Lynx_rufus 0.6032 1.0378 543
## total_shrub_cover-Didelphis_virginiana 0.6502 1.0056 939
## total_shrub_cover-Sylvilagus_floridanus 0.8367 1.0211 798
## total_shrub_cover-Sciurus_carolinensis 1.0005 1.0082 1232
## total_shrub_cover-Vulpes_vulpes 0.9547 1.0318 603
## total_shrub_cover-Sus_scrofa 1.4043 1.0398 799
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6350 0.1703 -2.9879 -2.6328 -2.3101 1.0176
## (Intercept)-Lynx_rufus -3.4115 0.2867 -4.0137 -3.3959 -2.8916 1.0338
## (Intercept)-Didelphis_virginiana -2.4453 0.2563 -2.9670 -2.4390 -1.9637 1.0036
## (Intercept)-Sylvilagus_floridanus -3.2262 0.3006 -3.8477 -3.2087 -2.6996 1.0403
## (Intercept)-Sciurus_carolinensis -2.5508 0.2661 -3.0848 -2.5416 -2.0573 1.0058
## (Intercept)-Vulpes_vulpes -3.6639 0.6591 -5.1605 -3.5505 -2.6421 1.1075
## (Intercept)-Sus_scrofa -3.0159 0.4140 -3.9681 -2.9815 -2.3132 1.0081
## week-Canis_latrans 0.0842 0.1340 -0.1943 0.0857 0.3374 1.0023
## week-Lynx_rufus -0.0300 0.2036 -0.4639 -0.0195 0.3471 1.0122
## week-Didelphis_virginiana -0.2078 0.2398 -0.7391 -0.1869 0.2157 1.0006
## week-Sylvilagus_floridanus -0.1595 0.2175 -0.6106 -0.1468 0.2259 1.0062
## week-Sciurus_carolinensis 0.1706 0.1913 -0.2036 0.1737 0.5363 1.0017
## week-Vulpes_vulpes -0.1013 0.2916 -0.7400 -0.0890 0.4225 1.0059
## week-Sus_scrofa 0.1392 0.2567 -0.3746 0.1402 0.6417 1.0059
## ESS
## (Intercept)-Canis_latrans 1119
## (Intercept)-Lynx_rufus 405
## (Intercept)-Didelphis_virginiana 1197
## (Intercept)-Sylvilagus_floridanus 351
## (Intercept)-Sciurus_carolinensis 1278
## (Intercept)-Vulpes_vulpes 142
## (Intercept)-Sus_scrofa 456
## week-Canis_latrans 1610
## week-Lynx_rufus 1046
## week-Didelphis_virginiana 1374
## week-Sylvilagus_floridanus 911
## week-Sciurus_carolinensis 2000
## week-Vulpes_vulpes 1132
## week-Sus_scrofa 1689
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4342
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2674 0.5705 -2.3523 -1.2882 -0.0604 1.0050 1202
## Tree_Density -0.7131 0.5350 -1.9060 -0.6716 0.2788 1.0124 682
## Avg_Canopy_Cover 0.9634 0.4405 0.1375 0.9479 1.9271 1.0061 1217
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0295 2.7758 0.1392 1.2896 9.2200 1.0493 767
## Tree_Density 1.2594 2.1633 0.0591 0.5815 6.0871 1.0043 670
## Avg_Canopy_Cover 0.9840 1.2462 0.0832 0.5938 4.3005 1.0222 668
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6143 0.7209 0.0494 0.3774 2.6243 1.2683 222
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8673 0.2674 -3.3907 -2.8700 -2.3302 1.0109 1373
## week -0.0103 0.1718 -0.3711 0.0003 0.3032 1.0014 1335
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4190 0.4541 0.0649 0.2829 1.6284 1.1343 312
## week 0.1388 0.1547 0.0279 0.0963 0.5185 1.0311 1806
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1103 0.6568 -1.3983 -0.1146 1.2404
## (Intercept)-Lynx_rufus -0.3067 0.9786 -1.9170 -0.4146 2.1130
## (Intercept)-Didelphis_virginiana -1.8755 0.6646 -3.2666 -1.8364 -0.7303
## (Intercept)-Sylvilagus_floridanus -1.0094 0.6882 -2.4052 -1.0155 0.3168
## (Intercept)-Sciurus_carolinensis -1.9224 0.6835 -3.4257 -1.8678 -0.7182
## (Intercept)-Vulpes_vulpes -2.0475 0.8452 -3.8627 -1.9940 -0.5572
## (Intercept)-Sus_scrofa -2.4301 0.8275 -4.2111 -2.3814 -0.9821
## Tree_Density-Canis_latrans -0.9424 0.6354 -2.4097 -0.8749 0.0777
## Tree_Density-Lynx_rufus 0.3317 0.7851 -0.9190 0.2191 2.3303
## Tree_Density-Didelphis_virginiana -1.0895 0.8892 -3.3382 -0.9089 0.1832
## Tree_Density-Sylvilagus_floridanus -1.2078 0.9218 -3.5429 -1.0295 0.1570
## Tree_Density-Sciurus_carolinensis -0.9474 0.8229 -2.9689 -0.8342 0.3284
## Tree_Density-Vulpes_vulpes -0.5813 0.8017 -2.2945 -0.5501 0.9020
## Tree_Density-Sus_scrofa -1.0155 0.9510 -3.4677 -0.8535 0.3811
## Avg_Canopy_Cover-Canis_latrans -0.0055 0.4763 -0.9319 -0.0024 0.9296
## Avg_Canopy_Cover-Lynx_rufus 0.6064 0.6589 -0.5813 0.5704 2.0395
## Avg_Canopy_Cover-Didelphis_virginiana 1.2864 0.5337 0.3937 1.2284 2.5420
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8124 0.8722 0.5806 1.6570 3.9874
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2348 0.5254 0.2993 1.1878 2.3875
## Avg_Canopy_Cover-Vulpes_vulpes 0.9585 0.5854 -0.0728 0.9291 2.2420
## Avg_Canopy_Cover-Sus_scrofa 1.2030 0.5614 0.1513 1.1661 2.3814
## Rhat ESS
## (Intercept)-Canis_latrans 1.0175 457
## (Intercept)-Lynx_rufus 1.0370 268
## (Intercept)-Didelphis_virginiana 1.0089 1222
## (Intercept)-Sylvilagus_floridanus 1.0069 929
## (Intercept)-Sciurus_carolinensis 1.0027 893
## (Intercept)-Vulpes_vulpes 1.0548 496
## (Intercept)-Sus_scrofa 1.0165 621
## Tree_Density-Canis_latrans 1.0101 1191
## Tree_Density-Lynx_rufus 1.0044 564
## Tree_Density-Didelphis_virginiana 1.0152 502
## Tree_Density-Sylvilagus_floridanus 1.0138 522
## Tree_Density-Sciurus_carolinensis 1.0114 784
## Tree_Density-Vulpes_vulpes 1.0251 706
## Tree_Density-Sus_scrofa 1.0193 651
## Avg_Canopy_Cover-Canis_latrans 1.0102 1182
## Avg_Canopy_Cover-Lynx_rufus 1.0148 635
## Avg_Canopy_Cover-Didelphis_virginiana 1.0153 1379
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0163 504
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0064 1315
## Avg_Canopy_Cover-Vulpes_vulpes 1.0009 1084
## Avg_Canopy_Cover-Sus_scrofa 1.0056 1398
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6556 0.1678 -3.0111 -2.6518 -2.3509 1.0126
## (Intercept)-Lynx_rufus -3.4287 0.3241 -4.1188 -3.4151 -2.8508 1.0486
## (Intercept)-Didelphis_virginiana -2.4276 0.2524 -2.9331 -2.4178 -1.9593 1.0306
## (Intercept)-Sylvilagus_floridanus -3.0861 0.2477 -3.5804 -3.0858 -2.6216 1.0033
## (Intercept)-Sciurus_carolinensis -2.5513 0.2553 -3.0529 -2.5493 -2.0649 1.0098
## (Intercept)-Vulpes_vulpes -3.4386 0.5006 -4.6304 -3.4004 -2.6022 1.1439
## (Intercept)-Sus_scrofa -2.8989 0.3674 -3.6623 -2.8784 -2.2244 1.0079
## week-Canis_latrans 0.0852 0.1353 -0.1889 0.0914 0.3343 1.0038
## week-Lynx_rufus -0.0244 0.1955 -0.4376 -0.0148 0.3327 0.9999
## week-Didelphis_virginiana -0.2086 0.2318 -0.7003 -0.1928 0.2006 1.0022
## week-Sylvilagus_floridanus -0.1326 0.2204 -0.6208 -0.1201 0.2580 1.0025
## week-Sciurus_carolinensis 0.1712 0.1852 -0.1963 0.1719 0.5239 1.0022
## week-Vulpes_vulpes -0.1101 0.2855 -0.7409 -0.0827 0.4021 1.0028
## week-Sus_scrofa 0.1287 0.2519 -0.3937 0.1350 0.6095 1.0050
## ESS
## (Intercept)-Canis_latrans 920
## (Intercept)-Lynx_rufus 267
## (Intercept)-Didelphis_virginiana 1226
## (Intercept)-Sylvilagus_floridanus 732
## (Intercept)-Sciurus_carolinensis 1213
## (Intercept)-Vulpes_vulpes 316
## (Intercept)-Sus_scrofa 941
## week-Canis_latrans 1566
## week-Lynx_rufus 900
## week-Didelphis_virginiana 1284
## week-Sylvilagus_floridanus 830
## week-Sciurus_carolinensis 1886
## week-Vulpes_vulpes 1252
## week-Sus_scrofa 1635
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4335
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0043 0.5470 -3.0965 -1.9924 -0.9216 1.0835 430
## Avg_Cogongrass_Cover -0.9414 0.4969 -1.9556 -0.9189 -0.0284 1.0953 490
## I(Avg_Cogongrass_Cover^2) 1.0513 0.4930 0.1419 1.0166 2.0911 1.0398 627
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9564 1.4293 0.0561 0.5400 4.4687 1.0378 807
## Avg_Cogongrass_Cover 0.6491 1.2761 0.0455 0.3175 3.3556 1.0887 1195
## I(Avg_Cogongrass_Cover^2) 1.1158 2.1575 0.0488 0.4476 6.3896 1.0493 295
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8666 0.8628 0.0639 0.5967 3.2042 1.0741 124
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8801 0.2756 -3.3976 -2.8879 -2.2856 1.0010 1495
## week -0.0091 0.1762 -0.3605 -0.0001 0.3154 1.0072 1496
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4016 0.5408 0.0524 0.2542 1.6627 1.0288 988
## week 0.1469 0.2573 0.0273 0.0999 0.4982 1.1864 1955
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3320 0.7401 -2.8197 -1.3276
## (Intercept)-Lynx_rufus -1.9151 0.7217 -3.3420 -1.8956
## (Intercept)-Didelphis_virginiana -2.1704 0.6645 -3.6083 -2.1405
## (Intercept)-Sylvilagus_floridanus -1.7461 0.6927 -3.1284 -1.7313
## (Intercept)-Sciurus_carolinensis -2.4904 0.7239 -4.0450 -2.4424
## (Intercept)-Vulpes_vulpes -2.5993 0.8361 -4.4757 -2.5124
## (Intercept)-Sus_scrofa -2.4799 0.7980 -4.2530 -2.3916
## Avg_Cogongrass_Cover-Canis_latrans -0.6536 0.6073 -1.8080 -0.6685
## Avg_Cogongrass_Cover-Lynx_rufus -0.8583 0.6472 -2.1706 -0.8468
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5792 0.6464 -1.7905 -0.6128
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4631 0.7301 -3.0911 -1.3838
## Avg_Cogongrass_Cover-Sciurus_carolinensis -1.0239 0.6263 -2.3324 -0.9868
## Avg_Cogongrass_Cover-Vulpes_vulpes -1.0215 0.7161 -2.5633 -0.9903
## Avg_Cogongrass_Cover-Sus_scrofa -1.2454 0.7426 -2.8353 -1.1881
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9419 1.2183 0.4543 1.5999
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5708 0.6904 0.5121 1.4787
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7158 0.4958 -0.1984 0.6934
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1147 0.6896 0.1104 1.0164
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1293 0.4532 0.3389 1.1023
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0581 0.5606 0.1605 0.9865
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3064 0.8471 -1.7719 0.4482
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.1824 1.0446 524
## (Intercept)-Lynx_rufus -0.5214 1.0278 596
## (Intercept)-Didelphis_virginiana -0.9441 1.0480 598
## (Intercept)-Sylvilagus_floridanus -0.3767 1.0345 647
## (Intercept)-Sciurus_carolinensis -1.2113 1.0711 604
## (Intercept)-Vulpes_vulpes -1.1990 1.0333 493
## (Intercept)-Sus_scrofa -1.1374 1.0322 529
## Avg_Cogongrass_Cover-Canis_latrans 0.6101 1.0434 943
## Avg_Cogongrass_Cover-Lynx_rufus 0.4031 1.0492 761
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8238 1.0467 620
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2595 1.0255 634
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1081 1.0638 682
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2939 1.0857 532
## Avg_Cogongrass_Cover-Sus_scrofa 0.0462 1.0385 562
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1213 1.0486 174
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.2749 1.0088 340
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.8350 1.0437 564
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8115 1.0261 316
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1170 1.0568 529
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3486 1.0272 561
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.6076 1.0295 362
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6680 0.1684 -2.9914 -2.6620 -2.3443 1.0032
## (Intercept)-Lynx_rufus -3.2990 0.2788 -3.8692 -3.2898 -2.7878 1.0036
## (Intercept)-Didelphis_virginiana -2.4924 0.2598 -3.0264 -2.4848 -2.0064 1.0016
## (Intercept)-Sylvilagus_floridanus -3.1561 0.2715 -3.7028 -3.1448 -2.6486 1.0133
## (Intercept)-Sciurus_carolinensis -2.5795 0.2538 -3.0863 -2.5703 -2.0980 1.0006
## (Intercept)-Vulpes_vulpes -3.4441 0.5072 -4.5389 -3.3973 -2.5805 1.0064
## (Intercept)-Sus_scrofa -2.9887 0.3879 -3.7848 -2.9702 -2.2659 1.0319
## week-Canis_latrans 0.0823 0.1355 -0.2006 0.0892 0.3343 0.9998
## week-Lynx_rufus -0.0107 0.1960 -0.4216 -0.0023 0.3464 1.0137
## week-Didelphis_virginiana -0.2100 0.2424 -0.7355 -0.1933 0.2231 1.0015
## week-Sylvilagus_floridanus -0.1439 0.2203 -0.6234 -0.1274 0.2417 1.0371
## week-Sciurus_carolinensis 0.1690 0.1896 -0.2074 0.1710 0.5263 1.0044
## week-Vulpes_vulpes -0.0995 0.2922 -0.7262 -0.0867 0.4131 1.0015
## week-Sus_scrofa 0.1409 0.2492 -0.3650 0.1407 0.6231 1.0015
## ESS
## (Intercept)-Canis_latrans 1002
## (Intercept)-Lynx_rufus 506
## (Intercept)-Didelphis_virginiana 962
## (Intercept)-Sylvilagus_floridanus 526
## (Intercept)-Sciurus_carolinensis 1118
## (Intercept)-Vulpes_vulpes 325
## (Intercept)-Sus_scrofa 755
## week-Canis_latrans 1550
## week-Lynx_rufus 1194
## week-Didelphis_virginiana 1101
## week-Sylvilagus_floridanus 661
## week-Sciurus_carolinensis 1816
## week-Vulpes_vulpes 1247
## week-Sus_scrofa 1482
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4678
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8704 1.1893 -4.9433 -2.9951 -0.1722 1.0475 407
## Cogon_Patch_Size -0.2006 1.0699 -2.4509 -0.1583 1.7916 1.0152 508
## Veg_shannon_index 0.8923 0.6944 -0.5596 0.8912 2.2154 1.0178 479
## total_shrub_cover -0.2493 0.5782 -1.4775 -0.2245 0.8423 1.0121 465
## Avg_Cogongrass_Cover -0.2451 1.1353 -2.5275 -0.2491 2.0555 1.1178 166
## Tree_Density -1.8349 0.9693 -3.7992 -1.8593 0.1862 1.0125 167
## Avg_Canopy_Cover 1.7856 0.8863 -0.0112 1.7601 3.5574 1.0006 637
## I(Avg_Cogongrass_Cover^2) 1.6263 0.8122 -0.0792 1.6357 3.2179 1.0388 296
## avg_veg_height -0.4175 0.6339 -1.7684 -0.3847 0.8277 1.0150 290
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.2267 28.6904 0.1017 4.1676 64.1990 1.2399 226
## Cogon_Patch_Size 11.2534 21.3655 0.3852 5.4803 56.2202 1.0415 242
## Veg_shannon_index 2.1688 3.6125 0.0566 0.8397 13.1792 1.0554 222
## total_shrub_cover 1.0382 2.2290 0.0480 0.4092 5.5827 1.3681 422
## Avg_Cogongrass_Cover 1.5911 3.0947 0.0508 0.5807 10.1042 1.0048 567
## Tree_Density 4.2033 9.3752 0.0680 1.2210 28.1019 1.1456 158
## Avg_Canopy_Cover 6.6647 11.9753 0.2401 3.1100 37.0205 1.0807 123
## I(Avg_Cogongrass_Cover^2) 3.7286 8.9799 0.0618 1.1363 23.9134 1.7257 153
## avg_veg_height 0.6635 1.1581 0.0451 0.3057 3.5263 1.0061 565
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.5381 3.5363 0.0811 1.4175 10.8316 1.1023 81
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9328 0.3309 -3.5692 -2.9395 -2.2456 1.0139 1151
## week -0.0089 0.1734 -0.3621 -0.0067 0.3203 1.0032 1485
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6661 0.7977 0.0957 0.4384 2.6538 1.0302 480
## week 0.1399 0.1482 0.0283 0.0973 0.5269 1.0074 1687
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.8888 1.5194 -4.7554 -1.9020
## (Intercept)-Lynx_rufus -1.8794 2.2134 -5.2577 -2.2553
## (Intercept)-Didelphis_virginiana -4.5505 1.5738 -8.2107 -4.3169
## (Intercept)-Sylvilagus_floridanus -3.3346 1.5836 -6.9021 -3.1886
## (Intercept)-Sciurus_carolinensis -5.1474 1.9659 -9.9219 -4.8025
## (Intercept)-Vulpes_vulpes -4.8134 2.0823 -9.6857 -4.5022
## (Intercept)-Sus_scrofa -5.7559 2.4209 -11.8882 -5.2507
## Cogon_Patch_Size-Canis_latrans 2.4492 1.9955 -0.0723 2.0434
## Cogon_Patch_Size-Lynx_rufus -0.7319 1.9046 -4.2822 -0.8157
## Cogon_Patch_Size-Didelphis_virginiana 2.3790 1.2848 0.2865 2.2437
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6229 2.6946 -9.8925 -2.0524
## Cogon_Patch_Size-Sciurus_carolinensis -1.7271 2.1288 -7.2113 -1.3024
## Cogon_Patch_Size-Vulpes_vulpes -1.2986 2.6947 -7.5517 -0.9136
## Cogon_Patch_Size-Sus_scrofa -1.1285 2.1761 -6.4173 -0.6994
## Veg_shannon_index-Canis_latrans 1.5450 0.8998 0.1008 1.4253
## Veg_shannon_index-Lynx_rufus 0.9514 1.2406 -1.5210 0.9545
## Veg_shannon_index-Didelphis_virginiana 1.0470 0.8617 -0.5402 1.0221
## Veg_shannon_index-Sylvilagus_floridanus 1.1163 0.9145 -0.5613 1.0567
## Veg_shannon_index-Sciurus_carolinensis -0.0691 1.1277 -2.8085 0.0762
## Veg_shannon_index-Vulpes_vulpes 0.2788 1.3120 -2.8020 0.4631
## Veg_shannon_index-Sus_scrofa 1.9411 1.4210 -0.0738 1.6318
## total_shrub_cover-Canis_latrans 0.0203 0.6704 -1.2283 -0.0151
## total_shrub_cover-Lynx_rufus -0.9634 1.1796 -3.8832 -0.7545
## total_shrub_cover-Didelphis_virginiana -0.4845 0.7801 -2.3075 -0.4048
## total_shrub_cover-Sylvilagus_floridanus -0.1952 0.7966 -1.8548 -0.2019
## total_shrub_cover-Sciurus_carolinensis 0.0373 0.6893 -1.2785 0.0259
## total_shrub_cover-Vulpes_vulpes -0.4013 0.9508 -2.4937 -0.3353
## total_shrub_cover-Sus_scrofa 0.0787 0.8567 -1.5262 0.0331
## Avg_Cogongrass_Cover-Canis_latrans -0.3910 1.3800 -3.1141 -0.3757
## Avg_Cogongrass_Cover-Lynx_rufus -0.0079 1.4976 -2.8117 -0.0642
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0663 1.3856 -2.6511 -0.1073
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7412 1.4833 -3.9953 -0.6742
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1365 1.4423 -2.8882 -0.1793
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0023 1.5461 -2.9526 -0.0837
## Avg_Cogongrass_Cover-Sus_scrofa -0.5657 1.5692 -3.9392 -0.5096
## Tree_Density-Canis_latrans -2.9217 1.6312 -6.9479 -2.6067
## Tree_Density-Lynx_rufus -0.6286 1.9152 -3.5046 -0.9361
## Tree_Density-Didelphis_virginiana -2.4162 1.3123 -5.5571 -2.2488
## Tree_Density-Sylvilagus_floridanus -2.6596 1.6017 -6.5457 -2.4418
## Tree_Density-Sciurus_carolinensis -2.5950 1.4356 -6.1222 -2.3819
## Tree_Density-Vulpes_vulpes -1.6603 2.2209 -4.9945 -1.8664
## Tree_Density-Sus_scrofa -2.1669 1.5699 -5.6240 -2.0610
## Avg_Canopy_Cover-Canis_latrans -0.0057 0.8646 -1.7811 -0.0176
## Avg_Canopy_Cover-Lynx_rufus 1.1038 1.5629 -1.4896 0.9283
## Avg_Canopy_Cover-Didelphis_virginiana 3.1986 1.2968 1.2733 2.9892
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.5456 2.3775 1.3116 4.0594
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5230 1.0898 0.8923 2.3536
## Avg_Canopy_Cover-Vulpes_vulpes 2.7258 2.1098 0.3818 2.2739
## Avg_Canopy_Cover-Sus_scrofa 2.1295 1.1257 0.3236 1.9750
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0752 1.9073 0.9568 2.6047
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9189 1.6729 0.8268 2.5994
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2842 0.8665 -0.3469 1.2645
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5868 1.1101 -0.1753 1.4813
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8169 0.8623 0.2471 1.7507
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1821 1.1244 0.4518 2.0302
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3317 1.8773 -4.3701 0.6964
## avg_veg_height-Canis_latrans -0.6091 0.6938 -2.0516 -0.5762
## avg_veg_height-Lynx_rufus -0.3766 0.9138 -2.1940 -0.3773
## avg_veg_height-Didelphis_virginiana -0.5258 0.8074 -2.2028 -0.5090
## avg_veg_height-Sylvilagus_floridanus -0.4796 0.8173 -2.1857 -0.4519
## avg_veg_height-Sciurus_carolinensis -0.0481 0.8023 -1.5592 -0.0792
## avg_veg_height-Vulpes_vulpes -0.5538 0.9432 -2.5569 -0.5175
## avg_veg_height-Sus_scrofa -0.4267 0.8051 -2.0740 -0.4067
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1535 1.0188 215
## (Intercept)-Lynx_rufus 3.6315 1.2169 109
## (Intercept)-Didelphis_virginiana -2.1153 1.0431 149
## (Intercept)-Sylvilagus_floridanus -0.6132 1.0364 253
## (Intercept)-Sciurus_carolinensis -2.2745 1.0857 137
## (Intercept)-Vulpes_vulpes -1.3704 1.0144 158
## (Intercept)-Sus_scrofa -2.3969 1.0588 97
## Cogon_Patch_Size-Canis_latrans 7.4305 1.0481 201
## Cogon_Patch_Size-Lynx_rufus 3.3868 1.0136 202
## Cogon_Patch_Size-Didelphis_virginiana 5.3072 1.0230 146
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8464 1.0148 230
## Cogon_Patch_Size-Sciurus_carolinensis 1.0429 1.0393 236
## Cogon_Patch_Size-Vulpes_vulpes 2.8875 1.1053 137
## Cogon_Patch_Size-Sus_scrofa 1.9603 1.0428 311
## Veg_shannon_index-Canis_latrans 3.5989 1.0391 428
## Veg_shannon_index-Lynx_rufus 3.4845 1.0201 335
## Veg_shannon_index-Didelphis_virginiana 2.8136 1.0083 759
## Veg_shannon_index-Sylvilagus_floridanus 3.2175 1.0100 629
## Veg_shannon_index-Sciurus_carolinensis 1.6608 1.0371 270
## Veg_shannon_index-Vulpes_vulpes 2.3447 1.0625 262
## Veg_shannon_index-Sus_scrofa 5.7249 1.0768 257
## total_shrub_cover-Canis_latrans 1.4516 1.0196 652
## total_shrub_cover-Lynx_rufus 0.7303 1.2178 212
## total_shrub_cover-Didelphis_virginiana 0.8240 1.0083 652
## total_shrub_cover-Sylvilagus_floridanus 1.5055 1.0364 749
## total_shrub_cover-Sciurus_carolinensis 1.4733 1.0307 754
## total_shrub_cover-Vulpes_vulpes 1.2097 1.0384 355
## total_shrub_cover-Sus_scrofa 1.9314 1.0368 650
## Avg_Cogongrass_Cover-Canis_latrans 2.3613 1.0480 247
## Avg_Cogongrass_Cover-Lynx_rufus 3.0748 1.0703 333
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7799 1.0591 223
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0706 1.0464 257
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8565 1.0903 231
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2327 1.1017 262
## Avg_Cogongrass_Cover-Sus_scrofa 2.4324 1.0812 210
## Tree_Density-Canis_latrans -0.5051 1.0136 214
## Tree_Density-Lynx_rufus 3.9552 1.0794 119
## Tree_Density-Didelphis_virginiana -0.4073 1.0174 279
## Tree_Density-Sylvilagus_floridanus -0.0705 1.0324 215
## Tree_Density-Sciurus_carolinensis -0.3916 1.0324 332
## Tree_Density-Vulpes_vulpes 3.2820 1.0626 98
## Tree_Density-Sus_scrofa 0.7260 1.0309 266
## Avg_Canopy_Cover-Canis_latrans 1.7734 1.0162 395
## Avg_Canopy_Cover-Lynx_rufus 4.5513 1.0392 223
## Avg_Canopy_Cover-Didelphis_virginiana 6.3094 1.0143 161
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.2742 1.0148 107
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1759 1.0090 337
## Avg_Canopy_Cover-Vulpes_vulpes 7.9491 1.1305 96
## Avg_Canopy_Cover-Sus_scrofa 4.7223 1.0180 353
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.1033 1.4266 70
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.2068 1.4440 133
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1084 1.0328 174
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.1503 1.0255 231
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.7693 1.0263 320
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.0564 1.0407 236
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.9124 1.3035 152
## avg_veg_height-Canis_latrans 0.7016 1.0143 435
## avg_veg_height-Lynx_rufus 1.4374 1.0192 400
## avg_veg_height-Didelphis_virginiana 1.0276 1.0033 422
## avg_veg_height-Sylvilagus_floridanus 1.0735 1.0134 435
## avg_veg_height-Sciurus_carolinensis 1.6855 1.0103 486
## avg_veg_height-Vulpes_vulpes 1.2144 1.0236 352
## avg_veg_height-Sus_scrofa 1.1779 1.0109 432
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6470 0.1688 -2.9898 -2.6466 -2.3309 1.0162
## (Intercept)-Lynx_rufus -3.5580 0.3005 -4.1225 -3.5597 -2.9700 1.0919
## (Intercept)-Didelphis_virginiana -2.4053 0.2535 -2.9266 -2.3918 -1.9321 1.0113
## (Intercept)-Sylvilagus_floridanus -3.2014 0.2619 -3.7313 -3.1933 -2.6990 1.0025
## (Intercept)-Sciurus_carolinensis -2.5505 0.2697 -3.1069 -2.5405 -2.0427 1.0072
## (Intercept)-Vulpes_vulpes -3.8729 0.5950 -5.0784 -3.8512 -2.8159 1.1285
## (Intercept)-Sus_scrofa -2.9742 0.4277 -3.9441 -2.9391 -2.2295 1.0167
## week-Canis_latrans 0.0808 0.1365 -0.2029 0.0835 0.3341 1.0276
## week-Lynx_rufus -0.0159 0.1967 -0.4519 -0.0048 0.3501 1.0005
## week-Didelphis_virginiana -0.2066 0.2331 -0.6991 -0.1894 0.2219 1.0009
## week-Sylvilagus_floridanus -0.1468 0.2178 -0.6029 -0.1304 0.2433 1.0081
## week-Sciurus_carolinensis 0.1701 0.1857 -0.2043 0.1754 0.5226 1.0071
## week-Vulpes_vulpes -0.0945 0.2754 -0.6630 -0.0779 0.4057 1.0062
## week-Sus_scrofa 0.1379 0.2595 -0.3894 0.1438 0.6350 1.0037
## ESS
## (Intercept)-Canis_latrans 784
## (Intercept)-Lynx_rufus 259
## (Intercept)-Didelphis_virginiana 1183
## (Intercept)-Sylvilagus_floridanus 471
## (Intercept)-Sciurus_carolinensis 1082
## (Intercept)-Vulpes_vulpes 166
## (Intercept)-Sus_scrofa 462
## week-Canis_latrans 1519
## week-Lynx_rufus 838
## week-Didelphis_virginiana 1512
## week-Sylvilagus_floridanus 778
## week-Sciurus_carolinensis 1820
## week-Vulpes_vulpes 1072
## week-Sus_scrofa 1270
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3338
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9342 0.4793 -1.9451 -0.9234 -0.0081 1.012 544
## Avg_Cogongrass_Cover 0.2905 0.3117 -0.3098 0.2867 0.8984 1.010 861
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8774 1.3493 0.0535 0.4916 4.0282 1.0150 919
## Avg_Cogongrass_Cover 0.3740 0.5421 0.0409 0.2128 1.7019 1.0084 1059
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3909 1.3712 0.1048 0.9848 4.8883 1.0156 163
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0142 0.2666 -3.5470 -3.0122 -2.5113 1.0111 1043
## shrub_cover 0.3496 0.3514 -0.3592 0.3441 1.0375 1.0012 1023
## veg_height -0.0413 0.2281 -0.5019 -0.0397 0.4109 0.9998 1537
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3613 0.4506 0.0442 0.2187 1.5476 1.0179 671
## shrub_cover 0.6530 0.7385 0.0921 0.4479 2.4821 1.0199 1201
## veg_height 0.2869 0.3054 0.0569 0.2040 0.9927 1.0424 1845
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1920 0.7148 -1.5381 -0.2053
## (Intercept)-Lynx_rufus -0.6761 0.6643 -1.9502 -0.6760
## (Intercept)-Didelphis_virginiana -1.1434 0.5955 -2.3437 -1.1233
## (Intercept)-Sylvilagus_floridanus -0.7385 0.6195 -1.9603 -0.7368
## (Intercept)-Sciurus_carolinensis -1.2188 0.6047 -2.4925 -1.2025
## (Intercept)-Vulpes_vulpes -1.4412 0.7366 -3.0069 -1.3849
## (Intercept)-Sus_scrofa -1.4634 0.7239 -3.0455 -1.4116
## Avg_Cogongrass_Cover-Canis_latrans 0.4887 0.4028 -0.2074 0.4602
## Avg_Cogongrass_Cover-Lynx_rufus 0.5462 0.4255 -0.1741 0.5024
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3987 0.3885 -0.3569 0.3943
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1559 0.4477 -1.1177 -0.1139
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4217 0.3796 -0.2831 0.4130
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3587 0.4407 -0.5134 0.3518
## Avg_Cogongrass_Cover-Sus_scrofa 0.0302 0.5423 -1.2197 0.0677
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3195 1.0070 434
## (Intercept)-Lynx_rufus 0.6772 1.0022 532
## (Intercept)-Didelphis_virginiana 0.0570 1.0152 557
## (Intercept)-Sylvilagus_floridanus 0.4587 1.0211 533
## (Intercept)-Sciurus_carolinensis -0.0736 1.0021 802
## (Intercept)-Vulpes_vulpes -0.0780 1.0276 556
## (Intercept)-Sus_scrofa -0.1818 1.0051 563
## Avg_Cogongrass_Cover-Canis_latrans 1.3972 1.0149 1437
## Avg_Cogongrass_Cover-Lynx_rufus 1.5023 1.0014 1408
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1674 1.0245 1516
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6616 1.0105 984
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2091 1.0114 1788
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2840 1.0016 1390
## Avg_Cogongrass_Cover-Sus_scrofa 1.0043 1.0027 959
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7936 0.1834 -3.1749 -2.7885 -2.4520 1.0188
## (Intercept)-Lynx_rufus -3.4025 0.3034 -4.0234 -3.3872 -2.8512 1.0038
## (Intercept)-Didelphis_virginiana -2.6810 0.2758 -3.2232 -2.6797 -2.1435 1.0095
## (Intercept)-Sylvilagus_floridanus -3.1119 0.2436 -3.6135 -3.1001 -2.6597 1.0173
## (Intercept)-Sciurus_carolinensis -2.7281 0.2905 -3.2914 -2.7259 -2.1747 1.0036
## (Intercept)-Vulpes_vulpes -3.5073 0.5299 -4.7844 -3.4216 -2.6947 1.0171
## (Intercept)-Sus_scrofa -3.2262 0.4359 -4.1400 -3.2103 -2.4235 1.0081
## shrub_cover-Canis_latrans -0.2593 0.2216 -0.6872 -0.2595 0.1705 1.0284
## shrub_cover-Lynx_rufus -0.0639 0.3552 -0.7520 -0.0691 0.6388 1.0160
## shrub_cover-Didelphis_virginiana 1.0010 0.3790 0.3099 0.9859 1.7919 1.0067
## shrub_cover-Sylvilagus_floridanus 0.3118 0.4162 -0.4677 0.3049 1.1580 1.0111
## shrub_cover-Sciurus_carolinensis 0.8765 0.4134 0.0847 0.8678 1.7101 1.0003
## shrub_cover-Vulpes_vulpes 0.1178 0.6163 -1.1717 0.1437 1.3016 1.0141
## shrub_cover-Sus_scrofa 0.6447 0.6797 -0.6223 0.6275 2.0832 1.0028
## veg_height-Canis_latrans -0.6180 0.1893 -1.0052 -0.6178 -0.2561 1.0080
## veg_height-Lynx_rufus -0.0001 0.2465 -0.4838 -0.0006 0.4649 1.0081
## veg_height-Didelphis_virginiana 0.4440 0.2677 -0.0520 0.4326 0.9793 1.0142
## veg_height-Sylvilagus_floridanus 0.1204 0.2389 -0.3396 0.1198 0.5928 1.0028
## veg_height-Sciurus_carolinensis 0.0956 0.2237 -0.3405 0.0967 0.5412 1.0065
## veg_height-Vulpes_vulpes -0.1379 0.3032 -0.8067 -0.1202 0.4165 0.9997
## veg_height-Sus_scrofa -0.1686 0.3536 -0.9113 -0.1575 0.4820 1.0036
## ESS
## (Intercept)-Canis_latrans 745
## (Intercept)-Lynx_rufus 343
## (Intercept)-Didelphis_virginiana 662
## (Intercept)-Sylvilagus_floridanus 768
## (Intercept)-Sciurus_carolinensis 887
## (Intercept)-Vulpes_vulpes 227
## (Intercept)-Sus_scrofa 642
## shrub_cover-Canis_latrans 780
## shrub_cover-Lynx_rufus 480
## shrub_cover-Didelphis_virginiana 596
## shrub_cover-Sylvilagus_floridanus 509
## shrub_cover-Sciurus_carolinensis 637
## shrub_cover-Vulpes_vulpes 627
## shrub_cover-Sus_scrofa 815
## veg_height-Canis_latrans 777
## veg_height-Lynx_rufus 821
## veg_height-Didelphis_virginiana 923
## veg_height-Sylvilagus_floridanus 900
## veg_height-Sciurus_carolinensis 1112
## veg_height-Vulpes_vulpes 750
## veg_height-Sus_scrofa 987
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4085
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3803 1.0635 -3.3198 -1.4166 0.8752 1.0043 129
## Cogon_Patch_Size -0.5199 1.0064 -2.5929 -0.4598 1.4447 1.0096 340
## Veg_shannon_index 0.9233 0.7576 -0.5690 0.8949 2.4507 1.0607 330
## total_shrub_cover -0.7127 0.9267 -2.7050 -0.6265 0.9363 1.0062 249
## Avg_Cogongrass_Cover 1.8824 0.9557 0.0474 1.8428 3.8900 1.0253 155
## Tree_Density -1.4058 1.0231 -3.4582 -1.3887 0.6669 1.0115 185
## Avg_Canopy_Cover 1.8841 1.1210 -0.6105 1.9044 4.0125 1.0101 1156
## avg_veg_height -0.3072 0.7508 -1.5757 -0.3796 1.3692 1.0111 196
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.5243 20.3706 0.0813 2.9708 54.2779 1.5776 49
## Cogon_Patch_Size 8.3235 16.6480 0.1392 3.5899 42.2083 1.5022 106
## Veg_shannon_index 2.3655 4.6002 0.0567 0.9390 12.8488 1.0206 326
## total_shrub_cover 5.6566 12.4497 0.0782 1.7873 33.4884 1.0617 129
## Avg_Cogongrass_Cover 1.8140 4.8197 0.0547 0.6008 12.3141 1.1652 412
## Tree_Density 5.1412 9.4760 0.0895 1.9978 28.3632 1.0473 246
## Avg_Canopy_Cover 16.0676 28.5910 0.4715 6.7728 84.5634 1.3687 142
## avg_veg_height 0.8875 1.4260 0.0472 0.3828 4.6883 1.0364 308
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.2684 8.746 0.0956 4.0058 31.5861 1.2205 45
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1632 0.314 -3.7547 -3.1644 -2.5239 1.0150 462
## shrub_cover 0.5835 0.405 -0.2108 0.5680 1.4458 0.9996 373
## veg_height -0.0234 0.240 -0.4791 -0.0244 0.4658 1.0083 1544
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5147 0.6510 0.0596 0.3291 2.1278 1.0212 779
## shrub_cover 0.8685 0.9492 0.1304 0.6018 3.2984 1.0151 544
## veg_height 0.3382 0.3215 0.0667 0.2419 1.1645 1.0109 1973
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1561 1.7530 -2.9792 0.1296
## (Intercept)-Lynx_rufus 0.1117 3.0567 -3.3287 -0.4894
## (Intercept)-Didelphis_virginiana -2.5413 1.4685 -5.8896 -2.4179
## (Intercept)-Sylvilagus_floridanus -1.5050 1.5611 -4.9759 -1.4453
## (Intercept)-Sciurus_carolinensis -2.7464 1.8332 -7.1421 -2.5382
## (Intercept)-Vulpes_vulpes -2.5857 1.8287 -6.7769 -2.4374
## (Intercept)-Sus_scrofa -3.3787 2.5426 -10.2884 -2.9307
## Cogon_Patch_Size-Canis_latrans 1.1130 1.5325 -1.0247 0.8185
## Cogon_Patch_Size-Lynx_rufus -1.0279 1.9396 -5.0280 -1.0411
## Cogon_Patch_Size-Didelphis_virginiana 1.6365 1.5057 -0.5164 1.3479
## Cogon_Patch_Size-Sylvilagus_floridanus -2.5659 2.5394 -8.7960 -2.0053
## Cogon_Patch_Size-Sciurus_carolinensis -1.9223 2.2689 -7.7764 -1.5060
## Cogon_Patch_Size-Vulpes_vulpes -1.4615 2.1883 -6.6018 -1.2107
## Cogon_Patch_Size-Sus_scrofa -1.3069 2.1506 -6.6132 -0.9899
## Veg_shannon_index-Canis_latrans 1.5180 0.8962 -0.0453 1.4240
## Veg_shannon_index-Lynx_rufus 0.9039 1.2104 -1.6722 0.8897
## Veg_shannon_index-Didelphis_virginiana 1.4027 1.0556 -0.3084 1.2553
## Veg_shannon_index-Sylvilagus_floridanus 1.3512 1.0481 -0.3774 1.2286
## Veg_shannon_index-Sciurus_carolinensis 0.0721 1.1945 -2.5197 0.1519
## Veg_shannon_index-Vulpes_vulpes 0.0963 1.3591 -2.9414 0.2725
## Veg_shannon_index-Sus_scrofa 1.8766 1.5211 -0.2025 1.5962
## total_shrub_cover-Canis_latrans 1.1034 1.5271 -0.8992 0.6705
## total_shrub_cover-Lynx_rufus -1.4561 1.9144 -5.9182 -1.2661
## total_shrub_cover-Didelphis_virginiana -1.4411 1.4645 -5.0447 -1.1803
## total_shrub_cover-Sylvilagus_floridanus -0.9593 1.5390 -4.4906 -0.7808
## total_shrub_cover-Sciurus_carolinensis -1.3064 1.9541 -6.2397 -0.8793
## total_shrub_cover-Vulpes_vulpes -1.9012 2.5310 -8.5138 -1.2151
## total_shrub_cover-Sus_scrofa -0.7375 1.7620 -5.2057 -0.5287
## Avg_Cogongrass_Cover-Canis_latrans 2.2674 1.1784 0.1694 2.1779
## Avg_Cogongrass_Cover-Lynx_rufus 2.2599 1.2691 0.0179 2.1779
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9889 1.1664 -0.1695 1.9141
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3063 1.3000 -1.6513 1.3817
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2642 1.3607 -0.0012 2.1322
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4745 1.5081 0.0948 2.2801
## Avg_Cogongrass_Cover-Sus_scrofa 1.6401 1.4425 -1.4758 1.7063
## Tree_Density-Canis_latrans -2.7081 1.8135 -7.0130 -2.3849
## Tree_Density-Lynx_rufus 0.1383 1.7353 -2.8938 -0.0333
## Tree_Density-Didelphis_virginiana -1.8138 1.6147 -5.2945 -1.7786
## Tree_Density-Sylvilagus_floridanus -2.4104 1.9867 -7.2163 -2.1274
## Tree_Density-Sciurus_carolinensis -1.9105 1.9502 -6.2459 -1.7421
## Tree_Density-Vulpes_vulpes -1.3827 1.8691 -5.1656 -1.3477
## Tree_Density-Sus_scrofa -1.8978 1.9455 -6.5488 -1.6455
## Avg_Canopy_Cover-Canis_latrans -0.2226 0.8513 -2.1875 -0.1432
## Avg_Canopy_Cover-Lynx_rufus 0.8830 2.1157 -2.5085 0.5976
## Avg_Canopy_Cover-Didelphis_virginiana 4.5846 2.2670 1.5235 4.0812
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.8698 3.1921 1.6402 5.2225
## Avg_Canopy_Cover-Sciurus_carolinensis 4.4728 2.7022 1.3311 3.8168
## Avg_Canopy_Cover-Vulpes_vulpes 3.6065 2.5693 0.3418 3.0052
## Avg_Canopy_Cover-Sus_scrofa 2.6671 1.8204 0.0266 2.3334
## avg_veg_height-Canis_latrans -0.3772 0.8145 -1.8855 -0.4086
## avg_veg_height-Lynx_rufus -0.4231 1.0586 -2.4746 -0.4741
## avg_veg_height-Didelphis_virginiana -0.5274 0.9619 -2.4492 -0.5498
## avg_veg_height-Sylvilagus_floridanus -0.5671 0.9504 -2.5757 -0.5698
## avg_veg_height-Sciurus_carolinensis 0.2024 1.0610 -1.4191 0.0563
## avg_veg_height-Vulpes_vulpes -0.2511 1.0748 -2.0581 -0.3550
## avg_veg_height-Sus_scrofa -0.3633 1.0514 -2.3548 -0.3878
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.6607 1.0231 102
## (Intercept)-Lynx_rufus 8.8838 1.2501 34
## (Intercept)-Didelphis_virginiana 0.0698 1.0148 154
## (Intercept)-Sylvilagus_floridanus 1.5260 1.1092 146
## (Intercept)-Sciurus_carolinensis 0.2687 1.0625 180
## (Intercept)-Vulpes_vulpes 0.6004 1.0115 163
## (Intercept)-Sus_scrofa 0.2977 1.0636 87
## Cogon_Patch_Size-Canis_latrans 4.9234 1.0668 282
## Cogon_Patch_Size-Lynx_rufus 3.1136 1.0576 139
## Cogon_Patch_Size-Didelphis_virginiana 5.2713 1.1412 140
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7098 1.1460 135
## Cogon_Patch_Size-Sciurus_carolinensis 1.1416 1.1117 146
## Cogon_Patch_Size-Vulpes_vulpes 2.2743 1.0302 180
## Cogon_Patch_Size-Sus_scrofa 2.0678 1.0528 219
## Veg_shannon_index-Canis_latrans 3.4471 1.0189 256
## Veg_shannon_index-Lynx_rufus 3.2700 1.0636 263
## Veg_shannon_index-Didelphis_virginiana 3.8524 1.0153 383
## Veg_shannon_index-Sylvilagus_floridanus 3.6441 1.0680 378
## Veg_shannon_index-Sciurus_carolinensis 2.2091 1.0671 278
## Veg_shannon_index-Vulpes_vulpes 2.2553 1.0429 235
## Veg_shannon_index-Sus_scrofa 5.5521 1.0130 163
## total_shrub_cover-Canis_latrans 4.8751 1.1738 122
## total_shrub_cover-Lynx_rufus 2.0595 1.0289 110
## total_shrub_cover-Didelphis_virginiana 0.7099 1.0498 165
## total_shrub_cover-Sylvilagus_floridanus 1.8506 1.0457 279
## total_shrub_cover-Sciurus_carolinensis 1.3641 1.0840 92
## total_shrub_cover-Vulpes_vulpes 1.2717 1.0403 109
## total_shrub_cover-Sus_scrofa 2.3801 1.0095 102
## Avg_Cogongrass_Cover-Canis_latrans 4.9612 1.0257 250
## Avg_Cogongrass_Cover-Lynx_rufus 5.1101 1.0555 240
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.5875 1.0117 274
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.7041 1.0107 257
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.3202 1.1391 201
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.0014 1.1580 173
## Avg_Cogongrass_Cover-Sus_scrofa 4.5378 1.0166 294
## Tree_Density-Canis_latrans -0.0924 1.0284 219
## Tree_Density-Lynx_rufus 4.1853 1.0443 123
## Tree_Density-Didelphis_virginiana 1.2375 1.0198 242
## Tree_Density-Sylvilagus_floridanus 0.8942 1.0185 188
## Tree_Density-Sciurus_carolinensis 1.6192 1.0078 217
## Tree_Density-Vulpes_vulpes 2.3759 1.0205 276
## Tree_Density-Sus_scrofa 1.3913 1.0106 314
## Avg_Canopy_Cover-Canis_latrans 1.2432 1.1006 278
## Avg_Canopy_Cover-Lynx_rufus 6.5278 1.0494 102
## Avg_Canopy_Cover-Didelphis_virginiana 10.4526 1.1954 133
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.7543 1.1692 104
## Avg_Canopy_Cover-Sciurus_carolinensis 11.1223 1.4583 75
## Avg_Canopy_Cover-Vulpes_vulpes 10.3374 1.3200 59
## Avg_Canopy_Cover-Sus_scrofa 7.4835 1.1709 179
## avg_veg_height-Canis_latrans 1.2961 1.0026 252
## avg_veg_height-Lynx_rufus 1.8185 1.0146 269
## avg_veg_height-Didelphis_virginiana 1.4175 1.0068 306
## avg_veg_height-Sylvilagus_floridanus 1.3806 1.0263 305
## avg_veg_height-Sciurus_carolinensis 2.6791 1.0258 185
## avg_veg_height-Vulpes_vulpes 2.1248 1.0225 220
## avg_veg_height-Sus_scrofa 1.8523 1.0096 241
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8157 0.1888 -3.1915 -2.8180 -2.4546 1.0012
## (Intercept)-Lynx_rufus -3.6159 0.3587 -4.3509 -3.5999 -2.9659 1.0273
## (Intercept)-Didelphis_virginiana -2.7561 0.3028 -3.3613 -2.7547 -2.1931 1.0027
## (Intercept)-Sylvilagus_floridanus -3.2142 0.2399 -3.7015 -3.2116 -2.7618 1.0183
## (Intercept)-Sciurus_carolinensis -2.9205 0.3077 -3.5368 -2.9240 -2.2998 1.0161
## (Intercept)-Vulpes_vulpes -3.9173 0.5818 -5.1122 -3.8875 -2.8968 1.1046
## (Intercept)-Sus_scrofa -3.5030 0.5275 -4.6140 -3.4805 -2.5287 1.0041
## shrub_cover-Canis_latrans -0.3700 0.2361 -0.8240 -0.3700 0.0852 1.0388
## shrub_cover-Lynx_rufus 0.0092 0.4228 -0.8724 0.0182 0.7892 1.0088
## shrub_cover-Didelphis_virginiana 1.1490 0.4052 0.4225 1.1332 1.9978 1.0034
## shrub_cover-Sylvilagus_floridanus 0.6443 0.4089 -0.2072 0.6608 1.4042 1.0088
## shrub_cover-Sciurus_carolinensis 1.1769 0.4110 0.3461 1.1815 1.9870 1.0064
## shrub_cover-Vulpes_vulpes 0.5021 0.6219 -0.7115 0.4822 1.7733 1.0024
## shrub_cover-Sus_scrofa 1.0431 0.8188 -0.4908 1.0054 2.7645 1.0010
## veg_height-Canis_latrans -0.6450 0.1911 -1.0303 -0.6396 -0.2878 1.0336
## veg_height-Lynx_rufus 0.0803 0.2545 -0.4284 0.0874 0.5516 1.0171
## veg_height-Didelphis_virginiana 0.5063 0.2551 0.0212 0.5006 1.0217 1.0146
## veg_height-Sylvilagus_floridanus 0.1403 0.2534 -0.3558 0.1425 0.6455 1.0081
## veg_height-Sciurus_carolinensis 0.1811 0.2326 -0.2656 0.1821 0.6351 1.0116
## veg_height-Vulpes_vulpes -0.2252 0.3538 -0.9697 -0.2073 0.4440 1.0401
## veg_height-Sus_scrofa -0.1936 0.3556 -0.9242 -0.1815 0.4851 1.0016
## ESS
## (Intercept)-Canis_latrans 602
## (Intercept)-Lynx_rufus 187
## (Intercept)-Didelphis_virginiana 304
## (Intercept)-Sylvilagus_floridanus 615
## (Intercept)-Sciurus_carolinensis 392
## (Intercept)-Vulpes_vulpes 165
## (Intercept)-Sus_scrofa 193
## shrub_cover-Canis_latrans 653
## shrub_cover-Lynx_rufus 166
## shrub_cover-Didelphis_virginiana 349
## shrub_cover-Sylvilagus_floridanus 343
## shrub_cover-Sciurus_carolinensis 274
## shrub_cover-Vulpes_vulpes 265
## shrub_cover-Sus_scrofa 238
## veg_height-Canis_latrans 670
## veg_height-Lynx_rufus 492
## veg_height-Didelphis_virginiana 783
## veg_height-Sylvilagus_floridanus 801
## veg_height-Sciurus_carolinensis 822
## veg_height-Vulpes_vulpes 520
## veg_height-Sus_scrofa 896
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4225
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5252 0.6871 -1.7732 -0.5409 0.9542 1.0717 112
## Avg_Cogongrass_Cover 0.2185 0.4861 -0.7741 0.2201 1.1760 1.0041 448
## total_shrub_cover -0.9057 0.7259 -2.5177 -0.8300 0.2745 1.0644 163
## avg_veg_height 0.1590 0.5055 -0.7607 0.1333 1.2600 1.0094 223
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1155 2.6963 0.0534 0.4941 5.4867 1.2023 262
## Avg_Cogongrass_Cover 0.6833 1.3045 0.0440 0.3314 3.4393 1.2396 248
## total_shrub_cover 2.2587 5.0079 0.0674 0.8375 12.8872 1.1658 166
## avg_veg_height 0.4371 1.0458 0.0349 0.2171 2.1650 1.1450 681
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.7517 4.7347 0.1559 2.3359 17.8491 1.4782 83
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1799 0.3225 -3.8103 -3.1751 -2.5579 1.0308 217
## shrub_cover 0.7050 0.4317 -0.1239 0.6975 1.5649 1.0156 291
## veg_height -0.0611 0.2494 -0.5695 -0.0591 0.4348 0.9995 1054
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4711 0.6968 0.0531 0.2885 1.9534 1.0284 389
## shrub_cover 0.9443 1.2065 0.1395 0.6674 3.2907 1.0434 999
## veg_height 0.3129 0.4179 0.0582 0.2209 1.1282 1.0647 1713
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0040 0.8820 -1.6551 -0.0254
## (Intercept)-Lynx_rufus -0.3670 0.9325 -1.9828 -0.3986
## (Intercept)-Didelphis_virginiana -0.7040 0.8584 -2.3838 -0.7185
## (Intercept)-Sylvilagus_floridanus -0.2000 0.9474 -1.8595 -0.2551
## (Intercept)-Sciurus_carolinensis -0.7746 0.8780 -2.4383 -0.7994
## (Intercept)-Vulpes_vulpes -0.8328 1.1366 -2.8386 -0.8823
## (Intercept)-Sus_scrofa -0.9961 1.0354 -3.1339 -0.9628
## Avg_Cogongrass_Cover-Canis_latrans 0.5294 0.6310 -0.5476 0.4713
## Avg_Cogongrass_Cover-Lynx_rufus 0.5931 0.7409 -0.5823 0.5289
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3576 0.6090 -0.8367 0.3371
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3273 0.7432 -2.0320 -0.2334
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1531 0.5703 -1.0546 0.1802
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3489 0.6926 -0.9930 0.3415
## Avg_Cogongrass_Cover-Sus_scrofa -0.0459 0.7904 -1.8018 0.0095
## total_shrub_cover-Canis_latrans 0.3738 0.8466 -1.0263 0.2690
## total_shrub_cover-Lynx_rufus -1.6239 1.2150 -4.6327 -1.4146
## total_shrub_cover-Didelphis_virginiana -1.1127 1.0287 -3.7307 -0.9247
## total_shrub_cover-Sylvilagus_floridanus -1.6684 1.4190 -5.4618 -1.3657
## total_shrub_cover-Sciurus_carolinensis -1.1544 1.0449 -3.7590 -0.9536
## total_shrub_cover-Vulpes_vulpes -1.3424 1.4476 -5.0903 -1.0360
## total_shrub_cover-Sus_scrofa -0.8073 1.2201 -3.6766 -0.7005
## avg_veg_height-Canis_latrans 0.1452 0.5739 -0.9276 0.1217
## avg_veg_height-Lynx_rufus 0.1047 0.7297 -1.2502 0.0775
## avg_veg_height-Didelphis_virginiana 0.0158 0.6429 -1.2116 0.0229
## avg_veg_height-Sylvilagus_floridanus 0.1011 0.6444 -1.0855 0.0660
## avg_veg_height-Sciurus_carolinensis 0.5046 0.6290 -0.5710 0.4504
## avg_veg_height-Vulpes_vulpes 0.0609 0.6786 -1.2895 0.0592
## avg_veg_height-Sus_scrofa 0.2214 0.6582 -0.9951 0.1926
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9021 1.0116 256
## (Intercept)-Lynx_rufus 1.6455 1.0948 148
## (Intercept)-Didelphis_virginiana 1.0769 1.0419 173
## (Intercept)-Sylvilagus_floridanus 1.9292 1.0539 230
## (Intercept)-Sciurus_carolinensis 1.0437 1.0504 219
## (Intercept)-Vulpes_vulpes 1.7228 1.1522 109
## (Intercept)-Sus_scrofa 1.0582 1.0289 174
## Avg_Cogongrass_Cover-Canis_latrans 1.9459 1.0136 525
## Avg_Cogongrass_Cover-Lynx_rufus 2.1198 1.0631 419
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5979 1.0056 706
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9148 1.0134 477
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2419 1.0012 573
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7970 1.0078 816
## Avg_Cogongrass_Cover-Sus_scrofa 1.3780 1.0165 513
## total_shrub_cover-Canis_latrans 2.3676 1.0496 178
## total_shrub_cover-Lynx_rufus 0.0785 1.2812 146
## total_shrub_cover-Didelphis_virginiana 0.3059 1.1657 118
## total_shrub_cover-Sylvilagus_floridanus 0.1712 1.0959 91
## total_shrub_cover-Sciurus_carolinensis 0.2943 1.0585 168
## total_shrub_cover-Vulpes_vulpes 0.6761 1.0817 130
## total_shrub_cover-Sus_scrofa 1.3398 1.0281 167
## avg_veg_height-Canis_latrans 1.3250 1.0113 339
## avg_veg_height-Lynx_rufus 1.6067 1.0149 352
## avg_veg_height-Didelphis_virginiana 1.3267 1.0241 330
## avg_veg_height-Sylvilagus_floridanus 1.4702 1.0145 263
## avg_veg_height-Sciurus_carolinensis 1.9504 1.0234 324
## avg_veg_height-Vulpes_vulpes 1.3982 1.0088 384
## avg_veg_height-Sus_scrofa 1.6608 1.0176 363
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8345 0.1966 -3.2250 -2.8319 -2.4547 1.0067
## (Intercept)-Lynx_rufus -3.4516 0.2979 -4.0583 -3.4403 -2.9040 1.0448
## (Intercept)-Didelphis_virginiana -2.8892 0.3259 -3.5416 -2.8767 -2.2639 1.0359
## (Intercept)-Sylvilagus_floridanus -3.2884 0.2643 -3.8133 -3.2801 -2.7893 1.0547
## (Intercept)-Sciurus_carolinensis -2.9396 0.3342 -3.5994 -2.9387 -2.2965 1.0524
## (Intercept)-Vulpes_vulpes -3.8510 0.6132 -5.2961 -3.7703 -2.8590 1.1025
## (Intercept)-Sus_scrofa -3.5715 0.5075 -4.6768 -3.5375 -2.6587 1.0077
## shrub_cover-Canis_latrans -0.3050 0.2571 -0.8041 -0.3085 0.2040 1.0078
## shrub_cover-Lynx_rufus 0.1779 0.3573 -0.5560 0.1890 0.8465 1.0225
## shrub_cover-Didelphis_virginiana 1.4000 0.4352 0.5899 1.3943 2.2709 1.0278
## shrub_cover-Sylvilagus_floridanus 0.8415 0.4445 -0.0653 0.8531 1.6796 1.0439
## shrub_cover-Sciurus_carolinensis 1.2902 0.4381 0.4303 1.2828 2.1397 1.0243
## shrub_cover-Vulpes_vulpes 0.5626 0.7163 -0.9240 0.5701 1.9583 1.0133
## shrub_cover-Sus_scrofa 1.2115 0.8293 -0.3972 1.2025 2.9561 1.0126
## veg_height-Canis_latrans -0.6349 0.1922 -1.0286 -0.6255 -0.2718 1.0073
## veg_height-Lynx_rufus 0.0145 0.2567 -0.5104 0.0183 0.5097 1.0066
## veg_height-Didelphis_virginiana 0.4351 0.2852 -0.0915 0.4292 1.0164 1.0282
## veg_height-Sylvilagus_floridanus 0.0267 0.2657 -0.5002 0.0324 0.5603 1.0238
## veg_height-Sciurus_carolinensis 0.1332 0.2456 -0.3321 0.1279 0.6433 1.0145
## veg_height-Vulpes_vulpes -0.1803 0.3400 -0.8920 -0.1663 0.4570 1.0030
## veg_height-Sus_scrofa -0.2417 0.3337 -0.9163 -0.2326 0.4037 1.0262
## ESS
## (Intercept)-Canis_latrans 563
## (Intercept)-Lynx_rufus 356
## (Intercept)-Didelphis_virginiana 190
## (Intercept)-Sylvilagus_floridanus 204
## (Intercept)-Sciurus_carolinensis 334
## (Intercept)-Vulpes_vulpes 140
## (Intercept)-Sus_scrofa 123
## shrub_cover-Canis_latrans 455
## shrub_cover-Lynx_rufus 484
## shrub_cover-Didelphis_virginiana 198
## shrub_cover-Sylvilagus_floridanus 227
## shrub_cover-Sciurus_carolinensis 294
## shrub_cover-Vulpes_vulpes 223
## shrub_cover-Sus_scrofa 104
## veg_height-Canis_latrans 765
## veg_height-Lynx_rufus 649
## veg_height-Didelphis_virginiana 540
## veg_height-Sylvilagus_floridanus 419
## veg_height-Sciurus_carolinensis 682
## veg_height-Vulpes_vulpes 516
## veg_height-Sus_scrofa 873
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3307
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7243 0.423 -1.5389 -0.7187 0.1478 1.0119 1297
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9657 1.043 0.1156 0.6526 3.6384 1.0449 1190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0291 0.2913 -3.6138 -3.0310 -2.4698 1.0209 1220
## shrub_cover 0.3212 0.3754 -0.4501 0.3108 1.0800 1.0041 1141
## veg_height -0.0368 0.2221 -0.4676 -0.0381 0.4067 1.0030 1486
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4453 0.5887 0.0518 0.2822 1.7292 1.0628 516
## shrub_cover 0.7899 0.8699 0.1115 0.5340 3.0113 1.0044 925
## veg_height 0.2954 0.3120 0.0555 0.2126 1.0182 1.0104 1802
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1896 0.4211 -0.5466 0.1686 1.0773 1.0127
## (Intercept)-Lynx_rufus -0.0687 0.6143 -1.0148 -0.1493 1.4012 1.0794
## (Intercept)-Didelphis_virginiana -1.1115 0.4392 -2.0056 -1.0993 -0.2684 1.0027
## (Intercept)-Sylvilagus_floridanus -0.4718 0.4166 -1.2537 -0.4786 0.3577 1.0104
## (Intercept)-Sciurus_carolinensis -1.0994 0.4431 -2.0302 -1.0815 -0.2843 0.9999
## (Intercept)-Vulpes_vulpes -1.3653 0.6150 -2.6487 -1.3387 -0.2190 1.0315
## (Intercept)-Sus_scrofa -1.4628 0.5984 -2.7265 -1.4281 -0.3841 1.0008
## ESS
## (Intercept)-Canis_latrans 1538
## (Intercept)-Lynx_rufus 430
## (Intercept)-Didelphis_virginiana 1471
## (Intercept)-Sylvilagus_floridanus 1591
## (Intercept)-Sciurus_carolinensis 1341
## (Intercept)-Vulpes_vulpes 723
## (Intercept)-Sus_scrofa 799
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7822 0.1793 -3.1389 -2.7778 -2.4368 1.0038
## (Intercept)-Lynx_rufus -3.4964 0.3439 -4.2099 -3.4639 -2.8930 1.1188
## (Intercept)-Didelphis_virginiana -2.6789 0.2832 -3.2525 -2.6711 -2.1465 1.0172
## (Intercept)-Sylvilagus_floridanus -3.1057 0.2590 -3.6574 -3.0977 -2.6351 1.0292
## (Intercept)-Sciurus_carolinensis -2.7228 0.2981 -3.3172 -2.7104 -2.1565 1.0039
## (Intercept)-Vulpes_vulpes -3.6241 0.5606 -4.8994 -3.5453 -2.7125 1.0843
## (Intercept)-Sus_scrofa -3.3041 0.4848 -4.2905 -3.2883 -2.3916 1.0312
## shrub_cover-Canis_latrans -0.3003 0.2321 -0.7469 -0.3016 0.1547 1.0077
## shrub_cover-Lynx_rufus -0.2192 0.3697 -0.9615 -0.2143 0.5035 1.0549
## shrub_cover-Didelphis_virginiana 1.0111 0.3854 0.2994 0.9947 1.8034 1.0104
## shrub_cover-Sylvilagus_floridanus 0.2817 0.4279 -0.4919 0.2659 1.1532 1.0211
## shrub_cover-Sciurus_carolinensis 0.8912 0.4275 0.0923 0.8790 1.7436 1.0030
## shrub_cover-Vulpes_vulpes -0.0699 0.6714 -1.4918 -0.0375 1.1760 1.0322
## shrub_cover-Sus_scrofa 0.7204 0.7704 -0.7225 0.7108 2.2746 1.0032
## veg_height-Canis_latrans -0.6223 0.1848 -0.9996 -0.6179 -0.2779 1.0009
## veg_height-Lynx_rufus 0.0193 0.2538 -0.4932 0.0291 0.5041 1.0037
## veg_height-Didelphis_virginiana 0.4677 0.2586 -0.0265 0.4604 1.0088 1.0075
## veg_height-Sylvilagus_floridanus 0.1172 0.2443 -0.3481 0.1159 0.5875 1.0034
## veg_height-Sciurus_carolinensis 0.0940 0.2242 -0.3273 0.0864 0.5599 1.0181
## veg_height-Vulpes_vulpes -0.1239 0.3227 -0.8011 -0.1120 0.4628 1.0144
## veg_height-Sus_scrofa -0.1724 0.3446 -0.8430 -0.1729 0.5320 1.0051
## ESS
## (Intercept)-Canis_latrans 678
## (Intercept)-Lynx_rufus 235
## (Intercept)-Didelphis_virginiana 795
## (Intercept)-Sylvilagus_floridanus 682
## (Intercept)-Sciurus_carolinensis 834
## (Intercept)-Vulpes_vulpes 271
## (Intercept)-Sus_scrofa 536
## shrub_cover-Canis_latrans 880
## shrub_cover-Lynx_rufus 388
## shrub_cover-Didelphis_virginiana 673
## shrub_cover-Sylvilagus_floridanus 519
## shrub_cover-Sciurus_carolinensis 666
## shrub_cover-Vulpes_vulpes 445
## shrub_cover-Sus_scrofa 673
## veg_height-Canis_latrans 663
## veg_height-Lynx_rufus 758
## veg_height-Didelphis_virginiana 971
## veg_height-Sylvilagus_floridanus 875
## veg_height-Sciurus_carolinensis 971
## veg_height-Vulpes_vulpes 595
## veg_height-Sus_scrofa 1069
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3383
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9747 0.5152 -2.0057 -0.9740 0.0542 1.0078 352
## Veg_shannon_index 0.3663 0.3776 -0.3824 0.3606 1.1286 1.0061 1123
## Avg_Cogongrass_Cover 0.3899 0.3637 -0.2882 0.3819 1.1047 1.0014 838
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0373 1.6736 0.0566 0.5670 4.8863 1.0373 326
## Veg_shannon_index 0.5658 0.8061 0.0511 0.3289 2.4987 1.0205 712
## Avg_Cogongrass_Cover 0.4855 0.9850 0.0400 0.2651 2.2290 1.1353 1217
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5143 1.7832 0.0963 1.0218 5.8876 1.1151 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0327 0.3045 -3.6258 -3.0322 -2.4394 1.0027 879
## shrub_cover 0.3394 0.3779 -0.4191 0.3386 1.1016 1.0067 1140
## veg_height -0.0368 0.2335 -0.4949 -0.0353 0.4388 1.0151 1234
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4941 0.8115 0.0542 0.2883 2.1023 1.0338 571
## shrub_cover 0.7483 0.9211 0.1010 0.5006 2.9465 1.0091 1233
## veg_height 0.2991 0.3334 0.0532 0.2148 1.0344 1.0189 1966
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2384 0.7469 -1.6188 -0.2470
## (Intercept)-Lynx_rufus -0.5544 0.8569 -1.9669 -0.6292
## (Intercept)-Didelphis_virginiana -1.2611 0.6462 -2.5927 -1.2324
## (Intercept)-Sylvilagus_floridanus -0.7626 0.6698 -2.0540 -0.7816
## (Intercept)-Sciurus_carolinensis -1.2756 0.6284 -2.5739 -1.2527
## (Intercept)-Vulpes_vulpes -1.4301 0.7901 -3.0222 -1.4119
## (Intercept)-Sus_scrofa -1.6591 0.7716 -3.3594 -1.6132
## Veg_shannon_index-Canis_latrans 0.7535 0.4570 -0.0693 0.7226
## Veg_shannon_index-Lynx_rufus 0.1949 0.6174 -1.1103 0.2254
## Veg_shannon_index-Didelphis_virginiana 0.5583 0.4503 -0.2749 0.5373
## Veg_shannon_index-Sylvilagus_floridanus 0.4607 0.4949 -0.4963 0.4490
## Veg_shannon_index-Sciurus_carolinensis -0.1242 0.4903 -1.1778 -0.0894
## Veg_shannon_index-Vulpes_vulpes 0.0035 0.5443 -1.1082 0.0302
## Veg_shannon_index-Sus_scrofa 0.7877 0.6442 -0.3085 0.7265
## Avg_Cogongrass_Cover-Canis_latrans 0.6956 0.4927 -0.0942 0.6502
## Avg_Cogongrass_Cover-Lynx_rufus 0.6849 0.5075 -0.1748 0.6427
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5132 0.4180 -0.2761 0.4929
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1139 0.5344 -1.2614 -0.0759
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4596 0.4082 -0.3297 0.4480
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4569 0.5437 -0.5595 0.4498
## Avg_Cogongrass_Cover-Sus_scrofa 0.0911 0.6312 -1.3423 0.1545
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2428 1.0162 283
## (Intercept)-Lynx_rufus 1.3363 1.0599 279
## (Intercept)-Didelphis_virginiana -0.0770 1.0207 820
## (Intercept)-Sylvilagus_floridanus 0.5904 1.0070 326
## (Intercept)-Sciurus_carolinensis -0.0844 1.0081 779
## (Intercept)-Vulpes_vulpes 0.1064 1.0047 414
## (Intercept)-Sus_scrofa -0.3511 1.0376 562
## Veg_shannon_index-Canis_latrans 1.7483 1.0109 1106
## Veg_shannon_index-Lynx_rufus 1.3728 1.0067 811
## Veg_shannon_index-Didelphis_virginiana 1.5276 1.0112 1563
## Veg_shannon_index-Sylvilagus_floridanus 1.4461 1.0023 1339
## Veg_shannon_index-Sciurus_carolinensis 0.7459 1.0118 1068
## Veg_shannon_index-Vulpes_vulpes 1.0131 1.0043 857
## Veg_shannon_index-Sus_scrofa 2.2799 1.0079 836
## Avg_Cogongrass_Cover-Canis_latrans 1.7838 1.0113 610
## Avg_Cogongrass_Cover-Lynx_rufus 1.8770 1.0065 952
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3717 1.0102 1518
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8437 1.0041 878
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3168 1.0029 1692
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6214 1.0008 892
## Avg_Cogongrass_Cover-Sus_scrofa 1.1795 1.0073 570
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7715 0.1846 -3.1611 -2.7609 -2.4399 1.0003
## (Intercept)-Lynx_rufus -3.5069 0.3513 -4.2637 -3.4875 -2.8749 1.0365
## (Intercept)-Didelphis_virginiana -2.6651 0.2806 -3.2027 -2.6648 -2.1173 1.0014
## (Intercept)-Sylvilagus_floridanus -3.1711 0.2719 -3.7362 -3.1595 -2.6733 1.0012
## (Intercept)-Sciurus_carolinensis -2.7192 0.3041 -3.3111 -2.7169 -2.1228 1.0198
## (Intercept)-Vulpes_vulpes -3.6552 0.5936 -5.0073 -3.5797 -2.7333 1.0016
## (Intercept)-Sus_scrofa -3.2979 0.4822 -4.3369 -3.2641 -2.4330 1.0016
## shrub_cover-Canis_latrans -0.2754 0.2259 -0.7416 -0.2716 0.1520 1.0026
## shrub_cover-Lynx_rufus -0.1694 0.3620 -0.8962 -0.1572 0.5316 1.0220
## shrub_cover-Didelphis_virginiana 0.9902 0.3904 0.2843 0.9692 1.7987 1.0075
## shrub_cover-Sylvilagus_floridanus 0.2917 0.4150 -0.4963 0.2787 1.1023 1.0170
## shrub_cover-Sciurus_carolinensis 0.9069 0.4306 0.1133 0.8924 1.8004 1.0053
## shrub_cover-Vulpes_vulpes 0.0259 0.5970 -1.1389 0.0163 1.2581 1.0229
## shrub_cover-Sus_scrofa 0.7050 0.7526 -0.7304 0.6722 2.3318 1.0037
## veg_height-Canis_latrans -0.6121 0.1868 -0.9781 -0.6084 -0.2641 1.0080
## veg_height-Lynx_rufus -0.0171 0.2563 -0.5409 -0.0111 0.4685 1.0147
## veg_height-Didelphis_virginiana 0.4563 0.2622 -0.0433 0.4459 0.9903 1.0026
## veg_height-Sylvilagus_floridanus 0.1384 0.2561 -0.3496 0.1403 0.6409 1.0172
## veg_height-Sciurus_carolinensis 0.0870 0.2222 -0.3404 0.0813 0.5474 1.0068
## veg_height-Vulpes_vulpes -0.1627 0.3196 -0.8783 -0.1392 0.4085 1.0169
## veg_height-Sus_scrofa -0.1954 0.3592 -0.9597 -0.1959 0.4893 1.0023
## ESS
## (Intercept)-Canis_latrans 747
## (Intercept)-Lynx_rufus 232
## (Intercept)-Didelphis_virginiana 731
## (Intercept)-Sylvilagus_floridanus 471
## (Intercept)-Sciurus_carolinensis 684
## (Intercept)-Vulpes_vulpes 214
## (Intercept)-Sus_scrofa 443
## shrub_cover-Canis_latrans 886
## shrub_cover-Lynx_rufus 368
## shrub_cover-Didelphis_virginiana 556
## shrub_cover-Sylvilagus_floridanus 562
## shrub_cover-Sciurus_carolinensis 715
## shrub_cover-Vulpes_vulpes 664
## shrub_cover-Sus_scrofa 512
## veg_height-Canis_latrans 720
## veg_height-Lynx_rufus 766
## veg_height-Didelphis_virginiana 865
## veg_height-Sylvilagus_floridanus 716
## veg_height-Sciurus_carolinensis 1153
## veg_height-Vulpes_vulpes 506
## veg_height-Sus_scrofa 943
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3948
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8045 0.6684 -2.0873 -0.8168 0.5489 1.0526 237
## Cogon_Patch_Size -0.4119 0.7198 -1.9357 -0.3840 0.9330 1.0182 791
## Avg_Cogongrass_Cover 0.4147 0.4575 -0.5238 0.4080 1.3264 1.0146 545
## total_shrub_cover -0.6398 0.6767 -2.1834 -0.5659 0.6051 1.0223 291
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7168 3.3877 0.0545 0.6284 9.7944 1.1940 104
## Cogon_Patch_Size 3.3904 6.5874 0.1365 1.4928 18.5179 1.0941 272
## Avg_Cogongrass_Cover 0.6000 1.0122 0.0414 0.2952 3.0496 1.0548 898
## total_shrub_cover 1.8859 4.3143 0.0569 0.6880 10.3907 1.1261 193
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.7197 3.3565 0.3781 2.7625 13.1559 1.0079 140
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1398 0.3062 -3.7542 -3.1359 -2.5221 1.0357 436
## shrub_cover 0.6126 0.4318 -0.2204 0.6073 1.5040 1.0123 427
## veg_height -0.0528 0.2329 -0.5053 -0.0591 0.4032 1.0029 1001
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4923 0.8001 0.0503 0.2681 2.2886 1.1087 287
## shrub_cover 0.9639 1.1299 0.1335 0.6420 3.9099 1.1225 220
## veg_height 0.2994 0.3185 0.0535 0.2136 1.0941 1.0048 1579
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0139 1.0282 -1.6501 -0.0912
## (Intercept)-Lynx_rufus -0.6069 1.0664 -2.4047 -0.6831
## (Intercept)-Didelphis_virginiana -1.0633 0.8510 -2.9942 -1.0021
## (Intercept)-Sylvilagus_floridanus -0.4928 1.0101 -2.2715 -0.5793
## (Intercept)-Sciurus_carolinensis -1.1879 0.9289 -3.3656 -1.1133
## (Intercept)-Vulpes_vulpes -1.2135 1.2873 -3.8643 -1.2069
## (Intercept)-Sus_scrofa -1.4903 1.1442 -4.2489 -1.3057
## Cogon_Patch_Size-Canis_latrans 0.9612 1.1106 -0.5053 0.7428
## Cogon_Patch_Size-Lynx_rufus -0.4054 1.2429 -2.4471 -0.5104
## Cogon_Patch_Size-Didelphis_virginiana 0.7572 0.6497 -0.3954 0.7027
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7260 1.6436 -6.2769 -1.3879
## Cogon_Patch_Size-Sciurus_carolinensis -1.3067 1.1492 -4.2195 -1.0782
## Cogon_Patch_Size-Vulpes_vulpes -0.9946 1.3990 -4.3072 -0.8058
## Cogon_Patch_Size-Sus_scrofa -0.8755 1.3422 -4.3509 -0.6244
## Avg_Cogongrass_Cover-Canis_latrans 0.5061 0.5177 -0.4010 0.4614
## Avg_Cogongrass_Cover-Lynx_rufus 0.7887 0.6807 -0.3384 0.7115
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3078 0.5526 -0.8752 0.3164
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0787 0.6921 -1.4836 0.1186
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6233 0.5756 -0.4697 0.5967
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5632 0.6051 -0.5771 0.5400
## Avg_Cogongrass_Cover-Sus_scrofa 0.1487 0.7910 -1.6414 0.2135
## total_shrub_cover-Canis_latrans 0.4022 0.8933 -0.9464 0.2131
## total_shrub_cover-Lynx_rufus -1.2026 1.1921 -4.0189 -1.0153
## total_shrub_cover-Didelphis_virginiana -0.9190 0.8530 -2.9969 -0.7819
## total_shrub_cover-Sylvilagus_floridanus -1.3534 1.2911 -4.8770 -1.0751
## total_shrub_cover-Sciurus_carolinensis -0.7634 0.9332 -3.2254 -0.6032
## total_shrub_cover-Vulpes_vulpes -0.7565 1.1789 -3.6911 -0.6090
## total_shrub_cover-Sus_scrofa -0.5151 1.1058 -3.0231 -0.3986
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4378 1.0995 158
## (Intercept)-Lynx_rufus 1.9200 1.2044 237
## (Intercept)-Didelphis_virginiana 0.4812 1.0327 355
## (Intercept)-Sylvilagus_floridanus 1.8023 1.0608 311
## (Intercept)-Sciurus_carolinensis 0.4291 1.0537 230
## (Intercept)-Vulpes_vulpes 0.9964 1.1280 147
## (Intercept)-Sus_scrofa 0.3464 1.0488 213
## Cogon_Patch_Size-Canis_latrans 3.6504 1.0228 294
## Cogon_Patch_Size-Lynx_rufus 2.2152 1.0656 334
## Cogon_Patch_Size-Didelphis_virginiana 2.1395 1.0305 539
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3253 1.0365 285
## Cogon_Patch_Size-Sciurus_carolinensis 0.2821 1.0235 407
## Cogon_Patch_Size-Vulpes_vulpes 1.2076 1.0275 311
## Cogon_Patch_Size-Sus_scrofa 1.0918 1.0359 366
## Avg_Cogongrass_Cover-Canis_latrans 1.6447 1.0139 782
## Avg_Cogongrass_Cover-Lynx_rufus 2.3836 1.0234 771
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4048 1.0136 790
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3251 1.0136 581
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8415 1.0092 659
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8452 1.0170 811
## Avg_Cogongrass_Cover-Sus_scrofa 1.5227 1.0192 496
## total_shrub_cover-Canis_latrans 2.6375 1.0509 259
## total_shrub_cover-Lynx_rufus 0.7065 1.0386 154
## total_shrub_cover-Didelphis_virginiana 0.3728 1.0343 151
## total_shrub_cover-Sylvilagus_floridanus 0.3088 1.0135 129
## total_shrub_cover-Sciurus_carolinensis 0.6175 1.0526 165
## total_shrub_cover-Vulpes_vulpes 1.2588 1.0172 200
## total_shrub_cover-Sus_scrofa 1.4338 1.0361 178
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8207 0.1896 -3.2003 -2.8118 -2.4611 1.0026
## (Intercept)-Lynx_rufus -3.4301 0.3270 -4.1668 -3.4042 -2.8657 1.0675
## (Intercept)-Didelphis_virginiana -2.7835 0.3063 -3.3823 -2.7740 -2.1926 1.0206
## (Intercept)-Sylvilagus_floridanus -3.2668 0.2614 -3.7951 -3.2606 -2.7714 1.0370
## (Intercept)-Sciurus_carolinensis -2.9122 0.3288 -3.5631 -2.9030 -2.3143 1.0138
## (Intercept)-Vulpes_vulpes -3.7489 0.6252 -5.2518 -3.6454 -2.7853 1.1247
## (Intercept)-Sus_scrofa -3.5170 0.5555 -4.8368 -3.4600 -2.5982 1.1021
## shrub_cover-Canis_latrans -0.3238 0.2546 -0.8003 -0.3239 0.1732 1.0113
## shrub_cover-Lynx_rufus 0.1426 0.3951 -0.7127 0.1725 0.8449 1.0546
## shrub_cover-Didelphis_virginiana 1.2444 0.4121 0.4702 1.2317 2.0671 1.0294
## shrub_cover-Sylvilagus_floridanus 0.8095 0.4054 -0.0377 0.8225 1.5912 1.0061
## shrub_cover-Sciurus_carolinensis 1.2030 0.4452 0.3471 1.2011 2.0860 1.0230
## shrub_cover-Vulpes_vulpes 0.3273 0.7209 -1.2023 0.3756 1.6554 1.0582
## shrub_cover-Sus_scrofa 1.1657 0.9252 -0.4406 1.0831 3.3432 1.1003
## veg_height-Canis_latrans -0.6274 0.1868 -1.0136 -0.6222 -0.2819 1.0181
## veg_height-Lynx_rufus 0.0218 0.2457 -0.4756 0.0308 0.4824 1.0095
## veg_height-Didelphis_virginiana 0.4286 0.2707 -0.0751 0.4237 0.9753 1.0032
## veg_height-Sylvilagus_floridanus 0.0085 0.2477 -0.4749 0.0089 0.4807 1.0165
## veg_height-Sciurus_carolinensis 0.1487 0.2427 -0.3102 0.1434 0.6269 1.0028
## veg_height-Vulpes_vulpes -0.1478 0.3279 -0.7991 -0.1444 0.4661 1.0140
## veg_height-Sus_scrofa -0.2280 0.3507 -0.9355 -0.2230 0.4514 1.0184
## ESS
## (Intercept)-Canis_latrans 675
## (Intercept)-Lynx_rufus 203
## (Intercept)-Didelphis_virginiana 457
## (Intercept)-Sylvilagus_floridanus 366
## (Intercept)-Sciurus_carolinensis 257
## (Intercept)-Vulpes_vulpes 128
## (Intercept)-Sus_scrofa 152
## shrub_cover-Canis_latrans 489
## shrub_cover-Lynx_rufus 269
## shrub_cover-Didelphis_virginiana 402
## shrub_cover-Sylvilagus_floridanus 317
## shrub_cover-Sciurus_carolinensis 330
## shrub_cover-Vulpes_vulpes 293
## shrub_cover-Sus_scrofa 122
## veg_height-Canis_latrans 730
## veg_height-Lynx_rufus 731
## veg_height-Didelphis_virginiana 851
## veg_height-Sylvilagus_floridanus 531
## veg_height-Sciurus_carolinensis 523
## veg_height-Vulpes_vulpes 538
## veg_height-Sus_scrofa 966
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.3552
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0827 0.5936 -2.2550 -1.0861 0.1288 1.0150 691
## Tree_Density -0.6793 0.5903 -1.9165 -0.6580 0.4373 1.0063 674
## Avg_Canopy_Cover 1.1521 0.5541 0.1044 1.1337 2.3293 1.0042 890
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8680 2.6948 0.0982 1.1164 8.3510 1.0501 601
## Tree_Density 1.5739 3.4993 0.0641 0.6851 7.9173 1.0529 749
## Avg_Canopy_Cover 1.8130 2.4904 0.1558 1.1030 7.5151 1.0757 606
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8375 1.0821 0.0499 0.4882 3.9188 1.0171 180
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0707 0.2839 -3.6221 -3.0686 -2.4979 1.0020 806
## shrub_cover 0.3900 0.3710 -0.3410 0.3906 1.1284 1.0175 1040
## veg_height -0.0126 0.2426 -0.4921 -0.0218 0.5067 1.0006 1421
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4063 0.5204 0.0470 0.2482 1.7367 1.0270 638
## shrub_cover 0.8198 0.9539 0.1140 0.5501 3.1780 1.0175 978
## veg_height 0.3354 0.3430 0.0609 0.2370 1.2346 1.0267 1997
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0458 0.7024 -1.4588 -0.0455 1.3775
## (Intercept)-Lynx_rufus -0.2215 1.0564 -1.9354 -0.3436 2.3768
## (Intercept)-Didelphis_virginiana -1.5647 0.7037 -3.0359 -1.5336 -0.2535
## (Intercept)-Sylvilagus_floridanus -0.8593 0.7090 -2.3151 -0.8682 0.5642
## (Intercept)-Sciurus_carolinensis -1.6693 0.7423 -3.2490 -1.6334 -0.2781
## (Intercept)-Vulpes_vulpes -1.7954 0.8622 -3.6189 -1.7232 -0.2215
## (Intercept)-Sus_scrofa -2.1985 0.9877 -4.3859 -2.1014 -0.5434
## Tree_Density-Canis_latrans -1.0175 0.6664 -2.5511 -0.9224 0.0702
## Tree_Density-Lynx_rufus 0.4377 0.8632 -0.8758 0.3103 2.5378
## Tree_Density-Didelphis_virginiana -1.0269 0.8710 -3.0631 -0.9024 0.3658
## Tree_Density-Sylvilagus_floridanus -1.1804 0.9572 -3.5748 -1.0169 0.3349
## Tree_Density-Sciurus_carolinensis -0.9199 0.9087 -3.0814 -0.8012 0.5130
## Tree_Density-Vulpes_vulpes -0.4901 1.0051 -2.4075 -0.5363 1.6257
## Tree_Density-Sus_scrofa -1.0262 1.0725 -3.6525 -0.8446 0.5060
## Avg_Canopy_Cover-Canis_latrans -0.1571 0.4776 -1.0925 -0.1542 0.8051
## Avg_Canopy_Cover-Lynx_rufus 0.6479 0.7782 -0.7284 0.5533 2.3935
## Avg_Canopy_Cover-Didelphis_virginiana 1.7819 0.8288 0.5162 1.6579 3.7757
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.3892 1.0896 0.7510 2.2296 4.8508
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6848 0.8001 0.4716 1.5745 3.5940
## Avg_Canopy_Cover-Vulpes_vulpes 1.1123 0.7078 -0.1178 1.0598 2.7307
## Avg_Canopy_Cover-Sus_scrofa 1.3999 0.7149 0.1797 1.3201 2.9715
## Rhat ESS
## (Intercept)-Canis_latrans 1.0276 563
## (Intercept)-Lynx_rufus 1.0795 255
## (Intercept)-Didelphis_virginiana 1.0028 891
## (Intercept)-Sylvilagus_floridanus 1.0088 756
## (Intercept)-Sciurus_carolinensis 1.0051 569
## (Intercept)-Vulpes_vulpes 1.0043 471
## (Intercept)-Sus_scrofa 1.0114 396
## Tree_Density-Canis_latrans 1.0133 1099
## Tree_Density-Lynx_rufus 1.0036 435
## Tree_Density-Didelphis_virginiana 1.0291 527
## Tree_Density-Sylvilagus_floridanus 1.0107 552
## Tree_Density-Sciurus_carolinensis 1.0495 545
## Tree_Density-Vulpes_vulpes 1.0423 443
## Tree_Density-Sus_scrofa 1.0228 580
## Avg_Canopy_Cover-Canis_latrans 1.0032 1418
## Avg_Canopy_Cover-Lynx_rufus 1.0019 557
## Avg_Canopy_Cover-Didelphis_virginiana 1.0020 510
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0087 404
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0070 578
## Avg_Canopy_Cover-Vulpes_vulpes 1.0162 833
## Avg_Canopy_Cover-Sus_scrofa 1.0070 949
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8106 0.1896 -3.1913 -2.8081 -2.4405 1.0063
## (Intercept)-Lynx_rufus -3.5325 0.3244 -4.1895 -3.5142 -2.9496 1.0794
## (Intercept)-Didelphis_virginiana -2.7416 0.2814 -3.2974 -2.7403 -2.1910 1.0077
## (Intercept)-Sylvilagus_floridanus -3.1181 0.2446 -3.6288 -3.1089 -2.6729 1.0032
## (Intercept)-Sciurus_carolinensis -2.8237 0.2998 -3.4274 -2.8221 -2.2489 1.0317
## (Intercept)-Vulpes_vulpes -3.6310 0.5370 -4.7873 -3.5607 -2.7583 1.0033
## (Intercept)-Sus_scrofa -3.2870 0.4712 -4.2990 -3.2677 -2.4145 1.0127
## shrub_cover-Canis_latrans -0.3089 0.2288 -0.7709 -0.3070 0.1480 1.0201
## shrub_cover-Lynx_rufus -0.2011 0.3558 -0.8953 -0.2053 0.5137 1.0697
## shrub_cover-Didelphis_virginiana 1.0760 0.3813 0.3671 1.0622 1.8742 1.0155
## shrub_cover-Sylvilagus_floridanus 0.4799 0.3975 -0.2828 0.4812 1.2836 1.0159
## shrub_cover-Sciurus_carolinensis 1.0067 0.4217 0.1831 1.0158 1.8236 1.0111
## shrub_cover-Vulpes_vulpes 0.0554 0.6236 -1.2654 0.0842 1.2334 1.0113
## shrub_cover-Sus_scrofa 0.7461 0.7204 -0.6692 0.7415 2.2219 1.0064
## veg_height-Canis_latrans -0.6352 0.1944 -1.0260 -0.6339 -0.2728 1.0059
## veg_height-Lynx_rufus 0.0318 0.2534 -0.4824 0.0390 0.4964 1.0011
## veg_height-Didelphis_virginiana 0.5368 0.2678 0.0478 0.5237 1.1048 1.0029
## veg_height-Sylvilagus_floridanus 0.1554 0.2417 -0.3202 0.1579 0.6409 1.0098
## veg_height-Sciurus_carolinensis 0.1600 0.2300 -0.2676 0.1564 0.6344 1.0169
## veg_height-Vulpes_vulpes -0.1352 0.3218 -0.8346 -0.1179 0.4583 1.0006
## veg_height-Sus_scrofa -0.1702 0.3419 -0.8581 -0.1592 0.4906 1.0088
## ESS
## (Intercept)-Canis_latrans 570
## (Intercept)-Lynx_rufus 293
## (Intercept)-Didelphis_virginiana 565
## (Intercept)-Sylvilagus_floridanus 710
## (Intercept)-Sciurus_carolinensis 486
## (Intercept)-Vulpes_vulpes 265
## (Intercept)-Sus_scrofa 496
## shrub_cover-Canis_latrans 752
## shrub_cover-Lynx_rufus 353
## shrub_cover-Didelphis_virginiana 552
## shrub_cover-Sylvilagus_floridanus 529
## shrub_cover-Sciurus_carolinensis 524
## shrub_cover-Vulpes_vulpes 734
## shrub_cover-Sus_scrofa 579
## veg_height-Canis_latrans 713
## veg_height-Lynx_rufus 685
## veg_height-Didelphis_virginiana 829
## veg_height-Sylvilagus_floridanus 968
## veg_height-Sciurus_carolinensis 908
## veg_height-Vulpes_vulpes 698
## veg_height-Sus_scrofa 1220
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.33
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9084 0.5528 -2.9841 -1.9088 -0.7472 1.0598 516
## Avg_Cogongrass_Cover -0.9307 0.4989 -1.9666 -0.9234 0.0535 1.0325 499
## I(Avg_Cogongrass_Cover^2) 1.1023 0.4737 0.2313 1.0931 2.0722 1.0095 484
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0590 1.8555 0.0563 0.5639 5.4295 1.0315 730
## Avg_Cogongrass_Cover 0.7851 1.3155 0.0465 0.3749 4.0878 1.0296 578
## I(Avg_Cogongrass_Cover^2) 0.8718 1.5013 0.0471 0.3667 4.9757 1.0785 388
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9676 1.0036 0.0672 0.6595 3.7182 1.0183 267
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0170 0.2733 -3.5642 -3.0178 -2.4769 1.0411 1022
## shrub_cover 0.3760 0.3711 -0.3388 0.3626 1.1593 1.0115 929
## veg_height -0.0325 0.2328 -0.5114 -0.0314 0.4189 1.0057 1214
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3737 0.5786 0.0471 0.2329 1.5149 1.0584 703
## shrub_cover 0.7019 0.8587 0.0926 0.4637 2.9757 1.0050 1015
## veg_height 0.2849 0.2869 0.0555 0.2066 0.9623 1.0070 1830
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.2364 0.7878 -2.7002 -1.2712
## (Intercept)-Lynx_rufus -1.7924 0.7411 -3.2595 -1.8041
## (Intercept)-Didelphis_virginiana -1.9905 0.6569 -3.2942 -1.9795
## (Intercept)-Sylvilagus_floridanus -1.6487 0.6998 -3.0219 -1.6518
## (Intercept)-Sciurus_carolinensis -2.4120 0.7578 -4.0304 -2.3358
## (Intercept)-Vulpes_vulpes -2.5314 0.8417 -4.4792 -2.4492
## (Intercept)-Sus_scrofa -2.4508 0.8423 -4.3260 -2.3558
## Avg_Cogongrass_Cover-Canis_latrans -0.4988 0.6472 -1.6484 -0.5370
## Avg_Cogongrass_Cover-Lynx_rufus -0.8602 0.6576 -2.2244 -0.8506
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5735 0.6583 -1.7778 -0.6021
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5615 0.7954 -3.3924 -1.4786
## Avg_Cogongrass_Cover-Sciurus_carolinensis -1.0228 0.6500 -2.4041 -0.9703
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9662 0.7106 -2.4393 -0.9484
## Avg_Cogongrass_Cover-Sus_scrofa -1.2296 0.8085 -3.1358 -1.1491
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8749 1.0448 0.4388 1.6493
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5119 0.6269 0.5140 1.4329
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8250 0.5552 -0.1777 0.7823
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0390 0.5368 0.0959 0.9866
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2123 0.5042 0.3688 1.1631
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0755 0.5530 0.1480 1.0258
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5293 0.7959 -1.4080 0.6020
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4035 1.0415 407
## (Intercept)-Lynx_rufus -0.2970 1.0463 457
## (Intercept)-Didelphis_virginiana -0.6441 1.0284 772
## (Intercept)-Sylvilagus_floridanus -0.1865 1.0253 478
## (Intercept)-Sciurus_carolinensis -1.1390 1.0470 564
## (Intercept)-Vulpes_vulpes -1.1024 1.0368 494
## (Intercept)-Sus_scrofa -1.0540 1.0358 466
## Avg_Cogongrass_Cover-Canis_latrans 0.8429 1.0104 740
## Avg_Cogongrass_Cover-Lynx_rufus 0.4604 1.0181 741
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8578 1.0038 748
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2691 1.0170 445
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1172 1.0240 659
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4075 1.0110 653
## Avg_Cogongrass_Cover-Sus_scrofa 0.1657 1.0418 489
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4368 1.0867 258
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9333 1.0503 495
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0525 1.0034 490
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.2526 1.0150 541
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.3123 1.0400 557
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3889 1.0078 409
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.8791 1.0389 335
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7949 0.1835 -3.1684 -2.7915 -2.4499 1.0223
## (Intercept)-Lynx_rufus -3.3702 0.3116 -4.0334 -3.3507 -2.8222 1.0178
## (Intercept)-Didelphis_virginiana -2.7175 0.2791 -3.2597 -2.7121 -2.1847 1.0204
## (Intercept)-Sylvilagus_floridanus -3.1533 0.2670 -3.7087 -3.1415 -2.6663 1.0077
## (Intercept)-Sciurus_carolinensis -2.7270 0.2927 -3.3179 -2.7263 -2.1770 1.0035
## (Intercept)-Vulpes_vulpes -3.5238 0.5235 -4.6952 -3.4517 -2.7243 1.0473
## (Intercept)-Sus_scrofa -3.2293 0.4486 -4.2035 -3.2050 -2.4061 1.0229
## shrub_cover-Canis_latrans -0.2303 0.2275 -0.6664 -0.2304 0.2177 1.0000
## shrub_cover-Lynx_rufus -0.1239 0.3572 -0.8372 -0.1176 0.5605 1.0170
## shrub_cover-Didelphis_virginiana 1.0742 0.4199 0.3182 1.0451 1.9244 1.0226
## shrub_cover-Sylvilagus_floridanus 0.3126 0.4187 -0.4687 0.2972 1.1545 1.0030
## shrub_cover-Sciurus_carolinensis 0.8689 0.4192 0.0548 0.8545 1.7145 1.0015
## shrub_cover-Vulpes_vulpes 0.1016 0.5929 -1.0780 0.1037 1.2839 1.0034
## shrub_cover-Sus_scrofa 0.6577 0.7332 -0.7949 0.6515 2.1772 1.0342
## veg_height-Canis_latrans -0.6128 0.1978 -1.0113 -0.6074 -0.2382 1.0156
## veg_height-Lynx_rufus 0.0559 0.2383 -0.4061 0.0568 0.5173 1.0107
## veg_height-Didelphis_virginiana 0.4114 0.2666 -0.0990 0.4052 0.9635 1.0027
## veg_height-Sylvilagus_floridanus 0.1421 0.2560 -0.3504 0.1335 0.6603 1.0020
## veg_height-Sciurus_carolinensis 0.1001 0.2195 -0.3371 0.0959 0.5414 1.0032
## veg_height-Vulpes_vulpes -0.1240 0.3046 -0.7475 -0.1130 0.4537 1.0054
## veg_height-Sus_scrofa -0.1533 0.3518 -0.8656 -0.1408 0.5262 1.0164
## ESS
## (Intercept)-Canis_latrans 810
## (Intercept)-Lynx_rufus 349
## (Intercept)-Didelphis_virginiana 590
## (Intercept)-Sylvilagus_floridanus 504
## (Intercept)-Sciurus_carolinensis 868
## (Intercept)-Vulpes_vulpes 288
## (Intercept)-Sus_scrofa 653
## shrub_cover-Canis_latrans 813
## shrub_cover-Lynx_rufus 511
## shrub_cover-Didelphis_virginiana 483
## shrub_cover-Sylvilagus_floridanus 555
## shrub_cover-Sciurus_carolinensis 747
## shrub_cover-Vulpes_vulpes 543
## shrub_cover-Sus_scrofa 590
## veg_height-Canis_latrans 671
## veg_height-Lynx_rufus 886
## veg_height-Didelphis_virginiana 627
## veg_height-Sylvilagus_floridanus 611
## veg_height-Sciurus_carolinensis 1116
## veg_height-Vulpes_vulpes 776
## veg_height-Sus_scrofa 885
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4863 1.2372 -4.6024 -2.5922 0.4224 1.0095 365
## Cogon_Patch_Size -0.0763 1.0801 -2.3251 -0.0301 1.9802 1.0123 556
## Veg_shannon_index 1.0771 0.8035 -0.4783 1.0867 2.6962 1.0406 265
## total_shrub_cover -0.6704 0.8617 -2.4972 -0.6475 1.0188 1.0411 177
## Avg_Cogongrass_Cover -0.4624 1.1864 -2.8346 -0.4422 1.7572 1.0010 172
## Tree_Density -1.7828 1.1081 -3.8924 -1.7592 0.5041 1.0483 168
## Avg_Canopy_Cover 1.9498 1.1581 -0.6541 1.9458 4.1431 1.0058 982
## I(Avg_Cogongrass_Cover^2) 1.8651 0.7932 0.3678 1.8569 3.4769 1.0119 247
## avg_veg_height -0.4007 0.7501 -1.9068 -0.3966 1.0617 1.0288 271
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.5883 29.3611 0.1481 6.1953 79.7314 1.0892 119
## Cogon_Patch_Size 10.9221 18.4178 0.1992 5.0442 64.7684 1.3774 111
## Veg_shannon_index 2.5821 6.0616 0.0666 0.8831 16.3624 1.1388 240
## total_shrub_cover 3.0075 12.0567 0.0592 0.8640 16.3189 1.6118 215
## Avg_Cogongrass_Cover 1.9000 4.5525 0.0524 0.6040 12.5196 1.0286 475
## Tree_Density 9.1647 29.7798 0.0643 1.8337 66.6740 1.7656 91
## Avg_Canopy_Cover 25.2258 74.4437 0.5966 7.9181 158.7647 1.4073 103
## I(Avg_Cogongrass_Cover^2) 3.1361 7.9428 0.0614 0.7486 24.6736 1.1132 100
## avg_veg_height 1.3562 3.3501 0.0473 0.4707 8.1746 1.0313 181
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4451 4.0057 0.0706 1.2016 11.4569 1.0908 83
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1331 0.3089 -3.7275 -3.1435 -2.5024 1.0030 953
## shrub_cover 0.5072 0.3998 -0.2929 0.5026 1.3137 1.0204 596
## veg_height 0.0247 0.2414 -0.4353 0.0245 0.5058 1.0117 1519
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5280 0.6601 0.0664 0.3463 2.0647 1.0295 430
## shrub_cover 0.7965 0.8260 0.1172 0.5777 2.9871 1.0123 1073
## veg_height 0.3365 0.3292 0.0608 0.2454 1.1556 1.0094 1045
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4676 1.5444 -4.3252 -1.5308
## (Intercept)-Lynx_rufus -0.9706 2.6137 -4.7733 -1.4642
## (Intercept)-Didelphis_virginiana -4.4731 1.8437 -8.7919 -4.2334
## (Intercept)-Sylvilagus_floridanus -3.1914 1.9542 -7.9655 -2.9867
## (Intercept)-Sciurus_carolinensis -5.0065 2.2484 -10.7450 -4.6077
## (Intercept)-Vulpes_vulpes -5.1184 2.3785 -10.4724 -4.7912
## (Intercept)-Sus_scrofa -6.0863 2.9091 -13.2066 -5.5795
## Cogon_Patch_Size-Canis_latrans 2.6011 2.1198 -0.1545 2.1224
## Cogon_Patch_Size-Lynx_rufus -0.6146 2.0308 -4.7033 -0.6720
## Cogon_Patch_Size-Didelphis_virginiana 2.3597 1.4805 0.0566 2.1712
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3154 2.9928 -10.8357 -1.5563
## Cogon_Patch_Size-Sciurus_carolinensis -1.3881 2.1237 -6.8260 -0.9461
## Cogon_Patch_Size-Vulpes_vulpes -0.9484 2.6149 -6.7424 -0.6423
## Cogon_Patch_Size-Sus_scrofa -1.2235 2.3252 -7.1608 -0.7532
## Veg_shannon_index-Canis_latrans 1.6608 0.9636 0.0306 1.5664
## Veg_shannon_index-Lynx_rufus 1.1321 1.4210 -2.2250 1.1577
## Veg_shannon_index-Didelphis_virginiana 1.4924 1.0957 -0.3685 1.3891
## Veg_shannon_index-Sylvilagus_floridanus 1.3548 1.0248 -0.4190 1.2698
## Veg_shannon_index-Sciurus_carolinensis 0.0916 1.4160 -3.2987 0.2810
## Veg_shannon_index-Vulpes_vulpes 0.4626 1.4133 -2.7937 0.5969
## Veg_shannon_index-Sus_scrofa 2.0576 1.3726 -0.0119 1.8106
## total_shrub_cover-Canis_latrans 0.2724 1.1173 -1.6989 0.1630
## total_shrub_cover-Lynx_rufus -1.4568 1.8992 -5.6269 -1.1799
## total_shrub_cover-Didelphis_virginiana -1.2063 1.2793 -4.1674 -1.0304
## total_shrub_cover-Sylvilagus_floridanus -0.8817 1.3827 -4.1144 -0.7634
## total_shrub_cover-Sciurus_carolinensis -0.8460 1.3106 -3.9310 -0.7006
## total_shrub_cover-Vulpes_vulpes -0.9551 1.6155 -4.3061 -0.8444
## total_shrub_cover-Sus_scrofa -0.3011 1.8888 -3.2002 -0.3648
## Avg_Cogongrass_Cover-Canis_latrans -0.3644 1.4919 -3.3628 -0.3636
## Avg_Cogongrass_Cover-Lynx_rufus -0.3279 1.5776 -3.4644 -0.3210
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4456 1.5083 -3.4331 -0.4434
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0802 1.6601 -4.8427 -0.9456
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3716 1.5000 -3.3718 -0.3590
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.1665 1.6224 -3.1536 -0.1765
## Avg_Cogongrass_Cover-Sus_scrofa -0.7611 1.6994 -4.4139 -0.6346
## Tree_Density-Canis_latrans -3.2521 2.0679 -8.8526 -2.8813
## Tree_Density-Lynx_rufus -0.1551 2.4683 -3.5687 -0.6443
## Tree_Density-Didelphis_virginiana -2.4399 1.8641 -6.8760 -2.2346
## Tree_Density-Sylvilagus_floridanus -3.2164 2.9670 -11.2830 -2.6781
## Tree_Density-Sciurus_carolinensis -2.7061 2.5463 -9.1189 -2.3463
## Tree_Density-Vulpes_vulpes -1.8818 2.2336 -7.1596 -1.7897
## Tree_Density-Sus_scrofa -2.6687 2.5211 -8.8690 -2.2127
## Avg_Canopy_Cover-Canis_latrans -0.1728 0.7726 -1.7402 -0.1532
## Avg_Canopy_Cover-Lynx_rufus 1.0881 2.2297 -2.8705 0.8217
## Avg_Canopy_Cover-Didelphis_virginiana 5.1973 2.9498 1.5094 4.5531
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.4598 4.1644 1.6603 5.4461
## Avg_Canopy_Cover-Sciurus_carolinensis 4.8057 3.2913 1.2119 3.9451
## Avg_Canopy_Cover-Vulpes_vulpes 4.2643 4.8290 0.3678 2.9197
## Avg_Canopy_Cover-Sus_scrofa 2.7912 1.8149 0.3352 2.4062
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.8588 1.7329 0.8638 2.4821
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8461 1.4672 0.8358 2.5569
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5453 0.9236 -0.2192 1.5228
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6106 1.0020 -0.2963 1.5873
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.2721 1.2015 0.5470 2.1036
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4992 1.5123 0.5618 2.2171
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0845 1.7038 -3.3496 1.3805
## avg_veg_height-Canis_latrans -0.4401 0.7977 -2.0020 -0.4322
## avg_veg_height-Lynx_rufus -0.7173 1.2558 -3.5128 -0.6321
## avg_veg_height-Didelphis_virginiana -0.6802 1.0947 -2.9925 -0.5915
## avg_veg_height-Sylvilagus_floridanus -0.4894 0.9937 -2.5456 -0.4475
## avg_veg_height-Sciurus_carolinensis 0.2017 1.1069 -1.5661 0.0680
## avg_veg_height-Vulpes_vulpes -0.4320 1.1676 -2.6726 -0.4204
## avg_veg_height-Sus_scrofa -0.4159 1.0852 -2.6087 -0.3968
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8025 1.0503 293
## (Intercept)-Lynx_rufus 5.7752 1.0044 87
## (Intercept)-Didelphis_virginiana -1.4682 1.0299 246
## (Intercept)-Sylvilagus_floridanus 0.1756 1.1056 158
## (Intercept)-Sciurus_carolinensis -1.7112 1.1360 98
## (Intercept)-Vulpes_vulpes -1.3247 1.0455 134
## (Intercept)-Sus_scrofa -1.9483 1.1365 126
## Cogon_Patch_Size-Canis_latrans 8.3192 1.1757 153
## Cogon_Patch_Size-Lynx_rufus 3.7802 1.0790 151
## Cogon_Patch_Size-Didelphis_virginiana 5.8765 1.0762 164
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3757 1.3434 86
## Cogon_Patch_Size-Sciurus_carolinensis 1.7253 1.1505 190
## Cogon_Patch_Size-Vulpes_vulpes 3.4184 1.0729 168
## Cogon_Patch_Size-Sus_scrofa 2.2673 1.0850 244
## Veg_shannon_index-Canis_latrans 3.9967 1.0208 340
## Veg_shannon_index-Lynx_rufus 3.8702 1.1198 251
## Veg_shannon_index-Didelphis_virginiana 3.8971 1.0049 379
## Veg_shannon_index-Sylvilagus_floridanus 3.5599 1.0187 348
## Veg_shannon_index-Sciurus_carolinensis 2.4319 1.0155 176
## Veg_shannon_index-Vulpes_vulpes 2.9709 1.0228 139
## Veg_shannon_index-Sus_scrofa 5.4086 1.0196 367
## total_shrub_cover-Canis_latrans 2.8868 1.0148 147
## total_shrub_cover-Lynx_rufus 1.2365 1.1719 87
## total_shrub_cover-Didelphis_virginiana 0.8377 1.0154 269
## total_shrub_cover-Sylvilagus_floridanus 1.4449 1.0162 270
## total_shrub_cover-Sciurus_carolinensis 1.2706 1.0055 242
## total_shrub_cover-Vulpes_vulpes 1.9236 1.0579 165
## total_shrub_cover-Sus_scrofa 2.9996 1.2120 90
## Avg_Cogongrass_Cover-Canis_latrans 2.5030 1.0046 241
## Avg_Cogongrass_Cover-Lynx_rufus 2.7685 1.0056 220
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.4798 1.0092 238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7795 1.0257 207
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5111 1.0021 269
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.1300 1.0056 289
## Avg_Cogongrass_Cover-Sus_scrofa 2.0519 1.0103 202
## Tree_Density-Canis_latrans -0.4408 1.0545 158
## Tree_Density-Lynx_rufus 6.6564 1.3396 93
## Tree_Density-Didelphis_virginiana 0.4830 1.0546 234
## Tree_Density-Sylvilagus_floridanus 0.2677 1.2698 61
## Tree_Density-Sciurus_carolinensis 0.7133 1.1081 135
## Tree_Density-Vulpes_vulpes 2.4211 1.0644 179
## Tree_Density-Sus_scrofa 0.7886 1.0682 121
## Avg_Canopy_Cover-Canis_latrans 1.3285 1.0066 450
## Avg_Canopy_Cover-Lynx_rufus 6.2640 1.0157 162
## Avg_Canopy_Cover-Didelphis_virginiana 13.3574 1.0582 76
## Avg_Canopy_Cover-Sylvilagus_floridanus 16.9126 1.1118 57
## Avg_Canopy_Cover-Sciurus_carolinensis 14.3389 1.0464 72
## Avg_Canopy_Cover-Vulpes_vulpes 15.9232 1.4836 47
## Avg_Canopy_Cover-Sus_scrofa 7.3483 1.0213 145
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.0739 1.0660 126
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.9050 1.0668 91
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.4506 1.0198 286
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7541 1.0050 291
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 5.0814 1.0396 122
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 6.7879 1.0564 88
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.5082 1.0017 177
## avg_veg_height-Canis_latrans 1.1137 1.0126 417
## avg_veg_height-Lynx_rufus 1.4728 1.0145 237
## avg_veg_height-Didelphis_virginiana 1.1833 1.0276 299
## avg_veg_height-Sylvilagus_floridanus 1.4029 1.0180 386
## avg_veg_height-Sciurus_carolinensis 2.8588 1.0125 287
## avg_veg_height-Vulpes_vulpes 1.9932 1.0420 296
## avg_veg_height-Sus_scrofa 1.6888 1.0462 329
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7688 0.1848 -3.1416 -2.7649 -2.4193 1.0048
## (Intercept)-Lynx_rufus -3.6769 0.3487 -4.3692 -3.6742 -3.0250 1.0280
## (Intercept)-Didelphis_virginiana -2.7864 0.2973 -3.3853 -2.7828 -2.2075 1.0218
## (Intercept)-Sylvilagus_floridanus -3.1876 0.2350 -3.6535 -3.1852 -2.7431 1.0260
## (Intercept)-Sciurus_carolinensis -2.8900 0.2974 -3.4630 -2.8889 -2.2981 1.0065
## (Intercept)-Vulpes_vulpes -3.8661 0.5350 -4.9887 -3.8285 -2.9301 1.0424
## (Intercept)-Sus_scrofa -3.3825 0.4926 -4.4247 -3.3729 -2.4564 1.0207
## shrub_cover-Canis_latrans -0.2819 0.2474 -0.7532 -0.2889 0.2055 1.0208
## shrub_cover-Lynx_rufus -0.0409 0.3976 -0.8300 -0.0440 0.7368 1.0767
## shrub_cover-Didelphis_virginiana 1.1812 0.3948 0.4465 1.1658 2.0036 1.0025
## shrub_cover-Sylvilagus_floridanus 0.5643 0.4001 -0.2150 0.5687 1.3580 1.0088
## shrub_cover-Sciurus_carolinensis 1.1228 0.4054 0.3231 1.1208 1.9120 1.0088
## shrub_cover-Vulpes_vulpes 0.3347 0.6026 -0.9763 0.3641 1.4402 1.0527
## shrub_cover-Sus_scrofa 0.9005 0.7992 -0.7558 0.9004 2.4720 1.0243
## veg_height-Canis_latrans -0.5759 0.1917 -0.9507 -0.5705 -0.2109 1.0181
## veg_height-Lynx_rufus 0.1645 0.2483 -0.3416 0.1672 0.6444 1.0094
## veg_height-Didelphis_virginiana 0.5497 0.2690 0.0304 0.5461 1.0810 1.0180
## veg_height-Sylvilagus_floridanus 0.1568 0.2526 -0.3364 0.1552 0.6611 1.0059
## veg_height-Sciurus_carolinensis 0.1899 0.2319 -0.2557 0.1847 0.6507 1.0013
## veg_height-Vulpes_vulpes -0.2020 0.3386 -0.9075 -0.1906 0.4136 1.0060
## veg_height-Sus_scrofa -0.1628 0.3431 -0.8708 -0.1557 0.5142 1.0109
## ESS
## (Intercept)-Canis_latrans 525
## (Intercept)-Lynx_rufus 184
## (Intercept)-Didelphis_virginiana 455
## (Intercept)-Sylvilagus_floridanus 794
## (Intercept)-Sciurus_carolinensis 429
## (Intercept)-Vulpes_vulpes 162
## (Intercept)-Sus_scrofa 315
## shrub_cover-Canis_latrans 373
## shrub_cover-Lynx_rufus 194
## shrub_cover-Didelphis_virginiana 462
## shrub_cover-Sylvilagus_floridanus 369
## shrub_cover-Sciurus_carolinensis 456
## shrub_cover-Vulpes_vulpes 356
## shrub_cover-Sus_scrofa 203
## veg_height-Canis_latrans 597
## veg_height-Lynx_rufus 552
## veg_height-Didelphis_virginiana 643
## veg_height-Sylvilagus_floridanus 777
## veg_height-Sciurus_carolinensis 858
## veg_height-Vulpes_vulpes 492
## veg_height-Sus_scrofa 747
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4452
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8832 0.4166 -1.6933 -0.886 -0.0015 1.0045 1773
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0998 1.2786 0.1437 0.7299 4.319 1.0014 1738
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6821 0.2876 -3.2473 -2.6840 -2.1206 1.0010 1532
## week 0.4251 0.2726 -0.1135 0.4269 0.9669 1.0061 923
## I(week^2) -0.2996 0.1556 -0.6148 -0.2927 -0.0113 1.0084 765
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4375 0.6015 0.0614 0.2821 1.6537 1.0249 660
## week 0.3045 0.4484 0.0381 0.1827 1.2310 1.0359 1346
## I(week^2) 0.1103 0.1332 0.0218 0.0740 0.4110 1.0178 1191
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1131 0.3985 -0.6143 0.1003 0.9249 1.0014
## (Intercept)-Lynx_rufus -0.2261 0.5217 -1.1141 -0.2589 0.8788 1.0108
## (Intercept)-Didelphis_virginiana -1.3087 0.4172 -2.1322 -1.2991 -0.5204 1.0054
## (Intercept)-Sylvilagus_floridanus -0.5427 0.4232 -1.3460 -0.5574 0.3173 1.0034
## (Intercept)-Sciurus_carolinensis -1.2842 0.4183 -2.1417 -1.2638 -0.5187 1.0007
## (Intercept)-Vulpes_vulpes -1.4908 0.6235 -2.6941 -1.4875 -0.2362 1.0098
## (Intercept)-Sus_scrofa -1.7291 0.5601 -2.8918 -1.7081 -0.7069 1.0011
## ESS
## (Intercept)-Canis_latrans 1911
## (Intercept)-Lynx_rufus 872
## (Intercept)-Didelphis_virginiana 1983
## (Intercept)-Sylvilagus_floridanus 1288
## (Intercept)-Sciurus_carolinensis 2158
## (Intercept)-Vulpes_vulpes 487
## (Intercept)-Sus_scrofa 945
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4512 0.1792 -2.8173 -2.4499 -2.0987 1.0039
## (Intercept)-Lynx_rufus -3.1531 0.2911 -3.7644 -3.1342 -2.6252 1.0017
## (Intercept)-Didelphis_virginiana -2.2154 0.2724 -2.7912 -2.2074 -1.7142 1.0053
## (Intercept)-Sylvilagus_floridanus -2.9509 0.2844 -3.5495 -2.9390 -2.4331 1.0082
## (Intercept)-Sciurus_carolinensis -2.3850 0.2813 -2.9319 -2.3798 -1.8553 1.0127
## (Intercept)-Vulpes_vulpes -3.2422 0.5569 -4.5219 -3.1630 -2.3834 1.0053
## (Intercept)-Sus_scrofa -2.8179 0.4145 -3.7131 -2.7953 -2.0718 1.0451
## week-Canis_latrans 0.5763 0.2625 0.0837 0.5713 1.0942 1.0024
## week-Lynx_rufus 0.4491 0.3288 -0.1781 0.4441 1.1096 1.0056
## week-Didelphis_virginiana 0.1233 0.3733 -0.6712 0.1383 0.8063 1.0062
## week-Sylvilagus_floridanus 0.1740 0.3346 -0.5011 0.1850 0.7974 1.0004
## week-Sciurus_carolinensis 0.7313 0.3688 0.0898 0.7012 1.5613 1.0108
## week-Vulpes_vulpes 0.3056 0.4535 -0.7128 0.3353 1.1357 1.0011
## week-Sus_scrofa 0.6645 0.4395 -0.1206 0.6291 1.5985 1.0054
## I(week^2)-Canis_latrans -0.2326 0.1085 -0.4540 -0.2298 -0.0245 1.0004
## I(week^2)-Lynx_rufus -0.2683 0.1543 -0.5774 -0.2654 0.0263 1.0044
## I(week^2)-Didelphis_virginiana -0.4191 0.2419 -0.9749 -0.3887 -0.0444 1.0471
## I(week^2)-Sylvilagus_floridanus -0.2024 0.1639 -0.5462 -0.1987 0.1116 1.0012
## I(week^2)-Sciurus_carolinensis -0.2529 0.1504 -0.5699 -0.2485 0.0286 1.0106
## I(week^2)-Vulpes_vulpes -0.4784 0.2946 -1.1958 -0.4374 -0.0110 1.0052
## I(week^2)-Sus_scrofa -0.2359 0.1802 -0.6059 -0.2258 0.1022 1.0045
## ESS
## (Intercept)-Canis_latrans 1357
## (Intercept)-Lynx_rufus 543
## (Intercept)-Didelphis_virginiana 1247
## (Intercept)-Sylvilagus_floridanus 651
## (Intercept)-Sciurus_carolinensis 1202
## (Intercept)-Vulpes_vulpes 282
## (Intercept)-Sus_scrofa 785
## week-Canis_latrans 1161
## week-Lynx_rufus 1020
## week-Didelphis_virginiana 1121
## week-Sylvilagus_floridanus 1058
## week-Sciurus_carolinensis 1002
## week-Vulpes_vulpes 907
## week-Sus_scrofa 1041
## I(week^2)-Canis_latrans 1195
## I(week^2)-Lynx_rufus 759
## I(week^2)-Didelphis_virginiana 384
## I(week^2)-Sylvilagus_floridanus 747
## I(week^2)-Sciurus_carolinensis 1205
## I(week^2)-Vulpes_vulpes 484
## I(week^2)-Sus_scrofa 1562
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4815
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5389 1.0463 -3.4679 -1.5954 0.6874 1.0792 281
## Cogon_Patch_Size -0.6729 0.9500 -2.6612 -0.6509 1.1935 1.0707 578
## Veg_shannon_index 0.6986 0.6216 -0.6374 0.7219 1.9003 1.0423 554
## total_shrub_cover -0.0850 0.5277 -1.1188 -0.0989 0.9876 1.0096 502
## Avg_Cogongrass_Cover 2.1070 0.7201 0.6539 2.1009 3.5542 1.0169 301
## Tree_Density -1.8009 0.8423 -3.4972 -1.7855 -0.1180 1.0412 308
## Avg_Canopy_Cover 1.8116 0.7581 0.2845 1.7812 3.3810 1.0108 739
## avg_veg_height -0.7829 0.5457 -1.8291 -0.7983 0.3072 1.0240 359
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.7818 14.6475 0.4256 5.2189 47.9225 1.0569 187
## Cogon_Patch_Size 7.9886 14.3782 0.2065 3.6493 45.7953 1.0590 263
## Veg_shannon_index 2.1674 4.6271 0.0681 0.8600 11.8548 1.0770 239
## total_shrub_cover 1.0079 2.2314 0.0469 0.3845 6.2718 1.0315 357
## Avg_Cogongrass_Cover 1.1966 2.2010 0.0495 0.5095 6.7257 1.1226 671
## Tree_Density 2.5120 4.9144 0.0710 0.9596 14.5804 1.1515 363
## Avg_Canopy_Cover 3.7304 6.5103 0.1620 1.9236 18.7335 1.1530 298
## avg_veg_height 0.5000 0.9531 0.0423 0.2434 2.4442 1.1158 502
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0879 2.9127 0.0983 1.1386 10.0097 1.3241 79
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7398 0.3421 -3.4271 -2.7414 -2.0366 1.0042 1332
## week 0.3982 0.2757 -0.1674 0.4009 0.9554 1.0029 913
## I(week^2) -0.2906 0.1542 -0.6291 -0.2852 0.0010 1.0051 915
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6862 0.8965 0.0840 0.4435 2.7267 1.0437 295
## week 0.2886 0.3491 0.0393 0.1831 1.1593 1.0064 955
## I(week^2) 0.1142 0.1342 0.0227 0.0776 0.4332 1.0025 696
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4403 1.2606 -1.8738 0.3803
## (Intercept)-Lynx_rufus 0.8135 2.7180 -2.6746 0.1184
## (Intercept)-Didelphis_virginiana -3.0240 1.1167 -5.4777 -2.9416
## (Intercept)-Sylvilagus_floridanus -1.7343 1.3218 -4.4467 -1.7005
## (Intercept)-Sciurus_carolinensis -3.3645 1.3825 -6.5177 -3.1497
## (Intercept)-Vulpes_vulpes -3.0034 2.0610 -7.2548 -2.9560
## (Intercept)-Sus_scrofa -4.5811 1.8755 -8.8475 -4.3248
## Cogon_Patch_Size-Canis_latrans 1.0818 1.4619 -0.9338 0.8269
## Cogon_Patch_Size-Lynx_rufus -0.6648 1.9290 -3.6789 -0.9480
## Cogon_Patch_Size-Didelphis_virginiana 1.2315 1.0469 -0.4858 1.1433
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8031 2.3507 -9.2392 -2.2115
## Cogon_Patch_Size-Sciurus_carolinensis -2.1537 1.7286 -6.4380 -1.7886
## Cogon_Patch_Size-Vulpes_vulpes -1.6985 2.3465 -7.2452 -1.4716
## Cogon_Patch_Size-Sus_scrofa -1.6495 2.1331 -6.9281 -1.2286
## Veg_shannon_index-Canis_latrans 1.2519 0.7709 -0.0288 1.1712
## Veg_shannon_index-Lynx_rufus 0.5898 1.1967 -2.1754 0.6656
## Veg_shannon_index-Didelphis_virginiana 0.9955 0.7981 -0.4745 0.9306
## Veg_shannon_index-Sylvilagus_floridanus 1.1079 0.8564 -0.3698 1.0202
## Veg_shannon_index-Sciurus_carolinensis -0.1479 0.9226 -2.1424 -0.0255
## Veg_shannon_index-Vulpes_vulpes -0.1763 1.1973 -2.9948 0.0221
## Veg_shannon_index-Sus_scrofa 1.8390 1.3706 0.0065 1.5468
## total_shrub_cover-Canis_latrans 0.2525 0.7052 -0.9191 0.1839
## total_shrub_cover-Lynx_rufus -0.6755 1.1083 -3.4837 -0.4843
## total_shrub_cover-Didelphis_virginiana -0.3381 0.7272 -1.9023 -0.3014
## total_shrub_cover-Sylvilagus_floridanus -0.0283 0.7720 -1.4772 -0.0371
## total_shrub_cover-Sciurus_carolinensis 0.1252 0.6640 -1.1064 0.0757
## total_shrub_cover-Vulpes_vulpes -0.2770 0.8595 -2.1843 -0.2391
## total_shrub_cover-Sus_scrofa 0.2209 0.7874 -1.2053 0.1768
## Avg_Cogongrass_Cover-Canis_latrans 2.3827 0.9102 0.7954 2.3173
## Avg_Cogongrass_Cover-Lynx_rufus 2.5557 1.1113 0.6345 2.4398
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2107 0.8533 0.6098 2.1722
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6532 0.9543 -0.3523 1.6863
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4445 0.9201 0.7611 2.3901
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5165 1.1163 0.5673 2.4226
## Avg_Cogongrass_Cover-Sus_scrofa 1.6644 1.1053 -0.9205 1.7660
## Tree_Density-Canis_latrans -2.4744 1.1867 -5.3107 -2.3145
## Tree_Density-Lynx_rufus -0.6690 1.4566 -3.0229 -0.7979
## Tree_Density-Didelphis_virginiana -2.2350 1.0669 -4.7175 -2.1167
## Tree_Density-Sylvilagus_floridanus -2.4339 1.3397 -5.5529 -2.2978
## Tree_Density-Sciurus_carolinensis -2.3885 1.2674 -5.6073 -2.2089
## Tree_Density-Vulpes_vulpes -1.8865 1.3172 -4.6373 -1.8877
## Tree_Density-Sus_scrofa -2.1187 1.3722 -5.3022 -1.9719
## Avg_Canopy_Cover-Canis_latrans 0.2712 0.7573 -1.2459 0.2758
## Avg_Canopy_Cover-Lynx_rufus 1.1581 1.4247 -1.3913 1.0695
## Avg_Canopy_Cover-Didelphis_virginiana 2.9067 1.0340 1.2842 2.7779
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6093 1.7568 1.2101 3.3065
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4507 0.9691 0.9481 2.3202
## Avg_Canopy_Cover-Vulpes_vulpes 2.3597 1.4841 0.4516 2.0586
## Avg_Canopy_Cover-Sus_scrofa 2.1154 0.9414 0.5490 2.0189
## avg_veg_height-Canis_latrans -0.9251 0.6242 -2.1821 -0.9199
## avg_veg_height-Lynx_rufus -0.7848 0.8045 -2.3484 -0.7990
## avg_veg_height-Didelphis_virginiana -0.8485 0.6758 -2.1725 -0.8435
## avg_veg_height-Sylvilagus_floridanus -0.9272 0.6898 -2.3444 -0.9100
## avg_veg_height-Sciurus_carolinensis -0.4539 0.7051 -1.7803 -0.4847
## avg_veg_height-Vulpes_vulpes -0.8010 0.8462 -2.3442 -0.8145
## avg_veg_height-Sus_scrofa -0.8198 0.6971 -2.1778 -0.8280
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.3224 1.0892 263
## (Intercept)-Lynx_rufus 8.0710 1.2607 58
## (Intercept)-Didelphis_virginiana -1.0199 1.0179 436
## (Intercept)-Sylvilagus_floridanus 0.7363 1.0805 309
## (Intercept)-Sciurus_carolinensis -1.2240 1.0062 279
## (Intercept)-Vulpes_vulpes 0.9140 1.0986 126
## (Intercept)-Sus_scrofa -1.6021 1.0254 181
## Cogon_Patch_Size-Canis_latrans 4.7522 1.0086 501
## Cogon_Patch_Size-Lynx_rufus 4.1878 1.2813 186
## Cogon_Patch_Size-Didelphis_virginiana 3.4369 1.0430 360
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2006 1.0339 206
## Cogon_Patch_Size-Sciurus_carolinensis 0.1546 1.0219 245
## Cogon_Patch_Size-Vulpes_vulpes 2.8167 1.0269 176
## Cogon_Patch_Size-Sus_scrofa 1.3449 1.0442 211
## Veg_shannon_index-Canis_latrans 3.0256 1.0236 512
## Veg_shannon_index-Lynx_rufus 2.5994 1.0668 328
## Veg_shannon_index-Didelphis_virginiana 2.7029 1.0229 950
## Veg_shannon_index-Sylvilagus_floridanus 3.0829 1.0462 641
## Veg_shannon_index-Sciurus_carolinensis 1.3260 1.0120 372
## Veg_shannon_index-Vulpes_vulpes 1.6089 1.0375 338
## Veg_shannon_index-Sus_scrofa 5.1593 1.0545 260
## total_shrub_cover-Canis_latrans 1.9056 1.0241 403
## total_shrub_cover-Lynx_rufus 0.9191 1.0158 271
## total_shrub_cover-Didelphis_virginiana 0.9571 1.0060 697
## total_shrub_cover-Sylvilagus_floridanus 1.5159 1.0095 689
## total_shrub_cover-Sciurus_carolinensis 1.5981 1.0179 747
## total_shrub_cover-Vulpes_vulpes 1.2241 1.0117 484
## total_shrub_cover-Sus_scrofa 1.9019 1.0039 597
## Avg_Cogongrass_Cover-Canis_latrans 4.4435 1.0055 459
## Avg_Cogongrass_Cover-Lynx_rufus 5.0682 1.0719 414
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9321 1.0096 523
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4480 1.0084 435
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4518 1.0223 392
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9509 1.0678 352
## Avg_Cogongrass_Cover-Sus_scrofa 3.6138 1.0192 503
## Tree_Density-Canis_latrans -0.5996 1.0131 434
## Tree_Density-Lynx_rufus 2.6782 1.2261 181
## Tree_Density-Didelphis_virginiana -0.4808 1.0085 438
## Tree_Density-Sylvilagus_floridanus -0.1878 1.0079 423
## Tree_Density-Sciurus_carolinensis -0.4686 1.0275 475
## Tree_Density-Vulpes_vulpes 0.7957 1.0018 434
## Tree_Density-Sus_scrofa 0.1576 1.0054 407
## Avg_Canopy_Cover-Canis_latrans 1.7062 1.0257 543
## Avg_Canopy_Cover-Lynx_rufus 4.2508 1.0579 214
## Avg_Canopy_Cover-Didelphis_virginiana 5.3451 1.0437 351
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1110 1.0579 247
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6928 1.0606 312
## Avg_Canopy_Cover-Vulpes_vulpes 6.1218 1.1321 159
## Avg_Canopy_Cover-Sus_scrofa 4.3476 1.0485 375
## avg_veg_height-Canis_latrans 0.2735 1.0028 477
## avg_veg_height-Lynx_rufus 0.8594 1.0329 446
## avg_veg_height-Didelphis_virginiana 0.4741 1.0011 638
## avg_veg_height-Sylvilagus_floridanus 0.3936 1.0147 601
## avg_veg_height-Sciurus_carolinensis 1.0803 1.0136 537
## avg_veg_height-Vulpes_vulpes 0.7603 1.0298 393
## avg_veg_height-Sus_scrofa 0.5483 1.0049 509
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4878 0.1918 -2.8940 -2.4803 -2.1331 1.0191
## (Intercept)-Lynx_rufus -3.4273 0.3733 -4.1822 -3.4027 -2.7643 1.1484
## (Intercept)-Didelphis_virginiana -2.1676 0.2728 -2.7124 -2.1625 -1.6404 1.0102
## (Intercept)-Sylvilagus_floridanus -3.0190 0.2795 -3.5988 -3.0051 -2.5043 1.0012
## (Intercept)-Sciurus_carolinensis -2.3596 0.2877 -2.9459 -2.3550 -1.8213 1.0068
## (Intercept)-Vulpes_vulpes -3.5715 0.6761 -5.1123 -3.5011 -2.4804 1.0636
## (Intercept)-Sus_scrofa -2.7988 0.4305 -3.7071 -2.7620 -2.0533 1.0061
## week-Canis_latrans 0.5582 0.2524 0.0825 0.5513 1.0667 1.0016
## week-Lynx_rufus 0.4123 0.3178 -0.2090 0.4115 1.0344 1.0050
## week-Didelphis_virginiana 0.1068 0.3723 -0.6311 0.1211 0.7995 1.0155
## week-Sylvilagus_floridanus 0.1478 0.3274 -0.5196 0.1521 0.7781 1.0070
## week-Sciurus_carolinensis 0.7021 0.3724 0.0415 0.6755 1.5079 1.0064
## week-Vulpes_vulpes 0.2814 0.4734 -0.7215 0.2971 1.1824 1.0048
## week-Sus_scrofa 0.6167 0.4338 -0.1436 0.5934 1.5479 1.0017
## I(week^2)-Canis_latrans -0.2315 0.1059 -0.4402 -0.2307 -0.0247 1.0096
## I(week^2)-Lynx_rufus -0.2506 0.1592 -0.5721 -0.2460 0.0507 1.0255
## I(week^2)-Didelphis_virginiana -0.4217 0.2420 -0.9775 -0.3945 -0.0333 1.0249
## I(week^2)-Sylvilagus_floridanus -0.2001 0.1641 -0.5431 -0.1962 0.1151 1.0221
## I(week^2)-Sciurus_carolinensis -0.2470 0.1500 -0.5621 -0.2388 0.0280 1.0032
## I(week^2)-Vulpes_vulpes -0.4782 0.3002 -1.2315 -0.4454 0.0013 1.0113
## I(week^2)-Sus_scrofa -0.2244 0.1859 -0.6033 -0.2198 0.1157 1.0013
## ESS
## (Intercept)-Canis_latrans 641
## (Intercept)-Lynx_rufus 100
## (Intercept)-Didelphis_virginiana 1322
## (Intercept)-Sylvilagus_floridanus 616
## (Intercept)-Sciurus_carolinensis 1160
## (Intercept)-Vulpes_vulpes 135
## (Intercept)-Sus_scrofa 815
## week-Canis_latrans 1106
## week-Lynx_rufus 832
## week-Didelphis_virginiana 885
## week-Sylvilagus_floridanus 941
## week-Sciurus_carolinensis 1085
## week-Vulpes_vulpes 720
## week-Sus_scrofa 1291
## I(week^2)-Canis_latrans 1201
## I(week^2)-Lynx_rufus 628
## I(week^2)-Didelphis_virginiana 359
## I(week^2)-Sylvilagus_floridanus 673
## I(week^2)-Sciurus_carolinensis 1310
## I(week^2)-Vulpes_vulpes 373
## I(week^2)-Sus_scrofa 1220
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4657
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0785 0.5259 -2.1288 -1.0705 -0.0212 1.0129 340
## Avg_Cogongrass_Cover 0.3506 0.4285 -0.4744 0.3553 1.2070 1.0341 413
## total_shrub_cover -0.1147 0.3572 -0.8510 -0.1070 0.5842 1.0133 811
## avg_veg_height -0.1179 0.3610 -0.8411 -0.1145 0.5803 1.0111 568
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0217 1.5435 0.0588 0.5630 4.8267 1.0190 633
## Avg_Cogongrass_Cover 0.4984 0.7314 0.0468 0.2758 2.4948 1.0165 965
## total_shrub_cover 0.4709 0.8341 0.0415 0.2390 2.3797 1.0502 937
## avg_veg_height 0.3004 0.3718 0.0358 0.1813 1.3299 1.0027 1468
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7613 1.6061 0.1057 1.3029 6.0146 1.0663 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7189 0.3250 -3.3572 -2.7212 -2.0802 1.0034 1754
## week 0.3992 0.2696 -0.1386 0.4028 0.9046 1.0025 929
## I(week^2) -0.2887 0.1514 -0.6044 -0.2841 -0.0003 1.0235 821
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6016 1.0161 0.0668 0.3521 2.5326 1.0347 257
## week 0.2685 0.3057 0.0376 0.1776 1.0120 1.0014 1031
## I(week^2) 0.1071 0.1762 0.0225 0.0691 0.3902 1.2475 345
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3703 0.7689 -1.8558 -0.3820
## (Intercept)-Lynx_rufus -0.7641 0.7847 -2.2812 -0.7939
## (Intercept)-Didelphis_virginiana -1.3906 0.6528 -2.6982 -1.3777
## (Intercept)-Sylvilagus_floridanus -0.7851 0.7195 -2.1465 -0.8008
## (Intercept)-Sciurus_carolinensis -1.4606 0.6707 -2.8829 -1.4194
## (Intercept)-Vulpes_vulpes -1.4962 0.8805 -3.3343 -1.4704
## (Intercept)-Sus_scrofa -1.7338 0.7544 -3.3723 -1.6656
## Avg_Cogongrass_Cover-Canis_latrans 0.5876 0.4886 -0.3000 0.5579
## Avg_Cogongrass_Cover-Lynx_rufus 0.6516 0.5488 -0.3305 0.6117
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5248 0.5032 -0.3969 0.5007
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1260 0.5784 -1.3596 -0.0859
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4237 0.4735 -0.4532 0.4169
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4688 0.5800 -0.6397 0.4567
## Avg_Cogongrass_Cover-Sus_scrofa -0.0006 0.6878 -1.5178 0.0721
## total_shrub_cover-Canis_latrans 0.1961 0.4329 -0.5808 0.1652
## total_shrub_cover-Lynx_rufus -0.6036 0.6554 -2.2042 -0.5224
## total_shrub_cover-Didelphis_virginiana -0.1416 0.4231 -0.9818 -0.1294
## total_shrub_cover-Sylvilagus_floridanus -0.2172 0.4898 -1.2176 -0.1915
## total_shrub_cover-Sciurus_carolinensis -0.0265 0.4160 -0.8328 -0.0309
## total_shrub_cover-Vulpes_vulpes -0.1793 0.5539 -1.3565 -0.1512
## total_shrub_cover-Sus_scrofa 0.1495 0.5356 -0.8273 0.1142
## avg_veg_height-Canis_latrans -0.1940 0.4318 -1.0746 -0.1917
## avg_veg_height-Lynx_rufus -0.1196 0.5200 -1.1120 -0.1217
## avg_veg_height-Didelphis_virginiana -0.1494 0.4678 -1.1218 -0.1376
## avg_veg_height-Sylvilagus_floridanus -0.2409 0.4697 -1.1954 -0.2192
## avg_veg_height-Sciurus_carolinensis 0.1850 0.4731 -0.6946 0.1687
## avg_veg_height-Vulpes_vulpes -0.1878 0.5192 -1.2808 -0.1840
## avg_veg_height-Sus_scrofa -0.1245 0.4839 -1.0999 -0.1116
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2579 1.0128 318
## (Intercept)-Lynx_rufus 0.8937 1.0081 309
## (Intercept)-Didelphis_virginiana -0.1732 1.0111 685
## (Intercept)-Sylvilagus_floridanus 0.7678 1.0035 361
## (Intercept)-Sciurus_carolinensis -0.2415 1.0233 606
## (Intercept)-Vulpes_vulpes 0.1999 1.0023 267
## (Intercept)-Sus_scrofa -0.4383 1.0061 536
## Avg_Cogongrass_Cover-Canis_latrans 1.6498 1.0162 703
## Avg_Cogongrass_Cover-Lynx_rufus 1.8710 1.0405 772
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5822 1.0073 773
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8916 1.0229 751
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.4180 1.0150 555
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6942 1.0109 734
## Avg_Cogongrass_Cover-Sus_scrofa 1.1917 1.0193 551
## total_shrub_cover-Canis_latrans 1.1313 1.0019 1405
## total_shrub_cover-Lynx_rufus 0.4360 1.0403 600
## total_shrub_cover-Didelphis_virginiana 0.6581 1.0056 1238
## total_shrub_cover-Sylvilagus_floridanus 0.6589 1.0291 532
## total_shrub_cover-Sciurus_carolinensis 0.8028 1.0055 1493
## total_shrub_cover-Vulpes_vulpes 0.8288 1.0018 945
## total_shrub_cover-Sus_scrofa 1.2929 1.0040 988
## avg_veg_height-Canis_latrans 0.6294 1.0064 869
## avg_veg_height-Lynx_rufus 0.9432 1.0094 1004
## avg_veg_height-Didelphis_virginiana 0.7352 1.0065 885
## avg_veg_height-Sylvilagus_floridanus 0.6383 1.0146 921
## avg_veg_height-Sciurus_carolinensis 1.1612 1.0107 904
## avg_veg_height-Vulpes_vulpes 0.8048 1.0074 821
## avg_veg_height-Sus_scrofa 0.8153 1.0064 1132
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4642 0.1837 -2.8330 -2.4639 -2.1215 1.0009
## (Intercept)-Lynx_rufus -3.2325 0.3084 -3.8954 -3.2139 -2.6658 1.0362
## (Intercept)-Didelphis_virginiana -2.2230 0.2822 -2.7976 -2.2133 -1.6810 1.0012
## (Intercept)-Sylvilagus_floridanus -3.0282 0.3145 -3.7013 -3.0059 -2.4677 1.0013
## (Intercept)-Sciurus_carolinensis -2.3673 0.2834 -2.9403 -2.3619 -1.8274 1.0025
## (Intercept)-Vulpes_vulpes -3.4791 0.6969 -5.2296 -3.3724 -2.4248 1.0041
## (Intercept)-Sus_scrofa -2.8293 0.4365 -3.7475 -2.8036 -2.0441 1.0137
## week-Canis_latrans 0.5482 0.2641 0.0447 0.5450 1.0798 1.0018
## week-Lynx_rufus 0.4050 0.3258 -0.2346 0.4028 1.0419 1.0025
## week-Didelphis_virginiana 0.1244 0.3608 -0.6275 0.1405 0.7839 1.0094
## week-Sylvilagus_floridanus 0.1397 0.3189 -0.5111 0.1541 0.7556 1.0018
## week-Sciurus_carolinensis 0.7022 0.3618 0.0469 0.6844 1.4767 1.0002
## week-Vulpes_vulpes 0.3020 0.4452 -0.6488 0.3215 1.1517 1.0037
## week-Sus_scrofa 0.6120 0.4245 -0.1652 0.5918 1.5426 1.0137
## I(week^2)-Canis_latrans -0.2237 0.1077 -0.4387 -0.2228 -0.0190 1.0032
## I(week^2)-Lynx_rufus -0.2593 0.1585 -0.5846 -0.2526 0.0351 1.0088
## I(week^2)-Didelphis_virginiana -0.4104 0.2411 -0.9659 -0.3793 -0.0366 1.0184
## I(week^2)-Sylvilagus_floridanus -0.1938 0.1584 -0.5238 -0.1876 0.1056 1.0080
## I(week^2)-Sciurus_carolinensis -0.2429 0.1509 -0.5545 -0.2356 0.0372 1.0027
## I(week^2)-Vulpes_vulpes -0.4629 0.3029 -1.1965 -0.4185 -0.0039 1.0527
## I(week^2)-Sus_scrofa -0.2238 0.1841 -0.6181 -0.2120 0.0980 1.0249
## ESS
## (Intercept)-Canis_latrans 1205
## (Intercept)-Lynx_rufus 456
## (Intercept)-Didelphis_virginiana 1127
## (Intercept)-Sylvilagus_floridanus 494
## (Intercept)-Sciurus_carolinensis 1177
## (Intercept)-Vulpes_vulpes 135
## (Intercept)-Sus_scrofa 640
## week-Canis_latrans 970
## week-Lynx_rufus 834
## week-Didelphis_virginiana 901
## week-Sylvilagus_floridanus 817
## week-Sciurus_carolinensis 1146
## week-Vulpes_vulpes 865
## week-Sus_scrofa 1218
## I(week^2)-Canis_latrans 1159
## I(week^2)-Lynx_rufus 688
## I(week^2)-Didelphis_virginiana 284
## I(week^2)-Sylvilagus_floridanus 742
## I(week^2)-Sciurus_carolinensis 1213
## I(week^2)-Vulpes_vulpes 301
## I(week^2)-Sus_scrofa 932
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.453
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2515 0.6133 -2.4066 -1.2607 0.0826 1.0196 535
## Tree_Density -0.7162 0.5675 -1.9961 -0.6710 0.2958 1.0016 656
## Avg_Canopy_Cover 0.9769 0.4563 0.1402 0.9515 2.0064 1.0117 1103
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8647 2.5834 0.1300 1.1734 8.3575 1.0456 775
## Tree_Density 1.3928 2.9564 0.0592 0.6287 6.9398 1.0797 946
## Avg_Canopy_Cover 1.1231 1.8634 0.0883 0.6676 4.8934 1.1155 969
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6639 0.7362 0.0465 0.4248 2.659 1.1162 234
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6920 0.3033 -3.3019 -2.6999 -2.0565 1.0064 2022
## week 0.3963 0.2740 -0.1385 0.3926 0.9615 1.0054 894
## I(week^2) -0.2836 0.1563 -0.6075 -0.2777 -0.0029 1.0092 771
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5140 0.6619 0.0664 0.3332 1.9891 1.0247 563
## week 0.2889 0.3801 0.0359 0.1768 1.2177 1.0128 1266
## I(week^2) 0.1128 0.1514 0.0221 0.0721 0.4468 1.0231 406
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1617 0.6803 -1.5797 -0.1461 1.0894
## (Intercept)-Lynx_rufus -0.3887 1.0573 -2.0672 -0.5058 2.0952
## (Intercept)-Didelphis_virginiana -1.8718 0.6935 -3.4178 -1.8242 -0.6422
## (Intercept)-Sylvilagus_floridanus -1.0016 0.6939 -2.3741 -1.0151 0.3828
## (Intercept)-Sciurus_carolinensis -1.9058 0.7073 -3.4113 -1.8486 -0.6306
## (Intercept)-Vulpes_vulpes -1.8550 0.9211 -3.6296 -1.8498 0.0491
## (Intercept)-Sus_scrofa -2.4004 0.8641 -4.3694 -2.3025 -1.0007
## Tree_Density-Canis_latrans -0.9532 0.6420 -2.4146 -0.8731 0.0916
## Tree_Density-Lynx_rufus 0.3844 0.8326 -0.8538 0.2475 2.6110
## Tree_Density-Didelphis_virginiana -1.1093 0.9429 -3.5968 -0.9346 0.1851
## Tree_Density-Sylvilagus_floridanus -1.2432 0.9703 -3.5814 -1.0702 0.1505
## Tree_Density-Sciurus_carolinensis -0.9654 0.8532 -3.0411 -0.8231 0.3155
## Tree_Density-Vulpes_vulpes -0.5628 0.9563 -2.5064 -0.5380 1.5084
## Tree_Density-Sus_scrofa -0.9454 0.9274 -3.1760 -0.7956 0.4615
## Avg_Canopy_Cover-Canis_latrans -0.0361 0.4810 -1.0019 -0.0206 0.8804
## Avg_Canopy_Cover-Lynx_rufus 0.5352 0.6344 -0.6530 0.5019 1.8569
## Avg_Canopy_Cover-Didelphis_virginiana 1.3028 0.5718 0.3676 1.2327 2.5746
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8913 0.9229 0.5819 1.7156 4.2424
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2902 0.5439 0.3680 1.2414 2.5055
## Avg_Canopy_Cover-Vulpes_vulpes 1.0179 0.6378 -0.0983 0.9439 2.3951
## Avg_Canopy_Cover-Sus_scrofa 1.2285 0.5932 0.2331 1.1849 2.5222
## Rhat ESS
## (Intercept)-Canis_latrans 1.0083 601
## (Intercept)-Lynx_rufus 1.1083 183
## (Intercept)-Didelphis_virginiana 1.0061 873
## (Intercept)-Sylvilagus_floridanus 1.0045 588
## (Intercept)-Sciurus_carolinensis 1.0028 611
## (Intercept)-Vulpes_vulpes 1.0438 295
## (Intercept)-Sus_scrofa 1.0037 710
## Tree_Density-Canis_latrans 1.0038 891
## Tree_Density-Lynx_rufus 1.0162 487
## Tree_Density-Didelphis_virginiana 1.0234 480
## Tree_Density-Sylvilagus_floridanus 1.0120 530
## Tree_Density-Sciurus_carolinensis 1.0071 664
## Tree_Density-Vulpes_vulpes 1.0095 578
## Tree_Density-Sus_scrofa 1.0002 637
## Avg_Canopy_Cover-Canis_latrans 1.0052 1135
## Avg_Canopy_Cover-Lynx_rufus 1.0021 938
## Avg_Canopy_Cover-Didelphis_virginiana 1.0211 941
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0249 347
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0029 1113
## Avg_Canopy_Cover-Vulpes_vulpes 1.0117 1078
## Avg_Canopy_Cover-Sus_scrofa 1.0151 1080
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4687 0.1905 -2.8601 -2.4619 -2.1218 1.0069
## (Intercept)-Lynx_rufus -3.2434 0.3356 -3.9816 -3.2049 -2.6427 1.0303
## (Intercept)-Didelphis_virginiana -2.2114 0.2656 -2.7439 -2.2033 -1.7083 1.0149
## (Intercept)-Sylvilagus_floridanus -2.9515 0.2757 -3.5314 -2.9390 -2.4493 1.0025
## (Intercept)-Sciurus_carolinensis -2.3591 0.2831 -2.9172 -2.3483 -1.8055 1.0150
## (Intercept)-Vulpes_vulpes -3.3809 0.5839 -4.6843 -3.3100 -2.4453 1.0338
## (Intercept)-Sus_scrofa -2.7786 0.3958 -3.6084 -2.7587 -2.0366 1.0047
## week-Canis_latrans 0.5426 0.2642 0.0403 0.5298 1.0794 1.0091
## week-Lynx_rufus 0.4168 0.3358 -0.2386 0.4164 1.1052 1.0027
## week-Didelphis_virginiana 0.1167 0.3643 -0.6778 0.1410 0.7499 1.0209
## week-Sylvilagus_floridanus 0.1281 0.3227 -0.5138 0.1380 0.7288 1.0048
## week-Sciurus_carolinensis 0.7008 0.3653 0.0672 0.6682 1.4859 1.0088
## week-Vulpes_vulpes 0.3100 0.4505 -0.6462 0.3221 1.2060 1.0015
## week-Sus_scrofa 0.6123 0.4234 -0.1570 0.5809 1.5032 1.0007
## I(week^2)-Canis_latrans -0.2291 0.1102 -0.4495 -0.2268 -0.0168 1.0014
## I(week^2)-Lynx_rufus -0.2592 0.1546 -0.5706 -0.2566 0.0185 1.0026
## I(week^2)-Didelphis_virginiana -0.4029 0.2354 -0.9308 -0.3734 -0.0178 1.0081
## I(week^2)-Sylvilagus_floridanus -0.1950 0.1602 -0.5350 -0.1890 0.0978 1.0026
## I(week^2)-Sciurus_carolinensis -0.2392 0.1485 -0.5609 -0.2300 0.0302 1.0011
## I(week^2)-Vulpes_vulpes -0.4731 0.3205 -1.3284 -0.4238 -0.0166 1.0456
## I(week^2)-Sus_scrofa -0.2156 0.1815 -0.5804 -0.2083 0.1078 1.0060
## ESS
## (Intercept)-Canis_latrans 1137
## (Intercept)-Lynx_rufus 267
## (Intercept)-Didelphis_virginiana 1358
## (Intercept)-Sylvilagus_floridanus 796
## (Intercept)-Sciurus_carolinensis 1316
## (Intercept)-Vulpes_vulpes 233
## (Intercept)-Sus_scrofa 879
## week-Canis_latrans 1125
## week-Lynx_rufus 924
## week-Didelphis_virginiana 849
## week-Sylvilagus_floridanus 951
## week-Sciurus_carolinensis 1022
## week-Vulpes_vulpes 764
## week-Sus_scrofa 1270
## I(week^2)-Canis_latrans 1110
## I(week^2)-Lynx_rufus 827
## I(week^2)-Didelphis_virginiana 438
## I(week^2)-Sylvilagus_floridanus 763
## I(week^2)-Sciurus_carolinensis 1257
## I(week^2)-Vulpes_vulpes 199
## I(week^2)-Sus_scrofa 1403
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4607
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1725 0.5750 -2.3501 -1.1722 0.0022 1.0083 276
## Cogon_Patch_Size -0.4377 0.6808 -1.8664 -0.3978 0.8026 1.0090 1108
## Avg_Cogongrass_Cover 0.4144 0.3634 -0.3185 0.4203 1.1413 1.0010 674
## total_shrub_cover -0.0193 0.3769 -0.8124 -0.0110 0.7048 1.0105 724
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2723 2.2493 0.0659 0.6381 6.0194 1.0307 563
## Cogon_Patch_Size 2.6982 5.3633 0.1524 1.4950 11.6841 1.1321 529
## Avg_Cogongrass_Cover 0.4422 1.0488 0.0406 0.2378 2.0633 1.2231 661
## total_shrub_cover 0.4618 0.8470 0.0419 0.2259 2.4172 1.1840 241
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4208 2.6185 0.2621 1.7876 8.4948 1.2375 110
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7252 0.3022 -3.3566 -2.7178 -2.1312 1.0098 942
## week 0.3980 0.2658 -0.1183 0.4012 0.9265 1.0050 865
## I(week^2) -0.2819 0.1522 -0.5953 -0.2781 0.0081 1.0128 902
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5201 0.7115 0.0642 0.3417 1.9833 1.0637 844
## week 0.2628 0.4010 0.0353 0.1605 1.0634 1.0174 1643
## I(week^2) 0.1102 0.1965 0.0222 0.0690 0.4455 1.0876 727
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3327 0.8949 -1.9616 -0.3800
## (Intercept)-Lynx_rufus -0.8865 0.8380 -2.4806 -0.9151
## (Intercept)-Didelphis_virginiana -1.4668 0.7244 -3.0032 -1.4485
## (Intercept)-Sylvilagus_floridanus -1.0621 0.8157 -2.6975 -1.0624
## (Intercept)-Sciurus_carolinensis -1.6153 0.7610 -3.2677 -1.5488
## (Intercept)-Vulpes_vulpes -1.5505 0.9431 -3.4189 -1.5293
## (Intercept)-Sus_scrofa -1.8835 0.8841 -3.9141 -1.7971
## Cogon_Patch_Size-Canis_latrans 0.8723 0.9317 -0.3836 0.6957
## Cogon_Patch_Size-Lynx_rufus -0.6577 0.9515 -2.4276 -0.6897
## Cogon_Patch_Size-Didelphis_virginiana 0.7782 0.6267 -0.2349 0.7276
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5912 1.3307 -5.0045 -1.2938
## Cogon_Patch_Size-Sciurus_carolinensis -1.2163 0.9765 -3.5488 -1.0594
## Cogon_Patch_Size-Vulpes_vulpes -1.0538 1.1932 -3.9018 -0.8728
## Cogon_Patch_Size-Sus_scrofa -0.6901 1.1059 -3.4040 -0.5348
## Avg_Cogongrass_Cover-Canis_latrans 0.4010 0.4380 -0.4259 0.3875
## Avg_Cogongrass_Cover-Lynx_rufus 0.7783 0.5742 -0.1309 0.7172
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3625 0.4414 -0.5519 0.3800
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0992 0.5158 -1.0099 0.1318
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6874 0.4479 -0.1360 0.6611
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5254 0.5207 -0.4591 0.5023
## Avg_Cogongrass_Cover-Sus_scrofa 0.1209 0.6276 -1.2835 0.1745
## total_shrub_cover-Canis_latrans 0.2277 0.4520 -0.5813 0.2059
## total_shrub_cover-Lynx_rufus -0.3998 0.6158 -1.8713 -0.3189
## total_shrub_cover-Didelphis_virginiana -0.1516 0.4507 -1.0918 -0.1280
## total_shrub_cover-Sylvilagus_floridanus -0.0578 0.5617 -1.1170 -0.0543
## total_shrub_cover-Sciurus_carolinensis 0.1048 0.4433 -0.7635 0.0975
## total_shrub_cover-Vulpes_vulpes -0.0973 0.6182 -1.3695 -0.0596
## total_shrub_cover-Sus_scrofa 0.2559 0.5929 -0.7583 0.2216
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5707 1.0139 320
## (Intercept)-Lynx_rufus 0.9263 1.0144 313
## (Intercept)-Didelphis_virginiana -0.0605 1.0075 554
## (Intercept)-Sylvilagus_floridanus 0.6103 1.0144 240
## (Intercept)-Sciurus_carolinensis -0.2689 1.0095 552
## (Intercept)-Vulpes_vulpes 0.4082 1.0302 369
## (Intercept)-Sus_scrofa -0.3437 1.0131 436
## Cogon_Patch_Size-Canis_latrans 3.1420 1.0118 634
## Cogon_Patch_Size-Lynx_rufus 1.3513 1.0662 472
## Cogon_Patch_Size-Didelphis_virginiana 2.1741 1.0063 898
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2238 1.0108 455
## Cogon_Patch_Size-Sciurus_carolinensis 0.1822 1.0107 561
## Cogon_Patch_Size-Vulpes_vulpes 0.8528 1.0202 425
## Cogon_Patch_Size-Sus_scrofa 1.0906 1.0073 577
## Avg_Cogongrass_Cover-Canis_latrans 1.3311 1.0070 1104
## Avg_Cogongrass_Cover-Lynx_rufus 2.0814 1.0311 867
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2011 1.0037 1192
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0189 1.0024 1005
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6262 1.0025 995
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6130 1.0037 1108
## Avg_Cogongrass_Cover-Sus_scrofa 1.1898 1.0113 722
## total_shrub_cover-Canis_latrans 1.2536 1.0032 1122
## total_shrub_cover-Lynx_rufus 0.5942 1.0208 467
## total_shrub_cover-Didelphis_virginiana 0.6749 1.0063 1091
## total_shrub_cover-Sylvilagus_floridanus 0.9578 1.0269 616
## total_shrub_cover-Sciurus_carolinensis 0.9871 1.0134 1115
## total_shrub_cover-Vulpes_vulpes 0.9847 1.0696 388
## total_shrub_cover-Sus_scrofa 1.5219 1.0137 760
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4629 0.1878 -2.8472 -2.4584 -2.1089 1.0023
## (Intercept)-Lynx_rufus -3.2021 0.3076 -3.8363 -3.1824 -2.6502 1.0296
## (Intercept)-Didelphis_virginiana -2.2298 0.2864 -2.8078 -2.2163 -1.6929 1.0034
## (Intercept)-Sylvilagus_floridanus -3.0182 0.3015 -3.6390 -3.0009 -2.4778 1.0104
## (Intercept)-Sciurus_carolinensis -2.3637 0.2777 -2.9399 -2.3567 -1.8253 1.0028
## (Intercept)-Vulpes_vulpes -3.4323 0.6214 -4.8062 -3.3643 -2.4258 1.0057
## (Intercept)-Sus_scrofa -2.8231 0.4257 -3.7677 -2.7949 -2.0521 1.0019
## week-Canis_latrans 0.5395 0.2576 0.0584 0.5354 1.0586 1.0144
## week-Lynx_rufus 0.4184 0.3141 -0.1892 0.4140 1.0591 1.0011
## week-Didelphis_virginiana 0.1274 0.3544 -0.6104 0.1439 0.7672 1.0094
## week-Sylvilagus_floridanus 0.1572 0.3254 -0.5113 0.1636 0.7803 1.0054
## week-Sciurus_carolinensis 0.6892 0.3637 0.0555 0.6673 1.4625 0.9999
## week-Vulpes_vulpes 0.3217 0.4357 -0.5958 0.3331 1.1642 1.0017
## week-Sus_scrofa 0.6008 0.4176 -0.1391 0.5844 1.4951 1.0193
## I(week^2)-Canis_latrans -0.2249 0.1078 -0.4364 -0.2240 -0.0193 1.0220
## I(week^2)-Lynx_rufus -0.2497 0.1532 -0.5511 -0.2478 0.0514 1.0029
## I(week^2)-Didelphis_virginiana -0.4063 0.2418 -1.0030 -0.3730 -0.0286 1.0481
## I(week^2)-Sylvilagus_floridanus -0.2043 0.1596 -0.5290 -0.1992 0.0926 1.0370
## I(week^2)-Sciurus_carolinensis -0.2437 0.1508 -0.5528 -0.2400 0.0380 1.0037
## I(week^2)-Vulpes_vulpes -0.4357 0.3030 -1.1478 -0.3903 0.0125 1.0431
## I(week^2)-Sus_scrofa -0.2162 0.1796 -0.5867 -0.2128 0.1186 1.0030
## ESS
## (Intercept)-Canis_latrans 1242
## (Intercept)-Lynx_rufus 432
## (Intercept)-Didelphis_virginiana 1012
## (Intercept)-Sylvilagus_floridanus 535
## (Intercept)-Sciurus_carolinensis 1182
## (Intercept)-Vulpes_vulpes 229
## (Intercept)-Sus_scrofa 703
## week-Canis_latrans 950
## week-Lynx_rufus 935
## week-Didelphis_virginiana 959
## week-Sylvilagus_floridanus 888
## week-Sciurus_carolinensis 946
## week-Vulpes_vulpes 966
## week-Sus_scrofa 945
## I(week^2)-Canis_latrans 1050
## I(week^2)-Lynx_rufus 664
## I(week^2)-Didelphis_virginiana 311
## I(week^2)-Sylvilagus_floridanus 679
## I(week^2)-Sciurus_carolinensis 1043
## I(week^2)-Vulpes_vulpes 275
## I(week^2)-Sus_scrofa 1146
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4533
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1090 0.4977 -2.0872 -1.1155 -0.1196 1.0017 521
## Veg_shannon_index 0.3822 0.3668 -0.3648 0.3754 1.0820 1.0035 1114
## Avg_Cogongrass_Cover 0.3456 0.3435 -0.3384 0.3402 1.0323 1.0101 992
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1185 1.5473 0.0683 0.6401 4.9914 1.0026 607
## Veg_shannon_index 0.5764 0.9066 0.0466 0.3105 2.6798 1.0069 793
## Avg_Cogongrass_Cover 0.4489 0.7561 0.0408 0.2528 2.0234 1.0146 1107
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.211 1.0872 0.0815 0.9097 4.0616 1.117 219
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7079 0.2985 -3.2996 -2.7090 -2.1171 1.0003 1594
## week 0.4123 0.2636 -0.0938 0.4069 0.9669 1.0028 920
## I(week^2) -0.2841 0.1552 -0.6100 -0.2794 0.0028 1.0008 1027
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4697 0.5186 0.0613 0.3131 1.7488 1.0099 685
## week 0.2722 0.3422 0.0354 0.1704 1.1527 1.0160 1216
## I(week^2) 0.1105 0.1268 0.0212 0.0722 0.4463 1.0138 638
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3299 0.6961 -1.6554 -0.3411
## (Intercept)-Lynx_rufus -0.6954 0.7320 -2.0567 -0.7348
## (Intercept)-Didelphis_virginiana -1.4434 0.5996 -2.7224 -1.4230
## (Intercept)-Sylvilagus_floridanus -0.8050 0.6723 -2.0619 -0.8219
## (Intercept)-Sciurus_carolinensis -1.4702 0.6286 -2.8886 -1.4329
## (Intercept)-Vulpes_vulpes -1.5086 0.7874 -3.1048 -1.4679
## (Intercept)-Sus_scrofa -1.8952 0.7993 -3.7280 -1.8211
## Veg_shannon_index-Canis_latrans 0.7526 0.4307 -0.0174 0.7238
## Veg_shannon_index-Lynx_rufus 0.1573 0.5814 -1.1321 0.1879
## Veg_shannon_index-Didelphis_virginiana 0.5658 0.4456 -0.2627 0.5464
## Veg_shannon_index-Sylvilagus_floridanus 0.5141 0.5086 -0.3997 0.4800
## Veg_shannon_index-Sciurus_carolinensis -0.0371 0.4555 -0.9675 -0.0212
## Veg_shannon_index-Vulpes_vulpes -0.0118 0.5634 -1.2148 0.0355
## Veg_shannon_index-Sus_scrofa 0.8233 0.6451 -0.1956 0.7362
## Avg_Cogongrass_Cover-Canis_latrans 0.5898 0.4152 -0.1257 0.5533
## Avg_Cogongrass_Cover-Lynx_rufus 0.6548 0.4681 -0.1672 0.6216
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5167 0.4050 -0.2205 0.4967
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1047 0.4891 -1.1893 -0.0635
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4464 0.3891 -0.3220 0.4498
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3971 0.5167 -0.5913 0.3841
## Avg_Cogongrass_Cover-Sus_scrofa -0.0044 0.6145 -1.4252 0.0656
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1179 1.0151 446
## (Intercept)-Lynx_rufus 0.9559 1.0218 412
## (Intercept)-Didelphis_virginiana -0.3078 1.0125 874
## (Intercept)-Sylvilagus_floridanus 0.6037 1.0032 695
## (Intercept)-Sciurus_carolinensis -0.3289 1.0045 787
## (Intercept)-Vulpes_vulpes -0.0100 1.0130 399
## (Intercept)-Sus_scrofa -0.5151 1.0123 625
## Veg_shannon_index-Canis_latrans 1.6753 1.0071 1430
## Veg_shannon_index-Lynx_rufus 1.2106 1.0026 852
## Veg_shannon_index-Didelphis_virginiana 1.5074 1.0009 1811
## Veg_shannon_index-Sylvilagus_floridanus 1.6048 1.0130 1225
## Veg_shannon_index-Sciurus_carolinensis 0.7895 1.0011 1853
## Veg_shannon_index-Vulpes_vulpes 0.9440 1.0012 650
## Veg_shannon_index-Sus_scrofa 2.4034 1.0032 852
## Avg_Cogongrass_Cover-Canis_latrans 1.4835 1.0081 1695
## Avg_Cogongrass_Cover-Lynx_rufus 1.7116 1.0093 1130
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3635 1.0122 1489
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7641 1.0007 1173
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2311 1.0016 1586
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4356 1.0211 933
## Avg_Cogongrass_Cover-Sus_scrofa 1.0150 1.0021 785
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4572 0.1877 -2.8439 -2.4500 -2.0943 1.0066
## (Intercept)-Lynx_rufus -3.2080 0.3099 -3.8589 -3.1875 -2.6467 1.0242
## (Intercept)-Didelphis_virginiana -2.2208 0.2716 -2.7584 -2.2180 -1.6895 1.0011
## (Intercept)-Sylvilagus_floridanus -3.0076 0.3040 -3.6810 -2.9890 -2.4604 1.0219
## (Intercept)-Sciurus_carolinensis -2.3791 0.2787 -2.9261 -2.3806 -1.8474 1.0073
## (Intercept)-Vulpes_vulpes -3.3605 0.5746 -4.5947 -3.2929 -2.4248 1.0205
## (Intercept)-Sus_scrofa -2.8020 0.4038 -3.6485 -2.7872 -2.0546 1.0238
## week-Canis_latrans 0.5475 0.2682 0.0342 0.5445 1.0963 1.0000
## week-Lynx_rufus 0.4245 0.3195 -0.2041 0.4192 1.0704 1.0053
## week-Didelphis_virginiana 0.1258 0.3572 -0.6243 0.1387 0.7725 1.0030
## week-Sylvilagus_floridanus 0.1670 0.3248 -0.5043 0.1694 0.7970 1.0063
## week-Sciurus_carolinensis 0.7025 0.3632 0.0650 0.6727 1.5077 1.0053
## week-Vulpes_vulpes 0.3085 0.4376 -0.5932 0.3224 1.1142 1.0045
## week-Sus_scrofa 0.6148 0.4201 -0.1211 0.5869 1.5060 1.0127
## I(week^2)-Canis_latrans -0.2267 0.1067 -0.4438 -0.2267 -0.0201 1.0030
## I(week^2)-Lynx_rufus -0.2525 0.1507 -0.5568 -0.2450 0.0364 1.0170
## I(week^2)-Didelphis_virginiana -0.4179 0.2343 -0.9560 -0.3920 -0.0475 1.0022
## I(week^2)-Sylvilagus_floridanus -0.1927 0.1549 -0.5125 -0.1824 0.0953 0.9998
## I(week^2)-Sciurus_carolinensis -0.2418 0.1504 -0.5484 -0.2340 0.0373 1.0096
## I(week^2)-Vulpes_vulpes -0.4728 0.3389 -1.3928 -0.4187 0.0202 1.0189
## I(week^2)-Sus_scrofa -0.2197 0.1892 -0.6082 -0.2149 0.1157 1.0098
## ESS
## (Intercept)-Canis_latrans 1109
## (Intercept)-Lynx_rufus 379
## (Intercept)-Didelphis_virginiana 1250
## (Intercept)-Sylvilagus_floridanus 475
## (Intercept)-Sciurus_carolinensis 1308
## (Intercept)-Vulpes_vulpes 270
## (Intercept)-Sus_scrofa 972
## week-Canis_latrans 902
## week-Lynx_rufus 856
## week-Didelphis_virginiana 957
## week-Sylvilagus_floridanus 805
## week-Sciurus_carolinensis 1080
## week-Vulpes_vulpes 850
## week-Sus_scrofa 1112
## I(week^2)-Canis_latrans 1091
## I(week^2)-Lynx_rufus 622
## I(week^2)-Didelphis_virginiana 461
## I(week^2)-Sylvilagus_floridanus 831
## I(week^2)-Sciurus_carolinensis 1113
## I(week^2)-Vulpes_vulpes 295
## I(week^2)-Sus_scrofa 1327
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4462
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0432 0.4583 -1.9430 -1.0574 -0.1229 1.0155 477
## Avg_Cogongrass_Cover 0.2503 0.3131 -0.3904 0.2482 0.8894 1.0012 1363
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8989 1.1127 0.0625 0.5440 3.9004 1.0482 886
## Avg_Cogongrass_Cover 0.3775 0.5454 0.0382 0.2089 1.7061 1.0500 1249
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1771 1.1704 0.0937 0.893 3.9071 1.0683 208
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6814 0.2863 -3.2300 -2.6793 -2.1179 1.0047 1482
## week 0.4102 0.2631 -0.1141 0.4103 0.9312 1.0303 1005
## I(week^2) -0.2879 0.1583 -0.6264 -0.2846 0.0165 1.0064 799
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4503 0.5475 0.0581 0.2894 1.8771 1.0165 363
## week 0.2647 0.2955 0.0362 0.1702 1.0959 1.0132 1160
## I(week^2) 0.1156 0.1475 0.0233 0.0748 0.4632 1.0191 445
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2857 0.6633 -1.4960 -0.3030
## (Intercept)-Lynx_rufus -0.7309 0.6590 -1.9540 -0.7642
## (Intercept)-Didelphis_virginiana -1.3320 0.5835 -2.5758 -1.3148
## (Intercept)-Sylvilagus_floridanus -0.7933 0.6140 -1.9658 -0.8165
## (Intercept)-Sciurus_carolinensis -1.3941 0.5819 -2.6454 -1.3596
## (Intercept)-Vulpes_vulpes -1.4944 0.7689 -3.1578 -1.4551
## (Intercept)-Sus_scrofa -1.6499 0.7136 -3.2155 -1.5981
## Avg_Cogongrass_Cover-Canis_latrans 0.3932 0.3589 -0.2553 0.3678
## Avg_Cogongrass_Cover-Lynx_rufus 0.5253 0.4276 -0.2094 0.4927
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3874 0.3713 -0.2920 0.3741
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1862 0.4470 -1.2028 -0.1494
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3932 0.3654 -0.3129 0.3865
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3326 0.4330 -0.5371 0.3316
## Avg_Cogongrass_Cover-Sus_scrofa -0.0677 0.5371 -1.2829 -0.0043
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0889 1.0515 639
## (Intercept)-Lynx_rufus 0.6775 1.0129 526
## (Intercept)-Didelphis_virginiana -0.2433 1.0028 911
## (Intercept)-Sylvilagus_floridanus 0.4227 1.0017 710
## (Intercept)-Sciurus_carolinensis -0.3259 1.0018 906
## (Intercept)-Vulpes_vulpes 0.0008 1.0113 325
## (Intercept)-Sus_scrofa -0.4178 1.0027 736
## Avg_Cogongrass_Cover-Canis_latrans 1.1566 1.0030 1986
## Avg_Cogongrass_Cover-Lynx_rufus 1.4708 1.0169 1541
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1408 1.0069 1701
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5686 1.0112 950
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1623 1.0013 1819
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2049 1.0029 1467
## Avg_Cogongrass_Cover-Sus_scrofa 0.8413 1.0125 1000
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4611 0.1826 -2.8352 -2.4581 -2.1121 1.0184
## (Intercept)-Lynx_rufus -3.1582 0.2951 -3.7609 -3.1492 -2.6237 1.0045
## (Intercept)-Didelphis_virginiana -2.2276 0.2744 -2.7962 -2.2200 -1.6986 1.0022
## (Intercept)-Sylvilagus_floridanus -2.9475 0.2772 -3.5097 -2.9384 -2.4417 1.0108
## (Intercept)-Sciurus_carolinensis -2.3764 0.2768 -2.9213 -2.3711 -1.8346 1.0056
## (Intercept)-Vulpes_vulpes -3.2678 0.5747 -4.6066 -3.1936 -2.3483 1.0210
## (Intercept)-Sus_scrofa -2.8065 0.4182 -3.7296 -2.7834 -2.0630 1.0067
## week-Canis_latrans 0.5460 0.2549 0.0652 0.5358 1.0584 1.0197
## week-Lynx_rufus 0.4067 0.3197 -0.2220 0.4094 1.0532 1.0127
## week-Didelphis_virginiana 0.1273 0.3493 -0.6211 0.1379 0.7809 1.0266
## week-Sylvilagus_floridanus 0.1621 0.3270 -0.4958 0.1684 0.7895 1.0051
## week-Sciurus_carolinensis 0.6986 0.3525 0.0675 0.6805 1.4407 1.0315
## week-Vulpes_vulpes 0.3186 0.4315 -0.5974 0.3268 1.1490 1.0173
## week-Sus_scrofa 0.6186 0.4248 -0.1595 0.6037 1.5619 1.0103
## I(week^2)-Canis_latrans -0.2279 0.1098 -0.4498 -0.2262 -0.0156 1.0040
## I(week^2)-Lynx_rufus -0.2535 0.1503 -0.5653 -0.2436 0.0312 1.0026
## I(week^2)-Didelphis_virginiana -0.4004 0.2289 -0.8933 -0.3760 -0.0293 1.0581
## I(week^2)-Sylvilagus_floridanus -0.2057 0.1571 -0.5394 -0.2019 0.0979 1.0242
## I(week^2)-Sciurus_carolinensis -0.2423 0.1477 -0.5432 -0.2370 0.0352 1.0090
## I(week^2)-Vulpes_vulpes -0.4822 0.3366 -1.2623 -0.4310 -0.0095 1.0078
## I(week^2)-Sus_scrofa -0.2209 0.1858 -0.6103 -0.2127 0.1202 1.0154
## ESS
## (Intercept)-Canis_latrans 1122
## (Intercept)-Lynx_rufus 485
## (Intercept)-Didelphis_virginiana 1172
## (Intercept)-Sylvilagus_floridanus 700
## (Intercept)-Sciurus_carolinensis 1013
## (Intercept)-Vulpes_vulpes 244
## (Intercept)-Sus_scrofa 554
## week-Canis_latrans 1044
## week-Lynx_rufus 929
## week-Didelphis_virginiana 1019
## week-Sylvilagus_floridanus 944
## week-Sciurus_carolinensis 1244
## week-Vulpes_vulpes 1038
## week-Sus_scrofa 1125
## I(week^2)-Canis_latrans 1181
## I(week^2)-Lynx_rufus 847
## I(week^2)-Didelphis_virginiana 448
## I(week^2)-Sylvilagus_floridanus 747
## I(week^2)-Sciurus_carolinensis 1368
## I(week^2)-Vulpes_vulpes 233
## I(week^2)-Sus_scrofa 1108
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4485
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0261 0.5499 -3.1111 -2.0288 -0.9588 1.0033 370
## Avg_Cogongrass_Cover -0.9162 0.4998 -1.8973 -0.8980 0.0347 1.0319 447
## I(Avg_Cogongrass_Cover^2) 1.0462 0.4720 0.1747 1.0182 2.0392 1.0242 424
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0052 1.6651 0.0562 0.5238 4.9005 1.0103 791
## Avg_Cogongrass_Cover 0.6869 1.2805 0.0449 0.3399 3.3835 1.0838 529
## I(Avg_Cogongrass_Cover^2) 0.9840 2.0793 0.0443 0.3762 6.1356 1.1256 375
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0514 1.0805 0.0675 0.7235 3.8791 1.0669 265
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6963 0.2849 -3.2736 -2.7027 -2.1152 1.0123 1376
## week 0.4126 0.2625 -0.1220 0.4160 0.9182 1.0140 987
## I(week^2) -0.2805 0.1550 -0.5917 -0.2754 0.0050 1.0044 942
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4082 0.4497 0.0522 0.2774 1.5834 1.0096 733
## week 0.2598 0.3291 0.0365 0.1624 1.0984 1.0428 1378
## I(week^2) 0.1064 0.1279 0.0211 0.0699 0.4040 1.0314 1113
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3471 0.7697 -2.8169 -1.3418
## (Intercept)-Lynx_rufus -1.9438 0.7427 -3.3543 -1.9459
## (Intercept)-Didelphis_virginiana -2.2135 0.6642 -3.6095 -2.1880
## (Intercept)-Sylvilagus_floridanus -1.7643 0.6938 -3.1662 -1.7379
## (Intercept)-Sciurus_carolinensis -2.4983 0.7372 -4.0975 -2.4427
## (Intercept)-Vulpes_vulpes -2.5641 0.8519 -4.5281 -2.4649
## (Intercept)-Sus_scrofa -2.5520 0.8345 -4.4366 -2.4686
## Avg_Cogongrass_Cover-Canis_latrans -0.6076 0.5944 -1.7237 -0.6350
## Avg_Cogongrass_Cover-Lynx_rufus -0.8016 0.6382 -2.0952 -0.7882
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5464 0.6203 -1.6772 -0.5775
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4740 0.7675 -3.2931 -1.3774
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.9884 0.6205 -2.3408 -0.9634
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9958 0.7036 -2.4122 -0.9591
## Avg_Cogongrass_Cover-Sus_scrofa -1.2532 0.7766 -3.0541 -1.1867
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8474 1.1674 0.4747 1.5130
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5013 0.6520 0.4938 1.4019
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7570 0.5839 -0.1391 0.6966
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0371 0.5827 0.0795 0.9768
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1257 0.4404 0.3579 1.0928
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0596 0.5613 0.1569 0.9969
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3901 0.7243 -1.3687 0.4954
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.1709 1.0026 471
## (Intercept)-Lynx_rufus -0.4059 1.0051 430
## (Intercept)-Didelphis_virginiana -0.9386 1.0164 586
## (Intercept)-Sylvilagus_floridanus -0.4448 1.0015 460
## (Intercept)-Sciurus_carolinensis -1.1663 1.0140 342
## (Intercept)-Vulpes_vulpes -1.1192 1.0276 275
## (Intercept)-Sus_scrofa -1.1638 1.0218 288
## Avg_Cogongrass_Cover-Canis_latrans 0.6501 1.0087 928
## Avg_Cogongrass_Cover-Lynx_rufus 0.4303 1.0223 864
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7629 1.0026 882
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2661 1.0457 380
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1362 1.0326 369
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3227 1.0275 527
## Avg_Cogongrass_Cover-Sus_scrofa 0.0150 1.0132 380
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.8531 1.1668 194
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0710 1.0330 339
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.9742 1.0248 309
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.3791 1.0572 390
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0995 1.0324 489
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3532 1.0329 341
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5628 1.0671 310
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4845 0.1844 -2.8558 -2.4744 -2.1291 1.0028
## (Intercept)-Lynx_rufus -3.1100 0.2894 -3.6852 -3.0989 -2.5857 1.0429
## (Intercept)-Didelphis_virginiana -2.2558 0.2765 -2.8349 -2.2429 -1.7452 1.0059
## (Intercept)-Sylvilagus_floridanus -2.9811 0.2906 -3.5933 -2.9648 -2.4665 1.0291
## (Intercept)-Sciurus_carolinensis -2.3716 0.2748 -2.8974 -2.3719 -1.8394 1.0013
## (Intercept)-Vulpes_vulpes -3.2656 0.5163 -4.3844 -3.2291 -2.3838 1.0585
## (Intercept)-Sus_scrofa -2.7762 0.4139 -3.6499 -2.7520 -2.0035 1.0169
## week-Canis_latrans 0.5496 0.2526 0.0669 0.5426 1.0666 1.0058
## week-Lynx_rufus 0.4223 0.3253 -0.2175 0.4171 1.0990 1.0172
## week-Didelphis_virginiana 0.1454 0.3372 -0.5623 0.1491 0.8075 1.0002
## week-Sylvilagus_floridanus 0.1735 0.3242 -0.4756 0.1734 0.7992 1.0034
## week-Sciurus_carolinensis 0.7003 0.3595 0.0597 0.6913 1.4528 1.0081
## week-Vulpes_vulpes 0.3311 0.4268 -0.5316 0.3403 1.1408 1.0271
## week-Sus_scrofa 0.6228 0.4000 -0.1379 0.6069 1.4301 1.0051
## I(week^2)-Canis_latrans -0.2279 0.1065 -0.4427 -0.2270 -0.0224 1.0185
## I(week^2)-Lynx_rufus -0.2578 0.1588 -0.5934 -0.2489 0.0331 1.0109
## I(week^2)-Didelphis_virginiana -0.3908 0.2133 -0.8795 -0.3676 -0.0383 1.0004
## I(week^2)-Sylvilagus_floridanus -0.2004 0.1559 -0.5293 -0.1922 0.0853 1.0032
## I(week^2)-Sciurus_carolinensis -0.2434 0.1437 -0.5422 -0.2415 0.0334 1.0019
## I(week^2)-Vulpes_vulpes -0.4546 0.2834 -1.1475 -0.4196 -0.0135 1.0067
## I(week^2)-Sus_scrofa -0.2220 0.1832 -0.6093 -0.2164 0.1155 1.0045
## ESS
## (Intercept)-Canis_latrans 1203
## (Intercept)-Lynx_rufus 602
## (Intercept)-Didelphis_virginiana 814
## (Intercept)-Sylvilagus_floridanus 586
## (Intercept)-Sciurus_carolinensis 1228
## (Intercept)-Vulpes_vulpes 306
## (Intercept)-Sus_scrofa 732
## week-Canis_latrans 1243
## week-Lynx_rufus 952
## week-Didelphis_virginiana 1194
## week-Sylvilagus_floridanus 915
## week-Sciurus_carolinensis 1163
## week-Vulpes_vulpes 1049
## week-Sus_scrofa 1163
## I(week^2)-Canis_latrans 1319
## I(week^2)-Lynx_rufus 711
## I(week^2)-Didelphis_virginiana 630
## I(week^2)-Sylvilagus_floridanus 732
## I(week^2)-Sciurus_carolinensis 1378
## I(week^2)-Vulpes_vulpes 435
## I(week^2)-Sus_scrofa 1295
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.4835
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6858 1.2327 -4.8392 -2.7869 0.0593 1.0330 189
## Cogon_Patch_Size -0.0252 1.0061 -2.0892 0.0007 1.9190 1.0085 443
## Veg_shannon_index 0.8519 0.7187 -0.6417 0.8686 2.2653 1.0413 463
## total_shrub_cover -0.2709 0.5584 -1.4149 -0.2631 0.7847 1.0018 572
## Avg_Cogongrass_Cover -0.4594 1.2161 -2.8815 -0.4426 1.9153 1.1009 104
## Tree_Density -1.9293 0.8879 -3.6469 -1.9376 -0.1750 1.0375 310
## Avg_Canopy_Cover 1.8061 0.8087 0.1874 1.7968 3.4541 1.0267 472
## I(Avg_Cogongrass_Cover^2) 1.6911 0.8261 0.0877 1.7044 3.3432 1.0736 213
## avg_veg_height -0.4292 0.6230 -1.7026 -0.4365 0.7702 1.0081 242
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.1372 21.9627 0.1241 5.4937 65.9879 1.2457 130
## Cogon_Patch_Size 8.6565 13.7900 0.2745 4.3862 45.4969 1.3033 123
## Veg_shannon_index 2.2060 3.8338 0.0649 1.0328 12.6183 1.0621 285
## total_shrub_cover 1.1709 2.3334 0.0449 0.4433 7.4954 1.0071 263
## Avg_Cogongrass_Cover 1.3297 2.5501 0.0524 0.4972 7.7461 1.0395 765
## Tree_Density 3.1520 5.9027 0.0567 1.0826 19.1044 1.0723 322
## Avg_Canopy_Cover 5.3029 10.1892 0.1908 2.6570 27.0716 1.2039 161
## I(Avg_Cogongrass_Cover^2) 3.7610 11.3952 0.0603 0.9256 25.0898 2.0687 41
## avg_veg_height 0.6313 1.1303 0.0442 0.3051 3.2433 1.0431 568
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8441 2.7401 0.0576 0.8844 10.1207 1.0297 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7454 0.3389 -3.3661 -2.7551 -2.0326 1.0026 1488
## week 0.3932 0.2720 -0.1251 0.3892 0.9391 1.0032 933
## I(week^2) -0.2841 0.1565 -0.6203 -0.2785 0.0034 1.0135 912
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6535 0.7589 0.1008 0.4498 2.3641 1.0301 552
## week 0.2836 0.5007 0.0372 0.1755 1.1120 1.2039 1388
## I(week^2) 0.1116 0.1322 0.0227 0.0741 0.4506 1.0414 603
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.6117 1.4177 -4.4655 -1.5924
## (Intercept)-Lynx_rufus -1.1852 2.6670 -4.9924 -1.6164
## (Intercept)-Didelphis_virginiana -4.6188 1.5704 -8.1227 -4.4678
## (Intercept)-Sylvilagus_floridanus -3.0869 1.4709 -6.0902 -3.0213
## (Intercept)-Sciurus_carolinensis -5.1669 1.7959 -9.5025 -4.9320
## (Intercept)-Vulpes_vulpes -5.1795 2.1472 -9.9277 -4.9377
## (Intercept)-Sus_scrofa -5.8785 2.3671 -11.2993 -5.4957
## Cogon_Patch_Size-Canis_latrans 2.4606 1.9882 0.0033 2.0073
## Cogon_Patch_Size-Lynx_rufus -0.2575 2.1235 -4.1369 -0.4270
## Cogon_Patch_Size-Didelphis_virginiana 2.2664 1.2554 0.2149 2.1272
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9761 2.3178 -8.2462 -1.5076
## Cogon_Patch_Size-Sciurus_carolinensis -1.2899 1.7882 -5.8495 -0.9627
## Cogon_Patch_Size-Vulpes_vulpes -1.0308 2.2141 -5.9633 -0.7873
## Cogon_Patch_Size-Sus_scrofa -0.7944 1.9820 -5.4557 -0.4838
## Veg_shannon_index-Canis_latrans 1.5176 0.8872 0.0750 1.4216
## Veg_shannon_index-Lynx_rufus 0.9208 1.3862 -2.0041 0.9363
## Veg_shannon_index-Didelphis_virginiana 0.9664 0.8591 -0.7141 0.9402
## Veg_shannon_index-Sylvilagus_floridanus 0.9886 0.9335 -0.8149 0.9684
## Veg_shannon_index-Sciurus_carolinensis -0.0939 1.0626 -2.5005 0.0176
## Veg_shannon_index-Vulpes_vulpes 0.2104 1.2668 -2.7040 0.3783
## Veg_shannon_index-Sus_scrofa 1.9718 1.3889 0.0199 1.6916
## total_shrub_cover-Canis_latrans -0.0236 0.6721 -1.2619 -0.0554
## total_shrub_cover-Lynx_rufus -1.0364 1.2315 -4.1960 -0.8222
## total_shrub_cover-Didelphis_virginiana -0.5009 0.7620 -2.1811 -0.4271
## total_shrub_cover-Sylvilagus_floridanus -0.1963 0.8396 -1.9546 -0.1645
## total_shrub_cover-Sciurus_carolinensis 0.0697 0.7329 -1.3268 0.0367
## total_shrub_cover-Vulpes_vulpes -0.4375 0.9251 -2.4707 -0.3578
## total_shrub_cover-Sus_scrofa 0.1101 0.8873 -1.4212 0.0326
## Avg_Cogongrass_Cover-Canis_latrans -0.5332 1.3996 -3.3550 -0.5323
## Avg_Cogongrass_Cover-Lynx_rufus -0.2771 1.4313 -3.0986 -0.2897
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2724 1.4922 -3.0398 -0.2729
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9603 1.5365 -4.1756 -0.8736
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3619 1.4504 -3.1902 -0.3634
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.3011 1.5437 -3.3717 -0.3232
## Avg_Cogongrass_Cover-Sus_scrofa -0.7599 1.5565 -4.0624 -0.6983
## Tree_Density-Canis_latrans -2.8424 1.4540 -6.3049 -2.6016
## Tree_Density-Lynx_rufus -0.8948 1.5863 -3.4268 -1.1040
## Tree_Density-Didelphis_virginiana -2.3190 1.2215 -5.2558 -2.1967
## Tree_Density-Sylvilagus_floridanus -2.5420 1.4240 -5.8882 -2.3165
## Tree_Density-Sciurus_carolinensis -2.6507 1.4917 -6.2478 -2.3861
## Tree_Density-Vulpes_vulpes -2.0518 1.5185 -5.3457 -1.9812
## Tree_Density-Sus_scrofa -2.2275 1.4926 -5.6913 -2.0909
## Avg_Canopy_Cover-Canis_latrans 0.0429 0.8134 -1.6382 0.0602
## Avg_Canopy_Cover-Lynx_rufus 1.2222 1.5823 -1.8236 1.1970
## Avg_Canopy_Cover-Didelphis_virginiana 3.0066 1.2054 1.2067 2.8219
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1135 2.1546 1.1929 3.7510
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4755 1.0601 0.8402 2.3383
## Avg_Canopy_Cover-Vulpes_vulpes 2.4888 1.5475 0.3015 2.1959
## Avg_Canopy_Cover-Sus_scrofa 2.1393 1.0019 0.4365 2.0369
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0014 2.1389 0.8732 2.4894
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8738 1.4871 0.8063 2.5686
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3484 0.8693 -0.3696 1.3388
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5722 1.1675 -0.2170 1.4797
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9412 0.9007 0.3693 1.8924
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2000 1.1144 0.4020 2.0623
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5020 1.7724 -4.3910 0.8425
## avg_veg_height-Canis_latrans -0.5609 0.6855 -1.9269 -0.5562
## avg_veg_height-Lynx_rufus -0.4591 0.9120 -2.4244 -0.4529
## avg_veg_height-Didelphis_virginiana -0.5056 0.7825 -2.1408 -0.4837
## avg_veg_height-Sylvilagus_floridanus -0.4582 0.7866 -1.9668 -0.4723
## avg_veg_height-Sciurus_carolinensis -0.0894 0.7806 -1.5152 -0.1279
## avg_veg_height-Vulpes_vulpes -0.5812 0.9024 -2.4627 -0.5494
## avg_veg_height-Sus_scrofa -0.4568 0.7930 -2.0259 -0.4422
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1988 1.0680 226
## (Intercept)-Lynx_rufus 5.0293 1.1448 67
## (Intercept)-Didelphis_virginiana -1.9808 1.1578 196
## (Intercept)-Sylvilagus_floridanus -0.1445 1.1055 196
## (Intercept)-Sciurus_carolinensis -2.3499 1.1678 162
## (Intercept)-Vulpes_vulpes -1.8234 1.1640 144
## (Intercept)-Sus_scrofa -2.4198 1.2442 128
## Cogon_Patch_Size-Canis_latrans 7.7723 1.2876 137
## Cogon_Patch_Size-Lynx_rufus 4.3533 1.0720 77
## Cogon_Patch_Size-Didelphis_virginiana 5.1717 1.0710 143
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1896 1.1578 192
## Cogon_Patch_Size-Sciurus_carolinensis 1.2409 1.0610 326
## Cogon_Patch_Size-Vulpes_vulpes 2.6235 1.0561 235
## Cogon_Patch_Size-Sus_scrofa 2.2856 1.0540 319
## Veg_shannon_index-Canis_latrans 3.6479 1.0075 551
## Veg_shannon_index-Lynx_rufus 3.6917 1.0339 270
## Veg_shannon_index-Didelphis_virginiana 2.7078 1.0381 646
## Veg_shannon_index-Sylvilagus_floridanus 3.0038 1.0088 700
## Veg_shannon_index-Sciurus_carolinensis 1.7741 1.0181 408
## Veg_shannon_index-Vulpes_vulpes 2.3759 1.0560 325
## Veg_shannon_index-Sus_scrofa 5.3524 1.0290 312
## total_shrub_cover-Canis_latrans 1.4041 1.0028 584
## total_shrub_cover-Lynx_rufus 0.7310 1.0035 215
## total_shrub_cover-Didelphis_virginiana 0.8517 1.0023 503
## total_shrub_cover-Sylvilagus_floridanus 1.4285 1.0222 574
## total_shrub_cover-Sciurus_carolinensis 1.6557 1.0025 865
## total_shrub_cover-Vulpes_vulpes 1.1736 1.0091 507
## total_shrub_cover-Sus_scrofa 2.1042 1.0139 484
## Avg_Cogongrass_Cover-Canis_latrans 2.2294 1.0977 151
## Avg_Cogongrass_Cover-Lynx_rufus 2.6017 1.0422 157
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6825 1.1046 126
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8205 1.0536 202
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5266 1.0803 135
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.7996 1.0738 141
## Avg_Cogongrass_Cover-Sus_scrofa 2.2215 1.0766 154
## Tree_Density-Canis_latrans -0.6312 1.1568 237
## Tree_Density-Lynx_rufus 3.0264 1.0405 207
## Tree_Density-Didelphis_virginiana -0.3191 1.0463 515
## Tree_Density-Sylvilagus_floridanus -0.2933 1.0390 356
## Tree_Density-Sciurus_carolinensis -0.5208 1.0239 263
## Tree_Density-Vulpes_vulpes 0.7985 1.0387 317
## Tree_Density-Sus_scrofa 0.3557 1.0052 430
## Avg_Canopy_Cover-Canis_latrans 1.6227 1.0076 534
## Avg_Canopy_Cover-Lynx_rufus 4.3754 1.0928 179
## Avg_Canopy_Cover-Didelphis_virginiana 5.8807 1.0210 150
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.0142 1.0099 157
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1152 1.0439 512
## Avg_Canopy_Cover-Vulpes_vulpes 6.5710 1.0022 224
## Avg_Canopy_Cover-Sus_scrofa 4.3621 1.0327 400
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 9.9586 1.7302 51
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.6357 1.0514 143
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1057 1.1019 166
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.8631 1.0621 154
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.9023 1.0663 249
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.8942 1.0154 242
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0671 1.1872 118
## avg_veg_height-Canis_latrans 0.7557 1.0071 335
## avg_veg_height-Lynx_rufus 1.3153 1.0030 315
## avg_veg_height-Didelphis_virginiana 0.9909 1.0225 384
## avg_veg_height-Sylvilagus_floridanus 1.0994 1.0031 390
## avg_veg_height-Sciurus_carolinensis 1.6926 1.0028 430
## avg_veg_height-Vulpes_vulpes 1.1078 1.0162 296
## avg_veg_height-Sus_scrofa 1.1336 1.0111 388
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4668 0.1850 -2.8459 -2.4627 -2.1137 1.0028
## (Intercept)-Lynx_rufus -3.4424 0.3353 -4.0963 -3.4469 -2.7840 1.0162
## (Intercept)-Didelphis_virginiana -2.1646 0.2675 -2.6901 -2.1603 -1.6644 1.0025
## (Intercept)-Sylvilagus_floridanus -3.0563 0.2853 -3.6423 -3.0442 -2.5396 1.0063
## (Intercept)-Sciurus_carolinensis -2.3599 0.2893 -2.9503 -2.3554 -1.8213 1.0104
## (Intercept)-Vulpes_vulpes -3.5784 0.5873 -4.8100 -3.5295 -2.5992 1.0232
## (Intercept)-Sus_scrofa -2.7802 0.4303 -3.7151 -2.7602 -2.0092 1.0103
## week-Canis_latrans 0.5474 0.2588 0.0627 0.5390 1.0725 1.0034
## week-Lynx_rufus 0.4061 0.3270 -0.2407 0.3976 1.0620 1.0117
## week-Didelphis_virginiana 0.1160 0.3530 -0.6364 0.1348 0.7691 1.0075
## week-Sylvilagus_floridanus 0.1300 0.3386 -0.5601 0.1403 0.7889 1.0102
## week-Sciurus_carolinensis 0.6870 0.3638 0.0249 0.6611 1.4765 1.0204
## week-Vulpes_vulpes 0.3035 0.4543 -0.6696 0.3246 1.1799 1.0095
## week-Sus_scrofa 0.6058 0.4316 -0.1642 0.5801 1.5500 1.0070
## I(week^2)-Canis_latrans -0.2236 0.1086 -0.4418 -0.2205 -0.0109 1.0002
## I(week^2)-Lynx_rufus -0.2531 0.1455 -0.5491 -0.2509 0.0308 1.0313
## I(week^2)-Didelphis_virginiana -0.4168 0.2344 -0.9652 -0.3856 -0.0513 1.0450
## I(week^2)-Sylvilagus_floridanus -0.2002 0.1659 -0.5409 -0.1925 0.1102 1.0344
## I(week^2)-Sciurus_carolinensis -0.2429 0.1476 -0.5499 -0.2384 0.0291 1.0156
## I(week^2)-Vulpes_vulpes -0.4670 0.3240 -1.2978 -0.4189 0.0051 1.0462
## I(week^2)-Sus_scrofa -0.2166 0.1891 -0.6149 -0.2049 0.1414 1.0125
## ESS
## (Intercept)-Canis_latrans 1106
## (Intercept)-Lynx_rufus 208
## (Intercept)-Didelphis_virginiana 1071
## (Intercept)-Sylvilagus_floridanus 564
## (Intercept)-Sciurus_carolinensis 1243
## (Intercept)-Vulpes_vulpes 213
## (Intercept)-Sus_scrofa 591
## week-Canis_latrans 1421
## week-Lynx_rufus 772
## week-Didelphis_virginiana 1073
## week-Sylvilagus_floridanus 835
## week-Sciurus_carolinensis 1113
## week-Vulpes_vulpes 776
## week-Sus_scrofa 1256
## I(week^2)-Canis_latrans 1348
## I(week^2)-Lynx_rufus 705
## I(week^2)-Didelphis_virginiana 428
## I(week^2)-Sylvilagus_floridanus 561
## I(week^2)-Sciurus_carolinensis 1298
## I(week^2)-Vulpes_vulpes 233
## I(week^2)-Sus_scrofa 1455
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.751 0.4134 -1.5642 -0.7466 0.0989 1.0085 1390
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0091 1.2482 0.1094 0.6573 4.0279 1.011 1433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8616 0.2938 -3.4202 -2.8602 -2.2429 1.0124 1311
## shrub_cover 0.3000 0.3601 -0.3730 0.2891 1.0436 1.0084 1281
## veg_height -0.0368 0.2317 -0.4805 -0.0407 0.4366 1.0031 1412
## week 0.3769 0.2827 -0.1808 0.3758 0.9366 1.0158 811
## I(week^2) -0.2956 0.1728 -0.6503 -0.2883 0.0080 1.0256 547
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4455 0.7456 0.0513 0.2804 1.7746 1.1180 1039
## shrub_cover 0.7348 0.8695 0.0983 0.4986 2.8347 1.0193 983
## veg_height 0.3098 0.3188 0.0561 0.2139 1.1410 1.0037 1711
## week 0.2876 0.3818 0.0380 0.1771 1.2043 1.0060 1378
## I(week^2) 0.1313 0.2236 0.0227 0.0789 0.5435 1.0816 335
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1781 0.4210 -0.6155 0.1642 1.0520 1.0040
## (Intercept)-Lynx_rufus -0.0784 0.6181 -1.0100 -0.1494 1.3164 1.0409
## (Intercept)-Didelphis_virginiana -1.1284 0.4313 -2.0241 -1.1081 -0.3501 1.0011
## (Intercept)-Sylvilagus_floridanus -0.4803 0.4278 -1.2681 -0.4922 0.4400 1.0192
## (Intercept)-Sciurus_carolinensis -1.1286 0.4450 -2.0609 -1.1020 -0.3206 1.0040
## (Intercept)-Vulpes_vulpes -1.3647 0.6334 -2.6473 -1.3447 -0.1592 1.0036
## (Intercept)-Sus_scrofa -1.4734 0.5761 -2.6767 -1.4312 -0.4798 1.0201
## ESS
## (Intercept)-Canis_latrans 1294
## (Intercept)-Lynx_rufus 327
## (Intercept)-Didelphis_virginiana 1689
## (Intercept)-Sylvilagus_floridanus 1353
## (Intercept)-Sciurus_carolinensis 1426
## (Intercept)-Vulpes_vulpes 661
## (Intercept)-Sus_scrofa 975
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6084 0.2015 -3.0216 -2.6039 -2.2264 1.0017
## (Intercept)-Lynx_rufus -3.3199 0.3383 -4.0232 -3.3070 -2.7084 1.0559
## (Intercept)-Didelphis_virginiana -2.4618 0.3034 -3.0572 -2.4599 -1.8633 1.0019
## (Intercept)-Sylvilagus_floridanus -3.0155 0.2794 -3.6264 -2.9973 -2.5117 1.0255
## (Intercept)-Sciurus_carolinensis -2.5630 0.3079 -3.1705 -2.5578 -1.9679 1.0049
## (Intercept)-Vulpes_vulpes -3.4027 0.5399 -4.6501 -3.3300 -2.5343 1.0161
## (Intercept)-Sus_scrofa -3.1460 0.4775 -4.1709 -3.1129 -2.2532 1.0169
## shrub_cover-Canis_latrans -0.2978 0.2232 -0.7464 -0.2933 0.1307 1.0161
## shrub_cover-Lynx_rufus -0.2084 0.3592 -0.9201 -0.1991 0.4799 1.0101
## shrub_cover-Didelphis_virginiana 0.9904 0.3929 0.2626 0.9699 1.8233 1.0118
## shrub_cover-Sylvilagus_floridanus 0.2415 0.4163 -0.5385 0.2230 1.1224 1.0034
## shrub_cover-Sciurus_carolinensis 0.8620 0.4185 0.0597 0.8575 1.6862 1.0026
## shrub_cover-Vulpes_vulpes -0.0075 0.6102 -1.2444 0.0041 1.2431 1.0099
## shrub_cover-Sus_scrofa 0.6281 0.7572 -0.8683 0.6057 2.1451 1.0182
## veg_height-Canis_latrans -0.6134 0.1890 -1.0011 -0.6056 -0.2553 1.0017
## veg_height-Lynx_rufus 0.0283 0.2534 -0.4987 0.0362 0.5082 1.0034
## veg_height-Didelphis_virginiana 0.4819 0.2688 -0.0165 0.4746 1.0398 1.0011
## veg_height-Sylvilagus_floridanus 0.1155 0.2536 -0.3697 0.1144 0.6037 1.0072
## veg_height-Sciurus_carolinensis 0.0877 0.2253 -0.3495 0.0793 0.5345 1.0080
## veg_height-Vulpes_vulpes -0.1192 0.3100 -0.7736 -0.1063 0.4552 1.0016
## veg_height-Sus_scrofa -0.1885 0.3618 -0.9207 -0.1908 0.5176 1.0016
## week-Canis_latrans 0.5539 0.2673 0.0430 0.5493 1.0992 1.0261
## week-Lynx_rufus 0.3978 0.3453 -0.2739 0.3874 1.1022 1.0034
## week-Didelphis_virginiana 0.1006 0.3724 -0.6864 0.1184 0.7596 1.0050
## week-Sylvilagus_floridanus 0.1254 0.3318 -0.5561 0.1307 0.7802 1.0017
## week-Sciurus_carolinensis 0.7008 0.3652 0.0619 0.6764 1.4861 1.0099
## week-Vulpes_vulpes 0.2593 0.4496 -0.6702 0.2636 1.1307 1.0144
## week-Sus_scrofa 0.5904 0.4272 -0.1833 0.5679 1.5042 1.0222
## I(week^2)-Canis_latrans -0.2266 0.1124 -0.4560 -0.2244 -0.0085 1.0030
## I(week^2)-Lynx_rufus -0.2711 0.1677 -0.6191 -0.2660 0.0313 1.0045
## I(week^2)-Didelphis_virginiana -0.4343 0.2493 -1.0339 -0.4044 -0.0423 1.0793
## I(week^2)-Sylvilagus_floridanus -0.1841 0.1636 -0.5337 -0.1741 0.1181 1.0277
## I(week^2)-Sciurus_carolinensis -0.2430 0.1533 -0.5517 -0.2351 0.0339 1.0019
## I(week^2)-Vulpes_vulpes -0.5054 0.3795 -1.3873 -0.4431 -0.0045 1.0930
## I(week^2)-Sus_scrofa -0.2274 0.1838 -0.6048 -0.2209 0.1165 1.0123
## ESS
## (Intercept)-Canis_latrans 844
## (Intercept)-Lynx_rufus 279
## (Intercept)-Didelphis_virginiana 738
## (Intercept)-Sylvilagus_floridanus 626
## (Intercept)-Sciurus_carolinensis 957
## (Intercept)-Vulpes_vulpes 366
## (Intercept)-Sus_scrofa 597
## shrub_cover-Canis_latrans 928
## shrub_cover-Lynx_rufus 456
## shrub_cover-Didelphis_virginiana 621
## shrub_cover-Sylvilagus_floridanus 627
## shrub_cover-Sciurus_carolinensis 646
## shrub_cover-Vulpes_vulpes 690
## shrub_cover-Sus_scrofa 574
## veg_height-Canis_latrans 707
## veg_height-Lynx_rufus 760
## veg_height-Didelphis_virginiana 830
## veg_height-Sylvilagus_floridanus 813
## veg_height-Sciurus_carolinensis 1131
## veg_height-Vulpes_vulpes 743
## veg_height-Sus_scrofa 1048
## week-Canis_latrans 867
## week-Lynx_rufus 721
## week-Didelphis_virginiana 889
## week-Sylvilagus_floridanus 847
## week-Sciurus_carolinensis 1052
## week-Vulpes_vulpes 885
## week-Sus_scrofa 1030
## I(week^2)-Canis_latrans 974
## I(week^2)-Lynx_rufus 515
## I(week^2)-Didelphis_virginiana 281
## I(week^2)-Sylvilagus_floridanus 588
## I(week^2)-Sciurus_carolinensis 1305
## I(week^2)-Vulpes_vulpes 188
## I(week^2)-Sus_scrofa 1127
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5795
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4036 1.0796 -3.4346 -1.4155 0.7307 1.0229 166
## Cogon_Patch_Size -0.5027 1.1135 -2.6600 -0.5142 1.7403 1.1212 245
## Veg_shannon_index 0.9463 0.8275 -0.7365 0.9137 2.5894 1.0215 236
## total_shrub_cover -0.8743 1.0701 -3.2245 -0.7957 0.9881 1.1465 165
## Avg_Cogongrass_Cover 1.8848 1.0468 -0.2349 1.8955 3.8235 1.0593 188
## Tree_Density -1.3662 1.1400 -3.4936 -1.4320 0.9524 1.2394 138
## Avg_Canopy_Cover 1.8101 1.1480 -0.8230 1.8808 3.8972 1.0170 1268
## avg_veg_height -0.3199 0.7777 -1.7416 -0.3635 1.3746 1.2595 183
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.3637 13.8102 0.1084 3.4268 39.3464 1.2064 131
## Cogon_Patch_Size 9.0090 13.9300 0.1807 4.2656 48.0889 1.0110 272
## Veg_shannon_index 4.8831 17.3603 0.0678 0.9501 40.7311 2.0270 28
## total_shrub_cover 10.0126 20.6839 0.0826 2.5663 60.1004 1.4407 97
## Avg_Cogongrass_Cover 2.5496 5.4332 0.0537 0.8218 15.8708 1.3754 166
## Tree_Density 6.3536 13.2546 0.0924 2.1462 36.6115 1.0760 205
## Avg_Canopy_Cover 23.1818 41.4268 0.5665 8.7646 126.5255 1.8096 66
## avg_veg_height 1.3073 3.1884 0.0507 0.4313 7.9932 1.1983 303
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.5621 17.5879 0.1022 4.4072 60.6977 2.296 29
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9860 0.3130 -3.5883 -2.9950 -2.3902 1.0947 615
## shrub_cover 0.5899 0.4171 -0.2655 0.5839 1.4344 1.0522 520
## veg_height -0.0194 0.2442 -0.5044 -0.0228 0.4850 1.0216 1736
## week 0.3947 0.2702 -0.1561 0.3934 0.9479 1.0123 1006
## I(week^2) -0.3117 0.1880 -0.7626 -0.2972 0.0156 1.0015 453
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4690 0.6116 0.0520 0.3071 1.8829 1.0872 634
## shrub_cover 0.8629 0.8513 0.1457 0.6105 2.9810 1.0562 1010
## veg_height 0.3753 0.3642 0.0700 0.2635 1.4220 1.0333 1405
## week 0.2920 0.3525 0.0388 0.1889 1.1062 1.0093 965
## I(week^2) 0.1510 0.2754 0.0223 0.0792 0.7171 1.0136 258
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3167 1.7185 -3.0056 0.3091
## (Intercept)-Lynx_rufus -0.3549 2.0298 -3.9945 -0.5036
## (Intercept)-Didelphis_virginiana -2.5908 1.6310 -6.4163 -2.4584
## (Intercept)-Sylvilagus_floridanus -1.3498 1.8162 -4.9735 -1.3817
## (Intercept)-Sciurus_carolinensis -2.6332 1.7583 -6.7440 -2.5064
## (Intercept)-Vulpes_vulpes -2.5286 1.9085 -6.6732 -2.4012
## (Intercept)-Sus_scrofa -3.6682 2.5344 -9.5944 -3.3039
## Cogon_Patch_Size-Canis_latrans 1.3966 1.7525 -0.9849 1.0422
## Cogon_Patch_Size-Lynx_rufus -0.9126 2.0302 -4.6107 -1.0091
## Cogon_Patch_Size-Didelphis_virginiana 1.6961 1.5652 -0.5417 1.4150
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6036 2.8403 -9.5819 -2.0813
## Cogon_Patch_Size-Sciurus_carolinensis -1.9191 2.4695 -7.8498 -1.5421
## Cogon_Patch_Size-Vulpes_vulpes -1.4861 2.4190 -7.0064 -1.3167
## Cogon_Patch_Size-Sus_scrofa -1.4408 2.2335 -7.0201 -1.1009
## Veg_shannon_index-Canis_latrans 1.5875 1.1104 -0.1407 1.4748
## Veg_shannon_index-Lynx_rufus 0.7028 1.9823 -3.8786 0.8996
## Veg_shannon_index-Didelphis_virginiana 1.6341 1.3638 -0.3017 1.3840
## Veg_shannon_index-Sylvilagus_floridanus 1.5429 1.3369 -0.4418 1.3154
## Veg_shannon_index-Sciurus_carolinensis -0.3304 2.6205 -5.7246 0.1339
## Veg_shannon_index-Vulpes_vulpes -0.0025 1.9795 -5.0454 0.3292
## Veg_shannon_index-Sus_scrofa 2.0367 1.7269 -0.1566 1.6728
## total_shrub_cover-Canis_latrans 1.4762 1.8819 -0.9030 0.9465
## total_shrub_cover-Lynx_rufus -2.5429 2.6649 -9.1768 -1.8421
## total_shrub_cover-Didelphis_virginiana -2.1788 2.4433 -8.9924 -1.4534
## total_shrub_cover-Sylvilagus_floridanus -1.6321 2.3768 -7.7497 -1.0468
## total_shrub_cover-Sciurus_carolinensis -1.7382 2.3299 -8.0187 -1.1621
## total_shrub_cover-Vulpes_vulpes -2.4536 3.1358 -10.9711 -1.4482
## total_shrub_cover-Sus_scrofa -0.6518 2.0118 -5.3523 -0.4171
## Avg_Cogongrass_Cover-Canis_latrans 2.3986 1.2995 0.0663 2.2862
## Avg_Cogongrass_Cover-Lynx_rufus 2.5379 1.5154 -0.0948 2.3701
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9833 1.3067 -0.6127 1.9646
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1897 1.6127 -2.5940 1.3253
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3223 1.4026 -0.3703 2.2439
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6180 1.5954 -0.1496 2.4650
## Avg_Cogongrass_Cover-Sus_scrofa 1.4611 1.8263 -2.8724 1.6189
## Tree_Density-Canis_latrans -2.8915 1.9326 -7.7945 -2.5320
## Tree_Density-Lynx_rufus 0.1891 1.8355 -2.6299 -0.0456
## Tree_Density-Didelphis_virginiana -1.7838 1.7917 -5.5713 -1.7860
## Tree_Density-Sylvilagus_floridanus -2.4739 2.3021 -8.0161 -2.1266
## Tree_Density-Sciurus_carolinensis -1.9217 2.2716 -7.2831 -1.8106
## Tree_Density-Vulpes_vulpes -1.3492 2.1206 -5.9895 -1.3947
## Tree_Density-Sus_scrofa -1.8752 2.1300 -6.9639 -1.7739
## Avg_Canopy_Cover-Canis_latrans -0.3062 0.9989 -2.5497 -0.1903
## Avg_Canopy_Cover-Lynx_rufus 0.2823 2.1676 -3.9036 0.2503
## Avg_Canopy_Cover-Didelphis_virginiana 5.4015 3.0194 1.7096 4.6590
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.4400 3.9497 1.7501 5.4172
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1483 3.3290 1.3144 4.2784
## Avg_Canopy_Cover-Vulpes_vulpes 3.9314 3.2849 0.4065 2.9036
## Avg_Canopy_Cover-Sus_scrofa 2.7614 1.9099 0.0749 2.3843
## avg_veg_height-Canis_latrans -0.3749 0.8730 -1.9861 -0.4144
## avg_veg_height-Lynx_rufus -0.4447 1.2558 -2.8007 -0.4748
## avg_veg_height-Didelphis_virginiana -0.5965 0.9976 -2.6646 -0.5905
## avg_veg_height-Sylvilagus_floridanus -0.5005 1.0142 -2.4726 -0.5039
## avg_veg_height-Sciurus_carolinensis 0.2550 1.1859 -1.5823 0.0669
## avg_veg_height-Vulpes_vulpes -0.2246 1.3094 -2.3106 -0.3308
## avg_veg_height-Sus_scrofa -0.3798 1.1642 -2.5557 -0.4228
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8018 1.0710 134
## (Intercept)-Lynx_rufus 4.0308 1.0251 95
## (Intercept)-Didelphis_virginiana 0.1802 1.0858 192
## (Intercept)-Sylvilagus_floridanus 2.3331 1.0846 170
## (Intercept)-Sciurus_carolinensis 0.3951 1.1233 149
## (Intercept)-Vulpes_vulpes 0.9042 1.1665 168
## (Intercept)-Sus_scrofa 0.2049 1.1991 56
## Cogon_Patch_Size-Canis_latrans 6.1109 1.1316 342
## Cogon_Patch_Size-Lynx_rufus 3.6355 1.0608 167
## Cogon_Patch_Size-Didelphis_virginiana 5.4943 1.2568 150
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3020 1.0183 169
## Cogon_Patch_Size-Sciurus_carolinensis 1.9173 1.0569 188
## Cogon_Patch_Size-Vulpes_vulpes 2.9063 1.1246 202
## Cogon_Patch_Size-Sus_scrofa 2.2323 1.0304 254
## Veg_shannon_index-Canis_latrans 3.9468 1.0471 228
## Veg_shannon_index-Lynx_rufus 3.5517 1.2430 88
## Veg_shannon_index-Didelphis_virginiana 5.2612 1.1692 115
## Veg_shannon_index-Sylvilagus_floridanus 5.0136 1.1166 258
## Veg_shannon_index-Sciurus_carolinensis 2.1310 1.5930 44
## Veg_shannon_index-Vulpes_vulpes 2.6413 1.1428 120
## Veg_shannon_index-Sus_scrofa 6.3394 1.1205 91
## total_shrub_cover-Canis_latrans 6.1241 1.6601 40
## total_shrub_cover-Lynx_rufus 0.8148 1.3873 48
## total_shrub_cover-Didelphis_virginiana 0.7332 1.5230 49
## total_shrub_cover-Sylvilagus_floridanus 1.4383 1.4394 85
## total_shrub_cover-Sciurus_carolinensis 1.0999 1.4894 114
## total_shrub_cover-Vulpes_vulpes 1.3513 1.5372 69
## total_shrub_cover-Sus_scrofa 2.9805 1.1195 114
## Avg_Cogongrass_Cover-Canis_latrans 5.3519 1.0257 200
## Avg_Cogongrass_Cover-Lynx_rufus 6.0553 1.0193 277
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.6541 1.0286 294
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.0064 1.1340 223
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.2949 1.0208 298
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.2284 1.0365 264
## Avg_Cogongrass_Cover-Sus_scrofa 4.4437 1.1093 155
## Tree_Density-Canis_latrans -0.0474 1.1000 176
## Tree_Density-Lynx_rufus 4.5320 1.0644 128
## Tree_Density-Didelphis_virginiana 1.9546 1.1613 161
## Tree_Density-Sylvilagus_floridanus 1.1206 1.0704 200
## Tree_Density-Sciurus_carolinensis 2.3678 1.2444 166
## Tree_Density-Vulpes_vulpes 3.0851 1.1589 234
## Tree_Density-Sus_scrofa 2.0224 1.1055 165
## Avg_Canopy_Cover-Canis_latrans 1.2546 1.3083 93
## Avg_Canopy_Cover-Lynx_rufus 5.0663 1.0773 102
## Avg_Canopy_Cover-Didelphis_virginiana 13.2175 1.4688 72
## Avg_Canopy_Cover-Sylvilagus_floridanus 16.5919 1.3865 91
## Avg_Canopy_Cover-Sciurus_carolinensis 13.8660 2.0051 65
## Avg_Canopy_Cover-Vulpes_vulpes 12.8712 1.8657 32
## Avg_Canopy_Cover-Sus_scrofa 7.7937 1.1628 167
## avg_veg_height-Canis_latrans 1.4727 1.1490 326
## avg_veg_height-Lynx_rufus 2.2334 1.1589 195
## avg_veg_height-Didelphis_virginiana 1.4084 1.1092 418
## avg_veg_height-Sylvilagus_floridanus 1.5857 1.0930 353
## avg_veg_height-Sciurus_carolinensis 3.2231 1.2698 173
## avg_veg_height-Vulpes_vulpes 2.6730 1.1727 161
## avg_veg_height-Sus_scrofa 2.1153 1.1689 365
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6599 0.2044 -3.0678 -2.6528 -2.2826 1.0206
## (Intercept)-Lynx_rufus -3.3723 0.3418 -4.0970 -3.3565 -2.7481 1.0656
## (Intercept)-Didelphis_virginiana -2.5882 0.3183 -3.1876 -2.5890 -1.9418 1.0389
## (Intercept)-Sylvilagus_floridanus -3.0960 0.2732 -3.6487 -3.0862 -2.5804 1.0631
## (Intercept)-Sciurus_carolinensis -2.7756 0.3146 -3.3897 -2.7749 -2.1544 1.1409
## (Intercept)-Vulpes_vulpes -3.6684 0.5647 -4.9231 -3.6153 -2.6893 1.3218
## (Intercept)-Sus_scrofa -3.2752 0.4815 -4.2812 -3.2469 -2.3752 1.0930
## shrub_cover-Canis_latrans -0.4137 0.2356 -0.8461 -0.4238 0.0560 1.0923
## shrub_cover-Lynx_rufus 0.1051 0.4063 -0.7093 0.1296 0.8525 1.0241
## shrub_cover-Didelphis_virginiana 1.2252 0.4186 0.4329 1.2129 2.0571 1.1144
## shrub_cover-Sylvilagus_floridanus 0.6719 0.4201 -0.1652 0.6694 1.4830 1.1315
## shrub_cover-Sciurus_carolinensis 1.2230 0.4054 0.4127 1.2385 1.9955 1.1911
## shrub_cover-Vulpes_vulpes 0.4495 0.6345 -0.8185 0.4719 1.7378 1.0546
## shrub_cover-Sus_scrofa 0.9532 0.7881 -0.6225 0.9673 2.4938 1.0742
## veg_height-Canis_latrans -0.6567 0.1931 -1.0368 -0.6550 -0.2909 1.0219
## veg_height-Lynx_rufus 0.0553 0.2576 -0.4608 0.0623 0.5385 1.0024
## veg_height-Didelphis_virginiana 0.5427 0.2694 0.0539 0.5304 1.1057 1.0024
## veg_height-Sylvilagus_floridanus 0.1462 0.2706 -0.3830 0.1512 0.6576 1.0239
## veg_height-Sciurus_carolinensis 0.1836 0.2453 -0.2789 0.1786 0.6628 1.0511
## veg_height-Vulpes_vulpes -0.2397 0.3503 -0.9541 -0.2297 0.4089 1.0395
## veg_height-Sus_scrofa -0.1909 0.3607 -0.9128 -0.1873 0.5067 1.0342
## week-Canis_latrans 0.5545 0.2602 0.0662 0.5491 1.0851 1.0037
## week-Lynx_rufus 0.3912 0.3274 -0.2433 0.3903 1.0505 1.0093
## week-Didelphis_virginiana 0.1128 0.3617 -0.6320 0.1278 0.7721 1.0044
## week-Sylvilagus_floridanus 0.1262 0.3301 -0.5624 0.1334 0.7514 1.0174
## week-Sciurus_carolinensis 0.7212 0.3808 0.0472 0.6881 1.5363 1.0061
## week-Vulpes_vulpes 0.2808 0.4453 -0.6398 0.2922 1.1453 1.0020
## week-Sus_scrofa 0.6378 0.4362 -0.1244 0.6131 1.5930 1.0130
## I(week^2)-Canis_latrans -0.2259 0.1096 -0.4423 -0.2236 -0.0209 1.0089
## I(week^2)-Lynx_rufus -0.2617 0.1639 -0.6121 -0.2536 0.0363 1.0029
## I(week^2)-Didelphis_virginiana -0.4582 0.2848 -1.2691 -0.4090 -0.0641 1.0175
## I(week^2)-Sylvilagus_floridanus -0.1786 0.1615 -0.5141 -0.1720 0.1247 1.0214
## I(week^2)-Sciurus_carolinensis -0.2544 0.1537 -0.5744 -0.2486 0.0268 1.0172
## I(week^2)-Vulpes_vulpes -0.5767 0.4456 -1.8569 -0.4663 -0.0212 1.0339
## I(week^2)-Sus_scrofa -0.2478 0.1945 -0.6524 -0.2365 0.0960 1.0038
## ESS
## (Intercept)-Canis_latrans 602
## (Intercept)-Lynx_rufus 267
## (Intercept)-Didelphis_virginiana 393
## (Intercept)-Sylvilagus_floridanus 420
## (Intercept)-Sciurus_carolinensis 315
## (Intercept)-Vulpes_vulpes 206
## (Intercept)-Sus_scrofa 183
## shrub_cover-Canis_latrans 333
## shrub_cover-Lynx_rufus 173
## shrub_cover-Didelphis_virginiana 252
## shrub_cover-Sylvilagus_floridanus 220
## shrub_cover-Sciurus_carolinensis 287
## shrub_cover-Vulpes_vulpes 225
## shrub_cover-Sus_scrofa 283
## veg_height-Canis_latrans 553
## veg_height-Lynx_rufus 564
## veg_height-Didelphis_virginiana 851
## veg_height-Sylvilagus_floridanus 766
## veg_height-Sciurus_carolinensis 672
## veg_height-Vulpes_vulpes 545
## veg_height-Sus_scrofa 834
## week-Canis_latrans 957
## week-Lynx_rufus 739
## week-Didelphis_virginiana 850
## week-Sylvilagus_floridanus 952
## week-Sciurus_carolinensis 907
## week-Vulpes_vulpes 801
## week-Sus_scrofa 864
## I(week^2)-Canis_latrans 895
## I(week^2)-Lynx_rufus 528
## I(week^2)-Didelphis_virginiana 139
## I(week^2)-Sylvilagus_floridanus 665
## I(week^2)-Sciurus_carolinensis 975
## I(week^2)-Vulpes_vulpes 81
## I(week^2)-Sus_scrofa 701
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.6018
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5223 0.6421 -1.7942 -0.5267 0.8166 1.0505 153
## Avg_Cogongrass_Cover 0.1957 0.4598 -0.7230 0.1774 1.0815 1.0480 481
## total_shrub_cover -0.8196 0.6595 -2.2759 -0.7523 0.3433 1.0147 195
## avg_veg_height 0.1425 0.4653 -0.7422 0.1437 1.1159 1.0350 341
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9948 1.7757 0.0516 0.4747 4.8720 1.0248 498
## Avg_Cogongrass_Cover 0.6760 1.2232 0.0483 0.3322 3.5454 1.0199 707
## total_shrub_cover 1.8620 3.0687 0.0683 0.8600 9.9276 1.0260 183
## avg_veg_height 0.4360 0.6317 0.0396 0.2379 2.0675 1.0022 1011
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.068 3.0209 0.2271 2.2022 10.8071 1.0186 156
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0166 0.2957 -3.6307 -3.0034 -2.4555 1.0337 459
## shrub_cover 0.6738 0.4231 -0.1781 0.6757 1.4895 1.0163 412
## veg_height -0.0691 0.2491 -0.5613 -0.0710 0.4332 1.0066 1153
## week 0.4111 0.2738 -0.1358 0.4147 0.9617 1.0015 800
## I(week^2) -0.2969 0.1537 -0.6219 -0.2917 0.0010 1.0125 875
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4263 0.5460 0.0495 0.2727 1.7168 1.0845 613
## shrub_cover 1.0250 1.3726 0.1631 0.6870 3.7851 1.0656 864
## veg_height 0.3161 0.3256 0.0561 0.2250 1.1576 1.0104 1583
## week 0.2783 0.3673 0.0371 0.1718 1.1554 1.0071 1210
## I(week^2) 0.1155 0.1387 0.0227 0.0760 0.4730 1.0381 705
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0404 0.8186 -1.4516 -0.0159
## (Intercept)-Lynx_rufus -0.4061 0.8364 -2.1356 -0.4063
## (Intercept)-Didelphis_virginiana -0.6714 0.7781 -2.1844 -0.6673
## (Intercept)-Sylvilagus_floridanus -0.2326 0.8449 -1.8035 -0.2707
## (Intercept)-Sciurus_carolinensis -0.7867 0.8191 -2.4183 -0.7542
## (Intercept)-Vulpes_vulpes -0.8435 0.9672 -2.8471 -0.8235
## (Intercept)-Sus_scrofa -1.0121 1.0182 -3.3450 -0.9311
## Avg_Cogongrass_Cover-Canis_latrans 0.5143 0.5959 -0.5733 0.4856
## Avg_Cogongrass_Cover-Lynx_rufus 0.5733 0.6458 -0.5085 0.5214
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3497 0.5962 -0.7372 0.3211
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3392 0.7005 -1.9201 -0.2566
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1417 0.5736 -1.0690 0.1506
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3430 0.6662 -0.9023 0.3185
## Avg_Cogongrass_Cover-Sus_scrofa -0.0972 0.7922 -1.8545 -0.0424
## total_shrub_cover-Canis_latrans 0.3607 0.8330 -1.1225 0.2679
## total_shrub_cover-Lynx_rufus -1.4609 1.0482 -3.9798 -1.3176
## total_shrub_cover-Didelphis_virginiana -1.0262 0.8171 -3.0352 -0.8945
## total_shrub_cover-Sylvilagus_floridanus -1.5131 1.1173 -4.4909 -1.2934
## total_shrub_cover-Sciurus_carolinensis -1.0300 0.9263 -3.2642 -0.8902
## total_shrub_cover-Vulpes_vulpes -0.9519 1.4084 -4.0575 -0.8834
## total_shrub_cover-Sus_scrofa -0.7104 1.1931 -3.3728 -0.6291
## avg_veg_height-Canis_latrans 0.1267 0.5509 -0.8897 0.1167
## avg_veg_height-Lynx_rufus 0.0561 0.6800 -1.2947 0.0475
## avg_veg_height-Didelphis_virginiana 0.0331 0.5920 -1.1484 0.0259
## avg_veg_height-Sylvilagus_floridanus 0.0688 0.6067 -1.0536 0.0678
## avg_veg_height-Sciurus_carolinensis 0.5271 0.5920 -0.4982 0.4882
## avg_veg_height-Vulpes_vulpes 0.0509 0.6430 -1.2259 0.0531
## avg_veg_height-Sus_scrofa 0.1988 0.6504 -1.0458 0.1705
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8558 1.0080 309
## (Intercept)-Lynx_rufus 1.2910 1.0473 223
## (Intercept)-Didelphis_virginiana 0.8868 1.0136 213
## (Intercept)-Sylvilagus_floridanus 1.5900 1.0301 216
## (Intercept)-Sciurus_carolinensis 0.8863 1.0218 313
## (Intercept)-Vulpes_vulpes 1.0482 1.0672 233
## (Intercept)-Sus_scrofa 0.7888 1.1165 214
## Avg_Cogongrass_Cover-Canis_latrans 1.7558 1.0305 608
## Avg_Cogongrass_Cover-Lynx_rufus 2.0455 1.0299 669
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.6094 1.0379 574
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8546 1.0105 661
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2324 1.0193 486
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7384 1.0363 681
## Avg_Cogongrass_Cover-Sus_scrofa 1.2651 1.0229 405
## total_shrub_cover-Canis_latrans 2.3287 1.0117 299
## total_shrub_cover-Lynx_rufus 0.2198 1.0032 185
## total_shrub_cover-Didelphis_virginiana 0.2197 1.0228 248
## total_shrub_cover-Sylvilagus_floridanus 0.0561 1.0219 119
## total_shrub_cover-Sciurus_carolinensis 0.4008 1.0152 243
## total_shrub_cover-Vulpes_vulpes 1.9409 1.0373 127
## total_shrub_cover-Sus_scrofa 1.4953 1.1386 132
## avg_veg_height-Canis_latrans 1.2828 1.0385 428
## avg_veg_height-Lynx_rufus 1.4067 1.0269 401
## avg_veg_height-Didelphis_virginiana 1.2051 1.0410 448
## avg_veg_height-Sylvilagus_floridanus 1.3098 1.0163 479
## avg_veg_height-Sciurus_carolinensis 1.7775 1.0053 557
## avg_veg_height-Vulpes_vulpes 1.3592 1.0438 409
## avg_veg_height-Sus_scrofa 1.5232 1.0507 395
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6704 0.2110 -3.1183 -2.6640 -2.2834 1.0258
## (Intercept)-Lynx_rufus -3.2946 0.3181 -3.9283 -3.2777 -2.6833 1.0669
## (Intercept)-Didelphis_virginiana -2.6675 0.3148 -3.2712 -2.6777 -2.0616 1.0022
## (Intercept)-Sylvilagus_floridanus -3.1461 0.2759 -3.7235 -3.1374 -2.6247 1.0081
## (Intercept)-Sciurus_carolinensis -2.7793 0.3328 -3.4308 -2.7792 -2.1346 1.0039
## (Intercept)-Vulpes_vulpes -3.6026 0.5646 -4.8475 -3.5334 -2.6584 1.1685
## (Intercept)-Sus_scrofa -3.3930 0.4991 -4.4172 -3.3595 -2.4838 1.1522
## shrub_cover-Canis_latrans -0.3176 0.2582 -0.8124 -0.3183 0.1907 1.0047
## shrub_cover-Lynx_rufus 0.1473 0.3791 -0.6683 0.1756 0.8229 1.0619
## shrub_cover-Didelphis_virginiana 1.3991 0.4378 0.5882 1.3823 2.2893 1.0167
## shrub_cover-Sylvilagus_floridanus 0.8382 0.4193 -0.0338 0.8594 1.6162 1.0132
## shrub_cover-Sciurus_carolinensis 1.3141 0.4383 0.4450 1.3131 2.1802 1.0118
## shrub_cover-Vulpes_vulpes 0.3984 0.8016 -1.2852 0.4521 1.8824 1.0248
## shrub_cover-Sus_scrofa 1.1810 0.8513 -0.6713 1.1890 2.7868 1.1691
## veg_height-Canis_latrans -0.6517 0.1995 -1.0671 -0.6427 -0.2763 1.0185
## veg_height-Lynx_rufus 0.0097 0.2606 -0.5159 0.0144 0.5065 1.0104
## veg_height-Didelphis_virginiana 0.4353 0.2831 -0.0835 0.4288 1.0260 1.0154
## veg_height-Sylvilagus_floridanus 0.0229 0.2620 -0.4729 0.0188 0.5435 1.0183
## veg_height-Sciurus_carolinensis 0.1247 0.2460 -0.3377 0.1175 0.6354 1.0116
## veg_height-Vulpes_vulpes -0.1728 0.3371 -0.8615 -0.1806 0.4707 1.0219
## veg_height-Sus_scrofa -0.2450 0.3415 -0.9475 -0.2449 0.4351 1.0154
## week-Canis_latrans 0.5424 0.2613 0.0327 0.5398 1.0593 1.0010
## week-Lynx_rufus 0.4271 0.3159 -0.1838 0.4263 1.0631 1.0009
## week-Didelphis_virginiana 0.1466 0.3524 -0.5988 0.1551 0.8215 1.0027
## week-Sylvilagus_floridanus 0.1483 0.3239 -0.5273 0.1557 0.7637 1.0032
## week-Sciurus_carolinensis 0.7137 0.3632 0.0359 0.6955 1.4925 1.0031
## week-Vulpes_vulpes 0.3272 0.4547 -0.6312 0.3252 1.1959 1.0062
## week-Sus_scrofa 0.6374 0.4183 -0.1161 0.6207 1.5122 1.0029
## I(week^2)-Canis_latrans -0.2259 0.1101 -0.4394 -0.2240 -0.0101 0.9999
## I(week^2)-Lynx_rufus -0.2508 0.1470 -0.5529 -0.2477 0.0252 1.0306
## I(week^2)-Didelphis_virginiana -0.4320 0.2385 -0.9922 -0.4022 -0.0520 1.0343
## I(week^2)-Sylvilagus_floridanus -0.1904 0.1577 -0.5238 -0.1863 0.1008 1.0041
## I(week^2)-Sciurus_carolinensis -0.2518 0.1473 -0.5575 -0.2475 0.0168 1.0080
## I(week^2)-Vulpes_vulpes -0.4938 0.3028 -1.2135 -0.4488 -0.0348 1.0380
## I(week^2)-Sus_scrofa -0.2518 0.1878 -0.6358 -0.2495 0.1032 1.0152
## ESS
## (Intercept)-Canis_latrans 723
## (Intercept)-Lynx_rufus 503
## (Intercept)-Didelphis_virginiana 378
## (Intercept)-Sylvilagus_floridanus 556
## (Intercept)-Sciurus_carolinensis 414
## (Intercept)-Vulpes_vulpes 156
## (Intercept)-Sus_scrofa 214
## shrub_cover-Canis_latrans 332
## shrub_cover-Lynx_rufus 375
## shrub_cover-Didelphis_virginiana 273
## shrub_cover-Sylvilagus_floridanus 257
## shrub_cover-Sciurus_carolinensis 268
## shrub_cover-Vulpes_vulpes 141
## shrub_cover-Sus_scrofa 192
## veg_height-Canis_latrans 619
## veg_height-Lynx_rufus 653
## veg_height-Didelphis_virginiana 648
## veg_height-Sylvilagus_floridanus 448
## veg_height-Sciurus_carolinensis 679
## veg_height-Vulpes_vulpes 512
## veg_height-Sus_scrofa 893
## week-Canis_latrans 1135
## week-Lynx_rufus 766
## week-Didelphis_virginiana 881
## week-Sylvilagus_floridanus 866
## week-Sciurus_carolinensis 883
## week-Vulpes_vulpes 676
## week-Sus_scrofa 820
## I(week^2)-Canis_latrans 1074
## I(week^2)-Lynx_rufus 807
## I(week^2)-Didelphis_virginiana 322
## I(week^2)-Sylvilagus_floridanus 565
## I(week^2)-Sciurus_carolinensis 1025
## I(week^2)-Vulpes_vulpes 247
## I(week^2)-Sus_scrofa 797
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5272
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1041 0.5924 -2.2499 -1.0998 0.0676 1.0196 506
## Tree_Density -0.6811 0.5952 -1.9395 -0.6525 0.4618 1.0596 800
## Avg_Canopy_Cover 1.1521 0.5754 0.0781 1.1124 2.4410 1.0095 937
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7865 2.8918 0.0959 1.0192 7.7073 1.2599 469
## Tree_Density 1.6672 3.1885 0.0616 0.7482 8.7139 1.0903 269
## Avg_Canopy_Cover 1.9651 2.6005 0.1427 1.1402 9.1312 1.0441 656
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9167 1.1106 0.0546 0.545 3.7801 1.1229 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8977 0.3031 -3.5180 -2.8964 -2.2917 1.0152 928
## shrub_cover 0.4040 0.3869 -0.3395 0.3980 1.1894 1.0083 1572
## veg_height -0.0115 0.2403 -0.4966 -0.0146 0.4638 1.0038 1700
## week 0.4189 0.2730 -0.1239 0.4225 0.9693 1.0081 865
## I(week^2) -0.2963 0.1582 -0.6318 -0.2888 -0.0041 1.0007 884
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4704 0.6208 0.0561 0.2874 2.0562 1.0544 346
## shrub_cover 0.8298 0.8785 0.1236 0.5550 3.2138 1.0163 968
## veg_height 0.3412 0.4808 0.0635 0.2350 1.1907 1.0440 2388
## week 0.2774 0.3201 0.0360 0.1795 1.1398 1.0061 1174
## I(week^2) 0.1126 0.1388 0.0220 0.0748 0.4285 1.0105 787
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1077 0.7425 -1.5723 -0.0981 1.3836
## (Intercept)-Lynx_rufus -0.2885 1.0581 -2.0025 -0.4121 2.0615
## (Intercept)-Didelphis_virginiana -1.5881 0.7108 -3.1168 -1.5486 -0.3153
## (Intercept)-Sylvilagus_floridanus -0.8671 0.7087 -2.2248 -0.8654 0.5437
## (Intercept)-Sciurus_carolinensis -1.6563 0.7356 -3.3243 -1.5985 -0.3754
## (Intercept)-Vulpes_vulpes -1.7136 0.9125 -3.5533 -1.6963 0.1069
## (Intercept)-Sus_scrofa -2.1174 0.8935 -4.1258 -2.0372 -0.5781
## Tree_Density-Canis_latrans -1.0167 0.6785 -2.5969 -0.9242 0.0905
## Tree_Density-Lynx_rufus 0.4685 0.9060 -1.0002 0.3419 2.6882
## Tree_Density-Didelphis_virginiana -1.0768 0.9233 -3.3078 -0.9391 0.3258
## Tree_Density-Sylvilagus_floridanus -1.2170 0.9745 -3.5085 -1.0624 0.2999
## Tree_Density-Sciurus_carolinensis -0.8824 0.8614 -3.0006 -0.7732 0.5534
## Tree_Density-Vulpes_vulpes -0.4945 1.1100 -2.5342 -0.5274 1.9092
## Tree_Density-Sus_scrofa -0.9994 1.0306 -3.6596 -0.8249 0.5650
## Avg_Canopy_Cover-Canis_latrans -0.1838 0.4935 -1.1739 -0.1650 0.7294
## Avg_Canopy_Cover-Lynx_rufus 0.6870 0.8337 -0.7139 0.6046 2.6122
## Avg_Canopy_Cover-Didelphis_virginiana 1.7505 0.8057 0.5164 1.6277 3.6866
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4894 1.2203 0.8135 2.2606 5.4780
## Avg_Canopy_Cover-Sciurus_carolinensis 1.7075 0.8174 0.4803 1.5916 3.5876
## Avg_Canopy_Cover-Vulpes_vulpes 1.1144 0.7696 -0.1245 1.0289 2.8408
## Avg_Canopy_Cover-Sus_scrofa 1.3768 0.7232 0.1568 1.3082 3.0254
## Rhat ESS
## (Intercept)-Canis_latrans 1.0536 371
## (Intercept)-Lynx_rufus 1.1753 204
## (Intercept)-Didelphis_virginiana 1.0275 631
## (Intercept)-Sylvilagus_floridanus 1.0291 646
## (Intercept)-Sciurus_carolinensis 1.0217 488
## (Intercept)-Vulpes_vulpes 1.0149 276
## (Intercept)-Sus_scrofa 1.0345 477
## Tree_Density-Canis_latrans 1.0356 971
## Tree_Density-Lynx_rufus 1.0185 428
## Tree_Density-Didelphis_virginiana 1.0557 599
## Tree_Density-Sylvilagus_floridanus 1.0605 364
## Tree_Density-Sciurus_carolinensis 1.1064 782
## Tree_Density-Vulpes_vulpes 1.0822 232
## Tree_Density-Sus_scrofa 1.0490 647
## Avg_Canopy_Cover-Canis_latrans 1.0126 962
## Avg_Canopy_Cover-Lynx_rufus 1.0817 329
## Avg_Canopy_Cover-Didelphis_virginiana 1.0047 656
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0384 417
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0565 368
## Avg_Canopy_Cover-Vulpes_vulpes 1.0362 767
## Avg_Canopy_Cover-Sus_scrofa 1.0093 846
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6221 0.1979 -3.0139 -2.6192 -2.2415 1.0299
## (Intercept)-Lynx_rufus -3.3791 0.3492 -4.0911 -3.3623 -2.7369 1.1001
## (Intercept)-Didelphis_virginiana -2.5300 0.3061 -3.1395 -2.5240 -1.9522 1.0069
## (Intercept)-Sylvilagus_floridanus -3.0101 0.2552 -3.5248 -3.0014 -2.5358 1.0164
## (Intercept)-Sciurus_carolinensis -2.6390 0.3199 -3.2927 -2.6386 -2.0226 1.0203
## (Intercept)-Vulpes_vulpes -3.5010 0.5762 -4.8596 -3.4378 -2.5671 1.0588
## (Intercept)-Sus_scrofa -3.1551 0.5036 -4.2226 -3.1231 -2.2317 1.0029
## shrub_cover-Canis_latrans -0.2940 0.2289 -0.7387 -0.2934 0.1565 1.0136
## shrub_cover-Lynx_rufus -0.2148 0.3612 -0.9643 -0.2068 0.4525 1.0189
## shrub_cover-Didelphis_virginiana 1.1006 0.3988 0.3843 1.0798 1.9241 1.0401
## shrub_cover-Sylvilagus_floridanus 0.4877 0.3969 -0.2858 0.4933 1.2756 1.0146
## shrub_cover-Sciurus_carolinensis 0.9971 0.4078 0.2061 0.9940 1.8200 1.0149
## shrub_cover-Vulpes_vulpes 0.0404 0.5996 -1.1928 0.0795 1.1769 1.0064
## shrub_cover-Sus_scrofa 0.8015 0.7829 -0.6441 0.7672 2.4947 1.0119
## veg_height-Canis_latrans -0.6286 0.1956 -1.0353 -0.6217 -0.2764 1.0290
## veg_height-Lynx_rufus 0.0528 0.2569 -0.4859 0.0535 0.5393 1.0201
## veg_height-Didelphis_virginiana 0.5362 0.2612 0.0428 0.5274 1.0696 1.0141
## veg_height-Sylvilagus_floridanus 0.1477 0.2427 -0.3287 0.1517 0.6212 1.0247
## veg_height-Sciurus_carolinensis 0.1418 0.2331 -0.3220 0.1429 0.6035 1.0099
## veg_height-Vulpes_vulpes -0.1566 0.3249 -0.8449 -0.1414 0.4571 1.0241
## veg_height-Sus_scrofa -0.1787 0.3423 -0.8763 -0.1712 0.4665 1.0053
## week-Canis_latrans 0.5569 0.2729 0.0518 0.5515 1.1095 1.0087
## week-Lynx_rufus 0.4154 0.3435 -0.2464 0.4219 1.0959 1.0038
## week-Didelphis_virginiana 0.1423 0.3632 -0.6264 0.1553 0.7962 1.0073
## week-Sylvilagus_floridanus 0.1435 0.3410 -0.5520 0.1595 0.8000 1.0080
## week-Sciurus_carolinensis 0.7268 0.3720 0.0728 0.7060 1.5410 1.0023
## week-Vulpes_vulpes 0.3257 0.4428 -0.5963 0.3346 1.1993 1.0011
## week-Sus_scrofa 0.6361 0.4313 -0.1496 0.6086 1.5623 1.0031
## I(week^2)-Canis_latrans -0.2275 0.1117 -0.4457 -0.2289 -0.0139 1.0031
## I(week^2)-Lynx_rufus -0.2594 0.1600 -0.5922 -0.2548 0.0322 1.0119
## I(week^2)-Didelphis_virginiana -0.4375 0.2412 -1.0003 -0.4060 -0.0549 1.0009
## I(week^2)-Sylvilagus_floridanus -0.1918 0.1574 -0.5075 -0.1856 0.1018 1.0013
## I(week^2)-Sciurus_carolinensis -0.2547 0.1530 -0.5697 -0.2510 0.0323 1.0053
## I(week^2)-Vulpes_vulpes -0.4724 0.3053 -1.2088 -0.4233 -0.0159 1.0354
## I(week^2)-Sus_scrofa -0.2391 0.1825 -0.6340 -0.2279 0.0963 1.0094
## ESS
## (Intercept)-Canis_latrans 883
## (Intercept)-Lynx_rufus 264
## (Intercept)-Didelphis_virginiana 634
## (Intercept)-Sylvilagus_floridanus 961
## (Intercept)-Sciurus_carolinensis 611
## (Intercept)-Vulpes_vulpes 206
## (Intercept)-Sus_scrofa 465
## shrub_cover-Canis_latrans 807
## shrub_cover-Lynx_rufus 465
## shrub_cover-Didelphis_virginiana 605
## shrub_cover-Sylvilagus_floridanus 528
## shrub_cover-Sciurus_carolinensis 613
## shrub_cover-Vulpes_vulpes 656
## shrub_cover-Sus_scrofa 566
## veg_height-Canis_latrans 685
## veg_height-Lynx_rufus 638
## veg_height-Didelphis_virginiana 906
## veg_height-Sylvilagus_floridanus 890
## veg_height-Sciurus_carolinensis 474
## veg_height-Vulpes_vulpes 634
## veg_height-Sus_scrofa 1283
## week-Canis_latrans 1077
## week-Lynx_rufus 839
## week-Didelphis_virginiana 811
## week-Sylvilagus_floridanus 799
## week-Sciurus_carolinensis 780
## week-Vulpes_vulpes 838
## week-Sus_scrofa 1086
## I(week^2)-Canis_latrans 1168
## I(week^2)-Lynx_rufus 648
## I(week^2)-Didelphis_virginiana 328
## I(week^2)-Sylvilagus_floridanus 688
## I(week^2)-Sciurus_carolinensis 1050
## I(week^2)-Vulpes_vulpes 276
## I(week^2)-Sus_scrofa 1202
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.581
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8319 0.6846 -2.1676 -0.8618 0.5334 1.0293 238
## Cogon_Patch_Size -0.4434 0.7381 -2.1629 -0.3853 0.9663 1.0255 697
## Avg_Cogongrass_Cover 0.4581 0.4449 -0.3911 0.4456 1.3737 1.0057 457
## total_shrub_cover -0.6489 0.6447 -2.1794 -0.5973 0.4655 1.0437 237
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4972 3.5601 0.0584 0.7294 7.1340 1.1023 483
## Cogon_Patch_Size 3.1761 5.5070 0.1157 1.5678 15.8440 1.0210 356
## Avg_Cogongrass_Cover 0.4841 0.7674 0.0398 0.2523 2.5368 1.0137 913
## total_shrub_cover 1.6334 3.1144 0.0572 0.6123 9.8944 1.0660 124
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.7413 4.0938 0.2936 2.6099 15.5883 1.1264 67
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9522 0.3017 -3.5294 -2.9537 -2.3578 1.0386 557
## shrub_cover 0.6161 0.4135 -0.2017 0.6119 1.4280 1.0192 550
## veg_height -0.0583 0.2401 -0.5223 -0.0578 0.4163 1.0034 1318
## week 0.4167 0.2727 -0.1346 0.4155 0.9625 1.0031 816
## I(week^2) -0.3001 0.1590 -0.6477 -0.2911 -0.0052 1.0183 645
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4286 0.5601 0.0498 0.2749 1.6766 1.0695 623
## shrub_cover 0.8794 0.8877 0.1397 0.6162 3.2500 1.0253 684
## veg_height 0.3135 0.3993 0.0589 0.2152 1.1577 1.0263 1920
## week 0.2847 0.3199 0.0399 0.1861 1.1585 1.0018 1289
## I(week^2) 0.1225 0.2116 0.0228 0.0735 0.5036 1.1773 309
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0162 0.9833 -1.6742 -0.0522
## (Intercept)-Lynx_rufus -0.5922 0.9760 -2.3857 -0.6427
## (Intercept)-Didelphis_virginiana -1.0204 0.9110 -2.8610 -1.0210
## (Intercept)-Sylvilagus_floridanus -0.5622 0.9662 -2.3540 -0.6159
## (Intercept)-Sciurus_carolinensis -1.1781 0.9393 -3.0142 -1.1817
## (Intercept)-Vulpes_vulpes -1.3560 1.1036 -3.6294 -1.3029
## (Intercept)-Sus_scrofa -1.4673 1.1022 -3.9494 -1.3797
## Cogon_Patch_Size-Canis_latrans 0.9284 1.0201 -0.4793 0.7467
## Cogon_Patch_Size-Lynx_rufus -0.3708 1.1422 -2.2846 -0.4589
## Cogon_Patch_Size-Didelphis_virginiana 0.7547 0.6486 -0.3590 0.7044
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7243 1.5694 -5.7265 -1.4150
## Cogon_Patch_Size-Sciurus_carolinensis -1.2572 1.1109 -4.0690 -1.0448
## Cogon_Patch_Size-Vulpes_vulpes -1.0875 1.3244 -4.4207 -0.8788
## Cogon_Patch_Size-Sus_scrofa -0.8203 1.2325 -4.0116 -0.6056
## Avg_Cogongrass_Cover-Canis_latrans 0.5185 0.5175 -0.3819 0.4762
## Avg_Cogongrass_Cover-Lynx_rufus 0.7696 0.6500 -0.3053 0.6998
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3367 0.5378 -0.7975 0.3409
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1599 0.6789 -1.2327 0.1666
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6464 0.5471 -0.3747 0.6187
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.6025 0.5991 -0.4537 0.5628
## Avg_Cogongrass_Cover-Sus_scrofa 0.2766 0.7087 -1.2106 0.2882
## total_shrub_cover-Canis_latrans 0.2823 0.7540 -0.8675 0.1967
## total_shrub_cover-Lynx_rufus -1.1936 1.0994 -3.9049 -0.9960
## total_shrub_cover-Didelphis_virginiana -0.9347 0.8779 -3.1387 -0.7950
## total_shrub_cover-Sylvilagus_floridanus -1.2449 1.2045 -4.3902 -0.9788
## total_shrub_cover-Sciurus_carolinensis -0.6702 0.8799 -2.7473 -0.5504
## total_shrub_cover-Vulpes_vulpes -0.7777 1.3780 -4.2471 -0.6342
## total_shrub_cover-Sus_scrofa -0.4646 0.9980 -2.7596 -0.3976
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2475 1.0396 260
## (Intercept)-Lynx_rufus 1.4907 1.0252 192
## (Intercept)-Didelphis_virginiana 0.7464 1.0407 314
## (Intercept)-Sylvilagus_floridanus 1.6266 1.0139 301
## (Intercept)-Sciurus_carolinensis 0.7601 1.0855 207
## (Intercept)-Vulpes_vulpes 0.7764 1.0666 264
## (Intercept)-Sus_scrofa 0.5600 1.0226 246
## Cogon_Patch_Size-Canis_latrans 3.4080 1.0036 487
## Cogon_Patch_Size-Lynx_rufus 2.3053 1.0802 340
## Cogon_Patch_Size-Didelphis_virginiana 2.2437 1.0054 398
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2961 1.0203 280
## Cogon_Patch_Size-Sciurus_carolinensis 0.3726 1.0422 403
## Cogon_Patch_Size-Vulpes_vulpes 0.9473 1.0236 338
## Cogon_Patch_Size-Sus_scrofa 1.0394 1.0306 441
## Avg_Cogongrass_Cover-Canis_latrans 1.7238 1.0046 710
## Avg_Cogongrass_Cover-Lynx_rufus 2.2429 1.0202 533
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3941 1.0252 511
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5076 1.0050 479
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8060 1.0083 707
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.9602 1.0315 626
## Avg_Cogongrass_Cover-Sus_scrofa 1.6167 1.0090 596
## total_shrub_cover-Canis_latrans 2.1037 1.0481 318
## total_shrub_cover-Lynx_rufus 0.5128 1.0551 221
## total_shrub_cover-Didelphis_virginiana 0.3613 1.1128 167
## total_shrub_cover-Sylvilagus_floridanus 0.4075 1.0672 110
## total_shrub_cover-Sciurus_carolinensis 0.7036 1.0487 264
## total_shrub_cover-Vulpes_vulpes 1.3453 1.0884 89
## total_shrub_cover-Sus_scrofa 1.3888 1.0196 228
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6342 0.2016 -3.0407 -2.6315 -2.2482 1.0003
## (Intercept)-Lynx_rufus -3.2509 0.3213 -3.9270 -3.2354 -2.6548 1.0411
## (Intercept)-Didelphis_virginiana -2.5721 0.3234 -3.2114 -2.5679 -1.9464 1.0465
## (Intercept)-Sylvilagus_floridanus -3.1344 0.2873 -3.7755 -3.1158 -2.6184 1.0261
## (Intercept)-Sciurus_carolinensis -2.7214 0.3474 -3.4238 -2.7217 -2.0535 1.0619
## (Intercept)-Vulpes_vulpes -3.5276 0.5864 -4.8882 -3.4559 -2.5790 1.1163
## (Intercept)-Sus_scrofa -3.3040 0.4756 -4.2662 -3.2856 -2.4020 1.0191
## shrub_cover-Canis_latrans -0.3085 0.2424 -0.7806 -0.3024 0.1682 1.0317
## shrub_cover-Lynx_rufus 0.1366 0.3770 -0.6454 0.1568 0.8057 1.0005
## shrub_cover-Didelphis_virginiana 1.2675 0.4408 0.4695 1.2431 2.1814 1.0730
## shrub_cover-Sylvilagus_floridanus 0.8057 0.4267 -0.0782 0.8213 1.6153 1.0252
## shrub_cover-Sciurus_carolinensis 1.1806 0.4468 0.2888 1.1910 2.0517 1.0672
## shrub_cover-Vulpes_vulpes 0.3674 0.7212 -1.0838 0.3925 1.7825 1.0198
## shrub_cover-Sus_scrofa 1.0722 0.7913 -0.4991 1.0621 2.6260 1.0399
## veg_height-Canis_latrans -0.6279 0.1926 -1.0061 -0.6226 -0.2662 1.0037
## veg_height-Lynx_rufus 0.0154 0.2406 -0.4604 0.0180 0.4749 1.0026
## veg_height-Didelphis_virginiana 0.4360 0.2748 -0.0843 0.4323 0.9971 1.0151
## veg_height-Sylvilagus_floridanus 0.0169 0.2499 -0.4729 0.0184 0.5072 1.0085
## veg_height-Sciurus_carolinensis 0.1424 0.2454 -0.3260 0.1396 0.6493 1.0309
## veg_height-Vulpes_vulpes -0.1389 0.3361 -0.8247 -0.1331 0.4793 1.0048
## veg_height-Sus_scrofa -0.2233 0.3389 -0.9255 -0.2116 0.4264 1.0365
## week-Canis_latrans 0.5614 0.2673 0.0562 0.5569 1.1029 1.0062
## week-Lynx_rufus 0.4167 0.3370 -0.1951 0.4035 1.1106 1.0136
## week-Didelphis_virginiana 0.1201 0.3634 -0.6570 0.1499 0.7817 1.0072
## week-Sylvilagus_floridanus 0.1349 0.3430 -0.5389 0.1367 0.8022 1.0026
## week-Sciurus_carolinensis 0.7300 0.3729 0.0852 0.7006 1.5352 1.0049
## week-Vulpes_vulpes 0.3206 0.4497 -0.5909 0.3278 1.2053 1.0007
## week-Sus_scrofa 0.6521 0.4254 -0.1446 0.6316 1.5732 1.0012
## I(week^2)-Canis_latrans -0.2281 0.1105 -0.4521 -0.2254 -0.0191 1.0031
## I(week^2)-Lynx_rufus -0.2599 0.1555 -0.5863 -0.2548 0.0282 1.0111
## I(week^2)-Didelphis_virginiana -0.4298 0.2292 -0.9715 -0.4060 -0.0522 1.0288
## I(week^2)-Sylvilagus_floridanus -0.1981 0.1627 -0.5394 -0.1917 0.1072 1.0027
## I(week^2)-Sciurus_carolinensis -0.2592 0.1519 -0.5710 -0.2574 0.0207 1.0025
## I(week^2)-Vulpes_vulpes -0.4913 0.3704 -1.3623 -0.4279 -0.0082 1.0497
## I(week^2)-Sus_scrofa -0.2478 0.1816 -0.6231 -0.2416 0.0867 1.0015
## ESS
## (Intercept)-Canis_latrans 731
## (Intercept)-Lynx_rufus 394
## (Intercept)-Didelphis_virginiana 408
## (Intercept)-Sylvilagus_floridanus 445
## (Intercept)-Sciurus_carolinensis 262
## (Intercept)-Vulpes_vulpes 193
## (Intercept)-Sus_scrofa 394
## shrub_cover-Canis_latrans 555
## shrub_cover-Lynx_rufus 460
## shrub_cover-Didelphis_virginiana 306
## shrub_cover-Sylvilagus_floridanus 323
## shrub_cover-Sciurus_carolinensis 280
## shrub_cover-Vulpes_vulpes 262
## shrub_cover-Sus_scrofa 302
## veg_height-Canis_latrans 736
## veg_height-Lynx_rufus 698
## veg_height-Didelphis_virginiana 812
## veg_height-Sylvilagus_floridanus 610
## veg_height-Sciurus_carolinensis 613
## veg_height-Vulpes_vulpes 505
## veg_height-Sus_scrofa 922
## week-Canis_latrans 1057
## week-Lynx_rufus 803
## week-Didelphis_virginiana 710
## week-Sylvilagus_floridanus 882
## week-Sciurus_carolinensis 769
## week-Vulpes_vulpes 704
## week-Sus_scrofa 897
## I(week^2)-Canis_latrans 965
## I(week^2)-Lynx_rufus 696
## I(week^2)-Didelphis_virginiana 352
## I(week^2)-Sylvilagus_floridanus 602
## I(week^2)-Sciurus_carolinensis 1019
## I(week^2)-Vulpes_vulpes 156
## I(week^2)-Sus_scrofa 953
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5118
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0099 0.4949 -2.0106 -0.9992 -0.0611 1.0428 432
## Veg_shannon_index 0.3658 0.3557 -0.3507 0.3589 1.0806 1.0052 1029
## Avg_Cogongrass_Cover 0.3591 0.3402 -0.3410 0.3671 1.0079 1.0430 898
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9892 1.5572 0.0704 0.5773 4.3415 1.0602 528
## Veg_shannon_index 0.5392 0.7613 0.0428 0.3067 2.5540 1.0151 1002
## Avg_Cogongrass_Cover 0.4609 0.7662 0.0408 0.2520 2.2400 1.0181 1282
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.36 1.4229 0.1161 0.9746 4.9334 1.1767 259
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8521 0.2881 -3.4327 -2.8545 -2.2712 1.0295 927
## shrub_cover 0.3420 0.3672 -0.3850 0.3374 1.0934 1.0151 1117
## veg_height -0.0529 0.2333 -0.5235 -0.0517 0.4115 1.0152 1343
## week 0.4132 0.2718 -0.1085 0.4075 0.9784 1.0051 813
## I(week^2) -0.3028 0.1648 -0.6491 -0.2971 0.0013 1.0163 784
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4100 0.5188 0.0517 0.2578 1.7920 1.0281 632
## shrub_cover 0.7057 0.7254 0.1042 0.4874 2.5698 1.0780 1090
## veg_height 0.2933 0.2592 0.0587 0.2179 0.9920 1.0026 1820
## week 0.2794 0.3328 0.0419 0.1794 1.2173 1.0048 1278
## I(week^2) 0.1142 0.1452 0.0211 0.0756 0.4352 1.0499 744
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2529 0.7186 -1.6670 -0.2874
## (Intercept)-Lynx_rufus -0.6668 0.7339 -2.0385 -0.6986
## (Intercept)-Didelphis_virginiana -1.2862 0.6149 -2.5822 -1.2506
## (Intercept)-Sylvilagus_floridanus -0.7540 0.6543 -1.9700 -0.7789
## (Intercept)-Sciurus_carolinensis -1.2863 0.6449 -2.6830 -1.2494
## (Intercept)-Vulpes_vulpes -1.4511 0.7503 -3.1084 -1.4001
## (Intercept)-Sus_scrofa -1.7110 0.7974 -3.4480 -1.6483
## Veg_shannon_index-Canis_latrans 0.7299 0.4508 -0.0775 0.6987
## Veg_shannon_index-Lynx_rufus 0.1700 0.5929 -1.1320 0.1951
## Veg_shannon_index-Didelphis_virginiana 0.5680 0.4463 -0.2550 0.5387
## Veg_shannon_index-Sylvilagus_floridanus 0.4637 0.4842 -0.4305 0.4531
## Veg_shannon_index-Sciurus_carolinensis -0.1037 0.4713 -1.1041 -0.0778
## Veg_shannon_index-Vulpes_vulpes 0.0030 0.5299 -1.1359 0.0245
## Veg_shannon_index-Sus_scrofa 0.7767 0.6125 -0.2286 0.7070
## Avg_Cogongrass_Cover-Canis_latrans 0.6571 0.4481 -0.1180 0.6249
## Avg_Cogongrass_Cover-Lynx_rufus 0.6408 0.4696 -0.1808 0.6057
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4952 0.4213 -0.3123 0.4917
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1143 0.4951 -1.1745 -0.0703
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4414 0.3966 -0.3328 0.4445
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4115 0.4878 -0.5460 0.3999
## Avg_Cogongrass_Cover-Sus_scrofa 0.0587 0.6204 -1.4131 0.1418
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2183 1.0541 481
## (Intercept)-Lynx_rufus 0.9271 1.0425 305
## (Intercept)-Didelphis_virginiana -0.1379 1.0120 950
## (Intercept)-Sylvilagus_floridanus 0.5919 1.0722 573
## (Intercept)-Sciurus_carolinensis -0.0836 1.0438 759
## (Intercept)-Vulpes_vulpes -0.0570 1.0033 492
## (Intercept)-Sus_scrofa -0.3325 1.0228 568
## Veg_shannon_index-Canis_latrans 1.7051 1.0004 1220
## Veg_shannon_index-Lynx_rufus 1.2945 1.0214 887
## Veg_shannon_index-Didelphis_virginiana 1.5322 1.0050 1244
## Veg_shannon_index-Sylvilagus_floridanus 1.5107 1.0042 1043
## Veg_shannon_index-Sciurus_carolinensis 0.7260 1.0062 1016
## Veg_shannon_index-Vulpes_vulpes 0.9615 1.0085 1006
## Veg_shannon_index-Sus_scrofa 2.1723 1.0037 857
## Avg_Cogongrass_Cover-Canis_latrans 1.6305 1.0067 1492
## Avg_Cogongrass_Cover-Lynx_rufus 1.6816 1.0243 1322
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3496 1.0128 1265
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7559 1.0089 708
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2486 1.0279 1718
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4384 1.0158 1077
## Avg_Cogongrass_Cover-Sus_scrofa 1.0887 1.0275 810
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6085 0.1935 -2.9743 -2.6062 -2.2367 1.0040
## (Intercept)-Lynx_rufus -3.2803 0.3242 -3.9654 -3.2698 -2.6934 1.0533
## (Intercept)-Didelphis_virginiana -2.4818 0.3013 -3.0815 -2.4820 -1.9081 1.0031
## (Intercept)-Sylvilagus_floridanus -3.0118 0.2907 -3.6063 -3.0013 -2.4877 1.0118
## (Intercept)-Sciurus_carolinensis -2.5606 0.3111 -3.1627 -2.5642 -1.9546 1.0168
## (Intercept)-Vulpes_vulpes -3.3634 0.5195 -4.4705 -3.3158 -2.4827 1.0440
## (Intercept)-Sus_scrofa -3.0826 0.4685 -4.0357 -3.0655 -2.1874 1.0215
## shrub_cover-Canis_latrans -0.2859 0.2263 -0.7301 -0.2881 0.1649 1.0031
## shrub_cover-Lynx_rufus -0.1767 0.3581 -0.9311 -0.1668 0.4913 1.0142
## shrub_cover-Didelphis_virginiana 1.0366 0.3980 0.2805 1.0250 1.8553 1.0066
## shrub_cover-Sylvilagus_floridanus 0.3080 0.4142 -0.4479 0.3018 1.1796 1.0065
## shrub_cover-Sciurus_carolinensis 0.8947 0.4264 0.0637 0.8972 1.7581 1.0414
## shrub_cover-Vulpes_vulpes 0.0219 0.6044 -1.1415 0.0124 1.2394 1.0193
## shrub_cover-Sus_scrofa 0.6134 0.7599 -0.8868 0.6085 2.1385 1.0169
## veg_height-Canis_latrans -0.6371 0.1879 -1.0340 -0.6301 -0.2868 1.0160
## veg_height-Lynx_rufus -0.0316 0.2571 -0.5664 -0.0253 0.4552 1.0161
## veg_height-Didelphis_virginiana 0.4515 0.2716 -0.0376 0.4391 1.0133 1.0062
## veg_height-Sylvilagus_floridanus 0.1264 0.2530 -0.3803 0.1282 0.6122 1.0001
## veg_height-Sciurus_carolinensis 0.0811 0.2224 -0.3468 0.0745 0.5302 1.0105
## veg_height-Vulpes_vulpes -0.1603 0.3113 -0.8258 -0.1468 0.4066 1.0069
## veg_height-Sus_scrofa -0.1671 0.3510 -0.8921 -0.1605 0.5161 1.0052
## week-Canis_latrans 0.5569 0.2669 0.0643 0.5495 1.0952 1.0099
## week-Lynx_rufus 0.4143 0.3190 -0.2041 0.4102 1.0564 1.0060
## week-Didelphis_virginiana 0.1159 0.3575 -0.6304 0.1343 0.7851 1.0024
## week-Sylvilagus_floridanus 0.1521 0.3252 -0.5019 0.1573 0.7853 1.0069
## week-Sciurus_carolinensis 0.7164 0.3792 0.0413 0.6908 1.5209 1.0107
## week-Vulpes_vulpes 0.3267 0.4501 -0.5916 0.3261 1.2337 1.0028
## week-Sus_scrofa 0.6279 0.4310 -0.1533 0.5997 1.5656 1.0057
## I(week^2)-Canis_latrans -0.2311 0.1079 -0.4510 -0.2290 -0.0280 1.0163
## I(week^2)-Lynx_rufus -0.2619 0.1570 -0.5966 -0.2556 0.0280 1.0175
## I(week^2)-Didelphis_virginiana -0.4268 0.2345 -0.9632 -0.3971 -0.0509 1.0265
## I(week^2)-Sylvilagus_floridanus -0.2078 0.1661 -0.5619 -0.1978 0.0970 1.0031
## I(week^2)-Sciurus_carolinensis -0.2489 0.1523 -0.5727 -0.2373 0.0264 1.0028
## I(week^2)-Vulpes_vulpes -0.4987 0.3243 -1.2708 -0.4399 -0.0070 1.0394
## I(week^2)-Sus_scrofa -0.2413 0.1837 -0.6298 -0.2362 0.1008 1.0037
## ESS
## (Intercept)-Canis_latrans 957
## (Intercept)-Lynx_rufus 435
## (Intercept)-Didelphis_virginiana 823
## (Intercept)-Sylvilagus_floridanus 616
## (Intercept)-Sciurus_carolinensis 739
## (Intercept)-Vulpes_vulpes 298
## (Intercept)-Sus_scrofa 559
## shrub_cover-Canis_latrans 788
## shrub_cover-Lynx_rufus 460
## shrub_cover-Didelphis_virginiana 635
## shrub_cover-Sylvilagus_floridanus 498
## shrub_cover-Sciurus_carolinensis 694
## shrub_cover-Vulpes_vulpes 562
## shrub_cover-Sus_scrofa 686
## veg_height-Canis_latrans 784
## veg_height-Lynx_rufus 676
## veg_height-Didelphis_virginiana 863
## veg_height-Sylvilagus_floridanus 805
## veg_height-Sciurus_carolinensis 948
## veg_height-Vulpes_vulpes 779
## veg_height-Sus_scrofa 1206
## week-Canis_latrans 824
## week-Lynx_rufus 874
## week-Didelphis_virginiana 905
## week-Sylvilagus_floridanus 942
## week-Sciurus_carolinensis 930
## week-Vulpes_vulpes 775
## week-Sus_scrofa 992
## I(week^2)-Canis_latrans 956
## I(week^2)-Lynx_rufus 678
## I(week^2)-Didelphis_virginiana 429
## I(week^2)-Sylvilagus_floridanus 706
## I(week^2)-Sciurus_carolinensis 932
## I(week^2)-Vulpes_vulpes 330
## I(week^2)-Sus_scrofa 1133
#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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.512
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9362 0.4992 -1.9825 -0.9361 0.0744 1.0083 364
## Avg_Cogongrass_Cover 0.2919 0.3331 -0.3656 0.2890 0.9944 1.0086 1144
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8406 1.2338 0.0566 0.4736 3.5719 1.0441 887
## Avg_Cogongrass_Cover 0.4068 0.6490 0.0384 0.2285 1.7033 1.0017 1354
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4516 1.3563 0.1009 1.084 4.9168 1.0155 247
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8591 0.3011 -3.4588 -2.8536 -2.2870 1.0114 1024
## shrub_cover 0.3511 0.3601 -0.3433 0.3471 1.0837 1.0102 1324
## veg_height -0.0547 0.2286 -0.5114 -0.0573 0.3897 1.0010 1600
## week 0.4126 0.2693 -0.1207 0.4081 0.9278 1.0021 895
## I(week^2) -0.2916 0.1509 -0.6076 -0.2836 -0.0175 1.0007 966
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4613 0.6440 0.0566 0.2913 1.8456 1.0660 627
## shrub_cover 0.6864 0.6808 0.0943 0.4851 2.5976 1.0067 1075
## veg_height 0.2987 0.3070 0.0583 0.2184 0.9896 1.0498 841
## week 0.2754 0.2989 0.0374 0.1825 1.1277 1.0018 1630
## I(week^2) 0.1096 0.1289 0.0217 0.0732 0.4399 1.0204 629
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2269 0.6893 -1.5872 -0.2386
## (Intercept)-Lynx_rufus -0.6479 0.7299 -2.0264 -0.6800
## (Intercept)-Didelphis_virginiana -1.1780 0.5958 -2.4489 -1.1623
## (Intercept)-Sylvilagus_floridanus -0.7433 0.6300 -1.9832 -0.7386
## (Intercept)-Sciurus_carolinensis -1.2580 0.6143 -2.5851 -1.2293
## (Intercept)-Vulpes_vulpes -1.3476 0.7419 -2.9189 -1.2975
## (Intercept)-Sus_scrofa -1.4334 0.6860 -2.8835 -1.3881
## Avg_Cogongrass_Cover-Canis_latrans 0.4864 0.4063 -0.2474 0.4615
## Avg_Cogongrass_Cover-Lynx_rufus 0.5402 0.4498 -0.2574 0.5059
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3888 0.3896 -0.3342 0.3720
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1784 0.4947 -1.3207 -0.1359
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4194 0.3809 -0.2980 0.4079
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3967 0.4769 -0.4920 0.3737
## Avg_Cogongrass_Cover-Sus_scrofa 0.0118 0.5814 -1.2801 0.0582
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1629 1.0026 369
## (Intercept)-Lynx_rufus 0.8780 1.0003 341
## (Intercept)-Didelphis_virginiana -0.0081 1.0185 676
## (Intercept)-Sylvilagus_floridanus 0.5375 1.0074 514
## (Intercept)-Sciurus_carolinensis -0.1603 1.0313 665
## (Intercept)-Vulpes_vulpes -0.0124 1.0060 523
## (Intercept)-Sus_scrofa -0.2022 1.0042 757
## Avg_Cogongrass_Cover-Canis_latrans 1.3593 1.0094 1323
## Avg_Cogongrass_Cover-Lynx_rufus 1.5315 1.0125 1108
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2091 1.0029 1470
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6744 1.0028 1097
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2229 1.0127 1597
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3922 1.0132 1193
## Avg_Cogongrass_Cover-Sus_scrofa 1.0551 1.0012 925
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6031 0.1995 -3.0093 -2.5999 -2.2251 1.0099
## (Intercept)-Lynx_rufus -3.3047 0.3377 -4.0100 -3.2861 -2.7102 1.0220
## (Intercept)-Didelphis_virginiana -2.4355 0.2954 -3.0065 -2.4309 -1.8648 1.0004
## (Intercept)-Sylvilagus_floridanus -3.0080 0.2761 -3.5698 -3.0079 -2.4821 1.0040
## (Intercept)-Sciurus_carolinensis -2.5452 0.3048 -3.1484 -2.5435 -1.9737 1.0107
## (Intercept)-Vulpes_vulpes -3.4547 0.5828 -4.7587 -3.3928 -2.4773 1.0096
## (Intercept)-Sus_scrofa -3.1103 0.4712 -4.0462 -3.0984 -2.2402 1.0169
## shrub_cover-Canis_latrans -0.2697 0.2251 -0.7329 -0.2613 0.1523 1.0242
## shrub_cover-Lynx_rufus -0.1465 0.3660 -0.8791 -0.1392 0.5777 1.0223
## shrub_cover-Didelphis_virginiana 1.0014 0.3933 0.2935 0.9761 1.8203 1.0030
## shrub_cover-Sylvilagus_floridanus 0.3355 0.4071 -0.4085 0.3272 1.1622 1.0094
## shrub_cover-Sciurus_carolinensis 0.8814 0.4268 0.0696 0.8766 1.7645 1.0037
## shrub_cover-Vulpes_vulpes 0.0755 0.5942 -1.1415 0.0861 1.2028 1.0202
## shrub_cover-Sus_scrofa 0.6531 0.7229 -0.7345 0.6209 2.1212 1.0260
## veg_height-Canis_latrans -0.6151 0.1846 -0.9868 -0.6105 -0.2571 1.0232
## veg_height-Lynx_rufus -0.0222 0.2454 -0.5226 -0.0140 0.4512 1.0000
## veg_height-Didelphis_virginiana 0.4457 0.2518 -0.0348 0.4359 0.9440 0.9997
## veg_height-Sylvilagus_floridanus 0.1187 0.2598 -0.3926 0.1140 0.6302 1.0013
## veg_height-Sciurus_carolinensis 0.0753 0.2210 -0.3235 0.0693 0.5183 1.0108
## veg_height-Vulpes_vulpes -0.1834 0.3192 -0.8651 -0.1701 0.3884 1.0033
## veg_height-Sus_scrofa -0.1916 0.3487 -0.8736 -0.1918 0.4806 1.0020
## week-Canis_latrans 0.5630 0.2606 0.0733 0.5563 1.0740 1.0012
## week-Lynx_rufus 0.4287 0.3313 -0.2386 0.4227 1.0795 1.0000
## week-Didelphis_virginiana 0.1329 0.3650 -0.6514 0.1503 0.8051 1.0023
## week-Sylvilagus_floridanus 0.1474 0.3333 -0.5037 0.1501 0.7785 1.0001
## week-Sciurus_carolinensis 0.7172 0.3653 0.0598 0.6958 1.4953 0.9997
## week-Vulpes_vulpes 0.3011 0.4352 -0.5936 0.3213 1.1161 1.0000
## week-Sus_scrofa 0.6377 0.4260 -0.1611 0.6220 1.5270 1.0058
## I(week^2)-Canis_latrans -0.2301 0.1082 -0.4432 -0.2298 -0.0273 1.0005
## I(week^2)-Lynx_rufus -0.2639 0.1532 -0.5903 -0.2590 0.0110 1.0014
## I(week^2)-Didelphis_virginiana -0.4117 0.2188 -0.9099 -0.3914 -0.0426 1.0018
## I(week^2)-Sylvilagus_floridanus -0.1971 0.1593 -0.5140 -0.1952 0.1158 1.0085
## I(week^2)-Sciurus_carolinensis -0.2466 0.1488 -0.5540 -0.2403 0.0354 1.0036
## I(week^2)-Vulpes_vulpes -0.4560 0.3027 -1.2225 -0.4129 0.0083 1.0044
## I(week^2)-Sus_scrofa -0.2366 0.1828 -0.6149 -0.2331 0.1040 1.0060
## ESS
## (Intercept)-Canis_latrans 1047
## (Intercept)-Lynx_rufus 306
## (Intercept)-Didelphis_virginiana 952
## (Intercept)-Sylvilagus_floridanus 754
## (Intercept)-Sciurus_carolinensis 957
## (Intercept)-Vulpes_vulpes 281
## (Intercept)-Sus_scrofa 606
## shrub_cover-Canis_latrans 754
## shrub_cover-Lynx_rufus 384
## shrub_cover-Didelphis_virginiana 677
## shrub_cover-Sylvilagus_floridanus 496
## shrub_cover-Sciurus_carolinensis 710
## shrub_cover-Vulpes_vulpes 702
## shrub_cover-Sus_scrofa 732
## veg_height-Canis_latrans 694
## veg_height-Lynx_rufus 1074
## veg_height-Didelphis_virginiana 895
## veg_height-Sylvilagus_floridanus 777
## veg_height-Sciurus_carolinensis 991
## veg_height-Vulpes_vulpes 560
## veg_height-Sus_scrofa 1025
## week-Canis_latrans 1133
## week-Lynx_rufus 940
## week-Didelphis_virginiana 885
## week-Sylvilagus_floridanus 958
## week-Sciurus_carolinensis 1010
## week-Vulpes_vulpes 847
## week-Sus_scrofa 1060
## I(week^2)-Canis_latrans 1350
## I(week^2)-Lynx_rufus 703
## I(week^2)-Didelphis_virginiana 506
## I(week^2)-Sylvilagus_floridanus 838
## I(week^2)-Sciurus_carolinensis 1210
## I(week^2)-Vulpes_vulpes 346
## I(week^2)-Sus_scrofa 1033
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5058
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9115 0.5676 -3.0503 -1.9095 -0.7845 1.0193 367
## Avg_Cogongrass_Cover -0.9306 0.5097 -1.9611 -0.9231 0.0187 1.0108 402
## I(Avg_Cogongrass_Cover^2) 1.0919 0.4898 0.2065 1.0666 2.1499 1.0144 343
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9732 1.5113 0.0601 0.5396 4.4924 1.0325 984
## Avg_Cogongrass_Cover 0.7077 1.2436 0.0481 0.3526 3.5588 1.0296 536
## I(Avg_Cogongrass_Cover^2) 0.8542 2.2655 0.0466 0.3504 4.4988 1.0915 827
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8723 0.9081 0.0731 0.5968 3.2835 1.0087 252
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8498 0.2734 -3.4125 -2.8453 -2.3114 1.0088 881
## shrub_cover 0.3839 0.3669 -0.3173 0.3776 1.1434 1.0258 1167
## veg_height -0.0399 0.2292 -0.4863 -0.0399 0.3934 1.0010 1396
## week 0.3986 0.2782 -0.1445 0.3886 0.9617 1.0199 861
## I(week^2) -0.2911 0.1556 -0.6100 -0.2837 0.0001 1.0153 882
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3727 0.4561 0.0492 0.2351 1.6483 1.0115 557
## shrub_cover 0.7226 0.8731 0.0952 0.4781 2.8889 1.0058 1047
## veg_height 0.2786 0.2946 0.0534 0.1964 1.0107 1.0151 1483
## week 0.2736 0.3371 0.0365 0.1761 1.1462 1.0029 1349
## I(week^2) 0.1076 0.1093 0.0227 0.0715 0.4170 1.0108 970
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.2317 0.7346 -2.6101 -1.2483
## (Intercept)-Lynx_rufus -1.7993 0.7173 -3.2041 -1.7980
## (Intercept)-Didelphis_virginiana -2.0169 0.6808 -3.4297 -2.0003
## (Intercept)-Sylvilagus_floridanus -1.6786 0.6872 -3.0633 -1.6847
## (Intercept)-Sciurus_carolinensis -2.3615 0.7277 -3.9054 -2.3150
## (Intercept)-Vulpes_vulpes -2.5272 0.8666 -4.5251 -2.4256
## (Intercept)-Sus_scrofa -2.4167 0.8320 -4.3034 -2.3236
## Avg_Cogongrass_Cover-Canis_latrans -0.5292 0.6239 -1.6946 -0.5563
## Avg_Cogongrass_Cover-Lynx_rufus -0.8688 0.6716 -2.1888 -0.8658
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.6122 0.6416 -1.8464 -0.6440
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5141 0.7516 -3.1826 -1.4309
## Avg_Cogongrass_Cover-Sciurus_carolinensis -1.0067 0.6576 -2.4127 -0.9831
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9876 0.7259 -2.5258 -0.9493
## Avg_Cogongrass_Cover-Sus_scrofa -1.2115 0.7892 -2.8366 -1.1451
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.7824 1.0023 0.4296 1.5709
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.5149 0.6446 0.5003 1.4296
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8118 0.5338 -0.1239 0.7661
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0444 0.5815 0.0690 0.9799
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1693 0.4844 0.3050 1.1235
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0825 0.5470 0.1603 1.0391
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4951 0.8145 -1.4142 0.5910
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2484 1.0018 470
## (Intercept)-Lynx_rufus -0.4309 1.0001 440
## (Intercept)-Didelphis_virginiana -0.7417 1.0024 442
## (Intercept)-Sylvilagus_floridanus -0.3340 1.0114 380
## (Intercept)-Sciurus_carolinensis -1.0566 1.0117 551
## (Intercept)-Vulpes_vulpes -1.0750 1.0044 479
## (Intercept)-Sus_scrofa -1.0439 1.0123 471
## Avg_Cogongrass_Cover-Canis_latrans 0.8289 1.0032 1008
## Avg_Cogongrass_Cover-Lynx_rufus 0.4432 1.0177 535
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7421 1.0108 579
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2619 1.0080 394
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2080 1.0085 407
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4001 1.0040 496
## Avg_Cogongrass_Cover-Sus_scrofa 0.1339 1.0053 513
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4020 1.0077 266
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9742 1.0072 342
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0102 1.0036 369
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.3821 1.0320 362
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.2633 1.0217 429
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2886 1.0025 519
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.8453 1.0062 359
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6122 0.1981 -3.0029 -2.6146 -2.2311 1.0101
## (Intercept)-Lynx_rufus -3.1999 0.3264 -3.9158 -3.1828 -2.6153 1.0452
## (Intercept)-Didelphis_virginiana -2.5034 0.3013 -3.1101 -2.4992 -1.9284 1.0003
## (Intercept)-Sylvilagus_floridanus -3.0181 0.2760 -3.5621 -3.0087 -2.4918 1.0123
## (Intercept)-Sciurus_carolinensis -2.5807 0.3116 -3.1775 -2.5733 -1.9792 1.0101
## (Intercept)-Vulpes_vulpes -3.3198 0.4944 -4.3880 -3.2775 -2.4811 1.0041
## (Intercept)-Sus_scrofa -3.0781 0.4586 -4.0327 -3.0753 -2.1900 1.0114
## shrub_cover-Canis_latrans -0.2266 0.2277 -0.6832 -0.2247 0.2015 1.0142
## shrub_cover-Lynx_rufus -0.1400 0.3512 -0.8175 -0.1355 0.5425 1.0144
## shrub_cover-Didelphis_virginiana 1.0957 0.4337 0.3141 1.0605 2.0162 1.0134
## shrub_cover-Sylvilagus_floridanus 0.3108 0.4108 -0.4830 0.3134 1.1143 1.0368
## shrub_cover-Sciurus_carolinensis 0.9075 0.4272 0.0771 0.9001 1.7859 1.0153
## shrub_cover-Vulpes_vulpes 0.1082 0.6217 -1.1738 0.1198 1.3311 1.0200
## shrub_cover-Sus_scrofa 0.6698 0.7325 -0.8072 0.6708 2.1100 1.0146
## veg_height-Canis_latrans -0.6063 0.1940 -1.0025 -0.5945 -0.2456 1.0103
## veg_height-Lynx_rufus 0.0370 0.2539 -0.4733 0.0392 0.5246 1.0094
## veg_height-Didelphis_virginiana 0.4028 0.2663 -0.1074 0.3964 0.9340 1.0105
## veg_height-Sylvilagus_floridanus 0.1204 0.2575 -0.3751 0.1189 0.6425 1.0015
## veg_height-Sciurus_carolinensis 0.1019 0.2283 -0.3356 0.0919 0.5656 1.0081
## veg_height-Vulpes_vulpes -0.1251 0.3024 -0.7367 -0.1141 0.4549 1.0117
## veg_height-Sus_scrofa -0.1585 0.3394 -0.8375 -0.1526 0.4929 1.0075
## week-Canis_latrans 0.5349 0.2671 0.0339 0.5294 1.0874 1.0031
## week-Lynx_rufus 0.4018 0.3346 -0.2509 0.4020 1.0497 1.0657
## week-Didelphis_virginiana 0.1220 0.3626 -0.6208 0.1374 0.8026 1.0048
## week-Sylvilagus_floridanus 0.1504 0.3390 -0.5240 0.1521 0.8216 1.0303
## week-Sciurus_carolinensis 0.7038 0.3723 0.0415 0.6748 1.5093 1.0063
## week-Vulpes_vulpes 0.3275 0.4386 -0.5564 0.3279 1.1879 1.0135
## week-Sus_scrofa 0.6223 0.4285 -0.1741 0.5900 1.5362 1.0280
## I(week^2)-Canis_latrans -0.2235 0.1106 -0.4437 -0.2242 -0.0109 1.0021
## I(week^2)-Lynx_rufus -0.2546 0.1539 -0.5729 -0.2501 0.0280 1.0164
## I(week^2)-Didelphis_virginiana -0.4256 0.2404 -0.9924 -0.3984 -0.0366 1.0403
## I(week^2)-Sylvilagus_floridanus -0.2031 0.1630 -0.5566 -0.1926 0.0841 1.0105
## I(week^2)-Sciurus_carolinensis -0.2426 0.1523 -0.5448 -0.2388 0.0422 1.0060
## I(week^2)-Vulpes_vulpes -0.4616 0.2933 -1.1662 -0.4221 -0.0054 1.0523
## I(week^2)-Sus_scrofa -0.2378 0.1857 -0.6282 -0.2344 0.1083 1.0053
## ESS
## (Intercept)-Canis_latrans 752
## (Intercept)-Lynx_rufus 409
## (Intercept)-Didelphis_virginiana 752
## (Intercept)-Sylvilagus_floridanus 678
## (Intercept)-Sciurus_carolinensis 746
## (Intercept)-Vulpes_vulpes 313
## (Intercept)-Sus_scrofa 543
## shrub_cover-Canis_latrans 823
## shrub_cover-Lynx_rufus 609
## shrub_cover-Didelphis_virginiana 349
## shrub_cover-Sylvilagus_floridanus 528
## shrub_cover-Sciurus_carolinensis 710
## shrub_cover-Vulpes_vulpes 647
## shrub_cover-Sus_scrofa 693
## veg_height-Canis_latrans 704
## veg_height-Lynx_rufus 735
## veg_height-Didelphis_virginiana 817
## veg_height-Sylvilagus_floridanus 518
## veg_height-Sciurus_carolinensis 850
## veg_height-Vulpes_vulpes 736
## veg_height-Sus_scrofa 1096
## week-Canis_latrans 924
## week-Lynx_rufus 927
## week-Didelphis_virginiana 998
## week-Sylvilagus_floridanus 834
## week-Sciurus_carolinensis 810
## week-Vulpes_vulpes 895
## week-Sus_scrofa 1070
## I(week^2)-Canis_latrans 1081
## I(week^2)-Lynx_rufus 755
## I(week^2)-Didelphis_virginiana 447
## I(week^2)-Sylvilagus_floridanus 712
## I(week^2)-Sciurus_carolinensis 878
## I(week^2)-Vulpes_vulpes 404
## I(week^2)-Sus_scrofa 1039
# 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 7 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.5727
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5089 1.2277 -4.7118 -2.5927 0.2843 1.0380 418
## Cogon_Patch_Size 0.0455 1.0601 -2.1496 0.1026 2.0426 1.0040 488
## Veg_shannon_index 0.9840 0.7984 -0.6019 1.0014 2.4837 1.0455 365
## total_shrub_cover -0.8711 0.8446 -2.6477 -0.8400 0.7583 1.0938 144
## Avg_Cogongrass_Cover -0.6600 1.2674 -3.1378 -0.6316 1.6918 1.0824 117
## Tree_Density -1.7091 1.0285 -3.6508 -1.6724 0.3264 1.1252 245
## Avg_Canopy_Cover 1.9619 1.0635 -0.2418 1.9776 3.9963 1.0310 1051
## I(Avg_Cogongrass_Cover^2) 1.9486 0.8491 0.3174 1.9250 3.6812 1.1593 171
## avg_veg_height -0.3031 0.7134 -1.7195 -0.2878 1.0941 1.0307 201
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7963 23.7101 0.1384 5.7730 71.8819 1.3930 204
## Cogon_Patch_Size 10.1935 21.1284 0.1757 4.7501 54.1285 1.1002 176
## Veg_shannon_index 2.6322 4.7266 0.0733 1.1497 13.8435 1.1602 246
## total_shrub_cover 3.1408 5.7604 0.0824 1.2715 17.3443 1.0214 291
## Avg_Cogongrass_Cover 2.0440 5.3698 0.0511 0.6717 11.8790 1.1292 590
## Tree_Density 4.4963 8.2633 0.0711 1.6345 27.2610 1.0311 191
## Avg_Canopy_Cover 12.7923 23.4899 0.4619 6.5265 64.5089 1.1566 199
## I(Avg_Cogongrass_Cover^2) 1.8410 4.2073 0.0558 0.6814 10.8929 1.0819 259
## avg_veg_height 0.9381 1.7208 0.0451 0.3997 4.9257 1.1108 605
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.72 2.376 0.0674 0.8312 8.4584 1.0204 76
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9830 0.3129 -3.6146 -2.9839 -2.3712 1.0237 648
## shrub_cover 0.5755 0.4185 -0.2210 0.5701 1.3869 1.0753 457
## veg_height -0.0036 0.2346 -0.4775 -0.0065 0.4560 1.0032 1349
## week 0.4045 0.2750 -0.1496 0.4098 0.9438 1.0014 900
## I(week^2) -0.3024 0.1699 -0.6342 -0.2931 0.0012 1.0072 810
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5068 0.5890 0.0610 0.3301 1.9081 1.0464 758
## shrub_cover 0.9049 1.0179 0.1320 0.6205 3.3422 1.0560 843
## veg_height 0.3267 0.3592 0.0636 0.2369 1.1629 1.0258 1598
## week 0.2871 0.3194 0.0367 0.1880 1.1606 1.0020 1396
## I(week^2) 0.1246 0.1666 0.0225 0.0786 0.5131 1.0386 486
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3974 1.4470 -4.0539 -1.4521
## (Intercept)-Lynx_rufus -1.1024 2.3144 -4.7332 -1.4783
## (Intercept)-Didelphis_virginiana -4.3494 1.6619 -8.0144 -4.1947
## (Intercept)-Sylvilagus_floridanus -2.8930 1.6591 -6.3373 -2.8344
## (Intercept)-Sciurus_carolinensis -4.8899 1.9644 -9.5671 -4.6243
## (Intercept)-Vulpes_vulpes -5.2122 2.3857 -10.6641 -4.8682
## (Intercept)-Sus_scrofa -5.6521 2.7155 -12.7017 -5.1691
## Cogon_Patch_Size-Canis_latrans 2.5813 2.0468 -0.0675 2.1889
## Cogon_Patch_Size-Lynx_rufus -0.0498 2.4493 -3.7067 -0.2685
## Cogon_Patch_Size-Didelphis_virginiana 2.2620 1.3335 0.1104 2.1130
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9032 2.3358 -7.4003 -1.4693
## Cogon_Patch_Size-Sciurus_carolinensis -1.3215 2.2741 -7.4061 -0.8890
## Cogon_Patch_Size-Vulpes_vulpes -0.8350 2.2687 -6.6018 -0.4587
## Cogon_Patch_Size-Sus_scrofa -1.0872 2.3497 -7.1908 -0.6505
## Veg_shannon_index-Canis_latrans 1.5875 0.9163 -0.0142 1.5009
## Veg_shannon_index-Lynx_rufus 1.0016 1.5070 -2.2415 1.0321
## Veg_shannon_index-Didelphis_virginiana 1.4732 1.1652 -0.4687 1.3641
## Veg_shannon_index-Sylvilagus_floridanus 1.2122 0.9918 -0.7557 1.1931
## Veg_shannon_index-Sciurus_carolinensis -0.0832 1.2981 -2.9363 0.0762
## Veg_shannon_index-Vulpes_vulpes 0.3499 1.2737 -2.5058 0.4773
## Veg_shannon_index-Sus_scrofa 2.1268 1.5718 -0.0344 1.8395
## total_shrub_cover-Canis_latrans 0.2746 0.9992 -1.4417 0.1616
## total_shrub_cover-Lynx_rufus -1.7586 1.5765 -5.3391 -1.5866
## total_shrub_cover-Didelphis_virginiana -1.6779 1.6945 -6.1768 -1.3481
## total_shrub_cover-Sylvilagus_floridanus -0.9962 1.3076 -3.9160 -0.8927
## total_shrub_cover-Sciurus_carolinensis -0.9549 1.3393 -4.0543 -0.7998
## total_shrub_cover-Vulpes_vulpes -1.4123 1.6078 -5.3490 -1.1574
## total_shrub_cover-Sus_scrofa -0.6753 1.5221 -4.0740 -0.5875
## Avg_Cogongrass_Cover-Canis_latrans -0.6251 1.5312 -3.5594 -0.6315
## Avg_Cogongrass_Cover-Lynx_rufus -0.5291 1.6128 -3.5657 -0.5308
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.7863 1.5824 -3.8757 -0.7562
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3283 1.7030 -4.9920 -1.2452
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6749 1.6024 -3.8166 -0.6520
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.3500 1.7193 -3.4042 -0.3602
## Avg_Cogongrass_Cover-Sus_scrofa -0.8813 1.7128 -4.3844 -0.8271
## Tree_Density-Canis_latrans -2.9239 1.6741 -7.0660 -2.6989
## Tree_Density-Lynx_rufus -0.4918 1.7918 -3.3782 -0.7540
## Tree_Density-Didelphis_virginiana -2.0592 1.5129 -5.5290 -1.9466
## Tree_Density-Sylvilagus_floridanus -2.6042 1.8660 -6.9872 -2.3599
## Tree_Density-Sciurus_carolinensis -2.5222 2.0091 -7.4462 -2.2666
## Tree_Density-Vulpes_vulpes -1.6527 1.7829 -5.2974 -1.6612
## Tree_Density-Sus_scrofa -2.0393 1.7559 -6.0110 -1.9145
## Avg_Canopy_Cover-Canis_latrans -0.1395 0.7606 -1.7124 -0.1092
## Avg_Canopy_Cover-Lynx_rufus 1.1855 1.9190 -2.0784 0.9300
## Avg_Canopy_Cover-Didelphis_virginiana 4.7391 2.8293 1.5929 4.1818
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.5661 2.9534 1.5697 4.9780
## Avg_Canopy_Cover-Sciurus_carolinensis 4.0533 2.1058 1.2136 3.6378
## Avg_Canopy_Cover-Vulpes_vulpes 3.2380 2.1724 0.4549 2.7719
## Avg_Canopy_Cover-Sus_scrofa 2.6339 1.5693 0.3617 2.3895
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5934 1.1214 0.8098 2.4641
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8356 1.2935 0.8565 2.6465
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6230 0.9714 -0.1788 1.5843
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7757 1.0288 -0.0865 1.7278
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1739 1.0209 0.4573 2.0651
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3525 1.1361 0.4876 2.2646
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3415 1.5898 -2.6585 1.5251
## avg_veg_height-Canis_latrans -0.3216 0.7641 -1.7943 -0.3176
## avg_veg_height-Lynx_rufus -0.5423 1.1037 -2.9906 -0.4698
## avg_veg_height-Didelphis_virginiana -0.4934 0.9600 -2.5016 -0.4571
## avg_veg_height-Sylvilagus_floridanus -0.3724 0.9709 -2.5718 -0.3319
## avg_veg_height-Sciurus_carolinensis 0.1780 0.9595 -1.5033 0.1018
## avg_veg_height-Vulpes_vulpes -0.3844 1.0354 -2.6325 -0.3546
## avg_veg_height-Sus_scrofa -0.2220 1.0099 -2.2564 -0.2383
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5783 1.0134 287
## (Intercept)-Lynx_rufus 4.4296 1.2704 91
## (Intercept)-Didelphis_virginiana -1.6081 1.1723 192
## (Intercept)-Sylvilagus_floridanus 0.2619 1.0315 228
## (Intercept)-Sciurus_carolinensis -1.8993 1.1207 142
## (Intercept)-Vulpes_vulpes -1.6136 1.1751 103
## (Intercept)-Sus_scrofa -1.8507 1.3548 120
## Cogon_Patch_Size-Canis_latrans 8.0135 1.0611 286
## Cogon_Patch_Size-Lynx_rufus 4.6642 1.1886 119
## Cogon_Patch_Size-Didelphis_virginiana 5.2339 1.0882 333
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4859 1.0386 281
## Cogon_Patch_Size-Sciurus_carolinensis 1.7563 1.0411 207
## Cogon_Patch_Size-Vulpes_vulpes 2.6692 1.0527 212
## Cogon_Patch_Size-Sus_scrofa 2.0861 1.1200 154
## Veg_shannon_index-Canis_latrans 3.6196 1.0739 467
## Veg_shannon_index-Lynx_rufus 4.0842 1.0425 248
## Veg_shannon_index-Didelphis_virginiana 4.0830 1.1018 347
## Veg_shannon_index-Sylvilagus_floridanus 3.3055 1.0334 472
## Veg_shannon_index-Sciurus_carolinensis 2.0567 1.1075 299
## Veg_shannon_index-Vulpes_vulpes 2.5147 1.0726 374
## Veg_shannon_index-Sus_scrofa 6.0090 1.1388 231
## total_shrub_cover-Canis_latrans 2.5524 1.0151 301
## total_shrub_cover-Lynx_rufus 0.9731 1.1364 157
## total_shrub_cover-Didelphis_virginiana 0.5173 1.0220 140
## total_shrub_cover-Sylvilagus_floridanus 1.3814 1.0457 345
## total_shrub_cover-Sciurus_carolinensis 1.2371 1.0045 277
## total_shrub_cover-Vulpes_vulpes 1.0585 1.2092 202
## total_shrub_cover-Sus_scrofa 2.1984 1.0697 223
## Avg_Cogongrass_Cover-Canis_latrans 2.2865 1.0631 181
## Avg_Cogongrass_Cover-Lynx_rufus 2.5574 1.0817 167
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1371 1.0914 198
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5859 1.1070 236
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3663 1.0975 187
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2973 1.0283 177
## Avg_Cogongrass_Cover-Sus_scrofa 2.3587 1.0459 199
## Tree_Density-Canis_latrans -0.4243 1.0786 290
## Tree_Density-Lynx_rufus 3.8329 1.0396 195
## Tree_Density-Didelphis_virginiana 0.8323 1.0464 386
## Tree_Density-Sylvilagus_floridanus 0.4961 1.0495 245
## Tree_Density-Sciurus_carolinensis 0.6778 1.1692 198
## Tree_Density-Vulpes_vulpes 1.9109 1.1210 285
## Tree_Density-Sus_scrofa 1.1625 1.0364 281
## Avg_Canopy_Cover-Canis_latrans 1.3107 1.0001 640
## Avg_Canopy_Cover-Lynx_rufus 5.5117 1.2919 122
## Avg_Canopy_Cover-Didelphis_virginiana 11.6001 1.1789 125
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.7831 1.0749 135
## Avg_Canopy_Cover-Sciurus_carolinensis 9.6245 1.1270 214
## Avg_Canopy_Cover-Vulpes_vulpes 9.0603 1.0039 166
## Avg_Canopy_Cover-Sus_scrofa 6.4884 1.0434 293
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1412 1.0673 298
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.9023 1.0416 179
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.5785 1.0973 195
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0356 1.0946 216
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.4465 1.1695 210
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.7919 1.0975 217
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.9930 1.1850 122
## avg_veg_height-Canis_latrans 1.2413 1.0467 349
## avg_veg_height-Lynx_rufus 1.4818 1.0132 257
## avg_veg_height-Didelphis_virginiana 1.3088 1.0098 380
## avg_veg_height-Sylvilagus_floridanus 1.4104 1.0140 352
## avg_veg_height-Sciurus_carolinensis 2.3227 1.0781 391
## avg_veg_height-Vulpes_vulpes 1.5445 1.0456 246
## avg_veg_height-Sus_scrofa 1.8386 1.0184 343
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5982 0.2007 -3.0035 -2.5883 -2.2193 1.0011
## (Intercept)-Lynx_rufus -3.4842 0.3661 -4.2549 -3.4640 -2.8157 1.1868
## (Intercept)-Didelphis_virginiana -2.5815 0.3042 -3.1622 -2.5792 -1.9740 1.0106
## (Intercept)-Sylvilagus_floridanus -3.0789 0.2682 -3.6500 -3.0715 -2.5690 1.0156
## (Intercept)-Sciurus_carolinensis -2.7366 0.3197 -3.3478 -2.7357 -2.0993 1.0111
## (Intercept)-Vulpes_vulpes -3.6128 0.5438 -4.7414 -3.5756 -2.6525 1.1149
## (Intercept)-Sus_scrofa -3.3393 0.5182 -4.3740 -3.3286 -2.3185 1.0816
## shrub_cover-Canis_latrans -0.2987 0.2493 -0.7714 -0.3030 0.1974 1.0292
## shrub_cover-Lynx_rufus 0.0008 0.4125 -0.8556 0.0022 0.7603 1.1768
## shrub_cover-Didelphis_virginiana 1.2409 0.4021 0.4982 1.2320 2.0668 1.0369
## shrub_cover-Sylvilagus_floridanus 0.5869 0.4060 -0.2412 0.5948 1.3477 1.0362
## shrub_cover-Sciurus_carolinensis 1.1558 0.4312 0.3209 1.1557 2.0144 1.0309
## shrub_cover-Vulpes_vulpes 0.4058 0.6046 -0.8026 0.4135 1.5632 1.0214
## shrub_cover-Sus_scrofa 1.1119 0.8363 -0.5626 1.1023 2.8192 1.1099
## veg_height-Canis_latrans -0.5879 0.1884 -0.9789 -0.5827 -0.2322 1.0147
## veg_height-Lynx_rufus 0.1464 0.2496 -0.3574 0.1527 0.6163 1.0122
## veg_height-Didelphis_virginiana 0.5356 0.2642 0.0329 0.5263 1.0681 1.0044
## veg_height-Sylvilagus_floridanus 0.1397 0.2616 -0.3592 0.1362 0.6447 1.0253
## veg_height-Sciurus_carolinensis 0.1789 0.2323 -0.2570 0.1726 0.6570 1.0139
## veg_height-Vulpes_vulpes -0.2083 0.3329 -0.9201 -0.1969 0.4129 1.0257
## veg_height-Sus_scrofa -0.2104 0.3280 -0.8621 -0.2077 0.4231 1.0227
## week-Canis_latrans 0.5539 0.2586 0.0532 0.5511 1.0601 1.0132
## week-Lynx_rufus 0.3971 0.3259 -0.2332 0.3889 1.0376 1.0084
## week-Didelphis_virginiana 0.1378 0.3637 -0.6329 0.1515 0.8171 1.0057
## week-Sylvilagus_floridanus 0.1249 0.3367 -0.5518 0.1384 0.7683 1.0009
## week-Sciurus_carolinensis 0.7172 0.3687 0.0509 0.7108 1.4981 1.0024
## week-Vulpes_vulpes 0.3173 0.4574 -0.6302 0.3287 1.1797 1.0029
## week-Sus_scrofa 0.6348 0.4120 -0.1326 0.6051 1.5009 1.0037
## I(week^2)-Canis_latrans -0.2298 0.1131 -0.4607 -0.2299 -0.0067 1.0118
## I(week^2)-Lynx_rufus -0.2575 0.1610 -0.5877 -0.2480 0.0258 1.0097
## I(week^2)-Didelphis_virginiana -0.4454 0.2473 -1.0229 -0.4189 -0.0411 1.0612
## I(week^2)-Sylvilagus_floridanus -0.1929 0.1609 -0.5241 -0.1855 0.1064 1.0083
## I(week^2)-Sciurus_carolinensis -0.2499 0.1558 -0.5617 -0.2434 0.0515 1.0012
## I(week^2)-Vulpes_vulpes -0.4968 0.3210 -1.2778 -0.4452 -0.0044 1.0683
## I(week^2)-Sus_scrofa -0.2428 0.1834 -0.6124 -0.2393 0.0978 1.0047
## ESS
## (Intercept)-Canis_latrans 863
## (Intercept)-Lynx_rufus 216
## (Intercept)-Didelphis_virginiana 460
## (Intercept)-Sylvilagus_floridanus 658
## (Intercept)-Sciurus_carolinensis 414
## (Intercept)-Vulpes_vulpes 268
## (Intercept)-Sus_scrofa 271
## shrub_cover-Canis_latrans 446
## shrub_cover-Lynx_rufus 140
## shrub_cover-Didelphis_virginiana 382
## shrub_cover-Sylvilagus_floridanus 366
## shrub_cover-Sciurus_carolinensis 416
## shrub_cover-Vulpes_vulpes 460
## shrub_cover-Sus_scrofa 223
## veg_height-Canis_latrans 785
## veg_height-Lynx_rufus 455
## veg_height-Didelphis_virginiana 717
## veg_height-Sylvilagus_floridanus 584
## veg_height-Sciurus_carolinensis 937
## veg_height-Vulpes_vulpes 531
## veg_height-Sus_scrofa 881
## week-Canis_latrans 1082
## week-Lynx_rufus 923
## week-Didelphis_virginiana 798
## week-Sylvilagus_floridanus 801
## week-Sciurus_carolinensis 997
## week-Vulpes_vulpes 725
## week-Sus_scrofa 934
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 483
## I(week^2)-Didelphis_virginiana 342
## I(week^2)-Sylvilagus_floridanus 794
## I(week^2)-Sciurus_carolinensis 954
## I(week^2)-Vulpes_vulpes 197
## I(week^2)-Sus_scrofa 972
waicOcc(ms_full_full, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -519.80039 51.11206 1141.82489
waicOcc(ms_full_cover, by.sp = FALSE)
## elpd pD WAIC
## -548.87075 49.77249 1197.28647
waicOcc(ms_full_canopy, by.sp = FALSE)
## elpd pD WAIC
## -544.93757 38.13621 1166.14756
waicOcc(ms_full_move, by.sp = FALSE)
## elpd pD WAIC
## -546.03120 46.87498 1185.81236
waicOcc(ms_full_forage, by.sp = FALSE)
## elpd pD WAIC
## -555.10226 40.35647 1190.91746
waicOcc(ms_full_cogon, by.sp = FALSE)
## elpd pD WAIC
## -558.21077 37.17611 1190.77376
waicOcc(ms_full_null, by.sp = FALSE)
## elpd pD WAIC
## -571.20830 26.75409 1195.92477
waicOcc(ms_full_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -552.54539 38.77318 1182.63715
waicOcc(ms_full_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -517.76318 47.69932 1130.92500
waicOcc(ms_null_null, by.sp = FALSE)
## elpd pD WAIC
## -589.33317 14.55709 1207.78052
waicOcc(ms_null_full, by.sp = FALSE)
## elpd pD WAIC
## -539.4933 41.9496 1162.8857
waicOcc(ms_null_cover, by.sp = FALSE)
## elpd pD WAIC
## -572.1833 35.4600 1215.2867
waicOcc(ms_null_canopy, by.sp = FALSE)
## elpd pD WAIC
## -565.02285 27.76524 1185.57617
waicOcc(ms_null_move, by.sp = FALSE)
## elpd pD WAIC
## -566.45069 34.01422 1200.92981
waicOcc(ms_null_forage, by.sp = FALSE)
## elpd pD WAIC
## -572.31626 30.37344 1205.37941
waicOcc(ms_null_cogon, by.sp = FALSE)
## elpd pD WAIC
## -576.05798 26.99224 1206.10044
waicOcc(ms_null_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -569.16798 29.26326 1196.86248
waicOcc(ms_null_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -535.18876 39.55485 1149.48722
waicOcc(ms_week_full, by.sp = FALSE)
## elpd pD WAIC
## -539.31621 45.28063 1169.19369
waicOcc(ms_week_cover, by.sp = FALSE)
## elpd pD WAIC
## -570.59100 39.97554 1221.13309
waicOcc(ms_week_null, by.sp = FALSE)
## elpd pD WAIC
## -587.39231 19.53302 1213.85066
waicOcc(ms_week_forage, by.sp = FALSE)
## elpd pD WAIC
## -570.40955 35.44803 1211.71516
waicOcc(ms_week_move, by.sp = FALSE)
## elpd pD WAIC
## -564.46241 38.97892 1206.88268
waicOcc(ms_week_canopy, by.sp = FALSE)
## elpd pD WAIC
## -563.60722 31.23431 1189.68306
waicOcc(ms_week_cogon, by.sp = FALSE)
## elpd pD WAIC
## -574.06867 31.49388 1211.12510
waicOcc(ms_week_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -567.75952 33.15919 1201.83742
waicOcc(ms_week_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -532.55167 44.23163 1153.56661
waicOcc(ms_cover_full, by.sp = FALSE)
## elpd pD WAIC
## -522.3809 45.9791 1136.7200
waicOcc(ms_cover_cover, by.sp = FALSE)
## elpd pD WAIC
## -551.95851 44.36149 1192.64000
waicOcc(ms_cover_null, by.sp = FALSE)
## elpd pD WAIC
## -573.2323 21.5988 1189.6621
waicOcc(ms_cover_forage, by.sp = FALSE)
## elpd pD WAIC
## -556.70017 37.26484 1187.93000
waicOcc(ms_cover_move, by.sp = FALSE)
## elpd pD WAIC
## -547.54055 42.47276 1180.02662
waicOcc(ms_cover_canopy, by.sp = FALSE)
## elpd pD WAIC
## -547.55670 33.12139 1161.35617
waicOcc(ms_cover_cogon, by.sp = FALSE)
## elpd pD WAIC
## -560.5325 33.0985 1187.2620
waicOcc(ms_cover_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -554.1979 35.9196 1180.2350
waicOcc(ms_cover_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -519.64462 43.11862 1125.52650
waicOcc(ms_weekQ_full, by.sp = FALSE)
## elpd pD WAIC
## -533.17199 52.00363 1170.35124
waicOcc(ms_weekQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -564.53331 47.16331 1223.39324
waicOcc(ms_weekQ_null, by.sp = FALSE)
## elpd pD WAIC
## -581.43442 26.21086 1215.29055
waicOcc(ms_weekQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -564.53985 41.53367 1212.14703
waicOcc(ms_weekQ_move, by.sp = FALSE)
## elpd pD WAIC
## -558.66931 45.43773 1208.21408
waicOcc(ms_weekQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -557.38587 38.65581 1192.08337
waicOcc(ms_weekQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -568.41279 37.75308 1212.33173
waicOcc(ms_weekQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -561.0582 40.2013 1202.5191
waicOcc(ms_weekQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -527.87454 50.06125 1155.87158
waicOcc(ms_fullQ_full, by.sp = FALSE)
## elpd pD WAIC
## -513.29922 58.77608 1144.15060
waicOcc(ms_fullQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -544.23316 54.26953 1197.00538
waicOcc(ms_fullQ_null, by.sp = FALSE)
## elpd pD WAIC
## -565.29702 33.79529 1198.18461
waicOcc(ms_fullQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -548.99216 48.05122 1194.08676
waicOcc(ms_fullQ_move, by.sp = FALSE)
## elpd pD WAIC
## -539.68166 53.52962 1186.42258
waicOcc(ms_fullQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -539.14734 44.89975 1168.09419
waicOcc(ms_fullQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -552.05601 44.32052 1192.75307
waicOcc(ms_fullQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -546.62519 46.94431 1187.13901
waicOcc(ms_fullQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -512.2573 54.8071 1134.1288
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 7
## Currently on species 2 out of 7
## Currently on species 3 out of 7
## Currently on species 4 out of 7
## Currently on species 5 out of 7
## Currently on species 6 out of 7
## Currently on species 7 out of 7
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: 0.414
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.6327
## Lynx_rufus Bayesian p-value: 0.325
## Didelphis_virginiana Bayesian p-value: 0.3887
## Sylvilagus_floridanus Bayesian p-value: 0.4077
## Sciurus_carolinensis Bayesian p-value: 0.3097
## Vulpes_vulpes Bayesian p-value: 0.262
## Sus_scrofa Bayesian p-value: 0.5723
## 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): 0.5727
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5089 1.2277 -4.7118 -2.5927 0.2843 1.0380 418
## Cogon_Patch_Size 0.0455 1.0601 -2.1496 0.1026 2.0426 1.0040 488
## Veg_shannon_index 0.9840 0.7984 -0.6019 1.0014 2.4837 1.0455 365
## total_shrub_cover -0.8711 0.8446 -2.6477 -0.8400 0.7583 1.0938 144
## Avg_Cogongrass_Cover -0.6600 1.2674 -3.1378 -0.6316 1.6918 1.0824 117
## Tree_Density -1.7091 1.0285 -3.6508 -1.6724 0.3264 1.1252 245
## Avg_Canopy_Cover 1.9619 1.0635 -0.2418 1.9776 3.9963 1.0310 1051
## I(Avg_Cogongrass_Cover^2) 1.9486 0.8491 0.3174 1.9250 3.6812 1.1593 171
## avg_veg_height -0.3031 0.7134 -1.7195 -0.2878 1.0941 1.0307 201
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7963 23.7101 0.1384 5.7730 71.8819 1.3930 204
## Cogon_Patch_Size 10.1935 21.1284 0.1757 4.7501 54.1285 1.1002 176
## Veg_shannon_index 2.6322 4.7266 0.0733 1.1497 13.8435 1.1602 246
## total_shrub_cover 3.1408 5.7604 0.0824 1.2715 17.3443 1.0214 291
## Avg_Cogongrass_Cover 2.0440 5.3698 0.0511 0.6717 11.8790 1.1292 590
## Tree_Density 4.4963 8.2633 0.0711 1.6345 27.2610 1.0311 191
## Avg_Canopy_Cover 12.7923 23.4899 0.4619 6.5265 64.5089 1.1566 199
## I(Avg_Cogongrass_Cover^2) 1.8410 4.2073 0.0558 0.6814 10.8929 1.0819 259
## avg_veg_height 0.9381 1.7208 0.0451 0.3997 4.9257 1.1108 605
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.72 2.376 0.0674 0.8312 8.4584 1.0204 76
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9830 0.3129 -3.6146 -2.9839 -2.3712 1.0237 648
## shrub_cover 0.5755 0.4185 -0.2210 0.5701 1.3869 1.0753 457
## veg_height -0.0036 0.2346 -0.4775 -0.0065 0.4560 1.0032 1349
## week 0.4045 0.2750 -0.1496 0.4098 0.9438 1.0014 900
## I(week^2) -0.3024 0.1699 -0.6342 -0.2931 0.0012 1.0072 810
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5068 0.5890 0.0610 0.3301 1.9081 1.0464 758
## shrub_cover 0.9049 1.0179 0.1320 0.6205 3.3422 1.0560 843
## veg_height 0.3267 0.3592 0.0636 0.2369 1.1629 1.0258 1598
## week 0.2871 0.3194 0.0367 0.1880 1.1606 1.0020 1396
## I(week^2) 0.1246 0.1666 0.0225 0.0786 0.5131 1.0386 486
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3974 1.4470 -4.0539 -1.4521
## (Intercept)-Lynx_rufus -1.1024 2.3144 -4.7332 -1.4783
## (Intercept)-Didelphis_virginiana -4.3494 1.6619 -8.0144 -4.1947
## (Intercept)-Sylvilagus_floridanus -2.8930 1.6591 -6.3373 -2.8344
## (Intercept)-Sciurus_carolinensis -4.8899 1.9644 -9.5671 -4.6243
## (Intercept)-Vulpes_vulpes -5.2122 2.3857 -10.6641 -4.8682
## (Intercept)-Sus_scrofa -5.6521 2.7155 -12.7017 -5.1691
## Cogon_Patch_Size-Canis_latrans 2.5813 2.0468 -0.0675 2.1889
## Cogon_Patch_Size-Lynx_rufus -0.0498 2.4493 -3.7067 -0.2685
## Cogon_Patch_Size-Didelphis_virginiana 2.2620 1.3335 0.1104 2.1130
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9032 2.3358 -7.4003 -1.4693
## Cogon_Patch_Size-Sciurus_carolinensis -1.3215 2.2741 -7.4061 -0.8890
## Cogon_Patch_Size-Vulpes_vulpes -0.8350 2.2687 -6.6018 -0.4587
## Cogon_Patch_Size-Sus_scrofa -1.0872 2.3497 -7.1908 -0.6505
## Veg_shannon_index-Canis_latrans 1.5875 0.9163 -0.0142 1.5009
## Veg_shannon_index-Lynx_rufus 1.0016 1.5070 -2.2415 1.0321
## Veg_shannon_index-Didelphis_virginiana 1.4732 1.1652 -0.4687 1.3641
## Veg_shannon_index-Sylvilagus_floridanus 1.2122 0.9918 -0.7557 1.1931
## Veg_shannon_index-Sciurus_carolinensis -0.0832 1.2981 -2.9363 0.0762
## Veg_shannon_index-Vulpes_vulpes 0.3499 1.2737 -2.5058 0.4773
## Veg_shannon_index-Sus_scrofa 2.1268 1.5718 -0.0344 1.8395
## total_shrub_cover-Canis_latrans 0.2746 0.9992 -1.4417 0.1616
## total_shrub_cover-Lynx_rufus -1.7586 1.5765 -5.3391 -1.5866
## total_shrub_cover-Didelphis_virginiana -1.6779 1.6945 -6.1768 -1.3481
## total_shrub_cover-Sylvilagus_floridanus -0.9962 1.3076 -3.9160 -0.8927
## total_shrub_cover-Sciurus_carolinensis -0.9549 1.3393 -4.0543 -0.7998
## total_shrub_cover-Vulpes_vulpes -1.4123 1.6078 -5.3490 -1.1574
## total_shrub_cover-Sus_scrofa -0.6753 1.5221 -4.0740 -0.5875
## Avg_Cogongrass_Cover-Canis_latrans -0.6251 1.5312 -3.5594 -0.6315
## Avg_Cogongrass_Cover-Lynx_rufus -0.5291 1.6128 -3.5657 -0.5308
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.7863 1.5824 -3.8757 -0.7562
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3283 1.7030 -4.9920 -1.2452
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6749 1.6024 -3.8166 -0.6520
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.3500 1.7193 -3.4042 -0.3602
## Avg_Cogongrass_Cover-Sus_scrofa -0.8813 1.7128 -4.3844 -0.8271
## Tree_Density-Canis_latrans -2.9239 1.6741 -7.0660 -2.6989
## Tree_Density-Lynx_rufus -0.4918 1.7918 -3.3782 -0.7540
## Tree_Density-Didelphis_virginiana -2.0592 1.5129 -5.5290 -1.9466
## Tree_Density-Sylvilagus_floridanus -2.6042 1.8660 -6.9872 -2.3599
## Tree_Density-Sciurus_carolinensis -2.5222 2.0091 -7.4462 -2.2666
## Tree_Density-Vulpes_vulpes -1.6527 1.7829 -5.2974 -1.6612
## Tree_Density-Sus_scrofa -2.0393 1.7559 -6.0110 -1.9145
## Avg_Canopy_Cover-Canis_latrans -0.1395 0.7606 -1.7124 -0.1092
## Avg_Canopy_Cover-Lynx_rufus 1.1855 1.9190 -2.0784 0.9300
## Avg_Canopy_Cover-Didelphis_virginiana 4.7391 2.8293 1.5929 4.1818
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.5661 2.9534 1.5697 4.9780
## Avg_Canopy_Cover-Sciurus_carolinensis 4.0533 2.1058 1.2136 3.6378
## Avg_Canopy_Cover-Vulpes_vulpes 3.2380 2.1724 0.4549 2.7719
## Avg_Canopy_Cover-Sus_scrofa 2.6339 1.5693 0.3617 2.3895
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5934 1.1214 0.8098 2.4641
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8356 1.2935 0.8565 2.6465
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6230 0.9714 -0.1788 1.5843
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7757 1.0288 -0.0865 1.7278
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1739 1.0209 0.4573 2.0651
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3525 1.1361 0.4876 2.2646
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3415 1.5898 -2.6585 1.5251
## avg_veg_height-Canis_latrans -0.3216 0.7641 -1.7943 -0.3176
## avg_veg_height-Lynx_rufus -0.5423 1.1037 -2.9906 -0.4698
## avg_veg_height-Didelphis_virginiana -0.4934 0.9600 -2.5016 -0.4571
## avg_veg_height-Sylvilagus_floridanus -0.3724 0.9709 -2.5718 -0.3319
## avg_veg_height-Sciurus_carolinensis 0.1780 0.9595 -1.5033 0.1018
## avg_veg_height-Vulpes_vulpes -0.3844 1.0354 -2.6325 -0.3546
## avg_veg_height-Sus_scrofa -0.2220 1.0099 -2.2564 -0.2383
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5783 1.0134 287
## (Intercept)-Lynx_rufus 4.4296 1.2704 91
## (Intercept)-Didelphis_virginiana -1.6081 1.1723 192
## (Intercept)-Sylvilagus_floridanus 0.2619 1.0315 228
## (Intercept)-Sciurus_carolinensis -1.8993 1.1207 142
## (Intercept)-Vulpes_vulpes -1.6136 1.1751 103
## (Intercept)-Sus_scrofa -1.8507 1.3548 120
## Cogon_Patch_Size-Canis_latrans 8.0135 1.0611 286
## Cogon_Patch_Size-Lynx_rufus 4.6642 1.1886 119
## Cogon_Patch_Size-Didelphis_virginiana 5.2339 1.0882 333
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4859 1.0386 281
## Cogon_Patch_Size-Sciurus_carolinensis 1.7563 1.0411 207
## Cogon_Patch_Size-Vulpes_vulpes 2.6692 1.0527 212
## Cogon_Patch_Size-Sus_scrofa 2.0861 1.1200 154
## Veg_shannon_index-Canis_latrans 3.6196 1.0739 467
## Veg_shannon_index-Lynx_rufus 4.0842 1.0425 248
## Veg_shannon_index-Didelphis_virginiana 4.0830 1.1018 347
## Veg_shannon_index-Sylvilagus_floridanus 3.3055 1.0334 472
## Veg_shannon_index-Sciurus_carolinensis 2.0567 1.1075 299
## Veg_shannon_index-Vulpes_vulpes 2.5147 1.0726 374
## Veg_shannon_index-Sus_scrofa 6.0090 1.1388 231
## total_shrub_cover-Canis_latrans 2.5524 1.0151 301
## total_shrub_cover-Lynx_rufus 0.9731 1.1364 157
## total_shrub_cover-Didelphis_virginiana 0.5173 1.0220 140
## total_shrub_cover-Sylvilagus_floridanus 1.3814 1.0457 345
## total_shrub_cover-Sciurus_carolinensis 1.2371 1.0045 277
## total_shrub_cover-Vulpes_vulpes 1.0585 1.2092 202
## total_shrub_cover-Sus_scrofa 2.1984 1.0697 223
## Avg_Cogongrass_Cover-Canis_latrans 2.2865 1.0631 181
## Avg_Cogongrass_Cover-Lynx_rufus 2.5574 1.0817 167
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1371 1.0914 198
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5859 1.1070 236
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3663 1.0975 187
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2973 1.0283 177
## Avg_Cogongrass_Cover-Sus_scrofa 2.3587 1.0459 199
## Tree_Density-Canis_latrans -0.4243 1.0786 290
## Tree_Density-Lynx_rufus 3.8329 1.0396 195
## Tree_Density-Didelphis_virginiana 0.8323 1.0464 386
## Tree_Density-Sylvilagus_floridanus 0.4961 1.0495 245
## Tree_Density-Sciurus_carolinensis 0.6778 1.1692 198
## Tree_Density-Vulpes_vulpes 1.9109 1.1210 285
## Tree_Density-Sus_scrofa 1.1625 1.0364 281
## Avg_Canopy_Cover-Canis_latrans 1.3107 1.0001 640
## Avg_Canopy_Cover-Lynx_rufus 5.5117 1.2919 122
## Avg_Canopy_Cover-Didelphis_virginiana 11.6001 1.1789 125
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.7831 1.0749 135
## Avg_Canopy_Cover-Sciurus_carolinensis 9.6245 1.1270 214
## Avg_Canopy_Cover-Vulpes_vulpes 9.0603 1.0039 166
## Avg_Canopy_Cover-Sus_scrofa 6.4884 1.0434 293
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1412 1.0673 298
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.9023 1.0416 179
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.5785 1.0973 195
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0356 1.0946 216
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.4465 1.1695 210
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.7919 1.0975 217
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.9930 1.1850 122
## avg_veg_height-Canis_latrans 1.2413 1.0467 349
## avg_veg_height-Lynx_rufus 1.4818 1.0132 257
## avg_veg_height-Didelphis_virginiana 1.3088 1.0098 380
## avg_veg_height-Sylvilagus_floridanus 1.4104 1.0140 352
## avg_veg_height-Sciurus_carolinensis 2.3227 1.0781 391
## avg_veg_height-Vulpes_vulpes 1.5445 1.0456 246
## avg_veg_height-Sus_scrofa 1.8386 1.0184 343
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5982 0.2007 -3.0035 -2.5883 -2.2193 1.0011
## (Intercept)-Lynx_rufus -3.4842 0.3661 -4.2549 -3.4640 -2.8157 1.1868
## (Intercept)-Didelphis_virginiana -2.5815 0.3042 -3.1622 -2.5792 -1.9740 1.0106
## (Intercept)-Sylvilagus_floridanus -3.0789 0.2682 -3.6500 -3.0715 -2.5690 1.0156
## (Intercept)-Sciurus_carolinensis -2.7366 0.3197 -3.3478 -2.7357 -2.0993 1.0111
## (Intercept)-Vulpes_vulpes -3.6128 0.5438 -4.7414 -3.5756 -2.6525 1.1149
## (Intercept)-Sus_scrofa -3.3393 0.5182 -4.3740 -3.3286 -2.3185 1.0816
## shrub_cover-Canis_latrans -0.2987 0.2493 -0.7714 -0.3030 0.1974 1.0292
## shrub_cover-Lynx_rufus 0.0008 0.4125 -0.8556 0.0022 0.7603 1.1768
## shrub_cover-Didelphis_virginiana 1.2409 0.4021 0.4982 1.2320 2.0668 1.0369
## shrub_cover-Sylvilagus_floridanus 0.5869 0.4060 -0.2412 0.5948 1.3477 1.0362
## shrub_cover-Sciurus_carolinensis 1.1558 0.4312 0.3209 1.1557 2.0144 1.0309
## shrub_cover-Vulpes_vulpes 0.4058 0.6046 -0.8026 0.4135 1.5632 1.0214
## shrub_cover-Sus_scrofa 1.1119 0.8363 -0.5626 1.1023 2.8192 1.1099
## veg_height-Canis_latrans -0.5879 0.1884 -0.9789 -0.5827 -0.2322 1.0147
## veg_height-Lynx_rufus 0.1464 0.2496 -0.3574 0.1527 0.6163 1.0122
## veg_height-Didelphis_virginiana 0.5356 0.2642 0.0329 0.5263 1.0681 1.0044
## veg_height-Sylvilagus_floridanus 0.1397 0.2616 -0.3592 0.1362 0.6447 1.0253
## veg_height-Sciurus_carolinensis 0.1789 0.2323 -0.2570 0.1726 0.6570 1.0139
## veg_height-Vulpes_vulpes -0.2083 0.3329 -0.9201 -0.1969 0.4129 1.0257
## veg_height-Sus_scrofa -0.2104 0.3280 -0.8621 -0.2077 0.4231 1.0227
## week-Canis_latrans 0.5539 0.2586 0.0532 0.5511 1.0601 1.0132
## week-Lynx_rufus 0.3971 0.3259 -0.2332 0.3889 1.0376 1.0084
## week-Didelphis_virginiana 0.1378 0.3637 -0.6329 0.1515 0.8171 1.0057
## week-Sylvilagus_floridanus 0.1249 0.3367 -0.5518 0.1384 0.7683 1.0009
## week-Sciurus_carolinensis 0.7172 0.3687 0.0509 0.7108 1.4981 1.0024
## week-Vulpes_vulpes 0.3173 0.4574 -0.6302 0.3287 1.1797 1.0029
## week-Sus_scrofa 0.6348 0.4120 -0.1326 0.6051 1.5009 1.0037
## I(week^2)-Canis_latrans -0.2298 0.1131 -0.4607 -0.2299 -0.0067 1.0118
## I(week^2)-Lynx_rufus -0.2575 0.1610 -0.5877 -0.2480 0.0258 1.0097
## I(week^2)-Didelphis_virginiana -0.4454 0.2473 -1.0229 -0.4189 -0.0411 1.0612
## I(week^2)-Sylvilagus_floridanus -0.1929 0.1609 -0.5241 -0.1855 0.1064 1.0083
## I(week^2)-Sciurus_carolinensis -0.2499 0.1558 -0.5617 -0.2434 0.0515 1.0012
## I(week^2)-Vulpes_vulpes -0.4968 0.3210 -1.2778 -0.4452 -0.0044 1.0683
## I(week^2)-Sus_scrofa -0.2428 0.1834 -0.6124 -0.2393 0.0978 1.0047
## ESS
## (Intercept)-Canis_latrans 863
## (Intercept)-Lynx_rufus 216
## (Intercept)-Didelphis_virginiana 460
## (Intercept)-Sylvilagus_floridanus 658
## (Intercept)-Sciurus_carolinensis 414
## (Intercept)-Vulpes_vulpes 268
## (Intercept)-Sus_scrofa 271
## shrub_cover-Canis_latrans 446
## shrub_cover-Lynx_rufus 140
## shrub_cover-Didelphis_virginiana 382
## shrub_cover-Sylvilagus_floridanus 366
## shrub_cover-Sciurus_carolinensis 416
## shrub_cover-Vulpes_vulpes 460
## shrub_cover-Sus_scrofa 223
## veg_height-Canis_latrans 785
## veg_height-Lynx_rufus 455
## veg_height-Didelphis_virginiana 717
## veg_height-Sylvilagus_floridanus 584
## veg_height-Sciurus_carolinensis 937
## veg_height-Vulpes_vulpes 531
## veg_height-Sus_scrofa 881
## week-Canis_latrans 1082
## week-Lynx_rufus 923
## week-Didelphis_virginiana 798
## week-Sylvilagus_floridanus 801
## week-Sciurus_carolinensis 997
## week-Vulpes_vulpes 725
## week-Sus_scrofa 934
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 483
## I(week^2)-Didelphis_virginiana 342
## I(week^2)-Sylvilagus_floridanus 794
## I(week^2)-Sciurus_carolinensis 954
## I(week^2)-Vulpes_vulpes 197
## I(week^2)-Sus_scrofa 972
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$beta.samples)
## 'mcmc' num [1:3000, 1:63] -1.246 0.291 -1.301 -1.237 -0.255 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:63] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0
MCMCplot(ms_fullQ_fullQ$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, 1:7, 1:100] 0.3066 0.5462 0.0387 0.0766 0.039 ...
## $ z.0.samples : int [1:3000, 1:7, 1:100] 0 1 0 0 0 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$psi.0.samples)
## num [1:3000, 1:7, 1:100] 0.3066 0.5462 0.0387 0.0766 0.039 ...
psi.0.quants <- apply(out.pred$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] 4.93e-05 2.95e-02 9.12e-01 5.45e-05 2.91e-02 ...
## - 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 0.0295 0.0291 0.0288 0.0301 0.0296 ...
## $ psi.low : num 4.93e-05 5.45e-05 5.09e-05 5.52e-05 5.81e-05 ...
## $ psi.high : num 0.912 0.906 0.893 0.896 0.888 ...
## $ 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): 0.5727
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5089 1.2277 -4.7118 -2.5927 0.2843 1.0380 418
## Cogon_Patch_Size 0.0455 1.0601 -2.1496 0.1026 2.0426 1.0040 488
## Veg_shannon_index 0.9840 0.7984 -0.6019 1.0014 2.4837 1.0455 365
## total_shrub_cover -0.8711 0.8446 -2.6477 -0.8400 0.7583 1.0938 144
## Avg_Cogongrass_Cover -0.6600 1.2674 -3.1378 -0.6316 1.6918 1.0824 117
## Tree_Density -1.7091 1.0285 -3.6508 -1.6724 0.3264 1.1252 245
## Avg_Canopy_Cover 1.9619 1.0635 -0.2418 1.9776 3.9963 1.0310 1051
## I(Avg_Cogongrass_Cover^2) 1.9486 0.8491 0.3174 1.9250 3.6812 1.1593 171
## avg_veg_height -0.3031 0.7134 -1.7195 -0.2878 1.0941 1.0307 201
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7963 23.7101 0.1384 5.7730 71.8819 1.3930 204
## Cogon_Patch_Size 10.1935 21.1284 0.1757 4.7501 54.1285 1.1002 176
## Veg_shannon_index 2.6322 4.7266 0.0733 1.1497 13.8435 1.1602 246
## total_shrub_cover 3.1408 5.7604 0.0824 1.2715 17.3443 1.0214 291
## Avg_Cogongrass_Cover 2.0440 5.3698 0.0511 0.6717 11.8790 1.1292 590
## Tree_Density 4.4963 8.2633 0.0711 1.6345 27.2610 1.0311 191
## Avg_Canopy_Cover 12.7923 23.4899 0.4619 6.5265 64.5089 1.1566 199
## I(Avg_Cogongrass_Cover^2) 1.8410 4.2073 0.0558 0.6814 10.8929 1.0819 259
## avg_veg_height 0.9381 1.7208 0.0451 0.3997 4.9257 1.1108 605
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.72 2.376 0.0674 0.8312 8.4584 1.0204 76
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9830 0.3129 -3.6146 -2.9839 -2.3712 1.0237 648
## shrub_cover 0.5755 0.4185 -0.2210 0.5701 1.3869 1.0753 457
## veg_height -0.0036 0.2346 -0.4775 -0.0065 0.4560 1.0032 1349
## week 0.4045 0.2750 -0.1496 0.4098 0.9438 1.0014 900
## I(week^2) -0.3024 0.1699 -0.6342 -0.2931 0.0012 1.0072 810
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5068 0.5890 0.0610 0.3301 1.9081 1.0464 758
## shrub_cover 0.9049 1.0179 0.1320 0.6205 3.3422 1.0560 843
## veg_height 0.3267 0.3592 0.0636 0.2369 1.1629 1.0258 1598
## week 0.2871 0.3194 0.0367 0.1880 1.1606 1.0020 1396
## I(week^2) 0.1246 0.1666 0.0225 0.0786 0.5131 1.0386 486
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3974 1.4470 -4.0539 -1.4521
## (Intercept)-Lynx_rufus -1.1024 2.3144 -4.7332 -1.4783
## (Intercept)-Didelphis_virginiana -4.3494 1.6619 -8.0144 -4.1947
## (Intercept)-Sylvilagus_floridanus -2.8930 1.6591 -6.3373 -2.8344
## (Intercept)-Sciurus_carolinensis -4.8899 1.9644 -9.5671 -4.6243
## (Intercept)-Vulpes_vulpes -5.2122 2.3857 -10.6641 -4.8682
## (Intercept)-Sus_scrofa -5.6521 2.7155 -12.7017 -5.1691
## Cogon_Patch_Size-Canis_latrans 2.5813 2.0468 -0.0675 2.1889
## Cogon_Patch_Size-Lynx_rufus -0.0498 2.4493 -3.7067 -0.2685
## Cogon_Patch_Size-Didelphis_virginiana 2.2620 1.3335 0.1104 2.1130
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9032 2.3358 -7.4003 -1.4693
## Cogon_Patch_Size-Sciurus_carolinensis -1.3215 2.2741 -7.4061 -0.8890
## Cogon_Patch_Size-Vulpes_vulpes -0.8350 2.2687 -6.6018 -0.4587
## Cogon_Patch_Size-Sus_scrofa -1.0872 2.3497 -7.1908 -0.6505
## Veg_shannon_index-Canis_latrans 1.5875 0.9163 -0.0142 1.5009
## Veg_shannon_index-Lynx_rufus 1.0016 1.5070 -2.2415 1.0321
## Veg_shannon_index-Didelphis_virginiana 1.4732 1.1652 -0.4687 1.3641
## Veg_shannon_index-Sylvilagus_floridanus 1.2122 0.9918 -0.7557 1.1931
## Veg_shannon_index-Sciurus_carolinensis -0.0832 1.2981 -2.9363 0.0762
## Veg_shannon_index-Vulpes_vulpes 0.3499 1.2737 -2.5058 0.4773
## Veg_shannon_index-Sus_scrofa 2.1268 1.5718 -0.0344 1.8395
## total_shrub_cover-Canis_latrans 0.2746 0.9992 -1.4417 0.1616
## total_shrub_cover-Lynx_rufus -1.7586 1.5765 -5.3391 -1.5866
## total_shrub_cover-Didelphis_virginiana -1.6779 1.6945 -6.1768 -1.3481
## total_shrub_cover-Sylvilagus_floridanus -0.9962 1.3076 -3.9160 -0.8927
## total_shrub_cover-Sciurus_carolinensis -0.9549 1.3393 -4.0543 -0.7998
## total_shrub_cover-Vulpes_vulpes -1.4123 1.6078 -5.3490 -1.1574
## total_shrub_cover-Sus_scrofa -0.6753 1.5221 -4.0740 -0.5875
## Avg_Cogongrass_Cover-Canis_latrans -0.6251 1.5312 -3.5594 -0.6315
## Avg_Cogongrass_Cover-Lynx_rufus -0.5291 1.6128 -3.5657 -0.5308
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.7863 1.5824 -3.8757 -0.7562
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3283 1.7030 -4.9920 -1.2452
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6749 1.6024 -3.8166 -0.6520
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.3500 1.7193 -3.4042 -0.3602
## Avg_Cogongrass_Cover-Sus_scrofa -0.8813 1.7128 -4.3844 -0.8271
## Tree_Density-Canis_latrans -2.9239 1.6741 -7.0660 -2.6989
## Tree_Density-Lynx_rufus -0.4918 1.7918 -3.3782 -0.7540
## Tree_Density-Didelphis_virginiana -2.0592 1.5129 -5.5290 -1.9466
## Tree_Density-Sylvilagus_floridanus -2.6042 1.8660 -6.9872 -2.3599
## Tree_Density-Sciurus_carolinensis -2.5222 2.0091 -7.4462 -2.2666
## Tree_Density-Vulpes_vulpes -1.6527 1.7829 -5.2974 -1.6612
## Tree_Density-Sus_scrofa -2.0393 1.7559 -6.0110 -1.9145
## Avg_Canopy_Cover-Canis_latrans -0.1395 0.7606 -1.7124 -0.1092
## Avg_Canopy_Cover-Lynx_rufus 1.1855 1.9190 -2.0784 0.9300
## Avg_Canopy_Cover-Didelphis_virginiana 4.7391 2.8293 1.5929 4.1818
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.5661 2.9534 1.5697 4.9780
## Avg_Canopy_Cover-Sciurus_carolinensis 4.0533 2.1058 1.2136 3.6378
## Avg_Canopy_Cover-Vulpes_vulpes 3.2380 2.1724 0.4549 2.7719
## Avg_Canopy_Cover-Sus_scrofa 2.6339 1.5693 0.3617 2.3895
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5934 1.1214 0.8098 2.4641
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8356 1.2935 0.8565 2.6465
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6230 0.9714 -0.1788 1.5843
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7757 1.0288 -0.0865 1.7278
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1739 1.0209 0.4573 2.0651
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3525 1.1361 0.4876 2.2646
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3415 1.5898 -2.6585 1.5251
## avg_veg_height-Canis_latrans -0.3216 0.7641 -1.7943 -0.3176
## avg_veg_height-Lynx_rufus -0.5423 1.1037 -2.9906 -0.4698
## avg_veg_height-Didelphis_virginiana -0.4934 0.9600 -2.5016 -0.4571
## avg_veg_height-Sylvilagus_floridanus -0.3724 0.9709 -2.5718 -0.3319
## avg_veg_height-Sciurus_carolinensis 0.1780 0.9595 -1.5033 0.1018
## avg_veg_height-Vulpes_vulpes -0.3844 1.0354 -2.6325 -0.3546
## avg_veg_height-Sus_scrofa -0.2220 1.0099 -2.2564 -0.2383
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5783 1.0134 287
## (Intercept)-Lynx_rufus 4.4296 1.2704 91
## (Intercept)-Didelphis_virginiana -1.6081 1.1723 192
## (Intercept)-Sylvilagus_floridanus 0.2619 1.0315 228
## (Intercept)-Sciurus_carolinensis -1.8993 1.1207 142
## (Intercept)-Vulpes_vulpes -1.6136 1.1751 103
## (Intercept)-Sus_scrofa -1.8507 1.3548 120
## Cogon_Patch_Size-Canis_latrans 8.0135 1.0611 286
## Cogon_Patch_Size-Lynx_rufus 4.6642 1.1886 119
## Cogon_Patch_Size-Didelphis_virginiana 5.2339 1.0882 333
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4859 1.0386 281
## Cogon_Patch_Size-Sciurus_carolinensis 1.7563 1.0411 207
## Cogon_Patch_Size-Vulpes_vulpes 2.6692 1.0527 212
## Cogon_Patch_Size-Sus_scrofa 2.0861 1.1200 154
## Veg_shannon_index-Canis_latrans 3.6196 1.0739 467
## Veg_shannon_index-Lynx_rufus 4.0842 1.0425 248
## Veg_shannon_index-Didelphis_virginiana 4.0830 1.1018 347
## Veg_shannon_index-Sylvilagus_floridanus 3.3055 1.0334 472
## Veg_shannon_index-Sciurus_carolinensis 2.0567 1.1075 299
## Veg_shannon_index-Vulpes_vulpes 2.5147 1.0726 374
## Veg_shannon_index-Sus_scrofa 6.0090 1.1388 231
## total_shrub_cover-Canis_latrans 2.5524 1.0151 301
## total_shrub_cover-Lynx_rufus 0.9731 1.1364 157
## total_shrub_cover-Didelphis_virginiana 0.5173 1.0220 140
## total_shrub_cover-Sylvilagus_floridanus 1.3814 1.0457 345
## total_shrub_cover-Sciurus_carolinensis 1.2371 1.0045 277
## total_shrub_cover-Vulpes_vulpes 1.0585 1.2092 202
## total_shrub_cover-Sus_scrofa 2.1984 1.0697 223
## Avg_Cogongrass_Cover-Canis_latrans 2.2865 1.0631 181
## Avg_Cogongrass_Cover-Lynx_rufus 2.5574 1.0817 167
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1371 1.0914 198
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5859 1.1070 236
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3663 1.0975 187
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2973 1.0283 177
## Avg_Cogongrass_Cover-Sus_scrofa 2.3587 1.0459 199
## Tree_Density-Canis_latrans -0.4243 1.0786 290
## Tree_Density-Lynx_rufus 3.8329 1.0396 195
## Tree_Density-Didelphis_virginiana 0.8323 1.0464 386
## Tree_Density-Sylvilagus_floridanus 0.4961 1.0495 245
## Tree_Density-Sciurus_carolinensis 0.6778 1.1692 198
## Tree_Density-Vulpes_vulpes 1.9109 1.1210 285
## Tree_Density-Sus_scrofa 1.1625 1.0364 281
## Avg_Canopy_Cover-Canis_latrans 1.3107 1.0001 640
## Avg_Canopy_Cover-Lynx_rufus 5.5117 1.2919 122
## Avg_Canopy_Cover-Didelphis_virginiana 11.6001 1.1789 125
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.7831 1.0749 135
## Avg_Canopy_Cover-Sciurus_carolinensis 9.6245 1.1270 214
## Avg_Canopy_Cover-Vulpes_vulpes 9.0603 1.0039 166
## Avg_Canopy_Cover-Sus_scrofa 6.4884 1.0434 293
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1412 1.0673 298
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.9023 1.0416 179
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.5785 1.0973 195
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0356 1.0946 216
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.4465 1.1695 210
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.7919 1.0975 217
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.9930 1.1850 122
## avg_veg_height-Canis_latrans 1.2413 1.0467 349
## avg_veg_height-Lynx_rufus 1.4818 1.0132 257
## avg_veg_height-Didelphis_virginiana 1.3088 1.0098 380
## avg_veg_height-Sylvilagus_floridanus 1.4104 1.0140 352
## avg_veg_height-Sciurus_carolinensis 2.3227 1.0781 391
## avg_veg_height-Vulpes_vulpes 1.5445 1.0456 246
## avg_veg_height-Sus_scrofa 1.8386 1.0184 343
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5982 0.2007 -3.0035 -2.5883 -2.2193 1.0011
## (Intercept)-Lynx_rufus -3.4842 0.3661 -4.2549 -3.4640 -2.8157 1.1868
## (Intercept)-Didelphis_virginiana -2.5815 0.3042 -3.1622 -2.5792 -1.9740 1.0106
## (Intercept)-Sylvilagus_floridanus -3.0789 0.2682 -3.6500 -3.0715 -2.5690 1.0156
## (Intercept)-Sciurus_carolinensis -2.7366 0.3197 -3.3478 -2.7357 -2.0993 1.0111
## (Intercept)-Vulpes_vulpes -3.6128 0.5438 -4.7414 -3.5756 -2.6525 1.1149
## (Intercept)-Sus_scrofa -3.3393 0.5182 -4.3740 -3.3286 -2.3185 1.0816
## shrub_cover-Canis_latrans -0.2987 0.2493 -0.7714 -0.3030 0.1974 1.0292
## shrub_cover-Lynx_rufus 0.0008 0.4125 -0.8556 0.0022 0.7603 1.1768
## shrub_cover-Didelphis_virginiana 1.2409 0.4021 0.4982 1.2320 2.0668 1.0369
## shrub_cover-Sylvilagus_floridanus 0.5869 0.4060 -0.2412 0.5948 1.3477 1.0362
## shrub_cover-Sciurus_carolinensis 1.1558 0.4312 0.3209 1.1557 2.0144 1.0309
## shrub_cover-Vulpes_vulpes 0.4058 0.6046 -0.8026 0.4135 1.5632 1.0214
## shrub_cover-Sus_scrofa 1.1119 0.8363 -0.5626 1.1023 2.8192 1.1099
## veg_height-Canis_latrans -0.5879 0.1884 -0.9789 -0.5827 -0.2322 1.0147
## veg_height-Lynx_rufus 0.1464 0.2496 -0.3574 0.1527 0.6163 1.0122
## veg_height-Didelphis_virginiana 0.5356 0.2642 0.0329 0.5263 1.0681 1.0044
## veg_height-Sylvilagus_floridanus 0.1397 0.2616 -0.3592 0.1362 0.6447 1.0253
## veg_height-Sciurus_carolinensis 0.1789 0.2323 -0.2570 0.1726 0.6570 1.0139
## veg_height-Vulpes_vulpes -0.2083 0.3329 -0.9201 -0.1969 0.4129 1.0257
## veg_height-Sus_scrofa -0.2104 0.3280 -0.8621 -0.2077 0.4231 1.0227
## week-Canis_latrans 0.5539 0.2586 0.0532 0.5511 1.0601 1.0132
## week-Lynx_rufus 0.3971 0.3259 -0.2332 0.3889 1.0376 1.0084
## week-Didelphis_virginiana 0.1378 0.3637 -0.6329 0.1515 0.8171 1.0057
## week-Sylvilagus_floridanus 0.1249 0.3367 -0.5518 0.1384 0.7683 1.0009
## week-Sciurus_carolinensis 0.7172 0.3687 0.0509 0.7108 1.4981 1.0024
## week-Vulpes_vulpes 0.3173 0.4574 -0.6302 0.3287 1.1797 1.0029
## week-Sus_scrofa 0.6348 0.4120 -0.1326 0.6051 1.5009 1.0037
## I(week^2)-Canis_latrans -0.2298 0.1131 -0.4607 -0.2299 -0.0067 1.0118
## I(week^2)-Lynx_rufus -0.2575 0.1610 -0.5877 -0.2480 0.0258 1.0097
## I(week^2)-Didelphis_virginiana -0.4454 0.2473 -1.0229 -0.4189 -0.0411 1.0612
## I(week^2)-Sylvilagus_floridanus -0.1929 0.1609 -0.5241 -0.1855 0.1064 1.0083
## I(week^2)-Sciurus_carolinensis -0.2499 0.1558 -0.5617 -0.2434 0.0515 1.0012
## I(week^2)-Vulpes_vulpes -0.4968 0.3210 -1.2778 -0.4452 -0.0044 1.0683
## I(week^2)-Sus_scrofa -0.2428 0.1834 -0.6124 -0.2393 0.0978 1.0047
## ESS
## (Intercept)-Canis_latrans 863
## (Intercept)-Lynx_rufus 216
## (Intercept)-Didelphis_virginiana 460
## (Intercept)-Sylvilagus_floridanus 658
## (Intercept)-Sciurus_carolinensis 414
## (Intercept)-Vulpes_vulpes 268
## (Intercept)-Sus_scrofa 271
## shrub_cover-Canis_latrans 446
## shrub_cover-Lynx_rufus 140
## shrub_cover-Didelphis_virginiana 382
## shrub_cover-Sylvilagus_floridanus 366
## shrub_cover-Sciurus_carolinensis 416
## shrub_cover-Vulpes_vulpes 460
## shrub_cover-Sus_scrofa 223
## veg_height-Canis_latrans 785
## veg_height-Lynx_rufus 455
## veg_height-Didelphis_virginiana 717
## veg_height-Sylvilagus_floridanus 584
## veg_height-Sciurus_carolinensis 937
## veg_height-Vulpes_vulpes 531
## veg_height-Sus_scrofa 881
## week-Canis_latrans 1082
## week-Lynx_rufus 923
## week-Didelphis_virginiana 798
## week-Sylvilagus_floridanus 801
## week-Sciurus_carolinensis 997
## week-Vulpes_vulpes 725
## week-Sus_scrofa 934
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 483
## I(week^2)-Didelphis_virginiana 342
## I(week^2)-Sylvilagus_floridanus 794
## I(week^2)-Sciurus_carolinensis 954
## I(week^2)-Vulpes_vulpes 197
## I(week^2)-Sus_scrofa 972
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$beta.samples)
## 'mcmc' num [1:3000, 1:63] -1.246 0.291 -1.301 -1.237 -0.255 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:63] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0
MCMCplot(ms_fullQ_fullQ$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 %in% c("Odocoileus_virginianus", "Dasypus_novemcinctus",
"Procyon_lotor", "Sciurus_niger")) %>%
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$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 8
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$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" "Lynx_rufus" "Didelphis_virginiana" "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 [1:8, 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" "Lynx_rufus" "Didelphis_virginiana" "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_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 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_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): 0.3658
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8145 0.366 -1.5362 -0.8144 -0.0583 1.0014 1512
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8554 0.9594 0.1157 0.5868 3.2316 1.0029 1268
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9283 0.2547 -3.4368 -2.9272 -2.4189 1.0029 1282
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3833 0.3976 0.0621 0.2728 1.3339 1.0043 886
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.0790 0.4041 -0.6374 0.0525 0.9432 1.0010
## (Intercept)-Lynx_rufus -0.2372 0.5398 -1.1243 -0.2873 0.9729 1.0035
## (Intercept)-Didelphis_virginiana -1.2684 0.4011 -2.1157 -1.2383 -0.5327 1.0012
## (Intercept)-Sylvilagus_floridanus -0.5210 0.4519 -1.3066 -0.5435 0.3713 1.0032
## (Intercept)-Meleagris_gallopavo -0.5525 0.4649 -1.3897 -0.5618 0.4527 1.0015
## (Intercept)-Sciurus_carolinensis -1.2482 0.4141 -2.0895 -1.2357 -0.4894 1.0063
## (Intercept)-Vulpes_vulpes -1.4240 0.5974 -2.6041 -1.4065 -0.2692 1.0055
## (Intercept)-Sus_scrofa -1.6277 0.5273 -2.7322 -1.6051 -0.6703 1.0028
## ESS
## (Intercept)-Canis_latrans 1656
## (Intercept)-Lynx_rufus 570
## (Intercept)-Didelphis_virginiana 2112
## (Intercept)-Sylvilagus_floridanus 744
## (Intercept)-Meleagris_gallopavo 1147
## (Intercept)-Sciurus_carolinensis 2171
## (Intercept)-Vulpes_vulpes 697
## (Intercept)-Sus_scrofa 1285
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6340 0.1675 -2.9655 -2.6309 -2.3177 1.0029
## (Intercept)-Lynx_rufus -3.3508 0.3025 -3.9871 -3.3301 -2.8110 1.0231
## (Intercept)-Didelphis_virginiana -2.4298 0.2461 -2.9279 -2.4194 -1.9724 1.0101
## (Intercept)-Sylvilagus_floridanus -3.1074 0.2751 -3.7125 -3.0877 -2.6307 1.0006
## (Intercept)-Meleagris_gallopavo -3.2737 0.2980 -3.9015 -3.2529 -2.7368 1.0020
## (Intercept)-Sciurus_carolinensis -2.5647 0.2580 -3.0893 -2.5623 -2.0687 1.0076
## (Intercept)-Vulpes_vulpes -3.4740 0.5070 -4.5537 -3.4180 -2.6183 1.0259
## (Intercept)-Sus_scrofa -2.9956 0.3965 -3.8285 -2.9688 -2.2744 1.0059
## ESS
## (Intercept)-Canis_latrans 994
## (Intercept)-Lynx_rufus 381
## (Intercept)-Didelphis_virginiana 1341
## (Intercept)-Sylvilagus_floridanus 530
## (Intercept)-Meleagris_gallopavo 546
## (Intercept)-Sciurus_carolinensis 1156
## (Intercept)-Vulpes_vulpes 332
## (Intercept)-Sus_scrofa 746
# 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 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_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): 0.6713
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2149 1.0826 -3.3121 -1.2244 0.9454 1.0674 132
## Cogon_Patch_Size -0.2144 0.9793 -2.2155 -0.2118 1.6998 1.0797 383
## Veg_shannon_index 1.2326 0.8094 -0.3251 1.2186 2.9174 1.1399 302
## total_shrub_cover -1.3975 1.1563 -3.7436 -1.3738 0.8197 1.1424 217
## Avg_Cogongrass_Cover 1.6943 0.9833 -0.3189 1.7234 3.5714 1.0236 175
## Tree_Density -1.4064 1.2152 -3.8120 -1.3967 1.0082 1.2629 96
## Avg_Canopy_Cover 2.1219 1.1524 -0.4558 2.1748 4.1699 1.0268 1110
## avg_veg_height -0.2048 0.8229 -1.7251 -0.2552 1.6485 1.3557 120
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5622 12.5357 0.0945 2.9854 34.0741 1.0660 170
## Cogon_Patch_Size 7.9827 14.3414 0.1547 3.6362 41.4548 1.1347 211
## Veg_shannon_index 3.3202 5.4606 0.0680 1.4518 18.2371 1.0687 256
## total_shrub_cover 17.7286 31.8944 0.3209 8.0407 92.5115 1.3293 65
## Avg_Cogongrass_Cover 3.3472 6.4195 0.0582 1.1426 20.2954 1.0860 297
## Tree_Density 5.8149 12.5908 0.0665 1.7259 37.5387 1.4969 128
## Avg_Canopy_Cover 19.4104 29.2637 0.7674 9.1973 93.7005 1.6181 64
## avg_veg_height 1.1239 2.3325 0.0513 0.4524 6.5198 1.1842 266
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 12.5087 15.3403 0.2198 7.2389 59.4436 1.7627 25
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.3246 0.2966 -3.9381 -3.3142 -2.7889 1.0565 405
## shrub_cover 0.4852 0.4192 -0.3464 0.4812 1.2973 1.0336 304
## veg_height -0.0920 0.2309 -0.5588 -0.0832 0.3496 1.0410 872
## week -0.0783 0.1710 -0.4342 -0.0694 0.2410 1.0055 794
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5247 0.6150 0.0705 0.3700 1.8611 1.0258 335
## shrub_cover 1.1250 1.1031 0.1994 0.8201 3.7772 1.0427 548
## veg_height 0.3246 0.3045 0.0645 0.2384 1.1391 1.0389 1358
## week 0.1495 0.1599 0.0304 0.1078 0.4971 1.0165 1559
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3904 1.8015 -2.7495 0.2518
## (Intercept)-Lynx_rufus -0.0801 2.3339 -3.6950 -0.4545
## (Intercept)-Didelphis_virginiana -2.4436 1.7777 -6.3289 -2.2863
## (Intercept)-Sylvilagus_floridanus -1.0703 1.6163 -4.3697 -1.0961
## (Intercept)-Meleagris_gallopavo -1.5945 1.9016 -5.7916 -1.5101
## (Intercept)-Sciurus_carolinensis -2.5015 1.8576 -7.1570 -2.3176
## (Intercept)-Vulpes_vulpes -2.0784 2.0601 -6.4051 -1.8519
## (Intercept)-Sus_scrofa -3.0718 2.6347 -9.3495 -2.6116
## Cogon_Patch_Size-Canis_latrans 1.4023 1.7266 -1.0710 1.0657
## Cogon_Patch_Size-Lynx_rufus -0.2951 2.1126 -4.3880 -0.3330
## Cogon_Patch_Size-Didelphis_virginiana 1.8839 1.7051 -0.5322 1.5599
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3268 2.4942 -8.5923 -1.8508
## Cogon_Patch_Size-Meleagris_gallopavo 0.5645 2.1602 -2.8122 0.2569
## Cogon_Patch_Size-Sciurus_carolinensis -1.5510 1.9983 -6.1315 -1.2565
## Cogon_Patch_Size-Vulpes_vulpes -0.9570 2.1297 -5.6110 -0.7762
## Cogon_Patch_Size-Sus_scrofa -0.9787 2.1098 -5.5171 -0.7432
## Veg_shannon_index-Canis_latrans 1.7503 1.0542 -0.0950 1.6352
## Veg_shannon_index-Lynx_rufus 1.1498 1.4837 -1.9111 1.1522
## Veg_shannon_index-Didelphis_virginiana 1.8887 1.3890 -0.1269 1.6492
## Veg_shannon_index-Sylvilagus_floridanus 1.6763 1.3136 -0.4378 1.4986
## Veg_shannon_index-Meleagris_gallopavo 2.1050 1.5022 -0.2733 1.8531
## Veg_shannon_index-Sciurus_carolinensis 0.0029 1.4750 -3.3864 0.2048
## Veg_shannon_index-Vulpes_vulpes 0.2511 1.5743 -3.5641 0.4869
## Veg_shannon_index-Sus_scrofa 2.2292 1.6341 -0.2372 1.9249
## total_shrub_cover-Canis_latrans 2.2225 2.0600 -0.6376 1.8600
## total_shrub_cover-Lynx_rufus -3.7694 3.2405 -11.7742 -3.2889
## total_shrub_cover-Didelphis_virginiana -2.6770 2.3850 -8.9109 -2.1851
## total_shrub_cover-Sylvilagus_floridanus -2.3039 3.2371 -10.1813 -1.7131
## total_shrub_cover-Meleagris_gallopavo -4.4354 2.7970 -11.0345 -3.9765
## total_shrub_cover-Sciurus_carolinensis -2.5345 2.8240 -9.4000 -1.8684
## total_shrub_cover-Vulpes_vulpes -4.2142 3.8643 -14.8063 -3.2974
## total_shrub_cover-Sus_scrofa -1.4298 2.9709 -8.8002 -0.9910
## Avg_Cogongrass_Cover-Canis_latrans 2.4349 1.3787 0.1491 2.3179
## Avg_Cogongrass_Cover-Lynx_rufus 2.5246 1.6937 -0.0798 2.2813
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9822 1.3154 -0.4379 1.9426
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8435 1.6251 -2.8861 1.0018
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0991 1.8770 -3.0783 1.3284
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2287 1.5074 -0.5510 2.1315
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5861 1.6203 -0.0696 2.4257
## Avg_Cogongrass_Cover-Sus_scrofa 1.4544 1.7208 -2.4974 1.5209
## Tree_Density-Canis_latrans -2.9321 2.1942 -8.6775 -2.5111
## Tree_Density-Lynx_rufus 0.0023 2.2466 -3.5588 -0.2414
## Tree_Density-Didelphis_virginiana -1.8273 2.0426 -6.2539 -1.7820
## Tree_Density-Sylvilagus_floridanus -2.2270 2.1559 -7.1752 -2.0764
## Tree_Density-Meleagris_gallopavo -1.7903 2.0909 -6.0531 -1.7861
## Tree_Density-Sciurus_carolinensis -1.8840 2.0715 -6.4352 -1.7967
## Tree_Density-Vulpes_vulpes -1.3134 2.0690 -5.3141 -1.3714
## Tree_Density-Sus_scrofa -1.7065 2.0505 -6.0458 -1.5713
## Avg_Canopy_Cover-Canis_latrans -0.4203 0.9753 -2.7016 -0.3268
## Avg_Canopy_Cover-Lynx_rufus 0.4085 2.0777 -4.0355 0.4110
## Avg_Canopy_Cover-Didelphis_virginiana 5.5794 2.8990 1.8464 4.8777
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.4689 3.6063 1.5727 5.6395
## Avg_Canopy_Cover-Meleagris_gallopavo 4.0027 2.7722 0.6638 3.3120
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2367 2.6747 1.5717 4.7437
## Avg_Canopy_Cover-Vulpes_vulpes 4.2348 2.6046 0.7526 3.6813
## Avg_Canopy_Cover-Sus_scrofa 3.1515 2.3878 -0.1982 2.7064
## avg_veg_height-Canis_latrans -0.2397 0.9205 -1.8779 -0.2844
## avg_veg_height-Lynx_rufus -0.3874 1.2487 -2.9808 -0.4092
## avg_veg_height-Didelphis_virginiana -0.4815 1.1070 -2.7842 -0.4725
## avg_veg_height-Sylvilagus_floridanus -0.2986 1.0667 -2.1541 -0.3753
## avg_veg_height-Meleagris_gallopavo -0.2217 1.2247 -2.7374 -0.2562
## avg_veg_height-Sciurus_carolinensis 0.3213 1.1971 -1.4684 0.1382
## avg_veg_height-Vulpes_vulpes -0.0927 1.2652 -2.2961 -0.2060
## avg_veg_height-Sus_scrofa -0.2328 1.1339 -2.4012 -0.2873
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.4382 1.0722 149
## (Intercept)-Lynx_rufus 5.5254 1.3159 89
## (Intercept)-Didelphis_virginiana 0.6514 1.0490 130
## (Intercept)-Sylvilagus_floridanus 2.2179 1.0301 223
## (Intercept)-Meleagris_gallopavo 1.9829 1.1337 187
## (Intercept)-Sciurus_carolinensis 0.6066 1.1123 156
## (Intercept)-Vulpes_vulpes 1.4886 1.0442 124
## (Intercept)-Sus_scrofa 0.9333 1.0942 80
## Cogon_Patch_Size-Canis_latrans 5.7742 1.1620 218
## Cogon_Patch_Size-Lynx_rufus 4.4746 1.0527 162
## Cogon_Patch_Size-Didelphis_virginiana 6.3759 1.2930 147
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3145 1.0573 172
## Cogon_Patch_Size-Meleagris_gallopavo 6.2628 1.1654 182
## Cogon_Patch_Size-Sciurus_carolinensis 1.5526 1.0409 182
## Cogon_Patch_Size-Vulpes_vulpes 3.3712 1.0761 213
## Cogon_Patch_Size-Sus_scrofa 2.5691 1.0370 228
## Veg_shannon_index-Canis_latrans 4.2378 1.1675 336
## Veg_shannon_index-Lynx_rufus 4.1854 1.0842 286
## Veg_shannon_index-Didelphis_virginiana 5.3755 1.1640 102
## Veg_shannon_index-Sylvilagus_floridanus 4.8100 1.2047 281
## Veg_shannon_index-Meleagris_gallopavo 5.7670 1.0896 217
## Veg_shannon_index-Sciurus_carolinensis 2.4700 1.0525 252
## Veg_shannon_index-Vulpes_vulpes 2.8799 1.0592 241
## Veg_shannon_index-Sus_scrofa 6.1085 1.0215 265
## total_shrub_cover-Canis_latrans 7.4214 1.5725 61
## total_shrub_cover-Lynx_rufus 1.0503 1.5261 77
## total_shrub_cover-Didelphis_virginiana 0.5419 1.3784 105
## total_shrub_cover-Sylvilagus_floridanus 2.4459 1.2116 67
## total_shrub_cover-Meleagris_gallopavo -0.2994 1.2534 80
## total_shrub_cover-Sciurus_carolinensis 1.3806 1.4404 88
## total_shrub_cover-Vulpes_vulpes 0.5413 1.4093 56
## total_shrub_cover-Sus_scrofa 3.4084 1.1041 71
## Avg_Cogongrass_Cover-Canis_latrans 5.7094 1.0389 335
## Avg_Cogongrass_Cover-Lynx_rufus 6.8495 1.0699 184
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.8051 1.0146 283
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5362 1.1175 208
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.4150 1.0333 221
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.5998 1.0105 260
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.3099 1.0295 233
## Avg_Cogongrass_Cover-Sus_scrofa 4.6856 1.0586 256
## Tree_Density-Canis_latrans 0.1347 1.0857 173
## Tree_Density-Lynx_rufus 5.6013 1.2987 74
## Tree_Density-Didelphis_virginiana 2.0609 1.1165 153
## Tree_Density-Sylvilagus_floridanus 1.7735 1.1730 99
## Tree_Density-Meleagris_gallopavo 2.3191 1.1346 136
## Tree_Density-Sciurus_carolinensis 2.0489 1.0779 149
## Tree_Density-Vulpes_vulpes 3.1415 1.0904 127
## Tree_Density-Sus_scrofa 1.8983 1.1675 246
## Avg_Canopy_Cover-Canis_latrans 1.3047 1.3373 149
## Avg_Canopy_Cover-Lynx_rufus 4.6854 1.2629 147
## Avg_Canopy_Cover-Didelphis_virginiana 13.3011 1.6894 91
## Avg_Canopy_Cover-Sylvilagus_floridanus 15.3106 1.3853 98
## Avg_Canopy_Cover-Meleagris_gallopavo 11.9419 1.6043 106
## Avg_Canopy_Cover-Sciurus_carolinensis 11.7114 1.3753 84
## Avg_Canopy_Cover-Vulpes_vulpes 11.0155 1.2946 112
## Avg_Canopy_Cover-Sus_scrofa 9.0426 1.0870 155
## avg_veg_height-Canis_latrans 1.7629 1.2134 197
## avg_veg_height-Lynx_rufus 2.2573 1.2006 221
## avg_veg_height-Didelphis_virginiana 1.7464 1.2012 227
## avg_veg_height-Sylvilagus_floridanus 1.9707 1.1578 174
## avg_veg_height-Meleagris_gallopavo 2.2897 1.2149 152
## avg_veg_height-Sciurus_carolinensis 3.1586 1.1691 186
## avg_veg_height-Vulpes_vulpes 2.9440 1.2719 154
## avg_veg_height-Sus_scrofa 2.1606 1.3009 226
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8587 0.1850 -3.2400 -2.8503 -2.5157 1.0519
## (Intercept)-Lynx_rufus -3.6435 0.3261 -4.3361 -3.6282 -3.0584 1.1289
## (Intercept)-Didelphis_virginiana -2.8487 0.2979 -3.4409 -2.8456 -2.2677 1.0492
## (Intercept)-Sylvilagus_floridanus -3.2957 0.2545 -3.8021 -3.2948 -2.8099 1.0093
## (Intercept)-Meleagris_gallopavo -3.8000 0.4245 -4.7217 -3.7901 -3.0341 1.0823
## (Intercept)-Sciurus_carolinensis -3.0123 0.2958 -3.6035 -3.0155 -2.4468 1.1397
## (Intercept)-Vulpes_vulpes -4.1111 0.5979 -5.3765 -4.0652 -3.1105 1.0811
## (Intercept)-Sus_scrofa -3.6562 0.5588 -4.7553 -3.6361 -2.6166 1.0947
## shrub_cover-Canis_latrans -0.4653 0.2257 -0.9021 -0.4658 0.0001 1.1082
## shrub_cover-Lynx_rufus 0.0804 0.4101 -0.8175 0.1204 0.8101 1.1833
## shrub_cover-Didelphis_virginiana 1.2608 0.3973 0.5037 1.2540 2.0450 1.0512
## shrub_cover-Sylvilagus_floridanus 0.6807 0.4510 -0.3112 0.6962 1.5265 1.0498
## shrub_cover-Meleagris_gallopavo -0.5091 0.4092 -1.3382 -0.5097 0.2892 1.1025
## shrub_cover-Sciurus_carolinensis 1.2949 0.4225 0.4392 1.3012 2.0948 1.2230
## shrub_cover-Vulpes_vulpes 0.5973 0.6329 -0.6388 0.5915 1.8975 1.1281
## shrub_cover-Sus_scrofa 1.1731 0.8998 -0.6294 1.2038 2.8799 1.0449
## veg_height-Canis_latrans -0.6696 0.1898 -1.0522 -0.6673 -0.3024 1.0356
## veg_height-Lynx_rufus 0.0360 0.2432 -0.4662 0.0399 0.5114 1.0038
## veg_height-Didelphis_virginiana 0.4937 0.2607 0.0062 0.4848 1.0414 1.0182
## veg_height-Sylvilagus_floridanus 0.0864 0.2671 -0.4485 0.0857 0.5958 1.0099
## veg_height-Meleagris_gallopavo -0.3028 0.4019 -1.1431 -0.2998 0.4744 1.1343
## veg_height-Sciurus_carolinensis 0.1733 0.2362 -0.2699 0.1700 0.6386 1.0140
## veg_height-Vulpes_vulpes -0.2733 0.3741 -1.0716 -0.2541 0.4182 1.0920
## veg_height-Sus_scrofa -0.2337 0.3530 -0.9445 -0.2205 0.4194 1.0137
## week-Canis_latrans 0.0736 0.1357 -0.2011 0.0764 0.3317 1.0053
## week-Lynx_rufus -0.0388 0.2052 -0.4699 -0.0291 0.3538 1.0040
## week-Didelphis_virginiana -0.2608 0.2435 -0.7739 -0.2422 0.1659 1.0031
## week-Sylvilagus_floridanus -0.1888 0.2286 -0.6718 -0.1744 0.2250 1.0202
## week-Meleagris_gallopavo -0.3335 0.2797 -0.9716 -0.3088 0.1389 0.9996
## week-Sciurus_carolinensis 0.1540 0.1926 -0.2274 0.1554 0.5232 1.0113
## week-Vulpes_vulpes -0.1395 0.2884 -0.7765 -0.1262 0.3814 1.0016
## week-Sus_scrofa 0.1046 0.2530 -0.4273 0.1070 0.5823 1.0070
## ESS
## (Intercept)-Canis_latrans 670
## (Intercept)-Lynx_rufus 148
## (Intercept)-Didelphis_virginiana 414
## (Intercept)-Sylvilagus_floridanus 435
## (Intercept)-Meleagris_gallopavo 221
## (Intercept)-Sciurus_carolinensis 277
## (Intercept)-Vulpes_vulpes 158
## (Intercept)-Sus_scrofa 108
## shrub_cover-Canis_latrans 368
## shrub_cover-Lynx_rufus 110
## shrub_cover-Didelphis_virginiana 322
## shrub_cover-Sylvilagus_floridanus 217
## shrub_cover-Meleagris_gallopavo 276
## shrub_cover-Sciurus_carolinensis 252
## shrub_cover-Vulpes_vulpes 209
## shrub_cover-Sus_scrofa 99
## veg_height-Canis_latrans 717
## veg_height-Lynx_rufus 666
## veg_height-Didelphis_virginiana 930
## veg_height-Sylvilagus_floridanus 630
## veg_height-Meleagris_gallopavo 234
## veg_height-Sciurus_carolinensis 918
## veg_height-Vulpes_vulpes 348
## veg_height-Sus_scrofa 626
## week-Canis_latrans 1611
## week-Lynx_rufus 738
## week-Didelphis_virginiana 1069
## week-Sylvilagus_floridanus 836
## week-Meleagris_gallopavo 557
## week-Sciurus_carolinensis 1367
## week-Vulpes_vulpes 699
## week-Sus_scrofa 1221
#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 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_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): 0.6028
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5533 0.411 -1.3461 -0.5632 0.3478 1.0054 1289
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9983 1.0991 0.1123 0.6813 3.8219 1.004 792
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2193 0.3300 -3.8593 -3.2222 -2.5337 1.0023 1205
## shrub_cover 0.1524 0.3890 -0.6108 0.1409 0.9366 1.0043 1156
## veg_height -0.1025 0.2171 -0.5474 -0.0957 0.3208 1.0020 1042
## week -0.0733 0.1696 -0.4402 -0.0665 0.2561 1.0009 1044
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6518 0.6706 0.0883 0.4707 2.4184 1.0031 585
## shrub_cover 0.9863 1.0984 0.1677 0.7093 3.3656 1.0298 865
## veg_height 0.2821 0.2970 0.0550 0.2044 0.8992 1.0358 1218
## week 0.1451 0.1424 0.0293 0.1075 0.4887 1.0234 1541
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2217 0.3976 -0.5227 0.2147 1.0435 1.0022
## (Intercept)-Lynx_rufus 0.0454 0.5798 -0.9038 -0.0148 1.4077 1.0146
## (Intercept)-Didelphis_virginiana -1.0474 0.4355 -1.9563 -1.0170 -0.2460 1.0041
## (Intercept)-Sylvilagus_floridanus -0.3855 0.4325 -1.2089 -0.3960 0.5003 1.0053
## (Intercept)-Meleagris_gallopavo 0.1090 0.7427 -0.9934 -0.0042 1.8851 1.0166
## (Intercept)-Sciurus_carolinensis -1.0374 0.4341 -1.8985 -1.0348 -0.1947 1.0013
## (Intercept)-Vulpes_vulpes -1.1450 0.7110 -2.5664 -1.1452 0.2874 1.0178
## (Intercept)-Sus_scrofa -1.3714 0.5918 -2.6307 -1.3312 -0.3123 1.0024
## ESS
## (Intercept)-Canis_latrans 1717
## (Intercept)-Lynx_rufus 535
## (Intercept)-Didelphis_virginiana 1593
## (Intercept)-Sylvilagus_floridanus 1665
## (Intercept)-Meleagris_gallopavo 342
## (Intercept)-Sciurus_carolinensis 1243
## (Intercept)-Vulpes_vulpes 372
## (Intercept)-Sus_scrofa 668
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7953 0.1829 -3.1578 -2.7907 -2.4595 1.0025
## (Intercept)-Lynx_rufus -3.6489 0.3325 -4.3201 -3.6301 -3.0292 1.0092
## (Intercept)-Didelphis_virginiana -2.7145 0.2936 -3.3139 -2.7054 -2.1708 1.0011
## (Intercept)-Sylvilagus_floridanus -3.2140 0.2871 -3.8019 -3.2023 -2.6873 1.0044
## (Intercept)-Meleagris_gallopavo -4.0576 0.4523 -4.9893 -4.0390 -3.1952 1.0020
## (Intercept)-Sciurus_carolinensis -2.7592 0.3162 -3.3895 -2.7567 -2.1564 1.0054
## (Intercept)-Vulpes_vulpes -3.9703 0.6289 -5.3709 -3.9101 -2.9312 1.0345
## (Intercept)-Sus_scrofa -3.4466 0.5267 -4.5061 -3.4545 -2.3897 1.0122
## shrub_cover-Canis_latrans -0.3311 0.2231 -0.7666 -0.3316 0.0990 1.0058
## shrub_cover-Lynx_rufus -0.3731 0.3623 -1.0942 -0.3704 0.3063 1.0127
## shrub_cover-Didelphis_virginiana 1.0626 0.4007 0.3093 1.0471 1.8915 1.0001
## shrub_cover-Sylvilagus_floridanus 0.2494 0.4156 -0.4987 0.2232 1.0787 1.0009
## shrub_cover-Meleagris_gallopavo -0.7697 0.3998 -1.5975 -0.7603 -0.0128 1.0057
## shrub_cover-Sciurus_carolinensis 0.9182 0.4362 0.1005 0.9130 1.8075 1.0117
## shrub_cover-Vulpes_vulpes -0.2197 0.6404 -1.5171 -0.2155 1.0522 1.0091
## shrub_cover-Sus_scrofa 0.7520 0.8497 -0.9096 0.7384 2.5236 1.0217
## veg_height-Canis_latrans -0.6234 0.1889 -1.0206 -0.6118 -0.2766 1.0094
## veg_height-Lynx_rufus -0.0189 0.2573 -0.5507 -0.0165 0.4849 1.0045
## veg_height-Didelphis_virginiana 0.4308 0.2599 -0.0498 0.4239 0.9705 1.0219
## veg_height-Sylvilagus_floridanus 0.0977 0.2585 -0.4121 0.0994 0.5959 1.0044
## veg_height-Meleagris_gallopavo -0.3699 0.3814 -1.1549 -0.3541 0.3580 1.0258
## veg_height-Sciurus_carolinensis 0.0779 0.2250 -0.3560 0.0810 0.5213 1.0037
## veg_height-Vulpes_vulpes -0.1726 0.3268 -0.8666 -0.1548 0.4165 1.0022
## veg_height-Sus_scrofa -0.2230 0.3477 -0.9372 -0.2183 0.4357 1.0049
## week-Canis_latrans 0.0759 0.1346 -0.2013 0.0812 0.3265 1.0014
## week-Lynx_rufus -0.0433 0.2009 -0.4593 -0.0371 0.3445 1.0088
## week-Didelphis_virginiana -0.2498 0.2452 -0.7708 -0.2353 0.1785 1.0192
## week-Sylvilagus_floridanus -0.1725 0.2140 -0.6295 -0.1606 0.2185 1.0065
## week-Meleagris_gallopavo -0.3004 0.2621 -0.8722 -0.2791 0.1575 1.0026
## week-Sciurus_carolinensis 0.1447 0.1946 -0.2429 0.1492 0.5137 1.0009
## week-Vulpes_vulpes -0.1574 0.3123 -0.8224 -0.1387 0.4111 1.0054
## week-Sus_scrofa 0.1098 0.2521 -0.4135 0.1131 0.5937 1.0022
## ESS
## (Intercept)-Canis_latrans 729
## (Intercept)-Lynx_rufus 349
## (Intercept)-Didelphis_virginiana 671
## (Intercept)-Sylvilagus_floridanus 588
## (Intercept)-Meleagris_gallopavo 208
## (Intercept)-Sciurus_carolinensis 791
## (Intercept)-Vulpes_vulpes 273
## (Intercept)-Sus_scrofa 443
## shrub_cover-Canis_latrans 760
## shrub_cover-Lynx_rufus 411
## shrub_cover-Didelphis_virginiana 655
## shrub_cover-Sylvilagus_floridanus 542
## shrub_cover-Meleagris_gallopavo 232
## shrub_cover-Sciurus_carolinensis 610
## shrub_cover-Vulpes_vulpes 442
## shrub_cover-Sus_scrofa 500
## veg_height-Canis_latrans 698
## veg_height-Lynx_rufus 713
## veg_height-Didelphis_virginiana 1024
## veg_height-Sylvilagus_floridanus 800
## veg_height-Meleagris_gallopavo 367
## veg_height-Sciurus_carolinensis 1043
## veg_height-Vulpes_vulpes 570
## veg_height-Sus_scrofa 1119
## week-Canis_latrans 1588
## week-Lynx_rufus 838
## week-Didelphis_virginiana 1001
## week-Sylvilagus_floridanus 1013
## week-Meleagris_gallopavo 585
## week-Sciurus_carolinensis 1748
## week-Vulpes_vulpes 890
## week-Sus_scrofa 1287
#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 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_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): 0.6783
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5913 0.6195 -1.7946 -0.6009 0.6995 1.0746 125
## Avg_Cogongrass_Cover 0.0258 0.5132 -1.0109 0.0591 0.9939 1.0241 350
## total_shrub_cover -1.1787 0.6790 -2.6775 -1.1326 -0.0057 1.0339 200
## avg_veg_height 0.0919 0.4983 -0.8377 0.0665 1.1778 1.0472 186
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7965 1.1737 0.0474 0.4162 3.7859 1.0227 325
## Avg_Cogongrass_Cover 0.8782 1.2366 0.0536 0.4718 4.0726 1.0514 427
## total_shrub_cover 2.2445 4.1277 0.0947 1.1243 11.0433 1.1055 233
## avg_veg_height 0.4932 0.8207 0.0416 0.2567 2.3049 1.0479 522
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.1869 3.8252 0.4558 3.1153 14.2637 1.1121 103
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.3052 0.2809 -3.8636 -3.3026 -2.7697 1.0241 299
## shrub_cover 0.5768 0.4063 -0.2318 0.5682 1.4267 1.0110 756
## veg_height -0.0642 0.2269 -0.5228 -0.0678 0.3970 1.0162 832
## week -0.0741 0.1591 -0.4053 -0.0697 0.2318 1.0091 969
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3914 0.5010 0.0525 0.2577 1.5175 1.0561 643
## shrub_cover 1.0505 0.9493 0.1884 0.7945 3.3681 1.0022 664
## veg_height 0.2984 0.3060 0.0625 0.2211 1.0007 1.0241 1840
## week 0.1359 0.1273 0.0291 0.1006 0.4478 1.0040 1425
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1037 0.8641 -1.6497 -0.1599
## (Intercept)-Lynx_rufus -0.4694 0.8516 -2.0356 -0.5138
## (Intercept)-Didelphis_virginiana -0.7285 0.7893 -2.2875 -0.7417
## (Intercept)-Sylvilagus_floridanus -0.3167 0.8825 -1.9150 -0.3712
## (Intercept)-Meleagris_gallopavo -0.6822 0.8030 -2.2599 -0.6731
## (Intercept)-Sciurus_carolinensis -0.7606 0.8490 -2.5044 -0.7274
## (Intercept)-Vulpes_vulpes -0.8330 0.9332 -2.6174 -0.8563
## (Intercept)-Sus_scrofa -0.9784 0.9007 -2.9283 -0.9167
## Avg_Cogongrass_Cover-Canis_latrans 0.4905 0.6632 -0.6975 0.4567
## Avg_Cogongrass_Cover-Lynx_rufus 0.5771 0.7142 -0.7226 0.5370
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2779 0.6432 -0.9492 0.2591
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5430 0.7821 -2.3053 -0.4584
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4958 0.8844 -2.5056 -0.4141
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0108 0.6748 -1.4630 0.0439
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2696 0.7394 -1.2128 0.2716
## Avg_Cogongrass_Cover-Sus_scrofa -0.2909 0.9037 -2.4145 -0.1802
## total_shrub_cover-Canis_latrans 0.3066 0.9446 -1.5582 0.2365
## total_shrub_cover-Lynx_rufus -1.8646 1.0945 -4.4610 -1.7417
## total_shrub_cover-Didelphis_virginiana -1.1860 0.8817 -3.1290 -1.0744
## total_shrub_cover-Sylvilagus_floridanus -1.7931 1.1658 -4.4674 -1.6607
## total_shrub_cover-Meleagris_gallopavo -1.9662 1.0748 -4.5120 -1.8056
## total_shrub_cover-Sciurus_carolinensis -1.5190 1.1036 -4.1353 -1.3574
## total_shrub_cover-Vulpes_vulpes -1.4821 1.4843 -4.8221 -1.2536
## total_shrub_cover-Sus_scrofa -0.9215 1.1558 -3.4620 -0.8795
## avg_veg_height-Canis_latrans 0.1081 0.5765 -1.0155 0.0879
## avg_veg_height-Lynx_rufus 0.0356 0.7124 -1.3872 0.0279
## avg_veg_height-Didelphis_virginiana -0.0533 0.6316 -1.2938 -0.0491
## avg_veg_height-Sylvilagus_floridanus 0.0693 0.6599 -1.1526 0.0279
## avg_veg_height-Meleagris_gallopavo -0.1681 0.8521 -1.9854 -0.1343
## avg_veg_height-Sciurus_carolinensis 0.5428 0.6588 -0.5672 0.4692
## avg_veg_height-Vulpes_vulpes 0.0476 0.6765 -1.2687 0.0409
## avg_veg_height-Sus_scrofa 0.1625 0.6590 -1.0951 0.1283
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8351 1.0676 196
## (Intercept)-Lynx_rufus 1.3246 1.0400 315
## (Intercept)-Didelphis_virginiana 0.8558 1.0212 222
## (Intercept)-Sylvilagus_floridanus 1.6240 1.0468 198
## (Intercept)-Meleagris_gallopavo 0.9432 1.0264 236
## (Intercept)-Sciurus_carolinensis 0.9432 1.0391 276
## (Intercept)-Vulpes_vulpes 0.9399 1.0792 160
## (Intercept)-Sus_scrofa 0.6791 1.0104 325
## Avg_Cogongrass_Cover-Canis_latrans 1.9080 1.0282 577
## Avg_Cogongrass_Cover-Lynx_rufus 2.1534 1.0189 563
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.6409 1.0038 807
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7660 1.0455 325
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0435 1.0217 363
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1999 1.0223 489
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7143 1.0167 608
## Avg_Cogongrass_Cover-Sus_scrofa 1.1777 1.0076 465
## total_shrub_cover-Canis_latrans 2.4142 1.0614 195
## total_shrub_cover-Lynx_rufus -0.0777 1.0482 167
## total_shrub_cover-Didelphis_virginiana 0.1482 1.0234 304
## total_shrub_cover-Sylvilagus_floridanus 0.0204 1.0716 172
## total_shrub_cover-Meleagris_gallopavo -0.3362 1.0247 274
## total_shrub_cover-Sciurus_carolinensis 0.1716 1.1161 190
## total_shrub_cover-Vulpes_vulpes 0.8725 1.0998 127
## total_shrub_cover-Sus_scrofa 1.3333 1.0131 234
## avg_veg_height-Canis_latrans 1.3022 1.0241 505
## avg_veg_height-Lynx_rufus 1.5107 1.0397 306
## avg_veg_height-Didelphis_virginiana 1.2293 1.0231 339
## avg_veg_height-Sylvilagus_floridanus 1.5059 1.0379 317
## avg_veg_height-Meleagris_gallopavo 1.4330 1.0795 300
## avg_veg_height-Sciurus_carolinensis 2.0561 1.0065 395
## avg_veg_height-Vulpes_vulpes 1.4651 1.0275 342
## avg_veg_height-Sus_scrofa 1.6095 1.0100 363
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8809 0.1992 -3.2885 -2.8750 -2.5091 1.0165
## (Intercept)-Lynx_rufus -3.5136 0.3017 -4.1631 -3.5012 -2.9735 1.0116
## (Intercept)-Didelphis_virginiana -2.9410 0.3057 -3.5567 -2.9451 -2.3673 1.0069
## (Intercept)-Sylvilagus_floridanus -3.3380 0.2551 -3.8610 -3.3388 -2.8471 1.0229
## (Intercept)-Meleagris_gallopavo -3.6389 0.4339 -4.5623 -3.6204 -2.8366 1.0041
## (Intercept)-Sciurus_carolinensis -3.0896 0.3355 -3.7550 -3.0959 -2.4281 1.0047
## (Intercept)-Vulpes_vulpes -3.8895 0.5543 -5.0770 -3.8285 -2.9657 1.0427
## (Intercept)-Sus_scrofa -3.6213 0.4407 -4.5475 -3.6122 -2.7791 1.0226
## shrub_cover-Canis_latrans -0.3054 0.2656 -0.7952 -0.3144 0.2425 1.0172
## shrub_cover-Lynx_rufus 0.1705 0.3563 -0.5887 0.1813 0.8362 1.0059
## shrub_cover-Didelphis_virginiana 1.3807 0.4136 0.6283 1.3692 2.2097 1.0078
## shrub_cover-Sylvilagus_floridanus 0.8433 0.4353 -0.0999 0.8539 1.6813 1.0531
## shrub_cover-Meleagris_gallopavo -0.3717 0.4301 -1.2247 -0.3630 0.4706 1.0061
## shrub_cover-Sciurus_carolinensis 1.4170 0.4338 0.5243 1.4169 2.2575 1.0055
## shrub_cover-Vulpes_vulpes 0.5266 0.7157 -0.9523 0.5701 1.8389 1.0416
## shrub_cover-Sus_scrofa 1.2325 0.7482 -0.2927 1.2402 2.7235 1.0619
## veg_height-Canis_latrans -0.6454 0.1893 -1.0447 -0.6387 -0.2926 1.0106
## veg_height-Lynx_rufus 0.0114 0.2509 -0.5177 0.0209 0.4762 1.0345
## veg_height-Didelphis_virginiana 0.4543 0.2814 -0.0602 0.4420 1.0504 1.0049
## veg_height-Sylvilagus_floridanus 0.0244 0.2538 -0.4715 0.0179 0.5243 1.0247
## veg_height-Meleagris_gallopavo -0.1854 0.4435 -1.0098 -0.1827 0.7177 1.0226
## veg_height-Sciurus_carolinensis 0.1837 0.2380 -0.2650 0.1760 0.6609 1.0007
## veg_height-Vulpes_vulpes -0.1650 0.3407 -0.8789 -0.1586 0.4832 1.0334
## veg_height-Sus_scrofa -0.2159 0.3452 -0.9151 -0.2170 0.4656 1.0339
## week-Canis_latrans 0.0755 0.1363 -0.2057 0.0760 0.3286 1.0053
## week-Lynx_rufus -0.0571 0.2006 -0.4732 -0.0467 0.3109 1.0060
## week-Didelphis_virginiana -0.2405 0.2392 -0.7519 -0.2195 0.1821 1.0023
## week-Sylvilagus_floridanus -0.1649 0.2178 -0.6371 -0.1511 0.2161 1.0146
## week-Meleagris_gallopavo -0.2869 0.2617 -0.8450 -0.2612 0.1597 1.0149
## week-Sciurus_carolinensis 0.1432 0.1993 -0.2700 0.1473 0.5233 1.0009
## week-Vulpes_vulpes -0.1369 0.2876 -0.7849 -0.1243 0.3683 1.0056
## week-Sus_scrofa 0.0952 0.2457 -0.3941 0.1025 0.5672 1.0125
## ESS
## (Intercept)-Canis_latrans 553
## (Intercept)-Lynx_rufus 485
## (Intercept)-Didelphis_virginiana 318
## (Intercept)-Sylvilagus_floridanus 351
## (Intercept)-Meleagris_gallopavo 342
## (Intercept)-Sciurus_carolinensis 298
## (Intercept)-Vulpes_vulpes 183
## (Intercept)-Sus_scrofa 291
## shrub_cover-Canis_latrans 273
## shrub_cover-Lynx_rufus 421
## shrub_cover-Didelphis_virginiana 309
## shrub_cover-Sylvilagus_floridanus 198
## shrub_cover-Meleagris_gallopavo 418
## shrub_cover-Sciurus_carolinensis 267
## shrub_cover-Vulpes_vulpes 180
## shrub_cover-Sus_scrofa 204
## veg_height-Canis_latrans 726
## veg_height-Lynx_rufus 617
## veg_height-Didelphis_virginiana 643
## veg_height-Sylvilagus_floridanus 604
## veg_height-Meleagris_gallopavo 377
## veg_height-Sciurus_carolinensis 704
## veg_height-Vulpes_vulpes 534
## veg_height-Sus_scrofa 715
## week-Canis_latrans 1547
## week-Lynx_rufus 886
## week-Didelphis_virginiana 852
## week-Sylvilagus_floridanus 806
## week-Meleagris_gallopavo 807
## week-Sciurus_carolinensis 1247
## week-Vulpes_vulpes 902
## week-Sus_scrofa 1279
#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 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_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): 0.6078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9323 0.5669 -2.0555 -0.9294 0.1872 1.0607 415
## Tree_Density -0.7709 0.5612 -1.9881 -0.7276 0.2456 1.0129 521
## Avg_Canopy_Cover 1.2247 0.5204 0.2180 1.2107 2.3081 1.0045 938
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6711 2.0364 0.0833 1.0743 6.7498 1.0076 514
## Tree_Density 1.2265 2.1687 0.0620 0.5971 6.3498 1.0373 647
## Avg_Canopy_Cover 1.5366 1.7062 0.1403 1.0272 5.8183 1.0141 729
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9435 0.9953 0.0783 0.5998 3.6942 1.1746 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2412 0.2869 -3.8118 -3.2313 -2.6863 1.0139 689
## shrub_cover 0.2266 0.3944 -0.5438 0.2182 1.0250 1.0037 1268
## veg_height -0.0500 0.2275 -0.4911 -0.0482 0.4018 1.0118 1384
## week -0.0734 0.1645 -0.4036 -0.0657 0.2354 1.0026 1254
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5272 0.5338 0.0780 0.3574 1.9456 1.0290 304
## shrub_cover 0.9740 0.9004 0.1592 0.7201 3.2758 1.0357 750
## veg_height 0.3030 0.2739 0.0645 0.2275 1.0051 1.0098 1008
## week 0.1440 0.1571 0.0289 0.1050 0.4982 1.0335 1691
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0162 0.7140 -1.4083 -0.0174 1.4460
## (Intercept)-Lynx_rufus -0.0752 1.0224 -1.7826 -0.1927 2.2116
## (Intercept)-Didelphis_virginiana -1.5435 0.7380 -3.2083 -1.5057 -0.1933
## (Intercept)-Sylvilagus_floridanus -0.8352 0.7111 -2.2014 -0.8414 0.5826
## (Intercept)-Meleagris_gallopavo -0.3871 0.8768 -1.9073 -0.4667 1.5670
## (Intercept)-Sciurus_carolinensis -1.5604 0.7549 -3.1453 -1.5031 -0.2327
## (Intercept)-Vulpes_vulpes -1.6336 0.9545 -3.6621 -1.5733 0.1114
## (Intercept)-Sus_scrofa -1.9845 0.9420 -4.1006 -1.8772 -0.4268
## Tree_Density-Canis_latrans -1.0456 0.6795 -2.6184 -0.9608 0.0574
## Tree_Density-Lynx_rufus 0.2910 0.7940 -0.9368 0.1816 2.2596
## Tree_Density-Didelphis_virginiana -1.0854 0.9249 -3.3471 -0.9334 0.2453
## Tree_Density-Sylvilagus_floridanus -1.1837 0.9674 -3.5852 -1.0412 0.2860
## Tree_Density-Meleagris_gallopavo -1.0811 0.9447 -3.3931 -0.9667 0.4263
## Tree_Density-Sciurus_carolinensis -0.9284 0.8667 -2.9413 -0.8155 0.4713
## Tree_Density-Vulpes_vulpes -0.6344 0.8837 -2.5506 -0.6087 1.0051
## Tree_Density-Sus_scrofa -1.0047 0.9534 -3.3325 -0.8581 0.4379
## Avg_Canopy_Cover-Canis_latrans -0.1260 0.4948 -1.1190 -0.1188 0.8364
## Avg_Canopy_Cover-Lynx_rufus 0.7403 0.8271 -0.7024 0.6650 2.6452
## Avg_Canopy_Cover-Didelphis_virginiana 1.7788 0.7999 0.5296 1.6769 3.7200
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.3388 1.0723 0.7777 2.1676 4.9262
## Avg_Canopy_Cover-Meleagris_gallopavo 1.7068 0.8799 0.3273 1.5848 3.8111
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6682 0.7250 0.4973 1.5662 3.3621
## Avg_Canopy_Cover-Vulpes_vulpes 1.1497 0.7493 -0.1556 1.0904 2.8351
## Avg_Canopy_Cover-Sus_scrofa 1.3692 0.6903 0.2119 1.3244 2.9448
## Rhat ESS
## (Intercept)-Canis_latrans 1.0302 388
## (Intercept)-Lynx_rufus 1.0612 227
## (Intercept)-Didelphis_virginiana 1.0158 674
## (Intercept)-Sylvilagus_floridanus 1.0284 605
## (Intercept)-Meleagris_gallopavo 1.0471 304
## (Intercept)-Sciurus_carolinensis 1.0131 613
## (Intercept)-Vulpes_vulpes 1.0802 332
## (Intercept)-Sus_scrofa 1.0018 357
## Tree_Density-Canis_latrans 1.0067 796
## Tree_Density-Lynx_rufus 1.0596 496
## Tree_Density-Didelphis_virginiana 1.0240 541
## Tree_Density-Sylvilagus_floridanus 1.0283 464
## Tree_Density-Meleagris_gallopavo 1.0165 534
## Tree_Density-Sciurus_carolinensis 1.0408 580
## Tree_Density-Vulpes_vulpes 1.0223 576
## Tree_Density-Sus_scrofa 1.0073 556
## Avg_Canopy_Cover-Canis_latrans 1.0044 1408
## Avg_Canopy_Cover-Lynx_rufus 1.0072 419
## Avg_Canopy_Cover-Didelphis_virginiana 1.0256 731
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0009 449
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0147 547
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0187 773
## Avg_Canopy_Cover-Vulpes_vulpes 1.0027 700
## Avg_Canopy_Cover-Sus_scrofa 1.0019 994
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8252 0.1853 -3.1950 -2.8209 -2.4770 1.0078
## (Intercept)-Lynx_rufus -3.6888 0.3358 -4.3791 -3.6781 -3.0715 1.0173
## (Intercept)-Didelphis_virginiana -2.7896 0.2994 -3.3770 -2.7829 -2.2123 1.0109
## (Intercept)-Sylvilagus_floridanus -3.1804 0.2520 -3.7057 -3.1722 -2.7229 1.0041
## (Intercept)-Meleagris_gallopavo -3.9083 0.4535 -4.9131 -3.8708 -3.1419 1.0601
## (Intercept)-Sciurus_carolinensis -2.8620 0.3214 -3.4889 -2.8680 -2.2104 1.0098
## (Intercept)-Vulpes_vulpes -3.8738 0.5762 -5.1342 -3.8310 -2.9055 1.0175
## (Intercept)-Sus_scrofa -3.4329 0.5091 -4.5263 -3.4196 -2.4711 1.0004
## shrub_cover-Canis_latrans -0.3355 0.2296 -0.7858 -0.3358 0.1202 1.0054
## shrub_cover-Lynx_rufus -0.3060 0.3587 -1.0287 -0.3036 0.4107 1.0203
## shrub_cover-Didelphis_virginiana 1.1127 0.3954 0.3768 1.0911 1.9326 1.0147
## shrub_cover-Sylvilagus_floridanus 0.4570 0.4097 -0.3432 0.4664 1.2575 1.0207
## shrub_cover-Meleagris_gallopavo -0.6841 0.4101 -1.5742 -0.6623 0.0470 1.0310
## shrub_cover-Sciurus_carolinensis 0.9964 0.4392 0.1390 0.9975 1.8483 1.0171
## shrub_cover-Vulpes_vulpes -0.1135 0.6479 -1.4447 -0.1032 1.1382 1.0261
## shrub_cover-Sus_scrofa 0.8040 0.8309 -0.7074 0.7801 2.5370 1.0068
## veg_height-Canis_latrans -0.6430 0.1868 -1.0222 -0.6421 -0.2833 1.0057
## veg_height-Lynx_rufus 0.0395 0.2520 -0.4770 0.0436 0.5178 1.0106
## veg_height-Didelphis_virginiana 0.5226 0.2626 0.0299 0.5187 1.0472 1.0056
## veg_height-Sylvilagus_floridanus 0.1305 0.2465 -0.3484 0.1269 0.6130 1.0005
## veg_height-Meleagris_gallopavo -0.2712 0.3710 -1.0377 -0.2588 0.4201 1.0049
## veg_height-Sciurus_carolinensis 0.1586 0.2310 -0.2921 0.1553 0.6165 1.0059
## veg_height-Vulpes_vulpes -0.1772 0.3230 -0.8522 -0.1648 0.4341 1.0325
## veg_height-Sus_scrofa -0.1858 0.3531 -0.9201 -0.1742 0.4815 1.0178
## week-Canis_latrans 0.0742 0.1362 -0.2071 0.0789 0.3325 1.0012
## week-Lynx_rufus -0.0351 0.2045 -0.4585 -0.0291 0.3451 1.0026
## week-Didelphis_virginiana -0.2614 0.2407 -0.7805 -0.2455 0.1587 1.0056
## week-Sylvilagus_floridanus -0.1726 0.2217 -0.6449 -0.1529 0.2165 1.0093
## week-Meleagris_gallopavo -0.2979 0.2638 -0.8762 -0.2739 0.1556 1.0084
## week-Sciurus_carolinensis 0.1466 0.1959 -0.2517 0.1544 0.5239 1.0004
## week-Vulpes_vulpes -0.1460 0.2987 -0.7753 -0.1283 0.3884 1.0057
## week-Sus_scrofa 0.1034 0.2534 -0.4154 0.1009 0.5839 1.0093
## ESS
## (Intercept)-Canis_latrans 796
## (Intercept)-Lynx_rufus 308
## (Intercept)-Didelphis_virginiana 659
## (Intercept)-Sylvilagus_floridanus 677
## (Intercept)-Meleagris_gallopavo 172
## (Intercept)-Sciurus_carolinensis 517
## (Intercept)-Vulpes_vulpes 196
## (Intercept)-Sus_scrofa 455
## shrub_cover-Canis_latrans 736
## shrub_cover-Lynx_rufus 395
## shrub_cover-Didelphis_virginiana 564
## shrub_cover-Sylvilagus_floridanus 653
## shrub_cover-Meleagris_gallopavo 255
## shrub_cover-Sciurus_carolinensis 552
## shrub_cover-Vulpes_vulpes 544
## shrub_cover-Sus_scrofa 568
## veg_height-Canis_latrans 865
## veg_height-Lynx_rufus 671
## veg_height-Didelphis_virginiana 860
## veg_height-Sylvilagus_floridanus 1071
## veg_height-Meleagris_gallopavo 594
## veg_height-Sciurus_carolinensis 851
## veg_height-Vulpes_vulpes 719
## veg_height-Sus_scrofa 1239
## week-Canis_latrans 1480
## week-Lynx_rufus 834
## week-Didelphis_virginiana 1113
## week-Sylvilagus_floridanus 733
## week-Meleagris_gallopavo 664
## week-Sciurus_carolinensis 1203
## week-Vulpes_vulpes 974
## week-Sus_scrofa 1479
#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 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_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): 0.6637
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4970 0.6957 -1.7101 -0.5562 0.9833 1.2384 114
## Cogon_Patch_Size -0.1845 0.6805 -1.6585 -0.1411 1.0973 1.0572 511
## Avg_Cogongrass_Cover 0.1586 0.4778 -0.8077 0.1615 1.0916 1.0603 312
## total_shrub_cover -1.1633 0.7534 -2.8792 -1.1037 0.1148 1.0972 76
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9945 1.5856 0.0546 0.4681 5.2444 1.0437 309
## Cogon_Patch_Size 2.5988 4.4734 0.0726 1.1551 14.0094 1.0522 254
## Avg_Cogongrass_Cover 0.7382 0.9986 0.0507 0.3878 3.5562 1.0208 494
## total_shrub_cover 2.4926 4.1940 0.0891 1.1038 14.5564 1.1564 155
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.0065 6.438 0.5099 3.1301 21.3254 1.4208 60
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.3298 0.2892 -3.9176 -3.3222 -2.7753 1.0356 266
## shrub_cover 0.5438 0.4101 -0.2886 0.5456 1.3423 1.0100 597
## veg_height -0.0643 0.2249 -0.5105 -0.0564 0.3740 1.0203 962
## week -0.0719 0.1712 -0.4212 -0.0676 0.2461 1.0186 1028
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4772 0.6731 0.0597 0.3157 1.8896 1.0461 506
## shrub_cover 1.0997 1.1505 0.2051 0.7995 3.9330 1.0229 443
## veg_height 0.3038 0.2910 0.0597 0.2302 1.0235 1.0236 1222
## week 0.1441 0.1371 0.0292 0.1021 0.5278 1.0074 1139
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0448 0.9464 -1.5675 -0.0394
## (Intercept)-Lynx_rufus -0.3476 0.9588 -2.0609 -0.3962
## (Intercept)-Didelphis_virginiana -0.6627 0.9282 -2.4188 -0.6846
## (Intercept)-Sylvilagus_floridanus -0.2687 0.9428 -1.9907 -0.3286
## (Intercept)-Meleagris_gallopavo -0.5069 0.9539 -2.2249 -0.5835
## (Intercept)-Sciurus_carolinensis -0.6722 0.9578 -2.3905 -0.7035
## (Intercept)-Vulpes_vulpes -0.7833 1.0644 -2.8180 -0.8161
## (Intercept)-Sus_scrofa -0.9432 1.0519 -3.0609 -0.9218
## Cogon_Patch_Size-Canis_latrans 0.9584 1.0858 -0.4206 0.7375
## Cogon_Patch_Size-Lynx_rufus -0.0418 1.1930 -2.1231 -0.1347
## Cogon_Patch_Size-Didelphis_virginiana 0.8611 0.6995 -0.3088 0.7961
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3001 1.4675 -5.2179 -0.9793
## Cogon_Patch_Size-Meleagris_gallopavo 0.0619 1.0424 -1.7614 -0.0116
## Cogon_Patch_Size-Sciurus_carolinensis -1.0852 1.3085 -4.6025 -0.7991
## Cogon_Patch_Size-Vulpes_vulpes -0.7257 1.3277 -4.1427 -0.5247
## Cogon_Patch_Size-Sus_scrofa -0.4929 1.1433 -3.3304 -0.3538
## Avg_Cogongrass_Cover-Canis_latrans 0.4062 0.5445 -0.5545 0.3667
## Avg_Cogongrass_Cover-Lynx_rufus 0.6102 0.7286 -0.5943 0.5214
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1088 0.6130 -1.1312 0.1209
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2251 0.7512 -1.7845 -0.1878
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3686 0.9442 -2.4972 -0.2928
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3753 0.6488 -0.8588 0.3690
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3884 0.6832 -0.7921 0.3462
## Avg_Cogongrass_Cover-Sus_scrofa -0.0328 0.8215 -1.8317 0.0197
## total_shrub_cover-Canis_latrans 0.3261 0.9178 -1.3119 0.2130
## total_shrub_cover-Lynx_rufus -1.8474 1.4314 -5.3238 -1.6146
## total_shrub_cover-Didelphis_virginiana -1.4045 1.0888 -4.2305 -1.2288
## total_shrub_cover-Sylvilagus_floridanus -1.8433 1.4324 -5.6395 -1.5463
## total_shrub_cover-Meleagris_gallopavo -1.9945 1.2690 -5.1652 -1.7941
## total_shrub_cover-Sciurus_carolinensis -1.3989 1.3201 -4.6977 -1.1323
## total_shrub_cover-Vulpes_vulpes -1.4279 1.5004 -4.8670 -1.2475
## total_shrub_cover-Sus_scrofa -0.9795 1.1682 -3.7175 -0.8755
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1800 1.1662 245
## (Intercept)-Lynx_rufus 1.7803 1.1246 157
## (Intercept)-Didelphis_virginiana 1.2719 1.1236 166
## (Intercept)-Sylvilagus_floridanus 1.7289 1.1639 253
## (Intercept)-Meleagris_gallopavo 1.6181 1.1010 224
## (Intercept)-Sciurus_carolinensis 1.3986 1.0876 191
## (Intercept)-Vulpes_vulpes 1.4868 1.0794 130
## (Intercept)-Sus_scrofa 1.1108 1.0891 144
## Cogon_Patch_Size-Canis_latrans 3.5860 1.0534 307
## Cogon_Patch_Size-Lynx_rufus 2.6811 1.1068 332
## Cogon_Patch_Size-Didelphis_virginiana 2.4185 1.0413 318
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5930 1.0261 174
## Cogon_Patch_Size-Meleagris_gallopavo 2.5576 1.0407 475
## Cogon_Patch_Size-Sciurus_carolinensis 0.5560 1.0284 163
## Cogon_Patch_Size-Vulpes_vulpes 1.3843 1.0198 319
## Cogon_Patch_Size-Sus_scrofa 1.3939 1.0402 321
## Avg_Cogongrass_Cover-Canis_latrans 1.6197 1.0224 773
## Avg_Cogongrass_Cover-Lynx_rufus 2.3225 1.0214 602
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3319 1.0439 428
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1439 1.0413 329
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2515 1.0434 298
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7070 1.0342 395
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8735 1.0546 476
## Avg_Cogongrass_Cover-Sus_scrofa 1.5136 1.0264 494
## total_shrub_cover-Canis_latrans 2.4870 1.0628 251
## total_shrub_cover-Lynx_rufus 0.3462 1.1530 94
## total_shrub_cover-Didelphis_virginiana 0.1208 1.1867 140
## total_shrub_cover-Sylvilagus_floridanus 0.2135 1.2226 134
## total_shrub_cover-Meleagris_gallopavo -0.0631 1.0776 164
## total_shrub_cover-Sciurus_carolinensis 0.3884 1.1495 120
## total_shrub_cover-Vulpes_vulpes 1.0156 1.0980 118
## total_shrub_cover-Sus_scrofa 1.0452 1.0867 120
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8558 0.1998 -3.2759 -2.8478 -2.4739 1.0035
## (Intercept)-Lynx_rufus -3.5428 0.3006 -4.1721 -3.5299 -2.9816 1.0087
## (Intercept)-Didelphis_virginiana -2.9226 0.3172 -3.5460 -2.9281 -2.3126 1.0356
## (Intercept)-Sylvilagus_floridanus -3.3600 0.2474 -3.8360 -3.3565 -2.8739 1.0174
## (Intercept)-Meleagris_gallopavo -3.7531 0.4898 -4.8224 -3.7160 -2.8701 1.1380
## (Intercept)-Sciurus_carolinensis -3.0995 0.3511 -3.7559 -3.1128 -2.3800 1.0653
## (Intercept)-Vulpes_vulpes -4.0072 0.6123 -5.3139 -3.9536 -2.9942 1.0231
## (Intercept)-Sus_scrofa -3.6903 0.5342 -4.9097 -3.6423 -2.7483 1.0519
## shrub_cover-Canis_latrans -0.3354 0.2592 -0.8296 -0.3410 0.1876 1.0256
## shrub_cover-Lynx_rufus 0.1331 0.3732 -0.6216 0.1399 0.8581 1.0122
## shrub_cover-Didelphis_virginiana 1.3503 0.4229 0.5409 1.3480 2.1726 1.0188
## shrub_cover-Sylvilagus_floridanus 0.8609 0.4209 -0.0443 0.8779 1.6506 1.0619
## shrub_cover-Meleagris_gallopavo -0.4475 0.4622 -1.3781 -0.4402 0.4412 1.0651
## shrub_cover-Sciurus_carolinensis 1.3208 0.4332 0.4374 1.3307 2.1346 1.0162
## shrub_cover-Vulpes_vulpes 0.4544 0.7010 -1.0243 0.4915 1.7156 1.0843
## shrub_cover-Sus_scrofa 1.3123 0.8859 -0.3537 1.2577 3.3401 1.0285
## veg_height-Canis_latrans -0.6476 0.1972 -1.0598 -0.6393 -0.2951 1.0057
## veg_height-Lynx_rufus 0.0100 0.2527 -0.5090 0.0139 0.4756 1.0053
## veg_height-Didelphis_virginiana 0.4716 0.2709 -0.0316 0.4621 1.0097 1.0066
## veg_height-Sylvilagus_floridanus 0.0418 0.2454 -0.4269 0.0370 0.5333 1.0423
## veg_height-Meleagris_gallopavo -0.2834 0.4063 -1.1041 -0.2873 0.5406 1.0985
## veg_height-Sciurus_carolinensis 0.2307 0.2580 -0.2640 0.2190 0.7479 1.0347
## veg_height-Vulpes_vulpes -0.1365 0.3409 -0.8424 -0.1261 0.5036 1.0031
## veg_height-Sus_scrofa -0.2350 0.3452 -0.9600 -0.2190 0.4212 1.0123
## week-Canis_latrans 0.0814 0.1392 -0.2058 0.0852 0.3417 1.0019
## week-Lynx_rufus -0.0415 0.2115 -0.4811 -0.0324 0.3494 1.0027
## week-Didelphis_virginiana -0.2549 0.2388 -0.7582 -0.2404 0.1671 1.0344
## week-Sylvilagus_floridanus -0.1668 0.2171 -0.6413 -0.1567 0.2302 1.0211
## week-Meleagris_gallopavo -0.3036 0.2640 -0.8881 -0.2809 0.1469 1.0179
## week-Sciurus_carolinensis 0.1560 0.1952 -0.2265 0.1575 0.5244 1.0071
## week-Vulpes_vulpes -0.1378 0.2867 -0.7251 -0.1225 0.4000 1.0091
## week-Sus_scrofa 0.1015 0.2498 -0.4071 0.1051 0.5878 1.0014
## ESS
## (Intercept)-Canis_latrans 514
## (Intercept)-Lynx_rufus 493
## (Intercept)-Didelphis_virginiana 354
## (Intercept)-Sylvilagus_floridanus 507
## (Intercept)-Meleagris_gallopavo 230
## (Intercept)-Sciurus_carolinensis 236
## (Intercept)-Vulpes_vulpes 138
## (Intercept)-Sus_scrofa 138
## shrub_cover-Canis_latrans 299
## shrub_cover-Lynx_rufus 431
## shrub_cover-Didelphis_virginiana 333
## shrub_cover-Sylvilagus_floridanus 297
## shrub_cover-Meleagris_gallopavo 372
## shrub_cover-Sciurus_carolinensis 276
## shrub_cover-Vulpes_vulpes 198
## shrub_cover-Sus_scrofa 150
## veg_height-Canis_latrans 614
## veg_height-Lynx_rufus 706
## veg_height-Didelphis_virginiana 728
## veg_height-Sylvilagus_floridanus 545
## veg_height-Meleagris_gallopavo 429
## veg_height-Sciurus_carolinensis 456
## veg_height-Vulpes_vulpes 524
## veg_height-Sus_scrofa 739
## week-Canis_latrans 1429
## week-Lynx_rufus 727
## week-Didelphis_virginiana 883
## week-Sylvilagus_floridanus 710
## week-Meleagris_gallopavo 534
## week-Sciurus_carolinensis 1148
## week-Vulpes_vulpes 809
## week-Sus_scrofa 1391
#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 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_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): 0.6028
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8271 0.4972 -1.7849 -0.8316 0.1888 1.0065 331
## Veg_shannon_index 0.4094 0.3636 -0.3058 0.3943 1.1559 1.0036 894
## Avg_Cogongrass_Cover 0.2693 0.3684 -0.4848 0.2659 1.0224 1.0082 717
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0283 1.6986 0.0608 0.5348 5.0399 1.0437 433
## Veg_shannon_index 0.5427 0.8077 0.0455 0.3080 2.4882 1.1513 504
## Avg_Cogongrass_Cover 0.5747 0.8101 0.0486 0.3296 2.6077 1.1065 479
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5995 1.6945 0.0871 1.0936 6.2218 1.0356 132
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2145 0.3211 -3.8716 -3.2095 -2.5790 1.0133 969
## shrub_cover 0.1615 0.3686 -0.5362 0.1589 0.9408 1.0090 1248
## veg_height -0.0925 0.2239 -0.5465 -0.0871 0.3619 1.0054 1153
## week -0.0719 0.1684 -0.4292 -0.0654 0.2321 1.0019 1185
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6166 0.8349 0.0874 0.4253 2.2622 1.1180 708
## shrub_cover 0.8796 0.7926 0.1443 0.6455 2.9727 1.0660 532
## veg_height 0.2867 0.2540 0.0565 0.2125 0.9405 1.0056 1532
## week 0.1468 0.1598 0.0298 0.1015 0.4962 1.0183 1572
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1823 0.7441 -1.5466 -0.2025
## (Intercept)-Lynx_rufus -0.5128 0.7666 -1.9115 -0.5667
## (Intercept)-Didelphis_virginiana -1.2072 0.6270 -2.5161 -1.1724
## (Intercept)-Sylvilagus_floridanus -0.6598 0.7263 -2.0316 -0.6921
## (Intercept)-Meleagris_gallopavo -0.3750 0.9065 -1.8593 -0.5018
## (Intercept)-Sciurus_carolinensis -1.2170 0.6459 -2.5805 -1.1897
## (Intercept)-Vulpes_vulpes -1.2282 0.7785 -2.8729 -1.2037
## (Intercept)-Sus_scrofa -1.5654 0.8054 -3.4352 -1.4885
## Veg_shannon_index-Canis_latrans 0.7537 0.4788 -0.0919 0.7152
## Veg_shannon_index-Lynx_rufus 0.2029 0.6062 -1.0768 0.2218
## Veg_shannon_index-Didelphis_virginiana 0.5654 0.4579 -0.2651 0.5465
## Veg_shannon_index-Sylvilagus_floridanus 0.5024 0.5486 -0.3723 0.4614
## Veg_shannon_index-Meleagris_gallopavo 0.5851 0.5874 -0.4696 0.5393
## Veg_shannon_index-Sciurus_carolinensis -0.1022 0.4833 -1.1642 -0.0661
## Veg_shannon_index-Vulpes_vulpes 0.0356 0.5777 -1.1660 0.0680
## Veg_shannon_index-Sus_scrofa 0.8072 0.6773 -0.3033 0.7269
## Avg_Cogongrass_Cover-Canis_latrans 0.6748 0.4903 -0.1222 0.6150
## Avg_Cogongrass_Cover-Lynx_rufus 0.6574 0.5491 -0.2516 0.5961
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4580 0.4361 -0.3904 0.4492
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1829 0.5371 -1.3057 -0.1532
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2052 0.7493 -1.9884 -0.1359
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4172 0.4160 -0.3750 0.4058
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4120 0.5689 -0.6426 0.3988
## Avg_Cogongrass_Cover-Sus_scrofa -0.0241 0.6459 -1.4665 0.0196
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2602 1.0012 273
## (Intercept)-Lynx_rufus 1.0832 1.0364 247
## (Intercept)-Didelphis_virginiana -0.0437 1.0119 729
## (Intercept)-Sylvilagus_floridanus 0.8344 1.0061 308
## (Intercept)-Meleagris_gallopavo 1.6886 1.0019 204
## (Intercept)-Sciurus_carolinensis -0.0275 1.0106 697
## (Intercept)-Vulpes_vulpes 0.2973 1.0110 372
## (Intercept)-Sus_scrofa -0.1870 1.0125 520
## Veg_shannon_index-Canis_latrans 1.7547 1.0261 852
## Veg_shannon_index-Lynx_rufus 1.3041 1.0083 742
## Veg_shannon_index-Didelphis_virginiana 1.5575 1.0139 879
## Veg_shannon_index-Sylvilagus_floridanus 1.6646 1.0188 477
## Veg_shannon_index-Meleagris_gallopavo 1.9029 1.0140 627
## Veg_shannon_index-Sciurus_carolinensis 0.7493 1.0166 988
## Veg_shannon_index-Vulpes_vulpes 1.1110 1.0099 876
## Veg_shannon_index-Sus_scrofa 2.4100 1.0154 753
## Avg_Cogongrass_Cover-Canis_latrans 1.8535 1.0715 721
## Avg_Cogongrass_Cover-Lynx_rufus 1.9224 1.0278 690
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3648 1.0024 1329
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7829 1.0024 840
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0546 1.0056 454
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2434 1.0083 1328
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6064 1.0112 857
## Avg_Cogongrass_Cover-Sus_scrofa 1.1478 1.0031 675
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7893 0.1925 -3.1727 -2.7877 -2.4253 1.0244
## (Intercept)-Lynx_rufus -3.6148 0.3249 -4.2681 -3.6053 -3.0144 1.0133
## (Intercept)-Didelphis_virginiana -2.7204 0.3022 -3.3277 -2.7118 -2.1457 1.0449
## (Intercept)-Sylvilagus_floridanus -3.2473 0.2828 -3.8234 -3.2338 -2.7287 1.0061
## (Intercept)-Meleagris_gallopavo -3.9723 0.4416 -4.8649 -3.9576 -3.1418 1.0940
## (Intercept)-Sciurus_carolinensis -2.7447 0.3194 -3.3835 -2.7320 -2.1507 1.0200
## (Intercept)-Vulpes_vulpes -3.9530 0.6283 -5.2961 -3.8838 -2.9067 1.0301
## (Intercept)-Sus_scrofa -3.3926 0.5196 -4.4601 -3.3775 -2.3974 1.0069
## shrub_cover-Canis_latrans -0.3305 0.2140 -0.7535 -0.3293 0.0942 1.0203
## shrub_cover-Lynx_rufus -0.2861 0.3545 -0.9903 -0.2835 0.3998 1.0831
## shrub_cover-Didelphis_virginiana 1.0157 0.4071 0.2853 0.9933 1.8792 1.0268
## shrub_cover-Sylvilagus_floridanus 0.2348 0.4202 -0.5484 0.2229 1.0747 1.0252
## shrub_cover-Meleagris_gallopavo -0.7500 0.4001 -1.5502 -0.7484 0.0204 1.1079
## shrub_cover-Sciurus_carolinensis 0.8783 0.4301 0.0336 0.8691 1.7316 1.0297
## shrub_cover-Vulpes_vulpes -0.1309 0.6206 -1.4213 -0.1184 1.0890 1.0053
## shrub_cover-Sus_scrofa 0.7147 0.8018 -0.8351 0.6973 2.3760 1.0153
## veg_height-Canis_latrans -0.6394 0.1982 -1.0452 -0.6356 -0.2606 1.0240
## veg_height-Lynx_rufus -0.0428 0.2522 -0.5353 -0.0393 0.4468 1.0159
## veg_height-Didelphis_virginiana 0.4507 0.2590 -0.0247 0.4337 0.9816 1.0035
## veg_height-Sylvilagus_floridanus 0.1132 0.2562 -0.3803 0.1140 0.6191 1.0021
## veg_height-Meleagris_gallopavo -0.2720 0.4026 -1.1002 -0.2564 0.5240 1.0045
## veg_height-Sciurus_carolinensis 0.0776 0.2290 -0.3589 0.0702 0.5583 1.0015
## veg_height-Vulpes_vulpes -0.1999 0.3333 -0.8976 -0.1811 0.4114 1.0056
## veg_height-Sus_scrofa -0.2189 0.3531 -0.9566 -0.2036 0.4400 1.0120
## week-Canis_latrans 0.0724 0.1368 -0.2067 0.0735 0.3291 1.0028
## week-Lynx_rufus -0.0486 0.2059 -0.4922 -0.0367 0.3197 1.0047
## week-Didelphis_virginiana -0.2477 0.2403 -0.7980 -0.2371 0.1656 1.0029
## week-Sylvilagus_floridanus -0.1694 0.2158 -0.6390 -0.1566 0.2076 1.0034
## week-Meleagris_gallopavo -0.2945 0.2584 -0.8800 -0.2787 0.1509 1.0095
## week-Sciurus_carolinensis 0.1614 0.1963 -0.2152 0.1622 0.5310 1.0047
## week-Vulpes_vulpes -0.1391 0.3033 -0.8033 -0.1231 0.4007 1.0118
## week-Sus_scrofa 0.1075 0.2507 -0.3888 0.1074 0.5996 1.0032
## ESS
## (Intercept)-Canis_latrans 703
## (Intercept)-Lynx_rufus 335
## (Intercept)-Didelphis_virginiana 729
## (Intercept)-Sylvilagus_floridanus 438
## (Intercept)-Meleagris_gallopavo 163
## (Intercept)-Sciurus_carolinensis 634
## (Intercept)-Vulpes_vulpes 175
## (Intercept)-Sus_scrofa 572
## shrub_cover-Canis_latrans 859
## shrub_cover-Lynx_rufus 417
## shrub_cover-Didelphis_virginiana 648
## shrub_cover-Sylvilagus_floridanus 466
## shrub_cover-Meleagris_gallopavo 155
## shrub_cover-Sciurus_carolinensis 642
## shrub_cover-Vulpes_vulpes 505
## shrub_cover-Sus_scrofa 540
## veg_height-Canis_latrans 664
## veg_height-Lynx_rufus 743
## veg_height-Didelphis_virginiana 969
## veg_height-Sylvilagus_floridanus 758
## veg_height-Meleagris_gallopavo 452
## veg_height-Sciurus_carolinensis 950
## veg_height-Vulpes_vulpes 614
## veg_height-Sus_scrofa 947
## week-Canis_latrans 1416
## week-Lynx_rufus 798
## week-Didelphis_virginiana 1051
## week-Sylvilagus_floridanus 944
## week-Meleagris_gallopavo 561
## week-Sciurus_carolinensis 1464
## week-Vulpes_vulpes 792
## week-Sus_scrofa 1567
#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 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_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): 0.5913
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6119 0.5855 -2.7718 -1.6187 -0.4645 1.0428 238
## Avg_Cogongrass_Cover -0.9239 0.5271 -2.0103 -0.9114 0.0680 1.0201 374
## I(Avg_Cogongrass_Cover^2) 0.9721 0.4686 0.0984 0.9480 1.9393 1.0460 458
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3245 2.0436 0.0635 0.7364 6.0795 1.0374 327
## Avg_Cogongrass_Cover 0.8483 1.8466 0.0507 0.4092 4.3756 1.0467 601
## I(Avg_Cogongrass_Cover^2) 0.7588 1.3191 0.0519 0.3608 3.8086 1.0121 362
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3072 1.6057 0.0688 0.814 5.4353 1.4749 156
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1992 0.3128 -3.8211 -3.1966 -2.5681 1.0175 811
## shrub_cover 0.2029 0.3751 -0.5420 0.1875 0.9783 1.0100 1008
## veg_height -0.0549 0.2106 -0.4824 -0.0526 0.3621 1.0118 981
## week -0.0656 0.1655 -0.4042 -0.0607 0.2461 1.0043 1453
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5614 0.6635 0.0757 0.3647 2.2006 1.0546 395
## shrub_cover 0.8782 0.9007 0.1532 0.6319 3.1234 1.0618 477
## veg_height 0.2536 0.2384 0.0501 0.1913 0.8473 1.0052 1199
## week 0.1471 0.1543 0.0301 0.1035 0.5013 1.0274 1606
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9948 0.7967 -2.5481 -0.9969
## (Intercept)-Lynx_rufus -1.5385 0.8258 -3.1677 -1.5478
## (Intercept)-Didelphis_virginiana -1.9053 0.7106 -3.4007 -1.8778
## (Intercept)-Sylvilagus_floridanus -1.4709 0.7632 -2.9902 -1.4685
## (Intercept)-Meleagris_gallopavo -0.8536 1.0970 -2.5990 -0.9893
## (Intercept)-Sciurus_carolinensis -2.2769 0.7893 -3.9272 -2.2339
## (Intercept)-Vulpes_vulpes -2.3219 0.9451 -4.2363 -2.2707
## (Intercept)-Sus_scrofa -2.2907 0.8755 -4.3073 -2.2164
## Avg_Cogongrass_Cover-Canis_latrans -0.4130 0.6612 -1.5589 -0.4621
## Avg_Cogongrass_Cover-Lynx_rufus -0.8228 0.6917 -2.2417 -0.8040
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5411 0.6641 -1.8039 -0.5677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5055 0.7957 -3.3286 -1.4210
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.3545 1.0158 -3.8973 -1.2401
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.9730 0.6836 -2.4098 -0.9346
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9426 0.7984 -2.6206 -0.9206
## Avg_Cogongrass_Cover-Sus_scrofa -1.1700 0.8196 -3.1062 -1.1184
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6480 0.9195 0.3738 1.4508
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.4636 0.6893 0.4523 1.3690
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7381 0.5307 -0.1957 0.7004
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9694 0.5447 0.0188 0.9316
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5622 0.8803 -1.0209 0.5290
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1211 0.4809 0.2564 1.0924
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0488 0.5834 0.1169 0.9798
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5362 0.7896 -1.2996 0.5962
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5916 1.0582 344
## (Intercept)-Lynx_rufus 0.0673 1.0490 345
## (Intercept)-Didelphis_virginiana -0.6214 1.0178 539
## (Intercept)-Sylvilagus_floridanus 0.0398 1.0653 460
## (Intercept)-Meleagris_gallopavo 1.8197 1.0956 202
## (Intercept)-Sciurus_carolinensis -0.8399 1.0287 572
## (Intercept)-Vulpes_vulpes -0.5512 1.0142 317
## (Intercept)-Sus_scrofa -0.7627 1.0587 306
## Avg_Cogongrass_Cover-Canis_latrans 1.0689 1.0048 733
## Avg_Cogongrass_Cover-Lynx_rufus 0.5463 1.0042 510
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8646 1.0173 684
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2131 1.0182 433
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2456 1.0012 217
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2788 1.0160 426
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5688 1.0179 498
## Avg_Cogongrass_Cover-Sus_scrofa 0.2721 1.0156 374
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.0400 1.0257 293
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0239 1.0405 420
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.9933 1.0183 460
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1273 1.0012 441
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2613 1.1378 224
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1575 1.0179 462
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3695 1.0786 445
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.9096 1.0275 352
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8154 0.1824 -3.1826 -2.8130 -2.4710 1.0255
## (Intercept)-Lynx_rufus -3.5356 0.3379 -4.2238 -3.5282 -2.9262 1.0104
## (Intercept)-Didelphis_virginiana -2.7334 0.3035 -3.3498 -2.7346 -2.1568 1.0042
## (Intercept)-Sylvilagus_floridanus -3.2208 0.2767 -3.7880 -3.2147 -2.6979 1.1043
## (Intercept)-Meleagris_gallopavo -3.9473 0.5018 -5.0037 -3.9241 -3.0594 1.0319
## (Intercept)-Sciurus_carolinensis -2.7652 0.3077 -3.3939 -2.7576 -2.1819 1.0543
## (Intercept)-Vulpes_vulpes -3.8034 0.6024 -5.1675 -3.7557 -2.8005 1.0077
## (Intercept)-Sus_scrofa -3.3859 0.5010 -4.4658 -3.3768 -2.4286 1.0882
## shrub_cover-Canis_latrans -0.2786 0.2285 -0.7259 -0.2776 0.1774 1.0386
## shrub_cover-Lynx_rufus -0.2438 0.3718 -1.0293 -0.2337 0.4428 1.0093
## shrub_cover-Didelphis_virginiana 1.0551 0.3968 0.3311 1.0382 1.8526 1.0015
## shrub_cover-Sylvilagus_floridanus 0.2731 0.4120 -0.5017 0.2662 1.0947 1.0043
## shrub_cover-Meleagris_gallopavo -0.7097 0.4319 -1.6069 -0.6894 0.0769 1.0380
## shrub_cover-Sciurus_carolinensis 0.8976 0.4106 0.1154 0.8872 1.7420 1.0295
## shrub_cover-Vulpes_vulpes -0.0694 0.6167 -1.3285 -0.0791 1.1364 1.0110
## shrub_cover-Sus_scrofa 0.7454 0.8221 -0.8667 0.7346 2.3997 1.1004
## veg_height-Canis_latrans -0.6217 0.1851 -0.9990 -0.6172 -0.2713 1.0661
## veg_height-Lynx_rufus 0.0367 0.2475 -0.4604 0.0371 0.5189 1.0227
## veg_height-Didelphis_virginiana 0.4039 0.2623 -0.0756 0.3940 0.9626 1.0167
## veg_height-Sylvilagus_floridanus 0.1213 0.2399 -0.3373 0.1146 0.6058 1.0148
## veg_height-Meleagris_gallopavo -0.1867 0.3850 -0.9383 -0.1922 0.6248 1.0271
## veg_height-Sciurus_carolinensis 0.1108 0.2252 -0.2946 0.1009 0.5851 1.0078
## veg_height-Vulpes_vulpes -0.1413 0.3041 -0.7522 -0.1336 0.4370 1.0039
## veg_height-Sus_scrofa -0.1716 0.3392 -0.8656 -0.1538 0.4897 1.0066
## week-Canis_latrans 0.0776 0.1381 -0.2040 0.0809 0.3423 1.0109
## week-Lynx_rufus -0.0398 0.2036 -0.4584 -0.0317 0.3356 1.0007
## week-Didelphis_virginiana -0.2491 0.2442 -0.7865 -0.2217 0.1720 1.0009
## week-Sylvilagus_floridanus -0.1700 0.2194 -0.6278 -0.1547 0.2295 1.0232
## week-Meleagris_gallopavo -0.3026 0.2574 -0.8707 -0.2820 0.1520 1.0219
## week-Sciurus_carolinensis 0.1620 0.1950 -0.2225 0.1629 0.5511 1.0127
## week-Vulpes_vulpes -0.1379 0.3033 -0.8002 -0.1112 0.3915 1.0107
## week-Sus_scrofa 0.1211 0.2557 -0.4014 0.1255 0.6105 1.0098
## ESS
## (Intercept)-Canis_latrans 732
## (Intercept)-Lynx_rufus 353
## (Intercept)-Didelphis_virginiana 611
## (Intercept)-Sylvilagus_floridanus 542
## (Intercept)-Meleagris_gallopavo 96
## (Intercept)-Sciurus_carolinensis 893
## (Intercept)-Vulpes_vulpes 201
## (Intercept)-Sus_scrofa 566
## shrub_cover-Canis_latrans 768
## shrub_cover-Lynx_rufus 511
## shrub_cover-Didelphis_virginiana 589
## shrub_cover-Sylvilagus_floridanus 565
## shrub_cover-Meleagris_gallopavo 145
## shrub_cover-Sciurus_carolinensis 803
## shrub_cover-Vulpes_vulpes 609
## shrub_cover-Sus_scrofa 382
## veg_height-Canis_latrans 786
## veg_height-Lynx_rufus 786
## veg_height-Didelphis_virginiana 670
## veg_height-Sylvilagus_floridanus 708
## veg_height-Meleagris_gallopavo 435
## veg_height-Sciurus_carolinensis 994
## veg_height-Vulpes_vulpes 759
## veg_height-Sus_scrofa 1060
## week-Canis_latrans 1487
## week-Lynx_rufus 989
## week-Didelphis_virginiana 1244
## week-Sylvilagus_floridanus 935
## week-Meleagris_gallopavo 640
## week-Sciurus_carolinensis 1750
## week-Vulpes_vulpes 793
## week-Sus_scrofa 1182
## 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 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_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): 0.6567
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6155 1.0176 -4.5924 -2.6153 -0.5111 1.0249 226
## Cogon_Patch_Size 0.1847 1.0042 -1.8610 0.2074 2.2405 1.0204 371
## Veg_shannon_index 1.2591 0.8033 -0.2965 1.2441 3.0242 1.1327 189
## total_shrub_cover -1.4726 0.9328 -3.4204 -1.4254 0.2861 1.1152 191
## Avg_Cogongrass_Cover -0.6343 1.1291 -2.7995 -0.6653 1.5439 1.0374 149
## Tree_Density -1.7965 1.0145 -3.8534 -1.7423 0.0889 1.0119 170
## Avg_Canopy_Cover 2.1022 1.0233 -0.0232 2.0991 4.0923 1.0189 549
## I(Avg_Cogongrass_Cover^2) 1.4149 0.9064 -0.5338 1.4718 3.0876 1.0014 157
## avg_veg_height -0.1790 0.7111 -1.5299 -0.1720 1.2615 1.0554 228
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.7940 10.1626 0.0873 2.4962 29.8847 1.1005 189
## Cogon_Patch_Size 8.9461 18.1122 0.1615 3.6779 49.4694 1.0706 116
## Veg_shannon_index 2.8748 6.8867 0.0662 0.9735 17.4685 1.1538 164
## total_shrub_cover 5.8338 12.2772 0.1329 2.6649 30.8259 1.1419 136
## Avg_Cogongrass_Cover 1.7709 4.1694 0.0516 0.6021 10.1788 1.1312 287
## Tree_Density 2.9626 5.7946 0.0663 1.1893 15.9809 1.0612 376
## Avg_Canopy_Cover 13.0669 26.8818 0.3269 5.2969 79.0421 1.2572 66
## I(Avg_Cogongrass_Cover^2) 8.5151 23.9875 0.0904 2.2947 59.7725 1.5113 32
## avg_veg_height 1.0993 2.1035 0.0529 0.4577 6.0655 1.0511 428
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.5597 6.0213 0.0638 1.5247 23.2946 1.5855 35
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2733 0.2918 -3.8376 -3.2691 -2.7065 1.0038 587
## shrub_cover 0.4741 0.4050 -0.3547 0.4790 1.2470 1.0157 426
## veg_height -0.0088 0.2218 -0.4523 -0.0094 0.4281 1.0157 623
## week -0.0748 0.1647 -0.4256 -0.0718 0.2390 1.0000 1185
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5089 0.7383 0.0699 0.3344 1.8829 1.2025 519
## shrub_cover 0.9623 0.9595 0.1757 0.7004 3.2360 1.0127 1050
## veg_height 0.2891 0.2688 0.0555 0.2163 0.9772 1.0054 1193
## week 0.1423 0.1464 0.0290 0.1024 0.4963 1.0035 1355
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.7440 1.5846 -4.5756 -1.8636
## (Intercept)-Lynx_rufus -1.9658 1.6309 -4.9419 -2.1434
## (Intercept)-Didelphis_virginiana -3.7584 1.5604 -7.4384 -3.5855
## (Intercept)-Sylvilagus_floridanus -2.5501 1.5080 -5.5971 -2.5725
## (Intercept)-Meleagris_gallopavo -2.3578 1.6438 -5.6117 -2.4234
## (Intercept)-Sciurus_carolinensis -4.0692 1.8133 -8.3922 -3.8382
## (Intercept)-Vulpes_vulpes -4.2915 2.1212 -9.4368 -3.9197
## (Intercept)-Sus_scrofa -4.4696 2.2111 -9.8001 -4.0338
## Cogon_Patch_Size-Canis_latrans 2.5098 2.0096 -0.1371 2.0952
## Cogon_Patch_Size-Lynx_rufus -0.2706 1.9476 -4.1923 -0.2356
## Cogon_Patch_Size-Didelphis_virginiana 2.2137 1.5051 -0.0289 2.0081
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7779 2.6049 -7.8694 -1.2928
## Cogon_Patch_Size-Meleagris_gallopavo 1.0694 2.0845 -2.0385 0.7382
## Cogon_Patch_Size-Sciurus_carolinensis -1.0907 2.1692 -6.5556 -0.7335
## Cogon_Patch_Size-Vulpes_vulpes -0.2280 2.6743 -5.1492 -0.2601
## Cogon_Patch_Size-Sus_scrofa -0.7078 2.0724 -5.7525 -0.3930
## Veg_shannon_index-Canis_latrans 1.7899 1.1326 0.0442 1.6428
## Veg_shannon_index-Lynx_rufus 1.3624 1.6702 -1.8156 1.2993
## Veg_shannon_index-Didelphis_virginiana 1.6724 1.1861 -0.2238 1.5193
## Veg_shannon_index-Sylvilagus_floridanus 1.4711 1.1448 -0.3960 1.3689
## Veg_shannon_index-Meleagris_gallopavo 2.0714 1.4225 -0.0860 1.8227
## Veg_shannon_index-Sciurus_carolinensis 0.1529 1.4679 -3.4100 0.3795
## Veg_shannon_index-Vulpes_vulpes 0.6119 1.4811 -2.8014 0.7726
## Veg_shannon_index-Sus_scrofa 2.2246 1.4020 0.0446 1.9821
## total_shrub_cover-Canis_latrans 0.3096 1.3307 -2.4018 0.2269
## total_shrub_cover-Lynx_rufus -2.4771 1.8035 -6.6833 -2.2074
## total_shrub_cover-Didelphis_virginiana -2.1996 1.6403 -5.9397 -1.9798
## total_shrub_cover-Sylvilagus_floridanus -1.7543 1.7690 -5.9979 -1.5155
## total_shrub_cover-Meleagris_gallopavo -3.5345 2.1207 -8.5442 -3.1987
## total_shrub_cover-Sciurus_carolinensis -1.6341 1.6647 -5.6480 -1.4364
## total_shrub_cover-Vulpes_vulpes -2.2671 2.1646 -7.6393 -1.9271
## total_shrub_cover-Sus_scrofa -0.9704 1.9195 -5.2004 -0.9180
## Avg_Cogongrass_Cover-Canis_latrans -0.5866 1.4337 -3.4155 -0.5877
## Avg_Cogongrass_Cover-Lynx_rufus -0.5608 1.5302 -3.5522 -0.5672
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4995 1.4372 -3.2197 -0.5236
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2401 1.6013 -4.7695 -1.1582
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9248 1.5957 -4.3282 -0.8191
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5556 1.5343 -3.4813 -0.5630
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.2632 1.6124 -3.0859 -0.3726
## Avg_Cogongrass_Cover-Sus_scrofa -0.7935 1.5459 -3.9153 -0.7837
## Tree_Density-Canis_latrans -2.7436 1.5778 -6.3677 -2.5708
## Tree_Density-Lynx_rufus -0.8438 1.5974 -3.7567 -0.9129
## Tree_Density-Didelphis_virginiana -2.0517 1.5299 -5.2924 -1.9654
## Tree_Density-Sylvilagus_floridanus -2.5226 1.7154 -6.5797 -2.3275
## Tree_Density-Meleagris_gallopavo -2.0507 1.5593 -5.2639 -1.9879
## Tree_Density-Sciurus_carolinensis -2.1385 1.6510 -5.7312 -2.0666
## Tree_Density-Vulpes_vulpes -1.7516 1.6581 -5.1653 -1.6907
## Tree_Density-Sus_scrofa -2.0333 1.6249 -5.7239 -1.9056
## Avg_Canopy_Cover-Canis_latrans -0.0335 1.0360 -2.0272 -0.0780
## Avg_Canopy_Cover-Lynx_rufus 1.0819 1.7932 -2.2349 0.9826
## Avg_Canopy_Cover-Didelphis_virginiana 4.6458 2.8350 1.4915 3.9649
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.6891 3.3755 1.5572 4.8566
## Avg_Canopy_Cover-Meleagris_gallopavo 3.2407 2.6641 0.3456 2.6412
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2480 2.4344 1.2345 3.6792
## Avg_Canopy_Cover-Vulpes_vulpes 3.2974 1.9522 0.4326 2.9237
## Avg_Canopy_Cover-Sus_scrofa 2.5854 1.5518 0.2541 2.3491
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5432 3.1413 0.7982 2.7013
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.5286 2.2746 0.7820 2.9799
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2796 0.9514 -0.5260 1.2617
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4949 1.3472 -0.5707 1.3633
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.1821 2.0820 -5.3833 0.1091
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0323 1.0941 0.2104 1.9218
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.5384 1.7249 0.2864 2.1886
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.1142 2.7742 -7.3843 0.5653
## avg_veg_height-Canis_latrans -0.2576 0.8037 -1.8815 -0.2557
## avg_veg_height-Lynx_rufus -0.6148 1.2061 -3.5489 -0.4877
## avg_veg_height-Didelphis_virginiana -0.3570 0.9845 -2.4443 -0.3062
## avg_veg_height-Sylvilagus_floridanus -0.2056 0.9514 -2.1047 -0.1942
## avg_veg_height-Meleagris_gallopavo -0.1906 1.0937 -2.4383 -0.1671
## avg_veg_height-Sciurus_carolinensis 0.4389 1.1257 -1.3351 0.2925
## avg_veg_height-Vulpes_vulpes -0.2164 1.1159 -2.4633 -0.2133
## avg_veg_height-Sus_scrofa -0.1359 0.9856 -2.1351 -0.1251
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5692 1.0241 213
## (Intercept)-Lynx_rufus 1.6222 1.0365 197
## (Intercept)-Didelphis_virginiana -1.1367 1.0499 177
## (Intercept)-Sylvilagus_floridanus 0.6719 1.0615 267
## (Intercept)-Meleagris_gallopavo 1.1187 1.0119 233
## (Intercept)-Sciurus_carolinensis -1.2491 1.0845 193
## (Intercept)-Vulpes_vulpes -1.0487 1.0396 183
## (Intercept)-Sus_scrofa -1.2143 1.0495 97
## Cogon_Patch_Size-Canis_latrans 7.4834 1.0256 122
## Cogon_Patch_Size-Lynx_rufus 3.4974 1.0192 237
## Cogon_Patch_Size-Didelphis_virginiana 6.0147 1.1385 153
## Cogon_Patch_Size-Sylvilagus_floridanus 1.9781 1.0297 184
## Cogon_Patch_Size-Meleagris_gallopavo 6.4380 1.0241 123
## Cogon_Patch_Size-Sciurus_carolinensis 2.2172 1.0269 248
## Cogon_Patch_Size-Vulpes_vulpes 4.7901 1.2686 99
## Cogon_Patch_Size-Sus_scrofa 2.4949 1.0627 229
## Veg_shannon_index-Canis_latrans 4.2905 1.0925 141
## Veg_shannon_index-Lynx_rufus 4.9105 1.0587 119
## Veg_shannon_index-Didelphis_virginiana 4.4390 1.0256 247
## Veg_shannon_index-Sylvilagus_floridanus 4.0060 1.1483 216
## Veg_shannon_index-Meleagris_gallopavo 5.6418 1.0833 167
## Veg_shannon_index-Sciurus_carolinensis 2.4778 1.0968 204
## Veg_shannon_index-Vulpes_vulpes 3.2115 1.0895 178
## Veg_shannon_index-Sus_scrofa 5.5100 1.0387 199
## total_shrub_cover-Canis_latrans 3.2572 1.0620 172
## total_shrub_cover-Lynx_rufus 0.4086 1.2630 108
## total_shrub_cover-Didelphis_virginiana 0.2769 1.1275 186
## total_shrub_cover-Sylvilagus_floridanus 1.1328 1.0396 189
## total_shrub_cover-Meleagris_gallopavo -0.4613 1.1099 108
## total_shrub_cover-Sciurus_carolinensis 1.1070 1.0382 166
## total_shrub_cover-Vulpes_vulpes 1.1118 1.1418 167
## total_shrub_cover-Sus_scrofa 2.7966 1.0771 119
## Avg_Cogongrass_Cover-Canis_latrans 2.2275 1.0176 214
## Avg_Cogongrass_Cover-Lynx_rufus 2.5346 1.0319 213
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.4104 1.0515 236
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5859 1.0157 214
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.9699 1.0422 213
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4961 1.0254 207
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3365 1.0439 226
## Avg_Cogongrass_Cover-Sus_scrofa 2.2193 1.0332 202
## Tree_Density-Canis_latrans -0.2237 1.0086 212
## Tree_Density-Lynx_rufus 2.7514 1.0157 200
## Tree_Density-Didelphis_virginiana 0.7681 1.0596 249
## Tree_Density-Sylvilagus_floridanus 0.4441 1.0860 283
## Tree_Density-Meleagris_gallopavo 0.8407 1.0434 315
## Tree_Density-Sciurus_carolinensis 0.8796 1.0162 205
## Tree_Density-Vulpes_vulpes 1.3439 1.0195 319
## Tree_Density-Sus_scrofa 0.8575 1.0078 206
## Avg_Canopy_Cover-Canis_latrans 2.5080 1.0868 92
## Avg_Canopy_Cover-Lynx_rufus 4.9754 1.0401 208
## Avg_Canopy_Cover-Didelphis_virginiana 12.2261 1.4285 62
## Avg_Canopy_Cover-Sylvilagus_floridanus 14.8431 1.2478 73
## Avg_Canopy_Cover-Meleagris_gallopavo 10.5050 1.3540 69
## Avg_Canopy_Cover-Sciurus_carolinensis 11.0412 1.2573 109
## Avg_Canopy_Cover-Vulpes_vulpes 7.9530 1.1320 197
## Avg_Canopy_Cover-Sus_scrofa 6.2414 1.1157 361
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 13.7127 1.3799 37
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 10.1355 1.1617 81
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2540 1.0089 241
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 5.0741 1.0088 165
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7776 1.0755 62
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.6819 1.0082 390
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 7.4514 1.2094 109
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.6673 1.0724 77
## avg_veg_height-Canis_latrans 1.3604 1.0259 296
## avg_veg_height-Lynx_rufus 1.4025 1.0565 333
## avg_veg_height-Didelphis_virginiana 1.4485 1.0404 370
## avg_veg_height-Sylvilagus_floridanus 1.7569 1.0198 372
## avg_veg_height-Meleagris_gallopavo 1.9589 1.0419 306
## avg_veg_height-Sciurus_carolinensis 3.0733 1.0651 276
## avg_veg_height-Vulpes_vulpes 2.0393 1.0368 348
## avg_veg_height-Sus_scrofa 1.8519 1.0562 394
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8100 0.1996 -3.2351 -2.7988 -2.4487 1.0164
## (Intercept)-Lynx_rufus -3.6769 0.3097 -4.3042 -3.6664 -3.0989 1.0125
## (Intercept)-Didelphis_virginiana -2.8496 0.2943 -3.4043 -2.8598 -2.2585 1.0091
## (Intercept)-Sylvilagus_floridanus -3.2523 0.2455 -3.7600 -3.2410 -2.8094 1.0001
## (Intercept)-Meleagris_gallopavo -3.6796 0.4383 -4.6122 -3.6619 -2.8751 1.0322
## (Intercept)-Sciurus_carolinensis -2.9916 0.3056 -3.6021 -2.9903 -2.4031 1.0175
## (Intercept)-Vulpes_vulpes -4.0339 0.5734 -5.2956 -3.9881 -3.0511 1.0750
## (Intercept)-Sus_scrofa -3.5224 0.5177 -4.5867 -3.5050 -2.4900 1.0126
## shrub_cover-Canis_latrans -0.2880 0.2672 -0.7752 -0.3062 0.2692 1.0764
## shrub_cover-Lynx_rufus -0.0056 0.3866 -0.7927 -0.0009 0.7167 1.0280
## shrub_cover-Didelphis_virginiana 1.2435 0.4013 0.5135 1.2290 2.0648 1.0388
## shrub_cover-Sylvilagus_floridanus 0.6659 0.4007 -0.0970 0.6621 1.4575 1.0105
## shrub_cover-Meleagris_gallopavo -0.4211 0.4386 -1.2745 -0.4182 0.4377 1.0187
## shrub_cover-Sciurus_carolinensis 1.2603 0.4201 0.4513 1.2570 2.1140 1.0411
## shrub_cover-Vulpes_vulpes 0.4141 0.6226 -0.8355 0.4036 1.6383 1.1251
## shrub_cover-Sus_scrofa 1.0497 0.8513 -0.7139 1.0543 2.7069 1.0530
## veg_height-Canis_latrans -0.5883 0.1953 -0.9843 -0.5783 -0.2310 1.0057
## veg_height-Lynx_rufus 0.1459 0.2429 -0.3316 0.1473 0.6244 1.0081
## veg_height-Didelphis_virginiana 0.5172 0.2764 0.0037 0.5065 1.0743 1.0198
## veg_height-Sylvilagus_floridanus 0.1232 0.2650 -0.3884 0.1152 0.6515 1.0186
## veg_height-Meleagris_gallopavo -0.1307 0.3792 -0.8880 -0.1315 0.6352 1.0618
## veg_height-Sciurus_carolinensis 0.1895 0.2360 -0.2797 0.1824 0.6530 1.0007
## veg_height-Vulpes_vulpes -0.1845 0.3406 -0.9091 -0.1685 0.4413 1.0270
## veg_height-Sus_scrofa -0.1749 0.3490 -0.8851 -0.1728 0.5089 1.0011
## week-Canis_latrans 0.0694 0.1366 -0.2023 0.0745 0.3295 1.0091
## week-Lynx_rufus -0.0469 0.2086 -0.4808 -0.0339 0.3394 1.0007
## week-Didelphis_virginiana -0.2532 0.2451 -0.7846 -0.2348 0.1738 1.0043
## week-Sylvilagus_floridanus -0.1794 0.2214 -0.6537 -0.1650 0.2015 1.0127
## week-Meleagris_gallopavo -0.3108 0.2664 -0.9251 -0.2812 0.1316 1.0013
## week-Sciurus_carolinensis 0.1495 0.1899 -0.2395 0.1497 0.5127 1.0019
## week-Vulpes_vulpes -0.1491 0.2873 -0.7794 -0.1298 0.3540 0.9999
## week-Sus_scrofa 0.1003 0.2516 -0.3977 0.1012 0.5899 1.0005
## ESS
## (Intercept)-Canis_latrans 469
## (Intercept)-Lynx_rufus 269
## (Intercept)-Didelphis_virginiana 383
## (Intercept)-Sylvilagus_floridanus 785
## (Intercept)-Meleagris_gallopavo 277
## (Intercept)-Sciurus_carolinensis 362
## (Intercept)-Vulpes_vulpes 181
## (Intercept)-Sus_scrofa 223
## shrub_cover-Canis_latrans 294
## shrub_cover-Lynx_rufus 351
## shrub_cover-Didelphis_virginiana 380
## shrub_cover-Sylvilagus_floridanus 365
## shrub_cover-Meleagris_gallopavo 380
## shrub_cover-Sciurus_carolinensis 371
## shrub_cover-Vulpes_vulpes 175
## shrub_cover-Sus_scrofa 162
## veg_height-Canis_latrans 687
## veg_height-Lynx_rufus 633
## veg_height-Didelphis_virginiana 540
## veg_height-Sylvilagus_floridanus 664
## veg_height-Meleagris_gallopavo 219
## veg_height-Sciurus_carolinensis 814
## veg_height-Vulpes_vulpes 525
## veg_height-Sus_scrofa 751
## week-Canis_latrans 1547
## week-Lynx_rufus 680
## week-Didelphis_virginiana 1073
## week-Sylvilagus_floridanus 759
## week-Meleagris_gallopavo 653
## week-Sciurus_carolinensis 1443
## week-Vulpes_vulpes 726
## week-Sus_scrofa 1251
# 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 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_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): 0.4005
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5823 0.9513 -3.2882 -1.6185 0.4477 1.0522 533
## Cogon_Patch_Size -0.4659 0.8514 -2.2492 -0.4507 1.2394 1.0637 656
## Veg_shannon_index 0.8353 0.6193 -0.3940 0.8418 2.0747 1.0074 660
## total_shrub_cover -0.6163 0.6888 -2.0796 -0.6025 0.8464 1.0016 764
## Avg_Cogongrass_Cover 1.7725 0.8108 0.2199 1.7558 3.3219 1.1431 223
## Tree_Density -1.8465 0.7629 -3.4028 -1.8089 -0.3944 1.0331 236
## Avg_Canopy_Cover 1.8272 0.7068 0.4351 1.8068 3.2851 1.0060 542
## avg_veg_height -0.7364 0.5825 -1.9258 -0.7147 0.3521 1.0781 208
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.8231 9.6123 0.3720 4.9530 32.9420 1.0019 133
## Cogon_Patch_Size 5.9022 8.9857 0.2331 3.1509 27.2153 1.0197 283
## Veg_shannon_index 2.0466 3.4529 0.0648 0.8973 11.7816 1.0352 185
## total_shrub_cover 3.4522 5.7291 0.1064 1.8200 16.1841 1.0732 190
## Avg_Cogongrass_Cover 1.4897 2.3986 0.0566 0.7055 8.2235 1.0029 655
## Tree_Density 1.9283 3.8535 0.0585 0.7273 11.2977 1.0288 388
## Avg_Canopy_Cover 3.0372 4.1446 0.1378 1.7267 13.1702 1.0590 306
## avg_veg_height 0.5802 0.9156 0.0432 0.3019 2.9772 1.0373 464
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6497 2.3193 0.0621 0.7981 7.3974 1.5016 92
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9796 0.2828 -3.5281 -2.9769 -2.3835 1.0173 1159
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5357 0.6169 0.0829 0.3805 2.0293 1.0674 676
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5372 1.2593 -1.7946 0.4714
## (Intercept)-Lynx_rufus 0.8019 2.2952 -2.6268 0.4547
## (Intercept)-Didelphis_virginiana -3.0470 1.1279 -5.4650 -2.9583
## (Intercept)-Sylvilagus_floridanus -1.6501 1.2134 -4.0908 -1.6344
## (Intercept)-Meleagris_gallopavo -1.7352 1.4484 -4.4413 -1.7759
## (Intercept)-Sciurus_carolinensis -3.3200 1.2289 -6.0818 -3.1759
## (Intercept)-Vulpes_vulpes -2.9481 1.6342 -6.4482 -2.8520
## (Intercept)-Sus_scrofa -4.6211 1.8427 -9.0207 -4.3868
## Cogon_Patch_Size-Canis_latrans 1.2257 1.5294 -0.7855 0.8863
## Cogon_Patch_Size-Lynx_rufus -0.2935 1.6574 -3.2375 -0.3513
## Cogon_Patch_Size-Didelphis_virginiana 1.3068 1.1101 -0.3534 1.1981
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3155 1.9556 -7.4587 -1.9598
## Cogon_Patch_Size-Meleagris_gallopavo -0.0144 1.5081 -2.3307 -0.1963
## Cogon_Patch_Size-Sciurus_carolinensis -1.9276 1.5476 -5.6031 -1.6528
## Cogon_Patch_Size-Vulpes_vulpes -1.5790 1.8807 -6.0580 -1.3390
## Cogon_Patch_Size-Sus_scrofa -1.2274 1.9184 -5.9269 -0.9569
## Veg_shannon_index-Canis_latrans 1.2770 0.7377 0.0521 1.1853
## Veg_shannon_index-Lynx_rufus 0.5602 1.2451 -2.2611 0.6642
## Veg_shannon_index-Didelphis_virginiana 1.1215 0.7837 -0.2875 1.0474
## Veg_shannon_index-Sylvilagus_floridanus 1.0476 0.8172 -0.4727 0.9907
## Veg_shannon_index-Meleagris_gallopavo 1.5314 1.0581 -0.0851 1.3517
## Veg_shannon_index-Sciurus_carolinensis -0.1309 0.9820 -2.4689 -0.0082
## Veg_shannon_index-Vulpes_vulpes -0.1073 1.2257 -3.1099 0.1193
## Veg_shannon_index-Sus_scrofa 1.9229 1.3787 0.0798 1.6434
## total_shrub_cover-Canis_latrans 0.2910 0.9006 -1.2355 0.1782
## total_shrub_cover-Lynx_rufus -1.6541 1.5300 -5.2834 -1.4249
## total_shrub_cover-Didelphis_virginiana -0.6897 0.8041 -2.3473 -0.6684
## total_shrub_cover-Sylvilagus_floridanus -0.3025 0.9774 -2.3013 -0.3022
## total_shrub_cover-Meleagris_gallopavo -2.8310 1.7603 -7.0911 -2.5706
## total_shrub_cover-Sciurus_carolinensis 0.0340 0.8258 -1.5079 -0.0136
## total_shrub_cover-Vulpes_vulpes -0.8377 1.2054 -3.5188 -0.7163
## total_shrub_cover-Sus_scrofa 0.3012 1.1223 -1.6182 0.2011
## Avg_Cogongrass_Cover-Canis_latrans 2.2179 0.9625 0.4917 2.1630
## Avg_Cogongrass_Cover-Lynx_rufus 2.3554 1.1197 0.3619 2.2801
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0404 0.9212 0.3086 1.9955
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3520 1.0682 -0.8139 1.3849
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1794 1.3384 -1.7708 1.3076
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2452 0.9883 0.4644 2.1840
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3683 1.2232 0.2164 2.2713
## Avg_Cogongrass_Cover-Sus_scrofa 1.3404 1.1776 -1.2744 1.4105
## Tree_Density-Canis_latrans -2.3823 1.1623 -5.0997 -2.2267
## Tree_Density-Lynx_rufus -0.8536 1.2764 -2.9479 -0.9750
## Tree_Density-Didelphis_virginiana -2.1622 1.0604 -4.7537 -2.0176
## Tree_Density-Sylvilagus_floridanus -2.3902 1.2581 -5.4351 -2.2082
## Tree_Density-Meleagris_gallopavo -2.0110 1.1945 -4.5607 -1.9383
## Tree_Density-Sciurus_carolinensis -2.3166 1.1130 -4.8113 -2.1536
## Tree_Density-Vulpes_vulpes -1.7937 1.3998 -4.5400 -1.8154
## Tree_Density-Sus_scrofa -2.0869 1.2733 -4.9766 -1.9421
## Avg_Canopy_Cover-Canis_latrans 0.2727 0.7550 -1.2678 0.2638
## Avg_Canopy_Cover-Lynx_rufus 0.9680 1.3644 -1.7246 0.9210
## Avg_Canopy_Cover-Didelphis_virginiana 2.8345 0.9876 1.2792 2.6897
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4274 1.7271 1.1937 3.0635
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3952 1.2591 0.5785 2.1373
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4324 0.9542 0.9924 2.2870
## Avg_Canopy_Cover-Vulpes_vulpes 2.2124 1.2106 0.3551 2.0419
## Avg_Canopy_Cover-Sus_scrofa 2.1204 0.9345 0.6103 2.0175
## avg_veg_height-Canis_latrans -0.8486 0.6561 -2.2379 -0.8147
## avg_veg_height-Lynx_rufus -0.8370 0.8680 -2.7321 -0.7845
## avg_veg_height-Didelphis_virginiana -0.7723 0.7205 -2.2871 -0.7473
## avg_veg_height-Sylvilagus_floridanus -0.8454 0.7631 -2.3796 -0.8381
## avg_veg_height-Meleagris_gallopavo -0.8741 0.8587 -2.7476 -0.8183
## avg_veg_height-Sciurus_carolinensis -0.2846 0.7671 -1.6651 -0.3136
## avg_veg_height-Vulpes_vulpes -0.7994 0.7756 -2.4091 -0.7779
## avg_veg_height-Sus_scrofa -0.7673 0.7753 -2.3422 -0.7542
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.4018 1.0606 148
## (Intercept)-Lynx_rufus 6.7639 1.0843 53
## (Intercept)-Didelphis_virginiana -1.0702 1.0203 383
## (Intercept)-Sylvilagus_floridanus 0.8839 1.0333 398
## (Intercept)-Meleagris_gallopavo 1.4643 1.0052 221
## (Intercept)-Sciurus_carolinensis -1.2258 1.0233 308
## (Intercept)-Vulpes_vulpes 0.2000 1.0789 180
## (Intercept)-Sus_scrofa -1.7043 1.0029 198
## Cogon_Patch_Size-Canis_latrans 5.1923 1.0050 417
## Cogon_Patch_Size-Lynx_rufus 3.3235 1.0445 269
## Cogon_Patch_Size-Didelphis_virginiana 3.6447 1.0289 225
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4795 1.1077 242
## Cogon_Patch_Size-Meleagris_gallopavo 3.5009 1.0568 252
## Cogon_Patch_Size-Sciurus_carolinensis 0.2947 1.0802 423
## Cogon_Patch_Size-Vulpes_vulpes 1.5667 1.0653 233
## Cogon_Patch_Size-Sus_scrofa 1.8581 1.0978 231
## Veg_shannon_index-Canis_latrans 3.0221 1.0035 540
## Veg_shannon_index-Lynx_rufus 2.7908 1.0219 350
## Veg_shannon_index-Didelphis_virginiana 2.8992 1.0057 905
## Veg_shannon_index-Sylvilagus_floridanus 2.8301 1.0083 831
## Veg_shannon_index-Meleagris_gallopavo 4.1195 1.0295 273
## Veg_shannon_index-Sciurus_carolinensis 1.4012 1.0145 227
## Veg_shannon_index-Vulpes_vulpes 1.6425 1.0711 243
## Veg_shannon_index-Sus_scrofa 5.1672 1.0250 200
## total_shrub_cover-Canis_latrans 2.3920 1.0260 454
## total_shrub_cover-Lynx_rufus 0.7513 1.0194 161
## total_shrub_cover-Didelphis_virginiana 0.8681 1.0237 1049
## total_shrub_cover-Sylvilagus_floridanus 1.5916 1.0044 605
## total_shrub_cover-Meleagris_gallopavo -0.2763 1.0118 163
## total_shrub_cover-Sciurus_carolinensis 1.7247 1.0018 1070
## total_shrub_cover-Vulpes_vulpes 1.2443 1.0154 366
## total_shrub_cover-Sus_scrofa 2.7216 1.0056 583
## Avg_Cogongrass_Cover-Canis_latrans 4.3584 1.0544 304
## Avg_Cogongrass_Cover-Lynx_rufus 4.7765 1.0576 316
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9327 1.0961 332
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3723 1.0789 304
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4610 1.0784 194
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4438 1.0632 290
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0380 1.0815 256
## Avg_Cogongrass_Cover-Sus_scrofa 3.3741 1.0842 320
## Tree_Density-Canis_latrans -0.5496 1.0380 350
## Tree_Density-Lynx_rufus 2.1402 1.0283 221
## Tree_Density-Didelphis_virginiana -0.4062 1.0310 330
## Tree_Density-Sylvilagus_floridanus -0.4389 1.0365 291
## Tree_Density-Meleagris_gallopavo 0.3174 1.0191 487
## Tree_Density-Sciurus_carolinensis -0.5504 1.0321 337
## Tree_Density-Vulpes_vulpes 1.2204 1.0217 218
## Tree_Density-Sus_scrofa -0.0043 1.0163 481
## Avg_Canopy_Cover-Canis_latrans 1.8105 1.0103 554
## Avg_Canopy_Cover-Lynx_rufus 3.9415 1.0022 300
## Avg_Canopy_Cover-Didelphis_virginiana 5.1202 1.0226 303
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6538 1.0447 191
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5738 1.0632 279
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6361 1.0122 325
## Avg_Canopy_Cover-Vulpes_vulpes 5.2762 1.0415 190
## Avg_Canopy_Cover-Sus_scrofa 4.2381 1.0245 507
## avg_veg_height-Canis_latrans 0.3808 1.0999 282
## avg_veg_height-Lynx_rufus 0.7304 1.0495 301
## avg_veg_height-Didelphis_virginiana 0.5673 1.0642 313
## avg_veg_height-Sylvilagus_floridanus 0.6234 1.0801 300
## avg_veg_height-Meleagris_gallopavo 0.6954 1.0609 281
## avg_veg_height-Sciurus_carolinensis 1.3543 1.0211 405
## avg_veg_height-Vulpes_vulpes 0.6667 1.0431 251
## avg_veg_height-Sus_scrofa 0.7126 1.0483 330
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6754 0.1787 -3.0450 -2.6676 -2.3485 1.0061
## (Intercept)-Lynx_rufus -3.5987 0.3239 -4.2399 -3.5954 -2.9814 1.0545
## (Intercept)-Didelphis_virginiana -2.3924 0.2376 -2.8732 -2.3856 -1.9401 1.0075
## (Intercept)-Sylvilagus_floridanus -3.1517 0.2644 -3.6936 -3.1413 -2.6483 1.0079
## (Intercept)-Meleagris_gallopavo -3.3495 0.2988 -3.9795 -3.3338 -2.8066 1.0057
## (Intercept)-Sciurus_carolinensis -2.5427 0.2668 -3.0831 -2.5369 -2.0459 1.0020
## (Intercept)-Vulpes_vulpes -3.7466 0.5460 -4.9080 -3.7003 -2.7970 1.0939
## (Intercept)-Sus_scrofa -2.9157 0.3954 -3.7865 -2.8996 -2.1832 1.0344
## ESS
## (Intercept)-Canis_latrans 805
## (Intercept)-Lynx_rufus 152
## (Intercept)-Didelphis_virginiana 1446
## (Intercept)-Sylvilagus_floridanus 563
## (Intercept)-Meleagris_gallopavo 430
## (Intercept)-Sciurus_carolinensis 989
## (Intercept)-Vulpes_vulpes 208
## (Intercept)-Sus_scrofa 681
# 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 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_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): 0.3795
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1209 0.4979 -2.1089 -1.1036 -0.1901 1.0265 431
## Avg_Cogongrass_Cover 0.2346 0.4057 -0.5986 0.2542 0.9935 1.0062 523
## total_shrub_cover -0.4169 0.3951 -1.2478 -0.4028 0.3407 1.0162 1170
## avg_veg_height -0.2051 0.3580 -0.9105 -0.2031 0.5124 1.0042 531
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9325 1.6080 0.0484 0.4698 4.4161 1.0143 400
## Avg_Cogongrass_Cover 0.5992 0.8282 0.0475 0.3340 2.6208 1.0069 821
## total_shrub_cover 0.8296 2.1797 0.0611 0.4807 3.3764 1.3233 1045
## avg_veg_height 0.3126 0.4255 0.0359 0.1857 1.3745 1.0113 1117
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1229 1.6655 0.2994 1.6517 6.5476 1.0289 212
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.956 0.2645 -3.4843 -2.9508 -2.4611 1.0102 1297
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4565 0.7542 0.0664 0.2959 1.8499 1.104 438
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4543 0.7831 -1.8982 -0.4830
## (Intercept)-Lynx_rufus -0.8932 0.7172 -2.3001 -0.8852
## (Intercept)-Didelphis_virginiana -1.3647 0.6600 -2.7925 -1.3382
## (Intercept)-Sylvilagus_floridanus -0.8576 0.7467 -2.2054 -0.8985
## (Intercept)-Meleagris_gallopavo -1.1437 0.7101 -2.6377 -1.1288
## (Intercept)-Sciurus_carolinensis -1.4613 0.6670 -2.8612 -1.4225
## (Intercept)-Vulpes_vulpes -1.4444 0.9058 -3.2197 -1.4545
## (Intercept)-Sus_scrofa -1.7106 0.7995 -3.5139 -1.6360
## Avg_Cogongrass_Cover-Canis_latrans 0.5840 0.5163 -0.3394 0.5576
## Avg_Cogongrass_Cover-Lynx_rufus 0.6269 0.5661 -0.3516 0.5803
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5033 0.4966 -0.4364 0.4868
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2059 0.5978 -1.5221 -0.1554
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3338 0.6921 -1.9210 -0.2427
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3903 0.4874 -0.5359 0.3743
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4247 0.5960 -0.7183 0.4046
## Avg_Cogongrass_Cover-Sus_scrofa -0.1066 0.7076 -1.6902 -0.0206
## total_shrub_cover-Canis_latrans 0.1472 0.4856 -0.6893 0.0993
## total_shrub_cover-Lynx_rufus -0.9528 0.7035 -2.5959 -0.8548
## total_shrub_cover-Didelphis_virginiana -0.2880 0.4579 -1.2075 -0.2833
## total_shrub_cover-Sylvilagus_floridanus -0.4245 0.5744 -1.6441 -0.4010
## total_shrub_cover-Meleagris_gallopavo -1.3737 0.7687 -3.1959 -1.2555
## total_shrub_cover-Sciurus_carolinensis -0.1254 0.4594 -1.0032 -0.1318
## total_shrub_cover-Vulpes_vulpes -0.4176 0.6434 -1.8135 -0.3925
## total_shrub_cover-Sus_scrofa 0.0154 0.6312 -1.1196 -0.0295
## avg_veg_height-Canis_latrans -0.2336 0.4444 -1.1064 -0.2359
## avg_veg_height-Lynx_rufus -0.2239 0.5318 -1.2994 -0.2122
## avg_veg_height-Didelphis_virginiana -0.2000 0.4495 -1.1250 -0.1870
## avg_veg_height-Sylvilagus_floridanus -0.3017 0.4767 -1.2521 -0.2883
## avg_veg_height-Meleagris_gallopavo -0.4163 0.5537 -1.5885 -0.3855
## avg_veg_height-Sciurus_carolinensis 0.1419 0.5003 -0.7859 0.1263
## avg_veg_height-Vulpes_vulpes -0.2678 0.5238 -1.3469 -0.2557
## avg_veg_height-Sus_scrofa -0.1638 0.5043 -1.1497 -0.1595
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2197 1.0097 317
## (Intercept)-Lynx_rufus 0.5320 1.0041 451
## (Intercept)-Didelphis_virginiana -0.1958 1.0039 759
## (Intercept)-Sylvilagus_floridanus 0.7639 1.0362 411
## (Intercept)-Meleagris_gallopavo 0.2301 1.0018 657
## (Intercept)-Sciurus_carolinensis -0.2461 1.0172 619
## (Intercept)-Vulpes_vulpes 0.2702 1.0631 299
## (Intercept)-Sus_scrofa -0.3585 1.0088 482
## Avg_Cogongrass_Cover-Canis_latrans 1.7301 1.0013 753
## Avg_Cogongrass_Cover-Lynx_rufus 1.8905 1.0039 934
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5631 1.0020 817
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8481 1.0058 838
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7955 1.0033 669
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.4298 1.0027 814
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6563 1.0104 595
## Avg_Cogongrass_Cover-Sus_scrofa 1.0899 1.0078 773
## total_shrub_cover-Canis_latrans 1.1853 1.0056 1367
## total_shrub_cover-Lynx_rufus 0.1534 1.0274 535
## total_shrub_cover-Didelphis_virginiana 0.6139 1.0135 1646
## total_shrub_cover-Sylvilagus_floridanus 0.6397 1.0105 1058
## total_shrub_cover-Meleagris_gallopavo -0.2105 1.0682 478
## total_shrub_cover-Sciurus_carolinensis 0.7856 1.0109 1621
## total_shrub_cover-Vulpes_vulpes 0.7955 1.0144 946
## total_shrub_cover-Sus_scrofa 1.4104 1.0162 1028
## avg_veg_height-Canis_latrans 0.6299 1.0039 780
## avg_veg_height-Lynx_rufus 0.8002 1.0025 821
## avg_veg_height-Didelphis_virginiana 0.6789 1.0090 928
## avg_veg_height-Sylvilagus_floridanus 0.6312 1.0038 875
## avg_veg_height-Meleagris_gallopavo 0.5932 1.0123 789
## avg_veg_height-Sciurus_carolinensis 1.1678 1.0054 992
## avg_veg_height-Vulpes_vulpes 0.7253 1.0050 576
## avg_veg_height-Sus_scrofa 0.8188 1.0046 861
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6537 0.1739 -3.0169 -2.6480 -2.3299 1.0109
## (Intercept)-Lynx_rufus -3.4058 0.2731 -3.9690 -3.4000 -2.8998 1.0052
## (Intercept)-Didelphis_virginiana -2.4552 0.2609 -2.9844 -2.4471 -1.9644 1.0008
## (Intercept)-Sylvilagus_floridanus -3.1582 0.2768 -3.7366 -3.1401 -2.6602 1.0099
## (Intercept)-Meleagris_gallopavo -3.2361 0.2767 -3.7997 -3.2270 -2.7208 1.0359
## (Intercept)-Sciurus_carolinensis -2.5713 0.2713 -3.1179 -2.5652 -2.0477 1.0061
## (Intercept)-Vulpes_vulpes -3.6107 0.6262 -5.1564 -3.5074 -2.6841 1.1275
## (Intercept)-Sus_scrofa -3.0129 0.4159 -3.9053 -2.9915 -2.2631 1.0018
## ESS
## (Intercept)-Canis_latrans 966
## (Intercept)-Lynx_rufus 424
## (Intercept)-Didelphis_virginiana 753
## (Intercept)-Sylvilagus_floridanus 451
## (Intercept)-Meleagris_gallopavo 561
## (Intercept)-Sciurus_carolinensis 1027
## (Intercept)-Vulpes_vulpes 123
## (Intercept)-Sus_scrofa 669
# 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 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_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): 0.3718
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2034 0.5075 -2.2074 -1.2042 -0.1494 1.0147 835
## Tree_Density -0.7573 0.4901 -1.8061 -0.7038 0.0716 1.0192 618
## Avg_Canopy_Cover 1.0583 0.4380 0.2514 1.0411 1.9570 1.0039 920
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5131 1.9040 0.0890 0.9593 6.0764 1.0102 670
## Tree_Density 0.9940 1.8132 0.0536 0.4839 4.7421 1.0258 584
## Avg_Canopy_Cover 1.0042 1.7004 0.0806 0.6206 4.0603 1.1138 840
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8671 0.9745 0.0541 0.5505 3.4741 1.0763 191
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9244 0.2602 -3.4339 -2.9272 -2.3846 1.0016 1509
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3971 0.4324 0.0658 0.2836 1.4008 1.0039 688
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1908 0.6877 -1.5436 -0.1977 1.2315
## (Intercept)-Lynx_rufus -0.4357 0.8985 -1.9805 -0.5099 1.6603
## (Intercept)-Didelphis_virginiana -1.7972 0.6558 -3.2413 -1.7557 -0.6113
## (Intercept)-Sylvilagus_floridanus -0.9965 0.6477 -2.2519 -1.0062 0.2921
## (Intercept)-Meleagris_gallopavo -0.8665 0.7260 -2.1824 -0.9008 0.7216
## (Intercept)-Sciurus_carolinensis -1.8545 0.7230 -3.5759 -1.7878 -0.6094
## (Intercept)-Vulpes_vulpes -1.8783 0.7977 -3.5332 -1.8337 -0.4133
## (Intercept)-Sus_scrofa -2.2427 0.8653 -4.1602 -2.1704 -0.7603
## Tree_Density-Canis_latrans -0.9441 0.6206 -2.3969 -0.8735 0.0648
## Tree_Density-Lynx_rufus 0.2479 0.7411 -0.9004 0.1538 1.9746
## Tree_Density-Didelphis_virginiana -1.0350 0.7673 -2.8825 -0.9136 0.1488
## Tree_Density-Sylvilagus_floridanus -1.1436 0.8357 -3.1536 -1.0003 0.1259
## Tree_Density-Meleagris_gallopavo -0.9334 0.7399 -2.6581 -0.8300 0.2866
## Tree_Density-Sciurus_carolinensis -0.9441 0.7875 -2.8325 -0.8245 0.2538
## Tree_Density-Vulpes_vulpes -0.6107 0.7495 -2.1971 -0.5630 0.7662
## Tree_Density-Sus_scrofa -0.9524 0.8483 -2.9470 -0.8341 0.4127
## Avg_Canopy_Cover-Canis_latrans 0.0143 0.4875 -0.9437 0.0300 0.9397
## Avg_Canopy_Cover-Lynx_rufus 0.6442 0.6746 -0.5664 0.6049 2.1216
## Avg_Canopy_Cover-Didelphis_virginiana 1.3265 0.5458 0.4216 1.2726 2.5606
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8737 0.8717 0.5906 1.7255 4.0150
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5016 0.7447 0.3254 1.4115 3.1691
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3125 0.5576 0.3761 1.2616 2.5258
## Avg_Canopy_Cover-Vulpes_vulpes 1.0224 0.6081 -0.1086 0.9845 2.3218
## Avg_Canopy_Cover-Sus_scrofa 1.2470 0.5811 0.2376 1.2025 2.5470
## Rhat ESS
## (Intercept)-Canis_latrans 1.0220 490
## (Intercept)-Lynx_rufus 1.0357 339
## (Intercept)-Didelphis_virginiana 1.0087 930
## (Intercept)-Sylvilagus_floridanus 1.0074 931
## (Intercept)-Meleagris_gallopavo 1.0235 612
## (Intercept)-Sciurus_carolinensis 0.9999 748
## (Intercept)-Vulpes_vulpes 1.0014 562
## (Intercept)-Sus_scrofa 1.0112 562
## Tree_Density-Canis_latrans 1.0151 1064
## Tree_Density-Lynx_rufus 1.0036 648
## Tree_Density-Didelphis_virginiana 1.0257 843
## Tree_Density-Sylvilagus_floridanus 1.0592 565
## Tree_Density-Meleagris_gallopavo 1.0337 657
## Tree_Density-Sciurus_carolinensis 1.0110 821
## Tree_Density-Vulpes_vulpes 1.0044 782
## Tree_Density-Sus_scrofa 1.0125 599
## Avg_Canopy_Cover-Canis_latrans 1.0018 1023
## Avg_Canopy_Cover-Lynx_rufus 1.0020 748
## Avg_Canopy_Cover-Didelphis_virginiana 1.0008 1180
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0131 576
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0007 637
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0002 1164
## Avg_Canopy_Cover-Vulpes_vulpes 1.0033 1043
## Avg_Canopy_Cover-Sus_scrofa 1.0007 1139
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6553 0.1741 -3.0023 -2.6472 -2.3369 1.0093
## (Intercept)-Lynx_rufus -3.4234 0.3061 -4.0617 -3.4072 -2.8810 1.0006
## (Intercept)-Didelphis_virginiana -2.4309 0.2478 -2.9208 -2.4261 -1.9586 1.0011
## (Intercept)-Sylvilagus_floridanus -3.0763 0.2486 -3.5897 -3.0657 -2.6209 1.0182
## (Intercept)-Meleagris_gallopavo -3.2808 0.2829 -3.8883 -3.2627 -2.7683 1.0071
## (Intercept)-Sciurus_carolinensis -2.5554 0.2622 -3.0967 -2.5427 -2.0669 1.0032
## (Intercept)-Vulpes_vulpes -3.5138 0.5097 -4.6365 -3.4629 -2.6404 1.0216
## (Intercept)-Sus_scrofa -2.9490 0.3753 -3.7171 -2.9346 -2.2269 1.0056
## ESS
## (Intercept)-Canis_latrans 980
## (Intercept)-Lynx_rufus 331
## (Intercept)-Didelphis_virginiana 1254
## (Intercept)-Sylvilagus_floridanus 755
## (Intercept)-Meleagris_gallopavo 518
## (Intercept)-Sciurus_carolinensis 967
## (Intercept)-Vulpes_vulpes 224
## (Intercept)-Sus_scrofa 897
# 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 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_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): 0.3792
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1638 0.5059 -2.2008 -1.1589 -0.2086 1.0154 380
## Cogon_Patch_Size -0.2354 0.5593 -1.4187 -0.2059 0.8365 1.0099 754
## Avg_Cogongrass_Cover 0.1468 0.3806 -0.6318 0.1496 0.8924 1.0006 531
## total_shrub_cover -0.3939 0.4152 -1.2929 -0.3795 0.4029 1.0042 760
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9189 1.2229 0.0587 0.5176 4.4459 1.0229 491
## Cogon_Patch_Size 1.8471 3.0518 0.0859 0.9563 9.3236 1.0105 475
## Avg_Cogongrass_Cover 0.6268 1.4887 0.0513 0.3476 2.7493 1.2168 1584
## total_shrub_cover 0.8933 1.6244 0.0593 0.4742 4.2105 1.1524 540
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3509 2.1941 0.2441 1.7718 7.8738 1.0366 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9427 0.2549 -3.4505 -2.9357 -2.4254 1.0013 1103
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4212 0.442 0.0639 0.293 1.6446 1.0083 476
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3863 0.7661 -1.7554 -0.4446
## (Intercept)-Lynx_rufus -0.9001 0.7420 -2.3540 -0.9105
## (Intercept)-Didelphis_virginiana -1.4106 0.6609 -2.7865 -1.3968
## (Intercept)-Sylvilagus_floridanus -0.9445 0.7291 -2.4043 -0.9471
## (Intercept)-Meleagris_gallopavo -1.1334 0.7165 -2.5737 -1.1427
## (Intercept)-Sciurus_carolinensis -1.5148 0.7345 -3.1067 -1.4737
## (Intercept)-Vulpes_vulpes -1.5642 0.8256 -3.4017 -1.5118
## (Intercept)-Sus_scrofa -1.7858 0.8912 -3.8743 -1.6703
## Cogon_Patch_Size-Canis_latrans 0.8243 0.7762 -0.3172 0.6982
## Cogon_Patch_Size-Lynx_rufus -0.3672 0.8911 -2.0518 -0.3864
## Cogon_Patch_Size-Didelphis_virginiana 0.7843 0.5896 -0.2390 0.7359
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2514 1.2655 -4.3334 -0.9794
## Cogon_Patch_Size-Meleagris_gallopavo -0.0609 0.7296 -1.4409 -0.0646
## Cogon_Patch_Size-Sciurus_carolinensis -0.9258 0.9038 -3.1339 -0.7659
## Cogon_Patch_Size-Vulpes_vulpes -0.7632 1.0991 -3.2753 -0.6019
## Cogon_Patch_Size-Sus_scrofa -0.4398 1.0028 -2.9086 -0.2911
## Avg_Cogongrass_Cover-Canis_latrans 0.2766 0.4407 -0.5552 0.2639
## Avg_Cogongrass_Cover-Lynx_rufus 0.6371 0.6059 -0.3531 0.5581
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2107 0.4641 -0.7239 0.2085
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1720 0.5565 -1.3881 -0.1403
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4899 0.7095 -2.0575 -0.4141
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5544 0.4691 -0.2940 0.5255
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3534 0.5425 -0.6829 0.3287
## Avg_Cogongrass_Cover-Sus_scrofa -0.1620 0.6815 -1.6546 -0.0969
## total_shrub_cover-Canis_latrans 0.1236 0.5072 -0.7478 0.0871
## total_shrub_cover-Lynx_rufus -0.8849 0.8412 -3.0242 -0.7506
## total_shrub_cover-Didelphis_virginiana -0.3923 0.4843 -1.4185 -0.3762
## total_shrub_cover-Sylvilagus_floridanus -0.3341 0.6131 -1.5893 -0.3133
## total_shrub_cover-Meleagris_gallopavo -1.3395 0.8234 -3.3062 -1.2043
## total_shrub_cover-Sciurus_carolinensis -0.0520 0.4929 -0.9711 -0.0652
## total_shrub_cover-Vulpes_vulpes -0.3983 0.7288 -1.9863 -0.3394
## total_shrub_cover-Sus_scrofa 0.0630 0.6629 -1.1111 0.0159
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2604 1.0002 519
## (Intercept)-Lynx_rufus 0.5854 1.0145 546
## (Intercept)-Didelphis_virginiana -0.1661 1.0028 479
## (Intercept)-Sylvilagus_floridanus 0.5515 1.0232 546
## (Intercept)-Meleagris_gallopavo 0.2766 1.0038 525
## (Intercept)-Sciurus_carolinensis -0.1737 1.0121 510
## (Intercept)-Vulpes_vulpes -0.0797 1.0322 258
## (Intercept)-Sus_scrofa -0.3233 1.0211 293
## Cogon_Patch_Size-Canis_latrans 2.7384 1.0115 617
## Cogon_Patch_Size-Lynx_rufus 1.4519 1.0126 517
## Cogon_Patch_Size-Didelphis_virginiana 2.0313 1.0137 903
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4272 1.0313 295
## Cogon_Patch_Size-Meleagris_gallopavo 1.4170 1.0338 810
## Cogon_Patch_Size-Sciurus_carolinensis 0.3863 1.0121 553
## Cogon_Patch_Size-Vulpes_vulpes 0.9923 1.0111 455
## Cogon_Patch_Size-Sus_scrofa 1.1366 1.0451 745
## Avg_Cogongrass_Cover-Canis_latrans 1.1924 0.9997 1063
## Avg_Cogongrass_Cover-Lynx_rufus 2.0709 1.0330 1063
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1076 1.0029 1345
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8409 1.0033 879
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7064 1.0373 588
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6169 1.0265 1031
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4824 1.0161 1123
## Avg_Cogongrass_Cover-Sus_scrofa 1.0250 1.0214 579
## total_shrub_cover-Canis_latrans 1.1857 1.0001 711
## total_shrub_cover-Lynx_rufus 0.3582 1.0202 387
## total_shrub_cover-Didelphis_virginiana 0.5704 0.9997 1895
## total_shrub_cover-Sylvilagus_floridanus 0.8295 1.0058 915
## total_shrub_cover-Meleagris_gallopavo -0.0823 1.0051 525
## total_shrub_cover-Sciurus_carolinensis 0.9851 1.0035 1425
## total_shrub_cover-Vulpes_vulpes 0.8125 1.0458 483
## total_shrub_cover-Sus_scrofa 1.4586 1.0076 867
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6300 0.1685 -2.9679 -2.6226 -2.3053 1.0044
## (Intercept)-Lynx_rufus -3.3909 0.2782 -3.9834 -3.3765 -2.8788 1.0004
## (Intercept)-Didelphis_virginiana -2.4391 0.2552 -2.9657 -2.4332 -1.9614 1.0014
## (Intercept)-Sylvilagus_floridanus -3.1795 0.2825 -3.7654 -3.1685 -2.6663 1.0056
## (Intercept)-Meleagris_gallopavo -3.2238 0.2790 -3.8163 -3.2137 -2.7212 1.0055
## (Intercept)-Sciurus_carolinensis -2.5513 0.2660 -3.0985 -2.5416 -2.0298 1.0021
## (Intercept)-Vulpes_vulpes -3.6019 0.5728 -4.8416 -3.5260 -2.6956 1.0033
## (Intercept)-Sus_scrofa -2.9943 0.3964 -3.8366 -2.9652 -2.2905 1.0046
## ESS
## (Intercept)-Canis_latrans 927
## (Intercept)-Lynx_rufus 532
## (Intercept)-Didelphis_virginiana 1173
## (Intercept)-Sylvilagus_floridanus 480
## (Intercept)-Meleagris_gallopavo 470
## (Intercept)-Sciurus_carolinensis 1058
## (Intercept)-Vulpes_vulpes 193
## (Intercept)-Sus_scrofa 653
# 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 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_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): 0.3727
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0922 0.4417 -2.0243 -1.0808 -0.2080 1.0120 554
## Veg_shannon_index 0.3719 0.3235 -0.2397 0.3635 1.0446 1.0165 897
## Avg_Cogongrass_Cover 0.2290 0.3127 -0.3929 0.2299 0.8598 1.0224 950
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7890 1.0024 0.0536 0.4797 3.2670 1.0029 819
## Veg_shannon_index 0.4388 0.5129 0.0481 0.2753 1.9043 1.0080 1017
## Avg_Cogongrass_Cover 0.4627 0.5898 0.0477 0.2745 2.0133 1.0170 836
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4392 1.2556 0.1461 1.1231 4.8289 1.0155 268
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9266 0.2458 -3.4238 -2.9187 -2.4588 1.0037 1347
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4064 0.4518 0.0652 0.2878 1.5121 1.0108 847
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4546 0.6649 -1.6714 -0.5001
## (Intercept)-Lynx_rufus -0.7959 0.6621 -2.0564 -0.8085
## (Intercept)-Didelphis_virginiana -1.4009 0.6079 -2.7050 -1.3644
## (Intercept)-Sylvilagus_floridanus -0.8606 0.6269 -2.0773 -0.8647
## (Intercept)-Meleagris_gallopavo -0.9648 0.6302 -2.2079 -0.9706
## (Intercept)-Sciurus_carolinensis -1.3827 0.5981 -2.6629 -1.3453
## (Intercept)-Vulpes_vulpes -1.4899 0.7364 -3.0403 -1.4473
## (Intercept)-Sus_scrofa -1.7550 0.7500 -3.3871 -1.6661
## Veg_shannon_index-Canis_latrans 0.7200 0.4159 -0.0434 0.7016
## Veg_shannon_index-Lynx_rufus 0.1409 0.5444 -1.0051 0.1592
## Veg_shannon_index-Didelphis_virginiana 0.5334 0.4406 -0.2682 0.5146
## Veg_shannon_index-Sylvilagus_floridanus 0.4730 0.4780 -0.3857 0.4453
## Veg_shannon_index-Meleagris_gallopavo 0.4863 0.4895 -0.4209 0.4614
## Veg_shannon_index-Sciurus_carolinensis -0.0584 0.4398 -0.9642 -0.0425
## Veg_shannon_index-Vulpes_vulpes -0.0066 0.5110 -1.0837 0.0215
## Veg_shannon_index-Sus_scrofa 0.7251 0.5842 -0.2159 0.6553
## Avg_Cogongrass_Cover-Canis_latrans 0.5432 0.4105 -0.1924 0.5232
## Avg_Cogongrass_Cover-Lynx_rufus 0.5913 0.4602 -0.2228 0.5539
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4782 0.4079 -0.2861 0.4709
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1694 0.5039 -1.2401 -0.1365
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1959 0.5610 -1.4334 -0.1593
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3952 0.3912 -0.3503 0.3799
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3248 0.4794 -0.5706 0.3198
## Avg_Cogongrass_Cover-Sus_scrofa -0.0481 0.6024 -1.3838 0.0092
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9343 1.0132 507
## (Intercept)-Lynx_rufus 0.5844 1.0190 491
## (Intercept)-Didelphis_virginiana -0.2763 1.0147 853
## (Intercept)-Sylvilagus_floridanus 0.4466 1.0014 676
## (Intercept)-Meleagris_gallopavo 0.3442 1.0018 714
## (Intercept)-Sciurus_carolinensis -0.3006 0.9999 922
## (Intercept)-Vulpes_vulpes -0.1402 1.0020 440
## (Intercept)-Sus_scrofa -0.5133 1.0026 567
## Veg_shannon_index-Canis_latrans 1.5819 1.0007 1642
## Veg_shannon_index-Lynx_rufus 1.1368 1.0276 1016
## Veg_shannon_index-Didelphis_virginiana 1.4787 1.0019 1611
## Veg_shannon_index-Sylvilagus_floridanus 1.4786 1.0125 1054
## Veg_shannon_index-Meleagris_gallopavo 1.5116 1.0133 1471
## Veg_shannon_index-Sciurus_carolinensis 0.7530 1.0051 1487
## Veg_shannon_index-Vulpes_vulpes 0.9369 1.0030 1135
## Veg_shannon_index-Sus_scrofa 2.0575 1.0175 961
## Avg_Cogongrass_Cover-Canis_latrans 1.4502 1.0072 1712
## Avg_Cogongrass_Cover-Lynx_rufus 1.6181 1.0012 1216
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3145 1.0018 1985
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7248 1.0336 866
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8120 1.0280 786
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2068 1.0006 1821
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3364 1.0011 1464
## Avg_Cogongrass_Cover-Sus_scrofa 0.9645 1.0206 859
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6206 0.1622 -2.9542 -2.6178 -2.3135 1.0040
## (Intercept)-Lynx_rufus -3.3792 0.2695 -3.9223 -3.3763 -2.8691 1.0083
## (Intercept)-Didelphis_virginiana -2.4352 0.2574 -2.9516 -2.4258 -1.9742 1.0031
## (Intercept)-Sylvilagus_floridanus -3.1314 0.2803 -3.7099 -3.1177 -2.6296 1.0174
## (Intercept)-Meleagris_gallopavo -3.2719 0.3000 -3.9049 -3.2517 -2.7304 1.0073
## (Intercept)-Sciurus_carolinensis -2.5430 0.2587 -3.0736 -2.5375 -2.0559 1.0021
## (Intercept)-Vulpes_vulpes -3.5112 0.5455 -4.7160 -3.4491 -2.6211 1.0077
## (Intercept)-Sus_scrofa -2.9536 0.3858 -3.7663 -2.9413 -2.2422 1.0213
## ESS
## (Intercept)-Canis_latrans 1203
## (Intercept)-Lynx_rufus 467
## (Intercept)-Didelphis_virginiana 1074
## (Intercept)-Sylvilagus_floridanus 567
## (Intercept)-Meleagris_gallopavo 498
## (Intercept)-Sciurus_carolinensis 1107
## (Intercept)-Vulpes_vulpes 309
## (Intercept)-Sus_scrofa 855
# 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 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_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): 0.3667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7976 0.5202 -2.8763 -1.8022 -0.7622 1.0094 355
## Avg_Cogongrass_Cover -0.8425 0.4375 -1.7776 -0.8299 -0.0026 1.0122 459
## I(Avg_Cogongrass_Cover^2) 0.8043 0.4315 -0.0479 0.7932 1.6844 1.0228 327
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8273 1.3365 0.0528 0.4622 3.7034 1.0829 802
## Avg_Cogongrass_Cover 0.5294 0.7920 0.0454 0.2891 2.5382 1.0411 354
## I(Avg_Cogongrass_Cover^2) 0.7764 1.3917 0.0533 0.3774 4.1060 1.1633 248
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2168 1.0692 0.0893 0.9534 3.9504 1.0947 183
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9194 0.2495 -3.4095 -2.9132 -2.4285 1.0127 1165
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3752 0.4339 0.0601 0.2574 1.3698 1.0179 476
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.2253 0.7526 -2.6524 -1.2595
## (Intercept)-Lynx_rufus -1.7630 0.7094 -3.1833 -1.7447
## (Intercept)-Didelphis_virginiana -1.9958 0.6552 -3.4277 -1.9438
## (Intercept)-Sylvilagus_floridanus -1.6008 0.6981 -2.9314 -1.6003
## (Intercept)-Meleagris_gallopavo -1.4616 0.7371 -2.8409 -1.5004
## (Intercept)-Sciurus_carolinensis -2.2584 0.6855 -3.7654 -2.2171
## (Intercept)-Vulpes_vulpes -2.3622 0.8013 -4.0883 -2.2968
## (Intercept)-Sus_scrofa -2.2854 0.7703 -3.9947 -2.2312
## Avg_Cogongrass_Cover-Canis_latrans -0.5741 0.5526 -1.6092 -0.6056
## Avg_Cogongrass_Cover-Lynx_rufus -0.7125 0.5972 -1.8770 -0.7041
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4507 0.6089 -1.5505 -0.4867
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3268 0.6931 -2.9502 -1.2447
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0932 0.6656 -2.4861 -1.0669
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8366 0.5800 -2.0105 -0.8258
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8285 0.6422 -2.1256 -0.8053
## Avg_Cogongrass_Cover-Sus_scrofa -1.0954 0.7060 -2.8442 -1.0265
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.5007 0.9462 0.3153 1.2721
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.3259 0.6006 0.3669 1.2459
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6313 0.5033 -0.2164 0.5900
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8672 0.5366 -0.0543 0.8034
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2204 0.6696 -1.2078 0.2521
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0062 0.4225 0.2561 0.9827
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9041 0.5084 0.0487 0.8501
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2034 0.7307 -1.6392 0.3242
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3211 1.0077 436
## (Intercept)-Lynx_rufus -0.3514 1.0116 546
## (Intercept)-Didelphis_virginiana -0.8066 1.0021 603
## (Intercept)-Sylvilagus_floridanus -0.1720 1.0031 499
## (Intercept)-Meleagris_gallopavo 0.1088 1.0182 458
## (Intercept)-Sciurus_carolinensis -1.0095 0.9997 532
## (Intercept)-Vulpes_vulpes -0.9227 1.0220 507
## (Intercept)-Sus_scrofa -0.9401 1.0038 680
## Avg_Cogongrass_Cover-Canis_latrans 0.5790 1.0133 937
## Avg_Cogongrass_Cover-Lynx_rufus 0.5275 1.0049 756
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8855 1.0253 867
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1956 1.0114 480
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1174 1.0056 747
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2936 1.0036 635
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3683 1.0022 708
## Avg_Cogongrass_Cover-Sus_scrofa 0.1228 1.0015 661
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.8899 1.0620 256
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7392 1.0043 409
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.7912 1.0632 418
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1024 1.0054 323
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5569 1.0102 367
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8987 1.0053 731
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1014 1.0330 469
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3046 1.0669 292
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6607 0.1692 -3.0002 -2.6558 -2.3404 1.0027
## (Intercept)-Lynx_rufus -3.2960 0.2751 -3.8873 -3.2835 -2.8112 1.0423
## (Intercept)-Didelphis_virginiana -2.4609 0.2622 -3.0042 -2.4526 -1.9906 1.0143
## (Intercept)-Sylvilagus_floridanus -3.1250 0.2632 -3.6820 -3.1143 -2.6577 1.0274
## (Intercept)-Meleagris_gallopavo -3.2662 0.3102 -3.9556 -3.2408 -2.7087 1.0499
## (Intercept)-Sciurus_carolinensis -2.5564 0.2577 -3.0854 -2.5535 -2.0611 1.0020
## (Intercept)-Vulpes_vulpes -3.4747 0.5486 -4.7767 -3.3987 -2.6185 1.0674
## (Intercept)-Sus_scrofa -2.9510 0.3825 -3.7508 -2.9358 -2.2548 1.0075
## ESS
## (Intercept)-Canis_latrans 1196
## (Intercept)-Lynx_rufus 469
## (Intercept)-Didelphis_virginiana 892
## (Intercept)-Sylvilagus_floridanus 407
## (Intercept)-Meleagris_gallopavo 384
## (Intercept)-Sciurus_carolinensis 1165
## (Intercept)-Vulpes_vulpes 203
## (Intercept)-Sus_scrofa 658
# 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 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_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): 0.408
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7318 0.9081 -4.4892 -2.7420 -0.8092 1.0023 604
## Cogon_Patch_Size 0.1077 0.8444 -1.6471 0.1121 1.7370 1.0365 488
## Veg_shannon_index 0.9910 0.6300 -0.2507 0.9845 2.3045 1.0219 466
## total_shrub_cover -0.7936 0.7178 -2.3522 -0.7632 0.5929 1.0024 843
## Avg_Cogongrass_Cover -0.2206 0.9736 -2.0663 -0.2382 1.7247 1.0518 179
## Tree_Density -2.0120 0.8580 -3.7557 -1.9579 -0.3713 1.0068 220
## Avg_Canopy_Cover 1.8174 0.7071 0.4281 1.8066 3.2320 1.0135 711
## I(Avg_Cogongrass_Cover^2) 1.2036 0.7948 -0.5308 1.2554 2.6971 1.0159 522
## avg_veg_height -0.4565 0.5654 -1.6275 -0.4659 0.6558 1.0053 221
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.4111 9.3441 0.1076 2.8183 26.1741 1.0664 281
## Cogon_Patch_Size 4.8417 7.2497 0.1702 2.8361 22.1302 1.0875 317
## Veg_shannon_index 1.6477 2.5816 0.0633 0.8193 8.2743 1.0131 450
## total_shrub_cover 3.4589 4.5697 0.1325 2.1094 14.5720 1.0064 440
## Avg_Cogongrass_Cover 1.2094 2.2128 0.0498 0.4895 6.6396 1.0089 580
## Tree_Density 1.7680 3.4539 0.0551 0.6819 10.4479 1.0175 408
## Avg_Canopy_Cover 3.0784 5.0655 0.1092 1.5679 15.7784 1.0254 360
## I(Avg_Cogongrass_Cover^2) 5.6252 11.6419 0.0997 2.1929 30.5967 1.0870 149
## avg_veg_height 0.5360 1.0732 0.0411 0.2831 2.6191 1.1378 1053
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.544 2.796 0.0648 0.7501 7.8142 1.3154 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9817 0.2916 -3.5299 -2.9882 -2.3567 1.0016 1495
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5626 0.5669 0.0932 0.4054 2.0491 1.0048 620
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.8427 1.1755 -4.0825 -1.8652
## (Intercept)-Lynx_rufus -1.8423 1.6202 -4.6335 -2.0418
## (Intercept)-Didelphis_virginiana -3.9448 1.3252 -6.9918 -3.7734
## (Intercept)-Sylvilagus_floridanus -2.6059 1.3010 -5.1269 -2.5814
## (Intercept)-Meleagris_gallopavo -2.4796 1.3058 -5.0161 -2.4924
## (Intercept)-Sciurus_carolinensis -4.4637 1.5310 -7.8591 -4.3165
## (Intercept)-Vulpes_vulpes -4.3469 1.8744 -8.5303 -4.0971
## (Intercept)-Sus_scrofa -4.7350 1.8239 -9.0026 -4.5268
## Cogon_Patch_Size-Canis_latrans 1.9435 1.3813 -0.1047 1.7056
## Cogon_Patch_Size-Lynx_rufus -0.0674 1.7676 -3.4545 -0.1251
## Cogon_Patch_Size-Didelphis_virginiana 1.9073 1.0530 0.1491 1.7995
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4443 1.7682 -5.7822 -1.1623
## Cogon_Patch_Size-Meleagris_gallopavo 0.4938 1.2027 -1.7114 0.4154
## Cogon_Patch_Size-Sciurus_carolinensis -0.9321 1.3534 -4.1640 -0.7291
## Cogon_Patch_Size-Vulpes_vulpes -0.5762 1.8707 -4.8471 -0.4383
## Cogon_Patch_Size-Sus_scrofa -0.5035 1.5201 -4.1021 -0.3009
## Veg_shannon_index-Canis_latrans 1.5359 0.8444 0.1166 1.4373
## Veg_shannon_index-Lynx_rufus 1.0185 1.1878 -1.4068 1.0038
## Veg_shannon_index-Didelphis_virginiana 1.1226 0.8327 -0.3933 1.0928
## Veg_shannon_index-Sylvilagus_floridanus 1.0257 0.8551 -0.5890 0.9962
## Veg_shannon_index-Meleagris_gallopavo 1.5087 0.9754 -0.1684 1.4118
## Veg_shannon_index-Sciurus_carolinensis 0.0295 0.9579 -2.0102 0.0892
## Veg_shannon_index-Vulpes_vulpes 0.4550 1.1304 -2.0023 0.5407
## Veg_shannon_index-Sus_scrofa 1.8528 1.2258 0.0608 1.6521
## total_shrub_cover-Canis_latrans 0.0277 0.7978 -1.4200 -0.0215
## total_shrub_cover-Lynx_rufus -2.1097 1.5017 -5.8793 -1.8380
## total_shrub_cover-Didelphis_virginiana -0.8206 0.8725 -2.6847 -0.7839
## total_shrub_cover-Sylvilagus_floridanus -0.4726 1.0351 -2.6280 -0.4431
## total_shrub_cover-Meleagris_gallopavo -3.1665 1.6200 -6.6918 -3.0258
## total_shrub_cover-Sciurus_carolinensis 0.0380 0.8495 -1.6096 0.0338
## total_shrub_cover-Vulpes_vulpes -0.9944 1.3375 -4.0392 -0.8830
## total_shrub_cover-Sus_scrofa 0.0741 1.0927 -1.9359 -0.0008
## Avg_Cogongrass_Cover-Canis_latrans -0.2925 1.1870 -2.6481 -0.2570
## Avg_Cogongrass_Cover-Lynx_rufus -0.0793 1.2710 -2.5515 -0.1079
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1144 1.2317 -2.1792 0.0708
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6645 1.3137 -3.4834 -0.5828
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4841 1.3074 -3.2359 -0.4181
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0592 1.2668 -2.4868 -0.0486
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0140 1.3197 -2.5041 -0.0517
## Avg_Cogongrass_Cover-Sus_scrofa -0.3972 1.3008 -3.1899 -0.3293
## Tree_Density-Canis_latrans -2.7152 1.3027 -5.6912 -2.5490
## Tree_Density-Lynx_rufus -1.3089 1.3617 -3.8030 -1.3835
## Tree_Density-Didelphis_virginiana -2.1793 1.0276 -4.4212 -2.1007
## Tree_Density-Sylvilagus_floridanus -2.4906 1.3190 -5.4105 -2.3285
## Tree_Density-Meleagris_gallopavo -2.1378 1.1780 -4.6193 -2.0903
## Tree_Density-Sciurus_carolinensis -2.4624 1.2592 -5.3707 -2.2802
## Tree_Density-Vulpes_vulpes -1.9310 1.3045 -4.4342 -1.9173
## Tree_Density-Sus_scrofa -2.1562 1.3545 -5.1400 -2.0318
## Avg_Canopy_Cover-Canis_latrans 0.3029 0.8285 -1.1765 0.2663
## Avg_Canopy_Cover-Lynx_rufus 1.2175 1.3414 -1.3456 1.2055
## Avg_Canopy_Cover-Didelphis_virginiana 2.6441 0.9735 1.1381 2.5340
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5043 1.7560 1.0961 3.1693
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2652 1.1173 0.5731 2.0978
## Avg_Canopy_Cover-Sciurus_carolinensis 2.3174 0.9423 0.8522 2.2063
## Avg_Canopy_Cover-Vulpes_vulpes 2.2626 1.2996 0.2648 2.0652
## Avg_Canopy_Cover-Sus_scrofa 2.0263 0.8917 0.4210 1.9477
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1498 2.0798 0.9160 2.5999
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.2919 1.8904 0.8306 2.8805
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9630 0.7535 -0.4759 0.9660
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2011 0.9432 -0.4704 1.1425
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2653 1.6361 -3.9963 -0.0972
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6824 0.9091 0.1610 1.6042
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9657 1.0440 0.2291 1.8520
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.5077 2.0400 -5.7472 -0.0305
## avg_veg_height-Canis_latrans -0.6014 0.6601 -2.0015 -0.5869
## avg_veg_height-Lynx_rufus -0.5807 0.8762 -2.4467 -0.5613
## avg_veg_height-Didelphis_virginiana -0.4523 0.7221 -1.9038 -0.4442
## avg_veg_height-Sylvilagus_floridanus -0.5016 0.7397 -1.9862 -0.4941
## avg_veg_height-Meleagris_gallopavo -0.4947 0.8190 -2.1409 -0.4986
## avg_veg_height-Sciurus_carolinensis -0.0888 0.7516 -1.4349 -0.1344
## avg_veg_height-Vulpes_vulpes -0.5413 0.8164 -2.2592 -0.5343
## avg_veg_height-Sus_scrofa -0.4440 0.7513 -1.9421 -0.4514
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5411 1.0162 444
## (Intercept)-Lynx_rufus 1.9276 1.0214 197
## (Intercept)-Didelphis_virginiana -1.8382 1.0128 219
## (Intercept)-Sylvilagus_floridanus -0.0613 1.0104 428
## (Intercept)-Meleagris_gallopavo 0.1466 1.0130 423
## (Intercept)-Sciurus_carolinensis -1.9826 1.0134 179
## (Intercept)-Vulpes_vulpes -1.3510 1.0413 181
## (Intercept)-Sus_scrofa -1.9263 1.0160 235
## Cogon_Patch_Size-Canis_latrans 5.3426 1.0238 399
## Cogon_Patch_Size-Lynx_rufus 3.5276 1.0212 261
## Cogon_Patch_Size-Didelphis_virginiana 4.2411 1.0305 353
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2163 1.0282 246
## Cogon_Patch_Size-Meleagris_gallopavo 3.1446 1.0014 625
## Cogon_Patch_Size-Sciurus_carolinensis 1.0196 1.0575 317
## Cogon_Patch_Size-Vulpes_vulpes 2.9202 1.0108 240
## Cogon_Patch_Size-Sus_scrofa 2.0554 1.0321 337
## Veg_shannon_index-Canis_latrans 3.5146 1.0169 548
## Veg_shannon_index-Lynx_rufus 3.6001 1.0266 370
## Veg_shannon_index-Didelphis_virginiana 2.8734 1.0066 820
## Veg_shannon_index-Sylvilagus_floridanus 2.7714 1.0097 614
## Veg_shannon_index-Meleagris_gallopavo 3.7504 1.0087 581
## Veg_shannon_index-Sciurus_carolinensis 1.7112 1.0095 409
## Veg_shannon_index-Vulpes_vulpes 2.5368 1.0350 256
## Veg_shannon_index-Sus_scrofa 4.7972 1.0469 362
## total_shrub_cover-Canis_latrans 1.8323 1.0162 753
## total_shrub_cover-Lynx_rufus 0.1050 1.0064 282
## total_shrub_cover-Didelphis_virginiana 0.7578 1.0035 1078
## total_shrub_cover-Sylvilagus_floridanus 1.5288 1.0102 537
## total_shrub_cover-Meleagris_gallopavo -0.5705 1.0037 240
## total_shrub_cover-Sciurus_carolinensis 1.7911 1.0171 895
## total_shrub_cover-Vulpes_vulpes 1.4462 1.0314 363
## total_shrub_cover-Sus_scrofa 2.3798 1.0068 674
## Avg_Cogongrass_Cover-Canis_latrans 1.9266 1.0260 250
## Avg_Cogongrass_Cover-Lynx_rufus 2.5230 1.0712 344
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7000 1.0205 292
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6573 1.0302 253
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.8650 1.0235 261
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5323 1.0380 271
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6899 1.0450 280
## Avg_Cogongrass_Cover-Sus_scrofa 1.9847 1.0126 290
## Tree_Density-Canis_latrans -0.7011 1.0053 284
## Tree_Density-Lynx_rufus 1.6142 1.0058 295
## Tree_Density-Didelphis_virginiana -0.3948 1.0132 321
## Tree_Density-Sylvilagus_floridanus -0.4690 1.0070 240
## Tree_Density-Meleagris_gallopavo 0.0594 1.0147 410
## Tree_Density-Sciurus_carolinensis -0.5069 1.0060 330
## Tree_Density-Vulpes_vulpes 0.7468 1.0117 342
## Tree_Density-Sus_scrofa 0.2332 1.0122 368
## Avg_Canopy_Cover-Canis_latrans 2.1047 1.0055 220
## Avg_Canopy_Cover-Lynx_rufus 3.9159 1.0542 290
## Avg_Canopy_Cover-Didelphis_virginiana 4.8662 1.0246 361
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.9958 1.0241 234
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9782 1.0026 317
## Avg_Canopy_Cover-Sciurus_carolinensis 4.5422 1.0066 654
## Avg_Canopy_Cover-Vulpes_vulpes 5.3924 1.0135 340
## Avg_Canopy_Cover-Sus_scrofa 3.9789 1.0369 640
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 9.0220 1.1297 85
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.1112 1.0573 133
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5682 1.0090 320
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3887 1.0444 391
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.4361 1.0249 107
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.7332 1.0219 330
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.4194 1.0544 327
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.1647 1.0462 114
## avg_veg_height-Canis_latrans 0.6415 1.0100 308
## avg_veg_height-Lynx_rufus 1.0192 1.0076 308
## avg_veg_height-Didelphis_virginiana 0.9569 1.0035 348
## avg_veg_height-Sylvilagus_floridanus 0.9218 1.0084 474
## avg_veg_height-Meleagris_gallopavo 1.1366 1.0063 364
## avg_veg_height-Sciurus_carolinensis 1.6257 1.0066 348
## avg_veg_height-Vulpes_vulpes 1.0873 1.0147 293
## avg_veg_height-Sus_scrofa 1.0544 1.0055 394
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6502 0.1702 -2.9900 -2.6463 -2.3214 1.0100
## (Intercept)-Lynx_rufus -3.6022 0.2815 -4.1766 -3.5941 -3.0688 1.0073
## (Intercept)-Didelphis_virginiana -2.3932 0.2549 -2.9197 -2.3818 -1.9232 1.0042
## (Intercept)-Sylvilagus_floridanus -3.1910 0.2574 -3.7180 -3.1873 -2.7077 1.0189
## (Intercept)-Meleagris_gallopavo -3.2825 0.2847 -3.9120 -3.2722 -2.7775 1.0159
## (Intercept)-Sciurus_carolinensis -2.5381 0.2688 -3.0907 -2.5259 -2.0397 1.0179
## (Intercept)-Vulpes_vulpes -3.8757 0.5596 -5.0754 -3.8514 -2.8606 1.0119
## (Intercept)-Sus_scrofa -2.9653 0.4043 -3.8112 -2.9540 -2.2101 1.0065
## ESS
## (Intercept)-Canis_latrans 901
## (Intercept)-Lynx_rufus 354
## (Intercept)-Didelphis_virginiana 1285
## (Intercept)-Sylvilagus_floridanus 568
## (Intercept)-Meleagris_gallopavo 356
## (Intercept)-Sciurus_carolinensis 790
## (Intercept)-Vulpes_vulpes 177
## (Intercept)-Sus_scrofa 781
# 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 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_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): 0.5953
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7642 0.4658 -1.6917 -0.7691 0.1906 1.0357 401
## Avg_Cogongrass_Cover 0.1507 0.3364 -0.5720 0.1534 0.8190 1.0124 603
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7728 1.0036 0.0512 0.4376 3.4153 1.0156 297
## Avg_Cogongrass_Cover 0.4895 0.6762 0.0425 0.2808 2.1973 1.0090 830
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4921 1.3044 0.1356 1.1283 4.978 1.1005 247
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2137 0.3087 -3.8090 -3.2072 -2.6053 1.0162 812
## shrub_cover 0.1675 0.3922 -0.5904 0.1650 0.9343 1.0012 1090
## veg_height -0.0833 0.2222 -0.5258 -0.0817 0.3480 1.0066 1023
## week -0.0812 0.1693 -0.4165 -0.0760 0.2321 1.0036 1104
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5996 0.6544 0.0860 0.4116 2.2149 1.0367 324
## shrub_cover 0.9488 1.0395 0.1555 0.6834 3.3600 1.0575 619
## veg_height 0.2754 0.2657 0.0532 0.2073 0.9098 1.0236 1344
## week 0.1448 0.1778 0.0283 0.1011 0.5014 1.0112 1076
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1522 0.6714 -1.3891 -0.1898
## (Intercept)-Lynx_rufus -0.5321 0.6837 -1.8241 -0.5533
## (Intercept)-Didelphis_virginiana -1.0280 0.6264 -2.3283 -0.9922
## (Intercept)-Sylvilagus_floridanus -0.6232 0.6038 -1.8185 -0.6218
## (Intercept)-Meleagris_gallopavo -0.4129 0.7382 -1.6710 -0.4884
## (Intercept)-Sciurus_carolinensis -1.1068 0.6268 -2.4743 -1.0601
## (Intercept)-Vulpes_vulpes -1.1027 0.7940 -2.7169 -1.0743
## (Intercept)-Sus_scrofa -1.2956 0.7435 -2.9266 -1.2157
## Avg_Cogongrass_Cover-Canis_latrans 0.4449 0.4192 -0.2972 0.4179
## Avg_Cogongrass_Cover-Lynx_rufus 0.4982 0.4598 -0.3075 0.4653
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3424 0.4106 -0.4478 0.3347
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2838 0.4961 -1.3867 -0.2315
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3398 0.7111 -2.0145 -0.2647
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3524 0.3825 -0.3649 0.3448
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3203 0.4954 -0.5948 0.3043
## Avg_Cogongrass_Cover-Sus_scrofa -0.0882 0.6239 -1.4979 -0.0284
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2426 1.0963 277
## (Intercept)-Lynx_rufus 0.8743 1.0326 511
## (Intercept)-Didelphis_virginiana 0.1061 1.0085 659
## (Intercept)-Sylvilagus_floridanus 0.6454 1.0090 644
## (Intercept)-Meleagris_gallopavo 1.3786 1.0717 269
## (Intercept)-Sciurus_carolinensis 0.0305 1.0044 579
## (Intercept)-Vulpes_vulpes 0.3267 1.0217 273
## (Intercept)-Sus_scrofa -0.0537 1.0020 417
## Avg_Cogongrass_Cover-Canis_latrans 1.3458 1.0036 1252
## Avg_Cogongrass_Cover-Lynx_rufus 1.5244 1.0032 1191
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2175 1.0068 1389
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5452 1.0022 710
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8355 1.0331 373
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1341 1.0053 1581
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4068 1.0139 1097
## Avg_Cogongrass_Cover-Sus_scrofa 1.0046 1.0102 601
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8026 0.1867 -3.1842 -2.7954 -2.4424 1.0139
## (Intercept)-Lynx_rufus -3.5618 0.3080 -4.1830 -3.5482 -3.0087 1.0134
## (Intercept)-Didelphis_virginiana -2.7045 0.3061 -3.3337 -2.6952 -2.1475 1.0249
## (Intercept)-Sylvilagus_floridanus -3.2020 0.2706 -3.7545 -3.1885 -2.7003 1.0038
## (Intercept)-Meleagris_gallopavo -3.9827 0.4691 -5.0552 -3.9614 -3.1325 1.0590
## (Intercept)-Sciurus_carolinensis -2.7614 0.3214 -3.4107 -2.7493 -2.1804 1.0250
## (Intercept)-Vulpes_vulpes -3.9587 0.6419 -5.3332 -3.8910 -2.9275 1.0444
## (Intercept)-Sus_scrofa -3.4243 0.5177 -4.5098 -3.4041 -2.4375 1.0041
## shrub_cover-Canis_latrans -0.3154 0.2225 -0.7618 -0.3139 0.1136 1.0246
## shrub_cover-Lynx_rufus -0.2330 0.3593 -0.9657 -0.2281 0.4516 1.0042
## shrub_cover-Didelphis_virginiana 1.0362 0.3994 0.3031 1.0203 1.8714 1.0303
## shrub_cover-Sylvilagus_floridanus 0.2863 0.4299 -0.5394 0.2854 1.1166 1.0018
## shrub_cover-Meleagris_gallopavo -0.7523 0.4090 -1.6216 -0.7388 0.0219 1.0450
## shrub_cover-Sciurus_carolinensis 0.9213 0.4527 0.0402 0.9116 1.8087 1.0218
## shrub_cover-Vulpes_vulpes -0.1844 0.6423 -1.5098 -0.1612 1.0489 1.0827
## shrub_cover-Sus_scrofa 0.7269 0.8442 -0.9679 0.6946 2.4509 1.0317
## veg_height-Canis_latrans -0.6324 0.1892 -1.0040 -0.6307 -0.2719 1.0102
## veg_height-Lynx_rufus -0.0284 0.2560 -0.5641 -0.0207 0.4610 1.0064
## veg_height-Didelphis_virginiana 0.4360 0.2629 -0.0486 0.4267 0.9711 0.9998
## veg_height-Sylvilagus_floridanus 0.1184 0.2574 -0.3787 0.1144 0.6214 0.9998
## veg_height-Meleagris_gallopavo -0.2672 0.3870 -1.0801 -0.2516 0.4724 1.0336
## veg_height-Sciurus_carolinensis 0.0922 0.2316 -0.3409 0.0782 0.5711 1.0065
## veg_height-Vulpes_vulpes -0.1935 0.3368 -0.9222 -0.1749 0.4277 1.0361
## veg_height-Sus_scrofa -0.1982 0.3649 -0.9410 -0.1858 0.5038 1.0335
## week-Canis_latrans 0.0668 0.1347 -0.2083 0.0745 0.3232 1.0079
## week-Lynx_rufus -0.0451 0.2012 -0.4733 -0.0331 0.3169 1.0040
## week-Didelphis_virginiana -0.2485 0.2363 -0.7510 -0.2292 0.1727 0.9999
## week-Sylvilagus_floridanus -0.1740 0.2167 -0.6355 -0.1640 0.2222 1.0010
## week-Meleagris_gallopavo -0.3125 0.2699 -0.9208 -0.2821 0.1253 1.0071
## week-Sciurus_carolinensis 0.1456 0.1892 -0.2373 0.1491 0.5102 1.0016
## week-Vulpes_vulpes -0.1508 0.2916 -0.7824 -0.1376 0.3772 1.0190
## week-Sus_scrofa 0.0896 0.2547 -0.4215 0.0903 0.5919 1.0072
## ESS
## (Intercept)-Canis_latrans 656
## (Intercept)-Lynx_rufus 405
## (Intercept)-Didelphis_virginiana 638
## (Intercept)-Sylvilagus_floridanus 638
## (Intercept)-Meleagris_gallopavo 122
## (Intercept)-Sciurus_carolinensis 613
## (Intercept)-Vulpes_vulpes 181
## (Intercept)-Sus_scrofa 499
## shrub_cover-Canis_latrans 810
## shrub_cover-Lynx_rufus 509
## shrub_cover-Didelphis_virginiana 566
## shrub_cover-Sylvilagus_floridanus 543
## shrub_cover-Meleagris_gallopavo 138
## shrub_cover-Sciurus_carolinensis 635
## shrub_cover-Vulpes_vulpes 377
## shrub_cover-Sus_scrofa 505
## veg_height-Canis_latrans 651
## veg_height-Lynx_rufus 656
## veg_height-Didelphis_virginiana 849
## veg_height-Sylvilagus_floridanus 689
## veg_height-Meleagris_gallopavo 474
## veg_height-Sciurus_carolinensis 933
## veg_height-Vulpes_vulpes 546
## veg_height-Sus_scrofa 972
## week-Canis_latrans 1486
## week-Lynx_rufus 907
## week-Didelphis_virginiana 1255
## week-Sylvilagus_floridanus 937
## week-Meleagris_gallopavo 428
## week-Sciurus_carolinensis 1765
## week-Vulpes_vulpes 979
## week-Sus_scrofa 1532
# 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 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_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): 0.3635
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0094 0.4249 -1.8473 -1.0085 -0.1414 1.0046 678
## Avg_Cogongrass_Cover 0.1409 0.2994 -0.5041 0.1478 0.7373 1.0035 1036
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7246 0.9434 0.0570 0.4535 3.0094 1.0198 838
## Avg_Cogongrass_Cover 0.4217 0.5659 0.0442 0.2569 1.8012 1.0087 1233
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.316 1.0794 0.1077 1.0539 4.1134 1.0058 251
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9222 0.2539 -3.4111 -2.9251 -2.3859 1.0162 1713
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3781 0.4162 0.0632 0.2723 1.2845 1.0215 860
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3287 0.6280 -1.5062 -0.3542
## (Intercept)-Lynx_rufus -0.7454 0.6339 -1.9692 -0.7598
## (Intercept)-Didelphis_virginiana -1.2727 0.5605 -2.4586 -1.2570
## (Intercept)-Sylvilagus_floridanus -0.7788 0.5823 -1.8931 -0.7954
## (Intercept)-Meleagris_gallopavo -0.8738 0.6119 -2.0467 -0.8924
## (Intercept)-Sciurus_carolinensis -1.3290 0.5845 -2.6426 -1.2896
## (Intercept)-Vulpes_vulpes -1.4573 0.6891 -2.9848 -1.3906
## (Intercept)-Sus_scrofa -1.5386 0.6638 -3.0104 -1.4855
## Avg_Cogongrass_Cover-Canis_latrans 0.3598 0.3836 -0.3419 0.3493
## Avg_Cogongrass_Cover-Lynx_rufus 0.4993 0.4203 -0.2223 0.4563
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3612 0.3836 -0.3702 0.3593
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2572 0.4589 -1.2630 -0.2184
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3247 0.5445 -1.6035 -0.2636
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3525 0.3738 -0.3632 0.3412
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2651 0.4463 -0.6408 0.2698
## Avg_Cogongrass_Cover-Sus_scrofa -0.1552 0.5522 -1.3647 -0.1031
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9622 1.0038 675
## (Intercept)-Lynx_rufus 0.5316 1.0040 687
## (Intercept)-Didelphis_virginiana -0.2447 1.0014 810
## (Intercept)-Sylvilagus_floridanus 0.4710 1.0024 828
## (Intercept)-Meleagris_gallopavo 0.3815 1.0056 697
## (Intercept)-Sciurus_carolinensis -0.2905 1.0019 912
## (Intercept)-Vulpes_vulpes -0.2410 1.0251 643
## (Intercept)-Sus_scrofa -0.3657 1.0080 577
## Avg_Cogongrass_Cover-Canis_latrans 1.1746 1.0018 2116
## Avg_Cogongrass_Cover-Lynx_rufus 1.4496 1.0006 1444
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1329 1.0002 1872
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5629 1.0012 979
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5828 1.0056 909
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1506 1.0119 1638
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1419 1.0090 1418
## Avg_Cogongrass_Cover-Sus_scrofa 0.7961 1.0081 997
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6456 0.1703 -2.9942 -2.6419 -2.3283 1.0022
## (Intercept)-Lynx_rufus -3.3574 0.2830 -3.9432 -3.3457 -2.8499 1.0239
## (Intercept)-Didelphis_virginiana -2.4404 0.2473 -2.9323 -2.4369 -1.9609 1.0131
## (Intercept)-Sylvilagus_floridanus -3.1061 0.2660 -3.6717 -3.0939 -2.6072 1.0142
## (Intercept)-Meleagris_gallopavo -3.2745 0.3004 -3.9175 -3.2529 -2.7385 1.0198
## (Intercept)-Sciurus_carolinensis -2.5553 0.2590 -3.0947 -2.5432 -2.0672 1.0017
## (Intercept)-Vulpes_vulpes -3.4685 0.4890 -4.5573 -3.4125 -2.6454 1.0238
## (Intercept)-Sus_scrofa -2.9571 0.3836 -3.7606 -2.9358 -2.2425 1.0064
## ESS
## (Intercept)-Canis_latrans 956
## (Intercept)-Lynx_rufus 415
## (Intercept)-Didelphis_virginiana 1172
## (Intercept)-Sylvilagus_floridanus 572
## (Intercept)-Meleagris_gallopavo 455
## (Intercept)-Sciurus_carolinensis 1151
## (Intercept)-Vulpes_vulpes 353
## (Intercept)-Sus_scrofa 883
# 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 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_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): 0.5037
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0278 0.4315 -1.9309 -1.0239 -0.1919 1.0399 354
## Avg_Cogongrass_Cover 0.1400 0.3080 -0.5069 0.1458 0.7476 1.0066 1213
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6493 0.8435 0.0486 0.3983 2.525 1.0314 811
## Avg_Cogongrass_Cover 0.4459 0.5502 0.0450 0.2674 1.952 1.0024 1094
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4793 1.2716 0.0858 1.1888 4.6943 1.0555 205
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9536 0.2554 -3.4611 -2.9529 -2.4223 1.0072 1250
## week -0.0679 0.1643 -0.4127 -0.0611 0.2289 1.0120 1258
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4281 0.5491 0.0632 0.2942 1.5467 1.0987 739
## week 0.1435 0.1288 0.0289 0.1056 0.4905 1.0121 1654
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4276 0.6682 -1.6848 -0.4448
## (Intercept)-Lynx_rufus -0.7892 0.6412 -1.9986 -0.8113
## (Intercept)-Didelphis_virginiana -1.2775 0.5734 -2.5057 -1.2443
## (Intercept)-Sylvilagus_floridanus -0.8334 0.6053 -1.9876 -0.8395
## (Intercept)-Meleagris_gallopavo -0.9220 0.6317 -2.1914 -0.9291
## (Intercept)-Sciurus_carolinensis -1.3275 0.6118 -2.7516 -1.2747
## (Intercept)-Vulpes_vulpes -1.3841 0.6985 -2.8948 -1.3268
## (Intercept)-Sus_scrofa -1.5478 0.6660 -3.0422 -1.4891
## Avg_Cogongrass_Cover-Canis_latrans 0.3611 0.3688 -0.3065 0.3442
## Avg_Cogongrass_Cover-Lynx_rufus 0.4936 0.4511 -0.2776 0.4428
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3721 0.3862 -0.3675 0.3575
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2686 0.4579 -1.2497 -0.2295
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3383 0.5197 -1.5449 -0.2793
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3612 0.3970 -0.3975 0.3452
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2731 0.4621 -0.6190 0.2634
## Avg_Cogongrass_Cover-Sus_scrofa -0.1719 0.5656 -1.4750 -0.1105
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9476 1.1180 430
## (Intercept)-Lynx_rufus 0.5226 1.0618 535
## (Intercept)-Didelphis_virginiana -0.1905 1.0059 642
## (Intercept)-Sylvilagus_floridanus 0.3667 1.0522 566
## (Intercept)-Meleagris_gallopavo 0.3207 1.0400 407
## (Intercept)-Sciurus_carolinensis -0.2466 1.0119 638
## (Intercept)-Vulpes_vulpes -0.1219 1.0092 537
## (Intercept)-Sus_scrofa -0.3549 1.0011 599
## Avg_Cogongrass_Cover-Canis_latrans 1.1275 1.0009 2063
## Avg_Cogongrass_Cover-Lynx_rufus 1.5210 1.0088 1132
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1915 1.0047 1902
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5147 1.0011 1367
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5308 1.0009 1037
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1788 1.0214 1758
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2167 1.0046 1381
## Avg_Cogongrass_Cover-Sus_scrofa 0.7914 1.0020 989
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6423 0.1665 -2.9785 -2.6355 -2.3314 1.0032
## (Intercept)-Lynx_rufus -3.3709 0.2835 -3.9645 -3.3542 -2.8516 1.0528
## (Intercept)-Didelphis_virginiana -2.4729 0.2585 -2.9885 -2.4740 -1.9786 1.0038
## (Intercept)-Sylvilagus_floridanus -3.1374 0.2738 -3.7254 -3.1311 -2.6298 1.0156
## (Intercept)-Meleagris_gallopavo -3.3107 0.2947 -3.9091 -3.2985 -2.7654 1.0690
## (Intercept)-Sciurus_carolinensis -2.5697 0.2604 -3.0969 -2.5602 -2.0758 1.0035
## (Intercept)-Vulpes_vulpes -3.5872 0.5623 -4.8947 -3.5100 -2.6785 1.0035
## (Intercept)-Sus_scrofa -2.9999 0.3934 -3.8354 -2.9807 -2.2798 1.0117
## week-Canis_latrans 0.0803 0.1322 -0.1760 0.0823 0.3308 1.0065
## week-Lynx_rufus -0.0408 0.2012 -0.4607 -0.0317 0.3206 1.0130
## week-Didelphis_virginiana -0.2506 0.2432 -0.7858 -0.2322 0.1726 1.0270
## week-Sylvilagus_floridanus -0.1666 0.2175 -0.6362 -0.1541 0.2212 1.0069
## week-Meleagris_gallopavo -0.3048 0.2608 -0.8766 -0.2840 0.1406 1.0364
## week-Sciurus_carolinensis 0.1544 0.1920 -0.2258 0.1593 0.5174 1.0025
## week-Vulpes_vulpes -0.1380 0.2911 -0.7525 -0.1205 0.3872 1.0356
## week-Sus_scrofa 0.1052 0.2576 -0.4218 0.1114 0.5892 1.0202
## ESS
## (Intercept)-Canis_latrans 1015
## (Intercept)-Lynx_rufus 328
## (Intercept)-Didelphis_virginiana 1146
## (Intercept)-Sylvilagus_floridanus 542
## (Intercept)-Meleagris_gallopavo 442
## (Intercept)-Sciurus_carolinensis 1129
## (Intercept)-Vulpes_vulpes 239
## (Intercept)-Sus_scrofa 722
## week-Canis_latrans 1302
## week-Lynx_rufus 1001
## week-Didelphis_virginiana 1086
## week-Sylvilagus_floridanus 954
## week-Meleagris_gallopavo 750
## week-Sciurus_carolinensis 1933
## week-Vulpes_vulpes 1090
## week-Sus_scrofa 1497
# 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 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_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): 0.545
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7535 0.9064 -3.4573 -1.7655 0.0676 1.0129 302
## Cogon_Patch_Size -0.4500 0.8117 -2.1508 -0.4245 1.0863 1.0485 431
## Veg_shannon_index 0.8927 0.6052 -0.3090 0.8834 2.1736 1.0242 402
## total_shrub_cover -0.6146 0.6759 -2.0778 -0.5819 0.6531 1.0006 615
## Avg_Cogongrass_Cover 1.8038 0.7635 0.3591 1.7981 3.3096 1.0133 295
## Tree_Density -1.7841 0.7855 -3.3514 -1.7684 -0.2269 1.0115 301
## Avg_Canopy_Cover 1.8211 0.7378 0.2978 1.8106 3.2605 1.0007 858
## avg_veg_height -0.6591 0.5612 -1.7643 -0.6586 0.4463 1.0064 274
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.6418 6.8855 0.1660 3.6099 23.7644 1.0256 287
## Cogon_Patch_Size 4.5629 6.4217 0.1615 2.5013 21.4174 1.1206 241
## Veg_shannon_index 1.7964 2.7266 0.0660 0.8988 9.0794 1.0528 315
## total_shrub_cover 3.1928 5.0073 0.1126 1.8747 13.1065 1.0405 509
## Avg_Cogongrass_Cover 1.6452 3.2945 0.0536 0.6781 8.9836 1.0284 485
## Tree_Density 2.4371 5.6487 0.0610 0.8880 13.3543 1.0919 262
## Avg_Canopy_Cover 4.0007 6.7726 0.1560 2.1492 19.3374 1.1052 314
## avg_veg_height 0.5466 0.7812 0.0441 0.2819 2.7251 1.0465 466
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2982 3.9885 0.1102 2.1169 12.4909 1.1129 118
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0193 0.2851 -3.5931 -3.0194 -2.4474 1.0054 1376
## week -0.0672 0.1632 -0.4080 -0.0624 0.2436 1.0114 1196
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5351 0.5259 0.0877 0.3868 1.8923 1.0295 534
## week 0.1433 0.1443 0.0300 0.1026 0.5159 1.0146 1350
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0118 1.3016 -2.4858 0.0285
## (Intercept)-Lynx_rufus -0.1241 1.8497 -3.1754 -0.3328
## (Intercept)-Didelphis_virginiana -3.0121 1.1803 -5.6616 -2.9154
## (Intercept)-Sylvilagus_floridanus -1.7320 1.2925 -4.4182 -1.7234
## (Intercept)-Meleagris_gallopavo -1.9190 1.4227 -4.8927 -1.8892
## (Intercept)-Sciurus_carolinensis -3.1370 1.2220 -5.9920 -3.0123
## (Intercept)-Vulpes_vulpes -2.7831 1.5041 -5.8004 -2.7888
## (Intercept)-Sus_scrofa -4.1500 1.7742 -8.1476 -3.9340
## Cogon_Patch_Size-Canis_latrans 0.8986 1.2356 -0.9370 0.6941
## Cogon_Patch_Size-Lynx_rufus -0.5420 1.6253 -3.4384 -0.6011
## Cogon_Patch_Size-Didelphis_virginiana 1.3122 1.0403 -0.3710 1.2019
## Cogon_Patch_Size-Sylvilagus_floridanus -2.1841 1.9660 -7.2664 -1.7787
## Cogon_Patch_Size-Meleagris_gallopavo -0.1417 1.3316 -2.3993 -0.2775
## Cogon_Patch_Size-Sciurus_carolinensis -1.8006 1.4372 -5.6326 -1.4943
## Cogon_Patch_Size-Vulpes_vulpes -1.1963 1.8244 -5.2720 -1.0477
## Cogon_Patch_Size-Sus_scrofa -0.9313 1.4720 -4.3112 -0.7465
## Veg_shannon_index-Canis_latrans 1.3230 0.7312 0.0887 1.2536
## Veg_shannon_index-Lynx_rufus 0.7073 1.1316 -1.9426 0.7956
## Veg_shannon_index-Didelphis_virginiana 1.1718 0.8490 -0.3134 1.0947
## Veg_shannon_index-Sylvilagus_floridanus 1.1542 0.8370 -0.3471 1.0898
## Veg_shannon_index-Meleagris_gallopavo 1.4682 1.0055 -0.2178 1.3346
## Veg_shannon_index-Sciurus_carolinensis -0.0821 0.9196 -2.1159 0.0127
## Veg_shannon_index-Vulpes_vulpes -0.0186 1.2002 -2.8592 0.1567
## Veg_shannon_index-Sus_scrofa 1.8483 1.2677 0.0169 1.6075
## total_shrub_cover-Canis_latrans 0.3339 0.8933 -1.1086 0.2153
## total_shrub_cover-Lynx_rufus -1.6580 1.3801 -4.9006 -1.4284
## total_shrub_cover-Didelphis_virginiana -0.7463 0.8882 -2.6466 -0.6750
## total_shrub_cover-Sylvilagus_floridanus -0.3006 1.0740 -2.6447 -0.2703
## total_shrub_cover-Meleagris_gallopavo -2.7535 1.6161 -6.4127 -2.5646
## total_shrub_cover-Sciurus_carolinensis 0.0945 0.8105 -1.5045 0.0884
## total_shrub_cover-Vulpes_vulpes -0.8325 1.1709 -3.4168 -0.7080
## total_shrub_cover-Sus_scrofa 0.2077 1.0987 -1.7750 0.1292
## Avg_Cogongrass_Cover-Canis_latrans 2.2202 0.9438 0.5980 2.1551
## Avg_Cogongrass_Cover-Lynx_rufus 2.4257 1.1421 0.5709 2.2858
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0926 0.9314 0.4315 2.0326
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2866 1.0520 -0.9167 1.3354
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2143 1.3662 -1.9879 1.3611
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2727 1.0332 0.5670 2.1885
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4697 1.2582 0.4148 2.3171
## Avg_Cogongrass_Cover-Sus_scrofa 1.3881 1.2082 -1.2768 1.4871
## Tree_Density-Canis_latrans -2.4794 1.3324 -5.7489 -2.2166
## Tree_Density-Lynx_rufus -0.5848 1.3261 -2.6540 -0.7523
## Tree_Density-Didelphis_virginiana -2.1756 1.1108 -4.7547 -2.0267
## Tree_Density-Sylvilagus_floridanus -2.4126 1.4119 -5.9163 -2.1785
## Tree_Density-Meleagris_gallopavo -2.0547 1.2547 -4.8676 -1.9408
## Tree_Density-Sciurus_carolinensis -2.2970 1.1984 -5.1236 -2.1006
## Tree_Density-Vulpes_vulpes -1.7814 1.5132 -4.9773 -1.7515
## Tree_Density-Sus_scrofa -2.0125 1.3350 -5.0417 -1.8577
## Avg_Canopy_Cover-Canis_latrans 0.1462 0.7718 -1.4239 0.1620
## Avg_Canopy_Cover-Lynx_rufus 0.8534 1.2887 -1.6395 0.8332
## Avg_Canopy_Cover-Didelphis_virginiana 2.9769 1.0867 1.2952 2.8160
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7477 1.7849 1.2386 3.4531
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5425 1.3425 0.6026 2.3035
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4535 0.9295 0.9692 2.3486
## Avg_Canopy_Cover-Vulpes_vulpes 2.3929 1.3909 0.3838 2.1786
## Avg_Canopy_Cover-Sus_scrofa 2.0972 0.9592 0.4563 2.0182
## avg_veg_height-Canis_latrans -0.7954 0.6407 -2.0942 -0.7912
## avg_veg_height-Lynx_rufus -0.7286 0.8358 -2.4526 -0.7157
## avg_veg_height-Didelphis_virginiana -0.7024 0.7230 -2.1546 -0.6931
## avg_veg_height-Sylvilagus_floridanus -0.7843 0.7273 -2.2902 -0.7615
## avg_veg_height-Meleagris_gallopavo -0.7530 0.8506 -2.4834 -0.7397
## avg_veg_height-Sciurus_carolinensis -0.2675 0.7258 -1.6293 -0.3113
## avg_veg_height-Vulpes_vulpes -0.7082 0.8286 -2.3955 -0.7150
## avg_veg_height-Sus_scrofa -0.6903 0.7498 -2.2181 -0.6865
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7915 1.0035 205
## (Intercept)-Lynx_rufus 3.9825 1.0291 143
## (Intercept)-Didelphis_virginiana -0.9220 1.0137 335
## (Intercept)-Sylvilagus_floridanus 0.7476 1.0549 289
## (Intercept)-Meleagris_gallopavo 0.8561 1.0104 328
## (Intercept)-Sciurus_carolinensis -1.1364 1.0417 340
## (Intercept)-Vulpes_vulpes 0.2682 1.0300 252
## (Intercept)-Sus_scrofa -1.3094 1.0286 199
## Cogon_Patch_Size-Canis_latrans 4.0529 1.0139 489
## Cogon_Patch_Size-Lynx_rufus 2.7745 1.0667 255
## Cogon_Patch_Size-Didelphis_virginiana 3.6449 1.0308 437
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4809 1.1625 199
## Cogon_Patch_Size-Meleagris_gallopavo 2.9283 1.0110 482
## Cogon_Patch_Size-Sciurus_carolinensis 0.1369 1.0437 378
## Cogon_Patch_Size-Vulpes_vulpes 2.1013 1.0684 232
## Cogon_Patch_Size-Sus_scrofa 1.6040 1.0206 411
## Veg_shannon_index-Canis_latrans 2.9911 1.0139 598
## Veg_shannon_index-Lynx_rufus 2.6994 1.0305 285
## Veg_shannon_index-Didelphis_virginiana 3.0738 1.0265 635
## Veg_shannon_index-Sylvilagus_floridanus 3.0419 1.0164 585
## Veg_shannon_index-Meleagris_gallopavo 3.8530 1.0219 309
## Veg_shannon_index-Sciurus_carolinensis 1.5381 1.0226 305
## Veg_shannon_index-Vulpes_vulpes 1.9024 1.0523 285
## Veg_shannon_index-Sus_scrofa 5.0464 1.0595 302
## total_shrub_cover-Canis_latrans 2.4986 1.0370 454
## total_shrub_cover-Lynx_rufus 0.4647 1.0124 243
## total_shrub_cover-Didelphis_virginiana 0.8557 1.0083 931
## total_shrub_cover-Sylvilagus_floridanus 1.8076 1.0033 550
## total_shrub_cover-Meleagris_gallopavo -0.1599 1.0355 224
## total_shrub_cover-Sciurus_carolinensis 1.6944 1.0000 928
## total_shrub_cover-Vulpes_vulpes 1.2096 1.0494 378
## total_shrub_cover-Sus_scrofa 2.6108 1.0154 643
## Avg_Cogongrass_Cover-Canis_latrans 4.3719 1.0090 382
## Avg_Cogongrass_Cover-Lynx_rufus 5.1693 1.0045 307
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0664 1.0080 367
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2052 1.0225 462
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5675 1.0028 400
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5998 1.0067 353
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.3102 1.0103 317
## Avg_Cogongrass_Cover-Sus_scrofa 3.4934 1.0014 469
## Tree_Density-Canis_latrans -0.6313 1.0317 258
## Tree_Density-Lynx_rufus 2.1932 1.0598 247
## Tree_Density-Didelphis_virginiana -0.3925 1.0123 401
## Tree_Density-Sylvilagus_floridanus -0.3052 1.0461 255
## Tree_Density-Meleagris_gallopavo 0.1128 1.0061 510
## Tree_Density-Sciurus_carolinensis -0.5012 1.0176 399
## Tree_Density-Vulpes_vulpes 1.3632 1.0375 189
## Tree_Density-Sus_scrofa 0.3103 1.0352 327
## Avg_Canopy_Cover-Canis_latrans 1.6352 1.0007 451
## Avg_Canopy_Cover-Lynx_rufus 3.6692 1.0063 330
## Avg_Canopy_Cover-Didelphis_virginiana 5.5063 1.0376 193
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1022 1.0403 230
## Avg_Canopy_Cover-Meleagris_gallopavo 6.0626 1.0055 369
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6190 1.0045 369
## Avg_Canopy_Cover-Vulpes_vulpes 5.7698 1.0884 224
## Avg_Canopy_Cover-Sus_scrofa 4.2481 1.0124 738
## avg_veg_height-Canis_latrans 0.4418 1.0070 447
## avg_veg_height-Lynx_rufus 0.9372 1.0094 451
## avg_veg_height-Didelphis_virginiana 0.6904 1.0026 523
## avg_veg_height-Sylvilagus_floridanus 0.5980 1.0140 488
## avg_veg_height-Meleagris_gallopavo 0.8520 1.0111 419
## avg_veg_height-Sciurus_carolinensis 1.2805 1.0093 508
## avg_veg_height-Vulpes_vulpes 0.9040 1.0108 415
## avg_veg_height-Sus_scrofa 0.7638 1.0041 556
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6807 0.1712 -3.0281 -2.6712 -2.3612 1.0051
## (Intercept)-Lynx_rufus -3.5851 0.3098 -4.1874 -3.5841 -3.0015 1.0113
## (Intercept)-Didelphis_virginiana -2.4392 0.2554 -2.9510 -2.4402 -1.9605 1.0135
## (Intercept)-Sylvilagus_floridanus -3.2202 0.2719 -3.7881 -3.2120 -2.7241 1.0150
## (Intercept)-Meleagris_gallopavo -3.3965 0.2888 -3.9683 -3.3897 -2.8625 1.0127
## (Intercept)-Sciurus_carolinensis -2.5672 0.2688 -3.1232 -2.5522 -2.0575 1.0076
## (Intercept)-Vulpes_vulpes -3.8370 0.5619 -5.0292 -3.7983 -2.8581 1.0368
## (Intercept)-Sus_scrofa -3.0143 0.3985 -3.8348 -3.0002 -2.3030 1.0003
## week-Canis_latrans 0.0695 0.1351 -0.2030 0.0737 0.3220 0.9998
## week-Lynx_rufus -0.0323 0.1992 -0.4498 -0.0236 0.3304 1.0123
## week-Didelphis_virginiana -0.2457 0.2334 -0.7603 -0.2241 0.1526 1.0052
## week-Sylvilagus_floridanus -0.1642 0.2201 -0.6396 -0.1526 0.2236 1.0204
## week-Meleagris_gallopavo -0.3066 0.2586 -0.8634 -0.2872 0.1467 1.0171
## week-Sciurus_carolinensis 0.1577 0.1915 -0.2312 0.1631 0.5217 1.0003
## week-Vulpes_vulpes -0.1377 0.2876 -0.7774 -0.1221 0.3910 1.0077
## week-Sus_scrofa 0.1101 0.2597 -0.3983 0.1120 0.6100 1.0032
## ESS
## (Intercept)-Canis_latrans 602
## (Intercept)-Lynx_rufus 223
## (Intercept)-Didelphis_virginiana 932
## (Intercept)-Sylvilagus_floridanus 490
## (Intercept)-Meleagris_gallopavo 454
## (Intercept)-Sciurus_carolinensis 932
## (Intercept)-Vulpes_vulpes 209
## (Intercept)-Sus_scrofa 734
## week-Canis_latrans 1589
## week-Lynx_rufus 889
## week-Didelphis_virginiana 1177
## week-Sylvilagus_floridanus 920
## week-Meleagris_gallopavo 701
## week-Sciurus_carolinensis 1830
## week-Vulpes_vulpes 793
## week-Sus_scrofa 1477
# 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 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_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): 0.5113
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0878 0.4758 -2.0710 -1.0856 -0.1459 1.0363 452
## Avg_Cogongrass_Cover 0.2221 0.4064 -0.5913 0.2205 1.0357 1.0648 566
## total_shrub_cover -0.4105 0.4122 -1.3132 -0.3960 0.3902 1.0114 1122
## avg_veg_height -0.1913 0.3562 -0.8834 -0.1935 0.5251 1.0813 519
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8345 1.1504 0.0519 0.4879 3.6819 1.0350 854
## Avg_Cogongrass_Cover 0.6132 0.8972 0.0476 0.3524 2.8325 1.0163 433
## total_shrub_cover 0.9281 1.3063 0.0633 0.5109 4.1031 1.0623 322
## avg_veg_height 0.2954 0.3696 0.0370 0.1834 1.1813 1.0310 1279
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9191 1.7946 0.1136 1.41 6.9587 1.2184 139
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9720 0.2703 -3.5055 -2.9680 -2.4491 1.0367 1005
## week -0.0772 0.1703 -0.4386 -0.0733 0.2576 1.0061 1246
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4456 0.5153 0.0634 0.3082 1.6406 1.1502 301
## week 0.1466 0.1388 0.0272 0.1076 0.5098 1.0095 1528
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4147 0.7318 -1.7945 -0.4294
## (Intercept)-Lynx_rufus -0.7922 0.7232 -2.1633 -0.8201
## (Intercept)-Didelphis_virginiana -1.3722 0.6266 -2.6806 -1.3375
## (Intercept)-Sylvilagus_floridanus -0.8047 0.6938 -2.1370 -0.8329
## (Intercept)-Meleagris_gallopavo -1.1368 0.6726 -2.5459 -1.0950
## (Intercept)-Sciurus_carolinensis -1.4213 0.6453 -2.7600 -1.3812
## (Intercept)-Vulpes_vulpes -1.4376 0.8298 -3.1798 -1.4045
## (Intercept)-Sus_scrofa -1.6504 0.7504 -3.3273 -1.6025
## Avg_Cogongrass_Cover-Canis_latrans 0.5637 0.5216 -0.3695 0.5371
## Avg_Cogongrass_Cover-Lynx_rufus 0.6315 0.5532 -0.3557 0.5923
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4968 0.5006 -0.4405 0.4818
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2332 0.5961 -1.5613 -0.1824
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3578 0.6907 -1.9004 -0.2883
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3794 0.4874 -0.5712 0.3713
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4174 0.5901 -0.6665 0.3961
## Avg_Cogongrass_Cover-Sus_scrofa -0.1393 0.7386 -1.7394 -0.0603
## total_shrub_cover-Canis_latrans 0.1751 0.5023 -0.7434 0.1405
## total_shrub_cover-Lynx_rufus -1.0147 0.7687 -2.9658 -0.8764
## total_shrub_cover-Didelphis_virginiana -0.2771 0.4635 -1.2248 -0.2631
## total_shrub_cover-Sylvilagus_floridanus -0.4171 0.5944 -1.6794 -0.3907
## total_shrub_cover-Meleagris_gallopavo -1.4338 0.8374 -3.3593 -1.2899
## total_shrub_cover-Sciurus_carolinensis -0.1045 0.4758 -1.0175 -0.1243
## total_shrub_cover-Vulpes_vulpes -0.4759 0.6959 -2.1014 -0.4222
## total_shrub_cover-Sus_scrofa 0.0627 0.6180 -1.0474 0.0148
## avg_veg_height-Canis_latrans -0.2137 0.4423 -1.1032 -0.1997
## avg_veg_height-Lynx_rufus -0.2021 0.5137 -1.2130 -0.2027
## avg_veg_height-Didelphis_virginiana -0.1955 0.4538 -1.1019 -0.1922
## avg_veg_height-Sylvilagus_floridanus -0.2942 0.4706 -1.2301 -0.2822
## avg_veg_height-Meleagris_gallopavo -0.4083 0.5638 -1.6096 -0.3696
## avg_veg_height-Sciurus_carolinensis 0.1544 0.4848 -0.7351 0.1302
## avg_veg_height-Vulpes_vulpes -0.2565 0.5159 -1.3038 -0.2426
## avg_veg_height-Sus_scrofa -0.1549 0.4969 -1.1375 -0.1546
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0783 1.0049 495
## (Intercept)-Lynx_rufus 0.7735 1.0239 439
## (Intercept)-Didelphis_virginiana -0.2043 1.0311 716
## (Intercept)-Sylvilagus_floridanus 0.7175 1.0465 577
## (Intercept)-Meleagris_gallopavo 0.1071 1.0334 458
## (Intercept)-Sciurus_carolinensis -0.1930 1.0137 656
## (Intercept)-Vulpes_vulpes 0.1938 1.0489 405
## (Intercept)-Sus_scrofa -0.2965 1.0092 614
## Avg_Cogongrass_Cover-Canis_latrans 1.7039 1.0232 918
## Avg_Cogongrass_Cover-Lynx_rufus 1.8931 1.0306 779
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5174 1.0516 626
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8227 1.0374 596
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8076 1.0395 469
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3581 1.0320 741
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6611 1.0226 654
## Avg_Cogongrass_Cover-Sus_scrofa 1.1152 1.0242 538
## total_shrub_cover-Canis_latrans 1.2410 1.0155 1059
## total_shrub_cover-Lynx_rufus 0.1402 1.0258 418
## total_shrub_cover-Didelphis_virginiana 0.5935 1.0014 2107
## total_shrub_cover-Sylvilagus_floridanus 0.6599 1.0257 721
## total_shrub_cover-Meleagris_gallopavo -0.2002 1.0349 314
## total_shrub_cover-Sciurus_carolinensis 0.8511 1.0066 1793
## total_shrub_cover-Vulpes_vulpes 0.7155 1.0593 665
## total_shrub_cover-Sus_scrofa 1.3988 1.0203 753
## avg_veg_height-Canis_latrans 0.6535 1.0302 998
## avg_veg_height-Lynx_rufus 0.7951 1.0378 949
## avg_veg_height-Didelphis_virginiana 0.6605 1.0261 992
## avg_veg_height-Sylvilagus_floridanus 0.6204 1.0591 739
## avg_veg_height-Meleagris_gallopavo 0.6412 1.0715 521
## avg_veg_height-Sciurus_carolinensis 1.1582 1.0183 1095
## avg_veg_height-Vulpes_vulpes 0.7262 1.0588 731
## avg_veg_height-Sus_scrofa 0.8209 1.0366 916
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6580 0.1717 -3.0021 -2.6494 -2.3354 1.0084
## (Intercept)-Lynx_rufus -3.4100 0.2851 -4.0240 -3.3932 -2.9022 1.0503
## (Intercept)-Didelphis_virginiana -2.4669 0.2530 -2.9729 -2.4644 -1.9952 1.0079
## (Intercept)-Sylvilagus_floridanus -3.2039 0.2897 -3.8096 -3.1896 -2.6907 1.0450
## (Intercept)-Meleagris_gallopavo -3.2708 0.2941 -3.8772 -3.2577 -2.7069 1.0670
## (Intercept)-Sciurus_carolinensis -2.5935 0.2663 -3.1369 -2.5837 -2.1083 1.0004
## (Intercept)-Vulpes_vulpes -3.6458 0.6130 -5.0313 -3.5443 -2.6849 1.1709
## (Intercept)-Sus_scrofa -3.0321 0.4213 -3.9372 -3.0079 -2.2752 1.0153
## week-Canis_latrans 0.0679 0.1337 -0.2145 0.0735 0.3087 1.0028
## week-Lynx_rufus -0.0358 0.1989 -0.4436 -0.0300 0.3320 1.0048
## week-Didelphis_virginiana -0.2572 0.2415 -0.7683 -0.2425 0.1703 1.0041
## week-Sylvilagus_floridanus -0.1810 0.2217 -0.6475 -0.1683 0.2236 1.0186
## week-Meleagris_gallopavo -0.3116 0.2776 -0.9185 -0.2831 0.1612 1.0034
## week-Sciurus_carolinensis 0.1440 0.1959 -0.2431 0.1455 0.5145 1.0020
## week-Vulpes_vulpes -0.1550 0.3020 -0.8000 -0.1310 0.3745 1.0052
## week-Sus_scrofa 0.1018 0.2508 -0.3866 0.0955 0.6091 1.0066
## ESS
## (Intercept)-Canis_latrans 1002
## (Intercept)-Lynx_rufus 441
## (Intercept)-Didelphis_virginiana 1186
## (Intercept)-Sylvilagus_floridanus 440
## (Intercept)-Meleagris_gallopavo 599
## (Intercept)-Sciurus_carolinensis 1135
## (Intercept)-Vulpes_vulpes 182
## (Intercept)-Sus_scrofa 631
## week-Canis_latrans 1556
## week-Lynx_rufus 847
## week-Didelphis_virginiana 891
## week-Sylvilagus_floridanus 734
## week-Meleagris_gallopavo 628
## week-Sciurus_carolinensis 1689
## week-Vulpes_vulpes 940
## week-Sus_scrofa 1638
# 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 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_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): 0.5007
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8104 0.3586 -1.5268 -0.8165 -0.0664 1.0103 1344
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8346 0.7887 0.1302 0.6222 2.9982 1.0238 1345
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9594 0.2451 -3.4405 -2.9611 -2.4634 1.0122 1318
## week -0.0702 0.1647 -0.3899 -0.0685 0.2474 1.0099 1417
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3737 0.4494 0.0617 0.2665 1.2687 1.0597 1133
## week 0.1429 0.1464 0.0300 0.1042 0.4926 1.0105 1895
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.0954 0.3872 -0.6202 0.0803 0.8841 1.0020
## (Intercept)-Lynx_rufus -0.2296 0.5078 -1.0976 -0.2719 0.9783 1.0012
## (Intercept)-Didelphis_virginiana -1.2615 0.4183 -2.1472 -1.2536 -0.4831 1.0009
## (Intercept)-Sylvilagus_floridanus -0.5154 0.4427 -1.3099 -0.5371 0.4089 1.0093
## (Intercept)-Meleagris_gallopavo -0.5646 0.4507 -1.4101 -0.5802 0.3776 1.0078
## (Intercept)-Sciurus_carolinensis -1.2555 0.4211 -2.1243 -1.2397 -0.4626 1.0056
## (Intercept)-Vulpes_vulpes -1.4534 0.6025 -2.6940 -1.4445 -0.2727 1.0262
## (Intercept)-Sus_scrofa -1.6330 0.5181 -2.7247 -1.6090 -0.7127 1.0065
## ESS
## (Intercept)-Canis_latrans 2090
## (Intercept)-Lynx_rufus 731
## (Intercept)-Didelphis_virginiana 2088
## (Intercept)-Sylvilagus_floridanus 861
## (Intercept)-Meleagris_gallopavo 1308
## (Intercept)-Sciurus_carolinensis 2324
## (Intercept)-Vulpes_vulpes 620
## (Intercept)-Sus_scrofa 1320
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6530 0.1657 -2.9856 -2.6542 -2.3410 1.0022
## (Intercept)-Lynx_rufus -3.3671 0.2850 -3.9384 -3.3623 -2.8382 1.0077
## (Intercept)-Didelphis_virginiana -2.4901 0.2546 -3.0063 -2.4814 -1.9956 1.0043
## (Intercept)-Sylvilagus_floridanus -3.1334 0.2651 -3.7036 -3.1250 -2.6401 1.0207
## (Intercept)-Meleagris_gallopavo -3.3120 0.3060 -3.9487 -3.2915 -2.7737 1.0052
## (Intercept)-Sciurus_carolinensis -2.5884 0.2624 -3.1191 -2.5803 -2.1096 1.0035
## (Intercept)-Vulpes_vulpes -3.5149 0.5037 -4.6895 -3.4713 -2.6650 1.0755
## (Intercept)-Sus_scrofa -3.0194 0.3786 -3.8154 -3.0089 -2.2982 1.0043
## week-Canis_latrans 0.0715 0.1375 -0.1981 0.0754 0.3363 1.0041
## week-Lynx_rufus -0.0389 0.2078 -0.4755 -0.0263 0.3499 1.0175
## week-Didelphis_virginiana -0.2464 0.2390 -0.7655 -0.2328 0.1719 1.0008
## week-Sylvilagus_floridanus -0.1723 0.2219 -0.6500 -0.1613 0.2296 1.0067
## week-Meleagris_gallopavo -0.2893 0.2596 -0.8590 -0.2692 0.1573 1.0080
## week-Sciurus_carolinensis 0.1480 0.1903 -0.2281 0.1475 0.5123 1.0078
## week-Vulpes_vulpes -0.1427 0.3036 -0.7795 -0.1291 0.4155 1.0005
## week-Sus_scrofa 0.1090 0.2573 -0.3944 0.1129 0.6033 1.0060
## ESS
## (Intercept)-Canis_latrans 1033
## (Intercept)-Lynx_rufus 455
## (Intercept)-Didelphis_virginiana 1104
## (Intercept)-Sylvilagus_floridanus 597
## (Intercept)-Meleagris_gallopavo 429
## (Intercept)-Sciurus_carolinensis 1170
## (Intercept)-Vulpes_vulpes 394
## (Intercept)-Sus_scrofa 700
## week-Canis_latrans 1501
## week-Lynx_rufus 934
## week-Didelphis_virginiana 1214
## week-Sylvilagus_floridanus 857
## week-Meleagris_gallopavo 699
## week-Sciurus_carolinensis 1786
## week-Vulpes_vulpes 1210
## week-Sus_scrofa 1639
#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 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_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): 0.5127
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1176 0.4397 -2.0064 -1.1134 -0.2765 1.0262 451
## Veg_shannon_index 0.3849 0.3200 -0.2544 0.3877 0.9953 1.0025 987
## Avg_Cogongrass_Cover 0.2516 0.3359 -0.4057 0.2504 0.9172 1.0123 813
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7858 1.0682 0.0496 0.4507 3.4586 1.0096 563
## Veg_shannon_index 0.4050 0.4789 0.0436 0.2552 1.6494 1.0073 1030
## Avg_Cogongrass_Cover 0.4750 0.6235 0.0446 0.2795 2.0757 1.0140 895
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4227 1.2448 0.1239 1.0952 4.5654 1.0602 213
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9719 0.2606 -3.5012 -2.9627 -2.4754 1.0026 1080
## week -0.0701 0.1681 -0.4178 -0.0634 0.2468 1.0018 1134
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4019 0.4295 0.0703 0.2879 1.3756 1.0047 572
## week 0.1417 0.1467 0.0293 0.1025 0.4799 1.0071 1693
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4649 0.6809 -1.7898 -0.4790
## (Intercept)-Lynx_rufus -0.8300 0.6808 -2.1004 -0.8469
## (Intercept)-Didelphis_virginiana -1.4176 0.6007 -2.7700 -1.3873
## (Intercept)-Sylvilagus_floridanus -0.8818 0.6424 -2.1241 -0.8895
## (Intercept)-Meleagris_gallopavo -0.9953 0.6438 -2.2585 -1.0124
## (Intercept)-Sciurus_carolinensis -1.4081 0.6027 -2.7023 -1.3747
## (Intercept)-Vulpes_vulpes -1.4947 0.7181 -2.9959 -1.4539
## (Intercept)-Sus_scrofa -1.7447 0.7741 -3.5125 -1.6545
## Veg_shannon_index-Canis_latrans 0.7191 0.4118 -0.0304 0.6988
## Veg_shannon_index-Lynx_rufus 0.1967 0.5367 -0.9320 0.2179
## Veg_shannon_index-Didelphis_virginiana 0.5378 0.4273 -0.2694 0.5269
## Veg_shannon_index-Sylvilagus_floridanus 0.4838 0.4619 -0.4102 0.4696
## Veg_shannon_index-Meleagris_gallopavo 0.4791 0.4869 -0.4354 0.4696
## Veg_shannon_index-Sciurus_carolinensis -0.0146 0.4353 -0.9547 0.0130
## Veg_shannon_index-Vulpes_vulpes 0.0447 0.4944 -1.0472 0.0725
## Veg_shannon_index-Sus_scrofa 0.7155 0.5714 -0.2477 0.6636
## Avg_Cogongrass_Cover-Canis_latrans 0.5419 0.4089 -0.1806 0.5105
## Avg_Cogongrass_Cover-Lynx_rufus 0.6344 0.5033 -0.1844 0.5799
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4797 0.4106 -0.2884 0.4669
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1615 0.4992 -1.2578 -0.1233
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2192 0.5763 -1.4741 -0.1659
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4102 0.3996 -0.3531 0.3963
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3481 0.4918 -0.5974 0.3226
## Avg_Cogongrass_Cover-Sus_scrofa -0.0598 0.6023 -1.5255 0.0042
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9254 1.0146 359
## (Intercept)-Lynx_rufus 0.5721 1.0238 457
## (Intercept)-Didelphis_virginiana -0.3383 1.0199 791
## (Intercept)-Sylvilagus_floridanus 0.4294 1.0214 537
## (Intercept)-Meleagris_gallopavo 0.2824 1.0320 460
## (Intercept)-Sciurus_carolinensis -0.3167 1.0101 650
## (Intercept)-Vulpes_vulpes -0.1827 1.0100 566
## (Intercept)-Sus_scrofa -0.4331 1.0040 448
## Veg_shannon_index-Canis_latrans 1.6004 0.9995 1617
## Veg_shannon_index-Lynx_rufus 1.2032 1.0042 970
## Veg_shannon_index-Didelphis_virginiana 1.4500 1.0025 1487
## Veg_shannon_index-Sylvilagus_floridanus 1.4546 1.0038 1280
## Veg_shannon_index-Meleagris_gallopavo 1.5039 1.0037 1127
## Veg_shannon_index-Sciurus_carolinensis 0.7746 1.0046 1622
## Veg_shannon_index-Vulpes_vulpes 0.9233 1.0039 1297
## Veg_shannon_index-Sus_scrofa 2.0559 1.0040 928
## Avg_Cogongrass_Cover-Canis_latrans 1.4359 1.0089 1792
## Avg_Cogongrass_Cover-Lynx_rufus 1.7674 1.0045 1143
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3205 1.0060 1592
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7046 1.0082 864
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8032 1.0045 698
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2222 1.0028 1656
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4068 1.0030 1197
## Avg_Cogongrass_Cover-Sus_scrofa 0.9228 1.0034 974
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6432 0.1680 -2.9749 -2.6367 -2.3284 1.0053
## (Intercept)-Lynx_rufus -3.3730 0.2793 -3.9451 -3.3614 -2.8611 1.0049
## (Intercept)-Didelphis_virginiana -2.4665 0.2537 -2.9853 -2.4562 -1.9979 1.0117
## (Intercept)-Sylvilagus_floridanus -3.1693 0.2809 -3.7548 -3.1508 -2.6728 1.0243
## (Intercept)-Meleagris_gallopavo -3.3436 0.3131 -4.0038 -3.3329 -2.7854 1.0056
## (Intercept)-Sciurus_carolinensis -2.5777 0.2774 -3.1358 -2.5642 -2.0737 1.0043
## (Intercept)-Vulpes_vulpes -3.5703 0.5587 -4.8252 -3.5066 -2.6634 1.0064
## (Intercept)-Sus_scrofa -3.0055 0.4000 -3.8520 -2.9814 -2.2563 1.0033
## week-Canis_latrans 0.0690 0.1362 -0.2083 0.0723 0.3357 1.0072
## week-Lynx_rufus -0.0425 0.1988 -0.4534 -0.0322 0.3182 1.0066
## week-Didelphis_virginiana -0.2532 0.2350 -0.7717 -0.2320 0.1608 1.0084
## week-Sylvilagus_floridanus -0.1598 0.2196 -0.6411 -0.1460 0.2332 1.0133
## week-Meleagris_gallopavo -0.3107 0.2612 -0.8794 -0.2900 0.1621 1.0080
## week-Sciurus_carolinensis 0.1510 0.1930 -0.2553 0.1558 0.5093 1.0051
## week-Vulpes_vulpes -0.1305 0.2920 -0.7480 -0.1186 0.4195 1.0009
## week-Sus_scrofa 0.1063 0.2549 -0.3970 0.1019 0.6131 1.0020
## ESS
## (Intercept)-Canis_latrans 1080
## (Intercept)-Lynx_rufus 502
## (Intercept)-Didelphis_virginiana 1222
## (Intercept)-Sylvilagus_floridanus 540
## (Intercept)-Meleagris_gallopavo 430
## (Intercept)-Sciurus_carolinensis 952
## (Intercept)-Vulpes_vulpes 252
## (Intercept)-Sus_scrofa 640
## week-Canis_latrans 1544
## week-Lynx_rufus 1006
## week-Didelphis_virginiana 1157
## week-Sylvilagus_floridanus 910
## week-Meleagris_gallopavo 687
## week-Sciurus_carolinensis 1925
## week-Vulpes_vulpes 1234
## week-Sus_scrofa 1439
# 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 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_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): 0.5158
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2044 0.5430 -2.3332 -1.1974 -0.1552 1.0052 323
## Cogon_Patch_Size -0.2575 0.6046 -1.6011 -0.2179 0.8699 1.0106 747
## Avg_Cogongrass_Cover 0.1795 0.3876 -0.5810 0.1863 0.9145 1.0026 890
## total_shrub_cover -0.3533 0.4313 -1.2469 -0.3372 0.4456 1.0040 1077
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1251 2.1064 0.0617 0.5803 5.0748 1.0416 712
## Cogon_Patch_Size 1.8846 2.7312 0.0968 1.0415 8.4347 1.0246 615
## Avg_Cogongrass_Cover 0.6366 0.8484 0.0497 0.3756 2.7522 1.0010 772
## total_shrub_cover 1.0112 1.6674 0.0599 0.5380 4.8785 1.0707 470
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.675 2.0793 0.435 2.1417 8.0065 1.0308 200
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9765 0.2828 -3.5493 -2.9742 -2.4237 1.0055 1054
## week -0.0782 0.1664 -0.4209 -0.0685 0.2404 1.0001 1267
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4715 0.5765 0.0688 0.3146 1.7699 1.0291 575
## week 0.1505 0.1606 0.0296 0.1054 0.5342 1.0023 1294
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4238 0.8583 -1.9502 -0.4897
## (Intercept)-Lynx_rufus -0.9239 0.8224 -2.5023 -0.9548
## (Intercept)-Didelphis_virginiana -1.4602 0.7080 -3.0445 -1.4093
## (Intercept)-Sylvilagus_floridanus -1.0151 0.7730 -2.5255 -1.0380
## (Intercept)-Meleagris_gallopavo -1.1603 0.7552 -2.7268 -1.1529
## (Intercept)-Sciurus_carolinensis -1.6423 0.7578 -3.3227 -1.5538
## (Intercept)-Vulpes_vulpes -1.6089 0.9332 -3.5895 -1.5878
## (Intercept)-Sus_scrofa -1.8626 0.9040 -3.9041 -1.7433
## Cogon_Patch_Size-Canis_latrans 0.8444 0.7964 -0.3465 0.7164
## Cogon_Patch_Size-Lynx_rufus -0.3593 0.9607 -2.1904 -0.4186
## Cogon_Patch_Size-Didelphis_virginiana 0.8202 0.6114 -0.2199 0.7678
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2643 1.2103 -4.2923 -1.0135
## Cogon_Patch_Size-Meleagris_gallopavo -0.0613 0.7317 -1.5376 -0.0352
## Cogon_Patch_Size-Sciurus_carolinensis -1.0038 0.9138 -3.2619 -0.8330
## Cogon_Patch_Size-Vulpes_vulpes -0.7908 1.1241 -3.5515 -0.6272
## Cogon_Patch_Size-Sus_scrofa -0.5326 1.0434 -3.2888 -0.3738
## Avg_Cogongrass_Cover-Canis_latrans 0.3004 0.4510 -0.5248 0.2830
## Avg_Cogongrass_Cover-Lynx_rufus 0.6824 0.6201 -0.3254 0.6001
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2463 0.5022 -0.7401 0.2463
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1599 0.5548 -1.3728 -0.1140
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4633 0.7209 -2.1051 -0.3783
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6020 0.4835 -0.2657 0.5727
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4006 0.5383 -0.6144 0.3837
## Avg_Cogongrass_Cover-Sus_scrofa -0.1310 0.7064 -1.7983 -0.0426
## total_shrub_cover-Canis_latrans 0.1777 0.5321 -0.7359 0.1261
## total_shrub_cover-Lynx_rufus -0.8925 0.8084 -2.8667 -0.7634
## total_shrub_cover-Didelphis_virginiana -0.3611 0.4909 -1.3086 -0.3682
## total_shrub_cover-Sylvilagus_floridanus -0.3335 0.6325 -1.6763 -0.3143
## total_shrub_cover-Meleagris_gallopavo -1.3992 0.8820 -3.5948 -1.2365
## total_shrub_cover-Sciurus_carolinensis 0.0068 0.5007 -0.9424 -0.0165
## total_shrub_cover-Vulpes_vulpes -0.3272 0.7231 -1.8001 -0.3144
## total_shrub_cover-Sus_scrofa 0.1121 0.6874 -1.0873 0.0617
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3721 1.0261 352
## (Intercept)-Lynx_rufus 0.8816 1.0403 475
## (Intercept)-Didelphis_virginiana -0.1906 1.0035 561
## (Intercept)-Sylvilagus_floridanus 0.6782 1.0100 397
## (Intercept)-Meleagris_gallopavo 0.3831 1.0021 461
## (Intercept)-Sciurus_carolinensis -0.3713 1.0052 428
## (Intercept)-Vulpes_vulpes 0.2265 1.0198 235
## (Intercept)-Sus_scrofa -0.4155 1.0147 321
## Cogon_Patch_Size-Canis_latrans 2.8727 1.0137 950
## Cogon_Patch_Size-Lynx_rufus 1.8419 1.0068 572
## Cogon_Patch_Size-Didelphis_virginiana 2.1241 1.0187 958
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3349 1.0396 365
## Cogon_Patch_Size-Meleagris_gallopavo 1.3738 1.0201 900
## Cogon_Patch_Size-Sciurus_carolinensis 0.2925 1.0141 578
## Cogon_Patch_Size-Vulpes_vulpes 0.9867 1.0090 410
## Cogon_Patch_Size-Sus_scrofa 1.0794 1.0025 650
## Avg_Cogongrass_Cover-Canis_latrans 1.2547 1.0071 1729
## Avg_Cogongrass_Cover-Lynx_rufus 2.1130 1.0033 846
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2323 1.0021 1478
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8250 1.0045 866
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7610 1.0048 609
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6308 1.0066 1280
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5099 1.0062 1138
## Avg_Cogongrass_Cover-Sus_scrofa 1.0319 1.0061 882
## total_shrub_cover-Canis_latrans 1.3651 1.0152 654
## total_shrub_cover-Lynx_rufus 0.3128 1.0086 500
## total_shrub_cover-Didelphis_virginiana 0.6038 1.0021 1500
## total_shrub_cover-Sylvilagus_floridanus 0.8617 1.0021 1073
## total_shrub_cover-Meleagris_gallopavo -0.1576 1.0022 416
## total_shrub_cover-Sciurus_carolinensis 1.0356 1.0035 1279
## total_shrub_cover-Vulpes_vulpes 1.0223 1.0167 655
## total_shrub_cover-Sus_scrofa 1.6403 1.0021 1119
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6500 0.1721 -2.9929 -2.6480 -2.3259 1.0024
## (Intercept)-Lynx_rufus -3.4151 0.2807 -3.9949 -3.4014 -2.9101 1.0114
## (Intercept)-Didelphis_virginiana -2.4822 0.2629 -3.0292 -2.4710 -1.9975 1.0081
## (Intercept)-Sylvilagus_floridanus -3.2033 0.2807 -3.7736 -3.1879 -2.6890 1.0022
## (Intercept)-Meleagris_gallopavo -3.2886 0.3012 -3.9037 -3.2705 -2.7331 1.0360
## (Intercept)-Sciurus_carolinensis -2.5684 0.2609 -3.0982 -2.5624 -2.0667 1.0079
## (Intercept)-Vulpes_vulpes -3.7111 0.6289 -5.0681 -3.6279 -2.6951 1.0038
## (Intercept)-Sus_scrofa -3.0464 0.4382 -4.0482 -3.0110 -2.2862 1.0184
## week-Canis_latrans 0.0734 0.1372 -0.2066 0.0776 0.3336 1.0089
## week-Lynx_rufus -0.0412 0.1993 -0.4546 -0.0282 0.3266 1.0014
## week-Didelphis_virginiana -0.2498 0.2391 -0.7746 -0.2287 0.1679 1.0020
## week-Sylvilagus_floridanus -0.1699 0.2147 -0.6205 -0.1564 0.2193 1.0042
## week-Meleagris_gallopavo -0.3154 0.2774 -0.9273 -0.2905 0.1466 1.0103
## week-Sciurus_carolinensis 0.1548 0.1957 -0.2267 0.1586 0.5205 1.0048
## week-Vulpes_vulpes -0.1628 0.3069 -0.8460 -0.1440 0.4018 1.0044
## week-Sus_scrofa 0.1084 0.2498 -0.3974 0.1089 0.5950 1.0002
## ESS
## (Intercept)-Canis_latrans 972
## (Intercept)-Lynx_rufus 423
## (Intercept)-Didelphis_virginiana 1139
## (Intercept)-Sylvilagus_floridanus 549
## (Intercept)-Meleagris_gallopavo 501
## (Intercept)-Sciurus_carolinensis 1162
## (Intercept)-Vulpes_vulpes 163
## (Intercept)-Sus_scrofa 478
## week-Canis_latrans 1575
## week-Lynx_rufus 964
## week-Didelphis_virginiana 1067
## week-Sylvilagus_floridanus 983
## week-Meleagris_gallopavo 604
## week-Sciurus_carolinensis 1683
## week-Vulpes_vulpes 854
## week-Sus_scrofa 1587
#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 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_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): 0.5322
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2026 0.4934 -2.1861 -1.2036 -0.2161 1.0312 690
## Tree_Density -0.7039 0.4946 -1.7929 -0.6613 0.1843 1.0088 695
## Avg_Canopy_Cover 1.0713 0.4304 0.2630 1.0383 2.0392 1.0235 951
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4341 1.7588 0.0912 0.8994 5.9722 1.0312 694
## Tree_Density 1.0297 1.4998 0.0543 0.5452 5.2579 1.0015 527
## Avg_Canopy_Cover 0.9667 1.2873 0.0709 0.5892 3.9084 1.0181 454
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9678 0.9963 0.0658 0.6469 3.6915 1.0571 198
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9677 0.2592 -3.5277 -2.9597 -2.4610 1.0138 1124
## week -0.0701 0.1632 -0.4114 -0.0645 0.2308 1.0040 1209
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4355 0.4489 0.0664 0.2996 1.6607 1.0106 358
## week 0.1379 0.1377 0.0289 0.1004 0.4666 1.0444 1432
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.2433 0.6966 -1.5850 -0.2398 1.1171
## (Intercept)-Lynx_rufus -0.5037 0.8644 -2.0054 -0.5870 1.5451
## (Intercept)-Didelphis_virginiana -1.7801 0.6837 -3.2650 -1.7221 -0.5987
## (Intercept)-Sylvilagus_floridanus -0.9963 0.6410 -2.2871 -0.9995 0.2743
## (Intercept)-Meleagris_gallopavo -0.8936 0.6631 -2.1512 -0.9215 0.5489
## (Intercept)-Sciurus_carolinensis -1.7974 0.6681 -3.2067 -1.7450 -0.6188
## (Intercept)-Vulpes_vulpes -1.8067 0.9000 -3.6812 -1.7525 -0.1318
## (Intercept)-Sus_scrofa -2.2122 0.8759 -4.2066 -2.1085 -0.8028
## Tree_Density-Canis_latrans -0.9334 0.6320 -2.3806 -0.8391 0.0934
## Tree_Density-Lynx_rufus 0.3264 0.7569 -0.8233 0.2149 2.1861
## Tree_Density-Didelphis_virginiana -1.0546 0.8276 -3.1735 -0.8987 0.1358
## Tree_Density-Sylvilagus_floridanus -1.1277 0.8327 -3.2021 -0.9900 0.1192
## Tree_Density-Meleagris_gallopavo -0.8768 0.7495 -2.6638 -0.7947 0.3910
## Tree_Density-Sciurus_carolinensis -0.9016 0.7994 -2.8016 -0.7748 0.3170
## Tree_Density-Vulpes_vulpes -0.5883 0.8068 -2.3195 -0.5458 0.9128
## Tree_Density-Sus_scrofa -0.9312 0.8944 -3.1590 -0.7620 0.4292
## Avg_Canopy_Cover-Canis_latrans 0.0155 0.4941 -0.9818 0.0165 0.9605
## Avg_Canopy_Cover-Lynx_rufus 0.6369 0.6516 -0.5843 0.6035 2.0545
## Avg_Canopy_Cover-Didelphis_virginiana 1.3203 0.5283 0.3851 1.2776 2.4721
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8731 0.9048 0.6519 1.7083 4.1076
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4963 0.7718 0.3184 1.3739 3.3795
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2725 0.5327 0.3491 1.2306 2.4547
## Avg_Canopy_Cover-Vulpes_vulpes 1.0890 0.6822 -0.0559 1.0328 2.5548
## Avg_Canopy_Cover-Sus_scrofa 1.2152 0.5846 0.2368 1.1706 2.5057
## Rhat ESS
## (Intercept)-Canis_latrans 1.0232 441
## (Intercept)-Lynx_rufus 1.0578 305
## (Intercept)-Didelphis_virginiana 1.0054 675
## (Intercept)-Sylvilagus_floridanus 1.0029 1011
## (Intercept)-Meleagris_gallopavo 1.0060 840
## (Intercept)-Sciurus_carolinensis 1.0005 905
## (Intercept)-Vulpes_vulpes 1.0045 414
## (Intercept)-Sus_scrofa 1.0042 510
## Tree_Density-Canis_latrans 1.0067 1093
## Tree_Density-Lynx_rufus 1.0090 644
## Tree_Density-Didelphis_virginiana 1.0074 528
## Tree_Density-Sylvilagus_floridanus 1.0044 651
## Tree_Density-Meleagris_gallopavo 1.0019 817
## Tree_Density-Sciurus_carolinensis 1.0059 730
## Tree_Density-Vulpes_vulpes 1.0025 794
## Tree_Density-Sus_scrofa 1.0160 626
## Avg_Canopy_Cover-Canis_latrans 1.0018 921
## Avg_Canopy_Cover-Lynx_rufus 1.0110 885
## Avg_Canopy_Cover-Didelphis_virginiana 1.0143 938
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0246 409
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0101 444
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0059 1044
## Avg_Canopy_Cover-Vulpes_vulpes 1.0260 604
## Avg_Canopy_Cover-Sus_scrofa 1.0173 1111
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6768 0.1772 -3.0240 -2.6758 -2.3438 1.0156
## (Intercept)-Lynx_rufus -3.4331 0.3000 -4.0639 -3.4186 -2.8850 1.0292
## (Intercept)-Didelphis_virginiana -2.4566 0.2589 -2.9805 -2.4516 -1.9729 1.0066
## (Intercept)-Sylvilagus_floridanus -3.1062 0.2509 -3.6174 -3.1066 -2.6370 1.0148
## (Intercept)-Meleagris_gallopavo -3.3479 0.2898 -3.9823 -3.3449 -2.8028 1.0372
## (Intercept)-Sciurus_carolinensis -2.5699 0.2626 -3.0824 -2.5688 -2.0634 1.0020
## (Intercept)-Vulpes_vulpes -3.6304 0.5870 -4.9757 -3.5385 -2.7087 1.0031
## (Intercept)-Sus_scrofa -2.9648 0.3822 -3.7912 -2.9369 -2.2591 1.0148
## week-Canis_latrans 0.0682 0.1350 -0.2026 0.0699 0.3316 1.0057
## week-Lynx_rufus -0.0437 0.2037 -0.4607 -0.0359 0.3246 1.0002
## week-Didelphis_virginiana -0.2411 0.2332 -0.7448 -0.2250 0.1674 1.0035
## week-Sylvilagus_floridanus -0.1706 0.2180 -0.6284 -0.1612 0.2202 1.0089
## week-Meleagris_gallopavo -0.2918 0.2609 -0.8852 -0.2749 0.1566 1.0158
## week-Sciurus_carolinensis 0.1492 0.1934 -0.2283 0.1487 0.5167 1.0041
## week-Vulpes_vulpes -0.1401 0.3011 -0.8025 -0.1150 0.3901 1.0095
## week-Sus_scrofa 0.1053 0.2553 -0.4154 0.1080 0.6082 1.0067
## ESS
## (Intercept)-Canis_latrans 889
## (Intercept)-Lynx_rufus 419
## (Intercept)-Didelphis_virginiana 1148
## (Intercept)-Sylvilagus_floridanus 725
## (Intercept)-Meleagris_gallopavo 497
## (Intercept)-Sciurus_carolinensis 1159
## (Intercept)-Vulpes_vulpes 179
## (Intercept)-Sus_scrofa 808
## week-Canis_latrans 1752
## week-Lynx_rufus 981
## week-Didelphis_virginiana 1269
## week-Sylvilagus_floridanus 1044
## week-Meleagris_gallopavo 680
## week-Sciurus_carolinensis 1760
## week-Vulpes_vulpes 985
## week-Sus_scrofa 1581
# 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 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_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): 0.5017
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8171 0.5008 -2.8549 -1.8186 -0.8146 1.0137 317
## Avg_Cogongrass_Cover -0.8583 0.4493 -1.7468 -0.8514 -0.0036 1.0021 529
## I(Avg_Cogongrass_Cover^2) 0.8394 0.4315 0.0216 0.8057 1.7517 1.0135 605
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7590 1.0314 0.0550 0.4330 3.3471 1.0133 848
## Avg_Cogongrass_Cover 0.5175 0.7123 0.0434 0.2920 2.4047 1.0374 981
## I(Avg_Cogongrass_Cover^2) 0.8366 1.1845 0.0524 0.4184 4.0753 1.0138 407
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1216 1.0832 0.0761 0.8224 4.0828 1.0342 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9423 0.2319 -3.4153 -2.9417 -2.4726 1.0002 1014
## week -0.0707 0.1661 -0.4377 -0.0660 0.2518 1.0042 1233
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3533 0.3991 0.0585 0.2491 1.2899 1.0356 992
## week 0.1401 0.1283 0.0280 0.1006 0.5035 1.0041 1627
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.2683 0.7219 -2.6138 -1.2912
## (Intercept)-Lynx_rufus -1.7794 0.6907 -3.1523 -1.7754
## (Intercept)-Didelphis_virginiana -1.9968 0.6421 -3.3525 -1.9683
## (Intercept)-Sylvilagus_floridanus -1.5977 0.6762 -2.8982 -1.6029
## (Intercept)-Meleagris_gallopavo -1.4982 0.7139 -2.8982 -1.5170
## (Intercept)-Sciurus_carolinensis -2.2776 0.6907 -3.7370 -2.2458
## (Intercept)-Vulpes_vulpes -2.3369 0.7796 -4.1246 -2.2609
## (Intercept)-Sus_scrofa -2.2801 0.7403 -3.9694 -2.2141
## Avg_Cogongrass_Cover-Canis_latrans -0.5767 0.5725 -1.6636 -0.5974
## Avg_Cogongrass_Cover-Lynx_rufus -0.7423 0.6003 -1.8744 -0.7489
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4721 0.6024 -1.5869 -0.5038
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3245 0.6678 -2.7515 -1.2644
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1121 0.6277 -2.4249 -1.0735
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8689 0.5844 -2.1419 -0.8537
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8497 0.6425 -2.0948 -0.8387
## Avg_Cogongrass_Cover-Sus_scrofa -1.0763 0.6901 -2.6344 -1.0330
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6850 1.0466 0.3691 1.4122
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.3699 0.6064 0.4188 1.2863
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6258 0.4993 -0.2403 0.5936
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8856 0.5322 -0.0077 0.8299
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2373 0.6673 -1.1798 0.2641
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0070 0.4241 0.2373 0.9836
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9066 0.4881 0.0785 0.8598
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2450 0.6702 -1.4414 0.3442
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.1941 1.0321 427
## (Intercept)-Lynx_rufus -0.4424 1.0128 576
## (Intercept)-Didelphis_virginiana -0.7823 1.0013 870
## (Intercept)-Sylvilagus_floridanus -0.2531 1.0214 511
## (Intercept)-Meleagris_gallopavo -0.0559 1.0268 414
## (Intercept)-Sciurus_carolinensis -1.0181 1.0036 664
## (Intercept)-Vulpes_vulpes -0.9841 1.0040 473
## (Intercept)-Sus_scrofa -0.9930 1.0021 740
## Avg_Cogongrass_Cover-Canis_latrans 0.6156 1.0006 1067
## Avg_Cogongrass_Cover-Lynx_rufus 0.4500 1.0021 806
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7963 1.0080 824
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1691 1.0043 605
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0320 1.0046 771
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2201 1.0065 704
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4009 1.0025 694
## Avg_Cogongrass_Cover-Sus_scrofa 0.1597 1.0107 783
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.2819 1.0224 272
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7658 1.0144 511
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.7149 1.0039 668
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.0413 1.0317 546
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5183 1.0061 307
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9411 1.0067 661
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0244 1.0152 734
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3212 1.0094 586
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6733 0.1658 -3.0076 -2.6675 -2.3535 1.0192
## (Intercept)-Lynx_rufus -3.3008 0.2678 -3.8501 -3.2948 -2.8169 1.0030
## (Intercept)-Didelphis_virginiana -2.5082 0.2647 -3.0580 -2.5024 -2.0286 1.0069
## (Intercept)-Sylvilagus_floridanus -3.1488 0.2592 -3.6709 -3.1413 -2.6678 1.0026
## (Intercept)-Meleagris_gallopavo -3.2939 0.3011 -3.9387 -3.2688 -2.7606 1.0000
## (Intercept)-Sciurus_carolinensis -2.5891 0.2607 -3.0986 -2.5850 -2.0828 0.9997
## (Intercept)-Vulpes_vulpes -3.4890 0.4976 -4.6166 -3.4347 -2.6706 1.0059
## (Intercept)-Sus_scrofa -2.9796 0.3739 -3.7629 -2.9648 -2.2764 1.0041
## week-Canis_latrans 0.0741 0.1350 -0.2015 0.0792 0.3280 1.0013
## week-Lynx_rufus -0.0315 0.1986 -0.4468 -0.0269 0.3335 1.0030
## week-Didelphis_virginiana -0.2489 0.2360 -0.7632 -0.2278 0.1599 1.0021
## week-Sylvilagus_floridanus -0.1725 0.2195 -0.6496 -0.1602 0.2235 1.0073
## week-Meleagris_gallopavo -0.3118 0.2717 -0.9257 -0.2826 0.1529 1.0030
## week-Sciurus_carolinensis 0.1602 0.1947 -0.2343 0.1598 0.5231 1.0049
## week-Vulpes_vulpes -0.1341 0.2946 -0.7771 -0.1143 0.3932 1.0017
## week-Sus_scrofa 0.1068 0.2546 -0.4046 0.1072 0.6059 1.0061
## ESS
## (Intercept)-Canis_latrans 922
## (Intercept)-Lynx_rufus 560
## (Intercept)-Didelphis_virginiana 948
## (Intercept)-Sylvilagus_floridanus 665
## (Intercept)-Meleagris_gallopavo 415
## (Intercept)-Sciurus_carolinensis 1091
## (Intercept)-Vulpes_vulpes 305
## (Intercept)-Sus_scrofa 813
## week-Canis_latrans 1556
## week-Lynx_rufus 1099
## week-Didelphis_virginiana 1247
## week-Sylvilagus_floridanus 862
## week-Meleagris_gallopavo 731
## week-Sciurus_carolinensis 1788
## week-Vulpes_vulpes 1101
## week-Sus_scrofa 1771
# 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 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_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): 0.5482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8326 1.0191 -4.7769 -2.8484 -0.7583 1.0026 306
## Cogon_Patch_Size 0.1647 0.9105 -1.6935 0.1910 1.9316 1.0198 306
## Veg_shannon_index 1.0366 0.6169 -0.1620 1.0318 2.2763 1.0086 539
## total_shrub_cover -0.8721 0.7724 -2.4384 -0.8708 0.6359 1.0056 954
## Avg_Cogongrass_Cover -0.3541 1.0685 -2.4589 -0.3551 1.6954 1.0491 149
## Tree_Density -2.1112 0.9013 -3.9385 -2.0588 -0.4041 1.0028 192
## Avg_Canopy_Cover 1.8999 0.7636 0.4313 1.8792 3.5307 1.0169 463
## I(Avg_Cogongrass_Cover^2) 1.2950 0.8642 -0.4894 1.2854 2.9860 1.0073 438
## avg_veg_height -0.4849 0.5678 -1.6130 -0.4938 0.6883 1.0252 203
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5268 11.7411 0.1114 3.0771 37.0571 1.0011 188
## Cogon_Patch_Size 6.2608 9.9398 0.2117 3.1707 31.4662 1.0162 181
## Veg_shannon_index 1.7629 2.8506 0.0618 0.7886 9.2748 1.0126 368
## total_shrub_cover 4.4995 6.1946 0.2041 2.6812 20.3904 1.0125 267
## Avg_Cogongrass_Cover 1.5216 3.2741 0.0503 0.5410 8.9645 1.0512 526
## Tree_Density 2.1233 4.8351 0.0569 0.7106 12.7117 1.1370 287
## Avg_Canopy_Cover 4.0207 6.9941 0.1289 2.0227 19.9685 1.2970 124
## I(Avg_Cogongrass_Cover^2) 6.8431 13.4432 0.1179 2.7099 40.2586 1.0889 78
## avg_veg_height 0.5949 0.8997 0.0427 0.3226 2.9637 1.0486 808
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.077 2.8087 0.0716 1.0933 10.0048 1.0975 77
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0244 0.2893 -3.5707 -3.0296 -2.4305 1.0000 1526
## week -0.0820 0.1685 -0.4450 -0.0764 0.2417 1.0057 997
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5404 0.5164 0.0907 0.3901 1.9382 1.0183 618
## week 0.1405 0.1385 0.0280 0.1008 0.4956 1.0040 1563
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.9047 1.3889 -4.6473 -1.9525
## (Intercept)-Lynx_rufus -2.0304 1.8147 -5.0041 -2.1644
## (Intercept)-Didelphis_virginiana -4.2004 1.6148 -8.0635 -3.9686
## (Intercept)-Sylvilagus_floridanus -2.8980 1.4331 -5.9211 -2.8570
## (Intercept)-Meleagris_gallopavo -2.6315 1.4331 -5.5374 -2.6593
## (Intercept)-Sciurus_carolinensis -4.7027 1.6673 -8.5659 -4.4971
## (Intercept)-Vulpes_vulpes -4.6211 1.9781 -9.4593 -4.3578
## (Intercept)-Sus_scrofa -4.9570 2.1092 -10.0263 -4.5581
## Cogon_Patch_Size-Canis_latrans 2.2635 1.6697 -0.0387 1.9355
## Cogon_Patch_Size-Lynx_rufus 0.0450 1.9081 -3.5934 -0.0103
## Cogon_Patch_Size-Didelphis_virginiana 2.1933 1.3923 0.1531 1.9820
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6752 1.9302 -6.2771 -1.3899
## Cogon_Patch_Size-Meleagris_gallopavo 0.6030 1.4674 -1.8520 0.4456
## Cogon_Patch_Size-Sciurus_carolinensis -1.0394 1.4823 -4.6680 -0.8042
## Cogon_Patch_Size-Vulpes_vulpes -0.1888 2.2407 -4.1117 -0.3226
## Cogon_Patch_Size-Sus_scrofa -0.4819 1.6799 -4.3340 -0.2951
## Veg_shannon_index-Canis_latrans 1.5896 0.9316 0.1518 1.4431
## Veg_shannon_index-Lynx_rufus 1.1132 1.1617 -1.1771 1.0611
## Veg_shannon_index-Didelphis_virginiana 1.1656 0.8722 -0.4233 1.1258
## Veg_shannon_index-Sylvilagus_floridanus 1.0997 0.9049 -0.6143 1.0577
## Veg_shannon_index-Meleagris_gallopavo 1.6161 1.0624 -0.0617 1.4511
## Veg_shannon_index-Sciurus_carolinensis 0.0810 0.9837 -2.0917 0.1982
## Veg_shannon_index-Vulpes_vulpes 0.4996 1.1285 -1.9845 0.6240
## Veg_shannon_index-Sus_scrofa 1.8614 1.2260 0.0809 1.6338
## total_shrub_cover-Canis_latrans 0.0420 0.8776 -1.6016 0.0105
## total_shrub_cover-Lynx_rufus -2.4079 1.7236 -6.3324 -2.1296
## total_shrub_cover-Didelphis_virginiana -0.9298 0.9592 -2.9691 -0.8868
## total_shrub_cover-Sylvilagus_floridanus -0.5157 1.1671 -3.0808 -0.4568
## total_shrub_cover-Meleagris_gallopavo -3.5841 1.8080 -7.6632 -3.3541
## total_shrub_cover-Sciurus_carolinensis 0.1175 0.8949 -1.6701 0.1333
## total_shrub_cover-Vulpes_vulpes -1.0909 1.3555 -4.0792 -0.9597
## total_shrub_cover-Sus_scrofa 0.1304 1.1997 -2.1214 0.0674
## Avg_Cogongrass_Cover-Canis_latrans -0.4330 1.2979 -3.0893 -0.4100
## Avg_Cogongrass_Cover-Lynx_rufus -0.2325 1.4249 -3.0086 -0.2612
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0192 1.3906 -2.6407 -0.0283
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8439 1.4197 -3.9499 -0.7397
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6740 1.4078 -3.7661 -0.6352
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1765 1.3508 -2.9366 -0.1968
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.1191 1.4559 -2.9210 -0.2080
## Avg_Cogongrass_Cover-Sus_scrofa -0.5907 1.4682 -3.7987 -0.5286
## Tree_Density-Canis_latrans -2.8363 1.4437 -6.2991 -2.6412
## Tree_Density-Lynx_rufus -1.3670 1.5042 -3.9291 -1.4856
## Tree_Density-Didelphis_virginiana -2.3557 1.1549 -4.9614 -2.2367
## Tree_Density-Sylvilagus_floridanus -2.5291 1.3029 -5.4059 -2.3651
## Tree_Density-Meleagris_gallopavo -2.2295 1.3220 -4.9486 -2.1530
## Tree_Density-Sciurus_carolinensis -2.5575 1.3697 -5.8976 -2.3791
## Tree_Density-Vulpes_vulpes -2.2094 1.5371 -5.3975 -2.0999
## Tree_Density-Sus_scrofa -2.2459 1.3256 -5.1022 -2.1367
## Avg_Canopy_Cover-Canis_latrans 0.1996 0.9011 -1.5458 0.1883
## Avg_Canopy_Cover-Lynx_rufus 1.2629 1.4775 -1.4837 1.2035
## Avg_Canopy_Cover-Didelphis_virginiana 2.8860 1.1941 1.1447 2.6776
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9415 2.0230 1.1991 3.5557
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5227 1.5017 0.5548 2.2349
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4455 1.0722 0.8477 2.2687
## Avg_Canopy_Cover-Vulpes_vulpes 2.4001 1.4353 0.2398 2.1567
## Avg_Canopy_Cover-Sus_scrofa 2.1016 0.9977 0.4252 1.9976
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5238 2.3561 0.8921 2.8680
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.5949 2.1768 0.8075 3.1446
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1286 0.8771 -0.5366 1.1049
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4842 1.3804 -0.3195 1.2523
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.5473 2.0087 -5.6838 -0.1803
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8001 0.9370 0.2335 1.7076
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2694 1.3631 0.3013 2.0198
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.5076 2.3293 -6.0005 -0.0083
## avg_veg_height-Canis_latrans -0.6203 0.6617 -1.9608 -0.6040
## avg_veg_height-Lynx_rufus -0.6774 0.8992 -2.6664 -0.6282
## avg_veg_height-Didelphis_virginiana -0.5065 0.7115 -1.9662 -0.5023
## avg_veg_height-Sylvilagus_floridanus -0.4992 0.7495 -2.0278 -0.4955
## avg_veg_height-Meleagris_gallopavo -0.5464 0.8344 -2.2800 -0.5464
## avg_veg_height-Sciurus_carolinensis -0.0833 0.7654 -1.4605 -0.1514
## avg_veg_height-Vulpes_vulpes -0.6082 0.8330 -2.3461 -0.5906
## avg_veg_height-Sus_scrofa -0.4688 0.7771 -2.0496 -0.4761
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8726 1.0074 260
## (Intercept)-Lynx_rufus 1.7899 1.0201 174
## (Intercept)-Didelphis_virginiana -1.7262 1.0200 227
## (Intercept)-Sylvilagus_floridanus -0.1957 1.0212 282
## (Intercept)-Meleagris_gallopavo 0.2695 1.0042 333
## (Intercept)-Sciurus_carolinensis -2.0823 1.0299 179
## (Intercept)-Vulpes_vulpes -1.5819 1.0032 175
## (Intercept)-Sus_scrofa -1.8469 1.0284 181
## Cogon_Patch_Size-Canis_latrans 6.2922 1.0315 146
## Cogon_Patch_Size-Lynx_rufus 4.0479 1.0360 155
## Cogon_Patch_Size-Didelphis_virginiana 5.4405 1.0172 169
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3488 1.0138 246
## Cogon_Patch_Size-Meleagris_gallopavo 3.9820 1.0289 279
## Cogon_Patch_Size-Sciurus_carolinensis 1.1772 1.0023 379
## Cogon_Patch_Size-Vulpes_vulpes 4.5428 1.1324 115
## Cogon_Patch_Size-Sus_scrofa 2.2094 1.0023 410
## Veg_shannon_index-Canis_latrans 3.8185 1.0028 400
## Veg_shannon_index-Lynx_rufus 3.5926 1.0078 361
## Veg_shannon_index-Didelphis_virginiana 3.0502 1.0035 631
## Veg_shannon_index-Sylvilagus_floridanus 3.0347 1.0050 564
## Veg_shannon_index-Meleagris_gallopavo 4.0491 1.0004 444
## Veg_shannon_index-Sciurus_carolinensis 1.6961 1.0131 488
## Veg_shannon_index-Vulpes_vulpes 2.5200 1.0043 372
## Veg_shannon_index-Sus_scrofa 4.8127 1.0132 319
## total_shrub_cover-Canis_latrans 1.9007 1.0098 700
## total_shrub_cover-Lynx_rufus 0.0302 1.0215 192
## total_shrub_cover-Didelphis_virginiana 0.8787 1.0017 813
## total_shrub_cover-Sylvilagus_floridanus 1.7461 1.0074 638
## total_shrub_cover-Meleagris_gallopavo -0.8226 1.0037 213
## total_shrub_cover-Sciurus_carolinensis 1.8637 1.0103 796
## total_shrub_cover-Vulpes_vulpes 1.2897 1.0063 365
## total_shrub_cover-Sus_scrofa 2.7170 1.0050 742
## Avg_Cogongrass_Cover-Canis_latrans 2.1803 1.0184 184
## Avg_Cogongrass_Cover-Lynx_rufus 2.6166 1.0501 248
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.9559 1.0268 253
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6083 1.0110 206
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.9187 1.0217 220
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5855 1.0277 218
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.0091 1.0702 222
## Avg_Cogongrass_Cover-Sus_scrofa 2.0663 1.0223 177
## Tree_Density-Canis_latrans -0.5991 1.0218 220
## Tree_Density-Lynx_rufus 2.0608 1.0161 195
## Tree_Density-Didelphis_virginiana -0.4393 1.0266 243
## Tree_Density-Sylvilagus_floridanus -0.3942 1.0127 281
## Tree_Density-Meleagris_gallopavo 0.2538 1.0145 256
## Tree_Density-Sciurus_carolinensis -0.4517 1.0418 272
## Tree_Density-Vulpes_vulpes 0.5240 1.0594 206
## Tree_Density-Sus_scrofa 0.1683 1.0136 227
## Avg_Canopy_Cover-Canis_latrans 2.1803 1.0854 144
## Avg_Canopy_Cover-Lynx_rufus 4.4538 1.0431 223
## Avg_Canopy_Cover-Didelphis_virginiana 5.6931 1.0238 99
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.9335 1.0720 183
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1150 1.0377 190
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1293 1.0130 222
## Avg_Canopy_Cover-Vulpes_vulpes 6.0276 1.0285 235
## Avg_Canopy_Cover-Sus_scrofa 4.3964 1.0062 319
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 9.7150 1.1458 49
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 9.3502 1.0347 116
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.9714 1.0135 287
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.9306 1.0757 135
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.4215 1.0495 84
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.8649 1.0367 232
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.4426 1.0310 154
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.7451 1.0293 97
## avg_veg_height-Canis_latrans 0.6915 1.0098 348
## avg_veg_height-Lynx_rufus 1.0009 1.0287 325
## avg_veg_height-Didelphis_virginiana 0.9058 1.0085 386
## avg_veg_height-Sylvilagus_floridanus 0.9805 1.0234 360
## avg_veg_height-Meleagris_gallopavo 1.0976 1.0129 314
## avg_veg_height-Sciurus_carolinensis 1.5457 1.0151 501
## avg_veg_height-Vulpes_vulpes 1.0213 1.0222 352
## avg_veg_height-Sus_scrofa 1.1352 1.0195 424
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6640 0.1782 -3.0439 -2.6544 -2.3373 1.0241
## (Intercept)-Lynx_rufus -3.6201 0.2687 -4.1414 -3.6193 -3.0909 1.0082
## (Intercept)-Didelphis_virginiana -2.4576 0.2606 -3.0004 -2.4449 -1.9790 1.0226
## (Intercept)-Sylvilagus_floridanus -3.2153 0.2567 -3.7609 -3.1994 -2.7456 1.0035
## (Intercept)-Meleagris_gallopavo -3.3287 0.2776 -3.8931 -3.3250 -2.7931 1.0195
## (Intercept)-Sciurus_carolinensis -2.5801 0.2810 -3.1649 -2.5720 -2.0600 1.0031
## (Intercept)-Vulpes_vulpes -3.9075 0.5258 -4.9937 -3.8861 -2.9353 1.0268
## (Intercept)-Sus_scrofa -2.9990 0.4220 -3.9170 -2.9706 -2.2400 1.0009
## week-Canis_latrans 0.0689 0.1339 -0.2077 0.0734 0.3170 1.0007
## week-Lynx_rufus -0.0514 0.2051 -0.4896 -0.0367 0.3103 1.0032
## week-Didelphis_virginiana -0.2456 0.2389 -0.7653 -0.2230 0.1773 1.0010
## week-Sylvilagus_floridanus -0.1753 0.2208 -0.6345 -0.1565 0.2226 1.0001
## week-Meleagris_gallopavo -0.2968 0.2594 -0.8502 -0.2775 0.1477 1.0716
## week-Sciurus_carolinensis 0.1439 0.1976 -0.2618 0.1526 0.5052 1.0113
## week-Vulpes_vulpes -0.1615 0.2977 -0.8320 -0.1452 0.3706 1.0038
## week-Sus_scrofa 0.1056 0.2585 -0.3995 0.0986 0.5995 1.0122
## ESS
## (Intercept)-Canis_latrans 514
## (Intercept)-Lynx_rufus 294
## (Intercept)-Didelphis_virginiana 1203
## (Intercept)-Sylvilagus_floridanus 500
## (Intercept)-Meleagris_gallopavo 472
## (Intercept)-Sciurus_carolinensis 908
## (Intercept)-Vulpes_vulpes 201
## (Intercept)-Sus_scrofa 453
## week-Canis_latrans 1494
## week-Lynx_rufus 723
## week-Didelphis_virginiana 1283
## week-Sylvilagus_floridanus 789
## week-Meleagris_gallopavo 755
## week-Sciurus_carolinensis 1601
## week-Vulpes_vulpes 652
## week-Sus_scrofa 1609
# 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 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_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): 0.4183
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7929 0.4719 -1.8033 -0.7745 0.0952 1.0148 380
## Avg_Cogongrass_Cover 0.1695 0.3228 -0.4837 0.1759 0.7756 1.0085 543
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7051 0.8689 0.0521 0.4183 3.1475 1.0126 582
## Avg_Cogongrass_Cover 0.4537 0.7198 0.0422 0.2571 1.9626 1.0575 939
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7149 1.6802 0.1239 1.2395 6.2451 1.007 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1844 0.3006 -3.8029 -3.1831 -2.5982 1.0000 1152
## shrub_cover 0.1879 0.3756 -0.5670 0.1884 0.9320 1.0009 1205
## veg_height -0.0909 0.2238 -0.5379 -0.0875 0.3491 1.0012 1055
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5450 0.6299 0.0800 0.3742 1.9974 1.0071 557
## shrub_cover 0.8671 0.8724 0.1533 0.6409 2.9149 1.0200 1098
## veg_height 0.2720 0.2421 0.0554 0.2014 0.9301 1.0040 1420
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2154 0.6677 -1.4880 -0.2163
## (Intercept)-Lynx_rufus -0.5551 0.6991 -1.8883 -0.5727
## (Intercept)-Didelphis_virginiana -1.0546 0.5889 -2.2648 -1.0239
## (Intercept)-Sylvilagus_floridanus -0.6370 0.6139 -1.8474 -0.6349
## (Intercept)-Meleagris_gallopavo -0.4813 0.7109 -1.7723 -0.5505
## (Intercept)-Sciurus_carolinensis -1.1066 0.6199 -2.4497 -1.0767
## (Intercept)-Vulpes_vulpes -1.1518 0.7737 -2.9235 -1.0912
## (Intercept)-Sus_scrofa -1.2961 0.7138 -2.8554 -1.2391
## Avg_Cogongrass_Cover-Canis_latrans 0.4380 0.3986 -0.2757 0.3979
## Avg_Cogongrass_Cover-Lynx_rufus 0.4869 0.4589 -0.2936 0.4485
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3423 0.3906 -0.4240 0.3372
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2354 0.4980 -1.3396 -0.1887
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2705 0.6673 -1.7742 -0.2046
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3645 0.3864 -0.3820 0.3598
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3288 0.4900 -0.5821 0.3061
## Avg_Cogongrass_Cover-Sus_scrofa -0.0595 0.6043 -1.4435 0.0021
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0897 1.0019 356
## (Intercept)-Lynx_rufus 0.9174 1.0040 410
## (Intercept)-Didelphis_virginiana 0.0630 1.0093 602
## (Intercept)-Sylvilagus_floridanus 0.5753 1.0043 531
## (Intercept)-Meleagris_gallopavo 1.0893 1.0039 294
## (Intercept)-Sciurus_carolinensis 0.0372 1.0168 550
## (Intercept)-Vulpes_vulpes 0.2651 1.0229 422
## (Intercept)-Sus_scrofa -0.0295 1.0214 486
## Avg_Cogongrass_Cover-Canis_latrans 1.3021 1.0088 1648
## Avg_Cogongrass_Cover-Lynx_rufus 1.4922 1.0010 1292
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1276 1.0011 1576
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6317 1.0069 450
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9152 1.0153 425
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1677 1.0005 1395
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3770 1.0020 1226
## Avg_Cogongrass_Cover-Sus_scrofa 0.9829 1.0051 613
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7929 0.1898 -3.1704 -2.7892 -2.4314 1.0020
## (Intercept)-Lynx_rufus -3.5657 0.3175 -4.2157 -3.5556 -2.9644 1.0164
## (Intercept)-Didelphis_virginiana -2.6896 0.2896 -3.2553 -2.6919 -2.1282 1.0030
## (Intercept)-Sylvilagus_floridanus -3.1935 0.2687 -3.7545 -3.1837 -2.6953 1.0218
## (Intercept)-Meleagris_gallopavo -3.8628 0.4270 -4.7250 -3.8547 -3.0569 1.0282
## (Intercept)-Sciurus_carolinensis -2.7462 0.3107 -3.3746 -2.7432 -2.1556 1.0046
## (Intercept)-Vulpes_vulpes -3.8265 0.5787 -5.1330 -3.7766 -2.8496 1.0073
## (Intercept)-Sus_scrofa -3.3706 0.4913 -4.3993 -3.3545 -2.3969 1.0265
## shrub_cover-Canis_latrans -0.3096 0.2215 -0.7312 -0.3100 0.1136 1.0022
## shrub_cover-Lynx_rufus -0.2570 0.3737 -1.0132 -0.2560 0.4737 1.0077
## shrub_cover-Didelphis_virginiana 1.0343 0.3750 0.3536 1.0256 1.7840 1.0208
## shrub_cover-Sylvilagus_floridanus 0.2871 0.4312 -0.5287 0.2787 1.1236 1.0049
## shrub_cover-Meleagris_gallopavo -0.6849 0.3840 -1.4807 -0.6766 0.0695 1.0504
## shrub_cover-Sciurus_carolinensis 0.9166 0.4243 0.1070 0.9146 1.7573 1.0202
## shrub_cover-Vulpes_vulpes -0.1236 0.6103 -1.3587 -0.1228 1.0651 1.0112
## shrub_cover-Sus_scrofa 0.7181 0.7731 -0.7947 0.7124 2.2503 1.0143
## veg_height-Canis_latrans -0.6244 0.1925 -1.0212 -0.6208 -0.2604 1.0050
## veg_height-Lynx_rufus -0.0480 0.2521 -0.5582 -0.0428 0.4391 1.0042
## veg_height-Didelphis_virginiana 0.4382 0.2653 -0.0585 0.4359 0.9792 1.0011
## veg_height-Sylvilagus_floridanus 0.0957 0.2638 -0.4277 0.0978 0.5950 1.0038
## veg_height-Meleagris_gallopavo -0.2435 0.3724 -0.9396 -0.2452 0.5023 1.0023
## veg_height-Sciurus_carolinensis 0.0905 0.2208 -0.3280 0.0860 0.5420 1.0023
## veg_height-Vulpes_vulpes -0.1955 0.3341 -0.8737 -0.1852 0.4170 1.0139
## veg_height-Sus_scrofa -0.1940 0.3536 -0.9298 -0.1825 0.4905 1.0046
## ESS
## (Intercept)-Canis_latrans 700
## (Intercept)-Lynx_rufus 324
## (Intercept)-Didelphis_virginiana 714
## (Intercept)-Sylvilagus_floridanus 600
## (Intercept)-Meleagris_gallopavo 238
## (Intercept)-Sciurus_carolinensis 615
## (Intercept)-Vulpes_vulpes 221
## (Intercept)-Sus_scrofa 628
## shrub_cover-Canis_latrans 862
## shrub_cover-Lynx_rufus 374
## shrub_cover-Didelphis_virginiana 727
## shrub_cover-Sylvilagus_floridanus 366
## shrub_cover-Meleagris_gallopavo 303
## shrub_cover-Sciurus_carolinensis 678
## shrub_cover-Vulpes_vulpes 570
## shrub_cover-Sus_scrofa 721
## veg_height-Canis_latrans 735
## veg_height-Lynx_rufus 727
## veg_height-Didelphis_virginiana 922
## veg_height-Sylvilagus_floridanus 558
## veg_height-Meleagris_gallopavo 475
## veg_height-Sciurus_carolinensis 997
## veg_height-Vulpes_vulpes 509
## veg_height-Sus_scrofa 924
# 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 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_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): 0.5078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1296 1.0804 -3.1639 -1.1532 1.0980 1.0456 174
## Cogon_Patch_Size -0.0539 1.1031 -2.1852 -0.0767 2.2596 1.0635 228
## Veg_shannon_index 1.1555 0.9861 -0.7531 1.1218 3.2386 1.1509 147
## total_shrub_cover -1.3475 1.1504 -3.7659 -1.2906 0.8881 1.0705 542
## Avg_Cogongrass_Cover 1.6417 1.0322 -0.6112 1.6588 3.5575 1.0207 222
## Tree_Density -1.5384 1.1329 -3.6853 -1.5765 0.7680 1.1829 181
## Avg_Canopy_Cover 1.9903 1.2838 -0.9033 2.0741 4.3928 1.0425 763
## avg_veg_height -0.2088 0.8253 -1.7806 -0.2266 1.4756 1.1967 168
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.4254 19.3561 0.0856 3.1513 65.0048 2.9980 29
## Cogon_Patch_Size 8.1601 16.5239 0.0884 2.8820 47.7560 1.4176 157
## Veg_shannon_index 8.2139 25.4171 0.0683 1.4234 65.1621 2.3108 35
## total_shrub_cover 34.2024 84.8162 0.2623 7.5004 250.7498 1.3691 42
## Avg_Cogongrass_Cover 7.1292 35.8588 0.0643 1.1872 41.6689 1.8302 93
## Tree_Density 7.3257 19.4571 0.0851 2.0344 45.6707 1.6775 81
## Avg_Canopy_Cover 43.4143 100.7571 0.7735 11.4336 299.9148 1.7378 33
## avg_veg_height 2.1221 6.4082 0.0524 0.5654 17.9841 1.1782 96
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 17.1866 25.8112 0.1545 8.5203 97.7334 1.3996 25
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.3266 0.2840 -3.9009 -3.3289 -2.7579 1.0694 429
## shrub_cover 0.4917 0.4175 -0.3151 0.4886 1.3124 1.0335 600
## veg_height -0.0793 0.2418 -0.5618 -0.0799 0.3857 1.0177 1070
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5081 0.5063 0.0765 0.3572 1.8039 1.0162 529
## shrub_cover 1.1799 1.2407 0.2110 0.8644 3.9743 1.0608 739
## veg_height 0.3436 0.3269 0.0687 0.2540 1.1400 1.0109 1652
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6687 2.2916 -2.9473 0.3564
## (Intercept)-Lynx_rufus 0.0980 2.5810 -3.6674 -0.3647
## (Intercept)-Didelphis_virginiana -2.4208 2.0243 -7.3212 -2.1585
## (Intercept)-Sylvilagus_floridanus -0.8803 2.1232 -4.9301 -1.0155
## (Intercept)-Meleagris_gallopavo -0.9722 2.7672 -5.5385 -1.2652
## (Intercept)-Sciurus_carolinensis -2.3715 2.0395 -7.3018 -2.1180
## (Intercept)-Vulpes_vulpes -2.2763 2.3135 -7.6856 -2.0013
## (Intercept)-Sus_scrofa -3.1021 2.9119 -11.3986 -2.5866
## Cogon_Patch_Size-Canis_latrans 1.3999 1.9404 -1.0115 0.9216
## Cogon_Patch_Size-Lynx_rufus -0.0191 2.1469 -4.0418 -0.0923
## Cogon_Patch_Size-Didelphis_virginiana 1.8663 1.8458 -0.6942 1.4869
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9690 2.9764 -10.0025 -1.3698
## Cogon_Patch_Size-Meleagris_gallopavo 0.6282 2.3696 -2.5928 0.2580
## Cogon_Patch_Size-Sciurus_carolinensis -1.3141 2.2057 -6.8117 -0.9847
## Cogon_Patch_Size-Vulpes_vulpes -0.3661 2.5563 -5.2035 -0.4422
## Cogon_Patch_Size-Sus_scrofa -0.6877 2.3098 -5.4337 -0.5208
## Veg_shannon_index-Canis_latrans 1.8064 1.5177 -0.6361 1.6667
## Veg_shannon_index-Lynx_rufus 1.1434 2.2224 -3.5183 1.0836
## Veg_shannon_index-Didelphis_virginiana 2.2586 2.1518 -0.1413 1.7428
## Veg_shannon_index-Sylvilagus_floridanus 1.8823 1.7788 -0.6511 1.5209
## Veg_shannon_index-Meleagris_gallopavo 2.4227 2.5111 -0.5619 1.8601
## Veg_shannon_index-Sciurus_carolinensis -0.5369 2.8070 -8.3491 0.0545
## Veg_shannon_index-Vulpes_vulpes 0.0364 2.0849 -5.3410 0.3616
## Veg_shannon_index-Sus_scrofa 2.8390 2.8602 -0.1459 2.0906
## total_shrub_cover-Canis_latrans 2.4584 2.8567 -0.9721 1.7995
## total_shrub_cover-Lynx_rufus -4.5993 4.8038 -17.5972 -3.3050
## total_shrub_cover-Didelphis_virginiana -3.6241 4.1295 -16.9218 -2.3631
## total_shrub_cover-Sylvilagus_floridanus -2.5818 3.2858 -11.4403 -1.8401
## total_shrub_cover-Meleagris_gallopavo -4.3315 4.0455 -14.8080 -3.5257
## total_shrub_cover-Sciurus_carolinensis -3.0106 3.6903 -13.4097 -2.0329
## total_shrub_cover-Vulpes_vulpes -4.7256 6.8365 -24.1145 -2.6840
## total_shrub_cover-Sus_scrofa -2.5321 4.3282 -15.4708 -1.4446
## Avg_Cogongrass_Cover-Canis_latrans 2.6272 1.8751 0.1962 2.3375
## Avg_Cogongrass_Cover-Lynx_rufus 2.6668 1.9919 -0.0503 2.3284
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0783 1.7764 -0.6946 1.9621
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7271 1.8710 -3.8739 0.9987
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1118 2.1740 -4.3028 1.3961
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3578 2.1577 -0.6217 2.0956
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8473 2.8147 -0.2742 2.4145
## Avg_Cogongrass_Cover-Sus_scrofa 1.0724 2.5411 -4.9772 1.4624
## Tree_Density-Canis_latrans -3.2274 2.5040 -10.6208 -2.6239
## Tree_Density-Lynx_rufus -0.1100 2.0887 -3.4549 -0.3603
## Tree_Density-Didelphis_virginiana -2.0590 2.0644 -6.5182 -2.0136
## Tree_Density-Sylvilagus_floridanus -2.4032 2.2435 -7.6822 -2.2324
## Tree_Density-Meleagris_gallopavo -2.3156 2.4280 -8.5154 -2.0880
## Tree_Density-Sciurus_carolinensis -1.9541 2.3171 -7.0176 -1.9181
## Tree_Density-Vulpes_vulpes -1.3046 2.3349 -5.5836 -1.4995
## Tree_Density-Sus_scrofa -2.1310 2.2346 -7.3989 -1.9629
## Avg_Canopy_Cover-Canis_latrans -0.4939 1.2256 -3.5628 -0.3696
## Avg_Canopy_Cover-Lynx_rufus -0.1104 3.0291 -8.2252 0.2330
## Avg_Canopy_Cover-Didelphis_virginiana 6.7048 5.3905 1.8022 5.0455
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.2502 5.5326 1.8842 6.5872
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8475 4.2854 0.4994 3.4585
## Avg_Canopy_Cover-Sciurus_carolinensis 6.2978 4.8808 1.5274 4.9663
## Avg_Canopy_Cover-Vulpes_vulpes 5.3738 4.2463 0.7895 4.0565
## Avg_Canopy_Cover-Sus_scrofa 3.2800 2.5984 -0.0172 2.6726
## avg_veg_height-Canis_latrans -0.2161 0.9578 -2.0433 -0.2569
## avg_veg_height-Lynx_rufus -0.4871 1.3472 -3.3725 -0.4180
## avg_veg_height-Didelphis_virginiana -0.5273 1.2657 -3.0960 -0.4586
## avg_veg_height-Sylvilagus_floridanus -0.4479 1.2686 -3.0340 -0.4129
## avg_veg_height-Meleagris_gallopavo -0.3195 1.5398 -3.6480 -0.2235
## avg_veg_height-Sciurus_carolinensis 0.6010 1.7263 -1.5964 0.2784
## avg_veg_height-Vulpes_vulpes -0.0368 1.5533 -2.5361 -0.1791
## avg_veg_height-Sus_scrofa -0.2801 1.2570 -2.7750 -0.2791
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.4613 1.5497 68
## (Intercept)-Lynx_rufus 6.6521 1.8424 52
## (Intercept)-Didelphis_virginiana 0.7222 1.3947 119
## (Intercept)-Sylvilagus_floridanus 3.9260 1.0886 148
## (Intercept)-Meleagris_gallopavo 6.4156 1.3491 67
## (Intercept)-Sciurus_carolinensis 0.9592 1.1752 187
## (Intercept)-Vulpes_vulpes 1.4283 1.4715 162
## (Intercept)-Sus_scrofa 0.8607 1.6634 85
## Cogon_Patch_Size-Canis_latrans 6.5534 1.1947 94
## Cogon_Patch_Size-Lynx_rufus 4.7339 1.0803 189
## Cogon_Patch_Size-Didelphis_virginiana 6.7384 1.3439 81
## Cogon_Patch_Size-Sylvilagus_floridanus 1.9496 1.4224 109
## Cogon_Patch_Size-Meleagris_gallopavo 6.7989 1.1815 132
## Cogon_Patch_Size-Sciurus_carolinensis 2.2322 1.0945 187
## Cogon_Patch_Size-Vulpes_vulpes 5.0650 1.3774 114
## Cogon_Patch_Size-Sus_scrofa 3.4635 1.0563 207
## Veg_shannon_index-Canis_latrans 5.1868 1.1609 137
## Veg_shannon_index-Lynx_rufus 5.7308 1.1580 111
## Veg_shannon_index-Didelphis_virginiana 8.2054 1.2452 42
## Veg_shannon_index-Sylvilagus_floridanus 6.7181 1.1503 55
## Veg_shannon_index-Meleagris_gallopavo 9.3053 1.5246 54
## Veg_shannon_index-Sciurus_carolinensis 2.9290 1.4701 71
## Veg_shannon_index-Vulpes_vulpes 3.2899 1.2206 72
## Veg_shannon_index-Sus_scrofa 11.5288 1.5903 69
## total_shrub_cover-Canis_latrans 9.1067 1.6090 38
## total_shrub_cover-Lynx_rufus 1.2023 1.5970 27
## total_shrub_cover-Didelphis_virginiana 0.3124 1.1946 55
## total_shrub_cover-Sylvilagus_floridanus 2.1546 1.2255 64
## total_shrub_cover-Meleagris_gallopavo 1.2893 1.1727 57
## total_shrub_cover-Sciurus_carolinensis 1.3192 1.3985 44
## total_shrub_cover-Vulpes_vulpes 2.2277 1.4954 34
## total_shrub_cover-Sus_scrofa 2.9622 1.1336 50
## Avg_Cogongrass_Cover-Canis_latrans 7.4882 1.3795 105
## Avg_Cogongrass_Cover-Lynx_rufus 7.8987 1.1550 110
## Avg_Cogongrass_Cover-Didelphis_virginiana 5.5580 1.1450 229
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.7108 1.0204 248
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.5904 1.0775 240
## Avg_Cogongrass_Cover-Sciurus_carolinensis 7.3518 1.2910 66
## Avg_Cogongrass_Cover-Vulpes_vulpes 8.7945 1.4040 83
## Avg_Cogongrass_Cover-Sus_scrofa 4.6020 1.0945 224
## Tree_Density-Canis_latrans -0.2081 1.3521 115
## Tree_Density-Lynx_rufus 4.8766 1.0406 125
## Tree_Density-Didelphis_virginiana 2.0301 1.0815 182
## Tree_Density-Sylvilagus_floridanus 1.6040 1.1267 185
## Tree_Density-Meleagris_gallopavo 1.6911 1.4572 179
## Tree_Density-Sciurus_carolinensis 2.7813 1.1955 193
## Tree_Density-Vulpes_vulpes 3.7926 1.0791 180
## Tree_Density-Sus_scrofa 1.8118 1.1556 211
## Avg_Canopy_Cover-Canis_latrans 1.5384 1.3026 91
## Avg_Canopy_Cover-Lynx_rufus 4.9183 1.4161 56
## Avg_Canopy_Cover-Didelphis_virginiana 22.9367 1.6648 18
## Avg_Canopy_Cover-Sylvilagus_floridanus 22.4273 1.3221 23
## Avg_Canopy_Cover-Meleagris_gallopavo 17.7129 1.2610 55
## Avg_Canopy_Cover-Sciurus_carolinensis 21.9533 1.6741 17
## Avg_Canopy_Cover-Vulpes_vulpes 16.5818 1.2295 22
## Avg_Canopy_Cover-Sus_scrofa 10.0616 1.1774 125
## avg_veg_height-Canis_latrans 1.8562 1.0601 351
## avg_veg_height-Lynx_rufus 1.9750 1.0721 239
## avg_veg_height-Didelphis_virginiana 1.6535 1.0653 243
## avg_veg_height-Sylvilagus_floridanus 1.8496 1.1415 256
## avg_veg_height-Meleagris_gallopavo 2.4723 1.1367 157
## avg_veg_height-Sciurus_carolinensis 5.2014 1.2001 105
## avg_veg_height-Vulpes_vulpes 3.7985 1.1958 169
## avg_veg_height-Sus_scrofa 2.1458 1.1228 304
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8597 0.1869 -3.2291 -2.8528 -2.5013 1.0110
## (Intercept)-Lynx_rufus -3.6276 0.3215 -4.2941 -3.6057 -3.0466 1.1655
## (Intercept)-Didelphis_virginiana -2.8537 0.2960 -3.4299 -2.8512 -2.2774 1.0465
## (Intercept)-Sylvilagus_floridanus -3.2603 0.2426 -3.7522 -3.2573 -2.7989 1.0138
## (Intercept)-Meleagris_gallopavo -3.8186 0.4371 -4.7390 -3.8005 -3.0056 1.1812
## (Intercept)-Sciurus_carolinensis -3.0132 0.3259 -3.6742 -2.9961 -2.4125 1.0671
## (Intercept)-Vulpes_vulpes -4.1088 0.5281 -5.2656 -4.0829 -3.1645 1.0659
## (Intercept)-Sus_scrofa -3.6764 0.5064 -4.6697 -3.6828 -2.7007 1.0743
## shrub_cover-Canis_latrans -0.4527 0.2328 -0.8965 -0.4610 0.0430 1.1243
## shrub_cover-Lynx_rufus 0.0745 0.4128 -0.8450 0.1038 0.8094 1.0644
## shrub_cover-Didelphis_virginiana 1.2972 0.4028 0.5319 1.2909 2.0918 1.0774
## shrub_cover-Sylvilagus_floridanus 0.7056 0.4169 -0.1356 0.7108 1.5308 1.0666
## shrub_cover-Meleagris_gallopavo -0.5470 0.4422 -1.4827 -0.5337 0.3108 1.1226
## shrub_cover-Sciurus_carolinensis 1.3257 0.4287 0.4644 1.3365 2.1973 1.0236
## shrub_cover-Vulpes_vulpes 0.5068 0.6609 -0.8214 0.5003 1.8107 1.1967
## shrub_cover-Sus_scrofa 1.2721 0.8938 -0.5500 1.2904 3.0158 1.0375
## veg_height-Canis_latrans -0.6726 0.1886 -1.0348 -0.6752 -0.3124 1.0208
## veg_height-Lynx_rufus 0.0343 0.2493 -0.4757 0.0376 0.5208 1.0574
## veg_height-Didelphis_virginiana 0.5223 0.2738 0.0131 0.5164 1.0833 1.0120
## veg_height-Sylvilagus_floridanus 0.1222 0.2524 -0.3863 0.1305 0.5943 1.0076
## veg_height-Meleagris_gallopavo -0.3242 0.4010 -1.1454 -0.3107 0.4249 1.0526
## veg_height-Sciurus_carolinensis 0.1801 0.2443 -0.2830 0.1727 0.6758 1.0138
## veg_height-Vulpes_vulpes -0.2783 0.3738 -1.0973 -0.2617 0.4353 1.0123
## veg_height-Sus_scrofa -0.2411 0.3643 -0.9583 -0.2355 0.4600 1.0420
## ESS
## (Intercept)-Canis_latrans 497
## (Intercept)-Lynx_rufus 135
## (Intercept)-Didelphis_virginiana 312
## (Intercept)-Sylvilagus_floridanus 656
## (Intercept)-Meleagris_gallopavo 226
## (Intercept)-Sciurus_carolinensis 265
## (Intercept)-Vulpes_vulpes 227
## (Intercept)-Sus_scrofa 194
## shrub_cover-Canis_latrans 243
## shrub_cover-Lynx_rufus 243
## shrub_cover-Didelphis_virginiana 291
## shrub_cover-Sylvilagus_floridanus 296
## shrub_cover-Meleagris_gallopavo 168
## shrub_cover-Sciurus_carolinensis 262
## shrub_cover-Vulpes_vulpes 170
## shrub_cover-Sus_scrofa 144
## veg_height-Canis_latrans 580
## veg_height-Lynx_rufus 549
## veg_height-Didelphis_virginiana 849
## veg_height-Sylvilagus_floridanus 601
## veg_height-Meleagris_gallopavo 282
## veg_height-Sciurus_carolinensis 670
## veg_height-Vulpes_vulpes 451
## veg_height-Sus_scrofa 800
# 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 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_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): 0.488
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5863 0.5515 -1.6521 -0.5959 0.5276 1.0215 168
## Avg_Cogongrass_Cover 0.0500 0.4748 -0.9191 0.0638 1.0004 1.0070 445
## total_shrub_cover -1.0889 0.6595 -2.5315 -1.0369 0.0672 1.0233 190
## avg_veg_height 0.1022 0.4575 -0.8182 0.1057 1.0588 1.0247 317
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7929 1.2204 0.0476 0.4101 3.9801 1.0418 671
## Avg_Cogongrass_Cover 0.7595 1.2716 0.0486 0.3779 3.7360 1.0105 549
## total_shrub_cover 1.7725 2.4394 0.0878 1.0220 8.1445 1.1567 274
## avg_veg_height 0.4729 0.7337 0.0405 0.2591 2.2140 1.1512 734
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.4654 3.2308 0.2856 2.4928 12.8962 1.1929 113
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2608 0.2722 -3.8109 -3.2545 -2.7159 1.0679 265
## shrub_cover 0.5400 0.4135 -0.2666 0.5491 1.3253 1.0028 478
## veg_height -0.0738 0.2324 -0.5583 -0.0737 0.4039 1.0020 910
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3530 0.4229 0.0479 0.2310 1.4631 1.0146 613
## shrub_cover 1.0253 0.9704 0.1754 0.7542 3.4008 1.0255 676
## veg_height 0.2921 0.2703 0.0572 0.2133 0.9669 1.0143 1192
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0947 0.7897 -1.5779 -0.1407
## (Intercept)-Lynx_rufus -0.4512 0.7670 -1.9590 -0.4691
## (Intercept)-Didelphis_virginiana -0.6926 0.7301 -2.1212 -0.7073
## (Intercept)-Sylvilagus_floridanus -0.2635 0.8127 -1.7243 -0.3305
## (Intercept)-Meleagris_gallopavo -0.6589 0.7470 -2.1292 -0.6584
## (Intercept)-Sciurus_carolinensis -0.7597 0.7773 -2.4388 -0.7398
## (Intercept)-Vulpes_vulpes -0.9227 0.9366 -2.8993 -0.8767
## (Intercept)-Sus_scrofa -0.9791 0.8358 -2.7628 -0.9186
## Avg_Cogongrass_Cover-Canis_latrans 0.4645 0.6446 -0.6332 0.4021
## Avg_Cogongrass_Cover-Lynx_rufus 0.4924 0.6932 -0.6915 0.4287
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2312 0.6297 -0.9716 0.2104
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4814 0.7014 -1.9951 -0.4136
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4675 0.8382 -2.4619 -0.3614
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0143 0.5906 -1.2151 0.0292
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3123 0.6843 -0.9450 0.2838
## Avg_Cogongrass_Cover-Sus_scrofa -0.1779 0.8186 -2.0163 -0.1073
## total_shrub_cover-Canis_latrans 0.2707 0.8532 -1.3075 0.2082
## total_shrub_cover-Lynx_rufus -1.6432 1.0099 -3.9992 -1.5112
## total_shrub_cover-Didelphis_virginiana -1.2314 0.8901 -3.3068 -1.1147
## total_shrub_cover-Sylvilagus_floridanus -1.5930 1.1438 -4.1697 -1.4591
## total_shrub_cover-Meleagris_gallopavo -1.9101 1.0119 -4.2680 -1.7819
## total_shrub_cover-Sciurus_carolinensis -1.2963 0.9857 -3.6306 -1.1524
## total_shrub_cover-Vulpes_vulpes -1.1924 1.3368 -4.0184 -1.0338
## total_shrub_cover-Sus_scrofa -0.7868 1.0503 -2.9506 -0.7340
## avg_veg_height-Canis_latrans 0.1219 0.5568 -0.9848 0.1227
## avg_veg_height-Lynx_rufus 0.0174 0.6872 -1.3715 0.0180
## avg_veg_height-Didelphis_virginiana -0.0184 0.5895 -1.2073 -0.0115
## avg_veg_height-Sylvilagus_floridanus 0.0476 0.6049 -1.1475 0.0383
## avg_veg_height-Meleagris_gallopavo -0.1316 0.7834 -1.7918 -0.1034
## avg_veg_height-Sciurus_carolinensis 0.5352 0.5990 -0.5459 0.4979
## avg_veg_height-Vulpes_vulpes 0.0328 0.6656 -1.3965 0.0588
## avg_veg_height-Sus_scrofa 0.1854 0.6511 -1.0521 0.1659
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6438 1.0355 298
## (Intercept)-Lynx_rufus 1.1430 1.0112 406
## (Intercept)-Didelphis_virginiana 0.7973 1.0090 316
## (Intercept)-Sylvilagus_floridanus 1.5085 1.0025 315
## (Intercept)-Meleagris_gallopavo 0.8053 1.0102 410
## (Intercept)-Sciurus_carolinensis 0.7190 1.0366 345
## (Intercept)-Vulpes_vulpes 0.7940 1.0359 263
## (Intercept)-Sus_scrofa 0.4945 1.0235 312
## Avg_Cogongrass_Cover-Canis_latrans 1.8907 1.0060 396
## Avg_Cogongrass_Cover-Lynx_rufus 2.1213 1.0188 497
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5675 1.0144 558
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7126 1.0036 561
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8568 1.0079 402
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1430 1.0073 649
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7462 1.0127 666
## Avg_Cogongrass_Cover-Sus_scrofa 1.2200 1.0254 574
## total_shrub_cover-Canis_latrans 2.2048 1.0597 290
## total_shrub_cover-Lynx_rufus -0.0284 1.0186 292
## total_shrub_cover-Didelphis_virginiana 0.2001 1.0099 277
## total_shrub_cover-Sylvilagus_floridanus 0.2583 1.0390 172
## total_shrub_cover-Meleagris_gallopavo -0.3253 1.0171 392
## total_shrub_cover-Sciurus_carolinensis 0.2210 1.0673 259
## total_shrub_cover-Vulpes_vulpes 0.9512 1.0759 167
## total_shrub_cover-Sus_scrofa 1.2256 1.0133 304
## avg_veg_height-Canis_latrans 1.2182 1.0059 461
## avg_veg_height-Lynx_rufus 1.3644 1.0083 436
## avg_veg_height-Didelphis_virginiana 1.1151 1.0129 389
## avg_veg_height-Sylvilagus_floridanus 1.2865 1.0086 540
## avg_veg_height-Meleagris_gallopavo 1.3692 1.0238 371
## avg_veg_height-Sciurus_carolinensis 1.8305 1.0590 668
## avg_veg_height-Vulpes_vulpes 1.3222 1.0099 442
## avg_veg_height-Sus_scrofa 1.5981 1.0315 500
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8729 0.1981 -3.2684 -2.8695 -2.4918 1.0085
## (Intercept)-Lynx_rufus -3.4779 0.3006 -4.1438 -3.4591 -2.9387 1.0373
## (Intercept)-Didelphis_virginiana -2.9397 0.3234 -3.5924 -2.9364 -2.3059 1.0129
## (Intercept)-Sylvilagus_floridanus -3.3040 0.2587 -3.8216 -3.2993 -2.8074 1.0617
## (Intercept)-Meleagris_gallopavo -3.5632 0.4267 -4.4784 -3.5437 -2.8020 1.0134
## (Intercept)-Sciurus_carolinensis -3.0104 0.3261 -3.6353 -3.0150 -2.3467 1.0353
## (Intercept)-Vulpes_vulpes -3.7857 0.5540 -5.0104 -3.7059 -2.8776 1.0651
## (Intercept)-Sus_scrofa -3.5383 0.4536 -4.5013 -3.5153 -2.6890 1.0533
## shrub_cover-Canis_latrans -0.3023 0.2649 -0.8095 -0.3110 0.2200 1.0480
## shrub_cover-Lynx_rufus 0.1470 0.3755 -0.6423 0.1655 0.8437 1.0169
## shrub_cover-Didelphis_virginiana 1.3944 0.4607 0.5538 1.3695 2.3354 1.0155
## shrub_cover-Sylvilagus_floridanus 0.7623 0.4503 -0.2369 0.7963 1.5850 1.0278
## shrub_cover-Meleagris_gallopavo -0.3529 0.4396 -1.2399 -0.3355 0.4947 1.0024
## shrub_cover-Sciurus_carolinensis 1.3591 0.4430 0.4615 1.3610 2.2332 1.0414
## shrub_cover-Vulpes_vulpes 0.4096 0.7366 -1.1287 0.4354 1.7679 1.0067
## shrub_cover-Sus_scrofa 1.0988 0.7423 -0.4604 1.1138 2.5384 1.0293
## veg_height-Canis_latrans -0.6539 0.1982 -1.0504 -0.6511 -0.2719 1.0009
## veg_height-Lynx_rufus 0.0086 0.2527 -0.5041 0.0143 0.5022 1.0031
## veg_height-Didelphis_virginiana 0.4520 0.2918 -0.0781 0.4359 1.0680 1.0293
## veg_height-Sylvilagus_floridanus 0.0423 0.2608 -0.4611 0.0296 0.5799 1.0054
## veg_height-Meleagris_gallopavo -0.2242 0.4342 -1.0892 -0.2381 0.6744 1.0171
## veg_height-Sciurus_carolinensis 0.1585 0.2406 -0.2944 0.1457 0.6616 1.0048
## veg_height-Vulpes_vulpes -0.1717 0.3287 -0.8492 -0.1602 0.4437 1.0027
## veg_height-Sus_scrofa -0.2197 0.3385 -0.9047 -0.2065 0.4053 1.0034
## ESS
## (Intercept)-Canis_latrans 440
## (Intercept)-Lynx_rufus 460
## (Intercept)-Didelphis_virginiana 261
## (Intercept)-Sylvilagus_floridanus 620
## (Intercept)-Meleagris_gallopavo 302
## (Intercept)-Sciurus_carolinensis 314
## (Intercept)-Vulpes_vulpes 194
## (Intercept)-Sus_scrofa 306
## shrub_cover-Canis_latrans 420
## shrub_cover-Lynx_rufus 396
## shrub_cover-Didelphis_virginiana 191
## shrub_cover-Sylvilagus_floridanus 216
## shrub_cover-Meleagris_gallopavo 396
## shrub_cover-Sciurus_carolinensis 257
## shrub_cover-Vulpes_vulpes 191
## shrub_cover-Sus_scrofa 287
## veg_height-Canis_latrans 615
## veg_height-Lynx_rufus 709
## veg_height-Didelphis_virginiana 598
## veg_height-Sylvilagus_floridanus 498
## veg_height-Meleagris_gallopavo 378
## veg_height-Sciurus_carolinensis 684
## veg_height-Vulpes_vulpes 608
## veg_height-Sus_scrofa 932
# 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 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_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): 0.4262
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5613 0.4061 -1.3465 -0.5608 0.2549 1.0139 997
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9676 1.0856 0.1041 0.6539 3.8227 1.0084 1055
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1927 0.3215 -3.8215 -3.1907 -2.5574 1.0392 704
## shrub_cover 0.1547 0.3909 -0.6317 0.1444 0.9423 1.0028 963
## veg_height -0.1017 0.2226 -0.5417 -0.1027 0.3456 1.0112 1083
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6485 0.6577 0.0924 0.4630 2.4522 1.0191 436
## shrub_cover 1.0148 1.0571 0.1507 0.7147 3.6570 1.0217 690
## veg_height 0.2879 0.2939 0.0564 0.2180 0.9321 1.0119 1196
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2141 0.4094 -0.5412 0.1877 1.0532 1.0066
## (Intercept)-Lynx_rufus 0.0450 0.5837 -0.9213 -0.0058 1.3924 1.0201
## (Intercept)-Didelphis_virginiana -1.0653 0.4331 -1.9828 -1.0435 -0.2260 1.0013
## (Intercept)-Sylvilagus_floridanus -0.3961 0.4343 -1.1902 -0.4143 0.4885 1.0161
## (Intercept)-Meleagris_gallopavo 0.0896 0.6994 -1.0591 0.0030 1.7269 1.0057
## (Intercept)-Sciurus_carolinensis -1.0657 0.4245 -1.9616 -1.0473 -0.2838 1.0013
## (Intercept)-Vulpes_vulpes -1.1353 0.7010 -2.4956 -1.1401 0.3355 1.0172
## (Intercept)-Sus_scrofa -1.3791 0.5748 -2.5642 -1.3568 -0.3420 1.0040
## ESS
## (Intercept)-Canis_latrans 1276
## (Intercept)-Lynx_rufus 364
## (Intercept)-Didelphis_virginiana 1388
## (Intercept)-Sylvilagus_floridanus 1176
## (Intercept)-Meleagris_gallopavo 345
## (Intercept)-Sciurus_carolinensis 1569
## (Intercept)-Vulpes_vulpes 280
## (Intercept)-Sus_scrofa 750
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7614 0.1848 -3.1398 -2.7596 -2.4221 1.0125
## (Intercept)-Lynx_rufus -3.6205 0.3388 -4.2795 -3.6167 -2.9785 1.0665
## (Intercept)-Didelphis_virginiana -2.6622 0.2846 -3.2491 -2.6508 -2.1266 1.0235
## (Intercept)-Sylvilagus_floridanus -3.1809 0.2720 -3.7362 -3.1756 -2.6823 1.0549
## (Intercept)-Meleagris_gallopavo -4.0067 0.4762 -4.9068 -4.0333 -3.0736 1.0179
## (Intercept)-Sciurus_carolinensis -2.7113 0.3150 -3.3576 -2.7079 -2.1101 1.0087
## (Intercept)-Vulpes_vulpes -3.8987 0.6097 -5.2556 -3.8500 -2.8341 1.0789
## (Intercept)-Sus_scrofa -3.4221 0.5382 -4.5188 -3.4164 -2.3773 1.0484
## shrub_cover-Canis_latrans -0.3344 0.2238 -0.7929 -0.3324 0.0769 1.0009
## shrub_cover-Lynx_rufus -0.3749 0.3952 -1.1649 -0.3743 0.4058 1.0364
## shrub_cover-Didelphis_virginiana 1.0390 0.3980 0.3020 1.0311 1.8269 1.0162
## shrub_cover-Sylvilagus_floridanus 0.2394 0.4319 -0.5536 0.2279 1.1286 1.0182
## shrub_cover-Meleagris_gallopavo -0.7984 0.4285 -1.6773 -0.8009 0.0314 1.0118
## shrub_cover-Sciurus_carolinensis 0.8725 0.4567 0.0085 0.8629 1.7972 1.0159
## shrub_cover-Vulpes_vulpes -0.2375 0.6664 -1.6157 -0.2130 1.0213 1.0020
## shrub_cover-Sus_scrofa 0.7855 0.8844 -0.9045 0.7591 2.6484 1.0481
## veg_height-Canis_latrans -0.6195 0.1896 -1.0065 -0.6160 -0.2574 1.0040
## veg_height-Lynx_rufus -0.0156 0.2603 -0.5375 -0.0164 0.4808 1.0013
## veg_height-Didelphis_virginiana 0.4370 0.2619 -0.0548 0.4316 0.9657 1.0055
## veg_height-Sylvilagus_floridanus 0.0925 0.2577 -0.3870 0.0915 0.6015 1.0491
## veg_height-Meleagris_gallopavo -0.3673 0.3896 -1.1422 -0.3653 0.3967 1.0038
## veg_height-Sciurus_carolinensis 0.0777 0.2147 -0.3394 0.0748 0.4961 1.0021
## veg_height-Vulpes_vulpes -0.2032 0.3287 -0.8951 -0.1850 0.4218 1.0464
## veg_height-Sus_scrofa -0.2380 0.3475 -0.9382 -0.2346 0.4367 1.0082
## ESS
## (Intercept)-Canis_latrans 705
## (Intercept)-Lynx_rufus 295
## (Intercept)-Didelphis_virginiana 818
## (Intercept)-Sylvilagus_floridanus 707
## (Intercept)-Meleagris_gallopavo 154
## (Intercept)-Sciurus_carolinensis 817
## (Intercept)-Vulpes_vulpes 175
## (Intercept)-Sus_scrofa 423
## shrub_cover-Canis_latrans 905
## shrub_cover-Lynx_rufus 350
## shrub_cover-Didelphis_virginiana 622
## shrub_cover-Sylvilagus_floridanus 446
## shrub_cover-Meleagris_gallopavo 229
## shrub_cover-Sciurus_carolinensis 617
## shrub_cover-Vulpes_vulpes 467
## shrub_cover-Sus_scrofa 492
## veg_height-Canis_latrans 728
## veg_height-Lynx_rufus 673
## veg_height-Didelphis_virginiana 938
## veg_height-Sylvilagus_floridanus 664
## veg_height-Meleagris_gallopavo 479
## veg_height-Sciurus_carolinensis 1040
## veg_height-Vulpes_vulpes 621
## veg_height-Sus_scrofa 1090
#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 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_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): 0.426
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8090 0.5421 -1.8443 -0.8342 0.2955 1.0168 218
## Veg_shannon_index 0.4113 0.3774 -0.3106 0.3989 1.1786 1.0371 667
## Avg_Cogongrass_Cover 0.3002 0.3665 -0.4432 0.3052 1.0262 1.0183 384
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0425 1.6663 0.0582 0.5477 5.1002 1.2680 289
## Veg_shannon_index 0.5401 0.7438 0.0503 0.3151 2.5219 1.0071 826
## Avg_Cogongrass_Cover 0.5291 0.8110 0.0418 0.2959 2.5304 1.0428 619
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9928 1.9996 0.1416 1.3967 7.2644 1.1514 177
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2092 0.3331 -3.9179 -3.2040 -2.5819 1.0027 567
## shrub_cover 0.1781 0.3789 -0.5485 0.1756 0.9449 1.0210 1132
## veg_height -0.0890 0.2179 -0.5006 -0.0880 0.3375 1.0001 1036
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6424 0.7706 0.0864 0.4189 2.6002 1.0047 312
## shrub_cover 0.9566 1.0095 0.1746 0.6850 3.3618 1.0350 879
## veg_height 0.2831 0.3109 0.0552 0.2066 0.9438 1.0102 1649
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1990 0.7522 -1.5585 -0.2246
## (Intercept)-Lynx_rufus -0.4861 0.8372 -1.9124 -0.5590
## (Intercept)-Didelphis_virginiana -1.1400 0.6731 -2.5348 -1.1330
## (Intercept)-Sylvilagus_floridanus -0.7132 0.7101 -2.1089 -0.7220
## (Intercept)-Meleagris_gallopavo -0.3748 0.9436 -1.8959 -0.4786
## (Intercept)-Sciurus_carolinensis -1.1689 0.6893 -2.6637 -1.1389
## (Intercept)-Vulpes_vulpes -1.1096 0.8820 -2.7471 -1.1391
## (Intercept)-Sus_scrofa -1.5219 0.8589 -3.3834 -1.4374
## Veg_shannon_index-Canis_latrans 0.7450 0.4764 -0.1291 0.7163
## Veg_shannon_index-Lynx_rufus 0.2509 0.6370 -1.0692 0.2735
## Veg_shannon_index-Didelphis_virginiana 0.5794 0.4590 -0.2541 0.5628
## Veg_shannon_index-Sylvilagus_floridanus 0.4784 0.5092 -0.4543 0.4491
## Veg_shannon_index-Meleagris_gallopavo 0.6033 0.6007 -0.4662 0.5456
## Veg_shannon_index-Sciurus_carolinensis -0.1142 0.5050 -1.1977 -0.0835
## Veg_shannon_index-Vulpes_vulpes 0.0569 0.6132 -1.2761 0.0870
## Veg_shannon_index-Sus_scrofa 0.7646 0.6573 -0.3620 0.7045
## Avg_Cogongrass_Cover-Canis_latrans 0.6694 0.4663 -0.1240 0.6277
## Avg_Cogongrass_Cover-Lynx_rufus 0.6601 0.5295 -0.2081 0.5958
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4918 0.4337 -0.3466 0.4768
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1679 0.5364 -1.3396 -0.1262
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0763 0.7450 -1.6033 -0.0289
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4311 0.4195 -0.3633 0.4302
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4269 0.5890 -0.6463 0.3934
## Avg_Cogongrass_Cover-Sus_scrofa 0.0330 0.6734 -1.5079 0.0912
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4062 1.0814 327
## (Intercept)-Lynx_rufus 1.4095 1.0931 311
## (Intercept)-Didelphis_virginiana 0.1877 1.0223 454
## (Intercept)-Sylvilagus_floridanus 0.7401 1.0157 494
## (Intercept)-Meleagris_gallopavo 1.8558 1.0579 232
## (Intercept)-Sciurus_carolinensis 0.1098 1.0206 571
## (Intercept)-Vulpes_vulpes 0.8988 1.0165 259
## (Intercept)-Sus_scrofa 0.0364 1.0809 359
## Veg_shannon_index-Canis_latrans 1.7448 1.0077 1113
## Veg_shannon_index-Lynx_rufus 1.4810 1.0740 563
## Veg_shannon_index-Didelphis_virginiana 1.5523 1.0096 1325
## Veg_shannon_index-Sylvilagus_floridanus 1.5419 1.0263 836
## Veg_shannon_index-Meleagris_gallopavo 1.9267 1.0178 692
## Veg_shannon_index-Sciurus_carolinensis 0.8013 1.0241 880
## Veg_shannon_index-Vulpes_vulpes 1.2222 1.0566 546
## Veg_shannon_index-Sus_scrofa 2.2579 1.0183 647
## Avg_Cogongrass_Cover-Canis_latrans 1.6928 1.0043 1201
## Avg_Cogongrass_Cover-Lynx_rufus 1.8551 1.0133 766
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4143 1.0223 1310
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7687 1.0146 657
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2498 1.0249 396
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2824 1.0011 1181
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6944 1.0230 568
## Avg_Cogongrass_Cover-Sus_scrofa 1.1817 1.0122 468
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7763 0.1875 -3.1610 -2.7670 -2.4352 1.0072
## (Intercept)-Lynx_rufus -3.6001 0.3141 -4.2369 -3.5908 -2.9958 1.0102
## (Intercept)-Didelphis_virginiana -2.7042 0.2972 -3.3246 -2.6998 -2.1533 1.0191
## (Intercept)-Sylvilagus_floridanus -3.2034 0.2845 -3.7899 -3.1882 -2.6774 1.0149
## (Intercept)-Meleagris_gallopavo -3.9463 0.4670 -4.9339 -3.9103 -3.1300 1.0214
## (Intercept)-Sciurus_carolinensis -2.7496 0.3169 -3.3901 -2.7490 -2.1482 1.0089
## (Intercept)-Vulpes_vulpes -3.9478 0.7349 -5.6377 -3.8091 -2.8414 1.0573
## (Intercept)-Sus_scrofa -3.4098 0.5195 -4.4771 -3.4006 -2.4146 1.0089
## shrub_cover-Canis_latrans -0.3070 0.2150 -0.7297 -0.3101 0.1170 1.0128
## shrub_cover-Lynx_rufus -0.2555 0.3633 -1.0375 -0.2435 0.4339 1.0075
## shrub_cover-Didelphis_virginiana 1.0429 0.3984 0.3197 1.0294 1.8610 1.0033
## shrub_cover-Sylvilagus_floridanus 0.2657 0.4367 -0.5517 0.2515 1.1385 1.0265
## shrub_cover-Meleagris_gallopavo -0.7591 0.3976 -1.5553 -0.7412 -0.0480 1.0520
## shrub_cover-Sciurus_carolinensis 0.9285 0.4245 0.1278 0.9171 1.7842 1.0280
## shrub_cover-Vulpes_vulpes -0.1678 0.6267 -1.4325 -0.1445 1.0456 1.0120
## shrub_cover-Sus_scrofa 0.7790 0.8211 -0.8098 0.7709 2.4308 1.0075
## veg_height-Canis_latrans -0.6285 0.1896 -1.0062 -0.6219 -0.2748 1.0031
## veg_height-Lynx_rufus -0.0461 0.2536 -0.5564 -0.0399 0.4524 1.0020
## veg_height-Didelphis_virginiana 0.4533 0.2644 -0.0425 0.4459 0.9920 1.0067
## veg_height-Sylvilagus_floridanus 0.1175 0.2496 -0.3603 0.1140 0.6147 1.0012
## veg_height-Meleagris_gallopavo -0.2960 0.3963 -1.0805 -0.2862 0.4840 1.0175
## veg_height-Sciurus_carolinensis 0.0825 0.2198 -0.3334 0.0779 0.5129 1.0047
## veg_height-Vulpes_vulpes -0.1916 0.3307 -0.9246 -0.1699 0.3918 1.0285
## veg_height-Sus_scrofa -0.2259 0.3554 -0.9802 -0.2060 0.4496 1.0038
## ESS
## (Intercept)-Canis_latrans 724
## (Intercept)-Lynx_rufus 342
## (Intercept)-Didelphis_virginiana 634
## (Intercept)-Sylvilagus_floridanus 466
## (Intercept)-Meleagris_gallopavo 139
## (Intercept)-Sciurus_carolinensis 742
## (Intercept)-Vulpes_vulpes 133
## (Intercept)-Sus_scrofa 594
## shrub_cover-Canis_latrans 866
## shrub_cover-Lynx_rufus 407
## shrub_cover-Didelphis_virginiana 599
## shrub_cover-Sylvilagus_floridanus 390
## shrub_cover-Meleagris_gallopavo 211
## shrub_cover-Sciurus_carolinensis 528
## shrub_cover-Vulpes_vulpes 448
## shrub_cover-Sus_scrofa 683
## veg_height-Canis_latrans 602
## veg_height-Lynx_rufus 662
## veg_height-Didelphis_virginiana 819
## veg_height-Sylvilagus_floridanus 706
## veg_height-Meleagris_gallopavo 438
## veg_height-Sciurus_carolinensis 1117
## veg_height-Vulpes_vulpes 478
## veg_height-Sus_scrofa 1021
# 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 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_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): 0.489
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7105 0.6070 -1.9036 -0.7318 0.5815 1.0182 204
## Cogon_Patch_Size -0.1316 0.6584 -1.5541 -0.1097 1.1428 1.0102 468
## Avg_Cogongrass_Cover 0.1401 0.5098 -0.9099 0.1404 1.1541 1.0315 245
## total_shrub_cover -1.1499 0.7031 -2.7165 -1.1005 0.0586 1.0248 171
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9307 1.6665 0.0508 0.4411 4.5541 1.1318 375
## Cogon_Patch_Size 2.5262 5.9731 0.0843 1.1337 11.7543 1.2629 130
## Avg_Cogongrass_Cover 0.8035 1.3450 0.0504 0.4034 3.8958 1.0819 604
## total_shrub_cover 2.3892 4.5932 0.0924 1.1044 13.2190 1.1186 117
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.2044 5.5159 0.5313 3.6508 19.833 1.2198 73
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2704 0.2803 -3.8564 -3.2628 -2.7523 1.0172 539
## shrub_cover 0.5410 0.4035 -0.2425 0.5385 1.3664 1.0095 795
## veg_height -0.0780 0.2320 -0.5275 -0.0755 0.3726 1.0003 835
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4140 0.4685 0.0533 0.2843 1.6343 1.1149 504
## shrub_cover 1.0312 1.2394 0.1606 0.7406 3.4330 1.0730 651
## veg_height 0.2958 0.2763 0.0604 0.2215 0.9865 1.0106 996
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1562 0.9433 -1.8023 -0.2755
## (Intercept)-Lynx_rufus -0.6367 0.8221 -2.2928 -0.6465
## (Intercept)-Didelphis_virginiana -0.8053 0.8489 -2.4781 -0.8098
## (Intercept)-Sylvilagus_floridanus -0.5309 0.9138 -2.3323 -0.5538
## (Intercept)-Meleagris_gallopavo -0.7793 0.8691 -2.5317 -0.7586
## (Intercept)-Sciurus_carolinensis -0.8926 0.8263 -2.6529 -0.8822
## (Intercept)-Vulpes_vulpes -1.0192 0.9415 -3.0092 -0.9690
## (Intercept)-Sus_scrofa -1.0716 0.9234 -3.0821 -1.0178
## Cogon_Patch_Size-Canis_latrans 0.9657 1.0377 -0.4296 0.7957
## Cogon_Patch_Size-Lynx_rufus 0.0893 1.5164 -2.2411 -0.0729
## Cogon_Patch_Size-Didelphis_virginiana 0.8505 0.6698 -0.3178 0.7825
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1976 1.3415 -4.5871 -0.9268
## Cogon_Patch_Size-Meleagris_gallopavo 0.0850 0.9260 -1.5871 0.0405
## Cogon_Patch_Size-Sciurus_carolinensis -0.9483 1.0331 -3.3969 -0.7797
## Cogon_Patch_Size-Vulpes_vulpes -0.7107 1.3147 -3.9208 -0.5083
## Cogon_Patch_Size-Sus_scrofa -0.4604 1.1704 -3.3504 -0.2733
## Avg_Cogongrass_Cover-Canis_latrans 0.4129 0.5621 -0.6112 0.3814
## Avg_Cogongrass_Cover-Lynx_rufus 0.6148 0.7540 -0.6660 0.5414
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0946 0.6123 -1.1447 0.1083
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2297 0.7640 -1.8852 -0.1838
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4279 0.9869 -2.7399 -0.3273
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4019 0.6399 -0.8844 0.3944
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4022 0.6871 -0.8432 0.3708
## Avg_Cogongrass_Cover-Sus_scrofa -0.0849 0.8755 -2.0345 0.0037
## total_shrub_cover-Canis_latrans 0.2864 0.9888 -1.3438 0.1625
## total_shrub_cover-Lynx_rufus -1.9048 1.3817 -5.2989 -1.6788
## total_shrub_cover-Didelphis_virginiana -1.3775 0.9557 -3.6534 -1.2571
## total_shrub_cover-Sylvilagus_floridanus -1.6757 1.2308 -4.6655 -1.4600
## total_shrub_cover-Meleagris_gallopavo -1.9884 1.1547 -4.8180 -1.8010
## total_shrub_cover-Sciurus_carolinensis -1.2154 1.1175 -3.7975 -1.0599
## total_shrub_cover-Vulpes_vulpes -1.3515 1.4935 -4.9416 -1.1659
## total_shrub_cover-Sus_scrofa -1.0949 1.2896 -4.1942 -0.9306
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9274 1.0609 285
## (Intercept)-Lynx_rufus 1.0274 1.0007 359
## (Intercept)-Didelphis_virginiana 0.9055 1.0191 280
## (Intercept)-Sylvilagus_floridanus 1.2910 1.0048 312
## (Intercept)-Meleagris_gallopavo 0.9145 1.0214 336
## (Intercept)-Sciurus_carolinensis 0.7496 1.0032 317
## (Intercept)-Vulpes_vulpes 0.7905 1.0120 275
## (Intercept)-Sus_scrofa 0.5614 1.0321 235
## Cogon_Patch_Size-Canis_latrans 3.5264 1.0409 245
## Cogon_Patch_Size-Lynx_rufus 3.5468 1.1219 145
## Cogon_Patch_Size-Didelphis_virginiana 2.3415 1.0252 531
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6800 1.0037 251
## Cogon_Patch_Size-Meleagris_gallopavo 2.1830 1.0171 588
## Cogon_Patch_Size-Sciurus_carolinensis 0.5748 1.0258 471
## Cogon_Patch_Size-Vulpes_vulpes 1.4010 1.0146 207
## Cogon_Patch_Size-Sus_scrofa 1.3620 1.0116 525
## Avg_Cogongrass_Cover-Canis_latrans 1.6221 1.0220 591
## Avg_Cogongrass_Cover-Lynx_rufus 2.2971 1.0046 630
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2509 1.0095 547
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1535 1.0092 348
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2298 1.0054 270
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7202 1.0093 421
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8662 1.0392 523
## Avg_Cogongrass_Cover-Sus_scrofa 1.4777 1.0156 367
## total_shrub_cover-Canis_latrans 2.6224 1.0452 168
## total_shrub_cover-Lynx_rufus 0.1335 1.0258 146
## total_shrub_cover-Didelphis_virginiana 0.1094 1.0097 210
## total_shrub_cover-Sylvilagus_floridanus 0.1143 1.0393 137
## total_shrub_cover-Meleagris_gallopavo -0.2972 1.0353 153
## total_shrub_cover-Sciurus_carolinensis 0.5531 1.0110 192
## total_shrub_cover-Vulpes_vulpes 1.0212 1.0893 144
## total_shrub_cover-Sus_scrofa 0.9746 1.0018 227
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8439 0.1966 -3.2387 -2.8340 -2.4752 1.0063
## (Intercept)-Lynx_rufus -3.4953 0.3007 -4.1214 -3.4681 -2.9487 1.0027
## (Intercept)-Didelphis_virginiana -2.8716 0.3094 -3.4888 -2.8722 -2.2770 1.0225
## (Intercept)-Sylvilagus_floridanus -3.3073 0.2559 -3.8317 -3.3030 -2.8382 1.0340
## (Intercept)-Meleagris_gallopavo -3.6424 0.4480 -4.6181 -3.6126 -2.8433 1.0543
## (Intercept)-Sciurus_carolinensis -3.0200 0.3490 -3.6971 -3.0220 -2.3609 1.0197
## (Intercept)-Vulpes_vulpes -3.8689 0.5783 -5.1517 -3.8058 -2.9168 1.0470
## (Intercept)-Sus_scrofa -3.6188 0.4676 -4.5661 -3.6037 -2.7460 1.0136
## shrub_cover-Canis_latrans -0.3259 0.2680 -0.8217 -0.3347 0.2427 1.0495
## shrub_cover-Lynx_rufus 0.1688 0.3659 -0.5826 0.1863 0.8477 1.0026
## shrub_cover-Didelphis_virginiana 1.3300 0.4505 0.4472 1.3235 2.2028 1.0115
## shrub_cover-Sylvilagus_floridanus 0.8137 0.4161 -0.0550 0.8171 1.6199 1.0685
## shrub_cover-Meleagris_gallopavo -0.3931 0.4635 -1.3196 -0.3893 0.5011 1.0513
## shrub_cover-Sciurus_carolinensis 1.2778 0.4470 0.4198 1.2771 2.1929 1.0252
## shrub_cover-Vulpes_vulpes 0.4326 0.7004 -0.9716 0.4449 1.8137 1.0132
## shrub_cover-Sus_scrofa 1.2623 0.7475 -0.1582 1.2422 2.7816 1.0304
## veg_height-Canis_latrans -0.6390 0.1871 -1.0044 -0.6368 -0.2721 1.0031
## veg_height-Lynx_rufus 0.0173 0.2491 -0.4817 0.0247 0.4926 1.0211
## veg_height-Didelphis_virginiana 0.4594 0.2758 -0.0491 0.4527 0.9997 1.0059
## veg_height-Sylvilagus_floridanus 0.0305 0.2496 -0.4543 0.0274 0.5375 1.0089
## veg_height-Meleagris_gallopavo -0.2883 0.4271 -1.1649 -0.2882 0.5362 1.0291
## veg_height-Sciurus_carolinensis 0.1987 0.2490 -0.2835 0.1958 0.6850 1.0148
## veg_height-Vulpes_vulpes -0.1715 0.3535 -0.8993 -0.1597 0.5095 1.0200
## veg_height-Sus_scrofa -0.2327 0.3464 -0.9459 -0.2124 0.4004 1.0101
## ESS
## (Intercept)-Canis_latrans 581
## (Intercept)-Lynx_rufus 351
## (Intercept)-Didelphis_virginiana 361
## (Intercept)-Sylvilagus_floridanus 372
## (Intercept)-Meleagris_gallopavo 228
## (Intercept)-Sciurus_carolinensis 268
## (Intercept)-Vulpes_vulpes 168
## (Intercept)-Sus_scrofa 237
## shrub_cover-Canis_latrans 393
## shrub_cover-Lynx_rufus 437
## shrub_cover-Didelphis_virginiana 262
## shrub_cover-Sylvilagus_floridanus 357
## shrub_cover-Meleagris_gallopavo 289
## shrub_cover-Sciurus_carolinensis 235
## shrub_cover-Vulpes_vulpes 300
## shrub_cover-Sus_scrofa 251
## veg_height-Canis_latrans 726
## veg_height-Lynx_rufus 690
## veg_height-Didelphis_virginiana 732
## veg_height-Sylvilagus_floridanus 673
## veg_height-Meleagris_gallopavo 309
## veg_height-Sciurus_carolinensis 531
## veg_height-Vulpes_vulpes 428
## veg_height-Sus_scrofa 666
#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 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_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): 0.4323
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9243 0.6025 -2.1578 -0.9365 0.3448 1.0585 304
## Tree_Density -0.7048 0.4874 -1.7680 -0.6620 0.1515 1.0131 596
## Avg_Canopy_Cover 1.2654 0.5523 0.2697 1.2355 2.4349 1.0019 777
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7007 2.3883 0.0838 0.9437 7.7389 1.0384 302
## Tree_Density 0.9676 1.4618 0.0671 0.4944 4.8488 1.0158 413
## Avg_Canopy_Cover 1.6975 2.0852 0.1479 1.0889 7.2579 1.0587 391
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3614 1.9344 0.0778 0.7967 5.7852 1.2739 90
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2214 0.2996 -3.8320 -3.2133 -2.6424 1.0401 573
## shrub_cover 0.2360 0.4089 -0.5670 0.2378 1.0553 1.0039 975
## veg_height -0.0506 0.2271 -0.4921 -0.0476 0.4099 1.0022 1547
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5196 0.5575 0.0724 0.3597 1.9014 1.1435 457
## shrub_cover 0.9881 1.0615 0.1567 0.7114 3.4592 1.0876 825
## veg_height 0.2981 0.2730 0.0657 0.2260 0.9680 1.0137 1286
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0797 0.7626 -1.5449 -0.0870 1.4639
## (Intercept)-Lynx_rufus -0.1354 1.2020 -2.0186 -0.3151 3.0064
## (Intercept)-Didelphis_virginiana -1.5084 0.7745 -3.1639 -1.4535 -0.1102
## (Intercept)-Sylvilagus_floridanus -0.7971 0.7344 -2.2610 -0.8171 0.7241
## (Intercept)-Meleagris_gallopavo -0.4193 0.9791 -2.0845 -0.5173 1.7572
## (Intercept)-Sciurus_carolinensis -1.5595 0.8025 -3.1981 -1.5253 -0.1453
## (Intercept)-Vulpes_vulpes -1.5844 0.9857 -3.5748 -1.5334 0.2097
## (Intercept)-Sus_scrofa -1.9083 0.9500 -4.0984 -1.8003 -0.3193
## Tree_Density-Canis_latrans -0.9653 0.6504 -2.4636 -0.8854 0.0911
## Tree_Density-Lynx_rufus 0.2433 0.7403 -0.9685 0.1484 1.9613
## Tree_Density-Didelphis_virginiana -0.9717 0.8178 -2.9442 -0.8545 0.3630
## Tree_Density-Sylvilagus_floridanus -0.9969 0.7986 -2.9004 -0.8814 0.3070
## Tree_Density-Meleagris_gallopavo -0.9419 0.8690 -2.9337 -0.8390 0.5260
## Tree_Density-Sciurus_carolinensis -0.8296 0.7731 -2.6364 -0.7322 0.4691
## Tree_Density-Vulpes_vulpes -0.5708 0.8515 -2.3501 -0.5428 0.9933
## Tree_Density-Sus_scrofa -0.8282 0.8736 -2.8349 -0.7198 0.5851
## Avg_Canopy_Cover-Canis_latrans -0.1696 0.5223 -1.2351 -0.1565 0.8162
## Avg_Canopy_Cover-Lynx_rufus 0.7784 0.8709 -0.7125 0.6994 2.7180
## Avg_Canopy_Cover-Didelphis_virginiana 1.8149 0.8094 0.5468 1.7008 3.6654
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4406 1.1113 0.8507 2.2527 5.0553
## Avg_Canopy_Cover-Meleagris_gallopavo 1.7683 0.9418 0.3083 1.6254 4.0169
## Avg_Canopy_Cover-Sciurus_carolinensis 1.7593 0.8242 0.5568 1.6423 3.7388
## Avg_Canopy_Cover-Vulpes_vulpes 1.2051 0.7750 -0.1090 1.1203 3.0251
## Avg_Canopy_Cover-Sus_scrofa 1.3902 0.7750 0.1237 1.3157 3.0511
## Rhat ESS
## (Intercept)-Canis_latrans 1.0248 448
## (Intercept)-Lynx_rufus 1.0354 177
## (Intercept)-Didelphis_virginiana 1.0150 546
## (Intercept)-Sylvilagus_floridanus 1.0113 534
## (Intercept)-Meleagris_gallopavo 1.0605 163
## (Intercept)-Sciurus_carolinensis 1.0154 580
## (Intercept)-Vulpes_vulpes 1.0966 282
## (Intercept)-Sus_scrofa 1.0503 457
## Tree_Density-Canis_latrans 1.0012 923
## Tree_Density-Lynx_rufus 1.0288 505
## Tree_Density-Didelphis_virginiana 1.0127 645
## Tree_Density-Sylvilagus_floridanus 1.0012 639
## Tree_Density-Meleagris_gallopavo 1.0095 592
## Tree_Density-Sciurus_carolinensis 1.0180 708
## Tree_Density-Vulpes_vulpes 1.0235 691
## Tree_Density-Sus_scrofa 1.0074 644
## Avg_Canopy_Cover-Canis_latrans 1.0168 663
## Avg_Canopy_Cover-Lynx_rufus 1.0131 425
## Avg_Canopy_Cover-Didelphis_virginiana 1.0089 465
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0023 311
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0023 352
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0126 394
## Avg_Canopy_Cover-Vulpes_vulpes 1.0150 645
## Avg_Canopy_Cover-Sus_scrofa 1.0225 640
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8068 0.1925 -3.1962 -2.8008 -2.4359 1.0350
## (Intercept)-Lynx_rufus -3.6516 0.3545 -4.3815 -3.6422 -2.9875 1.0700
## (Intercept)-Didelphis_virginiana -2.7562 0.2988 -3.3451 -2.7484 -2.1944 1.0058
## (Intercept)-Sylvilagus_floridanus -3.1655 0.2487 -3.6785 -3.1591 -2.6916 1.0147
## (Intercept)-Meleagris_gallopavo -3.8374 0.4213 -4.7552 -3.8041 -3.1007 1.0703
## (Intercept)-Sciurus_carolinensis -2.8305 0.3221 -3.4803 -2.8277 -2.2215 1.0088
## (Intercept)-Vulpes_vulpes -3.8613 0.6011 -5.2019 -3.7996 -2.8231 1.1944
## (Intercept)-Sus_scrofa -3.3994 0.4854 -4.3738 -3.3956 -2.4681 1.0094
## shrub_cover-Canis_latrans -0.3253 0.2246 -0.7608 -0.3300 0.1346 1.0160
## shrub_cover-Lynx_rufus -0.3012 0.3524 -1.0004 -0.2966 0.3991 1.0300
## shrub_cover-Didelphis_virginiana 1.1061 0.3986 0.3245 1.1051 1.9196 1.0300
## shrub_cover-Sylvilagus_floridanus 0.4757 0.3990 -0.3122 0.4728 1.2488 1.0187
## shrub_cover-Meleagris_gallopavo -0.6727 0.3939 -1.4758 -0.6611 0.0735 1.0539
## shrub_cover-Sciurus_carolinensis 1.0332 0.4187 0.2248 1.0261 1.8946 1.0261
## shrub_cover-Vulpes_vulpes -0.0920 0.6512 -1.5220 -0.0563 1.1213 1.0045
## shrub_cover-Sus_scrofa 0.8073 0.8237 -0.7810 0.7799 2.5328 1.0522
## veg_height-Canis_latrans -0.6358 0.1895 -1.0156 -0.6316 -0.2856 1.0242
## veg_height-Lynx_rufus 0.0362 0.2538 -0.4867 0.0355 0.5216 1.0037
## veg_height-Didelphis_virginiana 0.5204 0.2641 0.0348 0.5157 1.0572 1.0053
## veg_height-Sylvilagus_floridanus 0.1344 0.2440 -0.3449 0.1314 0.6225 1.0017
## veg_height-Meleagris_gallopavo -0.2617 0.3625 -0.9748 -0.2549 0.4244 1.0092
## veg_height-Sciurus_carolinensis 0.1509 0.2354 -0.3110 0.1477 0.6289 1.0029
## veg_height-Vulpes_vulpes -0.1917 0.3194 -0.8376 -0.1824 0.4134 1.0031
## veg_height-Sus_scrofa -0.1751 0.3494 -0.8851 -0.1602 0.4787 1.0081
## ESS
## (Intercept)-Canis_latrans 642
## (Intercept)-Lynx_rufus 174
## (Intercept)-Didelphis_virginiana 571
## (Intercept)-Sylvilagus_floridanus 602
## (Intercept)-Meleagris_gallopavo 215
## (Intercept)-Sciurus_carolinensis 565
## (Intercept)-Vulpes_vulpes 221
## (Intercept)-Sus_scrofa 632
## shrub_cover-Canis_latrans 884
## shrub_cover-Lynx_rufus 354
## shrub_cover-Didelphis_virginiana 413
## shrub_cover-Sylvilagus_floridanus 584
## shrub_cover-Meleagris_gallopavo 256
## shrub_cover-Sciurus_carolinensis 455
## shrub_cover-Vulpes_vulpes 472
## shrub_cover-Sus_scrofa 539
## veg_height-Canis_latrans 652
## veg_height-Lynx_rufus 700
## veg_height-Didelphis_virginiana 1005
## veg_height-Sylvilagus_floridanus 1019
## veg_height-Meleagris_gallopavo 589
## veg_height-Sciurus_carolinensis 810
## veg_height-Vulpes_vulpes 479
## veg_height-Sus_scrofa 1134
# 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 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_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): 0.4212
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6669 0.5970 -2.9112 -1.6474 -0.5096 1.0284 221
## Avg_Cogongrass_Cover -0.8759 0.5240 -1.9573 -0.8583 0.1301 1.0038 366
## I(Avg_Cogongrass_Cover^2) 0.9716 0.4829 0.0919 0.9341 2.0693 1.0184 316
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0199 1.5180 0.0573 0.5371 4.6468 1.0636 339
## Avg_Cogongrass_Cover 0.7269 1.0699 0.0492 0.3738 3.6408 1.0095 583
## I(Avg_Cogongrass_Cover^2) 0.7832 1.3526 0.0474 0.3748 3.9460 1.1463 460
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6414 1.8721 0.0832 1.0285 6.8563 1.0694 107
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1612 0.2992 -3.7772 -3.1508 -2.5848 1.0001 726
## shrub_cover 0.2118 0.3485 -0.4575 0.2013 0.9476 1.0123 1502
## veg_height -0.0678 0.2137 -0.4850 -0.0701 0.3532 1.0120 1028
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4965 0.5029 0.0668 0.3511 1.7926 1.0073 493
## shrub_cover 0.8063 0.7635 0.1257 0.5997 2.7530 1.0115 748
## veg_height 0.2644 0.3974 0.0489 0.1928 0.8744 1.1648 2456
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.1091 0.7839 -2.6542 -1.1249
## (Intercept)-Lynx_rufus -1.6423 0.8213 -3.3614 -1.6205
## (Intercept)-Didelphis_virginiana -1.8789 0.7308 -3.4379 -1.8329
## (Intercept)-Sylvilagus_floridanus -1.5169 0.7367 -2.9681 -1.4937
## (Intercept)-Meleagris_gallopavo -1.1303 0.8925 -2.8852 -1.1710
## (Intercept)-Sciurus_carolinensis -2.1698 0.8211 -3.9983 -2.0911
## (Intercept)-Vulpes_vulpes -2.2440 1.0311 -4.5426 -2.1332
## (Intercept)-Sus_scrofa -2.2060 0.8804 -4.1835 -2.1308
## Avg_Cogongrass_Cover-Canis_latrans -0.4191 0.6586 -1.5900 -0.4602
## Avg_Cogongrass_Cover-Lynx_rufus -0.7608 0.7180 -2.2129 -0.7537
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4717 0.6627 -1.7148 -0.4921
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4328 0.8064 -3.3313 -1.3459
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.2082 0.8635 -3.1371 -1.1087
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.9382 0.6769 -2.4440 -0.8931
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9168 0.7745 -2.6161 -0.8740
## Avg_Cogongrass_Cover-Sus_scrofa -1.0576 0.7981 -2.7562 -1.0044
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6502 0.9621 0.2898 1.4641
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.4618 0.6975 0.3908 1.3812
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7339 0.5685 -0.2785 0.6891
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9517 0.5795 0.0166 0.8908
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5579 0.8415 -1.1118 0.5279
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.1124 0.5046 0.2772 1.0516
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0593 0.5611 0.1374 1.0019
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5030 0.8169 -1.3924 0.5601
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4458 1.0408 322
## (Intercept)-Lynx_rufus 0.0163 1.0231 379
## (Intercept)-Didelphis_virginiana -0.5434 1.0183 328
## (Intercept)-Sylvilagus_floridanus -0.1106 1.0121 344
## (Intercept)-Meleagris_gallopavo 0.6936 1.0207 377
## (Intercept)-Sciurus_carolinensis -0.7584 1.0161 389
## (Intercept)-Vulpes_vulpes -0.5381 1.0308 263
## (Intercept)-Sus_scrofa -0.6884 1.0224 329
## Avg_Cogongrass_Cover-Canis_latrans 0.9814 1.0099 662
## Avg_Cogongrass_Cover-Lynx_rufus 0.6540 1.0136 503
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9555 1.0076 743
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1109 1.0033 375
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2254 1.0189 322
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2865 1.0013 539
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5907 1.0014 478
## Avg_Cogongrass_Cover-Sus_scrofa 0.3693 1.0031 478
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.0462 1.1609 264
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.1284 1.0496 359
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0493 1.0062 327
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.3115 1.0177 398
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3498 1.0367 178
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.2481 1.0013 459
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3293 1.0051 423
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.9785 1.0623 290
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7917 0.1819 -3.1548 -2.7855 -2.4435 1.0056
## (Intercept)-Lynx_rufus -3.4871 0.3181 -4.1253 -3.4832 -2.9089 1.0037
## (Intercept)-Didelphis_virginiana -2.7084 0.2955 -3.2927 -2.7001 -2.1494 1.0136
## (Intercept)-Sylvilagus_floridanus -3.1851 0.2726 -3.7599 -3.1808 -2.6644 1.0032
## (Intercept)-Meleagris_gallopavo -3.7991 0.4696 -4.7539 -3.7899 -2.9080 1.0442
## (Intercept)-Sciurus_carolinensis -2.7369 0.3117 -3.3521 -2.7332 -2.1404 1.0152
## (Intercept)-Vulpes_vulpes -3.7972 0.5880 -5.0901 -3.7479 -2.8317 1.0032
## (Intercept)-Sus_scrofa -3.3579 0.4828 -4.3535 -3.3407 -2.4561 1.0083
## shrub_cover-Canis_latrans -0.2792 0.2232 -0.7195 -0.2803 0.1482 1.0007
## shrub_cover-Lynx_rufus -0.2142 0.3605 -0.9482 -0.2159 0.4843 1.0095
## shrub_cover-Didelphis_virginiana 1.0716 0.4269 0.2860 1.0518 1.9414 1.0032
## shrub_cover-Sylvilagus_floridanus 0.2964 0.4201 -0.5052 0.2939 1.1285 1.0185
## shrub_cover-Meleagris_gallopavo -0.6238 0.4024 -1.4243 -0.6193 0.1695 1.0484
## shrub_cover-Sciurus_carolinensis 0.8604 0.4455 0.0030 0.8524 1.7658 1.0022
## shrub_cover-Vulpes_vulpes -0.0829 0.5824 -1.2241 -0.0841 1.1074 1.0036
## shrub_cover-Sus_scrofa 0.6974 0.7572 -0.7824 0.6806 2.2959 1.0124
## veg_height-Canis_latrans -0.6097 0.1924 -1.0214 -0.6020 -0.2519 1.0080
## veg_height-Lynx_rufus 0.0309 0.2519 -0.4916 0.0413 0.5042 1.0027
## veg_height-Didelphis_virginiana 0.3847 0.2708 -0.1250 0.3763 0.9255 1.0413
## veg_height-Sylvilagus_floridanus 0.1032 0.2546 -0.3823 0.0961 0.6106 1.0135
## veg_height-Meleagris_gallopavo -0.1855 0.3967 -0.9770 -0.1939 0.6290 1.0020
## veg_height-Sciurus_carolinensis 0.0983 0.2263 -0.3292 0.0889 0.5594 1.0090
## veg_height-Vulpes_vulpes -0.1425 0.3140 -0.7720 -0.1355 0.4471 1.0056
## veg_height-Sus_scrofa -0.1927 0.3425 -0.9202 -0.1849 0.4543 1.0038
## ESS
## (Intercept)-Canis_latrans 824
## (Intercept)-Lynx_rufus 412
## (Intercept)-Didelphis_virginiana 498
## (Intercept)-Sylvilagus_floridanus 557
## (Intercept)-Meleagris_gallopavo 186
## (Intercept)-Sciurus_carolinensis 603
## (Intercept)-Vulpes_vulpes 258
## (Intercept)-Sus_scrofa 576
## shrub_cover-Canis_latrans 785
## shrub_cover-Lynx_rufus 520
## shrub_cover-Didelphis_virginiana 522
## shrub_cover-Sylvilagus_floridanus 493
## shrub_cover-Meleagris_gallopavo 261
## shrub_cover-Sciurus_carolinensis 639
## shrub_cover-Vulpes_vulpes 701
## shrub_cover-Sus_scrofa 692
## veg_height-Canis_latrans 741
## veg_height-Lynx_rufus 783
## veg_height-Didelphis_virginiana 765
## veg_height-Sylvilagus_floridanus 571
## veg_height-Meleagris_gallopavo 399
## veg_height-Sciurus_carolinensis 777
## veg_height-Vulpes_vulpes 701
## veg_height-Sus_scrofa 883
# 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 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_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): 0.4873
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4913 1.0272 -4.3005 -2.5596 -0.2196 1.0215 243
## Cogon_Patch_Size 0.2888 0.9836 -1.7210 0.3095 2.1274 1.0412 283
## Veg_shannon_index 1.1713 0.7300 -0.3138 1.1534 2.6178 1.0444 345
## total_shrub_cover -1.5054 1.0175 -3.6807 -1.4541 0.5114 1.2173 156
## Avg_Cogongrass_Cover -0.7373 1.1780 -3.0660 -0.7191 1.5402 1.0255 119
## Tree_Density -1.7755 1.0216 -3.8401 -1.7518 0.2185 1.0911 163
## Avg_Canopy_Cover 2.1855 1.0282 -0.0105 2.2198 4.1228 1.0919 762
## I(Avg_Cogongrass_Cover^2) 1.4680 0.8765 -0.3962 1.5294 3.0521 1.0352 348
## avg_veg_height -0.2188 0.7452 -1.6733 -0.2241 1.3046 1.0737 161
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.9544 10.6137 0.0817 2.3717 34.7311 1.1898 124
## Cogon_Patch_Size 7.2026 11.0582 0.1446 3.7027 35.3854 1.1081 208
## Veg_shannon_index 2.7582 4.7480 0.0621 1.0917 15.8728 1.3692 132
## total_shrub_cover 5.8860 8.8532 0.1253 3.2779 26.9247 1.1856 220
## Avg_Cogongrass_Cover 1.5984 3.6443 0.0464 0.5281 9.9405 1.0758 440
## Tree_Density 4.0929 8.5579 0.0576 1.2064 25.5542 1.1181 131
## Avg_Canopy_Cover 10.4642 18.9776 0.3430 4.8923 54.9774 1.1336 72
## I(Avg_Cogongrass_Cover^2) 7.1632 15.4487 0.0980 2.4034 44.7919 1.4961 131
## avg_veg_height 1.1793 2.0599 0.0504 0.4767 6.7580 1.0381 397
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7458 4.5162 0.061 0.9357 16.029 1.3867 51
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.2779 0.3049 -3.9088 -3.2722 -2.6647 1.0073 394
## shrub_cover 0.4894 0.4088 -0.3497 0.4871 1.2883 1.0172 384
## veg_height -0.0104 0.2316 -0.4939 -0.0069 0.4488 1.0151 991
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5380 0.6812 0.0779 0.3603 2.0776 1.0503 303
## shrub_cover 1.0595 1.1959 0.1734 0.7503 3.9024 1.0477 566
## veg_height 0.3105 0.3115 0.0606 0.2288 1.0666 1.0158 1359
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.7477 1.4713 -4.3301 -1.8348
## (Intercept)-Lynx_rufus -1.6510 1.8237 -4.6515 -1.8480
## (Intercept)-Didelphis_virginiana -3.6175 1.5502 -7.0857 -3.4257
## (Intercept)-Sylvilagus_floridanus -2.5160 1.4678 -5.4453 -2.5164
## (Intercept)-Meleagris_gallopavo -2.1532 1.9953 -5.3974 -2.3749
## (Intercept)-Sciurus_carolinensis -4.0573 1.8107 -8.3603 -3.7755
## (Intercept)-Vulpes_vulpes -4.0690 2.1713 -9.2085 -3.7068
## (Intercept)-Sus_scrofa -4.2473 2.0990 -9.4194 -3.8332
## Cogon_Patch_Size-Canis_latrans 2.5430 1.9094 0.0462 2.1513
## Cogon_Patch_Size-Lynx_rufus 0.0675 2.1035 -3.9756 0.0754
## Cogon_Patch_Size-Didelphis_virginiana 2.2483 1.4920 -0.0260 1.9986
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4054 2.1104 -6.4672 -1.0939
## Cogon_Patch_Size-Meleagris_gallopavo 1.1548 1.9583 -1.9163 0.8582
## Cogon_Patch_Size-Sciurus_carolinensis -0.9052 1.8239 -5.2944 -0.7082
## Cogon_Patch_Size-Vulpes_vulpes -0.2476 2.1261 -5.1008 -0.0716
## Cogon_Patch_Size-Sus_scrofa -0.5598 2.0777 -5.6123 -0.2646
## Veg_shannon_index-Canis_latrans 1.7926 1.1409 0.1196 1.6272
## Veg_shannon_index-Lynx_rufus 1.2570 1.3391 -1.4212 1.2131
## Veg_shannon_index-Didelphis_virginiana 1.6540 1.2780 -0.3621 1.4743
## Veg_shannon_index-Sylvilagus_floridanus 1.3648 1.0765 -0.4952 1.2747
## Veg_shannon_index-Meleagris_gallopavo 1.9026 1.3179 -0.0991 1.7065
## Veg_shannon_index-Sciurus_carolinensis 0.0123 1.3393 -3.0215 0.2288
## Veg_shannon_index-Vulpes_vulpes 0.4106 1.5038 -3.1004 0.6246
## Veg_shannon_index-Sus_scrofa 2.1642 1.4575 0.0625 1.8816
## total_shrub_cover-Canis_latrans 0.2160 1.3539 -2.6871 0.1548
## total_shrub_cover-Lynx_rufus -2.6934 1.9718 -7.4024 -2.4026
## total_shrub_cover-Didelphis_virginiana -2.3561 1.7274 -6.6901 -2.0418
## total_shrub_cover-Sylvilagus_floridanus -1.5026 1.8601 -5.9061 -1.3078
## total_shrub_cover-Meleagris_gallopavo -3.7316 2.0469 -8.2358 -3.5576
## total_shrub_cover-Sciurus_carolinensis -1.6520 1.9342 -6.2394 -1.4028
## total_shrub_cover-Vulpes_vulpes -2.1754 2.4006 -8.1439 -1.8743
## total_shrub_cover-Sus_scrofa -1.3707 1.8138 -5.2605 -1.2613
## Avg_Cogongrass_Cover-Canis_latrans -0.7486 1.5243 -3.7773 -0.7022
## Avg_Cogongrass_Cover-Lynx_rufus -0.6808 1.5754 -3.7400 -0.7031
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.6216 1.4606 -3.4812 -0.6230
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2435 1.5544 -4.6546 -1.1399
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9900 1.6788 -4.5315 -0.9452
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6269 1.4842 -3.5532 -0.6341
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.4719 1.6385 -3.5524 -0.4889
## Avg_Cogongrass_Cover-Sus_scrofa -0.8400 1.6233 -4.0490 -0.8441
## Tree_Density-Canis_latrans -2.9564 1.7450 -7.3627 -2.6413
## Tree_Density-Lynx_rufus -0.7592 2.0367 -3.9819 -1.0521
## Tree_Density-Didelphis_virginiana -2.0724 1.3999 -5.1008 -2.0342
## Tree_Density-Sylvilagus_floridanus -2.6293 1.8250 -7.2825 -2.3829
## Tree_Density-Meleagris_gallopavo -2.0002 1.5720 -5.3364 -1.9807
## Tree_Density-Sciurus_carolinensis -2.2001 1.6807 -5.9007 -2.0912
## Tree_Density-Vulpes_vulpes -1.6189 1.9869 -5.2781 -1.7101
## Tree_Density-Sus_scrofa -2.0417 1.6918 -5.6136 -1.9980
## Avg_Canopy_Cover-Canis_latrans 0.0324 1.0138 -1.7560 -0.0216
## Avg_Canopy_Cover-Lynx_rufus 1.2671 1.9360 -2.1730 1.1967
## Avg_Canopy_Cover-Didelphis_virginiana 4.3475 2.0986 1.5231 3.9042
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.2498 2.5833 1.6310 4.7519
## Avg_Canopy_Cover-Meleagris_gallopavo 2.8178 1.6389 0.4303 2.5361
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2350 2.4736 1.4071 3.6764
## Avg_Canopy_Cover-Vulpes_vulpes 3.4514 2.3279 0.3576 2.9713
## Avg_Canopy_Cover-Sus_scrofa 2.9243 1.9346 0.3577 2.5504
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4955 2.3478 0.9795 2.8733
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.8139 2.6199 0.9580 3.1687
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2435 0.9169 -0.4942 1.2322
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4338 1.1431 -0.5843 1.3288
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2361 2.1381 -5.7520 0.1563
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.1175 1.2288 0.2464 1.9519
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3929 1.4410 0.3026 2.1654
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2448 2.2925 -5.7696 0.6892
## avg_veg_height-Canis_latrans -0.2811 0.8184 -1.8971 -0.2926
## avg_veg_height-Lynx_rufus -0.6862 1.2584 -3.7065 -0.5551
## avg_veg_height-Didelphis_virginiana -0.3947 1.0111 -2.5346 -0.3797
## avg_veg_height-Sylvilagus_floridanus -0.2650 0.9944 -2.3795 -0.2500
## avg_veg_height-Meleagris_gallopavo -0.2219 1.2819 -2.8046 -0.2231
## avg_veg_height-Sciurus_carolinensis 0.3850 1.0383 -1.3961 0.2819
## avg_veg_height-Vulpes_vulpes -0.2670 1.1397 -2.4533 -0.2543
## avg_veg_height-Sus_scrofa -0.2101 1.0500 -2.3727 -0.1920
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6885 1.0399 207
## (Intercept)-Lynx_rufus 2.6277 1.2100 125
## (Intercept)-Didelphis_virginiana -0.8424 1.0537 196
## (Intercept)-Sylvilagus_floridanus 0.3854 1.0438 282
## (Intercept)-Meleagris_gallopavo 2.8170 1.1370 107
## (Intercept)-Sciurus_carolinensis -1.1835 1.1263 128
## (Intercept)-Vulpes_vulpes -0.7203 1.1909 115
## (Intercept)-Sus_scrofa -1.1343 1.1716 113
## Cogon_Patch_Size-Canis_latrans 7.7504 1.1762 166
## Cogon_Patch_Size-Lynx_rufus 4.7035 1.0455 198
## Cogon_Patch_Size-Didelphis_virginiana 5.7921 1.0165 204
## Cogon_Patch_Size-Sylvilagus_floridanus 1.8755 1.0714 255
## Cogon_Patch_Size-Meleagris_gallopavo 6.5013 1.1400 168
## Cogon_Patch_Size-Sciurus_carolinensis 2.1239 1.0526 258
## Cogon_Patch_Size-Vulpes_vulpes 3.6989 1.0240 203
## Cogon_Patch_Size-Sus_scrofa 2.8415 1.0605 217
## Veg_shannon_index-Canis_latrans 4.4644 1.3712 193
## Veg_shannon_index-Lynx_rufus 4.0495 1.0487 350
## Veg_shannon_index-Didelphis_virginiana 4.8106 1.0998 293
## Veg_shannon_index-Sylvilagus_floridanus 3.7277 1.0409 360
## Veg_shannon_index-Meleagris_gallopavo 5.0922 1.1457 251
## Veg_shannon_index-Sciurus_carolinensis 2.1535 1.0841 203
## Veg_shannon_index-Vulpes_vulpes 2.9054 1.0394 221
## Veg_shannon_index-Sus_scrofa 5.6790 1.1539 271
## total_shrub_cover-Canis_latrans 3.1517 1.1364 131
## total_shrub_cover-Lynx_rufus 0.4444 1.2660 120
## total_shrub_cover-Didelphis_virginiana 0.1775 1.3219 154
## total_shrub_cover-Sylvilagus_floridanus 1.9197 1.2572 191
## total_shrub_cover-Meleagris_gallopavo -0.2685 1.1840 112
## total_shrub_cover-Sciurus_carolinensis 1.1975 1.2220 119
## total_shrub_cover-Vulpes_vulpes 1.7684 1.3042 131
## total_shrub_cover-Sus_scrofa 2.1009 1.0108 173
## Avg_Cogongrass_Cover-Canis_latrans 2.1580 1.0373 200
## Avg_Cogongrass_Cover-Lynx_rufus 2.4615 1.0359 157
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2442 1.0127 181
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5927 1.0152 182
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0076 1.0202 160
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2916 1.0219 207
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8633 1.0230 183
## Avg_Cogongrass_Cover-Sus_scrofa 2.2246 1.0318 169
## Tree_Density-Canis_latrans -0.5077 1.0966 218
## Tree_Density-Lynx_rufus 4.2091 1.0411 138
## Tree_Density-Didelphis_virginiana 0.7642 1.0794 343
## Tree_Density-Sylvilagus_floridanus 0.3278 1.1154 183
## Tree_Density-Meleagris_gallopavo 1.0883 1.0415 369
## Tree_Density-Sciurus_carolinensis 0.9576 1.0290 227
## Tree_Density-Vulpes_vulpes 2.8505 1.0531 208
## Tree_Density-Sus_scrofa 1.2178 1.1504 347
## Avg_Canopy_Cover-Canis_latrans 2.5773 1.2776 136
## Avg_Canopy_Cover-Lynx_rufus 5.2311 1.1399 161
## Avg_Canopy_Cover-Didelphis_virginiana 9.6380 1.0211 213
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.5388 1.0111 159
## Avg_Canopy_Cover-Meleagris_gallopavo 6.9964 1.0670 221
## Avg_Canopy_Cover-Sciurus_carolinensis 10.9286 1.0424 84
## Avg_Canopy_Cover-Vulpes_vulpes 9.6761 1.0549 120
## Avg_Canopy_Cover-Sus_scrofa 8.0526 1.0210 115
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 10.1255 1.4314 66
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 10.9924 1.4462 91
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1163 1.0167 318
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0070 1.0599 229
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.8337 1.3851 90
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 5.3500 1.0532 183
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.7627 1.1544 134
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.4659 1.0667 105
## avg_veg_height-Canis_latrans 1.3666 1.0715 259
## avg_veg_height-Lynx_rufus 1.4043 1.0916 267
## avg_veg_height-Didelphis_virginiana 1.5204 1.0353 284
## avg_veg_height-Sylvilagus_floridanus 1.6214 1.0186 308
## avg_veg_height-Meleagris_gallopavo 2.3641 1.0521 227
## avg_veg_height-Sciurus_carolinensis 2.8407 1.0485 321
## avg_veg_height-Vulpes_vulpes 2.0898 1.0417 228
## avg_veg_height-Sus_scrofa 1.8670 1.0218 372
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8012 0.1931 -3.1946 -2.7955 -2.4345 1.0692
## (Intercept)-Lynx_rufus -3.6885 0.3219 -4.3533 -3.6797 -3.1031 1.0863
## (Intercept)-Didelphis_virginiana -2.8238 0.2884 -3.3844 -2.8294 -2.2423 1.0488
## (Intercept)-Sylvilagus_floridanus -3.2326 0.2425 -3.7345 -3.2257 -2.7847 1.0201
## (Intercept)-Meleagris_gallopavo -3.6351 0.4518 -4.5960 -3.6056 -2.7629 1.0497
## (Intercept)-Sciurus_carolinensis -2.9669 0.3165 -3.5852 -2.9657 -2.3504 1.0152
## (Intercept)-Vulpes_vulpes -4.0704 0.6014 -5.4142 -4.0076 -3.0466 1.0353
## (Intercept)-Sus_scrofa -3.6117 0.5490 -4.6806 -3.6037 -2.5607 1.0174
## shrub_cover-Canis_latrans -0.2797 0.2727 -0.8026 -0.2929 0.2686 1.1277
## shrub_cover-Lynx_rufus -0.0095 0.3968 -0.8380 -0.0046 0.7361 1.0348
## shrub_cover-Didelphis_virginiana 1.2579 0.3937 0.5038 1.2525 2.0540 1.0140
## shrub_cover-Sylvilagus_floridanus 0.6389 0.4403 -0.2545 0.6456 1.4831 1.0894
## shrub_cover-Meleagris_gallopavo -0.4236 0.4458 -1.3292 -0.4264 0.4652 1.0470
## shrub_cover-Sciurus_carolinensis 1.2337 0.4067 0.4616 1.2342 2.0257 1.0415
## shrub_cover-Vulpes_vulpes 0.3996 0.6589 -0.9601 0.4159 1.6325 1.0700
## shrub_cover-Sus_scrofa 1.2793 0.8898 -0.5337 1.2701 3.0855 1.0483
## veg_height-Canis_latrans -0.5861 0.1890 -0.9589 -0.5833 -0.2190 1.0299
## veg_height-Lynx_rufus 0.1565 0.2405 -0.3299 0.1578 0.6033 1.0181
## veg_height-Didelphis_virginiana 0.5310 0.2655 0.0266 0.5230 1.0706 1.0093
## veg_height-Sylvilagus_floridanus 0.1222 0.2540 -0.3719 0.1206 0.6278 1.0040
## veg_height-Meleagris_gallopavo -0.1459 0.4147 -0.9759 -0.1363 0.6633 1.0609
## veg_height-Sciurus_carolinensis 0.1986 0.2392 -0.2655 0.1951 0.6675 1.0067
## veg_height-Vulpes_vulpes -0.2117 0.3608 -0.9882 -0.1918 0.4450 1.0791
## veg_height-Sus_scrofa -0.1807 0.3389 -0.8880 -0.1734 0.4673 1.0057
## ESS
## (Intercept)-Canis_latrans 341
## (Intercept)-Lynx_rufus 188
## (Intercept)-Didelphis_virginiana 413
## (Intercept)-Sylvilagus_floridanus 671
## (Intercept)-Meleagris_gallopavo 191
## (Intercept)-Sciurus_carolinensis 277
## (Intercept)-Vulpes_vulpes 151
## (Intercept)-Sus_scrofa 185
## shrub_cover-Canis_latrans 259
## shrub_cover-Lynx_rufus 222
## shrub_cover-Didelphis_virginiana 383
## shrub_cover-Sylvilagus_floridanus 297
## shrub_cover-Meleagris_gallopavo 236
## shrub_cover-Sciurus_carolinensis 284
## shrub_cover-Vulpes_vulpes 261
## shrub_cover-Sus_scrofa 139
## veg_height-Canis_latrans 512
## veg_height-Lynx_rufus 597
## veg_height-Didelphis_virginiana 822
## veg_height-Sylvilagus_floridanus 510
## veg_height-Meleagris_gallopavo 272
## veg_height-Sciurus_carolinensis 705
## veg_height-Vulpes_vulpes 407
## veg_height-Sus_scrofa 951
#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 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_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): 0.5168
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8211 0.3751 -1.5264 -0.8248 -0.0479 1.0082 1106
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8683 1.1778 0.1169 0.5863 3.2093 1.1177 253
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7771 0.2624 -3.3216 -2.7749 -2.2437 1.0064 1042
## week 0.2973 0.2613 -0.2237 0.2958 0.8135 1.0026 1125
## I(week^2) -0.3066 0.1623 -0.6688 -0.2887 -0.0395 1.0158 444
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4369 0.5206 0.0654 0.2877 1.7911 1.0551 497
## week 0.3130 0.3548 0.0415 0.2060 1.2466 1.0254 864
## I(week^2) 0.1183 0.1487 0.0219 0.0756 0.4730 1.0481 171
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.0751 0.4000 -0.6572 0.0629 0.8996 1.0012
## (Intercept)-Lynx_rufus -0.1879 0.7286 -1.1043 -0.2674 1.0596 1.1563
## (Intercept)-Didelphis_virginiana -1.2656 0.4067 -2.1414 -1.2381 -0.4769 1.0006
## (Intercept)-Sylvilagus_floridanus -0.5433 0.4281 -1.3548 -0.5604 0.3634 1.0128
## (Intercept)-Meleagris_gallopavo -0.5699 0.4888 -1.4579 -0.5926 0.4075 1.0049
## (Intercept)-Sciurus_carolinensis -1.2582 0.3992 -2.0905 -1.2400 -0.5403 1.0029
## (Intercept)-Vulpes_vulpes -1.3642 0.6253 -2.6146 -1.3545 -0.1355 1.0108
## (Intercept)-Sus_scrofa -1.6365 0.5507 -2.8130 -1.6024 -0.6184 1.0061
## ESS
## (Intercept)-Canis_latrans 1908
## (Intercept)-Lynx_rufus 195
## (Intercept)-Didelphis_virginiana 1868
## (Intercept)-Sylvilagus_floridanus 1003
## (Intercept)-Meleagris_gallopavo 741
## (Intercept)-Sciurus_carolinensis 2147
## (Intercept)-Vulpes_vulpes 547
## (Intercept)-Sus_scrofa 863
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4655 0.1852 -2.8487 -2.4636 -2.1037 1.0010
## (Intercept)-Lynx_rufus -3.2109 0.3034 -3.8641 -3.1950 -2.6653 1.0048
## (Intercept)-Didelphis_virginiana -2.2430 0.2861 -2.8096 -2.2380 -1.7038 1.0050
## (Intercept)-Sylvilagus_floridanus -2.9850 0.2746 -3.5540 -2.9747 -2.4469 1.0319
## (Intercept)-Meleagris_gallopavo -3.1082 0.3187 -3.8072 -3.0894 -2.5363 1.0023
## (Intercept)-Sciurus_carolinensis -2.4005 0.2845 -2.9708 -2.3999 -1.8759 1.0008
## (Intercept)-Vulpes_vulpes -3.3621 0.5814 -4.7035 -3.2848 -2.4616 1.0400
## (Intercept)-Sus_scrofa -2.8757 0.4150 -3.7808 -2.8524 -2.1089 1.0023
## week-Canis_latrans 0.5201 0.2613 0.0153 0.5134 1.0528 1.0053
## week-Lynx_rufus 0.3529 0.3329 -0.2848 0.3513 1.0441 1.0061
## week-Didelphis_virginiana 0.0366 0.3702 -0.7634 0.0632 0.6875 1.0083
## week-Sylvilagus_floridanus 0.0771 0.3192 -0.5540 0.0804 0.6890 1.0170
## week-Meleagris_gallopavo -0.1071 0.4065 -0.9938 -0.0841 0.6188 1.0493
## week-Sciurus_carolinensis 0.6673 0.3835 -0.0074 0.6398 1.5229 1.0023
## week-Vulpes_vulpes 0.2266 0.4588 -0.7078 0.2475 1.0917 1.0186
## week-Sus_scrofa 0.5817 0.4417 -0.2139 0.5554 1.5448 1.0202
## I(week^2)-Canis_latrans -0.2139 0.1077 -0.4273 -0.2103 -0.0092 1.0128
## I(week^2)-Lynx_rufus -0.2385 0.1544 -0.5602 -0.2349 0.0465 1.0025
## I(week^2)-Didelphis_virginiana -0.4192 0.2619 -1.0586 -0.3735 -0.0176 1.0322
## I(week^2)-Sylvilagus_floridanus -0.1841 0.1638 -0.5345 -0.1779 0.1189 1.0138
## I(week^2)-Meleagris_gallopavo -0.4634 0.2939 -1.2041 -0.4135 -0.0155 1.0630
## I(week^2)-Sciurus_carolinensis -0.2328 0.1523 -0.5543 -0.2235 0.0472 1.0010
## I(week^2)-Vulpes_vulpes -0.4943 0.3467 -1.3795 -0.4277 0.0026 1.0196
## I(week^2)-Sus_scrofa -0.2147 0.1908 -0.6034 -0.2073 0.1493 1.0020
## ESS
## (Intercept)-Canis_latrans 1209
## (Intercept)-Lynx_rufus 467
## (Intercept)-Didelphis_virginiana 1160
## (Intercept)-Sylvilagus_floridanus 545
## (Intercept)-Meleagris_gallopavo 454
## (Intercept)-Sciurus_carolinensis 1189
## (Intercept)-Vulpes_vulpes 272
## (Intercept)-Sus_scrofa 615
## week-Canis_latrans 1214
## week-Lynx_rufus 976
## week-Didelphis_virginiana 691
## week-Sylvilagus_floridanus 1125
## week-Meleagris_gallopavo 414
## week-Sciurus_carolinensis 1050
## week-Vulpes_vulpes 947
## week-Sus_scrofa 1094
## I(week^2)-Canis_latrans 1291
## I(week^2)-Lynx_rufus 849
## I(week^2)-Didelphis_virginiana 196
## I(week^2)-Sylvilagus_floridanus 597
## I(week^2)-Meleagris_gallopavo 185
## I(week^2)-Sciurus_carolinensis 1227
## I(week^2)-Vulpes_vulpes 269
## I(week^2)-Sus_scrofa 1181
#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 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_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): 0.5597
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6954 0.8905 -3.3740 -1.7299 0.2018 1.0220 482
## Cogon_Patch_Size -0.4644 0.8357 -2.1726 -0.4550 1.1929 1.0428 593
## Veg_shannon_index 0.8469 0.6068 -0.3112 0.8238 2.0254 1.0345 436
## total_shrub_cover -0.6512 0.6850 -2.0807 -0.6378 0.6868 1.0205 796
## Avg_Cogongrass_Cover 1.7939 0.8686 0.0256 1.7821 3.5600 1.1308 190
## Tree_Density -1.8107 0.8031 -3.4931 -1.7818 -0.3078 1.0927 218
## Avg_Canopy_Cover 1.8554 0.7183 0.4973 1.8207 3.3702 1.0116 531
## avg_veg_height -0.7430 0.5708 -1.9647 -0.7226 0.3033 1.0726 276
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.3668 8.4432 0.1516 3.7154 27.9760 1.0365 187
## Cogon_Patch_Size 4.9321 6.9839 0.2346 2.8310 22.4573 1.0476 415
## Veg_shannon_index 1.8877 3.1667 0.0636 0.8672 9.8270 1.0033 292
## total_shrub_cover 3.5065 4.6494 0.1282 1.9585 16.7952 1.0983 197
## Avg_Cogongrass_Cover 1.8044 3.1715 0.0586 0.7573 10.3473 1.1375 247
## Tree_Density 2.5129 5.8399 0.0540 0.9953 14.3949 1.1026 228
## Avg_Canopy_Cover 4.4499 8.8552 0.2056 2.1678 24.1417 1.1141 120
## avg_veg_height 0.5323 0.7272 0.0436 0.2987 2.3970 1.0842 810
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6858 3.6759 0.0738 1.5706 13.1302 1.2204 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8312 0.2928 -3.3951 -2.8315 -2.2209 1.0011 1192
## week 0.2937 0.2613 -0.2142 0.2937 0.8217 1.0082 914
## I(week^2) -0.2993 0.1626 -0.6592 -0.2833 -0.0244 1.0519 346
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5376 0.5467 0.0915 0.3919 1.8987 1.0312 812
## week 0.3054 0.3621 0.0417 0.2034 1.1802 1.0886 637
## I(week^2) 0.1192 0.1812 0.0222 0.0732 0.5503 1.1748 99
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1187 1.3270 -2.5095 0.1308
## (Intercept)-Lynx_rufus 0.1763 2.0258 -3.0171 -0.0786
## (Intercept)-Didelphis_virginiana -3.0632 1.2237 -5.8083 -2.9495
## (Intercept)-Sylvilagus_floridanus -1.6960 1.1576 -3.9743 -1.6804
## (Intercept)-Meleagris_gallopavo -1.9851 1.2619 -4.5586 -1.9429
## (Intercept)-Sciurus_carolinensis -3.1802 1.2456 -6.0259 -3.0555
## (Intercept)-Vulpes_vulpes -2.8471 1.6389 -6.4246 -2.7487
## (Intercept)-Sus_scrofa -4.2846 1.8920 -8.7119 -4.0350
## Cogon_Patch_Size-Canis_latrans 1.0445 1.2876 -0.7818 0.8285
## Cogon_Patch_Size-Lynx_rufus -0.4031 1.7213 -3.4632 -0.5486
## Cogon_Patch_Size-Didelphis_virginiana 1.3243 0.9950 -0.3565 1.2068
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2109 1.8495 -6.7241 -1.8893
## Cogon_Patch_Size-Meleagris_gallopavo -0.1682 1.3008 -2.4674 -0.2749
## Cogon_Patch_Size-Sciurus_carolinensis -1.8192 1.3971 -5.2490 -1.6045
## Cogon_Patch_Size-Vulpes_vulpes -1.3634 1.8560 -5.6455 -1.1866
## Cogon_Patch_Size-Sus_scrofa -1.1361 1.6440 -5.2687 -0.8843
## Veg_shannon_index-Canis_latrans 1.2437 0.7164 -0.0134 1.1822
## Veg_shannon_index-Lynx_rufus 0.6550 1.1658 -2.1469 0.7311
## Veg_shannon_index-Didelphis_virginiana 1.1420 0.8296 -0.3827 1.0680
## Veg_shannon_index-Sylvilagus_floridanus 1.1139 0.8548 -0.3952 1.0293
## Veg_shannon_index-Meleagris_gallopavo 1.4501 1.0624 -0.2110 1.2905
## Veg_shannon_index-Sciurus_carolinensis -0.0759 0.9136 -2.1676 0.0299
## Veg_shannon_index-Vulpes_vulpes -0.0524 1.1993 -2.9755 0.1517
## Veg_shannon_index-Sus_scrofa 1.8270 1.2490 0.0759 1.5920
## total_shrub_cover-Canis_latrans 0.3695 0.9218 -1.0377 0.2121
## total_shrub_cover-Lynx_rufus -1.7905 1.5359 -5.8041 -1.4981
## total_shrub_cover-Didelphis_virginiana -0.7946 0.9075 -2.7814 -0.7473
## total_shrub_cover-Sylvilagus_floridanus -0.3313 1.2125 -2.8961 -0.3164
## total_shrub_cover-Meleagris_gallopavo -2.8981 1.6417 -6.6746 -2.6958
## total_shrub_cover-Sciurus_carolinensis 0.0720 0.8353 -1.4709 0.0478
## total_shrub_cover-Vulpes_vulpes -0.9804 1.3868 -4.4090 -0.8007
## total_shrub_cover-Sus_scrofa 0.2508 1.1928 -1.7679 0.1219
## Avg_Cogongrass_Cover-Canis_latrans 2.2561 1.0242 0.4677 2.1916
## Avg_Cogongrass_Cover-Lynx_rufus 2.4665 1.2263 0.4566 2.3419
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1140 0.9812 0.3339 2.0367
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3080 1.1314 -0.9432 1.3272
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1054 1.5097 -2.5783 1.2658
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3019 1.0267 0.5107 2.2238
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5047 1.3055 0.3968 2.3594
## Avg_Cogongrass_Cover-Sus_scrofa 1.3296 1.3983 -1.5449 1.3890
## Tree_Density-Canis_latrans -2.4248 1.2065 -5.3991 -2.2331
## Tree_Density-Lynx_rufus -0.6100 1.4485 -2.9228 -0.7771
## Tree_Density-Didelphis_virginiana -2.2329 1.1385 -4.8630 -2.1115
## Tree_Density-Sylvilagus_floridanus -2.4364 1.3790 -5.7322 -2.2398
## Tree_Density-Meleagris_gallopavo -2.1042 1.2639 -4.9551 -2.0158
## Tree_Density-Sciurus_carolinensis -2.3535 1.3094 -5.5373 -2.1545
## Tree_Density-Vulpes_vulpes -1.7873 1.4865 -4.7282 -1.7953
## Tree_Density-Sus_scrofa -2.0395 1.3674 -5.3402 -1.8890
## Avg_Canopy_Cover-Canis_latrans 0.1581 0.7549 -1.3923 0.1818
## Avg_Canopy_Cover-Lynx_rufus 0.7842 1.4193 -1.8010 0.7279
## Avg_Canopy_Cover-Didelphis_virginiana 2.9627 1.0437 1.3351 2.8380
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8763 2.0921 1.3051 3.4549
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5784 1.5302 0.5581 2.2386
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4662 0.9829 0.9037 2.3411
## Avg_Canopy_Cover-Vulpes_vulpes 2.5207 1.8601 0.4063 2.1808
## Avg_Canopy_Cover-Sus_scrofa 2.2021 1.0246 0.4729 2.0758
## avg_veg_height-Canis_latrans -0.8660 0.6772 -2.2978 -0.8360
## avg_veg_height-Lynx_rufus -0.8181 0.8252 -2.6447 -0.7857
## avg_veg_height-Didelphis_virginiana -0.7714 0.7416 -2.3354 -0.7426
## avg_veg_height-Sylvilagus_floridanus -0.8539 0.7443 -2.4360 -0.8153
## avg_veg_height-Meleagris_gallopavo -0.8693 0.8361 -2.6154 -0.8101
## avg_veg_height-Sciurus_carolinensis -0.3304 0.7342 -1.7049 -0.3605
## avg_veg_height-Vulpes_vulpes -0.7883 0.7992 -2.4398 -0.7627
## avg_veg_height-Sus_scrofa -0.7655 0.7722 -2.3332 -0.7534
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.8149 1.0117 167
## (Intercept)-Lynx_rufus 5.0673 1.0196 87
## (Intercept)-Didelphis_virginiana -1.0222 1.0124 220
## (Intercept)-Sylvilagus_floridanus 0.6162 1.0469 434
## (Intercept)-Meleagris_gallopavo 0.4802 1.0307 416
## (Intercept)-Sciurus_carolinensis -1.1129 1.0424 272
## (Intercept)-Vulpes_vulpes 0.2799 1.0122 215
## (Intercept)-Sus_scrofa -1.3407 1.0954 139
## Cogon_Patch_Size-Canis_latrans 4.3401 1.0007 444
## Cogon_Patch_Size-Lynx_rufus 3.4362 1.0520 176
## Cogon_Patch_Size-Didelphis_virginiana 3.4711 1.0132 520
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6200 1.0851 259
## Cogon_Patch_Size-Meleagris_gallopavo 2.6228 1.0567 401
## Cogon_Patch_Size-Sciurus_carolinensis 0.2753 1.0930 355
## Cogon_Patch_Size-Vulpes_vulpes 1.8418 1.0437 342
## Cogon_Patch_Size-Sus_scrofa 1.3699 1.0116 440
## Veg_shannon_index-Canis_latrans 2.7668 1.0408 436
## Veg_shannon_index-Lynx_rufus 2.6607 1.0519 244
## Veg_shannon_index-Didelphis_virginiana 3.0021 1.0101 650
## Veg_shannon_index-Sylvilagus_floridanus 2.9768 1.0107 610
## Veg_shannon_index-Meleagris_gallopavo 3.9843 1.0143 271
## Veg_shannon_index-Sciurus_carolinensis 1.4738 1.0182 273
## Veg_shannon_index-Vulpes_vulpes 1.7948 1.0200 263
## Veg_shannon_index-Sus_scrofa 4.9484 1.0272 208
## total_shrub_cover-Canis_latrans 2.7066 1.0195 389
## total_shrub_cover-Lynx_rufus 0.4011 1.0734 196
## total_shrub_cover-Didelphis_virginiana 0.8566 1.0239 790
## total_shrub_cover-Sylvilagus_floridanus 2.2877 1.0551 378
## total_shrub_cover-Meleagris_gallopavo -0.3768 1.1519 228
## total_shrub_cover-Sciurus_carolinensis 1.7828 1.0117 847
## total_shrub_cover-Vulpes_vulpes 1.1757 1.0277 286
## total_shrub_cover-Sus_scrofa 3.0823 1.0233 368
## Avg_Cogongrass_Cover-Canis_latrans 4.4572 1.0970 196
## Avg_Cogongrass_Cover-Lynx_rufus 5.3081 1.1003 262
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.2837 1.1123 284
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4744 1.1122 248
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6814 1.0872 247
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4754 1.0888 265
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.5292 1.0530 196
## Avg_Cogongrass_Cover-Sus_scrofa 3.8968 1.0571 356
## Tree_Density-Canis_latrans -0.5759 1.0898 254
## Tree_Density-Lynx_rufus 2.6573 1.0374 214
## Tree_Density-Didelphis_virginiana -0.4331 1.0640 381
## Tree_Density-Sylvilagus_floridanus -0.3596 1.0897 189
## Tree_Density-Meleagris_gallopavo 0.1375 1.0396 350
## Tree_Density-Sciurus_carolinensis -0.4205 1.0921 238
## Tree_Density-Vulpes_vulpes 1.1947 1.0663 252
## Tree_Density-Sus_scrofa 0.3281 1.0318 293
## Avg_Canopy_Cover-Canis_latrans 1.5948 1.0156 417
## Avg_Canopy_Cover-Lynx_rufus 3.7399 1.0313 256
## Avg_Canopy_Cover-Didelphis_virginiana 5.4008 1.0507 201
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.6269 1.0881 112
## Avg_Canopy_Cover-Meleagris_gallopavo 6.4104 1.0714 173
## Avg_Canopy_Cover-Sciurus_carolinensis 4.8718 1.0005 437
## Avg_Canopy_Cover-Vulpes_vulpes 6.6407 1.0312 111
## Avg_Canopy_Cover-Sus_scrofa 4.5497 1.0026 393
## avg_veg_height-Canis_latrans 0.3689 1.0693 355
## avg_veg_height-Lynx_rufus 0.7673 1.0232 450
## avg_veg_height-Didelphis_virginiana 0.6815 1.0484 457
## avg_veg_height-Sylvilagus_floridanus 0.5304 1.0567 399
## avg_veg_height-Meleagris_gallopavo 0.6317 1.0404 448
## avg_veg_height-Sciurus_carolinensis 1.2144 1.0550 542
## avg_veg_height-Vulpes_vulpes 0.7301 1.0350 462
## avg_veg_height-Sus_scrofa 0.6897 1.0370 371
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5002 0.1930 -2.8800 -2.5002 -2.1229 1.0247
## (Intercept)-Lynx_rufus -3.4111 0.3321 -4.1147 -3.3949 -2.7995 1.0057
## (Intercept)-Didelphis_virginiana -2.1929 0.2613 -2.6980 -2.1881 -1.6881 1.0004
## (Intercept)-Sylvilagus_floridanus -3.0655 0.2802 -3.6203 -3.0573 -2.5191 1.0141
## (Intercept)-Meleagris_gallopavo -3.1652 0.3056 -3.8124 -3.1591 -2.5888 1.0003
## (Intercept)-Sciurus_carolinensis -2.4023 0.2929 -2.9980 -2.3967 -1.8470 0.9996
## (Intercept)-Vulpes_vulpes -3.6174 0.5714 -4.8181 -3.5755 -2.5983 1.0412
## (Intercept)-Sus_scrofa -2.8553 0.4236 -3.7699 -2.8389 -2.0717 1.0061
## week-Canis_latrans 0.5246 0.2587 0.0422 0.5124 1.0544 1.0035
## week-Lynx_rufus 0.3777 0.3355 -0.2438 0.3652 1.0704 1.0051
## week-Didelphis_virginiana 0.0485 0.3521 -0.6854 0.0695 0.7084 1.0010
## week-Sylvilagus_floridanus 0.0850 0.3219 -0.5842 0.0950 0.6889 1.0163
## week-Meleagris_gallopavo -0.1009 0.4258 -1.0050 -0.0532 0.5949 1.1021
## week-Sciurus_carolinensis 0.6632 0.3807 -0.0049 0.6382 1.4909 1.0163
## week-Vulpes_vulpes 0.2512 0.4416 -0.6014 0.2424 1.1294 1.0024
## week-Sus_scrofa 0.5461 0.4326 -0.2458 0.5287 1.4617 1.0031
## I(week^2)-Canis_latrans -0.2195 0.1092 -0.4453 -0.2175 -0.0077 1.0069
## I(week^2)-Lynx_rufus -0.2536 0.1595 -0.5872 -0.2432 0.0420 1.0090
## I(week^2)-Didelphis_virginiana -0.4079 0.2377 -0.9574 -0.3781 -0.0256 1.0221
## I(week^2)-Sylvilagus_floridanus -0.1912 0.1587 -0.5148 -0.1871 0.1055 1.0077
## I(week^2)-Meleagris_gallopavo -0.4536 0.3460 -1.3854 -0.3995 -0.0182 1.2925
## I(week^2)-Sciurus_carolinensis -0.2317 0.1551 -0.5588 -0.2226 0.0444 1.0118
## I(week^2)-Vulpes_vulpes -0.4671 0.3386 -1.2893 -0.4057 0.0192 1.1485
## I(week^2)-Sus_scrofa -0.1981 0.1872 -0.5972 -0.1858 0.1548 1.0030
## ESS
## (Intercept)-Canis_latrans 896
## (Intercept)-Lynx_rufus 181
## (Intercept)-Didelphis_virginiana 1471
## (Intercept)-Sylvilagus_floridanus 532
## (Intercept)-Meleagris_gallopavo 661
## (Intercept)-Sciurus_carolinensis 857
## (Intercept)-Vulpes_vulpes 220
## (Intercept)-Sus_scrofa 709
## week-Canis_latrans 1256
## week-Lynx_rufus 809
## week-Didelphis_virginiana 1130
## week-Sylvilagus_floridanus 965
## week-Meleagris_gallopavo 372
## week-Sciurus_carolinensis 935
## week-Vulpes_vulpes 678
## week-Sus_scrofa 1228
## I(week^2)-Canis_latrans 1280
## I(week^2)-Lynx_rufus 660
## I(week^2)-Didelphis_virginiana 365
## I(week^2)-Sylvilagus_floridanus 772
## I(week^2)-Meleagris_gallopavo 71
## I(week^2)-Sciurus_carolinensis 1073
## I(week^2)-Vulpes_vulpes 144
## I(week^2)-Sus_scrofa 1338
#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 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_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): 0.5288
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1026 0.4709 -1.9803 -1.1103 -0.1508 1.0239 528
## Avg_Cogongrass_Cover 0.2350 0.4130 -0.5878 0.2337 1.0351 1.0108 719
## total_shrub_cover -0.4116 0.4100 -1.2893 -0.3939 0.3764 1.0091 996
## avg_veg_height -0.1960 0.3626 -0.9177 -0.1997 0.5341 1.0034 575
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8529 1.1740 0.0578 0.4860 3.8637 1.0200 476
## Avg_Cogongrass_Cover 0.6003 0.9945 0.0479 0.3390 2.6378 1.0362 934
## total_shrub_cover 0.8660 1.0861 0.0642 0.5385 3.5624 1.0452 751
## avg_veg_height 0.3116 0.4677 0.0380 0.1816 1.3072 1.0204 1283
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1219 1.7801 0.2667 1.6398 7.105 1.0172 229
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7868 0.2553 -3.2998 -2.7837 -2.2941 1.0024 1264
## week 0.2999 0.2554 -0.2192 0.2966 0.8092 1.0198 840
## I(week^2) -0.3028 0.1506 -0.6252 -0.2961 -0.0354 1.0128 385
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3883 0.4032 0.0651 0.2772 1.3511 1.0244 1007
## week 0.2979 0.3354 0.0414 0.1957 1.1770 1.0158 1108
## I(week^2) 0.1118 0.1344 0.0222 0.0747 0.4352 1.0678 493
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4235 0.7618 -1.8069 -0.4580
## (Intercept)-Lynx_rufus -0.8051 0.7405 -2.1527 -0.8634
## (Intercept)-Didelphis_virginiana -1.3609 0.6451 -2.7010 -1.3353
## (Intercept)-Sylvilagus_floridanus -0.8649 0.7133 -2.1683 -0.8977
## (Intercept)-Meleagris_gallopavo -1.1140 0.6912 -2.5719 -1.0886
## (Intercept)-Sciurus_carolinensis -1.4411 0.6596 -2.8843 -1.4049
## (Intercept)-Vulpes_vulpes -1.5200 0.7681 -3.1718 -1.4915
## (Intercept)-Sus_scrofa -1.6729 0.7638 -3.3548 -1.5901
## Avg_Cogongrass_Cover-Canis_latrans 0.5778 0.5085 -0.3155 0.5424
## Avg_Cogongrass_Cover-Lynx_rufus 0.6285 0.5885 -0.4115 0.5703
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4852 0.4959 -0.4458 0.4810
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2104 0.5663 -1.4550 -0.1610
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3132 0.6706 -1.7549 -0.2560
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3836 0.4849 -0.5442 0.3662
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4312 0.5909 -0.6428 0.3878
## Avg_Cogongrass_Cover-Sus_scrofa -0.0639 0.6773 -1.5965 0.0011
## total_shrub_cover-Canis_latrans 0.1831 0.4921 -0.7117 0.1490
## total_shrub_cover-Lynx_rufus -1.0156 0.7563 -2.7167 -0.9117
## total_shrub_cover-Didelphis_virginiana -0.2947 0.4682 -1.2252 -0.2846
## total_shrub_cover-Sylvilagus_floridanus -0.4209 0.5787 -1.6121 -0.3907
## total_shrub_cover-Meleagris_gallopavo -1.4373 0.7927 -3.2195 -1.3420
## total_shrub_cover-Sciurus_carolinensis -0.0985 0.4548 -0.9816 -0.1143
## total_shrub_cover-Vulpes_vulpes -0.4211 0.6328 -1.7554 -0.3965
## total_shrub_cover-Sus_scrofa 0.0797 0.6403 -1.1563 0.0514
## avg_veg_height-Canis_latrans -0.2230 0.4416 -1.0939 -0.2247
## avg_veg_height-Lynx_rufus -0.1925 0.5441 -1.2555 -0.2039
## avg_veg_height-Didelphis_virginiana -0.1687 0.4641 -1.0574 -0.1659
## avg_veg_height-Sylvilagus_floridanus -0.2968 0.4797 -1.3119 -0.2851
## avg_veg_height-Meleagris_gallopavo -0.4177 0.5542 -1.6026 -0.3991
## avg_veg_height-Sciurus_carolinensis 0.1540 0.4895 -0.7297 0.1346
## avg_veg_height-Vulpes_vulpes -0.2634 0.5250 -1.3041 -0.2600
## avg_veg_height-Sus_scrofa -0.1557 0.5071 -1.1447 -0.1530
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2435 1.0154 481
## (Intercept)-Lynx_rufus 0.8727 1.0089 493
## (Intercept)-Didelphis_virginiana -0.1573 1.0038 799
## (Intercept)-Sylvilagus_floridanus 0.6087 1.0254 621
## (Intercept)-Meleagris_gallopavo 0.2587 1.0217 769
## (Intercept)-Sciurus_carolinensis -0.2501 1.0059 696
## (Intercept)-Vulpes_vulpes -0.1349 1.0026 540
## (Intercept)-Sus_scrofa -0.4108 1.0056 666
## Avg_Cogongrass_Cover-Canis_latrans 1.6723 1.0135 930
## Avg_Cogongrass_Cover-Lynx_rufus 1.9687 1.0110 914
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4897 1.0051 1117
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8016 1.0055 1095
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8642 1.0148 682
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3992 1.0178 1047
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6773 1.0025 1003
## Avg_Cogongrass_Cover-Sus_scrofa 1.0797 1.0086 861
## total_shrub_cover-Canis_latrans 1.2522 1.0017 1404
## total_shrub_cover-Lynx_rufus 0.1872 1.0108 427
## total_shrub_cover-Didelphis_virginiana 0.5985 1.0012 2219
## total_shrub_cover-Sylvilagus_floridanus 0.6042 1.0055 656
## total_shrub_cover-Meleagris_gallopavo -0.1624 1.0238 476
## total_shrub_cover-Sciurus_carolinensis 0.8328 0.9998 2078
## total_shrub_cover-Vulpes_vulpes 0.7970 1.0143 1043
## total_shrub_cover-Sus_scrofa 1.4682 1.0023 1176
## avg_veg_height-Canis_latrans 0.6547 1.0058 863
## avg_veg_height-Lynx_rufus 0.8988 1.0141 797
## avg_veg_height-Didelphis_virginiana 0.7592 1.0019 1014
## avg_veg_height-Sylvilagus_floridanus 0.6283 1.0030 966
## avg_veg_height-Meleagris_gallopavo 0.6148 1.0032 873
## avg_veg_height-Sciurus_carolinensis 1.1857 1.0107 947
## avg_veg_height-Vulpes_vulpes 0.7509 1.0033 852
## avg_veg_height-Sus_scrofa 0.8802 1.0019 980
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4977 0.1941 -2.8976 -2.4914 -2.1367 1.0336
## (Intercept)-Lynx_rufus -3.2276 0.2842 -3.7959 -3.2180 -2.6894 1.0019
## (Intercept)-Didelphis_virginiana -2.2693 0.2763 -2.8226 -2.2653 -1.7506 1.0132
## (Intercept)-Sylvilagus_floridanus -3.0288 0.2980 -3.6367 -3.0079 -2.4871 1.0271
## (Intercept)-Meleagris_gallopavo -3.0466 0.3029 -3.6734 -3.0329 -2.4867 1.0011
## (Intercept)-Sciurus_carolinensis -2.4215 0.2845 -3.0055 -2.4204 -1.8811 1.0122
## (Intercept)-Vulpes_vulpes -3.3459 0.5172 -4.4715 -3.2883 -2.4770 1.0016
## (Intercept)-Sus_scrofa -2.8605 0.3947 -3.6950 -2.8403 -2.1650 1.0004
## week-Canis_latrans 0.5166 0.2597 0.0300 0.5115 1.0410 1.0392
## week-Lynx_rufus 0.3728 0.3307 -0.2480 0.3681 1.0497 1.0006
## week-Didelphis_virginiana 0.0618 0.3509 -0.6535 0.0706 0.7026 1.0177
## week-Sylvilagus_floridanus 0.1055 0.3322 -0.5702 0.1085 0.7512 1.0091
## week-Meleagris_gallopavo -0.0966 0.3981 -0.9150 -0.0800 0.6441 1.0188
## week-Sciurus_carolinensis 0.6637 0.3714 0.0080 0.6369 1.4495 1.0317
## week-Vulpes_vulpes 0.2466 0.4494 -0.7007 0.2563 1.1196 1.0014
## week-Sus_scrofa 0.5738 0.4311 -0.2131 0.5445 1.5242 1.0102
## I(week^2)-Canis_latrans -0.2121 0.1092 -0.4278 -0.2120 -0.0031 1.0467
## I(week^2)-Lynx_rufus -0.2416 0.1535 -0.5688 -0.2340 0.0446 1.0114
## I(week^2)-Didelphis_virginiana -0.4055 0.2298 -0.9288 -0.3763 -0.0322 1.0053
## I(week^2)-Sylvilagus_floridanus -0.1990 0.1583 -0.5263 -0.1989 0.1088 1.0363
## I(week^2)-Meleagris_gallopavo -0.4643 0.2914 -1.1310 -0.4160 -0.0158 1.0567
## I(week^2)-Sciurus_carolinensis -0.2255 0.1473 -0.5328 -0.2211 0.0432 1.0268
## I(week^2)-Vulpes_vulpes -0.4649 0.3281 -1.2211 -0.4219 0.0229 1.0176
## I(week^2)-Sus_scrofa -0.2151 0.1874 -0.6136 -0.2062 0.1322 1.0091
## ESS
## (Intercept)-Canis_latrans 850
## (Intercept)-Lynx_rufus 538
## (Intercept)-Didelphis_virginiana 1133
## (Intercept)-Sylvilagus_floridanus 584
## (Intercept)-Meleagris_gallopavo 711
## (Intercept)-Sciurus_carolinensis 1198
## (Intercept)-Vulpes_vulpes 313
## (Intercept)-Sus_scrofa 921
## week-Canis_latrans 926
## week-Lynx_rufus 868
## week-Didelphis_virginiana 1003
## week-Sylvilagus_floridanus 758
## week-Meleagris_gallopavo 528
## week-Sciurus_carolinensis 1005
## week-Vulpes_vulpes 933
## week-Sus_scrofa 1041
## I(week^2)-Canis_latrans 1256
## I(week^2)-Lynx_rufus 744
## I(week^2)-Didelphis_virginiana 426
## I(week^2)-Sylvilagus_floridanus 697
## I(week^2)-Meleagris_gallopavo 180
## I(week^2)-Sciurus_carolinensis 1173
## I(week^2)-Vulpes_vulpes 202
## I(week^2)-Sus_scrofa 1202
#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 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_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): 0.5298
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2632 0.5340 -2.3463 -1.2724 -0.2000 1.0033 624
## Tree_Density -0.7416 0.5174 -1.8230 -0.7077 0.1913 1.0145 672
## Avg_Canopy_Cover 1.0957 0.4516 0.2609 1.0758 2.0384 1.0047 1212
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4788 1.9408 0.0877 0.9510 6.2193 1.0276 538
## Tree_Density 1.2688 2.1978 0.0608 0.6387 6.5043 1.0261 455
## Avg_Canopy_Cover 1.1063 1.5490 0.0843 0.6800 4.7719 1.0406 780
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1808 1.2474 0.0692 0.8095 4.2007 1.0622 155
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7848 0.2809 -3.3496 -2.7808 -2.2072 1.0049 943
## week 0.2955 0.2695 -0.2468 0.2961 0.8326 1.0052 914
## I(week^2) -0.3027 0.1578 -0.6367 -0.2954 -0.0201 1.1062 422
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4686 0.7280 0.0695 0.3221 1.6414 1.1706 1286
## week 0.3191 0.3384 0.0406 0.2167 1.2358 1.0722 872
## I(week^2) 0.1115 0.1259 0.0228 0.0743 0.4007 1.0920 545
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.3163 0.7183 -1.7273 -0.3032 1.0936
## (Intercept)-Lynx_rufus -0.6108 0.9512 -2.1767 -0.6834 1.6164
## (Intercept)-Didelphis_virginiana -1.8375 0.7226 -3.3965 -1.7888 -0.5676
## (Intercept)-Sylvilagus_floridanus -1.0473 0.7108 -2.5202 -1.0262 0.3353
## (Intercept)-Meleagris_gallopavo -0.9075 0.7422 -2.3320 -0.9419 0.6210
## (Intercept)-Sciurus_carolinensis -1.8811 0.7257 -3.4524 -1.8147 -0.6009
## (Intercept)-Vulpes_vulpes -1.7760 0.9094 -3.7969 -1.7371 -0.0714
## (Intercept)-Sus_scrofa -2.3096 0.9028 -4.2736 -2.2068 -0.7877
## Tree_Density-Canis_latrans -0.9389 0.6547 -2.5069 -0.8601 0.1280
## Tree_Density-Lynx_rufus 0.3723 0.7834 -0.8840 0.2654 2.2683
## Tree_Density-Didelphis_virginiana -1.1642 0.8889 -3.3354 -1.0037 0.1602
## Tree_Density-Sylvilagus_floridanus -1.2175 0.9203 -3.4138 -1.0632 0.1498
## Tree_Density-Meleagris_gallopavo -0.9487 0.8179 -2.8876 -0.8537 0.4398
## Tree_Density-Sciurus_carolinensis -0.9833 0.8123 -2.9141 -0.8886 0.3026
## Tree_Density-Vulpes_vulpes -0.5419 0.9847 -2.4949 -0.5498 1.5490
## Tree_Density-Sus_scrofa -0.9614 0.8976 -3.0516 -0.8408 0.5339
## Avg_Canopy_Cover-Canis_latrans -0.0249 0.5004 -1.0621 -0.0195 0.9504
## Avg_Canopy_Cover-Lynx_rufus 0.6315 0.7146 -0.6037 0.5828 2.2579
## Avg_Canopy_Cover-Didelphis_virginiana 1.3702 0.5645 0.4105 1.3255 2.6020
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9682 0.9145 0.6858 1.8064 4.1858
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5857 0.7763 0.3579 1.4493 3.4186
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3367 0.5529 0.3872 1.2900 2.5355
## Avg_Canopy_Cover-Vulpes_vulpes 1.0992 0.6608 -0.0531 1.0358 2.5483
## Avg_Canopy_Cover-Sus_scrofa 1.2467 0.5716 0.2282 1.2002 2.4890
## Rhat ESS
## (Intercept)-Canis_latrans 1.0287 442
## (Intercept)-Lynx_rufus 1.0203 274
## (Intercept)-Didelphis_virginiana 1.0001 744
## (Intercept)-Sylvilagus_floridanus 1.0114 612
## (Intercept)-Meleagris_gallopavo 1.0186 559
## (Intercept)-Sciurus_carolinensis 1.0360 637
## (Intercept)-Vulpes_vulpes 1.0393 398
## (Intercept)-Sus_scrofa 1.0234 561
## Tree_Density-Canis_latrans 1.0223 1137
## Tree_Density-Lynx_rufus 1.0402 473
## Tree_Density-Didelphis_virginiana 1.0254 417
## Tree_Density-Sylvilagus_floridanus 1.0291 482
## Tree_Density-Meleagris_gallopavo 1.0045 831
## Tree_Density-Sciurus_carolinensis 1.0120 853
## Tree_Density-Vulpes_vulpes 1.0022 440
## Tree_Density-Sus_scrofa 1.0193 755
## Avg_Canopy_Cover-Canis_latrans 1.0040 1041
## Avg_Canopy_Cover-Lynx_rufus 1.0163 654
## Avg_Canopy_Cover-Didelphis_virginiana 1.0044 995
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0073 540
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0132 580
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0119 1038
## Avg_Canopy_Cover-Vulpes_vulpes 1.0019 736
## Avg_Canopy_Cover-Sus_scrofa 1.0049 1314
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4887 0.1859 -2.8545 -2.4822 -2.1391 1.0057
## (Intercept)-Lynx_rufus -3.2545 0.3352 -3.9631 -3.2363 -2.6512 1.0386
## (Intercept)-Didelphis_virginiana -2.2380 0.2753 -2.8012 -2.2368 -1.7165 1.0294
## (Intercept)-Sylvilagus_floridanus -2.9821 0.2752 -3.5626 -2.9719 -2.4739 1.0014
## (Intercept)-Meleagris_gallopavo -3.1641 0.3145 -3.8118 -3.1397 -2.6027 1.0050
## (Intercept)-Sciurus_carolinensis -2.3992 0.2865 -2.9743 -2.3916 -1.8596 1.0000
## (Intercept)-Vulpes_vulpes -3.4476 0.5728 -4.6834 -3.3916 -2.4767 1.0979
## (Intercept)-Sus_scrofa -2.7829 0.3906 -3.5954 -2.7651 -2.0444 1.0025
## week-Canis_latrans 0.5274 0.2696 0.0141 0.5248 1.0760 1.0070
## week-Lynx_rufus 0.3709 0.3414 -0.2686 0.3584 1.0769 1.0114
## week-Didelphis_virginiana 0.0334 0.3659 -0.7213 0.0536 0.7106 1.0033
## week-Sylvilagus_floridanus 0.0738 0.3355 -0.6196 0.0796 0.7084 1.0135
## week-Meleagris_gallopavo -0.1252 0.4230 -1.0712 -0.0828 0.5792 1.0811
## week-Sciurus_carolinensis 0.6798 0.3897 -0.0178 0.6587 1.4993 1.0321
## week-Vulpes_vulpes 0.2427 0.4573 -0.6763 0.2384 1.1952 1.0125
## week-Sus_scrofa 0.5619 0.4426 -0.2320 0.5274 1.5318 1.0119
## I(week^2)-Canis_latrans -0.2203 0.1098 -0.4462 -0.2154 -0.0160 1.0084
## I(week^2)-Lynx_rufus -0.2387 0.1654 -0.5891 -0.2285 0.0654 1.0838
## I(week^2)-Didelphis_virginiana -0.4025 0.2364 -0.9541 -0.3740 -0.0150 1.0364
## I(week^2)-Sylvilagus_floridanus -0.1902 0.1635 -0.5324 -0.1805 0.1193 1.0009
## I(week^2)-Meleagris_gallopavo -0.4578 0.2806 -1.1509 -0.4167 -0.0177 1.3798
## I(week^2)-Sciurus_carolinensis -0.2334 0.1551 -0.5476 -0.2324 0.0648 1.0209
## I(week^2)-Vulpes_vulpes -0.4880 0.3028 -1.2055 -0.4432 -0.0154 1.0988
## I(week^2)-Sus_scrofa -0.2030 0.1853 -0.5785 -0.1957 0.1390 1.0082
## ESS
## (Intercept)-Canis_latrans 1137
## (Intercept)-Lynx_rufus 333
## (Intercept)-Didelphis_virginiana 885
## (Intercept)-Sylvilagus_floridanus 762
## (Intercept)-Meleagris_gallopavo 491
## (Intercept)-Sciurus_carolinensis 1249
## (Intercept)-Vulpes_vulpes 220
## (Intercept)-Sus_scrofa 968
## week-Canis_latrans 1205
## week-Lynx_rufus 806
## week-Didelphis_virginiana 1058
## week-Sylvilagus_floridanus 883
## week-Meleagris_gallopavo 450
## week-Sciurus_carolinensis 974
## week-Vulpes_vulpes 646
## week-Sus_scrofa 1005
## I(week^2)-Canis_latrans 1249
## I(week^2)-Lynx_rufus 515
## I(week^2)-Didelphis_virginiana 452
## I(week^2)-Sylvilagus_floridanus 713
## I(week^2)-Meleagris_gallopavo 131
## I(week^2)-Sciurus_carolinensis 1107
## I(week^2)-Vulpes_vulpes 320
## I(week^2)-Sus_scrofa 1268
#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 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_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): 0.5338
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1721 0.5005 -2.2045 -1.1656 -0.1934 1.0156 479
## Cogon_Patch_Size -0.2364 0.5454 -1.3636 -0.2199 0.7977 1.0088 987
## Avg_Cogongrass_Cover 0.1545 0.3692 -0.6134 0.1661 0.8690 1.0027 769
## total_shrub_cover -0.3539 0.4137 -1.2162 -0.3313 0.4396 1.0083 785
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0316 1.7863 0.0556 0.5402 4.9485 1.1514 342
## Cogon_Patch_Size 1.6777 2.5066 0.0934 0.9349 7.4704 1.0395 541
## Avg_Cogongrass_Cover 0.6407 0.8907 0.0484 0.3502 2.9821 1.0144 753
## total_shrub_cover 0.8186 1.0511 0.0528 0.4757 3.5640 1.0069 528
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3826 1.8713 0.2692 1.8652 7.2569 1.0007 193
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7782 0.2677 -3.3100 -2.7766 -2.2431 1.0025 1617
## week 0.3000 0.2567 -0.2113 0.3067 0.7900 1.0038 840
## I(week^2) -0.3048 0.1469 -0.6190 -0.2957 -0.0295 1.0153 622
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4234 0.4659 0.0642 0.2923 1.5318 1.0201 687
## week 0.2984 0.3333 0.0402 0.1960 1.2078 1.0339 802
## I(week^2) 0.1031 0.1056 0.0220 0.0707 0.3980 1.0461 729
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3856 0.8371 -1.8652 -0.4599
## (Intercept)-Lynx_rufus -0.8922 0.7712 -2.2880 -0.9198
## (Intercept)-Didelphis_virginiana -1.4284 0.6978 -2.9705 -1.3955
## (Intercept)-Sylvilagus_floridanus -1.0255 0.7690 -2.5213 -1.0449
## (Intercept)-Meleagris_gallopavo -1.1433 0.7361 -2.6171 -1.1372
## (Intercept)-Sciurus_carolinensis -1.5606 0.7041 -3.1387 -1.5025
## (Intercept)-Vulpes_vulpes -1.5679 0.8640 -3.4125 -1.5189
## (Intercept)-Sus_scrofa -1.8135 0.8408 -3.7187 -1.7185
## Cogon_Patch_Size-Canis_latrans 0.8002 0.7915 -0.3409 0.6701
## Cogon_Patch_Size-Lynx_rufus -0.3681 0.9297 -2.0503 -0.3971
## Cogon_Patch_Size-Didelphis_virginiana 0.7980 0.6479 -0.2784 0.7299
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2005 1.0792 -3.9923 -0.9767
## Cogon_Patch_Size-Meleagris_gallopavo -0.0967 0.6994 -1.5399 -0.0990
## Cogon_Patch_Size-Sciurus_carolinensis -0.9457 0.8539 -3.0535 -0.8140
## Cogon_Patch_Size-Vulpes_vulpes -0.7263 0.9977 -3.0332 -0.5840
## Cogon_Patch_Size-Sus_scrofa -0.4757 0.9621 -2.7578 -0.3433
## Avg_Cogongrass_Cover-Canis_latrans 0.2910 0.4476 -0.5551 0.2706
## Avg_Cogongrass_Cover-Lynx_rufus 0.6740 0.6074 -0.3311 0.6079
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2223 0.4694 -0.7418 0.2327
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2060 0.5589 -1.4521 -0.1621
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4881 0.7240 -2.0893 -0.4094
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5657 0.4697 -0.2816 0.5214
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3758 0.5456 -0.6293 0.3510
## Avg_Cogongrass_Cover-Sus_scrofa -0.1631 0.6947 -1.7659 -0.0704
## total_shrub_cover-Canis_latrans 0.1388 0.4935 -0.7523 0.1066
## total_shrub_cover-Lynx_rufus -0.7877 0.7249 -2.4639 -0.6846
## total_shrub_cover-Didelphis_virginiana -0.3703 0.4960 -1.4021 -0.3452
## total_shrub_cover-Sylvilagus_floridanus -0.2932 0.5950 -1.5703 -0.2768
## total_shrub_cover-Meleagris_gallopavo -1.3177 0.8189 -3.3158 -1.1885
## total_shrub_cover-Sciurus_carolinensis -0.0250 0.4919 -0.9693 -0.0416
## total_shrub_cover-Vulpes_vulpes -0.3433 0.6822 -1.8046 -0.3014
## total_shrub_cover-Sus_scrofa 0.0765 0.6359 -1.1176 0.0496
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4815 1.0112 331
## (Intercept)-Lynx_rufus 0.7268 1.0494 399
## (Intercept)-Didelphis_virginiana -0.1653 0.9999 648
## (Intercept)-Sylvilagus_floridanus 0.6223 1.0208 443
## (Intercept)-Meleagris_gallopavo 0.3391 1.0094 529
## (Intercept)-Sciurus_carolinensis -0.3115 0.9998 579
## (Intercept)-Vulpes_vulpes -0.0758 1.0209 361
## (Intercept)-Sus_scrofa -0.3504 1.0111 486
## Cogon_Patch_Size-Canis_latrans 2.7533 1.0255 648
## Cogon_Patch_Size-Lynx_rufus 1.6613 1.0229 542
## Cogon_Patch_Size-Didelphis_virginiana 2.1729 1.0075 646
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2788 1.0217 460
## Cogon_Patch_Size-Meleagris_gallopavo 1.2614 1.0167 957
## Cogon_Patch_Size-Sciurus_carolinensis 0.3132 1.0008 671
## Cogon_Patch_Size-Vulpes_vulpes 0.8737 1.0034 594
## Cogon_Patch_Size-Sus_scrofa 1.0991 1.0109 607
## Avg_Cogongrass_Cover-Canis_latrans 1.2381 0.9999 1715
## Avg_Cogongrass_Cover-Lynx_rufus 2.0656 1.0038 934
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1493 0.9999 1662
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7797 1.0081 881
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7779 1.0069 626
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.5909 1.0015 1121
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5518 1.0019 1134
## Avg_Cogongrass_Cover-Sus_scrofa 0.9777 1.0034 703
## total_shrub_cover-Canis_latrans 1.2003 1.0124 1152
## total_shrub_cover-Lynx_rufus 0.3195 1.0164 532
## total_shrub_cover-Didelphis_virginiana 0.5694 1.0076 1326
## total_shrub_cover-Sylvilagus_floridanus 0.8603 1.0007 888
## total_shrub_cover-Meleagris_gallopavo -0.0997 1.0184 495
## total_shrub_cover-Sciurus_carolinensis 0.9509 1.0059 1583
## total_shrub_cover-Vulpes_vulpes 0.9586 1.0100 931
## total_shrub_cover-Sus_scrofa 1.4028 1.0126 1053
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4802 0.1842 -2.8412 -2.4787 -2.1357 1.0042
## (Intercept)-Lynx_rufus -3.2093 0.2943 -3.8447 -3.1966 -2.6672 1.0147
## (Intercept)-Didelphis_virginiana -2.2540 0.2788 -2.7975 -2.2583 -1.7128 1.0001
## (Intercept)-Sylvilagus_floridanus -3.0470 0.2852 -3.6193 -3.0330 -2.5312 1.0003
## (Intercept)-Meleagris_gallopavo -3.0756 0.3195 -3.7505 -3.0602 -2.5028 1.0055
## (Intercept)-Sciurus_carolinensis -2.4013 0.2839 -2.9670 -2.3908 -1.8761 1.0096
## (Intercept)-Vulpes_vulpes -3.4042 0.5777 -4.7248 -3.3306 -2.4646 1.0023
## (Intercept)-Sus_scrofa -2.8500 0.4157 -3.7025 -2.8382 -2.0811 1.0076
## week-Canis_latrans 0.5258 0.2619 0.0413 0.5206 1.0690 1.0054
## week-Lynx_rufus 0.3785 0.3242 -0.2237 0.3698 1.0388 1.0095
## week-Didelphis_virginiana 0.0520 0.3615 -0.7162 0.0652 0.7037 1.0131
## week-Sylvilagus_floridanus 0.1116 0.3239 -0.5482 0.1238 0.7336 1.0096
## week-Meleagris_gallopavo -0.1000 0.4191 -1.0391 -0.0783 0.6439 1.0083
## week-Sciurus_carolinensis 0.6629 0.3623 -0.0015 0.6325 1.4548 1.0113
## week-Vulpes_vulpes 0.2141 0.4543 -0.7714 0.2242 1.0531 1.0106
## week-Sus_scrofa 0.5813 0.4299 -0.1931 0.5394 1.5575 1.0052
## I(week^2)-Canis_latrans -0.2165 0.1088 -0.4431 -0.2134 -0.0123 1.0050
## I(week^2)-Lynx_rufus -0.2499 0.1563 -0.5910 -0.2399 0.0349 1.0024
## I(week^2)-Didelphis_virginiana -0.4047 0.2363 -0.9778 -0.3696 -0.0315 1.0224
## I(week^2)-Sylvilagus_floridanus -0.2058 0.1592 -0.5306 -0.1987 0.0859 1.0262
## I(week^2)-Meleagris_gallopavo -0.4574 0.2665 -1.1028 -0.4154 -0.0390 1.0231
## I(week^2)-Sciurus_carolinensis -0.2332 0.1473 -0.5364 -0.2263 0.0404 1.0019
## I(week^2)-Vulpes_vulpes -0.4527 0.2882 -1.1143 -0.4186 0.0033 1.0931
## I(week^2)-Sus_scrofa -0.2169 0.1810 -0.5860 -0.2104 0.1156 1.0012
## ESS
## (Intercept)-Canis_latrans 1172
## (Intercept)-Lynx_rufus 512
## (Intercept)-Didelphis_virginiana 1345
## (Intercept)-Sylvilagus_floridanus 498
## (Intercept)-Meleagris_gallopavo 547
## (Intercept)-Sciurus_carolinensis 1276
## (Intercept)-Vulpes_vulpes 194
## (Intercept)-Sus_scrofa 723
## week-Canis_latrans 1077
## week-Lynx_rufus 1033
## week-Didelphis_virginiana 960
## week-Sylvilagus_floridanus 968
## week-Meleagris_gallopavo 350
## week-Sciurus_carolinensis 1138
## week-Vulpes_vulpes 768
## week-Sus_scrofa 1108
## I(week^2)-Canis_latrans 1233
## I(week^2)-Lynx_rufus 683
## I(week^2)-Didelphis_virginiana 392
## I(week^2)-Sylvilagus_floridanus 636
## I(week^2)-Meleagris_gallopavo 212
## I(week^2)-Sciurus_carolinensis 1295
## I(week^2)-Vulpes_vulpes 403
## I(week^2)-Sus_scrofa 1290
#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 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_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): 0.5333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0749 0.4424 -1.9528 -1.0737 -0.2294 1.0092 572
## Veg_shannon_index 0.3622 0.3247 -0.2959 0.3611 1.0177 1.0162 1056
## Avg_Cogongrass_Cover 0.2185 0.3293 -0.4640 0.2207 0.8756 1.0054 1097
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7767 0.9972 0.0554 0.4558 3.3743 1.0219 626
## Veg_shannon_index 0.4309 0.5720 0.0427 0.2572 1.8544 1.0369 860
## Avg_Cogongrass_Cover 0.4891 0.8013 0.0442 0.2715 2.0871 1.0103 1021
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3019 1.1253 0.0716 1.0271 4.2799 1.048 200
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7801 0.2601 -3.3027 -2.7768 -2.2658 1.0030 1234
## week 0.2891 0.2566 -0.2081 0.2885 0.8024 1.0014 936
## I(week^2) -0.3000 0.1615 -0.6370 -0.2924 -0.0055 1.0065 674
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4288 0.5338 0.0602 0.2929 1.5588 1.0380 813
## week 0.3123 0.3614 0.0422 0.2015 1.2257 1.0039 991
## I(week^2) 0.1243 0.1851 0.0216 0.0759 0.5250 1.0768 232
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4386 0.6775 -1.7019 -0.4788
## (Intercept)-Lynx_rufus -0.7995 0.6887 -2.1215 -0.8335
## (Intercept)-Didelphis_virginiana -1.3632 0.5724 -2.5476 -1.3477
## (Intercept)-Sylvilagus_floridanus -0.8149 0.6187 -1.9948 -0.8425
## (Intercept)-Meleagris_gallopavo -0.9440 0.6721 -2.2736 -0.9508
## (Intercept)-Sciurus_carolinensis -1.3806 0.5726 -2.5901 -1.3629
## (Intercept)-Vulpes_vulpes -1.4379 0.7081 -2.9264 -1.4007
## (Intercept)-Sus_scrofa -1.7457 0.7102 -3.3075 -1.6902
## Veg_shannon_index-Canis_latrans 0.6947 0.4220 -0.0530 0.6445
## Veg_shannon_index-Lynx_rufus 0.1600 0.5373 -0.9925 0.1826
## Veg_shannon_index-Didelphis_virginiana 0.5186 0.4203 -0.2493 0.4904
## Veg_shannon_index-Sylvilagus_floridanus 0.4746 0.4762 -0.3756 0.4487
## Veg_shannon_index-Meleagris_gallopavo 0.4746 0.4914 -0.4572 0.4511
## Veg_shannon_index-Sciurus_carolinensis -0.0436 0.4453 -0.9923 -0.0234
## Veg_shannon_index-Vulpes_vulpes 0.0125 0.5101 -1.0903 0.0466
## Veg_shannon_index-Sus_scrofa 0.6917 0.5526 -0.2769 0.6375
## Avg_Cogongrass_Cover-Canis_latrans 0.5266 0.4106 -0.1799 0.5013
## Avg_Cogongrass_Cover-Lynx_rufus 0.5922 0.4882 -0.1948 0.5274
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4697 0.4386 -0.3126 0.4423
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1908 0.4782 -1.2455 -0.1556
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2410 0.5646 -1.4778 -0.1911
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3832 0.3894 -0.3683 0.3696
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3114 0.4971 -0.6616 0.3000
## Avg_Cogongrass_Cover-Sus_scrofa -0.0901 0.6067 -1.5071 -0.0173
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9371 1.0409 415
## (Intercept)-Lynx_rufus 0.6169 1.0134 474
## (Intercept)-Didelphis_virginiana -0.2832 1.0103 1045
## (Intercept)-Sylvilagus_floridanus 0.5081 1.0040 601
## (Intercept)-Meleagris_gallopavo 0.4702 1.0536 559
## (Intercept)-Sciurus_carolinensis -0.2919 1.0022 893
## (Intercept)-Vulpes_vulpes -0.0616 1.0080 473
## (Intercept)-Sus_scrofa -0.4888 1.0161 563
## Veg_shannon_index-Canis_latrans 1.6262 1.0055 1455
## Veg_shannon_index-Lynx_rufus 1.1915 1.0076 1026
## Veg_shannon_index-Didelphis_virginiana 1.4188 1.0012 1736
## Veg_shannon_index-Sylvilagus_floridanus 1.5521 1.0078 1082
## Veg_shannon_index-Meleagris_gallopavo 1.5264 1.0055 1172
## Veg_shannon_index-Sciurus_carolinensis 0.7438 1.0094 1300
## Veg_shannon_index-Vulpes_vulpes 0.9372 1.0108 1122
## Veg_shannon_index-Sus_scrofa 1.9326 1.0323 815
## Avg_Cogongrass_Cover-Canis_latrans 1.3937 1.0013 1555
## Avg_Cogongrass_Cover-Lynx_rufus 1.7260 1.0001 1170
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3863 1.0049 1291
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6857 1.0121 902
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7224 1.0039 760
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1717 1.0025 1697
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3218 1.0013 926
## Avg_Cogongrass_Cover-Sus_scrofa 0.9694 1.0121 972
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4578 0.1812 -2.8230 -2.4513 -2.1090 1.0015
## (Intercept)-Lynx_rufus -3.2149 0.3007 -3.8520 -3.1970 -2.6780 1.0076
## (Intercept)-Didelphis_virginiana -2.2556 0.2848 -2.8371 -2.2507 -1.7226 1.0140
## (Intercept)-Sylvilagus_floridanus -3.0187 0.2927 -3.6053 -3.0064 -2.5005 1.0023
## (Intercept)-Meleagris_gallopavo -3.1344 0.3210 -3.7976 -3.1111 -2.5396 1.0192
## (Intercept)-Sciurus_carolinensis -2.4172 0.2775 -2.9682 -2.4149 -1.8940 1.0019
## (Intercept)-Vulpes_vulpes -3.3603 0.5634 -4.6284 -3.2840 -2.4558 1.0371
## (Intercept)-Sus_scrofa -2.8444 0.4094 -3.7412 -2.8174 -2.0995 1.0103
## week-Canis_latrans 0.5055 0.2639 0.0015 0.5056 1.0572 1.0047
## week-Lynx_rufus 0.3512 0.3289 -0.2717 0.3356 1.0230 1.0090
## week-Didelphis_virginiana 0.0318 0.3579 -0.7124 0.0507 0.7087 1.0059
## week-Sylvilagus_floridanus 0.0984 0.3202 -0.5300 0.0928 0.7249 1.0114
## week-Meleagris_gallopavo -0.1074 0.3989 -0.9933 -0.0597 0.5650 1.0045
## week-Sciurus_carolinensis 0.6722 0.3837 -0.0352 0.6466 1.4898 1.0042
## week-Vulpes_vulpes 0.2216 0.4653 -0.7440 0.2264 1.1279 1.0103
## week-Sus_scrofa 0.5628 0.4281 -0.2080 0.5256 1.5010 1.0079
## I(week^2)-Canis_latrans -0.2104 0.1086 -0.4220 -0.2099 0.0027 1.0008
## I(week^2)-Lynx_rufus -0.2456 0.1543 -0.5643 -0.2384 0.0446 1.0008
## I(week^2)-Didelphis_virginiana -0.4211 0.2634 -1.0630 -0.3851 -0.0188 1.0189
## I(week^2)-Sylvilagus_floridanus -0.1914 0.1618 -0.5321 -0.1894 0.1127 1.0240
## I(week^2)-Meleagris_gallopavo -0.4467 0.2671 -1.0946 -0.4139 -0.0183 1.0168
## I(week^2)-Sciurus_carolinensis -0.2335 0.1507 -0.5429 -0.2302 0.0516 1.0080
## I(week^2)-Vulpes_vulpes -0.4960 0.3692 -1.3495 -0.4371 -0.0063 1.1073
## I(week^2)-Sus_scrofa -0.2090 0.1896 -0.5976 -0.2008 0.1456 1.0005
## ESS
## (Intercept)-Canis_latrans 1039
## (Intercept)-Lynx_rufus 472
## (Intercept)-Didelphis_virginiana 941
## (Intercept)-Sylvilagus_floridanus 580
## (Intercept)-Meleagris_gallopavo 515
## (Intercept)-Sciurus_carolinensis 1388
## (Intercept)-Vulpes_vulpes 205
## (Intercept)-Sus_scrofa 770
## week-Canis_latrans 1079
## week-Lynx_rufus 881
## week-Didelphis_virginiana 1082
## week-Sylvilagus_floridanus 1075
## week-Meleagris_gallopavo 542
## week-Sciurus_carolinensis 1124
## week-Vulpes_vulpes 744
## week-Sus_scrofa 1081
## I(week^2)-Canis_latrans 1344
## I(week^2)-Lynx_rufus 633
## I(week^2)-Didelphis_virginiana 250
## I(week^2)-Sylvilagus_floridanus 584
## I(week^2)-Meleagris_gallopavo 213
## I(week^2)-Sciurus_carolinensis 1266
## I(week^2)-Vulpes_vulpes 183
## I(week^2)-Sus_scrofa 1288
#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 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_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): 0.5198
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0628 0.4434 -1.9302 -1.0612 -0.1999 1.0246 437
## Avg_Cogongrass_Cover 0.1239 0.3140 -0.5214 0.1350 0.7056 1.0422 952
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6874 0.9708 0.0518 0.3970 3.0226 1.0347 850
## Avg_Cogongrass_Cover 0.4368 0.6014 0.0471 0.2665 1.9664 1.0808 986
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5845 1.3944 0.1269 1.2166 5.4745 1.0297 205
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7754 0.2686 -3.3270 -2.7701 -2.2332 1.0111 1199
## week 0.3056 0.2671 -0.2283 0.3085 0.8280 1.0048 631
## I(week^2) -0.3107 0.1644 -0.6771 -0.2979 -0.0295 1.0341 447
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4504 0.5523 0.0612 0.3056 1.7172 1.0461 689
## week 0.3193 0.3844 0.0388 0.2095 1.2817 1.0298 791
## I(week^2) 0.1192 0.1672 0.0224 0.0768 0.4905 1.0798 188
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4563 0.6679 -1.7182 -0.4629
## (Intercept)-Lynx_rufus -0.8252 0.6803 -2.1270 -0.8400
## (Intercept)-Didelphis_virginiana -1.3209 0.5848 -2.5793 -1.3046
## (Intercept)-Sylvilagus_floridanus -0.8696 0.6213 -2.0980 -0.8715
## (Intercept)-Meleagris_gallopavo -0.9748 0.6515 -2.2681 -0.9769
## (Intercept)-Sciurus_carolinensis -1.3476 0.5785 -2.5409 -1.3327
## (Intercept)-Vulpes_vulpes -1.4224 0.7029 -2.8803 -1.3947
## (Intercept)-Sus_scrofa -1.5595 0.6714 -3.0275 -1.5073
## Avg_Cogongrass_Cover-Canis_latrans 0.3599 0.3703 -0.2936 0.3382
## Avg_Cogongrass_Cover-Lynx_rufus 0.4873 0.4432 -0.2280 0.4358
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3573 0.3920 -0.3714 0.3342
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2655 0.4531 -1.2679 -0.2298
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3454 0.5595 -1.6311 -0.2821
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3582 0.3807 -0.3615 0.3431
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2653 0.4480 -0.6111 0.2573
## Avg_Cogongrass_Cover-Sus_scrofa -0.1699 0.5638 -1.4470 -0.1047
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9363 1.0881 395
## (Intercept)-Lynx_rufus 0.5807 1.0441 506
## (Intercept)-Didelphis_virginiana -0.2426 1.0037 809
## (Intercept)-Sylvilagus_floridanus 0.3764 1.0112 379
## (Intercept)-Meleagris_gallopavo 0.3099 1.0135 389
## (Intercept)-Sciurus_carolinensis -0.2539 1.0011 813
## (Intercept)-Vulpes_vulpes -0.1132 1.0237 506
## (Intercept)-Sus_scrofa -0.3580 1.0076 746
## Avg_Cogongrass_Cover-Canis_latrans 1.1520 1.0138 1841
## Avg_Cogongrass_Cover-Lynx_rufus 1.4970 1.0268 1339
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1602 1.0111 1923
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5160 1.0028 1218
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5965 1.0215 912
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1599 1.0088 1567
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1965 1.0100 1499
## Avg_Cogongrass_Cover-Sus_scrofa 0.7823 1.0343 865
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4693 0.1833 -2.8234 -2.4672 -2.1135 1.0022
## (Intercept)-Lynx_rufus -3.2106 0.3048 -3.8474 -3.1925 -2.6641 1.0021
## (Intercept)-Didelphis_virginiana -2.2548 0.2805 -2.8418 -2.2500 -1.7180 1.0010
## (Intercept)-Sylvilagus_floridanus -2.9863 0.2803 -3.5721 -2.9716 -2.4773 1.0433
## (Intercept)-Meleagris_gallopavo -3.1223 0.3326 -3.8345 -3.0981 -2.5289 1.0041
## (Intercept)-Sciurus_carolinensis -2.3879 0.2817 -2.9594 -2.3730 -1.8435 1.0035
## (Intercept)-Vulpes_vulpes -3.3693 0.5879 -4.7095 -3.3114 -2.4065 1.0380
## (Intercept)-Sus_scrofa -2.8398 0.4037 -3.7220 -2.8130 -2.1084 1.0015
## week-Canis_latrans 0.5316 0.2645 0.0292 0.5263 1.0587 1.0006
## week-Lynx_rufus 0.3807 0.3432 -0.2805 0.3684 1.1012 1.0035
## week-Didelphis_virginiana 0.0421 0.3626 -0.7306 0.0641 0.7159 1.0135
## week-Sylvilagus_floridanus 0.0948 0.3318 -0.5813 0.1042 0.7295 1.0038
## week-Meleagris_gallopavo -0.1156 0.4093 -1.0001 -0.0871 0.6191 1.0470
## week-Sciurus_carolinensis 0.6708 0.3789 -0.0244 0.6481 1.4977 1.0007
## week-Vulpes_vulpes 0.2515 0.4631 -0.7319 0.2605 1.1989 1.0064
## week-Sus_scrofa 0.5860 0.4431 -0.1793 0.5536 1.5397 1.0161
## I(week^2)-Canis_latrans -0.2194 0.1101 -0.4425 -0.2193 -0.0052 1.0003
## I(week^2)-Lynx_rufus -0.2462 0.1570 -0.5671 -0.2398 0.0438 1.0104
## I(week^2)-Didelphis_virginiana -0.4261 0.2452 -0.9733 -0.3976 -0.0429 1.0502
## I(week^2)-Sylvilagus_floridanus -0.1890 0.1693 -0.5269 -0.1847 0.1256 1.0229
## I(week^2)-Meleagris_gallopavo -0.4662 0.2730 -1.1285 -0.4385 -0.0378 1.0054
## I(week^2)-Sciurus_carolinensis -0.2368 0.1541 -0.5503 -0.2286 0.0546 1.0053
## I(week^2)-Vulpes_vulpes -0.5034 0.3660 -1.4352 -0.4419 0.0002 1.1349
## I(week^2)-Sus_scrofa -0.2110 0.1891 -0.6045 -0.2018 0.1227 1.0014
## ESS
## (Intercept)-Canis_latrans 1135
## (Intercept)-Lynx_rufus 494
## (Intercept)-Didelphis_virginiana 1293
## (Intercept)-Sylvilagus_floridanus 746
## (Intercept)-Meleagris_gallopavo 462
## (Intercept)-Sciurus_carolinensis 1333
## (Intercept)-Vulpes_vulpes 243
## (Intercept)-Sus_scrofa 967
## week-Canis_latrans 1144
## week-Lynx_rufus 742
## week-Didelphis_virginiana 895
## week-Sylvilagus_floridanus 945
## week-Meleagris_gallopavo 434
## week-Sciurus_carolinensis 1120
## week-Vulpes_vulpes 813
## week-Sus_scrofa 1177
## I(week^2)-Canis_latrans 1323
## I(week^2)-Lynx_rufus 675
## I(week^2)-Didelphis_virginiana 359
## I(week^2)-Sylvilagus_floridanus 625
## I(week^2)-Meleagris_gallopavo 206
## I(week^2)-Sciurus_carolinensis 1172
## I(week^2)-Vulpes_vulpes 136
## I(week^2)-Sus_scrofa 1155
# 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 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_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): 0.525
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7868 0.5087 -2.8091 -1.7731 -0.8301 1.0356 435
## Avg_Cogongrass_Cover -0.8941 0.4191 -1.7166 -0.8949 -0.0462 1.0401 543
## I(Avg_Cogongrass_Cover^2) 0.8353 0.4146 0.0494 0.8100 1.7482 1.0217 704
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8579 1.0921 0.0661 0.5052 3.7311 1.0105 757
## Avg_Cogongrass_Cover 0.5009 0.7342 0.0436 0.2783 2.2575 1.0204 1006
## I(Avg_Cogongrass_Cover^2) 0.8742 1.6029 0.0492 0.3781 4.5519 1.0150 400
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0132 1.0231 0.0892 0.7063 3.7329 1.0597 162
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7669 0.2524 -3.2812 -2.7675 -2.2754 1.0044 1124
## week 0.3007 0.2663 -0.2329 0.3042 0.8296 1.0287 901
## I(week^2) -0.3197 0.1771 -0.7234 -0.3014 -0.0313 1.1402 126
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3780 0.4818 0.0588 0.2625 1.3619 1.0648 793
## week 0.3158 0.3567 0.0400 0.2033 1.2599 1.0181 517
## I(week^2) 0.1366 0.2490 0.0216 0.0744 0.6695 2.0205 40
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.1786 0.7044 -2.5149 -1.1846
## (Intercept)-Lynx_rufus -1.7478 0.6957 -3.2008 -1.7267
## (Intercept)-Didelphis_virginiana -2.0018 0.6574 -3.3498 -1.9799
## (Intercept)-Sylvilagus_floridanus -1.5743 0.6699 -2.8893 -1.5851
## (Intercept)-Meleagris_gallopavo -1.4237 0.7063 -2.8509 -1.4332
## (Intercept)-Sciurus_carolinensis -2.3076 0.7258 -3.9286 -2.2445
## (Intercept)-Vulpes_vulpes -2.3516 0.8441 -4.1525 -2.2950
## (Intercept)-Sus_scrofa -2.2902 0.7481 -3.9081 -2.2141
## Avg_Cogongrass_Cover-Canis_latrans -0.6174 0.5524 -1.6273 -0.6550
## Avg_Cogongrass_Cover-Lynx_rufus -0.7503 0.5958 -1.8727 -0.7560
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5150 0.5933 -1.6039 -0.5437
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3372 0.6416 -2.7880 -1.2725
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1393 0.6082 -2.4749 -1.1130
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.9082 0.5564 -2.0692 -0.9045
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.9138 0.6189 -2.2393 -0.8983
## Avg_Cogongrass_Cover-Sus_scrofa -1.1210 0.6714 -2.6312 -1.0892
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6599 1.0512 0.3756 1.3678
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.3850 0.6276 0.4284 1.2982
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6408 0.4846 -0.2572 0.6122
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9076 0.5496 0.0330 0.8471
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2453 0.6004 -1.0985 0.2958
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0261 0.4142 0.3023 1.0053
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9343 0.4848 0.1473 0.8873
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2246 0.7169 -1.5201 0.3325
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3047 1.0057 423
## (Intercept)-Lynx_rufus -0.3982 1.0512 515
## (Intercept)-Didelphis_virginiana -0.8013 1.0140 885
## (Intercept)-Sylvilagus_floridanus -0.2453 1.0119 639
## (Intercept)-Meleagris_gallopavo 0.0003 1.0459 572
## (Intercept)-Sciurus_carolinensis -1.0752 1.0493 454
## (Intercept)-Vulpes_vulpes -0.8449 1.0462 326
## (Intercept)-Sus_scrofa -0.9908 1.0149 682
## Avg_Cogongrass_Cover-Canis_latrans 0.5638 1.0087 1238
## Avg_Cogongrass_Cover-Lynx_rufus 0.4609 1.0588 804
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7198 1.0234 894
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2308 1.0362 675
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0140 1.0199 977
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1516 1.0352 778
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2206 1.0355 741
## Avg_Cogongrass_Cover-Sus_scrofa 0.0675 1.0380 692
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4135 1.0386 290
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9156 1.0129 488
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.7320 1.0143 721
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1541 1.0338 526
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3402 1.0077 616
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9022 1.0313 693
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0798 1.0050 682
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3038 1.0057 483
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5033 0.1847 -2.8595 -2.5003 -2.1485 1.0042
## (Intercept)-Lynx_rufus -3.1272 0.2764 -3.7124 -3.1135 -2.6278 1.0125
## (Intercept)-Didelphis_virginiana -2.2883 0.2898 -2.8601 -2.2879 -1.7240 1.0043
## (Intercept)-Sylvilagus_floridanus -2.9903 0.2812 -3.5707 -2.9848 -2.4650 1.0060
## (Intercept)-Meleagris_gallopavo -3.0957 0.3125 -3.7572 -3.0834 -2.5122 1.0055
## (Intercept)-Sciurus_carolinensis -2.3953 0.2756 -2.9600 -2.3913 -1.8550 1.0067
## (Intercept)-Vulpes_vulpes -3.3113 0.5424 -4.5678 -3.2504 -2.4331 1.0090
## (Intercept)-Sus_scrofa -2.8296 0.3887 -3.6848 -2.8104 -2.1057 1.0111
## week-Canis_latrans 0.5271 0.2537 0.0337 0.5177 1.0411 1.0124
## week-Lynx_rufus 0.3862 0.3371 -0.2381 0.3723 1.0691 1.0004
## week-Didelphis_virginiana 0.0326 0.3814 -0.7983 0.0536 0.6897 1.0232
## week-Sylvilagus_floridanus 0.1098 0.3356 -0.5701 0.1118 0.7643 1.0079
## week-Meleagris_gallopavo -0.0929 0.4164 -0.9728 -0.0611 0.6163 1.0567
## week-Sciurus_carolinensis 0.6821 0.3799 0.0064 0.6509 1.4967 1.0100
## week-Vulpes_vulpes 0.2360 0.4600 -0.7281 0.2457 1.0769 1.0156
## week-Sus_scrofa 0.5902 0.4460 -0.2310 0.5485 1.5766 1.0089
## I(week^2)-Canis_latrans -0.2210 0.1069 -0.4389 -0.2195 -0.0120 1.0138
## I(week^2)-Lynx_rufus -0.2475 0.1596 -0.5873 -0.2394 0.0519 1.0192
## I(week^2)-Didelphis_virginiana -0.4352 0.2615 -1.0736 -0.3947 -0.0343 1.1145
## I(week^2)-Sylvilagus_floridanus -0.1949 0.1627 -0.5382 -0.1872 0.1116 1.0014
## I(week^2)-Meleagris_gallopavo -0.4590 0.3216 -1.3988 -0.4081 -0.0189 1.4592
## I(week^2)-Sciurus_carolinensis -0.2342 0.1521 -0.5541 -0.2290 0.0407 1.0163
## I(week^2)-Vulpes_vulpes -0.5440 0.4674 -1.8758 -0.4519 -0.0080 1.5608
## I(week^2)-Sus_scrofa -0.2156 0.1919 -0.5940 -0.2050 0.1414 1.0163
## ESS
## (Intercept)-Canis_latrans 1142
## (Intercept)-Lynx_rufus 674
## (Intercept)-Didelphis_virginiana 894
## (Intercept)-Sylvilagus_floridanus 585
## (Intercept)-Meleagris_gallopavo 592
## (Intercept)-Sciurus_carolinensis 1264
## (Intercept)-Vulpes_vulpes 257
## (Intercept)-Sus_scrofa 953
## week-Canis_latrans 1177
## week-Lynx_rufus 912
## week-Didelphis_virginiana 658
## week-Sylvilagus_floridanus 974
## week-Meleagris_gallopavo 430
## week-Sciurus_carolinensis 960
## week-Vulpes_vulpes 794
## week-Sus_scrofa 1119
## I(week^2)-Canis_latrans 1050
## I(week^2)-Lynx_rufus 710
## I(week^2)-Didelphis_virginiana 190
## I(week^2)-Sylvilagus_floridanus 807
## I(week^2)-Meleagris_gallopavo 47
## I(week^2)-Sciurus_carolinensis 1153
## I(week^2)-Vulpes_vulpes 70
## I(week^2)-Sus_scrofa 1102
# 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 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_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): 0.559
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9041 0.9961 -4.7361 -2.9703 -0.7510 1.0069 341
## Cogon_Patch_Size 0.0325 0.8782 -1.8305 0.0745 1.7092 1.0146 431
## Veg_shannon_index 1.0571 0.6393 -0.2488 1.0689 2.2889 1.0070 581
## total_shrub_cover -0.8564 0.7509 -2.4205 -0.8227 0.6291 1.0031 689
## Avg_Cogongrass_Cover -0.0248 1.0450 -2.0145 -0.0258 2.0385 1.0851 143
## Tree_Density -2.2467 0.8279 -3.9629 -2.2335 -0.6340 1.0176 197
## Avg_Canopy_Cover 1.9018 0.7184 0.4430 1.8819 3.3980 1.0135 937
## I(Avg_Cogongrass_Cover^2) 1.1578 0.8611 -0.7695 1.1966 2.7544 1.0360 450
## avg_veg_height -0.6061 0.5967 -1.7739 -0.6081 0.5721 1.0281 227
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.7755 10.9696 0.0915 2.5512 29.6042 1.1674 297
## Cogon_Patch_Size 6.4449 13.1520 0.2676 3.3003 33.6862 1.1959 206
## Veg_shannon_index 1.7257 2.5599 0.0787 0.8614 8.3830 1.0041 371
## total_shrub_cover 4.6492 6.3399 0.2360 2.8302 19.8764 1.0314 408
## Avg_Cogongrass_Cover 1.5441 3.5163 0.0573 0.5566 9.4672 1.0201 531
## Tree_Density 2.1500 4.3693 0.0557 0.7239 13.0157 1.0007 263
## Avg_Canopy_Cover 4.3825 8.8608 0.1675 2.1938 21.6774 1.1756 180
## I(Avg_Cogongrass_Cover^2) 6.1194 10.2852 0.1245 2.7682 33.3085 1.0130 132
## avg_veg_height 0.6262 1.2979 0.0417 0.2900 3.0692 1.1252 407
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0969 2.9324 0.0683 1.0994 10.0092 1.2602 91
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8287 0.2962 -3.3906 -2.8376 -2.2129 1.0159 1691
## week 0.3005 0.2701 -0.2178 0.2883 0.8379 1.0406 994
## I(week^2) -0.2891 0.1434 -0.5901 -0.2818 -0.0261 1.0048 646
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.536 0.5463 0.0960 0.3888 1.8593 1.0217 890
## week 0.310 0.3426 0.0417 0.2064 1.1812 1.0119 1008
## I(week^2) 0.098 0.1073 0.0213 0.0680 0.3438 1.0518 693
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.9967 1.3368 -4.6074 -2.0115
## (Intercept)-Lynx_rufus -1.9650 1.8392 -4.9105 -2.2181
## (Intercept)-Didelphis_virginiana -4.1626 1.4305 -7.3192 -4.0104
## (Intercept)-Sylvilagus_floridanus -3.0528 1.4477 -5.9980 -2.9607
## (Intercept)-Meleagris_gallopavo -2.7804 1.4008 -5.5576 -2.7293
## (Intercept)-Sciurus_carolinensis -4.6120 1.4723 -7.9889 -4.4459
## (Intercept)-Vulpes_vulpes -4.6145 1.8537 -8.9014 -4.3218
## (Intercept)-Sus_scrofa -4.9753 1.9240 -9.5404 -4.6084
## Cogon_Patch_Size-Canis_latrans 2.0252 1.5519 -0.1734 1.7484
## Cogon_Patch_Size-Lynx_rufus -0.1086 2.1611 -4.3285 -0.0763
## Cogon_Patch_Size-Didelphis_virginiana 2.0916 1.2425 0.1124 1.9571
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9021 2.3289 -7.4623 -1.4715
## Cogon_Patch_Size-Meleagris_gallopavo 0.4194 1.3331 -1.9102 0.2957
## Cogon_Patch_Size-Sciurus_carolinensis -1.1725 1.6051 -5.4254 -0.8859
## Cogon_Patch_Size-Vulpes_vulpes -0.6827 1.8856 -4.9269 -0.5150
## Cogon_Patch_Size-Sus_scrofa -0.7291 1.6956 -5.0111 -0.4365
## Veg_shannon_index-Canis_latrans 1.6195 0.8817 0.1372 1.5351
## Veg_shannon_index-Lynx_rufus 1.1408 1.2021 -1.0718 1.0942
## Veg_shannon_index-Didelphis_virginiana 1.1935 0.8448 -0.3755 1.1410
## Veg_shannon_index-Sylvilagus_floridanus 1.1443 0.8742 -0.5364 1.1366
## Veg_shannon_index-Meleagris_gallopavo 1.6406 1.0360 -0.0705 1.5220
## Veg_shannon_index-Sciurus_carolinensis 0.1096 1.0053 -2.1533 0.2061
## Veg_shannon_index-Vulpes_vulpes 0.4452 1.1803 -2.3552 0.5836
## Veg_shannon_index-Sus_scrofa 1.8509 1.1569 -0.0095 1.6844
## total_shrub_cover-Canis_latrans 0.0846 0.8583 -1.5311 0.0454
## total_shrub_cover-Lynx_rufus -2.3793 1.5419 -6.0903 -2.1782
## total_shrub_cover-Didelphis_virginiana -0.9685 0.9462 -2.9676 -0.9300
## total_shrub_cover-Sylvilagus_floridanus -0.3625 1.2160 -2.9079 -0.3773
## total_shrub_cover-Meleagris_gallopavo -3.7512 1.8201 -7.9966 -3.5598
## total_shrub_cover-Sciurus_carolinensis 0.1072 0.8804 -1.5550 0.1074
## total_shrub_cover-Vulpes_vulpes -1.1408 1.4376 -4.4059 -0.9670
## total_shrub_cover-Sus_scrofa 0.1785 1.1687 -1.9237 0.1038
## Avg_Cogongrass_Cover-Canis_latrans -0.1385 1.3536 -3.1080 -0.0856
## Avg_Cogongrass_Cover-Lynx_rufus 0.1112 1.4007 -2.5707 0.0854
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3143 1.3913 -2.2876 0.2860
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4968 1.4616 -3.5634 -0.3790
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3364 1.4867 -3.5960 -0.2434
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1633 1.3000 -2.3638 0.1418
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2379 1.4185 -2.4555 0.1822
## Avg_Cogongrass_Cover-Sus_scrofa -0.2642 1.5036 -3.5621 -0.1737
## Tree_Density-Canis_latrans -3.1218 1.5443 -7.1583 -2.8670
## Tree_Density-Lynx_rufus -1.5042 1.3140 -3.8333 -1.6077
## Tree_Density-Didelphis_virginiana -2.3642 1.0806 -4.6993 -2.2986
## Tree_Density-Sylvilagus_floridanus -2.7835 1.3536 -6.0232 -2.6380
## Tree_Density-Meleagris_gallopavo -2.3981 1.3011 -5.2283 -2.3605
## Tree_Density-Sciurus_carolinensis -2.6658 1.2252 -5.5727 -2.5172
## Tree_Density-Vulpes_vulpes -2.2593 1.5046 -5.2363 -2.2567
## Tree_Density-Sus_scrofa -2.3334 1.4049 -5.4416 -2.2591
## Avg_Canopy_Cover-Canis_latrans 0.1755 0.8279 -1.5430 0.1888
## Avg_Canopy_Cover-Lynx_rufus 1.1408 1.4394 -1.7818 1.1360
## Avg_Canopy_Cover-Didelphis_virginiana 2.9941 1.1861 1.3171 2.7742
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0991 2.1945 1.4223 3.6372
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4501 1.2739 0.6599 2.2240
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4809 1.0099 0.9215 2.3239
## Avg_Canopy_Cover-Vulpes_vulpes 2.4045 1.3003 0.4454 2.1722
## Avg_Canopy_Cover-Sus_scrofa 2.1425 0.9887 0.5130 2.0479
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0688 1.6658 0.9216 2.6918
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.5346 2.0592 0.8962 3.0796
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9568 0.8370 -0.6691 0.9401
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3876 1.2435 -0.4342 1.2101
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.6680 1.9286 -5.4845 -0.4308
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6587 0.8691 0.1273 1.5887
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1373 1.2686 0.1285 1.9558
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.6006 2.0402 -5.7041 -0.1763
## avg_veg_height-Canis_latrans -0.7441 0.7029 -2.1831 -0.7274
## avg_veg_height-Lynx_rufus -0.7501 0.9214 -2.6255 -0.7120
## avg_veg_height-Didelphis_virginiana -0.5975 0.7626 -2.1538 -0.5864
## avg_veg_height-Sylvilagus_floridanus -0.6369 0.7669 -2.1977 -0.6483
## avg_veg_height-Meleagris_gallopavo -0.7106 0.8797 -2.5293 -0.6947
## avg_veg_height-Sciurus_carolinensis -0.1958 0.7787 -1.5441 -0.2461
## avg_veg_height-Vulpes_vulpes -0.7284 0.8844 -2.5964 -0.7047
## avg_veg_height-Sus_scrofa -0.6143 0.7831 -2.1616 -0.6128
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6974 1.0048 239
## (Intercept)-Lynx_rufus 2.3828 1.0253 186
## (Intercept)-Didelphis_virginiana -1.8339 1.1278 179
## (Intercept)-Sylvilagus_floridanus -0.4709 1.0868 236
## (Intercept)-Meleagris_gallopavo -0.0653 1.0175 404
## (Intercept)-Sciurus_carolinensis -2.1715 1.0805 261
## (Intercept)-Vulpes_vulpes -1.6232 1.0552 170
## (Intercept)-Sus_scrofa -2.1911 1.0867 163
## Cogon_Patch_Size-Canis_latrans 5.9073 1.0558 316
## Cogon_Patch_Size-Lynx_rufus 3.9165 1.1134 112
## Cogon_Patch_Size-Didelphis_virginiana 4.9581 1.0693 179
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2792 1.0717 152
## Cogon_Patch_Size-Meleagris_gallopavo 3.5091 1.0279 463
## Cogon_Patch_Size-Sciurus_carolinensis 1.0321 1.0105 247
## Cogon_Patch_Size-Vulpes_vulpes 2.8117 1.0283 265
## Cogon_Patch_Size-Sus_scrofa 1.8364 1.0091 413
## Veg_shannon_index-Canis_latrans 3.6870 1.0218 633
## Veg_shannon_index-Lynx_rufus 3.9364 1.0171 381
## Veg_shannon_index-Didelphis_virginiana 3.0338 1.0045 900
## Veg_shannon_index-Sylvilagus_floridanus 3.0279 1.0231 567
## Veg_shannon_index-Meleagris_gallopavo 4.1021 1.0033 466
## Veg_shannon_index-Sciurus_carolinensis 1.9011 1.0048 484
## Veg_shannon_index-Vulpes_vulpes 2.4732 1.0021 360
## Veg_shannon_index-Sus_scrofa 4.5920 1.0073 397
## total_shrub_cover-Canis_latrans 2.0041 1.0101 578
## total_shrub_cover-Lynx_rufus 0.1105 1.0019 161
## total_shrub_cover-Didelphis_virginiana 0.7257 1.0119 899
## total_shrub_cover-Sylvilagus_floridanus 2.1093 1.0380 483
## total_shrub_cover-Meleagris_gallopavo -0.8162 1.0085 256
## total_shrub_cover-Sciurus_carolinensis 1.8295 1.0085 981
## total_shrub_cover-Vulpes_vulpes 1.2835 1.0161 386
## total_shrub_cover-Sus_scrofa 2.7672 1.0060 730
## Avg_Cogongrass_Cover-Canis_latrans 2.3997 1.0554 166
## Avg_Cogongrass_Cover-Lynx_rufus 2.7809 1.0315 237
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1169 1.0545 206
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0399 1.0538 236
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1823 1.0658 267
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9343 1.0510 236
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2792 1.0233 298
## Avg_Cogongrass_Cover-Sus_scrofa 2.4780 1.1060 173
## Tree_Density-Canis_latrans -0.8668 1.0167 140
## Tree_Density-Lynx_rufus 1.3661 1.0193 307
## Tree_Density-Didelphis_virginiana -0.4485 1.0164 312
## Tree_Density-Sylvilagus_floridanus -0.6159 1.0030 193
## Tree_Density-Meleagris_gallopavo -0.0763 1.0086 347
## Tree_Density-Sciurus_carolinensis -0.6607 1.0184 247
## Tree_Density-Vulpes_vulpes 0.6770 1.0190 256
## Tree_Density-Sus_scrofa 0.4208 1.0080 342
## Avg_Canopy_Cover-Canis_latrans 1.7515 1.0193 232
## Avg_Canopy_Cover-Lynx_rufus 4.0252 1.0126 282
## Avg_Canopy_Cover-Didelphis_virginiana 6.0036 1.0949 274
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.4775 1.2395 119
## Avg_Canopy_Cover-Meleagris_gallopavo 5.7741 1.0275 349
## Avg_Canopy_Cover-Sciurus_carolinensis 4.8412 1.0445 497
## Avg_Canopy_Cover-Vulpes_vulpes 5.7035 1.0657 437
## Avg_Canopy_Cover-Sus_scrofa 4.4199 1.0143 595
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.4914 1.0195 126
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.9364 1.0145 128
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6249 1.0559 230
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.6031 1.0891 161
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2764 1.0157 117
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5418 1.0237 274
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.2561 1.0669 266
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.2597 1.0521 172
## avg_veg_height-Canis_latrans 0.5953 1.0075 321
## avg_veg_height-Lynx_rufus 0.9143 1.0209 255
## avg_veg_height-Didelphis_virginiana 0.9124 1.0112 407
## avg_veg_height-Sylvilagus_floridanus 0.8087 1.0137 374
## avg_veg_height-Meleagris_gallopavo 1.0233 1.0072 327
## avg_veg_height-Sciurus_carolinensis 1.4565 1.0298 446
## avg_veg_height-Vulpes_vulpes 0.9399 1.0085 329
## avg_veg_height-Sus_scrofa 0.9479 1.0118 418
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4893 0.1890 -2.8613 -2.4836 -2.1361 1.0029
## (Intercept)-Lynx_rufus -3.4423 0.2866 -4.0166 -3.4444 -2.8813 1.0081
## (Intercept)-Didelphis_virginiana -2.2149 0.2715 -2.7691 -2.2061 -1.7064 1.0108
## (Intercept)-Sylvilagus_floridanus -3.0660 0.2769 -3.6550 -3.0549 -2.5554 1.0419
## (Intercept)-Meleagris_gallopavo -3.1118 0.3109 -3.7709 -3.0945 -2.5395 1.0305
## (Intercept)-Sciurus_carolinensis -2.3784 0.2843 -2.9477 -2.3687 -1.8448 1.0007
## (Intercept)-Vulpes_vulpes -3.6343 0.5469 -4.7995 -3.6020 -2.6657 1.0447
## (Intercept)-Sus_scrofa -2.8255 0.4135 -3.6910 -2.8034 -2.0675 1.0077
## week-Canis_latrans 0.5248 0.2620 0.0353 0.5178 1.0521 1.0005
## week-Lynx_rufus 0.3630 0.3378 -0.2618 0.3584 1.0603 1.0022
## week-Didelphis_virginiana 0.0498 0.3574 -0.6832 0.0575 0.7344 1.0053
## week-Sylvilagus_floridanus 0.0841 0.3216 -0.5700 0.0886 0.7074 1.0113
## week-Meleagris_gallopavo -0.1025 0.3931 -0.9501 -0.0793 0.6120 1.0128
## week-Sciurus_carolinensis 0.6657 0.3760 -0.0038 0.6329 1.4704 1.0513
## week-Vulpes_vulpes 0.2192 0.4430 -0.6734 0.2278 1.0550 1.0126
## week-Sus_scrofa 0.5759 0.4483 -0.2143 0.5464 1.5287 1.0401
## I(week^2)-Canis_latrans -0.2161 0.1075 -0.4307 -0.2147 -0.0078 0.9998
## I(week^2)-Lynx_rufus -0.2440 0.1617 -0.5846 -0.2387 0.0576 1.0449
## I(week^2)-Didelphis_virginiana -0.3914 0.2230 -0.9077 -0.3652 -0.0178 0.9996
## I(week^2)-Sylvilagus_floridanus -0.1792 0.1557 -0.5017 -0.1727 0.1101 1.0369
## I(week^2)-Meleagris_gallopavo -0.3993 0.2573 -1.0253 -0.3610 0.0041 1.0120
## I(week^2)-Sciurus_carolinensis -0.2310 0.1497 -0.5434 -0.2244 0.0456 1.0283
## I(week^2)-Vulpes_vulpes -0.4528 0.2721 -1.0762 -0.4208 0.0013 1.0020
## I(week^2)-Sus_scrofa -0.2061 0.1886 -0.5903 -0.1998 0.1479 1.0047
## ESS
## (Intercept)-Canis_latrans 922
## (Intercept)-Lynx_rufus 347
## (Intercept)-Didelphis_virginiana 1324
## (Intercept)-Sylvilagus_floridanus 483
## (Intercept)-Meleagris_gallopavo 512
## (Intercept)-Sciurus_carolinensis 1075
## (Intercept)-Vulpes_vulpes 187
## (Intercept)-Sus_scrofa 826
## week-Canis_latrans 993
## week-Lynx_rufus 722
## week-Didelphis_virginiana 1015
## week-Sylvilagus_floridanus 1042
## week-Meleagris_gallopavo 601
## week-Sciurus_carolinensis 1050
## week-Vulpes_vulpes 733
## week-Sus_scrofa 1033
## I(week^2)-Canis_latrans 1013
## I(week^2)-Lynx_rufus 546
## I(week^2)-Didelphis_virginiana 512
## I(week^2)-Sylvilagus_floridanus 814
## I(week^2)-Meleagris_gallopavo 272
## I(week^2)-Sciurus_carolinensis 1338
## I(week^2)-Vulpes_vulpes 359
## I(week^2)-Sus_scrofa 1199
#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 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_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): 0.6268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5494 0.4008 -1.3046 -0.5592 0.2772 1.0172 1049
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0022 1.1913 0.1207 0.6847 3.9055 1.0195 702
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0355 0.3173 -3.6506 -3.0376 -2.4080 1.0359 990
## shrub_cover 0.1378 0.3724 -0.6204 0.1379 0.8584 1.0004 1280
## veg_height -0.1029 0.2183 -0.5485 -0.0981 0.3304 1.0010 1216
## week 0.2866 0.2701 -0.2458 0.2868 0.7913 1.0271 886
## I(week^2) -0.3126 0.1592 -0.6518 -0.3055 -0.0344 1.0096 331
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6341 0.7778 0.0874 0.4422 2.2755 1.0305 739
## shrub_cover 1.0033 0.9851 0.1629 0.7118 3.7488 1.0156 847
## veg_height 0.2844 0.2647 0.0569 0.2081 0.9418 1.0064 1733
## week 0.3238 0.3753 0.0432 0.2060 1.3193 1.0298 643
## I(week^2) 0.1198 0.1668 0.0221 0.0787 0.4566 1.0973 273
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2264 0.4109 -0.5287 0.2099 1.1012 1.0036
## (Intercept)-Lynx_rufus 0.0491 0.5998 -0.8961 -0.0342 1.4345 1.0263
## (Intercept)-Didelphis_virginiana -1.0650 0.4398 -2.0003 -1.0431 -0.2775 1.0035
## (Intercept)-Sylvilagus_floridanus -0.3918 0.4441 -1.2155 -0.4199 0.5613 1.0030
## (Intercept)-Meleagris_gallopavo 0.0541 0.6801 -1.0621 -0.0163 1.6349 1.0402
## (Intercept)-Sciurus_carolinensis -1.0672 0.4459 -1.9961 -1.0622 -0.2299 1.0007
## (Intercept)-Vulpes_vulpes -1.1116 0.6987 -2.4661 -1.1250 0.2647 1.0533
## (Intercept)-Sus_scrofa -1.3926 0.5920 -2.6177 -1.3682 -0.2803 1.0085
## ESS
## (Intercept)-Canis_latrans 1493
## (Intercept)-Lynx_rufus 527
## (Intercept)-Didelphis_virginiana 1386
## (Intercept)-Sylvilagus_floridanus 1065
## (Intercept)-Meleagris_gallopavo 270
## (Intercept)-Sciurus_carolinensis 1344
## (Intercept)-Vulpes_vulpes 407
## (Intercept)-Sus_scrofa 750
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6175 0.2045 -3.0269 -2.6065 -2.2294 1.0080
## (Intercept)-Lynx_rufus -3.4502 0.3508 -4.1626 -3.4346 -2.8128 1.0448
## (Intercept)-Didelphis_virginiana -2.4755 0.3207 -3.1331 -2.4639 -1.8895 1.0024
## (Intercept)-Sylvilagus_floridanus -3.0772 0.2947 -3.7243 -3.0613 -2.5635 1.0080
## (Intercept)-Meleagris_gallopavo -3.7906 0.4766 -4.7442 -3.7873 -2.8912 1.0424
## (Intercept)-Sciurus_carolinensis -2.5663 0.3427 -3.2471 -2.5683 -1.8925 1.0133
## (Intercept)-Vulpes_vulpes -3.7160 0.6054 -5.0007 -3.6648 -2.6771 1.0837
## (Intercept)-Sus_scrofa -3.2910 0.5385 -4.3956 -3.2967 -2.2408 1.0804
## shrub_cover-Canis_latrans -0.3462 0.2284 -0.7902 -0.3446 0.0901 1.0031
## shrub_cover-Lynx_rufus -0.3522 0.3669 -1.0703 -0.3454 0.3830 1.0117
## shrub_cover-Didelphis_virginiana 1.0441 0.3995 0.3059 1.0308 1.8422 1.0090
## shrub_cover-Sylvilagus_floridanus 0.2345 0.4444 -0.5785 0.2129 1.1747 1.0053
## shrub_cover-Meleagris_gallopavo -0.7831 0.4271 -1.6246 -0.7690 0.0426 1.0259
## shrub_cover-Sciurus_carolinensis 0.9024 0.4533 0.0624 0.8913 1.8256 1.0046
## shrub_cover-Vulpes_vulpes -0.2297 0.6341 -1.5156 -0.2246 1.0224 1.0055
## shrub_cover-Sus_scrofa 0.6974 0.8643 -1.0179 0.6763 2.3878 1.0519
## veg_height-Canis_latrans -0.6433 0.1934 -1.0569 -0.6332 -0.2958 1.0003
## veg_height-Lynx_rufus -0.0254 0.2464 -0.5125 -0.0218 0.4546 1.0015
## veg_height-Didelphis_virginiana 0.4562 0.2634 -0.0399 0.4478 0.9939 1.0136
## veg_height-Sylvilagus_floridanus 0.0877 0.2613 -0.4195 0.0811 0.6034 1.0006
## veg_height-Meleagris_gallopavo -0.3689 0.3745 -1.1046 -0.3665 0.3952 1.0132
## veg_height-Sciurus_carolinensis 0.0778 0.2244 -0.3439 0.0703 0.5405 1.0015
## veg_height-Vulpes_vulpes -0.1816 0.3216 -0.8223 -0.1701 0.4224 1.0034
## veg_height-Sus_scrofa -0.2309 0.3522 -0.9448 -0.2228 0.4244 1.0045
## week-Canis_latrans 0.5248 0.2657 0.0156 0.5221 1.0409 1.0167
## week-Lynx_rufus 0.3488 0.3305 -0.2892 0.3459 1.0016 1.0128
## week-Didelphis_virginiana 0.0397 0.3660 -0.7321 0.0539 0.7163 1.0108
## week-Sylvilagus_floridanus 0.0615 0.3432 -0.6624 0.0753 0.6926 1.0100
## week-Meleagris_gallopavo -0.0884 0.4027 -1.0026 -0.0556 0.6170 1.0009
## week-Sciurus_carolinensis 0.6757 0.3787 0.0129 0.6518 1.4779 1.0037
## week-Vulpes_vulpes 0.1979 0.4846 -0.8204 0.2169 1.0962 1.0265
## week-Sus_scrofa 0.5793 0.4465 -0.2000 0.5508 1.5432 1.0049
## I(week^2)-Canis_latrans -0.2208 0.1130 -0.4449 -0.2213 -0.0084 1.0126
## I(week^2)-Lynx_rufus -0.2299 0.1550 -0.5439 -0.2268 0.0602 1.0012
## I(week^2)-Didelphis_virginiana -0.4385 0.2582 -1.0695 -0.4123 -0.0354 1.1985
## I(week^2)-Sylvilagus_floridanus -0.1755 0.1617 -0.4991 -0.1703 0.1289 1.0127
## I(week^2)-Meleagris_gallopavo -0.4969 0.2948 -1.2185 -0.4553 -0.0464 1.0892
## I(week^2)-Sciurus_carolinensis -0.2378 0.1542 -0.5511 -0.2360 0.0575 1.0002
## I(week^2)-Vulpes_vulpes -0.4790 0.3228 -1.2567 -0.4318 -0.0024 1.0310
## I(week^2)-Sus_scrofa -0.2187 0.1890 -0.5977 -0.2107 0.1243 1.0019
## ESS
## (Intercept)-Canis_latrans 815
## (Intercept)-Lynx_rufus 313
## (Intercept)-Didelphis_virginiana 657
## (Intercept)-Sylvilagus_floridanus 632
## (Intercept)-Meleagris_gallopavo 193
## (Intercept)-Sciurus_carolinensis 675
## (Intercept)-Vulpes_vulpes 172
## (Intercept)-Sus_scrofa 495
## shrub_cover-Canis_latrans 846
## shrub_cover-Lynx_rufus 469
## shrub_cover-Didelphis_virginiana 559
## shrub_cover-Sylvilagus_floridanus 526
## shrub_cover-Meleagris_gallopavo 213
## shrub_cover-Sciurus_carolinensis 580
## shrub_cover-Vulpes_vulpes 476
## shrub_cover-Sus_scrofa 507
## veg_height-Canis_latrans 616
## veg_height-Lynx_rufus 793
## veg_height-Didelphis_virginiana 972
## veg_height-Sylvilagus_floridanus 663
## veg_height-Meleagris_gallopavo 411
## veg_height-Sciurus_carolinensis 945
## veg_height-Vulpes_vulpes 702
## veg_height-Sus_scrofa 1035
## week-Canis_latrans 1084
## week-Lynx_rufus 895
## week-Didelphis_virginiana 698
## week-Sylvilagus_floridanus 717
## week-Meleagris_gallopavo 492
## week-Sciurus_carolinensis 1138
## week-Vulpes_vulpes 504
## week-Sus_scrofa 1050
## I(week^2)-Canis_latrans 1115
## I(week^2)-Lynx_rufus 664
## I(week^2)-Didelphis_virginiana 154
## I(week^2)-Sylvilagus_floridanus 817
## I(week^2)-Meleagris_gallopavo 141
## I(week^2)-Sciurus_carolinensis 1128
## I(week^2)-Vulpes_vulpes 296
## I(week^2)-Sus_scrofa 1122
#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 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_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): 0.6908
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3028 0.9429 -3.1416 -1.3045 0.5701 1.0304 317
## Cogon_Patch_Size -0.2816 0.8858 -2.0210 -0.2849 1.6455 1.0183 239
## Veg_shannon_index 1.0298 0.7431 -0.5069 1.0352 2.4895 1.0239 255
## total_shrub_cover -1.1933 0.9074 -3.1224 -1.1514 0.4635 1.0330 350
## Avg_Cogongrass_Cover 1.5927 0.9175 -0.2316 1.5864 3.4219 1.0447 201
## Tree_Density -1.5628 0.8995 -3.3431 -1.5799 0.2385 1.0065 214
## Avg_Canopy_Cover 2.0289 0.9289 0.1191 1.9799 3.9480 1.0158 925
## avg_veg_height -0.2885 0.7150 -1.5861 -0.3153 1.1588 1.1057 169
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.6916 8.3927 0.1175 2.8772 28.6914 1.0050 108
## Cogon_Patch_Size 4.9664 9.3321 0.1141 2.2584 26.3810 1.0947 117
## Veg_shannon_index 3.0890 9.6429 0.0625 0.9321 17.3755 1.3671 148
## total_shrub_cover 5.9546 9.2640 0.1231 2.8975 29.5520 1.0890 143
## Avg_Cogongrass_Cover 2.3584 4.9671 0.0571 0.7643 14.8151 1.0720 240
## Tree_Density 3.2641 6.4604 0.0665 1.1724 20.5517 1.3369 105
## Avg_Canopy_Cover 8.7304 12.6255 0.3114 4.5396 44.0793 1.0195 188
## avg_veg_height 0.8162 1.6214 0.0428 0.3416 4.6447 1.0082 410
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.805 6.5825 0.1049 3.8494 24.5886 1.102 49
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0932 0.2971 -3.6848 -3.0845 -2.5289 1.0108 563
## shrub_cover 0.4440 0.3928 -0.3264 0.4412 1.2380 1.0195 508
## veg_height -0.0640 0.2301 -0.5358 -0.0615 0.3796 1.0114 822
## week 0.2854 0.2609 -0.2401 0.2815 0.8022 1.0154 910
## I(week^2) -0.3052 0.1564 -0.6367 -0.2955 -0.0329 1.0439 463
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4931 0.4783 0.0692 0.3579 1.7275 1.0473 656
## shrub_cover 0.9397 0.8609 0.1616 0.7076 3.1907 1.0353 1014
## veg_height 0.3133 0.2973 0.0666 0.2287 1.0475 1.0101 1437
## week 0.3174 0.3680 0.0449 0.2124 1.1703 1.0170 827
## I(week^2) 0.1155 0.1321 0.0227 0.0783 0.4683 1.1374 243
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4135 1.5834 -2.5123 0.3341
## (Intercept)-Lynx_rufus -0.0147 1.9958 -3.2320 -0.2884
## (Intercept)-Didelphis_virginiana -2.3925 1.3406 -5.2065 -2.3447
## (Intercept)-Sylvilagus_floridanus -1.0963 1.5074 -4.0918 -1.1136
## (Intercept)-Meleagris_gallopavo -1.4664 1.5732 -4.8804 -1.4038
## (Intercept)-Sciurus_carolinensis -2.5987 1.6246 -6.3809 -2.4102
## (Intercept)-Vulpes_vulpes -2.2767 1.7326 -6.0137 -2.2097
## (Intercept)-Sus_scrofa -3.3188 2.1557 -8.5742 -3.0054
## Cogon_Patch_Size-Canis_latrans 1.0112 1.4154 -0.9590 0.7511
## Cogon_Patch_Size-Lynx_rufus -0.2784 1.7477 -3.4698 -0.3378
## Cogon_Patch_Size-Didelphis_virginiana 1.2783 1.1981 -0.6261 1.1282
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7955 2.0032 -6.5371 -1.5162
## Cogon_Patch_Size-Meleagris_gallopavo 0.3219 1.7701 -2.3429 0.0640
## Cogon_Patch_Size-Sciurus_carolinensis -1.4446 1.7692 -5.5059 -1.1903
## Cogon_Patch_Size-Vulpes_vulpes -0.9215 1.8489 -4.8353 -0.8080
## Cogon_Patch_Size-Sus_scrofa -0.8940 1.6790 -4.6524 -0.7477
## Veg_shannon_index-Canis_latrans 1.5451 0.9034 -0.0102 1.4490
## Veg_shannon_index-Lynx_rufus 0.7664 1.4497 -2.7729 0.8757
## Veg_shannon_index-Didelphis_virginiana 1.5496 1.0474 -0.1190 1.4169
## Veg_shannon_index-Sylvilagus_floridanus 1.3981 1.0571 -0.3868 1.2941
## Veg_shannon_index-Meleagris_gallopavo 1.7875 1.3410 -0.0629 1.5484
## Veg_shannon_index-Sciurus_carolinensis -0.1762 1.8372 -4.4629 0.1835
## Veg_shannon_index-Vulpes_vulpes 0.1020 1.5608 -3.9475 0.3803
## Veg_shannon_index-Sus_scrofa 2.1001 1.7059 0.0552 1.7895
## total_shrub_cover-Canis_latrans 0.9768 1.4156 -1.1668 0.6748
## total_shrub_cover-Lynx_rufus -2.4622 1.9894 -7.0763 -2.1072
## total_shrub_cover-Didelphis_virginiana -1.7514 1.4188 -5.1756 -1.5264
## total_shrub_cover-Sylvilagus_floridanus -1.5120 1.8839 -6.1500 -1.2390
## total_shrub_cover-Meleagris_gallopavo -3.0875 1.9569 -7.6481 -2.7570
## total_shrub_cover-Sciurus_carolinensis -1.2676 1.5515 -4.8178 -1.1124
## total_shrub_cover-Vulpes_vulpes -2.0492 2.3292 -8.1980 -1.5727
## total_shrub_cover-Sus_scrofa -1.0063 1.9540 -5.7737 -0.8705
## Avg_Cogongrass_Cover-Canis_latrans 2.1962 1.2467 0.0987 2.0796
## Avg_Cogongrass_Cover-Lynx_rufus 2.2838 1.4114 -0.0459 2.1309
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7855 1.1470 -0.4312 1.7500
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9431 1.3343 -1.9726 1.0454
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9941 1.7748 -3.2473 1.2426
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0362 1.2412 -0.1028 1.9384
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3585 1.4591 -0.1339 2.1932
## Avg_Cogongrass_Cover-Sus_scrofa 1.2910 1.5321 -2.2554 1.4325
## Tree_Density-Canis_latrans -2.5989 1.6901 -6.8276 -2.2515
## Tree_Density-Lynx_rufus -0.4501 1.4190 -2.8208 -0.5919
## Tree_Density-Didelphis_virginiana -1.8858 1.4186 -5.1299 -1.8069
## Tree_Density-Sylvilagus_floridanus -2.1652 1.6723 -6.4113 -1.9442
## Tree_Density-Meleagris_gallopavo -1.8686 1.5346 -5.1283 -1.8189
## Tree_Density-Sciurus_carolinensis -1.9760 1.5497 -5.5377 -1.8394
## Tree_Density-Vulpes_vulpes -1.3549 1.6757 -4.6176 -1.4436
## Tree_Density-Sus_scrofa -1.9363 1.7092 -6.4425 -1.7354
## Avg_Canopy_Cover-Canis_latrans -0.0955 0.8383 -1.8070 -0.0941
## Avg_Canopy_Cover-Lynx_rufus 0.5344 1.6162 -2.8060 0.5622
## Avg_Canopy_Cover-Didelphis_virginiana 4.1337 1.8793 1.4877 3.7979
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.8887 2.5705 1.4117 4.4326
## Avg_Canopy_Cover-Meleagris_gallopavo 2.8362 1.6526 0.2239 2.5190
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9988 2.0818 1.2691 3.5929
## Avg_Canopy_Cover-Vulpes_vulpes 3.1234 1.8741 0.5515 2.7220
## Avg_Canopy_Cover-Sus_scrofa 2.5717 1.5958 0.3073 2.2762
## avg_veg_height-Canis_latrans -0.3159 0.7820 -1.7705 -0.3355
## avg_veg_height-Lynx_rufus -0.3960 1.0640 -2.5142 -0.4181
## avg_veg_height-Didelphis_virginiana -0.5017 0.8895 -2.3248 -0.4957
## avg_veg_height-Sylvilagus_floridanus -0.4202 0.9077 -2.1771 -0.4250
## avg_veg_height-Meleagris_gallopavo -0.3453 1.1273 -2.7686 -0.3470
## avg_veg_height-Sciurus_carolinensis 0.1292 0.9349 -1.4716 0.0346
## avg_veg_height-Vulpes_vulpes -0.2714 0.9987 -2.1103 -0.3243
## avg_veg_height-Sus_scrofa -0.2859 0.9715 -2.1792 -0.3149
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.2073 1.0105 161
## (Intercept)-Lynx_rufus 4.8777 1.0617 106
## (Intercept)-Didelphis_virginiana 0.0071 1.0138 335
## (Intercept)-Sylvilagus_floridanus 2.0858 1.0473 241
## (Intercept)-Meleagris_gallopavo 1.6803 1.0012 208
## (Intercept)-Sciurus_carolinensis 0.0306 1.0391 145
## (Intercept)-Vulpes_vulpes 0.9294 1.0970 157
## (Intercept)-Sus_scrofa -0.0840 1.0021 91
## Cogon_Patch_Size-Canis_latrans 4.5561 1.0393 356
## Cogon_Patch_Size-Lynx_rufus 3.4442 1.0959 201
## Cogon_Patch_Size-Didelphis_virginiana 4.1298 1.0532 274
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2745 1.0245 208
## Cogon_Patch_Size-Meleagris_gallopavo 4.7226 1.0374 142
## Cogon_Patch_Size-Sciurus_carolinensis 1.3923 1.0097 204
## Cogon_Patch_Size-Vulpes_vulpes 2.5492 1.0435 267
## Cogon_Patch_Size-Sus_scrofa 2.1140 1.0506 369
## Veg_shannon_index-Canis_latrans 3.5619 1.0048 294
## Veg_shannon_index-Lynx_rufus 3.1259 1.1430 161
## Veg_shannon_index-Didelphis_virginiana 4.0306 1.0042 284
## Veg_shannon_index-Sylvilagus_floridanus 3.7394 1.0334 298
## Veg_shannon_index-Meleagris_gallopavo 4.8893 1.0027 231
## Veg_shannon_index-Sciurus_carolinensis 2.0449 1.1212 100
## Veg_shannon_index-Vulpes_vulpes 2.3234 1.0622 178
## Veg_shannon_index-Sus_scrofa 5.8511 1.0681 174
## total_shrub_cover-Canis_latrans 4.3528 1.0477 160
## total_shrub_cover-Lynx_rufus 0.5262 1.0894 135
## total_shrub_cover-Didelphis_virginiana 0.4374 1.0673 282
## total_shrub_cover-Sylvilagus_floridanus 1.7405 1.0820 233
## total_shrub_cover-Meleagris_gallopavo -0.2882 1.0447 127
## total_shrub_cover-Sciurus_carolinensis 1.3680 1.0053 252
## total_shrub_cover-Vulpes_vulpes 1.2931 1.0190 135
## total_shrub_cover-Sus_scrofa 2.4255 1.0569 109
## Avg_Cogongrass_Cover-Canis_latrans 5.0971 1.0586 144
## Avg_Cogongrass_Cover-Lynx_rufus 5.6614 1.0106 177
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.2378 1.0973 267
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3162 1.0207 313
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6910 1.0462 161
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.8379 1.0312 213
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.8593 1.0387 204
## Avg_Cogongrass_Cover-Sus_scrofa 3.9105 1.0012 289
## Tree_Density-Canis_latrans -0.3551 1.0936 187
## Tree_Density-Lynx_rufus 2.8041 1.0323 171
## Tree_Density-Didelphis_virginiana 0.8650 1.0592 289
## Tree_Density-Sylvilagus_floridanus 0.5861 1.0315 253
## Tree_Density-Meleagris_gallopavo 1.2632 1.0193 332
## Tree_Density-Sciurus_carolinensis 0.9449 1.0434 324
## Tree_Density-Vulpes_vulpes 2.4897 1.0332 250
## Tree_Density-Sus_scrofa 0.7869 1.1099 184
## Avg_Canopy_Cover-Canis_latrans 1.5713 1.0821 234
## Avg_Canopy_Cover-Lynx_rufus 3.8902 1.0532 262
## Avg_Canopy_Cover-Didelphis_virginiana 8.7694 1.0740 90
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.2956 1.0645 148
## Avg_Canopy_Cover-Meleagris_gallopavo 6.7557 1.0318 275
## Avg_Canopy_Cover-Sciurus_carolinensis 8.7847 1.0233 133
## Avg_Canopy_Cover-Vulpes_vulpes 8.0973 1.0477 174
## Avg_Canopy_Cover-Sus_scrofa 6.5482 1.0508 322
## avg_veg_height-Canis_latrans 1.2962 1.0732 176
## avg_veg_height-Lynx_rufus 1.8574 1.0686 199
## avg_veg_height-Didelphis_virginiana 1.2090 1.0498 286
## avg_veg_height-Sylvilagus_floridanus 1.3988 1.0634 282
## avg_veg_height-Meleagris_gallopavo 1.8427 1.0659 221
## avg_veg_height-Sciurus_carolinensis 2.2286 1.0451 250
## avg_veg_height-Vulpes_vulpes 1.8251 1.0419 195
## avg_veg_height-Sus_scrofa 1.6980 1.0960 234
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6451 0.2049 -3.0756 -2.6379 -2.2612 1.0074
## (Intercept)-Lynx_rufus -3.4705 0.3468 -4.1758 -3.4581 -2.8334 1.0196
## (Intercept)-Didelphis_virginiana -2.5795 0.3116 -3.1851 -2.5707 -1.9790 1.0152
## (Intercept)-Sylvilagus_floridanus -3.1315 0.2703 -3.6888 -3.1234 -2.6162 1.0126
## (Intercept)-Meleagris_gallopavo -3.5570 0.4387 -4.4495 -3.5281 -2.7581 1.0377
## (Intercept)-Sciurus_carolinensis -2.7622 0.3408 -3.4233 -2.7653 -2.1014 1.0073
## (Intercept)-Vulpes_vulpes -3.7527 0.5410 -4.8873 -3.7357 -2.7974 1.1202
## (Intercept)-Sus_scrofa -3.3868 0.5276 -4.4429 -3.3793 -2.3791 1.0316
## shrub_cover-Canis_latrans -0.3870 0.2432 -0.8411 -0.3982 0.1170 1.0576
## shrub_cover-Lynx_rufus 0.0636 0.3899 -0.7153 0.0690 0.8000 1.0195
## shrub_cover-Didelphis_virginiana 1.2157 0.4114 0.4319 1.1975 2.0637 1.0760
## shrub_cover-Sylvilagus_floridanus 0.6505 0.4235 -0.2354 0.6613 1.4722 1.1559
## shrub_cover-Meleagris_gallopavo -0.4850 0.4337 -1.3333 -0.4880 0.3407 1.0336
## shrub_cover-Sciurus_carolinensis 1.2109 0.4398 0.3412 1.2067 2.0731 1.0026
## shrub_cover-Vulpes_vulpes 0.4042 0.6157 -0.8751 0.4344 1.6168 1.0284
## shrub_cover-Sus_scrofa 1.0387 0.8146 -0.6952 1.0479 2.5539 1.0835
## veg_height-Canis_latrans -0.6370 0.1829 -1.0023 -0.6351 -0.2934 1.0114
## veg_height-Lynx_rufus 0.0510 0.2603 -0.4652 0.0511 0.5497 1.0313
## veg_height-Didelphis_virginiana 0.4937 0.2601 0.0047 0.4914 1.0093 1.0044
## veg_height-Sylvilagus_floridanus 0.1097 0.2554 -0.4039 0.1097 0.6039 1.0727
## veg_height-Meleagris_gallopavo -0.2706 0.3951 -1.0513 -0.2781 0.5568 1.0136
## veg_height-Sciurus_carolinensis 0.1662 0.2381 -0.2822 0.1664 0.6370 1.0147
## veg_height-Vulpes_vulpes -0.2560 0.3488 -0.9832 -0.2329 0.3685 1.0064
## veg_height-Sus_scrofa -0.2233 0.3630 -0.9693 -0.2074 0.4626 1.0069
## week-Canis_latrans 0.5203 0.2671 0.0252 0.5101 1.0721 1.0037
## week-Lynx_rufus 0.3320 0.3487 -0.3409 0.3297 1.0277 1.0066
## week-Didelphis_virginiana 0.0390 0.3628 -0.7262 0.0501 0.7374 1.0012
## week-Sylvilagus_floridanus 0.0539 0.3467 -0.6735 0.0595 0.6907 1.0092
## week-Meleagris_gallopavo -0.1190 0.4008 -0.9949 -0.0934 0.5827 1.0424
## week-Sciurus_carolinensis 0.6775 0.3749 0.0187 0.6467 1.4798 1.0044
## week-Vulpes_vulpes 0.2099 0.4463 -0.6947 0.2231 1.0499 1.0157
## week-Sus_scrofa 0.5881 0.4371 -0.1987 0.5571 1.5589 1.0257
## I(week^2)-Canis_latrans -0.2154 0.1112 -0.4393 -0.2144 0.0010 1.0036
## I(week^2)-Lynx_rufus -0.2387 0.1578 -0.5739 -0.2338 0.0452 1.0146
## I(week^2)-Didelphis_virginiana -0.4127 0.2393 -0.9803 -0.3836 -0.0119 1.0464
## I(week^2)-Sylvilagus_floridanus -0.1690 0.1647 -0.5083 -0.1626 0.1384 1.0098
## I(week^2)-Meleagris_gallopavo -0.4548 0.3331 -1.2425 -0.4024 -0.0013 1.0812
## I(week^2)-Sciurus_carolinensis -0.2455 0.1480 -0.5619 -0.2403 0.0247 1.0028
## I(week^2)-Vulpes_vulpes -0.4700 0.3181 -1.2641 -0.4172 0.0107 1.0980
## I(week^2)-Sus_scrofa -0.2324 0.1892 -0.6151 -0.2279 0.1257 1.0077
## ESS
## (Intercept)-Canis_latrans 681
## (Intercept)-Lynx_rufus 220
## (Intercept)-Didelphis_virginiana 627
## (Intercept)-Sylvilagus_floridanus 828
## (Intercept)-Meleagris_gallopavo 316
## (Intercept)-Sciurus_carolinensis 376
## (Intercept)-Vulpes_vulpes 230
## (Intercept)-Sus_scrofa 206
## shrub_cover-Canis_latrans 400
## shrub_cover-Lynx_rufus 300
## shrub_cover-Didelphis_virginiana 347
## shrub_cover-Sylvilagus_floridanus 313
## shrub_cover-Meleagris_gallopavo 348
## shrub_cover-Sciurus_carolinensis 283
## shrub_cover-Vulpes_vulpes 247
## shrub_cover-Sus_scrofa 179
## veg_height-Canis_latrans 657
## veg_height-Lynx_rufus 527
## veg_height-Didelphis_virginiana 985
## veg_height-Sylvilagus_floridanus 588
## veg_height-Meleagris_gallopavo 336
## veg_height-Sciurus_carolinensis 873
## veg_height-Vulpes_vulpes 558
## veg_height-Sus_scrofa 750
## week-Canis_latrans 1219
## week-Lynx_rufus 829
## week-Didelphis_virginiana 928
## week-Sylvilagus_floridanus 941
## week-Meleagris_gallopavo 485
## week-Sciurus_carolinensis 951
## week-Vulpes_vulpes 647
## week-Sus_scrofa 828
## I(week^2)-Canis_latrans 1216
## I(week^2)-Lynx_rufus 594
## I(week^2)-Didelphis_virginiana 380
## I(week^2)-Sylvilagus_floridanus 697
## I(week^2)-Meleagris_gallopavo 116
## I(week^2)-Sciurus_carolinensis 1079
## I(week^2)-Vulpes_vulpes 162
## I(week^2)-Sus_scrofa 1016
#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 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_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): 0.714
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6110 0.6320 -1.8332 -0.6140 0.7258 1.1179 163
## Avg_Cogongrass_Cover 0.0747 0.5236 -0.9723 0.0751 1.1006 1.0057 407
## total_shrub_cover -1.1612 0.7103 -2.6908 -1.1094 0.1174 1.1035 190
## avg_veg_height 0.0899 0.4906 -0.8346 0.0780 1.0872 1.0140 296
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0153 1.9112 0.0560 0.4457 5.8054 1.0200 248
## Avg_Cogongrass_Cover 0.8983 1.6394 0.0500 0.4130 5.1376 1.0085 432
## total_shrub_cover 2.4845 3.5944 0.1104 1.3690 11.6361 1.0148 116
## avg_veg_height 0.5077 0.8661 0.0431 0.2647 2.3827 1.0756 689
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.6146 4.2759 0.368 3.3021 16.0639 1.0857 59
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1173 0.2921 -3.7120 -3.1165 -2.5698 1.0348 259
## shrub_cover 0.5604 0.4084 -0.2440 0.5696 1.3562 1.0437 777
## veg_height -0.0717 0.2292 -0.5342 -0.0739 0.3783 1.0009 808
## week 0.2870 0.2640 -0.2485 0.2924 0.7920 1.0045 818
## I(week^2) -0.3036 0.1505 -0.6244 -0.2977 -0.0183 1.0045 499
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4317 0.5116 0.0517 0.2743 1.7590 1.0432 314
## shrub_cover 1.1145 1.0379 0.1885 0.8169 3.9132 1.0480 237
## veg_height 0.3059 0.2655 0.0624 0.2321 0.9741 1.0121 1464
## week 0.3108 0.3591 0.0409 0.2033 1.2793 1.0071 700
## I(week^2) 0.1096 0.1168 0.0225 0.0754 0.4120 1.0076 643
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0988 0.9432 -1.7827 -0.1622
## (Intercept)-Lynx_rufus -0.5105 0.8499 -2.1398 -0.5266
## (Intercept)-Didelphis_virginiana -0.7458 0.8520 -2.5126 -0.7241
## (Intercept)-Sylvilagus_floridanus -0.2985 0.9501 -2.0007 -0.3544
## (Intercept)-Meleagris_gallopavo -0.7255 1.0106 -2.6431 -0.7152
## (Intercept)-Sciurus_carolinensis -0.8622 0.8756 -2.7336 -0.8128
## (Intercept)-Vulpes_vulpes -0.8261 0.9735 -2.7509 -0.8424
## (Intercept)-Sus_scrofa -1.0578 1.0068 -3.3257 -0.9831
## Avg_Cogongrass_Cover-Canis_latrans 0.5110 0.6466 -0.6664 0.4877
## Avg_Cogongrass_Cover-Lynx_rufus 0.5568 0.7583 -0.6711 0.4764
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2740 0.6444 -0.9734 0.2551
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4612 0.7756 -2.2814 -0.3861
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4600 0.9463 -2.6102 -0.3548
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0299 0.6474 -1.3864 0.0526
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3011 0.7570 -1.1417 0.2633
## Avg_Cogongrass_Cover-Sus_scrofa -0.2120 0.8757 -2.2764 -0.1200
## total_shrub_cover-Canis_latrans 0.4789 0.9495 -1.0851 0.3619
## total_shrub_cover-Lynx_rufus -1.9920 1.1926 -4.8030 -1.8201
## total_shrub_cover-Didelphis_virginiana -1.2325 0.9294 -3.4104 -1.1041
## total_shrub_cover-Sylvilagus_floridanus -1.8821 1.2340 -4.8963 -1.6469
## total_shrub_cover-Meleagris_gallopavo -2.0970 1.1987 -4.9692 -1.9155
## total_shrub_cover-Sciurus_carolinensis -1.4026 1.0529 -3.8883 -1.2547
## total_shrub_cover-Vulpes_vulpes -1.2990 1.6373 -4.5983 -1.2093
## total_shrub_cover-Sus_scrofa -0.9772 1.1551 -3.6122 -0.8887
## avg_veg_height-Canis_latrans 0.1234 0.5857 -0.9624 0.1001
## avg_veg_height-Lynx_rufus 0.0732 0.7096 -1.2624 0.0434
## avg_veg_height-Didelphis_virginiana -0.0653 0.6370 -1.4048 -0.0607
## avg_veg_height-Sylvilagus_floridanus 0.0538 0.6353 -1.1509 0.0227
## avg_veg_height-Meleagris_gallopavo -0.1793 0.8447 -1.9565 -0.1498
## avg_veg_height-Sciurus_carolinensis 0.5314 0.6572 -0.6216 0.4764
## avg_veg_height-Vulpes_vulpes 0.0260 0.6672 -1.3011 0.0240
## avg_veg_height-Sus_scrofa 0.1607 0.6711 -1.0560 0.1393
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0784 1.0412 311
## (Intercept)-Lynx_rufus 1.2965 1.0633 342
## (Intercept)-Didelphis_virginiana 0.9810 1.0953 258
## (Intercept)-Sylvilagus_floridanus 1.8832 1.0967 208
## (Intercept)-Meleagris_gallopavo 1.0483 1.0700 179
## (Intercept)-Sciurus_carolinensis 0.7958 1.0492 347
## (Intercept)-Vulpes_vulpes 1.2892 1.1288 269
## (Intercept)-Sus_scrofa 0.7352 1.0794 259
## Avg_Cogongrass_Cover-Canis_latrans 1.9579 1.0026 471
## Avg_Cogongrass_Cover-Lynx_rufus 2.3256 1.0076 516
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.6305 1.0006 512
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8435 1.0051 379
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1353 1.0111 315
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2346 1.0182 556
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8480 1.0253 552
## Avg_Cogongrass_Cover-Sus_scrofa 1.3111 1.0222 525
## total_shrub_cover-Canis_latrans 2.7618 1.0037 167
## total_shrub_cover-Lynx_rufus -0.1215 1.0686 123
## total_shrub_cover-Didelphis_virginiana 0.2334 1.0329 237
## total_shrub_cover-Sylvilagus_floridanus -0.0608 1.0422 190
## total_shrub_cover-Meleagris_gallopavo -0.2572 1.0106 223
## total_shrub_cover-Sciurus_carolinensis 0.2364 1.0676 179
## total_shrub_cover-Vulpes_vulpes 2.1209 1.2320 103
## total_shrub_cover-Sus_scrofa 1.0383 1.0835 226
## avg_veg_height-Canis_latrans 1.3805 1.0022 504
## avg_veg_height-Lynx_rufus 1.5200 1.0033 408
## avg_veg_height-Didelphis_virginiana 1.1894 1.0116 452
## avg_veg_height-Sylvilagus_floridanus 1.3382 1.0308 367
## avg_veg_height-Meleagris_gallopavo 1.4070 1.0197 297
## avg_veg_height-Sciurus_carolinensis 1.9820 1.0135 570
## avg_veg_height-Vulpes_vulpes 1.3197 1.0100 456
## avg_veg_height-Sus_scrofa 1.5284 1.0085 506
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6967 0.2132 -3.1133 -2.6982 -2.2872 1.0034
## (Intercept)-Lynx_rufus -3.3279 0.3160 -3.9941 -3.3107 -2.7580 1.0357
## (Intercept)-Didelphis_virginiana -2.7387 0.3362 -3.3936 -2.7473 -2.0508 1.0130
## (Intercept)-Sylvilagus_floridanus -3.2022 0.2727 -3.7699 -3.1983 -2.6862 1.0227
## (Intercept)-Meleagris_gallopavo -3.4756 0.4681 -4.4965 -3.4496 -2.6752 1.0281
## (Intercept)-Sciurus_carolinensis -2.8639 0.3500 -3.5369 -2.8671 -2.1775 1.0536
## (Intercept)-Vulpes_vulpes -3.7131 0.6191 -5.1594 -3.6340 -2.7255 1.0983
## (Intercept)-Sus_scrofa -3.4684 0.5031 -4.5442 -3.4436 -2.5246 1.0727
## shrub_cover-Canis_latrans -0.3344 0.2696 -0.8313 -0.3429 0.2213 1.0165
## shrub_cover-Lynx_rufus 0.1687 0.3539 -0.5652 0.1890 0.8087 1.0201
## shrub_cover-Didelphis_virginiana 1.4134 0.4591 0.5496 1.3950 2.3789 1.0872
## shrub_cover-Sylvilagus_floridanus 0.8748 0.4348 0.0014 0.8706 1.7278 1.0776
## shrub_cover-Meleagris_gallopavo -0.4048 0.4560 -1.3304 -0.3830 0.4252 1.0110
## shrub_cover-Sciurus_carolinensis 1.3481 0.4287 0.4683 1.3415 2.2200 1.1149
## shrub_cover-Vulpes_vulpes 0.3795 0.7645 -1.2349 0.4176 1.8088 1.1653
## shrub_cover-Sus_scrofa 1.2558 0.7999 -0.3087 1.2545 2.8938 1.1147
## veg_height-Canis_latrans -0.6520 0.1947 -1.0391 -0.6502 -0.2769 1.0122
## veg_height-Lynx_rufus 0.0215 0.2479 -0.4677 0.0190 0.5028 1.0151
## veg_height-Didelphis_virginiana 0.4812 0.2933 -0.0619 0.4700 1.0831 1.0136
## veg_height-Sylvilagus_floridanus 0.0231 0.2616 -0.4764 0.0202 0.5493 1.0082
## veg_height-Meleagris_gallopavo -0.2176 0.4465 -1.0595 -0.2337 0.7171 1.0221
## veg_height-Sciurus_carolinensis 0.1578 0.2514 -0.3080 0.1511 0.6812 1.0069
## veg_height-Vulpes_vulpes -0.1536 0.3296 -0.8050 -0.1533 0.4804 1.0066
## veg_height-Sus_scrofa -0.2308 0.3548 -0.9397 -0.2305 0.4552 1.0039
## week-Canis_latrans 0.5176 0.2639 0.0168 0.5115 1.0679 1.0060
## week-Lynx_rufus 0.3418 0.3335 -0.3180 0.3337 1.0009 1.0003
## week-Didelphis_virginiana 0.0430 0.3638 -0.7049 0.0685 0.7193 1.0060
## week-Sylvilagus_floridanus 0.0957 0.3311 -0.5948 0.1094 0.6940 1.0120
## week-Meleagris_gallopavo -0.1343 0.4211 -1.0531 -0.0960 0.6116 1.0035
## week-Sciurus_carolinensis 0.6615 0.3749 -0.0198 0.6395 1.4435 1.0013
## week-Vulpes_vulpes 0.2161 0.4326 -0.7214 0.2244 1.0359 1.0033
## week-Sus_scrofa 0.5781 0.4214 -0.1715 0.5493 1.5080 1.0020
## I(week^2)-Canis_latrans -0.2145 0.1111 -0.4295 -0.2135 0.0040 1.0104
## I(week^2)-Lynx_rufus -0.2265 0.1567 -0.5446 -0.2225 0.0622 1.0055
## I(week^2)-Didelphis_virginiana -0.4224 0.2443 -1.0121 -0.3903 -0.0381 1.0139
## I(week^2)-Sylvilagus_floridanus -0.1764 0.1564 -0.4980 -0.1724 0.1194 1.0031
## I(week^2)-Meleagris_gallopavo -0.4748 0.2786 -1.1053 -0.4385 -0.0125 1.0100
## I(week^2)-Sciurus_carolinensis -0.2354 0.1515 -0.5526 -0.2317 0.0505 1.0075
## I(week^2)-Vulpes_vulpes -0.4663 0.2947 -1.1696 -0.4185 -0.0101 1.0248
## I(week^2)-Sus_scrofa -0.2319 0.1829 -0.5993 -0.2292 0.1223 1.0008
## ESS
## (Intercept)-Canis_latrans 336
## (Intercept)-Lynx_rufus 284
## (Intercept)-Didelphis_virginiana 306
## (Intercept)-Sylvilagus_floridanus 520
## (Intercept)-Meleagris_gallopavo 208
## (Intercept)-Sciurus_carolinensis 197
## (Intercept)-Vulpes_vulpes 165
## (Intercept)-Sus_scrofa 269
## shrub_cover-Canis_latrans 327
## shrub_cover-Lynx_rufus 563
## shrub_cover-Didelphis_virginiana 278
## shrub_cover-Sylvilagus_floridanus 291
## shrub_cover-Meleagris_gallopavo 315
## shrub_cover-Sciurus_carolinensis 304
## shrub_cover-Vulpes_vulpes 221
## shrub_cover-Sus_scrofa 238
## veg_height-Canis_latrans 650
## veg_height-Lynx_rufus 703
## veg_height-Didelphis_virginiana 564
## veg_height-Sylvilagus_floridanus 554
## veg_height-Meleagris_gallopavo 360
## veg_height-Sciurus_carolinensis 619
## veg_height-Vulpes_vulpes 608
## veg_height-Sus_scrofa 706
## week-Canis_latrans 1013
## week-Lynx_rufus 880
## week-Didelphis_virginiana 558
## week-Sylvilagus_floridanus 797
## week-Meleagris_gallopavo 484
## week-Sciurus_carolinensis 767
## week-Vulpes_vulpes 687
## week-Sus_scrofa 819
## I(week^2)-Canis_latrans 1213
## I(week^2)-Lynx_rufus 680
## I(week^2)-Didelphis_virginiana 306
## I(week^2)-Sylvilagus_floridanus 580
## I(week^2)-Meleagris_gallopavo 227
## I(week^2)-Sciurus_carolinensis 851
## I(week^2)-Vulpes_vulpes 245
## I(week^2)-Sus_scrofa 824
#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 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_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): 0.6383
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9223 0.5665 -1.9770 -0.9376 0.2531 1.0129 334
## Tree_Density -0.7590 0.5306 -1.9240 -0.7167 0.1998 1.0021 434
## Avg_Canopy_Cover 1.2943 0.5371 0.3383 1.2525 2.4243 1.0084 576
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8701 3.1976 0.1066 1.0753 8.0647 1.2703 163
## Tree_Density 1.1216 1.9913 0.0543 0.5213 6.0319 1.1217 203
## Avg_Canopy_Cover 1.6858 2.1072 0.1364 1.0596 6.9567 1.0647 327
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0728 1.2542 0.053 0.6298 4.4702 1.2947 90
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0737 0.3153 -3.6766 -3.0784 -2.4342 1.0091 1061
## shrub_cover 0.2280 0.3841 -0.5044 0.2223 0.9703 1.0040 1451
## veg_height -0.0498 0.2263 -0.4870 -0.0484 0.4043 0.9996 1362
## week 0.3042 0.2715 -0.2355 0.3036 0.8376 1.0146 977
## I(week^2) -0.3242 0.1697 -0.7089 -0.3131 -0.0328 1.0663 258
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5909 0.6975 0.0858 0.4070 2.2162 1.0993 422
## shrub_cover 1.0172 0.9725 0.1837 0.7477 3.4774 1.0309 1074
## veg_height 0.3123 0.3165 0.0641 0.2324 1.0413 1.0059 1885
## week 0.3326 0.4138 0.0424 0.2150 1.3046 1.1074 879
## I(week^2) 0.1342 0.2369 0.0239 0.0828 0.5697 1.2878 356
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0187 0.7225 -1.4231 -0.0142 1.3982
## (Intercept)-Lynx_rufus 0.0387 1.3696 -1.7337 -0.1802 3.1201
## (Intercept)-Didelphis_virginiana -1.5338 0.7288 -3.1331 -1.4896 -0.2374
## (Intercept)-Sylvilagus_floridanus -0.7771 0.6951 -2.0475 -0.8120 0.6798
## (Intercept)-Meleagris_gallopavo -0.3731 0.9246 -2.0219 -0.4414 1.7343
## (Intercept)-Sciurus_carolinensis -1.5715 0.7325 -3.0984 -1.5297 -0.2396
## (Intercept)-Vulpes_vulpes -1.5312 0.9930 -3.4987 -1.5310 0.5488
## (Intercept)-Sus_scrofa -2.0385 0.9770 -4.2349 -1.9324 -0.3973
## Tree_Density-Canis_latrans -1.0216 0.6695 -2.6433 -0.9446 0.0503
## Tree_Density-Lynx_rufus 0.2487 0.8228 -1.0301 0.1475 2.3080
## Tree_Density-Didelphis_virginiana -1.0253 0.8265 -3.0765 -0.8948 0.2474
## Tree_Density-Sylvilagus_floridanus -1.1445 0.9218 -3.2789 -1.0053 0.2227
## Tree_Density-Meleagris_gallopavo -1.0285 0.8657 -3.1501 -0.8991 0.4039
## Tree_Density-Sciurus_carolinensis -0.8814 0.8226 -2.7989 -0.7747 0.4988
## Tree_Density-Vulpes_vulpes -0.6437 0.9403 -2.6465 -0.6085 1.1934
## Tree_Density-Sus_scrofa -0.9495 0.9262 -3.1633 -0.8178 0.4685
## Avg_Canopy_Cover-Canis_latrans -0.1562 0.4953 -1.1484 -0.1491 0.8025
## Avg_Canopy_Cover-Lynx_rufus 0.8261 0.8474 -0.6533 0.7537 2.7368
## Avg_Canopy_Cover-Didelphis_virginiana 1.8101 0.7912 0.5694 1.6721 3.6772
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4714 1.1875 0.7574 2.2692 5.3923
## Avg_Canopy_Cover-Meleagris_gallopavo 1.8008 0.9247 0.3814 1.6577 4.0618
## Avg_Canopy_Cover-Sciurus_carolinensis 1.7246 0.7897 0.5131 1.6084 3.5998
## Avg_Canopy_Cover-Vulpes_vulpes 1.2855 0.8474 -0.0592 1.1848 3.2837
## Avg_Canopy_Cover-Sus_scrofa 1.4361 0.7273 0.1976 1.3606 3.1271
## Rhat ESS
## (Intercept)-Canis_latrans 1.0084 424
## (Intercept)-Lynx_rufus 1.2388 136
## (Intercept)-Didelphis_virginiana 1.0076 747
## (Intercept)-Sylvilagus_floridanus 1.0012 940
## (Intercept)-Meleagris_gallopavo 1.0108 392
## (Intercept)-Sciurus_carolinensis 1.0012 802
## (Intercept)-Vulpes_vulpes 1.0442 280
## (Intercept)-Sus_scrofa 1.0006 418
## Tree_Density-Canis_latrans 1.0008 784
## Tree_Density-Lynx_rufus 1.0244 385
## Tree_Density-Didelphis_virginiana 1.0235 530
## Tree_Density-Sylvilagus_floridanus 1.0116 480
## Tree_Density-Meleagris_gallopavo 1.0211 433
## Tree_Density-Sciurus_carolinensis 1.0028 662
## Tree_Density-Vulpes_vulpes 1.0815 243
## Tree_Density-Sus_scrofa 1.0093 494
## Avg_Canopy_Cover-Canis_latrans 1.0090 1004
## Avg_Canopy_Cover-Lynx_rufus 1.0029 489
## Avg_Canopy_Cover-Didelphis_virginiana 1.0072 554
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0298 345
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0523 389
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0567 444
## Avg_Canopy_Cover-Vulpes_vulpes 1.0158 554
## Avg_Canopy_Cover-Sus_scrofa 1.0030 607
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6349 0.2109 -3.0653 -2.6268 -2.2275 1.0037
## (Intercept)-Lynx_rufus -3.5299 0.3391 -4.1965 -3.5235 -2.8939 1.0304
## (Intercept)-Didelphis_virginiana -2.5407 0.3152 -3.1759 -2.5364 -1.9377 1.0103
## (Intercept)-Sylvilagus_floridanus -3.0565 0.2686 -3.6255 -3.0514 -2.5541 1.0159
## (Intercept)-Meleagris_gallopavo -3.7343 0.4271 -4.5981 -3.7194 -2.9411 1.0528
## (Intercept)-Sciurus_carolinensis -2.6672 0.3367 -3.3400 -2.6597 -2.0259 1.0040
## (Intercept)-Vulpes_vulpes -3.7743 0.6210 -5.1232 -3.7169 -2.7319 1.0656
## (Intercept)-Sus_scrofa -3.2749 0.5265 -4.3185 -3.2554 -2.2292 1.0307
## shrub_cover-Canis_latrans -0.3463 0.2262 -0.7873 -0.3442 0.0978 1.0225
## shrub_cover-Lynx_rufus -0.3245 0.3392 -1.0077 -0.3252 0.3398 1.0270
## shrub_cover-Didelphis_virginiana 1.1269 0.3907 0.4227 1.1154 1.9350 1.0031
## shrub_cover-Sylvilagus_floridanus 0.4410 0.4143 -0.3676 0.4334 1.2674 1.0153
## shrub_cover-Meleagris_gallopavo -0.7147 0.3850 -1.5032 -0.7103 0.0087 1.0280
## shrub_cover-Sciurus_carolinensis 1.0353 0.4511 0.1665 1.0264 1.9619 1.0021
## shrub_cover-Vulpes_vulpes -0.1118 0.6447 -1.4537 -0.0984 1.0909 1.0474
## shrub_cover-Sus_scrofa 0.7892 0.8291 -0.8349 0.7772 2.4732 1.0344
## veg_height-Canis_latrans -0.6431 0.1921 -1.0292 -0.6360 -0.2830 1.0015
## veg_height-Lynx_rufus 0.0445 0.2517 -0.4342 0.0474 0.5164 1.0042
## veg_height-Didelphis_virginiana 0.5262 0.2645 0.0291 0.5166 1.0888 1.0036
## veg_height-Sylvilagus_floridanus 0.1388 0.2443 -0.3523 0.1376 0.6173 1.0014
## veg_height-Meleagris_gallopavo -0.2852 0.3742 -1.0534 -0.2775 0.4315 1.0122
## veg_height-Sciurus_carolinensis 0.1393 0.2333 -0.2960 0.1358 0.6240 1.0077
## veg_height-Vulpes_vulpes -0.1688 0.3277 -0.8752 -0.1495 0.4510 1.0086
## veg_height-Sus_scrofa -0.1746 0.3497 -0.8937 -0.1571 0.4811 1.0034
## week-Canis_latrans 0.5304 0.2658 0.0280 0.5246 1.0775 1.0088
## week-Lynx_rufus 0.3633 0.3315 -0.2666 0.3615 1.0324 1.0110
## week-Didelphis_virginiana 0.0602 0.3671 -0.6924 0.0766 0.7339 1.0353
## week-Sylvilagus_floridanus 0.0612 0.3408 -0.6450 0.0732 0.7021 1.0197
## week-Meleagris_gallopavo -0.1032 0.4286 -1.0761 -0.0744 0.6441 1.1959
## week-Sciurus_carolinensis 0.6990 0.3755 0.0019 0.6878 1.4801 1.0118
## week-Vulpes_vulpes 0.2026 0.4756 -0.8514 0.2252 1.0859 1.0268
## week-Sus_scrofa 0.6010 0.4565 -0.2167 0.5619 1.5899 1.0297
## I(week^2)-Canis_latrans -0.2266 0.1111 -0.4510 -0.2233 -0.0125 1.0076
## I(week^2)-Lynx_rufus -0.2536 0.1649 -0.6046 -0.2429 0.0420 1.0211
## I(week^2)-Didelphis_virginiana -0.4498 0.2674 -1.1184 -0.4056 -0.0548 1.0283
## I(week^2)-Sylvilagus_floridanus -0.1704 0.1667 -0.5147 -0.1653 0.1444 1.0101
## I(week^2)-Meleagris_gallopavo -0.5100 0.3374 -1.4004 -0.4445 -0.0328 1.2043
## I(week^2)-Sciurus_carolinensis -0.2436 0.1502 -0.5425 -0.2361 0.0277 1.0030
## I(week^2)-Vulpes_vulpes -0.5263 0.3628 -1.4799 -0.4679 0.0030 1.1190
## I(week^2)-Sus_scrofa -0.2231 0.1913 -0.6141 -0.2156 0.1347 1.0125
## ESS
## (Intercept)-Canis_latrans 770
## (Intercept)-Lynx_rufus 307
## (Intercept)-Didelphis_virginiana 721
## (Intercept)-Sylvilagus_floridanus 888
## (Intercept)-Meleagris_gallopavo 287
## (Intercept)-Sciurus_carolinensis 602
## (Intercept)-Vulpes_vulpes 202
## (Intercept)-Sus_scrofa 581
## shrub_cover-Canis_latrans 836
## shrub_cover-Lynx_rufus 508
## shrub_cover-Didelphis_virginiana 658
## shrub_cover-Sylvilagus_floridanus 594
## shrub_cover-Meleagris_gallopavo 296
## shrub_cover-Sciurus_carolinensis 399
## shrub_cover-Vulpes_vulpes 538
## shrub_cover-Sus_scrofa 667
## veg_height-Canis_latrans 663
## veg_height-Lynx_rufus 670
## veg_height-Didelphis_virginiana 914
## veg_height-Sylvilagus_floridanus 1000
## veg_height-Meleagris_gallopavo 565
## veg_height-Sciurus_carolinensis 813
## veg_height-Vulpes_vulpes 688
## veg_height-Sus_scrofa 1129
## week-Canis_latrans 1022
## week-Lynx_rufus 841
## week-Didelphis_virginiana 704
## week-Sylvilagus_floridanus 936
## week-Meleagris_gallopavo 212
## week-Sciurus_carolinensis 1028
## week-Vulpes_vulpes 811
## week-Sus_scrofa 1027
## I(week^2)-Canis_latrans 1104
## I(week^2)-Lynx_rufus 521
## I(week^2)-Didelphis_virginiana 232
## I(week^2)-Sylvilagus_floridanus 718
## I(week^2)-Meleagris_gallopavo 102
## I(week^2)-Sciurus_carolinensis 1253
## I(week^2)-Vulpes_vulpes 159
## I(week^2)-Sus_scrofa 1240
#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 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_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): 0.6922
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6806 0.6248 -1.8617 -0.6990 0.6481 1.0165 206
## Cogon_Patch_Size -0.1756 0.6236 -1.5102 -0.1456 0.9987 1.0016 639
## Avg_Cogongrass_Cover 0.1943 0.4630 -0.7458 0.1862 1.1506 1.0399 370
## total_shrub_cover -1.0168 0.6575 -2.4882 -0.9754 0.1532 1.0029 242
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0939 1.7001 0.0501 0.5368 5.6274 1.1114 245
## Cogon_Patch_Size 2.2071 3.9321 0.0805 1.0589 10.9793 1.1221 311
## Avg_Cogongrass_Cover 0.7873 1.2743 0.0469 0.3901 3.8725 1.0244 490
## total_shrub_cover 2.0641 3.1240 0.0857 1.1011 10.1770 1.1264 199
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.2785 3.9882 0.5931 3.0505 14.3023 1.0273 104
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0967 0.3056 -3.7390 -3.0905 -2.5064 1.0063 534
## shrub_cover 0.5029 0.4050 -0.3184 0.5079 1.2686 1.0039 820
## veg_height -0.0883 0.2398 -0.5581 -0.0881 0.3794 1.0105 670
## week 0.3001 0.2749 -0.2651 0.3002 0.8330 1.0124 860
## I(week^2) -0.3113 0.1482 -0.6201 -0.3021 -0.0340 1.0218 628
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4932 0.5481 0.0644 0.3348 1.8852 1.0597 422
## shrub_cover 1.1025 1.1668 0.1837 0.7902 4.1952 1.0305 716
## veg_height 0.2959 0.2703 0.0586 0.2176 1.0253 1.0051 1280
## week 0.3211 0.3670 0.0410 0.2149 1.3237 1.0088 691
## I(week^2) 0.1059 0.1018 0.0227 0.0758 0.3754 1.0199 494
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0447 0.9320 -1.6579 -0.1496
## (Intercept)-Lynx_rufus -0.4914 0.9641 -2.2426 -0.5495
## (Intercept)-Didelphis_virginiana -0.8146 0.8657 -2.4705 -0.8556
## (Intercept)-Sylvilagus_floridanus -0.4853 0.8877 -2.2188 -0.5128
## (Intercept)-Meleagris_gallopavo -0.6755 0.9489 -2.5862 -0.7013
## (Intercept)-Sciurus_carolinensis -0.9939 0.9069 -2.9160 -0.9458
## (Intercept)-Vulpes_vulpes -1.0293 1.0205 -3.1042 -0.9603
## (Intercept)-Sus_scrofa -1.1526 1.0199 -3.4087 -1.0642
## Cogon_Patch_Size-Canis_latrans 0.9156 0.9906 -0.4558 0.7334
## Cogon_Patch_Size-Lynx_rufus -0.1594 1.0694 -2.1525 -0.2187
## Cogon_Patch_Size-Didelphis_virginiana 0.8099 0.6493 -0.3377 0.7525
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2190 1.3440 -4.7405 -0.9026
## Cogon_Patch_Size-Meleagris_gallopavo 0.0754 0.9983 -1.6103 0.0196
## Cogon_Patch_Size-Sciurus_carolinensis -1.0036 1.0692 -3.6354 -0.7994
## Cogon_Patch_Size-Vulpes_vulpes -0.6686 1.2429 -3.7079 -0.5102
## Cogon_Patch_Size-Sus_scrofa -0.4610 1.0843 -3.1409 -0.3156
## Avg_Cogongrass_Cover-Canis_latrans 0.4213 0.5568 -0.5356 0.3748
## Avg_Cogongrass_Cover-Lynx_rufus 0.6787 0.7710 -0.5587 0.5881
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1621 0.5926 -1.0197 0.1621
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2152 0.7127 -1.7424 -0.1685
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3516 0.9592 -2.4080 -0.2853
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4581 0.6116 -0.6933 0.4362
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4171 0.6767 -0.8433 0.3906
## Avg_Cogongrass_Cover-Sus_scrofa -0.0108 0.7663 -1.6343 0.0284
## total_shrub_cover-Canis_latrans 0.3666 0.9104 -1.1570 0.2440
## total_shrub_cover-Lynx_rufus -1.6540 1.2160 -4.3140 -1.4848
## total_shrub_cover-Didelphis_virginiana -1.2194 0.9430 -3.6763 -1.0569
## total_shrub_cover-Sylvilagus_floridanus -1.5563 1.2616 -4.8828 -1.3284
## total_shrub_cover-Meleagris_gallopavo -1.9275 1.1616 -4.7986 -1.7161
## total_shrub_cover-Sciurus_carolinensis -1.0252 0.9744 -3.3369 -0.8884
## total_shrub_cover-Vulpes_vulpes -1.0764 1.3970 -4.1857 -0.9930
## total_shrub_cover-Sus_scrofa -0.7720 1.1777 -3.3507 -0.7226
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1197 1.0023 320
## (Intercept)-Lynx_rufus 1.5722 1.0119 330
## (Intercept)-Didelphis_virginiana 1.0296 1.0133 278
## (Intercept)-Sylvilagus_floridanus 1.4048 1.0074 355
## (Intercept)-Meleagris_gallopavo 1.3039 1.0066 249
## (Intercept)-Sciurus_carolinensis 0.7092 1.0539 257
## (Intercept)-Vulpes_vulpes 0.8914 1.0828 215
## (Intercept)-Sus_scrofa 0.6773 1.0421 212
## Cogon_Patch_Size-Canis_latrans 3.2521 1.0155 470
## Cogon_Patch_Size-Lynx_rufus 2.4118 1.0554 390
## Cogon_Patch_Size-Didelphis_virginiana 2.1868 1.0040 597
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5550 1.0462 315
## Cogon_Patch_Size-Meleagris_gallopavo 2.3768 1.0188 465
## Cogon_Patch_Size-Sciurus_carolinensis 0.5233 1.0278 384
## Cogon_Patch_Size-Vulpes_vulpes 1.3177 1.0071 331
## Cogon_Patch_Size-Sus_scrofa 1.2821 1.0047 551
## Avg_Cogongrass_Cover-Canis_latrans 1.6701 1.0093 737
## Avg_Cogongrass_Cover-Lynx_rufus 2.5173 1.0062 597
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3312 1.0064 769
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0571 1.0186 406
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.3255 1.0286 283
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7105 1.0213 689
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.9060 1.0205 676
## Avg_Cogongrass_Cover-Sus_scrofa 1.3897 1.0263 488
## total_shrub_cover-Canis_latrans 2.5771 1.0166 181
## total_shrub_cover-Lynx_rufus 0.2752 1.0528 199
## total_shrub_cover-Didelphis_virginiana 0.1438 1.0096 170
## total_shrub_cover-Sylvilagus_floridanus 0.2437 1.0663 220
## total_shrub_cover-Meleagris_gallopavo -0.2430 1.0626 261
## total_shrub_cover-Sciurus_carolinensis 0.5759 1.0098 263
## total_shrub_cover-Vulpes_vulpes 1.8240 1.0187 143
## total_shrub_cover-Sus_scrofa 1.5623 1.0845 201
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6612 0.2106 -3.0970 -2.6533 -2.2627 1.0027
## (Intercept)-Lynx_rufus -3.3680 0.3329 -4.0839 -3.3504 -2.7669 1.0361
## (Intercept)-Didelphis_virginiana -2.6323 0.3346 -3.3001 -2.6358 -1.9950 1.0141
## (Intercept)-Sylvilagus_floridanus -3.1897 0.2771 -3.7583 -3.1810 -2.6705 1.0026
## (Intercept)-Meleagris_gallopavo -3.5319 0.4792 -4.5214 -3.5110 -2.6382 1.0153
## (Intercept)-Sciurus_carolinensis -2.8172 0.3599 -3.5217 -2.8118 -2.1296 1.0270
## (Intercept)-Vulpes_vulpes -3.7754 0.6006 -5.1010 -3.7292 -2.7686 1.0463
## (Intercept)-Sus_scrofa -3.4622 0.4947 -4.4979 -3.4522 -2.5349 1.0176
## shrub_cover-Canis_latrans -0.3552 0.2584 -0.8526 -0.3604 0.1612 1.0367
## shrub_cover-Lynx_rufus 0.1272 0.3719 -0.6571 0.1454 0.8235 1.0408
## shrub_cover-Didelphis_virginiana 1.2931 0.4308 0.5071 1.2827 2.1865 1.0125
## shrub_cover-Sylvilagus_floridanus 0.8013 0.4105 -0.0630 0.8235 1.5533 1.0536
## shrub_cover-Meleagris_gallopavo -0.4506 0.4540 -1.3362 -0.4514 0.4168 1.0099
## shrub_cover-Sciurus_carolinensis 1.2768 0.4511 0.3616 1.2825 2.1572 1.0006
## shrub_cover-Vulpes_vulpes 0.3265 0.7713 -1.2755 0.3438 1.8180 0.9999
## shrub_cover-Sus_scrofa 1.1867 0.8146 -0.4909 1.1845 2.8182 1.0219
## veg_height-Canis_latrans -0.6357 0.1929 -1.0291 -0.6334 -0.2872 1.0032
## veg_height-Lynx_rufus 0.0183 0.2528 -0.4890 0.0180 0.5002 1.0094
## veg_height-Didelphis_virginiana 0.4574 0.2779 -0.0348 0.4476 1.0354 1.0007
## veg_height-Sylvilagus_floridanus 0.0317 0.2634 -0.4806 0.0261 0.5386 1.0528
## veg_height-Meleagris_gallopavo -0.3076 0.4125 -1.1127 -0.3118 0.5542 1.0068
## veg_height-Sciurus_carolinensis 0.1702 0.2551 -0.3132 0.1696 0.6872 1.0247
## veg_height-Vulpes_vulpes -0.1611 0.3323 -0.8467 -0.1575 0.4614 1.0279
## veg_height-Sus_scrofa -0.2266 0.3509 -0.9422 -0.2203 0.4502 1.0016
## week-Canis_latrans 0.5457 0.2729 0.0353 0.5288 1.1038 1.0168
## week-Lynx_rufus 0.3785 0.3501 -0.2954 0.3710 1.0879 1.0034
## week-Didelphis_virginiana 0.0663 0.3695 -0.6984 0.0806 0.7745 1.0119
## week-Sylvilagus_floridanus 0.0828 0.3384 -0.6072 0.0857 0.7275 1.0167
## week-Meleagris_gallopavo -0.1132 0.4029 -1.0025 -0.0826 0.5832 1.0096
## week-Sciurus_carolinensis 0.6885 0.3811 0.0177 0.6652 1.5239 1.0046
## week-Vulpes_vulpes 0.2317 0.4706 -0.7118 0.2449 1.1546 1.0091
## week-Sus_scrofa 0.5762 0.4295 -0.2222 0.5453 1.5108 1.0017
## I(week^2)-Canis_latrans -0.2249 0.1134 -0.4602 -0.2209 -0.0185 1.0167
## I(week^2)-Lynx_rufus -0.2414 0.1602 -0.5911 -0.2345 0.0518 1.0061
## I(week^2)-Didelphis_virginiana -0.4220 0.2376 -0.9861 -0.3969 -0.0441 1.0299
## I(week^2)-Sylvilagus_floridanus -0.1796 0.1644 -0.5065 -0.1775 0.1225 1.0044
## I(week^2)-Meleagris_gallopavo -0.4737 0.2754 -1.1409 -0.4427 -0.0433 1.0048
## I(week^2)-Sciurus_carolinensis -0.2424 0.1522 -0.5654 -0.2338 0.0355 1.0014
## I(week^2)-Vulpes_vulpes -0.4736 0.2911 -1.1456 -0.4360 0.0065 1.0653
## I(week^2)-Sus_scrofa -0.2349 0.1856 -0.6192 -0.2242 0.1090 0.9998
## ESS
## (Intercept)-Canis_latrans 538
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 459
## (Intercept)-Sylvilagus_floridanus 600
## (Intercept)-Meleagris_gallopavo 306
## (Intercept)-Sciurus_carolinensis 372
## (Intercept)-Vulpes_vulpes 216
## (Intercept)-Sus_scrofa 320
## shrub_cover-Canis_latrans 281
## shrub_cover-Lynx_rufus 301
## shrub_cover-Didelphis_virginiana 386
## shrub_cover-Sylvilagus_floridanus 314
## shrub_cover-Meleagris_gallopavo 364
## shrub_cover-Sciurus_carolinensis 376
## shrub_cover-Vulpes_vulpes 214
## shrub_cover-Sus_scrofa 264
## veg_height-Canis_latrans 553
## veg_height-Lynx_rufus 716
## veg_height-Didelphis_virginiana 758
## veg_height-Sylvilagus_floridanus 466
## veg_height-Meleagris_gallopavo 462
## veg_height-Sciurus_carolinensis 655
## veg_height-Vulpes_vulpes 548
## veg_height-Sus_scrofa 708
## week-Canis_latrans 1064
## week-Lynx_rufus 893
## week-Didelphis_virginiana 867
## week-Sylvilagus_floridanus 830
## week-Meleagris_gallopavo 499
## week-Sciurus_carolinensis 824
## week-Vulpes_vulpes 566
## week-Sus_scrofa 968
## I(week^2)-Canis_latrans 1105
## I(week^2)-Lynx_rufus 551
## I(week^2)-Didelphis_virginiana 337
## I(week^2)-Sylvilagus_floridanus 692
## I(week^2)-Meleagris_gallopavo 202
## I(week^2)-Sciurus_carolinensis 901
## I(week^2)-Vulpes_vulpes 280
## I(week^2)-Sus_scrofa 886
#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 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_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): 0.6322
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8384 0.4846 -1.7369 -0.8611 0.1757 1.0393 346
## Veg_shannon_index 0.4104 0.3582 -0.2821 0.4110 1.1537 1.0141 689
## Avg_Cogongrass_Cover 0.3093 0.3603 -0.4013 0.3084 1.0542 1.0013 590
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9587 1.3990 0.0558 0.5167 4.7200 1.0248 313
## Veg_shannon_index 0.5398 0.7589 0.0456 0.3075 2.4636 1.0557 449
## Avg_Cogongrass_Cover 0.5438 0.6933 0.0487 0.3267 2.3819 1.0048 855
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5067 1.4257 0.1074 1.0911 5.2597 1.058 170
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0160 0.3202 -3.6717 -3.0207 -2.3987 1.0049 791
## shrub_cover 0.1877 0.3879 -0.5955 0.1758 0.9807 1.0066 1246
## veg_height -0.0857 0.2140 -0.5000 -0.0884 0.3434 1.0039 1086
## week 0.3117 0.2572 -0.1998 0.3108 0.8265 1.0075 932
## I(week^2) -0.3076 0.1552 -0.6269 -0.2991 -0.0129 1.0031 666
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6357 0.7467 0.0902 0.4298 2.5142 1.0496 471
## shrub_cover 0.9264 0.8899 0.1493 0.6757 3.0911 1.0216 495
## veg_height 0.2758 0.4247 0.0579 0.2033 0.8404 1.1253 1364
## week 0.3078 0.4012 0.0400 0.1994 1.1898 1.0266 1113
## I(week^2) 0.1151 0.1332 0.0217 0.0762 0.4626 1.0299 339
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2125 0.7178 -1.5335 -0.2350
## (Intercept)-Lynx_rufus -0.5409 0.7526 -1.9118 -0.6010
## (Intercept)-Didelphis_virginiana -1.1937 0.6124 -2.4742 -1.1702
## (Intercept)-Sylvilagus_floridanus -0.7022 0.6653 -1.9820 -0.7148
## (Intercept)-Meleagris_gallopavo -0.4146 0.9123 -1.8901 -0.5286
## (Intercept)-Sciurus_carolinensis -1.2018 0.6297 -2.5295 -1.1659
## (Intercept)-Vulpes_vulpes -1.1765 0.8455 -2.7890 -1.1861
## (Intercept)-Sus_scrofa -1.5409 0.7608 -3.3136 -1.4567
## Veg_shannon_index-Canis_latrans 0.7646 0.4559 -0.0298 0.7209
## Veg_shannon_index-Lynx_rufus 0.2448 0.5798 -0.9894 0.2621
## Veg_shannon_index-Didelphis_virginiana 0.5456 0.4457 -0.2514 0.5241
## Veg_shannon_index-Sylvilagus_floridanus 0.4893 0.5224 -0.4067 0.4528
## Veg_shannon_index-Meleagris_gallopavo 0.5889 0.6281 -0.5618 0.5545
## Veg_shannon_index-Sciurus_carolinensis -0.0896 0.4805 -1.1348 -0.0621
## Veg_shannon_index-Vulpes_vulpes 0.0323 0.6007 -1.2368 0.0624
## Veg_shannon_index-Sus_scrofa 0.7506 0.6333 -0.2737 0.6793
## Avg_Cogongrass_Cover-Canis_latrans 0.6895 0.4658 -0.0889 0.6407
## Avg_Cogongrass_Cover-Lynx_rufus 0.6774 0.5302 -0.2231 0.6282
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4891 0.4420 -0.3289 0.4854
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2032 0.5337 -1.3765 -0.1549
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1230 0.7321 -1.7091 -0.0717
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4369 0.4180 -0.3817 0.4231
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4406 0.5826 -0.6100 0.4041
## Avg_Cogongrass_Cover-Sus_scrofa 0.0187 0.6382 -1.4194 0.0650
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2935 1.0351 330
## (Intercept)-Lynx_rufus 1.0644 1.0293 351
## (Intercept)-Didelphis_virginiana -0.0629 1.0023 764
## (Intercept)-Sylvilagus_floridanus 0.6043 1.0111 400
## (Intercept)-Meleagris_gallopavo 1.7271 1.0798 203
## (Intercept)-Sciurus_carolinensis -0.0297 1.0033 686
## (Intercept)-Vulpes_vulpes 0.5265 1.0669 328
## (Intercept)-Sus_scrofa -0.2521 1.0173 489
## Veg_shannon_index-Canis_latrans 1.7522 1.0044 1241
## Veg_shannon_index-Lynx_rufus 1.3153 1.0090 908
## Veg_shannon_index-Didelphis_virginiana 1.4743 1.0020 1545
## Veg_shannon_index-Sylvilagus_floridanus 1.6267 1.0097 532
## Veg_shannon_index-Meleagris_gallopavo 1.9718 1.0256 589
## Veg_shannon_index-Sciurus_carolinensis 0.7621 1.0316 1130
## Veg_shannon_index-Vulpes_vulpes 1.1463 1.0486 638
## Veg_shannon_index-Sus_scrofa 2.1823 1.0032 778
## Avg_Cogongrass_Cover-Canis_latrans 1.7555 1.0041 989
## Avg_Cogongrass_Cover-Lynx_rufus 1.8842 1.0003 864
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3880 1.0011 1319
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7197 1.0028 739
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1953 1.0077 379
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2875 1.0017 1465
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7159 1.0075 849
## Avg_Cogongrass_Cover-Sus_scrofa 1.2070 1.0224 630
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5931 0.1954 -2.9865 -2.5896 -2.2159 1.0209
## (Intercept)-Lynx_rufus -3.3835 0.3289 -4.0711 -3.3841 -2.7730 1.0077
## (Intercept)-Didelphis_virginiana -2.4773 0.3071 -3.0901 -2.4782 -1.8907 1.0009
## (Intercept)-Sylvilagus_floridanus -3.0742 0.2958 -3.6980 -3.0623 -2.5469 1.0148
## (Intercept)-Meleagris_gallopavo -3.8024 0.4930 -4.8122 -3.7926 -2.8631 1.0271
## (Intercept)-Sciurus_carolinensis -2.5842 0.3198 -3.2218 -2.5750 -1.9798 1.0004
## (Intercept)-Vulpes_vulpes -3.7201 0.6401 -5.0963 -3.6396 -2.6572 1.0249
## (Intercept)-Sus_scrofa -3.2300 0.5125 -4.2662 -3.2246 -2.2370 1.0138
## shrub_cover-Canis_latrans -0.3196 0.2168 -0.7347 -0.3261 0.1040 1.0012
## shrub_cover-Lynx_rufus -0.2609 0.3666 -1.0277 -0.2551 0.4212 1.0049
## shrub_cover-Didelphis_virginiana 1.0535 0.4129 0.2737 1.0390 1.9360 1.0199
## shrub_cover-Sylvilagus_floridanus 0.2551 0.4341 -0.5305 0.2425 1.1644 1.0030
## shrub_cover-Meleagris_gallopavo -0.7806 0.4221 -1.6538 -0.7693 0.0235 1.0508
## shrub_cover-Sciurus_carolinensis 0.9344 0.4224 0.1112 0.9374 1.7580 1.0007
## shrub_cover-Vulpes_vulpes -0.0868 0.6217 -1.3131 -0.0860 1.1353 1.0286
## shrub_cover-Sus_scrofa 0.7086 0.8463 -0.9575 0.6908 2.4681 1.0253
## veg_height-Canis_latrans -0.6281 0.1826 -0.9932 -0.6244 -0.2725 1.0064
## veg_height-Lynx_rufus -0.0543 0.2528 -0.5797 -0.0450 0.4300 1.0041
## veg_height-Didelphis_virginiana 0.4251 0.2604 -0.0778 0.4178 0.9408 1.0133
## veg_height-Sylvilagus_floridanus 0.1237 0.2543 -0.3699 0.1192 0.6223 1.0001
## veg_height-Meleagris_gallopavo -0.2820 0.3966 -1.0884 -0.2739 0.4712 1.0084
## veg_height-Sciurus_carolinensis 0.0786 0.2201 -0.3483 0.0743 0.5212 1.0047
## veg_height-Vulpes_vulpes -0.1765 0.3283 -0.8483 -0.1630 0.4494 1.0210
## veg_height-Sus_scrofa -0.2032 0.3492 -0.9197 -0.1923 0.4633 1.0077
## week-Canis_latrans 0.5294 0.2657 0.0383 0.5276 1.0709 1.0075
## week-Lynx_rufus 0.3660 0.3305 -0.2564 0.3603 1.0330 1.0057
## week-Didelphis_virginiana 0.0784 0.3626 -0.7017 0.1023 0.7468 1.0027
## week-Sylvilagus_floridanus 0.1068 0.3387 -0.5881 0.1110 0.7608 0.9999
## week-Meleagris_gallopavo -0.0787 0.4137 -0.9975 -0.0433 0.6420 1.0392
## week-Sciurus_carolinensis 0.6963 0.3837 0.0539 0.6671 1.5691 1.0038
## week-Vulpes_vulpes 0.2330 0.4491 -0.6535 0.2318 1.1312 1.0184
## week-Sus_scrofa 0.5836 0.4255 -0.1740 0.5495 1.5105 1.0083
## I(week^2)-Canis_latrans -0.2234 0.1101 -0.4377 -0.2240 -0.0100 1.0041
## I(week^2)-Lynx_rufus -0.2446 0.1587 -0.5724 -0.2404 0.0558 1.0209
## I(week^2)-Didelphis_virginiana -0.4401 0.2614 -1.0951 -0.4006 -0.0463 1.0076
## I(week^2)-Sylvilagus_floridanus -0.1840 0.1639 -0.5234 -0.1775 0.1239 1.0095
## I(week^2)-Meleagris_gallopavo -0.4892 0.2990 -1.1766 -0.4527 -0.0348 1.0601
## I(week^2)-Sciurus_carolinensis -0.2480 0.1542 -0.5809 -0.2450 0.0368 1.0037
## I(week^2)-Vulpes_vulpes -0.4808 0.3047 -1.1737 -0.4418 -0.0009 1.0070
## I(week^2)-Sus_scrofa -0.2209 0.1909 -0.6338 -0.2092 0.1233 1.0083
## ESS
## (Intercept)-Canis_latrans 842
## (Intercept)-Lynx_rufus 436
## (Intercept)-Didelphis_virginiana 874
## (Intercept)-Sylvilagus_floridanus 623
## (Intercept)-Meleagris_gallopavo 179
## (Intercept)-Sciurus_carolinensis 889
## (Intercept)-Vulpes_vulpes 173
## (Intercept)-Sus_scrofa 570
## shrub_cover-Canis_latrans 848
## shrub_cover-Lynx_rufus 432
## shrub_cover-Didelphis_virginiana 614
## shrub_cover-Sylvilagus_floridanus 483
## shrub_cover-Meleagris_gallopavo 179
## shrub_cover-Sciurus_carolinensis 739
## shrub_cover-Vulpes_vulpes 496
## shrub_cover-Sus_scrofa 654
## veg_height-Canis_latrans 755
## veg_height-Lynx_rufus 711
## veg_height-Didelphis_virginiana 1010
## veg_height-Sylvilagus_floridanus 769
## veg_height-Meleagris_gallopavo 373
## veg_height-Sciurus_carolinensis 1115
## veg_height-Vulpes_vulpes 538
## veg_height-Sus_scrofa 1135
## week-Canis_latrans 1131
## week-Lynx_rufus 1000
## week-Didelphis_virginiana 631
## week-Sylvilagus_floridanus 932
## week-Meleagris_gallopavo 420
## week-Sciurus_carolinensis 1033
## week-Vulpes_vulpes 909
## week-Sus_scrofa 1148
## I(week^2)-Canis_latrans 1157
## I(week^2)-Lynx_rufus 666
## I(week^2)-Didelphis_virginiana 308
## I(week^2)-Sylvilagus_floridanus 690
## I(week^2)-Meleagris_gallopavo 161
## I(week^2)-Sciurus_carolinensis 1228
## I(week^2)-Vulpes_vulpes 325
## I(week^2)-Sus_scrofa 953
#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 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_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): 0.627
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7395 0.4709 -1.6539 -0.7422 0.2819 1.0079 398
## Avg_Cogongrass_Cover 0.1604 0.3456 -0.5743 0.1674 0.8165 1.0014 643
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8830 1.2403 0.0583 0.4859 4.3942 1.0118 277
## Avg_Cogongrass_Cover 0.4951 0.6946 0.0445 0.3004 2.2599 1.1175 690
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5864 1.4068 0.1523 1.182 5.2089 1.0134 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0382 0.3347 -3.6947 -3.0423 -2.3892 1.0043 982
## shrub_cover 0.1845 0.3721 -0.5500 0.1948 0.9289 1.0011 1373
## veg_height -0.0750 0.2149 -0.5058 -0.0729 0.3556 1.0152 1167
## week 0.3017 0.2723 -0.2510 0.3061 0.8434 1.0327 636
## I(week^2) -0.3187 0.1694 -0.7069 -0.3049 -0.0323 1.1555 252
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6586 0.7321 0.0864 0.4574 2.5337 1.0014 388
## shrub_cover 0.9421 0.9483 0.1507 0.6858 3.1241 1.0224 598
## veg_height 0.2763 0.2329 0.0583 0.2062 0.9111 1.0130 1267
## week 0.3439 0.4063 0.0430 0.2226 1.3555 1.1166 334
## I(week^2) 0.1314 0.1989 0.0232 0.0799 0.5283 1.5845 151
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1389 0.6829 -1.3779 -0.1865
## (Intercept)-Lynx_rufus -0.4816 0.7958 -1.8563 -0.5317
## (Intercept)-Didelphis_virginiana -1.0455 0.6174 -2.3212 -1.0326
## (Intercept)-Sylvilagus_floridanus -0.6002 0.6552 -1.8223 -0.6156
## (Intercept)-Meleagris_gallopavo -0.3376 0.8373 -1.6979 -0.4310
## (Intercept)-Sciurus_carolinensis -1.1260 0.6391 -2.4999 -1.0915
## (Intercept)-Vulpes_vulpes -1.0871 0.7890 -2.6009 -1.0798
## (Intercept)-Sus_scrofa -1.2951 0.7364 -2.9216 -1.2188
## Avg_Cogongrass_Cover-Canis_latrans 0.4545 0.4110 -0.3149 0.4361
## Avg_Cogongrass_Cover-Lynx_rufus 0.5170 0.4695 -0.2612 0.4765
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3486 0.4130 -0.4059 0.3325
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2604 0.5055 -1.3941 -0.2199
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3263 0.7271 -2.0215 -0.2368
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3800 0.3878 -0.3643 0.3766
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3175 0.4922 -0.6666 0.2982
## Avg_Cogongrass_Cover-Sus_scrofa -0.0790 0.6115 -1.4498 -0.0328
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2680 1.0007 436
## (Intercept)-Lynx_rufus 1.0828 1.0545 268
## (Intercept)-Didelphis_virginiana 0.1562 1.0076 557
## (Intercept)-Sylvilagus_floridanus 0.7896 1.0082 469
## (Intercept)-Meleagris_gallopavo 1.5061 1.0113 271
## (Intercept)-Sciurus_carolinensis 0.0216 1.0040 622
## (Intercept)-Vulpes_vulpes 0.4896 1.0166 270
## (Intercept)-Sus_scrofa 0.0127 1.0014 527
## Avg_Cogongrass_Cover-Canis_latrans 1.3593 1.0153 1670
## Avg_Cogongrass_Cover-Lynx_rufus 1.5769 1.0013 1194
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2470 1.0082 1303
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6291 1.0145 698
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8511 1.0480 469
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1474 1.0015 1466
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3373 1.0022 1040
## Avg_Cogongrass_Cover-Sus_scrofa 0.9572 1.0008 769
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6087 0.2044 -3.0227 -2.6058 -2.2113 1.0037
## (Intercept)-Lynx_rufus -3.4281 0.3475 -4.1394 -3.4180 -2.7780 1.0321
## (Intercept)-Didelphis_virginiana -2.4855 0.3115 -3.1158 -2.4789 -1.8938 1.0188
## (Intercept)-Sylvilagus_floridanus -3.0751 0.3033 -3.7398 -3.0589 -2.5418 1.0233
## (Intercept)-Meleagris_gallopavo -3.8294 0.5291 -5.0021 -3.7743 -2.9194 1.0075
## (Intercept)-Sciurus_carolinensis -2.5856 0.3453 -3.2713 -2.5799 -1.9183 1.0012
## (Intercept)-Vulpes_vulpes -3.7511 0.6582 -5.2491 -3.6861 -2.6521 1.0064
## (Intercept)-Sus_scrofa -3.2679 0.5153 -4.2415 -3.2711 -2.2216 1.0003
## shrub_cover-Canis_latrans -0.2993 0.2290 -0.7522 -0.3031 0.1507 1.0020
## shrub_cover-Lynx_rufus -0.2615 0.3466 -0.9556 -0.2608 0.4015 1.0087
## shrub_cover-Didelphis_virginiana 1.0373 0.3907 0.3281 1.0225 1.8392 1.0115
## shrub_cover-Sylvilagus_floridanus 0.2883 0.4271 -0.5312 0.2891 1.1433 1.0049
## shrub_cover-Meleagris_gallopavo -0.7973 0.4464 -1.7624 -0.7674 0.0187 1.0152
## shrub_cover-Sciurus_carolinensis 0.9234 0.4397 0.1030 0.9174 1.8059 1.0029
## shrub_cover-Vulpes_vulpes -0.1547 0.6637 -1.5225 -0.1371 1.1462 1.0021
## shrub_cover-Sus_scrofa 0.7420 0.8147 -0.8651 0.7294 2.4153 1.0103
## veg_height-Canis_latrans -0.6197 0.1859 -0.9959 -0.6184 -0.2736 1.0075
## veg_height-Lynx_rufus -0.0272 0.2620 -0.5367 -0.0283 0.4733 1.0071
## veg_height-Didelphis_virginiana 0.4485 0.2647 -0.0416 0.4427 0.9852 0.9997
## veg_height-Sylvilagus_floridanus 0.1149 0.2530 -0.3756 0.1096 0.6044 1.0078
## veg_height-Meleagris_gallopavo -0.2708 0.4039 -1.0595 -0.2712 0.5353 1.0148
## veg_height-Sciurus_carolinensis 0.0918 0.2227 -0.3246 0.0858 0.5382 1.0051
## veg_height-Vulpes_vulpes -0.1864 0.3221 -0.8597 -0.1724 0.4045 1.0086
## veg_height-Sus_scrofa -0.1969 0.3586 -0.9212 -0.1879 0.4668 1.0025
## week-Canis_latrans 0.5308 0.2653 0.0171 0.5321 1.0514 1.0261
## week-Lynx_rufus 0.3463 0.3360 -0.2987 0.3330 1.0265 1.0166
## week-Didelphis_virginiana 0.0619 0.3783 -0.7345 0.0893 0.7297 1.0155
## week-Sylvilagus_floridanus 0.0681 0.3408 -0.6016 0.0791 0.7274 1.0224
## week-Meleagris_gallopavo -0.1474 0.4507 -1.1299 -0.1114 0.6213 1.2265
## week-Sciurus_carolinensis 0.7110 0.3890 0.0379 0.6803 1.5703 1.0010
## week-Vulpes_vulpes 0.2091 0.4914 -0.8502 0.2376 1.1465 1.0469
## week-Sus_scrofa 0.6116 0.4525 -0.1968 0.5857 1.6179 1.0040
## I(week^2)-Canis_latrans -0.2226 0.1113 -0.4507 -0.2198 -0.0155 1.0197
## I(week^2)-Lynx_rufus -0.2409 0.1597 -0.5728 -0.2382 0.0550 1.0027
## I(week^2)-Didelphis_virginiana -0.4388 0.2684 -1.0607 -0.3962 -0.0405 1.0926
## I(week^2)-Sylvilagus_floridanus -0.1732 0.1647 -0.5237 -0.1658 0.1298 1.0047
## I(week^2)-Meleagris_gallopavo -0.5202 0.3378 -1.3775 -0.4564 -0.0306 1.5208
## I(week^2)-Sciurus_carolinensis -0.2513 0.1577 -0.5884 -0.2443 0.0382 1.0051
## I(week^2)-Vulpes_vulpes -0.5044 0.3653 -1.3599 -0.4332 -0.0016 1.2128
## I(week^2)-Sus_scrofa -0.2286 0.1954 -0.6394 -0.2176 0.1228 1.0084
## ESS
## (Intercept)-Canis_latrans 747
## (Intercept)-Lynx_rufus 296
## (Intercept)-Didelphis_virginiana 729
## (Intercept)-Sylvilagus_floridanus 572
## (Intercept)-Meleagris_gallopavo 141
## (Intercept)-Sciurus_carolinensis 612
## (Intercept)-Vulpes_vulpes 150
## (Intercept)-Sus_scrofa 733
## shrub_cover-Canis_latrans 796
## shrub_cover-Lynx_rufus 462
## shrub_cover-Didelphis_virginiana 543
## shrub_cover-Sylvilagus_floridanus 504
## shrub_cover-Meleagris_gallopavo 158
## shrub_cover-Sciurus_carolinensis 614
## shrub_cover-Vulpes_vulpes 515
## shrub_cover-Sus_scrofa 684
## veg_height-Canis_latrans 676
## veg_height-Lynx_rufus 697
## veg_height-Didelphis_virginiana 707
## veg_height-Sylvilagus_floridanus 739
## veg_height-Meleagris_gallopavo 375
## veg_height-Sciurus_carolinensis 1195
## veg_height-Vulpes_vulpes 692
## veg_height-Sus_scrofa 1080
## week-Canis_latrans 1322
## week-Lynx_rufus 795
## week-Didelphis_virginiana 448
## week-Sylvilagus_floridanus 888
## week-Meleagris_gallopavo 189
## week-Sciurus_carolinensis 917
## week-Vulpes_vulpes 710
## week-Sus_scrofa 938
## I(week^2)-Canis_latrans 1169
## I(week^2)-Lynx_rufus 708
## I(week^2)-Didelphis_virginiana 194
## I(week^2)-Sylvilagus_floridanus 647
## I(week^2)-Meleagris_gallopavo 117
## I(week^2)-Sciurus_carolinensis 873
## I(week^2)-Vulpes_vulpes 118
## I(week^2)-Sus_scrofa 1044
# 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 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_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): 0.617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6191 0.5649 -2.8176 -1.6158 -0.5641 1.0107 282
## Avg_Cogongrass_Cover -0.8662 0.4678 -1.8292 -0.8608 0.0336 1.0441 378
## I(Avg_Cogongrass_Cover^2) 0.8956 0.4416 0.0578 0.8795 1.8080 1.0282 438
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9306 1.2170 0.0595 0.5356 4.0447 1.0066 618
## Avg_Cogongrass_Cover 0.5915 0.8719 0.0491 0.3068 2.7505 1.0056 736
## I(Avg_Cogongrass_Cover^2) 0.7954 1.4874 0.0427 0.3508 4.2608 1.2884 217
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3048 1.3792 0.0906 0.8459 4.8765 1.0473 140
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0073 0.3082 -3.6186 -3.0050 -2.3651 1.0057 907
## shrub_cover 0.1736 0.3771 -0.5523 0.1740 0.9368 1.0030 832
## veg_height -0.0458 0.2215 -0.4843 -0.0449 0.3937 0.9996 933
## week 0.2844 0.2634 -0.2519 0.2947 0.7876 1.0312 954
## I(week^2) -0.3033 0.1531 -0.6428 -0.2924 -0.0326 1.0047 589
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5554 0.6155 0.0793 0.3786 2.0477 1.0352 462
## shrub_cover 0.8402 0.7747 0.1482 0.6356 2.8485 1.0010 1157
## veg_height 0.2723 0.2388 0.0590 0.2066 0.8821 1.0080 1120
## week 0.2917 0.3473 0.0387 0.1943 1.1347 1.0471 1114
## I(week^2) 0.1154 0.1454 0.0217 0.0747 0.4160 1.0331 551
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0644 0.7324 -2.5505 -1.0557
## (Intercept)-Lynx_rufus -1.5876 0.7486 -3.2003 -1.5586
## (Intercept)-Didelphis_virginiana -1.8388 0.6962 -3.3175 -1.8150
## (Intercept)-Sylvilagus_floridanus -1.4310 0.7317 -2.9321 -1.4450
## (Intercept)-Meleagris_gallopavo -1.0553 0.8753 -2.7201 -1.1022
## (Intercept)-Sciurus_carolinensis -2.1388 0.7610 -3.7613 -2.0903
## (Intercept)-Vulpes_vulpes -2.1340 0.8959 -4.0753 -2.0438
## (Intercept)-Sus_scrofa -2.1689 0.8512 -4.0812 -2.0981
## Avg_Cogongrass_Cover-Canis_latrans -0.4765 0.5850 -1.5644 -0.5207
## Avg_Cogongrass_Cover-Lynx_rufus -0.7713 0.6271 -2.0463 -0.7725
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5011 0.6224 -1.6856 -0.5192
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3710 0.7380 -3.0110 -1.2908
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1737 0.7761 -2.9589 -1.1021
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8932 0.6329 -2.2152 -0.8664
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8698 0.7008 -2.3081 -0.8624
## Avg_Cogongrass_Cover-Sus_scrofa -1.0252 0.7138 -2.5914 -0.9767
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.5628 0.9278 0.3244 1.3878
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.3986 0.6706 0.4130 1.3005
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6393 0.4898 -0.2642 0.6149
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8829 0.5455 -0.0367 0.8323
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4938 0.7976 -1.0636 0.4699
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0411 0.4720 0.2388 0.9950
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0488 0.6992 0.0842 0.9435
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4247 0.7853 -1.3853 0.4917
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3595 1.0315 419
## (Intercept)-Lynx_rufus -0.1716 1.0044 362
## (Intercept)-Didelphis_virginiana -0.4783 1.0079 441
## (Intercept)-Sylvilagus_floridanus 0.0142 1.0036 490
## (Intercept)-Meleagris_gallopavo 0.7844 1.0330 312
## (Intercept)-Sciurus_carolinensis -0.7935 0.9997 435
## (Intercept)-Vulpes_vulpes -0.6055 1.0297 228
## (Intercept)-Sus_scrofa -0.7331 1.0132 363
## Avg_Cogongrass_Cover-Canis_latrans 0.7888 1.0181 828
## Avg_Cogongrass_Cover-Lynx_rufus 0.4275 1.0372 719
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7536 1.0084 661
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1572 1.0355 444
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2121 1.0343 400
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2848 1.0291 610
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4275 1.0263 605
## Avg_Cogongrass_Cover-Sus_scrofa 0.2401 1.0164 596
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.9028 1.0962 267
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.1705 1.1027 354
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6689 1.0537 410
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1129 1.0276 474
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2037 1.0266 239
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0838 1.0643 410
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.8473 1.1582 161
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.7808 1.0365 305
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6246 0.1973 -3.0191 -2.6237 -2.2411 1.0068
## (Intercept)-Lynx_rufus -3.3355 0.3338 -4.0252 -3.3286 -2.7109 1.0099
## (Intercept)-Didelphis_virginiana -2.5050 0.3145 -3.1262 -2.5001 -1.8930 1.0191
## (Intercept)-Sylvilagus_floridanus -3.0840 0.2845 -3.6709 -3.0736 -2.5626 1.0012
## (Intercept)-Meleagris_gallopavo -3.6974 0.4793 -4.7019 -3.6707 -2.8346 1.0652
## (Intercept)-Sciurus_carolinensis -2.5977 0.3244 -3.2601 -2.6000 -1.9587 1.0019
## (Intercept)-Vulpes_vulpes -3.6783 0.6317 -5.0745 -3.6084 -2.6559 1.0536
## (Intercept)-Sus_scrofa -3.1891 0.5134 -4.1848 -3.1814 -2.1827 1.0383
## shrub_cover-Canis_latrans -0.2918 0.2279 -0.7444 -0.2923 0.1469 1.0013
## shrub_cover-Lynx_rufus -0.2270 0.3475 -0.9267 -0.2224 0.4494 1.0039
## shrub_cover-Didelphis_virginiana 1.0348 0.4026 0.3149 1.0110 1.8678 1.0237
## shrub_cover-Sylvilagus_floridanus 0.2664 0.4136 -0.5029 0.2555 1.0974 1.0072
## shrub_cover-Meleagris_gallopavo -0.6898 0.4110 -1.5563 -0.6848 0.1012 1.0295
## shrub_cover-Sciurus_carolinensis 0.8930 0.4338 0.0447 0.8871 1.7658 1.0116
## shrub_cover-Vulpes_vulpes -0.1171 0.6185 -1.3341 -0.1083 1.1393 1.0278
## shrub_cover-Sus_scrofa 0.6846 0.8161 -0.8890 0.6661 2.3600 1.0434
## veg_height-Canis_latrans -0.6209 0.1898 -1.0040 -0.6145 -0.2681 1.0309
## veg_height-Lynx_rufus 0.0519 0.2458 -0.4223 0.0530 0.5361 1.0046
## veg_height-Didelphis_virginiana 0.4386 0.2773 -0.0893 0.4319 0.9851 1.0043
## veg_height-Sylvilagus_floridanus 0.1398 0.2643 -0.3611 0.1362 0.6696 1.0041
## veg_height-Meleagris_gallopavo -0.2135 0.4017 -0.9982 -0.2161 0.5785 1.0064
## veg_height-Sciurus_carolinensis 0.1101 0.2251 -0.3205 0.1042 0.5828 1.0005
## veg_height-Vulpes_vulpes -0.1465 0.3180 -0.8207 -0.1237 0.4328 1.0211
## veg_height-Sus_scrofa -0.1758 0.3539 -0.9169 -0.1666 0.5364 1.0138
## week-Canis_latrans 0.5154 0.2664 0.0126 0.5079 1.0608 1.0025
## week-Lynx_rufus 0.3410 0.3100 -0.2542 0.3372 0.9701 1.0130
## week-Didelphis_virginiana 0.0640 0.3535 -0.6613 0.0802 0.7105 1.0070
## week-Sylvilagus_floridanus 0.0906 0.3294 -0.5886 0.0982 0.6949 1.0109
## week-Meleagris_gallopavo -0.0885 0.3975 -0.9325 -0.0688 0.6354 1.0653
## week-Sciurus_carolinensis 0.6579 0.3665 0.0077 0.6315 1.4411 1.0080
## week-Vulpes_vulpes 0.1982 0.4560 -0.7860 0.2304 1.0438 1.0562
## week-Sus_scrofa 0.5543 0.4306 -0.2368 0.5286 1.4855 1.0045
## I(week^2)-Canis_latrans -0.2171 0.1109 -0.4419 -0.2126 -0.0059 1.0027
## I(week^2)-Lynx_rufus -0.2316 0.1532 -0.5488 -0.2242 0.0490 1.0240
## I(week^2)-Didelphis_virginiana -0.4263 0.2451 -1.0129 -0.3995 -0.0282 1.0111
## I(week^2)-Sylvilagus_floridanus -0.1874 0.1639 -0.5166 -0.1798 0.1192 1.0072
## I(week^2)-Meleagris_gallopavo -0.4631 0.2668 -1.1067 -0.4311 -0.0312 1.0571
## I(week^2)-Sciurus_carolinensis -0.2305 0.1496 -0.5280 -0.2292 0.0568 1.0073
## I(week^2)-Vulpes_vulpes -0.4828 0.3213 -1.2549 -0.4438 0.0069 1.0427
## I(week^2)-Sus_scrofa -0.2078 0.1818 -0.5843 -0.1996 0.1263 1.0204
## ESS
## (Intercept)-Canis_latrans 876
## (Intercept)-Lynx_rufus 393
## (Intercept)-Didelphis_virginiana 682
## (Intercept)-Sylvilagus_floridanus 720
## (Intercept)-Meleagris_gallopavo 218
## (Intercept)-Sciurus_carolinensis 805
## (Intercept)-Vulpes_vulpes 158
## (Intercept)-Sus_scrofa 439
## shrub_cover-Canis_latrans 813
## shrub_cover-Lynx_rufus 604
## shrub_cover-Didelphis_virginiana 453
## shrub_cover-Sylvilagus_floridanus 546
## shrub_cover-Meleagris_gallopavo 245
## shrub_cover-Sciurus_carolinensis 732
## shrub_cover-Vulpes_vulpes 529
## shrub_cover-Sus_scrofa 506
## veg_height-Canis_latrans 796
## veg_height-Lynx_rufus 680
## veg_height-Didelphis_virginiana 700
## veg_height-Sylvilagus_floridanus 584
## veg_height-Meleagris_gallopavo 325
## veg_height-Sciurus_carolinensis 926
## veg_height-Vulpes_vulpes 536
## veg_height-Sus_scrofa 797
## week-Canis_latrans 1071
## week-Lynx_rufus 951
## week-Didelphis_virginiana 875
## week-Sylvilagus_floridanus 747
## week-Meleagris_gallopavo 496
## week-Sciurus_carolinensis 1124
## week-Vulpes_vulpes 810
## week-Sus_scrofa 1167
## I(week^2)-Canis_latrans 1303
## I(week^2)-Lynx_rufus 623
## I(week^2)-Didelphis_virginiana 332
## I(week^2)-Sylvilagus_floridanus 613
## I(week^2)-Meleagris_gallopavo 174
## I(week^2)-Sciurus_carolinensis 1304
## I(week^2)-Vulpes_vulpes 307
## I(week^2)-Sus_scrofa 1191
# 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 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_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): 0.6895
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4014 1.0101 -4.2052 -2.4640 -0.1671 1.0101 266
## Cogon_Patch_Size 0.2054 0.9734 -1.7737 0.2143 2.1182 1.0374 437
## Veg_shannon_index 1.3101 0.7532 -0.0925 1.2872 2.9430 1.0072 257
## total_shrub_cover -1.3702 0.9981 -3.3752 -1.3461 0.5909 1.0231 232
## Avg_Cogongrass_Cover -0.5804 1.2797 -3.0347 -0.5787 1.8981 1.0302 114
## Tree_Density -1.8318 1.0473 -3.8845 -1.8647 0.3495 1.2167 138
## Avg_Canopy_Cover 2.1276 1.0370 -0.1676 2.1293 4.1616 1.0132 916
## I(Avg_Cogongrass_Cover^2) 1.4529 0.9331 -0.5379 1.4898 3.1014 1.0688 194
## avg_veg_height -0.1515 0.7243 -1.6550 -0.1446 1.2909 1.0986 176
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.9593 10.4130 0.0775 2.2725 33.0788 1.0744 151
## Cogon_Patch_Size 7.1986 11.4810 0.1881 3.5710 38.8722 1.0137 135
## Veg_shannon_index 2.4147 5.5730 0.0646 0.8328 15.3821 1.0843 229
## total_shrub_cover 7.8075 17.2867 0.1176 3.3661 41.6034 1.0639 134
## Avg_Cogongrass_Cover 1.9154 4.6312 0.0548 0.6431 11.2951 1.2204 330
## Tree_Density 2.9664 6.7314 0.0570 0.8915 17.3224 1.2786 220
## Avg_Canopy_Cover 13.5361 29.6351 0.4526 5.9445 71.7334 1.0801 166
## I(Avg_Cogongrass_Cover^2) 5.0611 9.0081 0.0765 2.0029 28.1433 1.1511 103
## avg_veg_height 1.2424 2.3736 0.0499 0.5085 7.4414 1.0309 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.3569 7.1562 0.0753 1.4941 24.8197 1.0098 38
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1281 0.3190 -3.7559 -3.1270 -2.5043 1.0208 490
## shrub_cover 0.4349 0.4140 -0.3802 0.4318 1.2747 1.0206 506
## veg_height -0.0278 0.2275 -0.4979 -0.0232 0.4164 1.0230 1001
## week 0.2873 0.2632 -0.2437 0.2884 0.8193 1.0122 792
## I(week^2) -0.3350 0.1515 -0.6685 -0.3252 -0.0650 1.0103 481
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5987 0.6591 0.0797 0.4155 2.3236 1.0094 643
## shrub_cover 1.0715 0.9468 0.1929 0.8029 3.6963 1.0477 566
## veg_height 0.3057 0.2597 0.0628 0.2288 1.0247 1.0025 1583
## week 0.3396 0.3678 0.0437 0.2276 1.3055 1.0141 668
## I(week^2) 0.1240 0.1349 0.0233 0.0830 0.4919 1.0085 382
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5727 1.5646 -3.9745 -1.7871
## (Intercept)-Lynx_rufus -1.5767 2.0324 -4.7114 -1.9025
## (Intercept)-Didelphis_virginiana -3.5308 1.5260 -6.8868 -3.3756
## (Intercept)-Sylvilagus_floridanus -2.4200 1.5664 -5.4655 -2.4709
## (Intercept)-Meleagris_gallopavo -2.1483 2.1387 -5.9271 -2.2979
## (Intercept)-Sciurus_carolinensis -3.8114 1.6139 -7.4449 -3.6107
## (Intercept)-Vulpes_vulpes -3.8691 2.1126 -8.7011 -3.5748
## (Intercept)-Sus_scrofa -4.1513 2.1505 -9.3270 -3.7692
## Cogon_Patch_Size-Canis_latrans 2.3235 1.7889 -0.2719 1.9904
## Cogon_Patch_Size-Lynx_rufus 0.0140 2.1561 -4.0481 -0.0265
## Cogon_Patch_Size-Didelphis_virginiana 2.2525 1.5294 0.0346 2.0068
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7373 2.2532 -7.0957 -1.3534
## Cogon_Patch_Size-Meleagris_gallopavo 0.8435 1.7845 -2.0901 0.6575
## Cogon_Patch_Size-Sciurus_carolinensis -0.9659 1.8009 -5.3040 -0.6888
## Cogon_Patch_Size-Vulpes_vulpes -0.3094 2.0493 -4.8737 -0.2089
## Cogon_Patch_Size-Sus_scrofa -0.4949 1.8669 -5.0360 -0.2971
## Veg_shannon_index-Canis_latrans 1.7383 0.9787 0.0713 1.6299
## Veg_shannon_index-Lynx_rufus 1.4835 1.3912 -1.1262 1.4139
## Veg_shannon_index-Didelphis_virginiana 1.6083 1.0789 -0.2878 1.5038
## Veg_shannon_index-Sylvilagus_floridanus 1.4972 1.0946 -0.4034 1.3954
## Veg_shannon_index-Meleagris_gallopavo 1.9208 1.5006 -0.2772 1.7494
## Veg_shannon_index-Sciurus_carolinensis 0.3255 1.3676 -2.7985 0.4559
## Veg_shannon_index-Vulpes_vulpes 0.7330 1.4458 -2.8778 0.8658
## Veg_shannon_index-Sus_scrofa 2.1029 1.4348 0.0126 1.8980
## total_shrub_cover-Canis_latrans 0.6532 1.4643 -1.7125 0.3920
## total_shrub_cover-Lynx_rufus -2.5463 2.2384 -7.7202 -2.3357
## total_shrub_cover-Didelphis_virginiana -2.3338 2.0786 -7.7426 -1.9034
## total_shrub_cover-Sylvilagus_floridanus -1.5945 1.9116 -6.1907 -1.3400
## total_shrub_cover-Meleagris_gallopavo -3.7161 2.5324 -9.8319 -3.3535
## total_shrub_cover-Sciurus_carolinensis -1.5526 1.8671 -5.7653 -1.3307
## total_shrub_cover-Vulpes_vulpes -2.0693 2.4296 -8.2306 -1.6660
## total_shrub_cover-Sus_scrofa -1.2455 2.0677 -6.2073 -1.0469
## Avg_Cogongrass_Cover-Canis_latrans -0.4626 1.5325 -3.3456 -0.5008
## Avg_Cogongrass_Cover-Lynx_rufus -0.4513 1.6853 -3.5032 -0.5153
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4096 1.5925 -3.4631 -0.4384
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1688 1.6406 -4.5980 -1.1421
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8322 1.8393 -4.5593 -0.8147
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4954 1.5996 -3.5110 -0.5275
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.2439 1.7976 -3.4022 -0.3484
## Avg_Cogongrass_Cover-Sus_scrofa -0.7820 1.6242 -3.9827 -0.7836
## Tree_Density-Canis_latrans -2.8373 1.6610 -7.1312 -2.5858
## Tree_Density-Lynx_rufus -1.0651 1.8430 -3.9963 -1.2779
## Tree_Density-Didelphis_virginiana -2.0449 1.4454 -5.0363 -2.0204
## Tree_Density-Sylvilagus_floridanus -2.3967 1.5886 -5.8353 -2.2896
## Tree_Density-Meleagris_gallopavo -2.0583 1.5433 -5.2401 -2.0199
## Tree_Density-Sciurus_carolinensis -2.0874 1.4788 -5.2348 -2.0941
## Tree_Density-Vulpes_vulpes -1.7802 1.6720 -4.8771 -1.8596
## Tree_Density-Sus_scrofa -2.0754 1.7094 -5.6992 -2.0031
## Avg_Canopy_Cover-Canis_latrans -0.1880 0.8813 -2.0462 -0.1618
## Avg_Canopy_Cover-Lynx_rufus 1.3772 2.2257 -2.7489 1.3132
## Avg_Canopy_Cover-Didelphis_virginiana 4.7263 3.0537 1.4822 4.1041
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.6513 2.8119 1.6357 5.0975
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3796 2.4834 0.6233 2.8831
## Avg_Canopy_Cover-Sciurus_carolinensis 4.3256 2.4293 1.2281 3.7579
## Avg_Canopy_Cover-Vulpes_vulpes 3.4854 2.1963 0.3612 3.0428
## Avg_Canopy_Cover-Sus_scrofa 2.6307 1.6939 0.1284 2.4183
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9020 1.7233 0.6879 2.5583
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.4140 1.9625 0.7559 2.9883
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2449 0.9641 -0.5718 1.1937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4521 1.1561 -0.6078 1.3620
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1731 2.1149 -4.6448 0.3253
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9378 1.1093 0.1213 1.8243
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4153 1.3728 0.3817 2.2287
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1846 2.1896 -5.3058 0.6663
## avg_veg_height-Canis_latrans -0.2090 0.7944 -1.7893 -0.2080
## avg_veg_height-Lynx_rufus -0.5948 1.3445 -3.8736 -0.4450
## avg_veg_height-Didelphis_virginiana -0.3334 0.9840 -2.3562 -0.3058
## avg_veg_height-Sylvilagus_floridanus -0.2186 0.9845 -2.2214 -0.2394
## avg_veg_height-Meleagris_gallopavo -0.1992 1.1658 -2.6045 -0.1714
## avg_veg_height-Sciurus_carolinensis 0.5038 1.1173 -1.3333 0.3634
## avg_veg_height-Vulpes_vulpes -0.1510 1.1518 -2.4050 -0.1665
## avg_veg_height-Sus_scrofa -0.1187 1.0418 -2.1534 -0.1077
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1049 1.0395 190
## (Intercept)-Lynx_rufus 3.4770 1.0808 108
## (Intercept)-Didelphis_virginiana -0.9010 1.0739 317
## (Intercept)-Sylvilagus_floridanus 0.8377 1.0241 294
## (Intercept)-Meleagris_gallopavo 2.6227 1.1045 96
## (Intercept)-Sciurus_carolinensis -1.0358 1.1003 204
## (Intercept)-Vulpes_vulpes -0.3342 1.0072 118
## (Intercept)-Sus_scrofa -1.1530 1.0623 115
## Cogon_Patch_Size-Canis_latrans 6.7711 1.0138 271
## Cogon_Patch_Size-Lynx_rufus 4.8803 1.1381 161
## Cogon_Patch_Size-Didelphis_virginiana 6.0781 1.0026 186
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4290 1.0355 190
## Cogon_Patch_Size-Meleagris_gallopavo 5.0788 1.0220 240
## Cogon_Patch_Size-Sciurus_carolinensis 1.9390 1.0064 285
## Cogon_Patch_Size-Vulpes_vulpes 3.6431 1.0290 262
## Cogon_Patch_Size-Sus_scrofa 2.6359 1.0517 320
## Veg_shannon_index-Canis_latrans 4.0161 1.0178 320
## Veg_shannon_index-Lynx_rufus 4.5896 1.0218 283
## Veg_shannon_index-Didelphis_virginiana 4.0768 1.0087 310
## Veg_shannon_index-Sylvilagus_floridanus 3.9199 1.0186 330
## Veg_shannon_index-Meleagris_gallopavo 5.2576 1.0621 130
## Veg_shannon_index-Sciurus_carolinensis 2.5184 1.0258 233
## Veg_shannon_index-Vulpes_vulpes 3.1703 1.0073 273
## Veg_shannon_index-Sus_scrofa 5.6256 1.0100 171
## total_shrub_cover-Canis_latrans 4.2951 1.1108 122
## total_shrub_cover-Lynx_rufus 0.8346 1.1424 102
## total_shrub_cover-Didelphis_virginiana 0.3370 1.0651 94
## total_shrub_cover-Sylvilagus_floridanus 1.3452 1.0572 127
## total_shrub_cover-Meleagris_gallopavo 0.0461 1.0132 112
## total_shrub_cover-Sciurus_carolinensis 1.3910 1.0922 130
## total_shrub_cover-Vulpes_vulpes 1.9051 1.1988 101
## total_shrub_cover-Sus_scrofa 2.2743 1.0223 139
## Avg_Cogongrass_Cover-Canis_latrans 2.5241 1.0178 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.9939 1.0112 162
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8106 1.0243 156
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7880 1.0375 191
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6882 1.0195 166
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7012 1.0161 148
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.7401 1.0263 189
## Avg_Cogongrass_Cover-Sus_scrofa 2.2837 1.0344 216
## Tree_Density-Canis_latrans -0.2629 1.0271 204
## Tree_Density-Lynx_rufus 2.6385 1.1910 122
## Tree_Density-Didelphis_virginiana 0.9825 1.1771 251
## Tree_Density-Sylvilagus_floridanus 0.6805 1.0680 270
## Tree_Density-Meleagris_gallopavo 0.8895 1.0829 261
## Tree_Density-Sciurus_carolinensis 0.9682 1.0814 228
## Tree_Density-Vulpes_vulpes 1.8252 1.1309 229
## Tree_Density-Sus_scrofa 1.0241 1.0563 238
## Avg_Canopy_Cover-Canis_latrans 1.4025 1.0682 219
## Avg_Canopy_Cover-Lynx_rufus 6.1803 1.0674 128
## Avg_Canopy_Cover-Didelphis_virginiana 12.2846 1.0718 66
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.8760 1.0119 143
## Avg_Canopy_Cover-Meleagris_gallopavo 8.3498 1.2966 90
## Avg_Canopy_Cover-Sciurus_carolinensis 10.3455 1.0413 111
## Avg_Canopy_Cover-Vulpes_vulpes 8.9382 1.0382 159
## Avg_Canopy_Cover-Sus_scrofa 6.4841 1.0660 261
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3310 1.2848 60
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.4305 1.0232 123
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2201 1.0158 145
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.9600 1.0207 213
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.8253 1.1809 94
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.5497 1.0056 322
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.7377 1.0592 202
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.3389 1.1658 78
## avg_veg_height-Canis_latrans 1.4107 1.0402 313
## avg_veg_height-Lynx_rufus 1.6764 1.0486 261
## avg_veg_height-Didelphis_virginiana 1.5729 1.0740 218
## avg_veg_height-Sylvilagus_floridanus 1.7507 1.0421 260
## avg_veg_height-Meleagris_gallopavo 2.1611 1.1190 219
## avg_veg_height-Sciurus_carolinensis 3.1262 1.0274 339
## avg_veg_height-Vulpes_vulpes 2.2521 1.0706 277
## avg_veg_height-Sus_scrofa 1.9258 1.0234 380
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6236 0.2001 -3.0371 -2.6178 -2.2415 1.0056
## (Intercept)-Lynx_rufus -3.5581 0.3374 -4.2641 -3.5470 -2.9098 1.0158
## (Intercept)-Didelphis_virginiana -2.6092 0.3164 -3.1989 -2.6130 -1.9630 1.0179
## (Intercept)-Sylvilagus_floridanus -3.1318 0.2691 -3.6761 -3.1246 -2.6412 1.0151
## (Intercept)-Meleagris_gallopavo -3.5885 0.4896 -4.6628 -3.5674 -2.6972 1.0128
## (Intercept)-Sciurus_carolinensis -2.7968 0.3262 -3.4716 -2.7944 -2.1733 1.0271
## (Intercept)-Vulpes_vulpes -3.9554 0.6331 -5.3349 -3.9362 -2.8260 1.0146
## (Intercept)-Sus_scrofa -3.4678 0.5332 -4.5451 -3.4610 -2.3993 1.0869
## shrub_cover-Canis_latrans -0.3360 0.2564 -0.8160 -0.3432 0.1729 1.0665
## shrub_cover-Lynx_rufus -0.0351 0.4124 -0.8567 -0.0282 0.7439 1.0653
## shrub_cover-Didelphis_virginiana 1.2741 0.4130 0.5076 1.2554 2.1155 1.0590
## shrub_cover-Sylvilagus_floridanus 0.6559 0.4246 -0.1600 0.6418 1.5102 1.0217
## shrub_cover-Meleagris_gallopavo -0.5277 0.4629 -1.4681 -0.5178 0.3433 1.0230
## shrub_cover-Sciurus_carolinensis 1.2370 0.4232 0.3904 1.2313 2.0502 1.0319
## shrub_cover-Vulpes_vulpes 0.3030 0.6633 -1.1185 0.3308 1.5587 1.0208
## shrub_cover-Sus_scrofa 1.1814 0.8426 -0.5826 1.2130 2.7754 1.0935
## veg_height-Canis_latrans -0.6046 0.1823 -0.9749 -0.6000 -0.2657 1.0065
## veg_height-Lynx_rufus 0.1437 0.2413 -0.3632 0.1524 0.5812 1.0129
## veg_height-Didelphis_virginiana 0.5250 0.2643 0.0344 0.5265 1.0438 1.0249
## veg_height-Sylvilagus_floridanus 0.1269 0.2670 -0.3707 0.1243 0.6618 1.0409
## veg_height-Meleagris_gallopavo -0.1914 0.4146 -1.0675 -0.1832 0.5819 1.0471
## veg_height-Sciurus_carolinensis 0.1815 0.2352 -0.2770 0.1773 0.6607 1.0272
## veg_height-Vulpes_vulpes -0.2082 0.3488 -0.9251 -0.1978 0.4464 1.0088
## veg_height-Sus_scrofa -0.2058 0.3462 -0.8837 -0.2043 0.4733 1.0022
## week-Canis_latrans 0.5323 0.2651 0.0270 0.5253 1.0551 1.0025
## week-Lynx_rufus 0.3860 0.3411 -0.2436 0.3792 1.0775 1.0008
## week-Didelphis_virginiana 0.0436 0.3725 -0.7473 0.0706 0.7299 1.0135
## week-Sylvilagus_floridanus 0.0454 0.3402 -0.6278 0.0569 0.7120 1.0062
## week-Meleagris_gallopavo -0.1619 0.4299 -1.1193 -0.1273 0.5776 1.0274
## week-Sciurus_carolinensis 0.7065 0.3791 0.0476 0.6802 1.5195 1.0022
## week-Vulpes_vulpes 0.2164 0.4731 -0.7891 0.2332 1.1453 1.0107
## week-Sus_scrofa 0.5987 0.4386 -0.1835 0.5726 1.4953 1.0084
## I(week^2)-Canis_latrans -0.2225 0.1104 -0.4493 -0.2182 -0.0129 1.0206
## I(week^2)-Lynx_rufus -0.2559 0.1608 -0.5741 -0.2507 0.0459 1.0042
## I(week^2)-Didelphis_virginiana -0.4642 0.2564 -1.0922 -0.4302 -0.0633 1.0096
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1733 -0.5306 -0.1766 0.1521 1.0315
## I(week^2)-Meleagris_gallopavo -0.5271 0.3009 -1.2535 -0.4820 -0.0597 1.0451
## I(week^2)-Sciurus_carolinensis -0.2463 0.1504 -0.5569 -0.2430 0.0343 1.0006
## I(week^2)-Vulpes_vulpes -0.5257 0.3145 -1.2766 -0.4852 -0.0366 1.0744
## I(week^2)-Sus_scrofa -0.2372 0.1945 -0.6450 -0.2314 0.1326 1.0126
## ESS
## (Intercept)-Canis_latrans 690
## (Intercept)-Lynx_rufus 247
## (Intercept)-Didelphis_virginiana 492
## (Intercept)-Sylvilagus_floridanus 734
## (Intercept)-Meleagris_gallopavo 180
## (Intercept)-Sciurus_carolinensis 345
## (Intercept)-Vulpes_vulpes 149
## (Intercept)-Sus_scrofa 216
## shrub_cover-Canis_latrans 215
## shrub_cover-Lynx_rufus 199
## shrub_cover-Didelphis_virginiana 263
## shrub_cover-Sylvilagus_floridanus 276
## shrub_cover-Meleagris_gallopavo 220
## shrub_cover-Sciurus_carolinensis 241
## shrub_cover-Vulpes_vulpes 216
## shrub_cover-Sus_scrofa 239
## veg_height-Canis_latrans 809
## veg_height-Lynx_rufus 623
## veg_height-Didelphis_virginiana 851
## veg_height-Sylvilagus_floridanus 537
## veg_height-Meleagris_gallopavo 257
## veg_height-Sciurus_carolinensis 703
## veg_height-Vulpes_vulpes 494
## veg_height-Sus_scrofa 722
## week-Canis_latrans 1056
## week-Lynx_rufus 740
## week-Didelphis_virginiana 679
## week-Sylvilagus_floridanus 826
## week-Meleagris_gallopavo 411
## week-Sciurus_carolinensis 730
## week-Vulpes_vulpes 566
## week-Sus_scrofa 859
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 539
## I(week^2)-Didelphis_virginiana 335
## I(week^2)-Sylvilagus_floridanus 559
## I(week^2)-Meleagris_gallopavo 186
## I(week^2)-Sciurus_carolinensis 1072
## I(week^2)-Vulpes_vulpes 258
## I(week^2)-Sus_scrofa 998
waicOcc(ms_full_full_T, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -584.15375 53.92461 1276.15671
waicOcc(ms_full_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -616.42792 52.72713 1338.31010
waicOcc(ms_full_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -614.40718 41.83224 1312.47883
waicOcc(ms_full_move_T, by.sp = FALSE)
## elpd pD WAIC
## -613.17754 52.30185 1330.95878
waicOcc(ms_full_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -624.33094 46.01716 1340.69619
waicOcc(ms_full_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -627.86267 42.13101 1339.98735
waicOcc(ms_full_null_T, by.sp = FALSE)
## elpd pD WAIC
## -642.8792 29.0613 1343.8809
waicOcc(ms_full_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -621.99276 44.86835 1333.72222
waicOcc(ms_full_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -584.29772 53.11723 1274.82989
waicOcc(ms_null_null_T, by.sp = FALSE)
## elpd pD WAIC
## -667.002 16.146 1366.296
waicOcc(ms_null_full_T, by.sp = FALSE)
## elpd pD WAIC
## -609.93889 45.27764 1310.43306
waicOcc(ms_null_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -641.82308 39.84012 1363.32639
waicOcc(ms_null_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -638.37899 30.19276 1337.14350
waicOcc(ms_null_move_T, by.sp = FALSE)
## elpd pD WAIC
## -637.54524 38.40168 1351.89382
waicOcc(ms_null_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -646.41796 33.69258 1360.22108
waicOcc(ms_null_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -650.27013 29.75197 1360.04420
waicOcc(ms_null_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -643.36184 32.98471 1352.69309
waicOcc(ms_null_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -604.81566 42.53969 1294.71071
waicOcc(ms_week_full_T, by.sp = FALSE)
## elpd pD WAIC
## -605.56872 50.75903 1312.65550
waicOcc(ms_week_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -639.57399 43.90499 1366.95797
waicOcc(ms_week_null_T, by.sp = FALSE)
## elpd pD WAIC
## -664.22425 20.83147 1370.11143
waicOcc(ms_week_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -643.97907 38.60715 1365.17244
waicOcc(ms_week_move_T, by.sp = FALSE)
## elpd pD WAIC
## -633.62625 44.13114 1355.51477
waicOcc(ms_week_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -635.14582 35.34852 1340.98868
waicOcc(ms_week_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -647.02540 34.75456 1363.55993
waicOcc(ms_week_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -641.10997 36.96539 1356.15072
waicOcc(ms_week_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -600.55479 48.81044 1298.73045
waicOcc(ms_cover_full_T, by.sp = FALSE)
## elpd pD WAIC
## -587.64621 49.75546 1274.80334
waicOcc(ms_cover_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -620.94532 47.88495 1337.66054
waicOcc(ms_cover_null_T, by.sp = FALSE)
## elpd pD WAIC
## -645.72492 24.07856 1339.60698
waicOcc(ms_cover_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -626.18416 42.09221 1336.55275
waicOcc(ms_cover_move_T, by.sp = FALSE)
## elpd pD WAIC
## -615.50866 48.84419 1328.70569
waicOcc(ms_cover_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -616.34684 37.35277 1307.39923
waicOcc(ms_cover_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -630.68711 37.37807 1336.13037
waicOcc(ms_cover_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -624.80178 41.28171 1332.16698
waicOcc(ms_cover_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -587.39117 48.57204 1271.92641
waicOcc(ms_weekQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -599.41089 58.00166 1314.82508
waicOcc(ms_weekQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -632.57667 51.14631 1367.44596
waicOcc(ms_weekQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -657.64330 27.92798 1371.14257
waicOcc(ms_weekQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -637.83752 45.48108 1366.63721
waicOcc(ms_weekQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -628.21908 50.49991 1357.43800
waicOcc(ms_weekQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -627.53653 43.55965 1342.19236
waicOcc(ms_weekQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -640.14555 42.55926 1365.40963
waicOcc(ms_weekQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -635.17170 43.29503 1356.93347
waicOcc(ms_weekQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -593.28639 53.92564 1294.42405
waicOcc(ms_fullQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -581.03537 61.92246 1285.91564
waicOcc(ms_fullQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -609.27911 60.17259 1338.90339
waicOcc(ms_fullQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -636.45501 36.25114 1345.41230
waicOcc(ms_fullQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -617.75278 53.12003 1341.74563
waicOcc(ms_fullQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -606.57732 59.05295 1331.26055
waicOcc(ms_fullQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -607.11875 49.33399 1312.90547
waicOcc(ms_fullQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -620.56390 50.40609 1341.93996
waicOcc(ms_fullQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -615.97805 51.08329 1334.12269
waicOcc(ms_fullQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -577.22330 59.39714 1273.24089
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 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_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: 0.4188
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.6007
## Lynx_rufus Bayesian p-value: 0.3143
## Didelphis_virginiana Bayesian p-value: 0.3593
## Sylvilagus_floridanus Bayesian p-value: 0.4197
## Meleagris_gallopavo Bayesian p-value: 0.615
## Sciurus_carolinensis Bayesian p-value: 0.2667
## Vulpes_vulpes Bayesian p-value: 0.2307
## Sus_scrofa Bayesian p-value: 0.5443
## 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): 0.6895
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4014 1.0101 -4.2052 -2.4640 -0.1671 1.0101 266
## Cogon_Patch_Size 0.2054 0.9734 -1.7737 0.2143 2.1182 1.0374 437
## Veg_shannon_index 1.3101 0.7532 -0.0925 1.2872 2.9430 1.0072 257
## total_shrub_cover -1.3702 0.9981 -3.3752 -1.3461 0.5909 1.0231 232
## Avg_Cogongrass_Cover -0.5804 1.2797 -3.0347 -0.5787 1.8981 1.0302 114
## Tree_Density -1.8318 1.0473 -3.8845 -1.8647 0.3495 1.2167 138
## Avg_Canopy_Cover 2.1276 1.0370 -0.1676 2.1293 4.1616 1.0132 916
## I(Avg_Cogongrass_Cover^2) 1.4529 0.9331 -0.5379 1.4898 3.1014 1.0688 194
## avg_veg_height -0.1515 0.7243 -1.6550 -0.1446 1.2909 1.0986 176
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.9593 10.4130 0.0775 2.2725 33.0788 1.0744 151
## Cogon_Patch_Size 7.1986 11.4810 0.1881 3.5710 38.8722 1.0137 135
## Veg_shannon_index 2.4147 5.5730 0.0646 0.8328 15.3821 1.0843 229
## total_shrub_cover 7.8075 17.2867 0.1176 3.3661 41.6034 1.0639 134
## Avg_Cogongrass_Cover 1.9154 4.6312 0.0548 0.6431 11.2951 1.2204 330
## Tree_Density 2.9664 6.7314 0.0570 0.8915 17.3224 1.2786 220
## Avg_Canopy_Cover 13.5361 29.6351 0.4526 5.9445 71.7334 1.0801 166
## I(Avg_Cogongrass_Cover^2) 5.0611 9.0081 0.0765 2.0029 28.1433 1.1511 103
## avg_veg_height 1.2424 2.3736 0.0499 0.5085 7.4414 1.0309 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.3569 7.1562 0.0753 1.4941 24.8197 1.0098 38
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1281 0.3190 -3.7559 -3.1270 -2.5043 1.0208 490
## shrub_cover 0.4349 0.4140 -0.3802 0.4318 1.2747 1.0206 506
## veg_height -0.0278 0.2275 -0.4979 -0.0232 0.4164 1.0230 1001
## week 0.2873 0.2632 -0.2437 0.2884 0.8193 1.0122 792
## I(week^2) -0.3350 0.1515 -0.6685 -0.3252 -0.0650 1.0103 481
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5987 0.6591 0.0797 0.4155 2.3236 1.0094 643
## shrub_cover 1.0715 0.9468 0.1929 0.8029 3.6963 1.0477 566
## veg_height 0.3057 0.2597 0.0628 0.2288 1.0247 1.0025 1583
## week 0.3396 0.3678 0.0437 0.2276 1.3055 1.0141 668
## I(week^2) 0.1240 0.1349 0.0233 0.0830 0.4919 1.0085 382
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5727 1.5646 -3.9745 -1.7871
## (Intercept)-Lynx_rufus -1.5767 2.0324 -4.7114 -1.9025
## (Intercept)-Didelphis_virginiana -3.5308 1.5260 -6.8868 -3.3756
## (Intercept)-Sylvilagus_floridanus -2.4200 1.5664 -5.4655 -2.4709
## (Intercept)-Meleagris_gallopavo -2.1483 2.1387 -5.9271 -2.2979
## (Intercept)-Sciurus_carolinensis -3.8114 1.6139 -7.4449 -3.6107
## (Intercept)-Vulpes_vulpes -3.8691 2.1126 -8.7011 -3.5748
## (Intercept)-Sus_scrofa -4.1513 2.1505 -9.3270 -3.7692
## Cogon_Patch_Size-Canis_latrans 2.3235 1.7889 -0.2719 1.9904
## Cogon_Patch_Size-Lynx_rufus 0.0140 2.1561 -4.0481 -0.0265
## Cogon_Patch_Size-Didelphis_virginiana 2.2525 1.5294 0.0346 2.0068
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7373 2.2532 -7.0957 -1.3534
## Cogon_Patch_Size-Meleagris_gallopavo 0.8435 1.7845 -2.0901 0.6575
## Cogon_Patch_Size-Sciurus_carolinensis -0.9659 1.8009 -5.3040 -0.6888
## Cogon_Patch_Size-Vulpes_vulpes -0.3094 2.0493 -4.8737 -0.2089
## Cogon_Patch_Size-Sus_scrofa -0.4949 1.8669 -5.0360 -0.2971
## Veg_shannon_index-Canis_latrans 1.7383 0.9787 0.0713 1.6299
## Veg_shannon_index-Lynx_rufus 1.4835 1.3912 -1.1262 1.4139
## Veg_shannon_index-Didelphis_virginiana 1.6083 1.0789 -0.2878 1.5038
## Veg_shannon_index-Sylvilagus_floridanus 1.4972 1.0946 -0.4034 1.3954
## Veg_shannon_index-Meleagris_gallopavo 1.9208 1.5006 -0.2772 1.7494
## Veg_shannon_index-Sciurus_carolinensis 0.3255 1.3676 -2.7985 0.4559
## Veg_shannon_index-Vulpes_vulpes 0.7330 1.4458 -2.8778 0.8658
## Veg_shannon_index-Sus_scrofa 2.1029 1.4348 0.0126 1.8980
## total_shrub_cover-Canis_latrans 0.6532 1.4643 -1.7125 0.3920
## total_shrub_cover-Lynx_rufus -2.5463 2.2384 -7.7202 -2.3357
## total_shrub_cover-Didelphis_virginiana -2.3338 2.0786 -7.7426 -1.9034
## total_shrub_cover-Sylvilagus_floridanus -1.5945 1.9116 -6.1907 -1.3400
## total_shrub_cover-Meleagris_gallopavo -3.7161 2.5324 -9.8319 -3.3535
## total_shrub_cover-Sciurus_carolinensis -1.5526 1.8671 -5.7653 -1.3307
## total_shrub_cover-Vulpes_vulpes -2.0693 2.4296 -8.2306 -1.6660
## total_shrub_cover-Sus_scrofa -1.2455 2.0677 -6.2073 -1.0469
## Avg_Cogongrass_Cover-Canis_latrans -0.4626 1.5325 -3.3456 -0.5008
## Avg_Cogongrass_Cover-Lynx_rufus -0.4513 1.6853 -3.5032 -0.5153
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4096 1.5925 -3.4631 -0.4384
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1688 1.6406 -4.5980 -1.1421
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8322 1.8393 -4.5593 -0.8147
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4954 1.5996 -3.5110 -0.5275
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.2439 1.7976 -3.4022 -0.3484
## Avg_Cogongrass_Cover-Sus_scrofa -0.7820 1.6242 -3.9827 -0.7836
## Tree_Density-Canis_latrans -2.8373 1.6610 -7.1312 -2.5858
## Tree_Density-Lynx_rufus -1.0651 1.8430 -3.9963 -1.2779
## Tree_Density-Didelphis_virginiana -2.0449 1.4454 -5.0363 -2.0204
## Tree_Density-Sylvilagus_floridanus -2.3967 1.5886 -5.8353 -2.2896
## Tree_Density-Meleagris_gallopavo -2.0583 1.5433 -5.2401 -2.0199
## Tree_Density-Sciurus_carolinensis -2.0874 1.4788 -5.2348 -2.0941
## Tree_Density-Vulpes_vulpes -1.7802 1.6720 -4.8771 -1.8596
## Tree_Density-Sus_scrofa -2.0754 1.7094 -5.6992 -2.0031
## Avg_Canopy_Cover-Canis_latrans -0.1880 0.8813 -2.0462 -0.1618
## Avg_Canopy_Cover-Lynx_rufus 1.3772 2.2257 -2.7489 1.3132
## Avg_Canopy_Cover-Didelphis_virginiana 4.7263 3.0537 1.4822 4.1041
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.6513 2.8119 1.6357 5.0975
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3796 2.4834 0.6233 2.8831
## Avg_Canopy_Cover-Sciurus_carolinensis 4.3256 2.4293 1.2281 3.7579
## Avg_Canopy_Cover-Vulpes_vulpes 3.4854 2.1963 0.3612 3.0428
## Avg_Canopy_Cover-Sus_scrofa 2.6307 1.6939 0.1284 2.4183
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9020 1.7233 0.6879 2.5583
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.4140 1.9625 0.7559 2.9883
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2449 0.9641 -0.5718 1.1937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4521 1.1561 -0.6078 1.3620
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1731 2.1149 -4.6448 0.3253
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9378 1.1093 0.1213 1.8243
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4153 1.3728 0.3817 2.2287
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1846 2.1896 -5.3058 0.6663
## avg_veg_height-Canis_latrans -0.2090 0.7944 -1.7893 -0.2080
## avg_veg_height-Lynx_rufus -0.5948 1.3445 -3.8736 -0.4450
## avg_veg_height-Didelphis_virginiana -0.3334 0.9840 -2.3562 -0.3058
## avg_veg_height-Sylvilagus_floridanus -0.2186 0.9845 -2.2214 -0.2394
## avg_veg_height-Meleagris_gallopavo -0.1992 1.1658 -2.6045 -0.1714
## avg_veg_height-Sciurus_carolinensis 0.5038 1.1173 -1.3333 0.3634
## avg_veg_height-Vulpes_vulpes -0.1510 1.1518 -2.4050 -0.1665
## avg_veg_height-Sus_scrofa -0.1187 1.0418 -2.1534 -0.1077
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1049 1.0395 190
## (Intercept)-Lynx_rufus 3.4770 1.0808 108
## (Intercept)-Didelphis_virginiana -0.9010 1.0739 317
## (Intercept)-Sylvilagus_floridanus 0.8377 1.0241 294
## (Intercept)-Meleagris_gallopavo 2.6227 1.1045 96
## (Intercept)-Sciurus_carolinensis -1.0358 1.1003 204
## (Intercept)-Vulpes_vulpes -0.3342 1.0072 118
## (Intercept)-Sus_scrofa -1.1530 1.0623 115
## Cogon_Patch_Size-Canis_latrans 6.7711 1.0138 271
## Cogon_Patch_Size-Lynx_rufus 4.8803 1.1381 161
## Cogon_Patch_Size-Didelphis_virginiana 6.0781 1.0026 186
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4290 1.0355 190
## Cogon_Patch_Size-Meleagris_gallopavo 5.0788 1.0220 240
## Cogon_Patch_Size-Sciurus_carolinensis 1.9390 1.0064 285
## Cogon_Patch_Size-Vulpes_vulpes 3.6431 1.0290 262
## Cogon_Patch_Size-Sus_scrofa 2.6359 1.0517 320
## Veg_shannon_index-Canis_latrans 4.0161 1.0178 320
## Veg_shannon_index-Lynx_rufus 4.5896 1.0218 283
## Veg_shannon_index-Didelphis_virginiana 4.0768 1.0087 310
## Veg_shannon_index-Sylvilagus_floridanus 3.9199 1.0186 330
## Veg_shannon_index-Meleagris_gallopavo 5.2576 1.0621 130
## Veg_shannon_index-Sciurus_carolinensis 2.5184 1.0258 233
## Veg_shannon_index-Vulpes_vulpes 3.1703 1.0073 273
## Veg_shannon_index-Sus_scrofa 5.6256 1.0100 171
## total_shrub_cover-Canis_latrans 4.2951 1.1108 122
## total_shrub_cover-Lynx_rufus 0.8346 1.1424 102
## total_shrub_cover-Didelphis_virginiana 0.3370 1.0651 94
## total_shrub_cover-Sylvilagus_floridanus 1.3452 1.0572 127
## total_shrub_cover-Meleagris_gallopavo 0.0461 1.0132 112
## total_shrub_cover-Sciurus_carolinensis 1.3910 1.0922 130
## total_shrub_cover-Vulpes_vulpes 1.9051 1.1988 101
## total_shrub_cover-Sus_scrofa 2.2743 1.0223 139
## Avg_Cogongrass_Cover-Canis_latrans 2.5241 1.0178 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.9939 1.0112 162
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8106 1.0243 156
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7880 1.0375 191
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6882 1.0195 166
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7012 1.0161 148
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.7401 1.0263 189
## Avg_Cogongrass_Cover-Sus_scrofa 2.2837 1.0344 216
## Tree_Density-Canis_latrans -0.2629 1.0271 204
## Tree_Density-Lynx_rufus 2.6385 1.1910 122
## Tree_Density-Didelphis_virginiana 0.9825 1.1771 251
## Tree_Density-Sylvilagus_floridanus 0.6805 1.0680 270
## Tree_Density-Meleagris_gallopavo 0.8895 1.0829 261
## Tree_Density-Sciurus_carolinensis 0.9682 1.0814 228
## Tree_Density-Vulpes_vulpes 1.8252 1.1309 229
## Tree_Density-Sus_scrofa 1.0241 1.0563 238
## Avg_Canopy_Cover-Canis_latrans 1.4025 1.0682 219
## Avg_Canopy_Cover-Lynx_rufus 6.1803 1.0674 128
## Avg_Canopy_Cover-Didelphis_virginiana 12.2846 1.0718 66
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.8760 1.0119 143
## Avg_Canopy_Cover-Meleagris_gallopavo 8.3498 1.2966 90
## Avg_Canopy_Cover-Sciurus_carolinensis 10.3455 1.0413 111
## Avg_Canopy_Cover-Vulpes_vulpes 8.9382 1.0382 159
## Avg_Canopy_Cover-Sus_scrofa 6.4841 1.0660 261
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3310 1.2848 60
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.4305 1.0232 123
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2201 1.0158 145
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.9600 1.0207 213
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.8253 1.1809 94
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.5497 1.0056 322
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.7377 1.0592 202
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.3389 1.1658 78
## avg_veg_height-Canis_latrans 1.4107 1.0402 313
## avg_veg_height-Lynx_rufus 1.6764 1.0486 261
## avg_veg_height-Didelphis_virginiana 1.5729 1.0740 218
## avg_veg_height-Sylvilagus_floridanus 1.7507 1.0421 260
## avg_veg_height-Meleagris_gallopavo 2.1611 1.1190 219
## avg_veg_height-Sciurus_carolinensis 3.1262 1.0274 339
## avg_veg_height-Vulpes_vulpes 2.2521 1.0706 277
## avg_veg_height-Sus_scrofa 1.9258 1.0234 380
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6236 0.2001 -3.0371 -2.6178 -2.2415 1.0056
## (Intercept)-Lynx_rufus -3.5581 0.3374 -4.2641 -3.5470 -2.9098 1.0158
## (Intercept)-Didelphis_virginiana -2.6092 0.3164 -3.1989 -2.6130 -1.9630 1.0179
## (Intercept)-Sylvilagus_floridanus -3.1318 0.2691 -3.6761 -3.1246 -2.6412 1.0151
## (Intercept)-Meleagris_gallopavo -3.5885 0.4896 -4.6628 -3.5674 -2.6972 1.0128
## (Intercept)-Sciurus_carolinensis -2.7968 0.3262 -3.4716 -2.7944 -2.1733 1.0271
## (Intercept)-Vulpes_vulpes -3.9554 0.6331 -5.3349 -3.9362 -2.8260 1.0146
## (Intercept)-Sus_scrofa -3.4678 0.5332 -4.5451 -3.4610 -2.3993 1.0869
## shrub_cover-Canis_latrans -0.3360 0.2564 -0.8160 -0.3432 0.1729 1.0665
## shrub_cover-Lynx_rufus -0.0351 0.4124 -0.8567 -0.0282 0.7439 1.0653
## shrub_cover-Didelphis_virginiana 1.2741 0.4130 0.5076 1.2554 2.1155 1.0590
## shrub_cover-Sylvilagus_floridanus 0.6559 0.4246 -0.1600 0.6418 1.5102 1.0217
## shrub_cover-Meleagris_gallopavo -0.5277 0.4629 -1.4681 -0.5178 0.3433 1.0230
## shrub_cover-Sciurus_carolinensis 1.2370 0.4232 0.3904 1.2313 2.0502 1.0319
## shrub_cover-Vulpes_vulpes 0.3030 0.6633 -1.1185 0.3308 1.5587 1.0208
## shrub_cover-Sus_scrofa 1.1814 0.8426 -0.5826 1.2130 2.7754 1.0935
## veg_height-Canis_latrans -0.6046 0.1823 -0.9749 -0.6000 -0.2657 1.0065
## veg_height-Lynx_rufus 0.1437 0.2413 -0.3632 0.1524 0.5812 1.0129
## veg_height-Didelphis_virginiana 0.5250 0.2643 0.0344 0.5265 1.0438 1.0249
## veg_height-Sylvilagus_floridanus 0.1269 0.2670 -0.3707 0.1243 0.6618 1.0409
## veg_height-Meleagris_gallopavo -0.1914 0.4146 -1.0675 -0.1832 0.5819 1.0471
## veg_height-Sciurus_carolinensis 0.1815 0.2352 -0.2770 0.1773 0.6607 1.0272
## veg_height-Vulpes_vulpes -0.2082 0.3488 -0.9251 -0.1978 0.4464 1.0088
## veg_height-Sus_scrofa -0.2058 0.3462 -0.8837 -0.2043 0.4733 1.0022
## week-Canis_latrans 0.5323 0.2651 0.0270 0.5253 1.0551 1.0025
## week-Lynx_rufus 0.3860 0.3411 -0.2436 0.3792 1.0775 1.0008
## week-Didelphis_virginiana 0.0436 0.3725 -0.7473 0.0706 0.7299 1.0135
## week-Sylvilagus_floridanus 0.0454 0.3402 -0.6278 0.0569 0.7120 1.0062
## week-Meleagris_gallopavo -0.1619 0.4299 -1.1193 -0.1273 0.5776 1.0274
## week-Sciurus_carolinensis 0.7065 0.3791 0.0476 0.6802 1.5195 1.0022
## week-Vulpes_vulpes 0.2164 0.4731 -0.7891 0.2332 1.1453 1.0107
## week-Sus_scrofa 0.5987 0.4386 -0.1835 0.5726 1.4953 1.0084
## I(week^2)-Canis_latrans -0.2225 0.1104 -0.4493 -0.2182 -0.0129 1.0206
## I(week^2)-Lynx_rufus -0.2559 0.1608 -0.5741 -0.2507 0.0459 1.0042
## I(week^2)-Didelphis_virginiana -0.4642 0.2564 -1.0922 -0.4302 -0.0633 1.0096
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1733 -0.5306 -0.1766 0.1521 1.0315
## I(week^2)-Meleagris_gallopavo -0.5271 0.3009 -1.2535 -0.4820 -0.0597 1.0451
## I(week^2)-Sciurus_carolinensis -0.2463 0.1504 -0.5569 -0.2430 0.0343 1.0006
## I(week^2)-Vulpes_vulpes -0.5257 0.3145 -1.2766 -0.4852 -0.0366 1.0744
## I(week^2)-Sus_scrofa -0.2372 0.1945 -0.6450 -0.2314 0.1326 1.0126
## ESS
## (Intercept)-Canis_latrans 690
## (Intercept)-Lynx_rufus 247
## (Intercept)-Didelphis_virginiana 492
## (Intercept)-Sylvilagus_floridanus 734
## (Intercept)-Meleagris_gallopavo 180
## (Intercept)-Sciurus_carolinensis 345
## (Intercept)-Vulpes_vulpes 149
## (Intercept)-Sus_scrofa 216
## shrub_cover-Canis_latrans 215
## shrub_cover-Lynx_rufus 199
## shrub_cover-Didelphis_virginiana 263
## shrub_cover-Sylvilagus_floridanus 276
## shrub_cover-Meleagris_gallopavo 220
## shrub_cover-Sciurus_carolinensis 241
## shrub_cover-Vulpes_vulpes 216
## shrub_cover-Sus_scrofa 239
## veg_height-Canis_latrans 809
## veg_height-Lynx_rufus 623
## veg_height-Didelphis_virginiana 851
## veg_height-Sylvilagus_floridanus 537
## veg_height-Meleagris_gallopavo 257
## veg_height-Sciurus_carolinensis 703
## veg_height-Vulpes_vulpes 494
## veg_height-Sus_scrofa 722
## week-Canis_latrans 1056
## week-Lynx_rufus 740
## week-Didelphis_virginiana 679
## week-Sylvilagus_floridanus 826
## week-Meleagris_gallopavo 411
## week-Sciurus_carolinensis 730
## week-Vulpes_vulpes 566
## week-Sus_scrofa 859
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 539
## I(week^2)-Didelphis_virginiana 335
## I(week^2)-Sylvilagus_floridanus 559
## I(week^2)-Meleagris_gallopavo 186
## I(week^2)-Sciurus_carolinensis 1072
## I(week^2)-Vulpes_vulpes 258
## I(week^2)-Sus_scrofa 998
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] 0.212 0.817 -0.145 -0.486 -2.422 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.09933333
MCMCplot(ms_fullQ_fullQ_T$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, 1:8, 1:100] 0.0114 0.0723 0.385 0.5249 0.0011 ...
## $ z.0.samples : int [1:3000, 1:8, 1:100] 0 0 0 0 0 0 0 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:8, 1:100] 0.0114 0.0723 0.385 0.5249 0.0011 ...
psi.0.quants <- apply(out.pred$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] 6.81e-05 4.41e-02 9.73e-01 7.64e-05 4.56e-02 ...
## - 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 0.0441 0.0456 0.0455 0.0464 0.0463 ...
## $ psi.low : num 6.81e-05 7.64e-05 7.88e-05 8.83e-05 8.83e-05 ...
## $ psi.high : num 0.973 0.976 0.971 0.97 0.965 ...
## $ 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): 0.6895
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4014 1.0101 -4.2052 -2.4640 -0.1671 1.0101 266
## Cogon_Patch_Size 0.2054 0.9734 -1.7737 0.2143 2.1182 1.0374 437
## Veg_shannon_index 1.3101 0.7532 -0.0925 1.2872 2.9430 1.0072 257
## total_shrub_cover -1.3702 0.9981 -3.3752 -1.3461 0.5909 1.0231 232
## Avg_Cogongrass_Cover -0.5804 1.2797 -3.0347 -0.5787 1.8981 1.0302 114
## Tree_Density -1.8318 1.0473 -3.8845 -1.8647 0.3495 1.2167 138
## Avg_Canopy_Cover 2.1276 1.0370 -0.1676 2.1293 4.1616 1.0132 916
## I(Avg_Cogongrass_Cover^2) 1.4529 0.9331 -0.5379 1.4898 3.1014 1.0688 194
## avg_veg_height -0.1515 0.7243 -1.6550 -0.1446 1.2909 1.0986 176
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.9593 10.4130 0.0775 2.2725 33.0788 1.0744 151
## Cogon_Patch_Size 7.1986 11.4810 0.1881 3.5710 38.8722 1.0137 135
## Veg_shannon_index 2.4147 5.5730 0.0646 0.8328 15.3821 1.0843 229
## total_shrub_cover 7.8075 17.2867 0.1176 3.3661 41.6034 1.0639 134
## Avg_Cogongrass_Cover 1.9154 4.6312 0.0548 0.6431 11.2951 1.2204 330
## Tree_Density 2.9664 6.7314 0.0570 0.8915 17.3224 1.2786 220
## Avg_Canopy_Cover 13.5361 29.6351 0.4526 5.9445 71.7334 1.0801 166
## I(Avg_Cogongrass_Cover^2) 5.0611 9.0081 0.0765 2.0029 28.1433 1.1511 103
## avg_veg_height 1.2424 2.3736 0.0499 0.5085 7.4414 1.0309 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.3569 7.1562 0.0753 1.4941 24.8197 1.0098 38
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1281 0.3190 -3.7559 -3.1270 -2.5043 1.0208 490
## shrub_cover 0.4349 0.4140 -0.3802 0.4318 1.2747 1.0206 506
## veg_height -0.0278 0.2275 -0.4979 -0.0232 0.4164 1.0230 1001
## week 0.2873 0.2632 -0.2437 0.2884 0.8193 1.0122 792
## I(week^2) -0.3350 0.1515 -0.6685 -0.3252 -0.0650 1.0103 481
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5987 0.6591 0.0797 0.4155 2.3236 1.0094 643
## shrub_cover 1.0715 0.9468 0.1929 0.8029 3.6963 1.0477 566
## veg_height 0.3057 0.2597 0.0628 0.2288 1.0247 1.0025 1583
## week 0.3396 0.3678 0.0437 0.2276 1.3055 1.0141 668
## I(week^2) 0.1240 0.1349 0.0233 0.0830 0.4919 1.0085 382
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5727 1.5646 -3.9745 -1.7871
## (Intercept)-Lynx_rufus -1.5767 2.0324 -4.7114 -1.9025
## (Intercept)-Didelphis_virginiana -3.5308 1.5260 -6.8868 -3.3756
## (Intercept)-Sylvilagus_floridanus -2.4200 1.5664 -5.4655 -2.4709
## (Intercept)-Meleagris_gallopavo -2.1483 2.1387 -5.9271 -2.2979
## (Intercept)-Sciurus_carolinensis -3.8114 1.6139 -7.4449 -3.6107
## (Intercept)-Vulpes_vulpes -3.8691 2.1126 -8.7011 -3.5748
## (Intercept)-Sus_scrofa -4.1513 2.1505 -9.3270 -3.7692
## Cogon_Patch_Size-Canis_latrans 2.3235 1.7889 -0.2719 1.9904
## Cogon_Patch_Size-Lynx_rufus 0.0140 2.1561 -4.0481 -0.0265
## Cogon_Patch_Size-Didelphis_virginiana 2.2525 1.5294 0.0346 2.0068
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7373 2.2532 -7.0957 -1.3534
## Cogon_Patch_Size-Meleagris_gallopavo 0.8435 1.7845 -2.0901 0.6575
## Cogon_Patch_Size-Sciurus_carolinensis -0.9659 1.8009 -5.3040 -0.6888
## Cogon_Patch_Size-Vulpes_vulpes -0.3094 2.0493 -4.8737 -0.2089
## Cogon_Patch_Size-Sus_scrofa -0.4949 1.8669 -5.0360 -0.2971
## Veg_shannon_index-Canis_latrans 1.7383 0.9787 0.0713 1.6299
## Veg_shannon_index-Lynx_rufus 1.4835 1.3912 -1.1262 1.4139
## Veg_shannon_index-Didelphis_virginiana 1.6083 1.0789 -0.2878 1.5038
## Veg_shannon_index-Sylvilagus_floridanus 1.4972 1.0946 -0.4034 1.3954
## Veg_shannon_index-Meleagris_gallopavo 1.9208 1.5006 -0.2772 1.7494
## Veg_shannon_index-Sciurus_carolinensis 0.3255 1.3676 -2.7985 0.4559
## Veg_shannon_index-Vulpes_vulpes 0.7330 1.4458 -2.8778 0.8658
## Veg_shannon_index-Sus_scrofa 2.1029 1.4348 0.0126 1.8980
## total_shrub_cover-Canis_latrans 0.6532 1.4643 -1.7125 0.3920
## total_shrub_cover-Lynx_rufus -2.5463 2.2384 -7.7202 -2.3357
## total_shrub_cover-Didelphis_virginiana -2.3338 2.0786 -7.7426 -1.9034
## total_shrub_cover-Sylvilagus_floridanus -1.5945 1.9116 -6.1907 -1.3400
## total_shrub_cover-Meleagris_gallopavo -3.7161 2.5324 -9.8319 -3.3535
## total_shrub_cover-Sciurus_carolinensis -1.5526 1.8671 -5.7653 -1.3307
## total_shrub_cover-Vulpes_vulpes -2.0693 2.4296 -8.2306 -1.6660
## total_shrub_cover-Sus_scrofa -1.2455 2.0677 -6.2073 -1.0469
## Avg_Cogongrass_Cover-Canis_latrans -0.4626 1.5325 -3.3456 -0.5008
## Avg_Cogongrass_Cover-Lynx_rufus -0.4513 1.6853 -3.5032 -0.5153
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4096 1.5925 -3.4631 -0.4384
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1688 1.6406 -4.5980 -1.1421
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8322 1.8393 -4.5593 -0.8147
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4954 1.5996 -3.5110 -0.5275
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.2439 1.7976 -3.4022 -0.3484
## Avg_Cogongrass_Cover-Sus_scrofa -0.7820 1.6242 -3.9827 -0.7836
## Tree_Density-Canis_latrans -2.8373 1.6610 -7.1312 -2.5858
## Tree_Density-Lynx_rufus -1.0651 1.8430 -3.9963 -1.2779
## Tree_Density-Didelphis_virginiana -2.0449 1.4454 -5.0363 -2.0204
## Tree_Density-Sylvilagus_floridanus -2.3967 1.5886 -5.8353 -2.2896
## Tree_Density-Meleagris_gallopavo -2.0583 1.5433 -5.2401 -2.0199
## Tree_Density-Sciurus_carolinensis -2.0874 1.4788 -5.2348 -2.0941
## Tree_Density-Vulpes_vulpes -1.7802 1.6720 -4.8771 -1.8596
## Tree_Density-Sus_scrofa -2.0754 1.7094 -5.6992 -2.0031
## Avg_Canopy_Cover-Canis_latrans -0.1880 0.8813 -2.0462 -0.1618
## Avg_Canopy_Cover-Lynx_rufus 1.3772 2.2257 -2.7489 1.3132
## Avg_Canopy_Cover-Didelphis_virginiana 4.7263 3.0537 1.4822 4.1041
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.6513 2.8119 1.6357 5.0975
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3796 2.4834 0.6233 2.8831
## Avg_Canopy_Cover-Sciurus_carolinensis 4.3256 2.4293 1.2281 3.7579
## Avg_Canopy_Cover-Vulpes_vulpes 3.4854 2.1963 0.3612 3.0428
## Avg_Canopy_Cover-Sus_scrofa 2.6307 1.6939 0.1284 2.4183
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9020 1.7233 0.6879 2.5583
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.4140 1.9625 0.7559 2.9883
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2449 0.9641 -0.5718 1.1937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4521 1.1561 -0.6078 1.3620
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1731 2.1149 -4.6448 0.3253
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9378 1.1093 0.1213 1.8243
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4153 1.3728 0.3817 2.2287
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1846 2.1896 -5.3058 0.6663
## avg_veg_height-Canis_latrans -0.2090 0.7944 -1.7893 -0.2080
## avg_veg_height-Lynx_rufus -0.5948 1.3445 -3.8736 -0.4450
## avg_veg_height-Didelphis_virginiana -0.3334 0.9840 -2.3562 -0.3058
## avg_veg_height-Sylvilagus_floridanus -0.2186 0.9845 -2.2214 -0.2394
## avg_veg_height-Meleagris_gallopavo -0.1992 1.1658 -2.6045 -0.1714
## avg_veg_height-Sciurus_carolinensis 0.5038 1.1173 -1.3333 0.3634
## avg_veg_height-Vulpes_vulpes -0.1510 1.1518 -2.4050 -0.1665
## avg_veg_height-Sus_scrofa -0.1187 1.0418 -2.1534 -0.1077
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1049 1.0395 190
## (Intercept)-Lynx_rufus 3.4770 1.0808 108
## (Intercept)-Didelphis_virginiana -0.9010 1.0739 317
## (Intercept)-Sylvilagus_floridanus 0.8377 1.0241 294
## (Intercept)-Meleagris_gallopavo 2.6227 1.1045 96
## (Intercept)-Sciurus_carolinensis -1.0358 1.1003 204
## (Intercept)-Vulpes_vulpes -0.3342 1.0072 118
## (Intercept)-Sus_scrofa -1.1530 1.0623 115
## Cogon_Patch_Size-Canis_latrans 6.7711 1.0138 271
## Cogon_Patch_Size-Lynx_rufus 4.8803 1.1381 161
## Cogon_Patch_Size-Didelphis_virginiana 6.0781 1.0026 186
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4290 1.0355 190
## Cogon_Patch_Size-Meleagris_gallopavo 5.0788 1.0220 240
## Cogon_Patch_Size-Sciurus_carolinensis 1.9390 1.0064 285
## Cogon_Patch_Size-Vulpes_vulpes 3.6431 1.0290 262
## Cogon_Patch_Size-Sus_scrofa 2.6359 1.0517 320
## Veg_shannon_index-Canis_latrans 4.0161 1.0178 320
## Veg_shannon_index-Lynx_rufus 4.5896 1.0218 283
## Veg_shannon_index-Didelphis_virginiana 4.0768 1.0087 310
## Veg_shannon_index-Sylvilagus_floridanus 3.9199 1.0186 330
## Veg_shannon_index-Meleagris_gallopavo 5.2576 1.0621 130
## Veg_shannon_index-Sciurus_carolinensis 2.5184 1.0258 233
## Veg_shannon_index-Vulpes_vulpes 3.1703 1.0073 273
## Veg_shannon_index-Sus_scrofa 5.6256 1.0100 171
## total_shrub_cover-Canis_latrans 4.2951 1.1108 122
## total_shrub_cover-Lynx_rufus 0.8346 1.1424 102
## total_shrub_cover-Didelphis_virginiana 0.3370 1.0651 94
## total_shrub_cover-Sylvilagus_floridanus 1.3452 1.0572 127
## total_shrub_cover-Meleagris_gallopavo 0.0461 1.0132 112
## total_shrub_cover-Sciurus_carolinensis 1.3910 1.0922 130
## total_shrub_cover-Vulpes_vulpes 1.9051 1.1988 101
## total_shrub_cover-Sus_scrofa 2.2743 1.0223 139
## Avg_Cogongrass_Cover-Canis_latrans 2.5241 1.0178 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.9939 1.0112 162
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8106 1.0243 156
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7880 1.0375 191
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6882 1.0195 166
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7012 1.0161 148
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.7401 1.0263 189
## Avg_Cogongrass_Cover-Sus_scrofa 2.2837 1.0344 216
## Tree_Density-Canis_latrans -0.2629 1.0271 204
## Tree_Density-Lynx_rufus 2.6385 1.1910 122
## Tree_Density-Didelphis_virginiana 0.9825 1.1771 251
## Tree_Density-Sylvilagus_floridanus 0.6805 1.0680 270
## Tree_Density-Meleagris_gallopavo 0.8895 1.0829 261
## Tree_Density-Sciurus_carolinensis 0.9682 1.0814 228
## Tree_Density-Vulpes_vulpes 1.8252 1.1309 229
## Tree_Density-Sus_scrofa 1.0241 1.0563 238
## Avg_Canopy_Cover-Canis_latrans 1.4025 1.0682 219
## Avg_Canopy_Cover-Lynx_rufus 6.1803 1.0674 128
## Avg_Canopy_Cover-Didelphis_virginiana 12.2846 1.0718 66
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.8760 1.0119 143
## Avg_Canopy_Cover-Meleagris_gallopavo 8.3498 1.2966 90
## Avg_Canopy_Cover-Sciurus_carolinensis 10.3455 1.0413 111
## Avg_Canopy_Cover-Vulpes_vulpes 8.9382 1.0382 159
## Avg_Canopy_Cover-Sus_scrofa 6.4841 1.0660 261
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3310 1.2848 60
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.4305 1.0232 123
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2201 1.0158 145
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.9600 1.0207 213
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.8253 1.1809 94
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.5497 1.0056 322
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.7377 1.0592 202
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.3389 1.1658 78
## avg_veg_height-Canis_latrans 1.4107 1.0402 313
## avg_veg_height-Lynx_rufus 1.6764 1.0486 261
## avg_veg_height-Didelphis_virginiana 1.5729 1.0740 218
## avg_veg_height-Sylvilagus_floridanus 1.7507 1.0421 260
## avg_veg_height-Meleagris_gallopavo 2.1611 1.1190 219
## avg_veg_height-Sciurus_carolinensis 3.1262 1.0274 339
## avg_veg_height-Vulpes_vulpes 2.2521 1.0706 277
## avg_veg_height-Sus_scrofa 1.9258 1.0234 380
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6236 0.2001 -3.0371 -2.6178 -2.2415 1.0056
## (Intercept)-Lynx_rufus -3.5581 0.3374 -4.2641 -3.5470 -2.9098 1.0158
## (Intercept)-Didelphis_virginiana -2.6092 0.3164 -3.1989 -2.6130 -1.9630 1.0179
## (Intercept)-Sylvilagus_floridanus -3.1318 0.2691 -3.6761 -3.1246 -2.6412 1.0151
## (Intercept)-Meleagris_gallopavo -3.5885 0.4896 -4.6628 -3.5674 -2.6972 1.0128
## (Intercept)-Sciurus_carolinensis -2.7968 0.3262 -3.4716 -2.7944 -2.1733 1.0271
## (Intercept)-Vulpes_vulpes -3.9554 0.6331 -5.3349 -3.9362 -2.8260 1.0146
## (Intercept)-Sus_scrofa -3.4678 0.5332 -4.5451 -3.4610 -2.3993 1.0869
## shrub_cover-Canis_latrans -0.3360 0.2564 -0.8160 -0.3432 0.1729 1.0665
## shrub_cover-Lynx_rufus -0.0351 0.4124 -0.8567 -0.0282 0.7439 1.0653
## shrub_cover-Didelphis_virginiana 1.2741 0.4130 0.5076 1.2554 2.1155 1.0590
## shrub_cover-Sylvilagus_floridanus 0.6559 0.4246 -0.1600 0.6418 1.5102 1.0217
## shrub_cover-Meleagris_gallopavo -0.5277 0.4629 -1.4681 -0.5178 0.3433 1.0230
## shrub_cover-Sciurus_carolinensis 1.2370 0.4232 0.3904 1.2313 2.0502 1.0319
## shrub_cover-Vulpes_vulpes 0.3030 0.6633 -1.1185 0.3308 1.5587 1.0208
## shrub_cover-Sus_scrofa 1.1814 0.8426 -0.5826 1.2130 2.7754 1.0935
## veg_height-Canis_latrans -0.6046 0.1823 -0.9749 -0.6000 -0.2657 1.0065
## veg_height-Lynx_rufus 0.1437 0.2413 -0.3632 0.1524 0.5812 1.0129
## veg_height-Didelphis_virginiana 0.5250 0.2643 0.0344 0.5265 1.0438 1.0249
## veg_height-Sylvilagus_floridanus 0.1269 0.2670 -0.3707 0.1243 0.6618 1.0409
## veg_height-Meleagris_gallopavo -0.1914 0.4146 -1.0675 -0.1832 0.5819 1.0471
## veg_height-Sciurus_carolinensis 0.1815 0.2352 -0.2770 0.1773 0.6607 1.0272
## veg_height-Vulpes_vulpes -0.2082 0.3488 -0.9251 -0.1978 0.4464 1.0088
## veg_height-Sus_scrofa -0.2058 0.3462 -0.8837 -0.2043 0.4733 1.0022
## week-Canis_latrans 0.5323 0.2651 0.0270 0.5253 1.0551 1.0025
## week-Lynx_rufus 0.3860 0.3411 -0.2436 0.3792 1.0775 1.0008
## week-Didelphis_virginiana 0.0436 0.3725 -0.7473 0.0706 0.7299 1.0135
## week-Sylvilagus_floridanus 0.0454 0.3402 -0.6278 0.0569 0.7120 1.0062
## week-Meleagris_gallopavo -0.1619 0.4299 -1.1193 -0.1273 0.5776 1.0274
## week-Sciurus_carolinensis 0.7065 0.3791 0.0476 0.6802 1.5195 1.0022
## week-Vulpes_vulpes 0.2164 0.4731 -0.7891 0.2332 1.1453 1.0107
## week-Sus_scrofa 0.5987 0.4386 -0.1835 0.5726 1.4953 1.0084
## I(week^2)-Canis_latrans -0.2225 0.1104 -0.4493 -0.2182 -0.0129 1.0206
## I(week^2)-Lynx_rufus -0.2559 0.1608 -0.5741 -0.2507 0.0459 1.0042
## I(week^2)-Didelphis_virginiana -0.4642 0.2564 -1.0922 -0.4302 -0.0633 1.0096
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1733 -0.5306 -0.1766 0.1521 1.0315
## I(week^2)-Meleagris_gallopavo -0.5271 0.3009 -1.2535 -0.4820 -0.0597 1.0451
## I(week^2)-Sciurus_carolinensis -0.2463 0.1504 -0.5569 -0.2430 0.0343 1.0006
## I(week^2)-Vulpes_vulpes -0.5257 0.3145 -1.2766 -0.4852 -0.0366 1.0744
## I(week^2)-Sus_scrofa -0.2372 0.1945 -0.6450 -0.2314 0.1326 1.0126
## ESS
## (Intercept)-Canis_latrans 690
## (Intercept)-Lynx_rufus 247
## (Intercept)-Didelphis_virginiana 492
## (Intercept)-Sylvilagus_floridanus 734
## (Intercept)-Meleagris_gallopavo 180
## (Intercept)-Sciurus_carolinensis 345
## (Intercept)-Vulpes_vulpes 149
## (Intercept)-Sus_scrofa 216
## shrub_cover-Canis_latrans 215
## shrub_cover-Lynx_rufus 199
## shrub_cover-Didelphis_virginiana 263
## shrub_cover-Sylvilagus_floridanus 276
## shrub_cover-Meleagris_gallopavo 220
## shrub_cover-Sciurus_carolinensis 241
## shrub_cover-Vulpes_vulpes 216
## shrub_cover-Sus_scrofa 239
## veg_height-Canis_latrans 809
## veg_height-Lynx_rufus 623
## veg_height-Didelphis_virginiana 851
## veg_height-Sylvilagus_floridanus 537
## veg_height-Meleagris_gallopavo 257
## veg_height-Sciurus_carolinensis 703
## veg_height-Vulpes_vulpes 494
## veg_height-Sus_scrofa 722
## week-Canis_latrans 1056
## week-Lynx_rufus 740
## week-Didelphis_virginiana 679
## week-Sylvilagus_floridanus 826
## week-Meleagris_gallopavo 411
## week-Sciurus_carolinensis 730
## week-Vulpes_vulpes 566
## week-Sus_scrofa 859
## I(week^2)-Canis_latrans 1154
## I(week^2)-Lynx_rufus 539
## I(week^2)-Didelphis_virginiana 335
## I(week^2)-Sylvilagus_floridanus 559
## I(week^2)-Meleagris_gallopavo 186
## I(week^2)-Sciurus_carolinensis 1072
## I(week^2)-Vulpes_vulpes 258
## I(week^2)-Sus_scrofa 998
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] 0.212 0.817 -0.145 -0.486 -2.422 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.09933333
MCMCplot(ms_fullQ_fullQ_T$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): 0.4567
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3789 0.3255 -1.0367 -0.3833 0.2837 1.0045 2146
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6656 0.6416 0.1137 0.4873 2.2013 1.0032 1870
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5564 0.2901 -3.1196 -2.5615 -1.9495 1.0019 2224
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6021 0.5938 0.1464 0.4613 1.9341 1.0224 1816
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1475 0.3671 -0.5374 0.1337 0.9158 1.0009
## (Intercept)-Procyon_lotor 0.4691 0.3759 -0.2187 0.4508 1.2364 1.0037
## (Intercept)-Dasypus_novemcinctus -0.5918 0.3352 -1.2502 -0.5900 0.0350 1.0013
## (Intercept)-Lynx_rufus -0.1162 0.4880 -0.9414 -0.1622 0.9387 1.0160
## (Intercept)-Didelphis_virginiana -1.1230 0.4161 -1.9735 -1.1021 -0.3545 1.0031
## (Intercept)-Sylvilagus_floridanus -0.3970 0.4085 -1.1726 -0.4098 0.4594 1.0084
## (Intercept)-Meleagris_gallopavo -0.3890 0.4602 -1.2290 -0.4238 0.5701 1.0196
## (Intercept)-Sciurus_carolinensis -1.0972 0.4081 -1.9557 -1.0748 -0.3571 1.0039
## ESS
## (Intercept)-Canis_latrans 2513
## (Intercept)-Procyon_lotor 2092
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 744
## (Intercept)-Didelphis_virginiana 1998
## (Intercept)-Sylvilagus_floridanus 1314
## (Intercept)-Meleagris_gallopavo 787
## (Intercept)-Sciurus_carolinensis 1726
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5971 0.1667 -2.9321 -2.5902 -2.2866 1.0193
## (Intercept)-Procyon_lotor -2.2651 0.1271 -2.5143 -2.2628 -2.0251 1.0054
## (Intercept)-Dasypus_novemcinctus -1.6124 0.1421 -1.8954 -1.6110 -1.3390 1.0007
## (Intercept)-Lynx_rufus -3.3729 0.3030 -4.0177 -3.3589 -2.8195 1.0289
## (Intercept)-Didelphis_virginiana -2.3446 0.2464 -2.8493 -2.3335 -1.8927 1.0043
## (Intercept)-Sylvilagus_floridanus -3.0998 0.2731 -3.6630 -3.0950 -2.5820 1.0027
## (Intercept)-Meleagris_gallopavo -3.3070 0.3325 -4.0150 -3.2926 -2.7254 1.0316
## (Intercept)-Sciurus_carolinensis -2.4658 0.2625 -3.0148 -2.4516 -1.9828 1.0016
## ESS
## (Intercept)-Canis_latrans 1044
## (Intercept)-Procyon_lotor 1241
## (Intercept)-Dasypus_novemcinctus 2246
## (Intercept)-Lynx_rufus 400
## (Intercept)-Didelphis_virginiana 1312
## (Intercept)-Sylvilagus_floridanus 496
## (Intercept)-Meleagris_gallopavo 391
## (Intercept)-Sciurus_carolinensis 1236
# 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): 0.705
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7211 0.6492 -1.9723 -0.7184 0.5963 1.0152 419
## Cogon_Patch_Size -0.3816 0.6449 -1.6457 -0.3903 0.9303 1.0097 568
## Veg_shannon_index 0.9682 0.4913 0.0438 0.9559 1.9717 1.0453 250
## total_shrub_cover -0.9047 0.6621 -2.3373 -0.8691 0.3260 1.0355 363
## Avg_Cogongrass_Cover 1.8884 0.7403 0.4690 1.9000 3.3641 1.1608 215
## Tree_Density -1.8217 0.7979 -3.4206 -1.8163 -0.2064 1.0906 513
## Avg_Canopy_Cover 1.8527 0.7321 0.4207 1.8237 3.3822 1.0540 739
## avg_veg_height -0.3912 0.5139 -1.3958 -0.3985 0.6355 1.0571 319
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1880 3.0860 0.0731 1.2722 9.8349 1.0300 284
## Cogon_Patch_Size 2.1327 3.5136 0.0901 1.0735 10.8089 1.0490 236
## Veg_shannon_index 0.6709 1.0190 0.0445 0.3681 3.1190 1.0618 737
## total_shrub_cover 2.4725 4.7916 0.1007 1.2758 11.6952 1.1428 267
## Avg_Cogongrass_Cover 1.1091 2.1607 0.0449 0.4391 6.2823 1.1477 295
## Tree_Density 3.5766 5.4106 0.0898 1.9823 17.4154 1.1395 345
## Avg_Canopy_Cover 4.2678 7.0008 0.1931 2.4033 20.1317 1.0938 226
## avg_veg_height 0.5667 1.0274 0.0431 0.2900 2.7003 1.0863 419
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7828 3.0954 0.0907 1.8156 10.8286 1.0896 95
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7922 0.3143 -3.3872 -2.7992 -2.1396 1.0032 2125
## shrub_cover 0.3992 0.3179 -0.2529 0.4002 1.0325 1.0156 828
## veg_height 0.0981 0.2045 -0.3117 0.0968 0.4967 1.0059 1392
## week -0.1050 0.1405 -0.3953 -0.1007 0.1646 1.0005 1467
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6870 0.5899 0.1593 0.5212 2.2248 1.0077 999
## shrub_cover 0.7298 0.6602 0.1586 0.5534 2.4353 1.0042 1041
## veg_height 0.2748 0.2936 0.0644 0.2058 0.8685 1.0623 2103
## week 0.1112 0.1030 0.0257 0.0811 0.3733 1.0050 1595
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1709 0.9655 -1.5366 0.1054
## (Intercept)-Procyon_lotor 0.1829 1.0238 -1.7109 0.1829
## (Intercept)-Dasypus_novemcinctus -1.1496 0.8903 -3.1233 -1.0875
## (Intercept)-Lynx_rufus -0.1948 1.1409 -2.1732 -0.3174
## (Intercept)-Didelphis_virginiana -1.7836 1.0920 -4.2173 -1.6368
## (Intercept)-Sylvilagus_floridanus -0.8933 0.9951 -2.9826 -0.8941
## (Intercept)-Meleagris_gallopavo -0.8424 1.1867 -3.2086 -0.8594
## (Intercept)-Sciurus_carolinensis -1.7710 1.1659 -4.3208 -1.6499
## Cogon_Patch_Size-Canis_latrans 0.5275 1.0027 -0.9990 0.3755
## Cogon_Patch_Size-Procyon_lotor -0.9176 0.7260 -2.4550 -0.8668
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3973 0.7642 -1.8364 -0.4309
## Cogon_Patch_Size-Lynx_rufus -0.6077 1.1785 -2.8063 -0.6360
## Cogon_Patch_Size-Didelphis_virginiana 0.7311 0.9797 -0.8258 0.5849
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4413 1.4401 -4.8306 -1.2034
## Cogon_Patch_Size-Meleagris_gallopavo -0.1380 1.2160 -2.1183 -0.2902
## Cogon_Patch_Size-Sciurus_carolinensis -1.0839 1.0907 -3.5761 -0.9680
## Veg_shannon_index-Canis_latrans 1.2795 0.6462 0.1548 1.2116
## Veg_shannon_index-Procyon_lotor 1.2770 0.6350 0.1752 1.2285
## Veg_shannon_index-Dasypus_novemcinctus 0.6558 0.5859 -0.5608 0.6613
## Veg_shannon_index-Lynx_rufus 0.8560 0.7935 -0.8015 0.8635
## Veg_shannon_index-Didelphis_virginiana 1.1752 0.6886 -0.0394 1.1204
## Veg_shannon_index-Sylvilagus_floridanus 1.1094 0.7035 -0.1538 1.0452
## Veg_shannon_index-Meleagris_gallopavo 1.2727 0.7847 -0.0823 1.2009
## Veg_shannon_index-Sciurus_carolinensis 0.3877 0.7685 -1.3311 0.4503
## total_shrub_cover-Canis_latrans 0.5069 0.9920 -1.0110 0.3523
## total_shrub_cover-Procyon_lotor -1.2093 0.6849 -2.6388 -1.1709
## total_shrub_cover-Dasypus_novemcinctus -0.4095 0.8479 -2.2847 -0.3518
## total_shrub_cover-Lynx_rufus -1.5680 1.3166 -4.6991 -1.3655
## total_shrub_cover-Didelphis_virginiana -1.1684 1.0057 -3.5189 -1.0555
## total_shrub_cover-Sylvilagus_floridanus -0.9403 1.2489 -3.9260 -0.8397
## total_shrub_cover-Meleagris_gallopavo -2.2675 1.5229 -6.0021 -2.0455
## total_shrub_cover-Sciurus_carolinensis -1.0846 1.2489 -3.9222 -0.9241
## Avg_Cogongrass_Cover-Canis_latrans 2.2470 0.9434 0.5949 2.1803
## Avg_Cogongrass_Cover-Procyon_lotor 2.0161 0.8673 0.3779 2.0049
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4232 1.0105 0.7395 2.3174
## Avg_Cogongrass_Cover-Lynx_rufus 2.2301 1.0495 0.4964 2.1343
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9625 0.9430 0.1835 1.9307
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3128 1.0681 -1.0417 1.3688
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4980 1.2567 -1.4244 1.6193
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1250 0.9584 0.3997 2.0726
## Tree_Density-Canis_latrans -2.7124 1.3964 -6.0660 -2.4895
## Tree_Density-Procyon_lotor -1.4498 0.7935 -2.9999 -1.4612
## Tree_Density-Dasypus_novemcinctus -3.7431 1.8514 -8.1768 -3.3641
## Tree_Density-Lynx_rufus -0.3015 1.2082 -2.4011 -0.4006
## Tree_Density-Didelphis_virginiana -2.0432 1.2663 -4.8542 -1.9659
## Tree_Density-Sylvilagus_floridanus -2.4197 1.5004 -5.9106 -2.2760
## Tree_Density-Meleagris_gallopavo -2.2258 1.3998 -5.2589 -2.1499
## Tree_Density-Sciurus_carolinensis -2.0428 1.4629 -5.2706 -1.9605
## Avg_Canopy_Cover-Canis_latrans 0.1827 0.6647 -1.1112 0.1942
## Avg_Canopy_Cover-Procyon_lotor 1.7248 0.7668 0.3637 1.6774
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2830 0.8906 0.8822 2.1613
## Avg_Canopy_Cover-Lynx_rufus 0.7553 1.1311 -1.3831 0.7225
## Avg_Canopy_Cover-Didelphis_virginiana 3.2043 1.4329 1.2435 2.9287
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8238 1.8808 1.2061 3.5000
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5914 1.3985 0.5369 2.3701
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0853 1.4784 1.0301 2.7820
## avg_veg_height-Canis_latrans -0.4878 0.6295 -1.7360 -0.4845
## avg_veg_height-Procyon_lotor -0.3681 0.6102 -1.6247 -0.3654
## avg_veg_height-Dasypus_novemcinctus -0.1159 0.6386 -1.3199 -0.1418
## avg_veg_height-Lynx_rufus -0.5465 0.7958 -2.0308 -0.5533
## avg_veg_height-Didelphis_virginiana -0.5841 0.6918 -2.0791 -0.5456
## avg_veg_height-Sylvilagus_floridanus -0.6219 0.7068 -2.1450 -0.6088
## avg_veg_height-Meleagris_gallopavo -0.4567 0.9042 -2.3065 -0.4346
## avg_veg_height-Sciurus_carolinensis 0.0057 0.7320 -1.2313 -0.0471
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3153 1.0294 279
## (Intercept)-Procyon_lotor 2.2548 1.0051 195
## (Intercept)-Dasypus_novemcinctus 0.4873 1.0326 376
## (Intercept)-Lynx_rufus 2.2599 1.0823 193
## (Intercept)-Didelphis_virginiana -0.0256 1.0395 273
## (Intercept)-Sylvilagus_floridanus 1.0555 1.0254 430
## (Intercept)-Meleagris_gallopavo 1.7502 1.0219 244
## (Intercept)-Sciurus_carolinensis 0.2322 1.0180 313
## Cogon_Patch_Size-Canis_latrans 2.9622 1.0016 352
## Cogon_Patch_Size-Procyon_lotor 0.3759 1.0491 246
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2192 1.0186 642
## Cogon_Patch_Size-Lynx_rufus 1.9568 1.0305 462
## Cogon_Patch_Size-Didelphis_virginiana 3.0829 1.0201 412
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5616 1.0318 307
## Cogon_Patch_Size-Meleagris_gallopavo 2.8776 1.0203 404
## Cogon_Patch_Size-Sciurus_carolinensis 0.8344 1.0011 416
## Veg_shannon_index-Canis_latrans 2.6889 1.0672 375
## Veg_shannon_index-Procyon_lotor 2.6597 1.0747 241
## Veg_shannon_index-Dasypus_novemcinctus 1.7800 1.0094 699
## Veg_shannon_index-Lynx_rufus 2.4470 1.0065 295
## Veg_shannon_index-Didelphis_virginiana 2.7650 1.0747 407
## Veg_shannon_index-Sylvilagus_floridanus 2.6621 1.0264 413
## Veg_shannon_index-Meleagris_gallopavo 3.0498 1.0620 325
## Veg_shannon_index-Sciurus_carolinensis 1.7569 1.0149 532
## total_shrub_cover-Canis_latrans 2.8406 1.1066 294
## total_shrub_cover-Procyon_lotor -0.0361 1.0139 623
## total_shrub_cover-Dasypus_novemcinctus 1.1389 1.0207 435
## total_shrub_cover-Lynx_rufus 0.5092 1.1902 184
## total_shrub_cover-Didelphis_virginiana 0.5082 1.0514 569
## total_shrub_cover-Sylvilagus_floridanus 1.3747 1.0556 238
## total_shrub_cover-Meleagris_gallopavo 0.0023 1.1164 135
## total_shrub_cover-Sciurus_carolinensis 0.8962 1.0513 258
## Avg_Cogongrass_Cover-Canis_latrans 4.2597 1.1208 315
## Avg_Cogongrass_Cover-Procyon_lotor 3.8078 1.1542 310
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.7248 1.1407 248
## Avg_Cogongrass_Cover-Lynx_rufus 4.4175 1.1888 248
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8419 1.1266 345
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2703 1.0786 297
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6801 1.0708 261
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1514 1.1241 298
## Tree_Density-Canis_latrans -0.4953 1.1642 323
## Tree_Density-Procyon_lotor 0.1283 1.0429 644
## Tree_Density-Dasypus_novemcinctus -1.2025 1.1324 269
## Tree_Density-Lynx_rufus 2.2561 1.0079 341
## Tree_Density-Didelphis_virginiana 0.3418 1.0892 488
## Tree_Density-Sylvilagus_floridanus 0.2631 1.2311 272
## Tree_Density-Meleagris_gallopavo 0.5775 1.0656 457
## Tree_Density-Sciurus_carolinensis 0.7144 1.1076 446
## Avg_Canopy_Cover-Canis_latrans 1.4925 1.0303 711
## Avg_Canopy_Cover-Procyon_lotor 3.4500 1.0306 506
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2925 1.0757 356
## Avg_Canopy_Cover-Lynx_rufus 3.0215 1.0262 394
## Avg_Canopy_Cover-Didelphis_virginiana 6.7321 1.2294 173
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.3385 1.1840 171
## Avg_Canopy_Cover-Meleagris_gallopavo 6.0716 1.1619 160
## Avg_Canopy_Cover-Sciurus_carolinensis 6.6433 1.2118 213
## avg_veg_height-Canis_latrans 0.7897 1.0172 537
## avg_veg_height-Procyon_lotor 0.8245 1.0548 579
## avg_veg_height-Dasypus_novemcinctus 1.2351 1.0374 491
## avg_veg_height-Lynx_rufus 1.0346 1.0021 417
## avg_veg_height-Didelphis_virginiana 0.6797 1.0475 489
## avg_veg_height-Sylvilagus_floridanus 0.6955 1.0254 497
## avg_veg_height-Meleagris_gallopavo 1.3230 1.0400 395
## avg_veg_height-Sciurus_carolinensis 1.7002 1.0137 492
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7520 0.1791 -3.1086 -2.7494 -2.4192 1.0234
## (Intercept)-Procyon_lotor -2.3115 0.1401 -2.5936 -2.3079 -2.0440 1.0028
## (Intercept)-Dasypus_novemcinctus -1.8305 0.1680 -2.1637 -1.8271 -1.5128 1.0025
## (Intercept)-Lynx_rufus -3.5544 0.3287 -4.2394 -3.5438 -2.9457 1.0301
## (Intercept)-Didelphis_virginiana -2.6937 0.2981 -3.2784 -2.6908 -2.1403 1.0100
## (Intercept)-Sylvilagus_floridanus -3.1961 0.2524 -3.7083 -3.1847 -2.7157 1.0225
## (Intercept)-Meleagris_gallopavo -3.7443 0.4723 -4.6814 -3.7181 -2.8442 1.0033
## (Intercept)-Sciurus_carolinensis -2.8523 0.3258 -3.5239 -2.8421 -2.2362 1.0285
## shrub_cover-Canis_latrans -0.3435 0.2293 -0.7702 -0.3519 0.1425 1.0335
## shrub_cover-Procyon_lotor 0.2760 0.1559 -0.0284 0.2786 0.5722 1.0003
## shrub_cover-Dasypus_novemcinctus 1.0152 0.3190 0.3967 1.0124 1.6461 1.0134
## shrub_cover-Lynx_rufus 0.0590 0.3879 -0.7810 0.0854 0.7691 1.0788
## shrub_cover-Didelphis_virginiana 1.1004 0.3864 0.3936 1.0819 1.9084 1.0058
## shrub_cover-Sylvilagus_floridanus 0.5801 0.4268 -0.2638 0.5865 1.4123 1.0321
## shrub_cover-Meleagris_gallopavo -0.4448 0.4422 -1.3399 -0.4336 0.3973 1.0034
## shrub_cover-Sciurus_carolinensis 1.1208 0.4073 0.3302 1.1237 1.9014 1.0518
## veg_height-Canis_latrans -0.5802 0.1795 -0.9373 -0.5780 -0.2339 1.0116
## veg_height-Procyon_lotor 0.3562 0.1228 0.1176 0.3539 0.5993 1.0019
## veg_height-Dasypus_novemcinctus 0.2812 0.1368 0.0210 0.2807 0.5458 1.0024
## veg_height-Lynx_rufus 0.0964 0.2487 -0.3965 0.0911 0.5804 1.0213
## veg_height-Didelphis_virginiana 0.5014 0.2519 0.0588 0.4888 1.0195 1.0010
## veg_height-Sylvilagus_floridanus 0.1810 0.2465 -0.3149 0.1826 0.6523 1.0029
## veg_height-Meleagris_gallopavo -0.2114 0.3886 -0.9861 -0.2071 0.5580 1.0045
## veg_height-Sciurus_carolinensis 0.1688 0.2313 -0.2744 0.1648 0.6344 1.0055
## week-Canis_latrans 0.0605 0.1357 -0.2160 0.0668 0.3204 1.0079
## week-Procyon_lotor -0.0598 0.1215 -0.3098 -0.0533 0.1665 1.0008
## week-Dasypus_novemcinctus -0.1877 0.1384 -0.4777 -0.1799 0.0742 1.0040
## week-Lynx_rufus -0.0543 0.1992 -0.4526 -0.0492 0.3255 1.0018
## week-Didelphis_virginiana -0.2353 0.2196 -0.6924 -0.2226 0.1540 1.0080
## week-Sylvilagus_floridanus -0.1695 0.2032 -0.6002 -0.1606 0.1986 1.0040
## week-Meleagris_gallopavo -0.2963 0.2554 -0.8843 -0.2749 0.1417 1.0014
## week-Sciurus_carolinensis 0.1181 0.1875 -0.2531 0.1195 0.4746 1.0010
## ESS
## (Intercept)-Canis_latrans 709
## (Intercept)-Procyon_lotor 1185
## (Intercept)-Dasypus_novemcinctus 1060
## (Intercept)-Lynx_rufus 288
## (Intercept)-Didelphis_virginiana 551
## (Intercept)-Sylvilagus_floridanus 567
## (Intercept)-Meleagris_gallopavo 221
## (Intercept)-Sciurus_carolinensis 324
## shrub_cover-Canis_latrans 617
## shrub_cover-Procyon_lotor 1188
## shrub_cover-Dasypus_novemcinctus 597
## shrub_cover-Lynx_rufus 257
## shrub_cover-Didelphis_virginiana 435
## shrub_cover-Sylvilagus_floridanus 290
## shrub_cover-Meleagris_gallopavo 233
## shrub_cover-Sciurus_carolinensis 394
## veg_height-Canis_latrans 897
## veg_height-Procyon_lotor 1586
## veg_height-Dasypus_novemcinctus 1559
## veg_height-Lynx_rufus 604
## veg_height-Didelphis_virginiana 810
## veg_height-Sylvilagus_floridanus 829
## veg_height-Meleagris_gallopavo 376
## veg_height-Sciurus_carolinensis 806
## week-Canis_latrans 1611
## week-Procyon_lotor 1583
## week-Dasypus_novemcinctus 1945
## week-Lynx_rufus 803
## week-Didelphis_virginiana 1199
## week-Sylvilagus_floridanus 1038
## week-Meleagris_gallopavo 669
## week-Sciurus_carolinensis 1519
#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): 0.676
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2011 0.3491 -0.8637 -0.2082 0.5244 1.0033 1246
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6964 0.7486 0.0905 0.5051 2.484 1.03 694
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7618 0.3342 -3.4106 -2.7692 -2.0681 1.0017 2256
## shrub_cover 0.2411 0.3051 -0.3861 0.2447 0.8648 1.0082 1878
## veg_height 0.0658 0.2085 -0.3709 0.0717 0.4757 1.0015 1862
## week -0.0969 0.1396 -0.3882 -0.0979 0.1752 1.0058 1596
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8006 0.6995 0.1718 0.6138 2.6784 1.0140 706
## shrub_cover 0.6983 0.6192 0.1375 0.5289 2.2596 1.0229 1086
## veg_height 0.2646 0.2389 0.0591 0.1992 0.8693 1.0079 1496
## week 0.1114 0.1232 0.0244 0.0808 0.3828 1.0197 1782
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2500 0.3822 -0.4504 0.2310 1.0649 1.0119
## (Intercept)-Procyon_lotor 0.5244 0.3865 -0.1720 0.5039 1.2949 1.0003
## (Intercept)-Dasypus_novemcinctus -0.4840 0.3480 -1.1981 -0.4746 0.1663 1.0013
## (Intercept)-Lynx_rufus 0.0975 0.5347 -0.8124 0.0401 1.3297 1.0096
## (Intercept)-Didelphis_virginiana -0.9529 0.4358 -1.8646 -0.9352 -0.1425 1.0011
## (Intercept)-Sylvilagus_floridanus -0.3157 0.4182 -1.1441 -0.3194 0.5405 1.0014
## (Intercept)-Meleagris_gallopavo 0.0886 0.6510 -0.9701 0.0073 1.6120 1.0091
## (Intercept)-Sciurus_carolinensis -0.9060 0.4363 -1.7990 -0.8827 -0.0948 1.0128
## ESS
## (Intercept)-Canis_latrans 1741
## (Intercept)-Procyon_lotor 1737
## (Intercept)-Dasypus_novemcinctus 2593
## (Intercept)-Lynx_rufus 545
## (Intercept)-Didelphis_virginiana 1491
## (Intercept)-Sylvilagus_floridanus 1189
## (Intercept)-Meleagris_gallopavo 292
## (Intercept)-Sciurus_carolinensis 1566
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7415 0.1898 -3.1173 -2.7373 -2.3762 1.0080
## (Intercept)-Procyon_lotor -2.3102 0.1432 -2.5962 -2.3069 -2.0432 1.0052
## (Intercept)-Dasypus_novemcinctus -1.7815 0.1600 -2.1048 -1.7802 -1.4777 1.0088
## (Intercept)-Lynx_rufus -3.6010 0.3528 -4.3291 -3.5911 -2.9361 1.0187
## (Intercept)-Didelphis_virginiana -2.6066 0.2863 -3.1829 -2.6020 -2.0724 1.0038
## (Intercept)-Sylvilagus_floridanus -3.1635 0.2917 -3.7715 -3.1512 -2.6272 1.0044
## (Intercept)-Meleagris_gallopavo -3.9527 0.5052 -5.0028 -3.9479 -2.9760 1.0252
## (Intercept)-Sciurus_carolinensis -2.6891 0.3168 -3.3656 -2.6756 -2.1176 1.0139
## shrub_cover-Canis_latrans -0.2989 0.2149 -0.7354 -0.2986 0.1230 1.0001
## shrub_cover-Procyon_lotor 0.2581 0.1624 -0.0704 0.2586 0.5729 1.0034
## shrub_cover-Dasypus_novemcinctus 0.8635 0.2944 0.3028 0.8627 1.4510 1.0046
## shrub_cover-Lynx_rufus -0.2712 0.3649 -0.9802 -0.2770 0.4544 1.0518
## shrub_cover-Didelphis_virginiana 0.9433 0.3552 0.2846 0.9289 1.6981 1.0013
## shrub_cover-Sylvilagus_floridanus 0.2736 0.4160 -0.5069 0.2749 1.1131 1.0047
## shrub_cover-Meleagris_gallopavo -0.6856 0.4271 -1.5454 -0.6715 0.1690 1.0135
## shrub_cover-Sciurus_carolinensis 0.8712 0.4071 0.0940 0.8631 1.6845 1.0169
## veg_height-Canis_latrans -0.5762 0.1807 -0.9415 -0.5717 -0.2460 1.0218
## veg_height-Procyon_lotor 0.3523 0.1211 0.1236 0.3543 0.5875 1.0003
## veg_height-Dasypus_novemcinctus 0.2570 0.1331 -0.0049 0.2564 0.5215 1.0026
## veg_height-Lynx_rufus 0.0455 0.2424 -0.4441 0.0516 0.5057 1.0095
## veg_height-Didelphis_virginiana 0.4755 0.2442 0.0161 0.4694 0.9755 1.0017
## veg_height-Sylvilagus_floridanus 0.1347 0.2384 -0.3254 0.1334 0.6045 1.0024
## veg_height-Meleagris_gallopavo -0.2262 0.3946 -1.0082 -0.2202 0.5588 1.0087
## veg_height-Sciurus_carolinensis 0.0971 0.2251 -0.3342 0.0932 0.5583 1.0017
## week-Canis_latrans 0.0636 0.1350 -0.2099 0.0673 0.3225 1.0183
## week-Procyon_lotor -0.0572 0.1193 -0.3005 -0.0550 0.1666 1.0017
## week-Dasypus_novemcinctus -0.1781 0.1369 -0.4631 -0.1717 0.0753 1.0024
## week-Lynx_rufus -0.0539 0.1915 -0.4498 -0.0504 0.3059 1.0051
## week-Didelphis_virginiana -0.2369 0.2287 -0.7317 -0.2184 0.1691 1.0132
## week-Sylvilagus_floridanus -0.1693 0.2071 -0.6079 -0.1568 0.2197 1.0105
## week-Meleagris_gallopavo -0.2861 0.2407 -0.8043 -0.2698 0.1441 1.0051
## week-Sciurus_carolinensis 0.1268 0.1821 -0.2325 0.1269 0.4851 1.0022
## ESS
## (Intercept)-Canis_latrans 924
## (Intercept)-Procyon_lotor 1403
## (Intercept)-Dasypus_novemcinctus 1447
## (Intercept)-Lynx_rufus 363
## (Intercept)-Didelphis_virginiana 757
## (Intercept)-Sylvilagus_floridanus 537
## (Intercept)-Meleagris_gallopavo 175
## (Intercept)-Sciurus_carolinensis 608
## shrub_cover-Canis_latrans 919
## shrub_cover-Procyon_lotor 1413
## shrub_cover-Dasypus_novemcinctus 1519
## shrub_cover-Lynx_rufus 442
## shrub_cover-Didelphis_virginiana 762
## shrub_cover-Sylvilagus_floridanus 530
## shrub_cover-Meleagris_gallopavo 175
## shrub_cover-Sciurus_carolinensis 759
## veg_height-Canis_latrans 732
## veg_height-Procyon_lotor 1598
## veg_height-Dasypus_novemcinctus 1915
## veg_height-Lynx_rufus 815
## veg_height-Didelphis_virginiana 1199
## veg_height-Sylvilagus_floridanus 828
## veg_height-Meleagris_gallopavo 391
## veg_height-Sciurus_carolinensis 829
## week-Canis_latrans 1681
## week-Procyon_lotor 1764
## week-Dasypus_novemcinctus 2038
## week-Lynx_rufus 1059
## week-Didelphis_virginiana 1343
## week-Sylvilagus_floridanus 1048
## week-Meleagris_gallopavo 856
## week-Sciurus_carolinensis 1837
#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): 0.7268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1041 0.4549 -0.9607 -0.1013 0.7656 1.0392 361
## Avg_Cogongrass_Cover -0.0058 0.3812 -0.7746 0.0052 0.7288 1.0164 550
## total_shrub_cover -0.9504 0.5026 -2.0469 -0.9113 -0.0517 1.0257 402
## avg_veg_height 0.1747 0.4152 -0.6457 0.1552 1.0248 1.0081 407
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6573 0.8116 0.0545 0.3906 2.8468 1.0130 634
## Avg_Cogongrass_Cover 0.4915 0.6252 0.0441 0.2868 2.1967 1.0393 941
## total_shrub_cover 1.0756 1.7802 0.0772 0.6314 4.6508 1.0910 651
## avg_veg_height 0.4936 1.0198 0.0402 0.2395 2.4810 1.0483 447
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0717 1.0196 0.0795 0.777 3.721 1.1351 227
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7973 0.2916 -3.3764 -2.7992 -2.1911 1.0145 1516
## shrub_cover 0.5435 0.3367 -0.1324 0.5423 1.1995 1.0121 823
## veg_height 0.0947 0.2086 -0.3262 0.0950 0.5072 1.0006 866
## week -0.0999 0.1356 -0.3781 -0.0958 0.1610 1.0048 1392
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5929 0.5679 0.1323 0.4347 1.9527 1.0187 809
## shrub_cover 0.8035 0.8189 0.1353 0.5882 2.7450 1.0076 1301
## veg_height 0.2580 0.2183 0.0601 0.1946 0.8636 1.0014 1442
## week 0.1075 0.0992 0.0261 0.0812 0.3488 1.0033 1867
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2853 0.6263 -0.8406 0.2528
## (Intercept)-Procyon_lotor 0.4220 0.6534 -0.7738 0.3913
## (Intercept)-Dasypus_novemcinctus -0.2424 0.6284 -1.4713 -0.2574
## (Intercept)-Lynx_rufus -0.0717 0.6459 -1.2942 -0.0787
## (Intercept)-Didelphis_virginiana -0.5164 0.6504 -1.8052 -0.4967
## (Intercept)-Sylvilagus_floridanus 0.0626 0.6791 -1.1477 0.0255
## (Intercept)-Meleagris_gallopavo -0.3153 0.6819 -1.6417 -0.3178
## (Intercept)-Sciurus_carolinensis -0.4893 0.6780 -1.8576 -0.4486
## Avg_Cogongrass_Cover-Canis_latrans 0.3268 0.5246 -0.6002 0.2913
## Avg_Cogongrass_Cover-Procyon_lotor -0.1206 0.4872 -1.1002 -0.1022
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1143 0.4782 -0.8073 0.1201
## Avg_Cogongrass_Cover-Lynx_rufus 0.3921 0.5809 -0.6279 0.3510
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1284 0.5330 -0.8951 0.1124
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4418 0.5916 -1.7553 -0.3974
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4851 0.6652 -1.9168 -0.4314
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0241 0.5327 -1.1434 -0.0056
## total_shrub_cover-Canis_latrans 0.1241 0.6810 -1.1242 0.0790
## total_shrub_cover-Procyon_lotor -1.2696 0.5985 -2.6949 -1.2052
## total_shrub_cover-Dasypus_novemcinctus -0.6217 0.7326 -2.3969 -0.5140
## total_shrub_cover-Lynx_rufus -1.3687 0.7875 -3.0699 -1.2796
## total_shrub_cover-Didelphis_virginiana -0.9960 0.7201 -2.6998 -0.9176
## total_shrub_cover-Sylvilagus_floridanus -1.3108 0.9376 -3.5263 -1.2085
## total_shrub_cover-Meleagris_gallopavo -1.6037 0.8433 -3.5589 -1.4953
## total_shrub_cover-Sciurus_carolinensis -1.0476 0.7871 -2.7924 -0.9642
## avg_veg_height-Canis_latrans 0.1510 0.5053 -0.8521 0.1568
## avg_veg_height-Procyon_lotor 0.1764 0.4908 -0.7761 0.1724
## avg_veg_height-Dasypus_novemcinctus 0.4339 0.5294 -0.4917 0.3881
## avg_veg_height-Lynx_rufus 0.0777 0.6423 -1.2309 0.0845
## avg_veg_height-Didelphis_virginiana 0.0471 0.5439 -1.0991 0.0496
## avg_veg_height-Sylvilagus_floridanus 0.0854 0.5771 -1.0743 0.0856
## avg_veg_height-Meleagris_gallopavo -0.1952 0.8538 -2.1272 -0.1288
## avg_veg_height-Sciurus_carolinensis 0.5779 0.5627 -0.3744 0.5180
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7177 1.0248 579
## (Intercept)-Procyon_lotor 1.7825 1.0300 470
## (Intercept)-Dasypus_novemcinctus 1.0216 1.0272 477
## (Intercept)-Lynx_rufus 1.2148 1.0160 590
## (Intercept)-Didelphis_virginiana 0.6741 1.0166 373
## (Intercept)-Sylvilagus_floridanus 1.5357 1.0230 356
## (Intercept)-Meleagris_gallopavo 1.0821 1.0247 392
## (Intercept)-Sciurus_carolinensis 0.7787 1.0609 449
## Avg_Cogongrass_Cover-Canis_latrans 1.4946 1.0111 745
## Avg_Cogongrass_Cover-Procyon_lotor 0.8130 1.0052 869
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0834 1.0151 983
## Avg_Cogongrass_Cover-Lynx_rufus 1.7125 1.0136 780
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2355 1.0054 737
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6076 1.0180 561
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6629 1.0346 488
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9654 1.0453 644
## total_shrub_cover-Canis_latrans 1.6934 1.0354 431
## total_shrub_cover-Procyon_lotor -0.2962 1.0105 719
## total_shrub_cover-Dasypus_novemcinctus 0.5236 1.0141 285
## total_shrub_cover-Lynx_rufus -0.0434 1.0057 401
## total_shrub_cover-Didelphis_virginiana 0.2019 1.0108 417
## total_shrub_cover-Sylvilagus_floridanus 0.2147 1.0423 227
## total_shrub_cover-Meleagris_gallopavo -0.2538 1.0224 375
## total_shrub_cover-Sciurus_carolinensis 0.2644 1.0756 358
## avg_veg_height-Canis_latrans 1.1712 1.0007 676
## avg_veg_height-Procyon_lotor 1.1477 1.0011 769
## avg_veg_height-Dasypus_novemcinctus 1.6133 1.0347 429
## avg_veg_height-Lynx_rufus 1.3186 1.0064 532
## avg_veg_height-Didelphis_virginiana 1.0691 1.0056 592
## avg_veg_height-Sylvilagus_floridanus 1.1984 1.0037 520
## avg_veg_height-Meleagris_gallopavo 1.2154 1.0395 253
## avg_veg_height-Sciurus_carolinensis 1.8731 1.0635 542
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7739 0.1897 -3.1608 -2.7655 -2.4319 1.0124
## (Intercept)-Procyon_lotor -2.3131 0.1433 -2.6026 -2.3054 -2.0461 1.0042
## (Intercept)-Dasypus_novemcinctus -1.8893 0.1870 -2.2593 -1.8842 -1.5422 1.0020
## (Intercept)-Lynx_rufus -3.4543 0.3129 -4.1022 -3.4436 -2.8848 1.0176
## (Intercept)-Didelphis_virginiana -2.8113 0.3112 -3.4293 -2.8069 -2.2255 1.0269
## (Intercept)-Sylvilagus_floridanus -3.2850 0.2776 -3.8595 -3.2712 -2.7667 1.0136
## (Intercept)-Meleagris_gallopavo -3.4965 0.5150 -4.5909 -3.4725 -2.5626 1.0150
## (Intercept)-Sciurus_carolinensis -2.8796 0.3285 -3.5499 -2.8687 -2.2621 1.0971
## shrub_cover-Canis_latrans -0.2671 0.2455 -0.7388 -0.2695 0.2100 1.0131
## shrub_cover-Procyon_lotor 0.3192 0.1616 -0.0121 0.3205 0.6309 1.0089
## shrub_cover-Dasypus_novemcinctus 1.1551 0.3698 0.4591 1.1522 1.8606 1.0014
## shrub_cover-Lynx_rufus 0.1969 0.3602 -0.5529 0.2140 0.8699 1.0056
## shrub_cover-Didelphis_virginiana 1.3031 0.4203 0.5173 1.2878 2.1528 1.0463
## shrub_cover-Sylvilagus_floridanus 0.7737 0.4538 -0.2251 0.8074 1.6115 1.0129
## shrub_cover-Meleagris_gallopavo -0.2414 0.4552 -1.2054 -0.2278 0.6105 1.0056
## shrub_cover-Sciurus_carolinensis 1.2403 0.4225 0.3918 1.2562 2.0351 1.0874
## veg_height-Canis_latrans -0.5816 0.1903 -0.9806 -0.5765 -0.2218 1.0097
## veg_height-Procyon_lotor 0.3461 0.1222 0.1094 0.3463 0.5915 1.0308
## veg_height-Dasypus_novemcinctus 0.2857 0.1451 0.0004 0.2865 0.5627 1.0023
## veg_height-Lynx_rufus 0.0691 0.2593 -0.4555 0.0777 0.5562 1.0184
## veg_height-Didelphis_virginiana 0.4559 0.2534 -0.0145 0.4475 0.9883 1.0120
## veg_height-Sylvilagus_floridanus 0.0829 0.2534 -0.4225 0.0757 0.5945 1.0188
## veg_height-Meleagris_gallopavo -0.0345 0.4800 -0.9440 -0.0481 0.9731 1.0120
## veg_height-Sciurus_carolinensis 0.1160 0.2386 -0.3373 0.1096 0.6176 1.0168
## week-Canis_latrans 0.0572 0.1353 -0.2157 0.0624 0.3034 1.0125
## week-Procyon_lotor -0.0574 0.1187 -0.3011 -0.0534 0.1614 1.0012
## week-Dasypus_novemcinctus -0.1780 0.1347 -0.4531 -0.1695 0.0774 0.9998
## week-Lynx_rufus -0.0509 0.1947 -0.4536 -0.0449 0.3165 1.0019
## week-Didelphis_virginiana -0.2390 0.2172 -0.6988 -0.2242 0.1566 1.0016
## week-Sylvilagus_floridanus -0.1672 0.2013 -0.5845 -0.1528 0.2007 1.0043
## week-Meleagris_gallopavo -0.2888 0.2428 -0.8039 -0.2699 0.1338 1.0014
## week-Sciurus_carolinensis 0.1190 0.1861 -0.2585 0.1200 0.4713 1.0031
## ESS
## (Intercept)-Canis_latrans 556
## (Intercept)-Procyon_lotor 1229
## (Intercept)-Dasypus_novemcinctus 503
## (Intercept)-Lynx_rufus 415
## (Intercept)-Didelphis_virginiana 396
## (Intercept)-Sylvilagus_floridanus 403
## (Intercept)-Meleagris_gallopavo 280
## (Intercept)-Sciurus_carolinensis 390
## shrub_cover-Canis_latrans 553
## shrub_cover-Procyon_lotor 1449
## shrub_cover-Dasypus_novemcinctus 407
## shrub_cover-Lynx_rufus 442
## shrub_cover-Didelphis_virginiana 254
## shrub_cover-Sylvilagus_floridanus 222
## shrub_cover-Meleagris_gallopavo 370
## shrub_cover-Sciurus_carolinensis 314
## veg_height-Canis_latrans 637
## veg_height-Procyon_lotor 1447
## veg_height-Dasypus_novemcinctus 1715
## veg_height-Lynx_rufus 564
## veg_height-Didelphis_virginiana 701
## veg_height-Sylvilagus_floridanus 555
## veg_height-Meleagris_gallopavo 317
## veg_height-Sciurus_carolinensis 675
## week-Canis_latrans 1469
## week-Procyon_lotor 1696
## week-Dasypus_novemcinctus 1511
## week-Lynx_rufus 992
## week-Didelphis_virginiana 1106
## week-Sylvilagus_floridanus 946
## week-Meleagris_gallopavo 815
## week-Sciurus_carolinensis 1585
#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): 0.6938
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4593 0.4683 -1.4131 -0.4545 0.4277 1.0201 661
## Tree_Density -0.7834 0.4600 -1.8203 -0.7412 0.0081 1.0044 829
## Avg_Canopy_Cover 1.1484 0.4726 0.2793 1.1053 2.1982 1.0014 1020
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1277 2.0511 0.0790 0.7155 4.2036 1.1496 411
## Tree_Density 1.0861 1.4914 0.0588 0.6154 4.9348 1.0126 630
## Avg_Canopy_Cover 1.3514 1.5816 0.0994 0.8630 5.5507 1.0194 505
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6513 0.7948 0.0501 0.3747 2.6961 1.15 194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7664 0.3278 -3.3825 -2.7775 -2.0944 1.0029 2298
## shrub_cover 0.2888 0.3268 -0.3595 0.2871 0.9315 1.0011 1506
## veg_height 0.0946 0.2040 -0.3043 0.0909 0.5080 1.0148 1496
## week -0.0981 0.1358 -0.3747 -0.0958 0.1548 1.0033 1362
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7857 0.6396 0.1788 0.6205 2.3979 1.0075 1114
## shrub_cover 0.7569 0.6367 0.1591 0.5678 2.4378 1.0075 1407
## veg_height 0.2640 0.2262 0.0602 0.1989 0.8298 1.0092 1787
## week 0.1035 0.0964 0.0256 0.0778 0.3371 1.0043 1697
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0625 0.5796 -1.0666 0.0513 1.2215
## (Intercept)-Procyon_lotor 0.3521 0.6387 -0.9290 0.3683 1.5590
## (Intercept)-Dasypus_novemcinctus -0.8327 0.5777 -2.0157 -0.8171 0.2874
## (Intercept)-Lynx_rufus 0.0591 0.9750 -1.3878 -0.0332 1.9700
## (Intercept)-Didelphis_virginiana -1.2327 0.6620 -2.6231 -1.1846 -0.0780
## (Intercept)-Sylvilagus_floridanus -0.5818 0.6022 -1.8126 -0.5693 0.6157
## (Intercept)-Meleagris_gallopavo -0.2135 0.7662 -1.5834 -0.2641 1.4897
## (Intercept)-Sciurus_carolinensis -1.2736 0.6955 -2.7525 -1.2256 -0.0200
## Tree_Density-Canis_latrans -1.0258 0.6597 -2.5787 -0.9240 0.0087
## Tree_Density-Procyon_lotor -0.4791 0.4325 -1.3495 -0.4784 0.3781
## Tree_Density-Dasypus_novemcinctus -1.4941 0.9458 -3.7478 -1.2852 -0.2084
## Tree_Density-Lynx_rufus 0.3081 0.7859 -0.8783 0.1940 2.2109
## Tree_Density-Didelphis_virginiana -0.9553 0.7789 -2.8631 -0.8443 0.2809
## Tree_Density-Sylvilagus_floridanus -1.1012 0.8142 -3.0355 -0.9756 0.1868
## Tree_Density-Meleagris_gallopavo -1.0582 0.8465 -3.0745 -0.9479 0.3261
## Tree_Density-Sciurus_carolinensis -0.8366 0.7523 -2.6007 -0.7590 0.4415
## Avg_Canopy_Cover-Canis_latrans -0.1023 0.4971 -1.0826 -0.0984 0.8489
## Avg_Canopy_Cover-Procyon_lotor 1.0318 0.4772 0.1928 0.9961 2.0645
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1309 0.5023 0.2525 1.0881 2.2323
## Avg_Canopy_Cover-Lynx_rufus 0.6780 0.7433 -0.6653 0.6635 2.2051
## Avg_Canopy_Cover-Didelphis_virginiana 1.6724 0.8017 0.5030 1.5368 3.6050
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.2374 1.0450 0.7117 2.0486 4.7616
## Avg_Canopy_Cover-Meleagris_gallopavo 1.6384 0.8495 0.3262 1.4990 3.6445
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5706 0.7289 0.4020 1.4619 3.3718
## Rhat ESS
## (Intercept)-Canis_latrans 1.0081 635
## (Intercept)-Procyon_lotor 1.0122 458
## (Intercept)-Dasypus_novemcinctus 1.0088 780
## (Intercept)-Lynx_rufus 1.0948 180
## (Intercept)-Didelphis_virginiana 1.0021 792
## (Intercept)-Sylvilagus_floridanus 1.0078 1062
## (Intercept)-Meleagris_gallopavo 1.0228 518
## (Intercept)-Sciurus_carolinensis 1.0113 624
## Tree_Density-Canis_latrans 1.0107 1026
## Tree_Density-Procyon_lotor 1.0028 2070
## Tree_Density-Dasypus_novemcinctus 1.0004 538
## Tree_Density-Lynx_rufus 1.0194 643
## Tree_Density-Didelphis_virginiana 1.0045 819
## Tree_Density-Sylvilagus_floridanus 1.0028 716
## Tree_Density-Meleagris_gallopavo 1.0118 550
## Tree_Density-Sciurus_carolinensis 1.0054 920
## Avg_Canopy_Cover-Canis_latrans 1.0186 809
## Avg_Canopy_Cover-Procyon_lotor 1.0048 1222
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0019 1746
## Avg_Canopy_Cover-Lynx_rufus 1.0381 557
## Avg_Canopy_Cover-Didelphis_virginiana 1.0322 408
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0147 494
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0238 424
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0157 636
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7573 0.1871 -3.1344 -2.7525 -2.4128 1.0068
## (Intercept)-Procyon_lotor -2.3059 0.1432 -2.5905 -2.3017 -2.0382 1.0009
## (Intercept)-Dasypus_novemcinctus -1.7875 0.1622 -2.1116 -1.7825 -1.4800 1.0032
## (Intercept)-Lynx_rufus -3.6374 0.3485 -4.3708 -3.6182 -2.9855 1.0306
## (Intercept)-Didelphis_virginiana -2.6849 0.2959 -3.2773 -2.6755 -2.1227 1.0023
## (Intercept)-Sylvilagus_floridanus -3.1254 0.2556 -3.6427 -3.1171 -2.6526 1.0131
## (Intercept)-Meleagris_gallopavo -3.8834 0.4510 -4.8156 -3.8601 -3.0400 1.0133
## (Intercept)-Sciurus_carolinensis -2.7695 0.3219 -3.4336 -2.7608 -2.1564 1.0261
## shrub_cover-Canis_latrans -0.2975 0.2200 -0.7452 -0.3000 0.1354 1.0022
## shrub_cover-Procyon_lotor 0.2620 0.1620 -0.0634 0.2636 0.5699 1.0007
## shrub_cover-Dasypus_novemcinctus 0.8992 0.2972 0.3099 0.8946 1.4868 1.0008
## shrub_cover-Lynx_rufus -0.2809 0.3619 -1.0122 -0.2821 0.4504 1.0305
## shrub_cover-Didelphis_virginiana 1.0454 0.3649 0.3633 1.0466 1.7699 1.0008
## shrub_cover-Sylvilagus_floridanus 0.4458 0.3860 -0.3064 0.4479 1.2165 1.0070
## shrub_cover-Meleagris_gallopavo -0.6533 0.4045 -1.4763 -0.6397 0.1103 1.0307
## shrub_cover-Sciurus_carolinensis 0.9659 0.4170 0.1748 0.9513 1.7999 1.0118
## veg_height-Canis_latrans -0.5819 0.1837 -0.9581 -0.5779 -0.2359 1.0015
## veg_height-Procyon_lotor 0.3538 0.1222 0.1179 0.3509 0.5990 1.0058
## veg_height-Dasypus_novemcinctus 0.2669 0.1397 0.0026 0.2645 0.5449 1.0071
## veg_height-Lynx_rufus 0.0758 0.2458 -0.4055 0.0738 0.5550 1.0204
## veg_height-Didelphis_virginiana 0.5145 0.2498 0.0526 0.5052 1.0133 1.0034
## veg_height-Sylvilagus_floridanus 0.1817 0.2335 -0.2931 0.1825 0.6342 1.0003
## veg_height-Meleagris_gallopavo -0.1938 0.3486 -0.8993 -0.1863 0.4483 1.0079
## veg_height-Sciurus_carolinensis 0.1533 0.2250 -0.2777 0.1514 0.6067 1.0056
## week-Canis_latrans 0.0610 0.1349 -0.2210 0.0647 0.3216 1.0034
## week-Procyon_lotor -0.0587 0.1200 -0.3057 -0.0546 0.1676 1.0054
## week-Dasypus_novemcinctus -0.1729 0.1390 -0.4586 -0.1685 0.0889 1.0015
## week-Lynx_rufus -0.0527 0.1910 -0.4615 -0.0476 0.2911 1.0030
## week-Didelphis_virginiana -0.2307 0.2139 -0.6922 -0.2186 0.1552 1.0003
## week-Sylvilagus_floridanus -0.1679 0.2010 -0.5963 -0.1610 0.2053 1.0004
## week-Meleagris_gallopavo -0.2806 0.2370 -0.8081 -0.2621 0.1346 1.0162
## week-Sciurus_carolinensis 0.1215 0.1825 -0.2549 0.1253 0.4739 1.0022
## ESS
## (Intercept)-Canis_latrans 742
## (Intercept)-Procyon_lotor 1323
## (Intercept)-Dasypus_novemcinctus 1463
## (Intercept)-Lynx_rufus 260
## (Intercept)-Didelphis_virginiana 533
## (Intercept)-Sylvilagus_floridanus 868
## (Intercept)-Meleagris_gallopavo 224
## (Intercept)-Sciurus_carolinensis 657
## shrub_cover-Canis_latrans 881
## shrub_cover-Procyon_lotor 1577
## shrub_cover-Dasypus_novemcinctus 1053
## shrub_cover-Lynx_rufus 310
## shrub_cover-Didelphis_virginiana 754
## shrub_cover-Sylvilagus_floridanus 419
## shrub_cover-Meleagris_gallopavo 271
## shrub_cover-Sciurus_carolinensis 586
## veg_height-Canis_latrans 799
## veg_height-Procyon_lotor 1331
## veg_height-Dasypus_novemcinctus 1901
## veg_height-Lynx_rufus 741
## veg_height-Didelphis_virginiana 1009
## veg_height-Sylvilagus_floridanus 1006
## veg_height-Meleagris_gallopavo 656
## veg_height-Sciurus_carolinensis 970
## week-Canis_latrans 1606
## week-Procyon_lotor 1615
## week-Dasypus_novemcinctus 1790
## week-Lynx_rufus 978
## week-Didelphis_virginiana 1254
## week-Sylvilagus_floridanus 1105
## week-Meleagris_gallopavo 776
## week-Sciurus_carolinensis 1546
#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): 0.707
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1669 0.4187 -0.9743 -0.1706 0.6628 1.0340 377
## Cogon_Patch_Size 0.0352 0.4123 -0.8229 0.0370 0.8446 1.0046 892
## Avg_Cogongrass_Cover 0.0813 0.3538 -0.6287 0.0852 0.7645 1.0042 525
## total_shrub_cover -0.9422 0.4908 -2.0322 -0.8814 -0.1074 1.0481 259
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6393 0.8464 0.0501 0.3754 2.7463 1.0568 256
## Cogon_Patch_Size 0.8084 1.4863 0.0545 0.4230 3.8167 1.0299 1104
## Avg_Cogongrass_Cover 0.4425 0.5885 0.0393 0.2500 1.9857 1.0100 837
## total_shrub_cover 0.8534 1.2239 0.0608 0.4717 3.9223 1.0593 507
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2617 1.1825 0.0947 0.9297 4.2971 1.0963 154
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7803 0.2868 -3.3496 -2.7804 -2.2120 1.0107 1285
## shrub_cover 0.5275 0.3304 -0.1139 0.5229 1.2114 1.0016 677
## veg_height 0.0874 0.2042 -0.3123 0.0888 0.4831 1.0067 1109
## week -0.0974 0.1379 -0.3787 -0.0934 0.1637 1.0142 1360
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5819 0.5252 0.1277 0.4376 2.0354 1.0058 983
## shrub_cover 0.7052 0.6442 0.1363 0.5351 2.3866 1.0233 955
## veg_height 0.2624 0.2596 0.0561 0.1919 0.8763 1.0238 1666
## week 0.1082 0.0946 0.0261 0.0791 0.3514 1.0051 1636
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2558 0.6172 -0.7946 0.2016
## (Intercept)-Procyon_lotor 0.3460 0.6562 -0.8423 0.2956
## (Intercept)-Dasypus_novemcinctus -0.2898 0.5827 -1.4599 -0.2920
## (Intercept)-Lynx_rufus -0.1306 0.6262 -1.3597 -0.1339
## (Intercept)-Didelphis_virginiana -0.5985 0.6581 -1.9812 -0.5550
## (Intercept)-Sylvilagus_floridanus -0.0746 0.6556 -1.2923 -0.0967
## (Intercept)-Meleagris_gallopavo -0.3370 0.6468 -1.6138 -0.3215
## (Intercept)-Sciurus_carolinensis -0.6031 0.6905 -2.1057 -0.5585
## Cogon_Patch_Size-Canis_latrans 0.6979 0.6833 -0.3347 0.5951
## Cogon_Patch_Size-Procyon_lotor -0.1085 0.4679 -1.1072 -0.0920
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0361 0.4585 -0.8744 0.0273
## Cogon_Patch_Size-Lynx_rufus 0.0517 0.7025 -1.3022 0.0027
## Cogon_Patch_Size-Didelphis_virginiana 0.6033 0.5062 -0.2914 0.5740
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6043 0.8255 -2.6671 -0.4635
## Cogon_Patch_Size-Meleagris_gallopavo 0.0958 0.6532 -1.1523 0.0682
## Cogon_Patch_Size-Sciurus_carolinensis -0.4707 0.6942 -2.2005 -0.3672
## Avg_Cogongrass_Cover-Canis_latrans 0.2592 0.4573 -0.5794 0.2229
## Avg_Cogongrass_Cover-Procyon_lotor 0.0366 0.4480 -0.8611 0.0387
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2959 0.4459 -0.5091 0.2874
## Avg_Cogongrass_Cover-Lynx_rufus 0.4327 0.5380 -0.4837 0.3847
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0399 0.4705 -0.9285 0.0490
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2802 0.5826 -1.5727 -0.2446
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4159 0.6973 -2.0114 -0.3478
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2951 0.4868 -0.6421 0.2826
## total_shrub_cover-Canis_latrans -0.0744 0.6418 -1.2510 -0.1003
## total_shrub_cover-Procyon_lotor -1.2181 0.5920 -2.5441 -1.1399
## total_shrub_cover-Dasypus_novemcinctus -0.5898 0.6193 -2.0720 -0.5197
## total_shrub_cover-Lynx_rufus -1.3264 0.8231 -3.2512 -1.2240
## total_shrub_cover-Didelphis_virginiana -0.9746 0.6613 -2.4658 -0.9044
## total_shrub_cover-Sylvilagus_floridanus -1.1903 0.8222 -3.1419 -1.0939
## total_shrub_cover-Meleagris_gallopavo -1.5076 0.8384 -3.6062 -1.3707
## total_shrub_cover-Sciurus_carolinensis -0.9528 0.7610 -2.7965 -0.8423
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6304 1.0233 481
## (Intercept)-Procyon_lotor 1.7438 1.0012 638
## (Intercept)-Dasypus_novemcinctus 0.8747 1.0173 540
## (Intercept)-Lynx_rufus 1.1140 1.0003 669
## (Intercept)-Didelphis_virginiana 0.5976 1.0175 414
## (Intercept)-Sylvilagus_floridanus 1.2449 1.0285 416
## (Intercept)-Meleagris_gallopavo 0.9364 1.0396 452
## (Intercept)-Sciurus_carolinensis 0.6505 1.0246 381
## Cogon_Patch_Size-Canis_latrans 2.3279 1.0012 803
## Cogon_Patch_Size-Procyon_lotor 0.7928 1.0033 1133
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9611 1.0055 1429
## Cogon_Patch_Size-Lynx_rufus 1.5655 1.0053 748
## Cogon_Patch_Size-Didelphis_virginiana 1.6675 1.0107 909
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6275 1.0013 595
## Cogon_Patch_Size-Meleagris_gallopavo 1.5081 1.0090 1002
## Cogon_Patch_Size-Sciurus_carolinensis 0.6074 1.0045 683
## Avg_Cogongrass_Cover-Canis_latrans 1.2697 1.0079 1192
## Avg_Cogongrass_Cover-Procyon_lotor 0.9047 1.0047 1110
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2234 1.0061 1193
## Avg_Cogongrass_Cover-Lynx_rufus 1.6836 1.0032 840
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9521 1.0034 906
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7758 1.0054 765
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7969 1.0042 496
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2760 1.0036 576
## total_shrub_cover-Canis_latrans 1.3446 1.0293 425
## total_shrub_cover-Procyon_lotor -0.2660 1.0139 507
## total_shrub_cover-Dasypus_novemcinctus 0.4544 1.0187 335
## total_shrub_cover-Lynx_rufus 0.0496 1.0841 276
## total_shrub_cover-Didelphis_virginiana 0.0775 1.0215 328
## total_shrub_cover-Sylvilagus_floridanus 0.2282 1.0910 253
## total_shrub_cover-Meleagris_gallopavo -0.2499 1.0294 355
## total_shrub_cover-Sciurus_carolinensis 0.2893 1.0145 222
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7679 0.1879 -3.1525 -2.7639 -2.4194 1.0091
## (Intercept)-Procyon_lotor -2.3169 0.1388 -2.5896 -2.3133 -2.0614 1.0097
## (Intercept)-Dasypus_novemcinctus -1.8686 0.1853 -2.2357 -1.8621 -1.5269 1.0352
## (Intercept)-Lynx_rufus -3.4540 0.3070 -4.0726 -3.4479 -2.8802 1.0176
## (Intercept)-Didelphis_virginiana -2.7520 0.2998 -3.3574 -2.7433 -2.2025 1.0092
## (Intercept)-Sylvilagus_floridanus -3.2457 0.2672 -3.7920 -3.2452 -2.7345 1.0038
## (Intercept)-Meleagris_gallopavo -3.5308 0.5047 -4.6028 -3.5169 -2.5930 1.0116
## (Intercept)-Sciurus_carolinensis -2.8780 0.3346 -3.5525 -2.8680 -2.2671 1.0203
## shrub_cover-Canis_latrans -0.2395 0.2434 -0.7210 -0.2349 0.2314 1.0138
## shrub_cover-Procyon_lotor 0.3265 0.1576 0.0044 0.3336 0.6288 1.0071
## shrub_cover-Dasypus_novemcinctus 1.0928 0.3526 0.4028 1.0961 1.7833 1.0462
## shrub_cover-Lynx_rufus 0.1936 0.3651 -0.5459 0.2041 0.8811 1.0089
## shrub_cover-Didelphis_virginiana 1.2124 0.4119 0.4435 1.2076 2.0492 1.0233
## shrub_cover-Sylvilagus_floridanus 0.7445 0.4098 -0.1230 0.7645 1.5226 1.0433
## shrub_cover-Meleagris_gallopavo -0.2524 0.4523 -1.1621 -0.2411 0.6034 1.0180
## shrub_cover-Sciurus_carolinensis 1.2158 0.4344 0.3501 1.2351 2.0696 1.0492
## veg_height-Canis_latrans -0.5785 0.1880 -0.9584 -0.5726 -0.2288 1.0025
## veg_height-Procyon_lotor 0.3576 0.1248 0.1213 0.3584 0.6085 1.0000
## veg_height-Dasypus_novemcinctus 0.2862 0.1438 0.0171 0.2802 0.5689 1.0052
## veg_height-Lynx_rufus 0.0769 0.2486 -0.4427 0.0844 0.5288 1.0124
## veg_height-Didelphis_virginiana 0.4614 0.2565 -0.0083 0.4515 0.9851 1.0017
## veg_height-Sylvilagus_floridanus 0.0852 0.2387 -0.3866 0.0867 0.5419 1.0404
## veg_height-Meleagris_gallopavo -0.1366 0.4349 -1.0214 -0.1357 0.7585 1.0095
## veg_height-Sciurus_carolinensis 0.1502 0.2269 -0.2760 0.1458 0.6117 1.0084
## week-Canis_latrans 0.0622 0.1331 -0.2069 0.0636 0.3171 1.0010
## week-Procyon_lotor -0.0611 0.1206 -0.3007 -0.0606 0.1657 1.0113
## week-Dasypus_novemcinctus -0.1867 0.1401 -0.4732 -0.1811 0.0705 1.0021
## week-Lynx_rufus -0.0527 0.1938 -0.4454 -0.0448 0.3173 1.0102
## week-Didelphis_virginiana -0.2349 0.2166 -0.7020 -0.2200 0.1648 1.0031
## week-Sylvilagus_floridanus -0.1523 0.2042 -0.6041 -0.1433 0.2256 1.0041
## week-Meleagris_gallopavo -0.2986 0.2486 -0.8361 -0.2797 0.1266 1.0448
## week-Sciurus_carolinensis 0.1277 0.1822 -0.2305 0.1287 0.4852 1.0169
## ESS
## (Intercept)-Canis_latrans 740
## (Intercept)-Procyon_lotor 1417
## (Intercept)-Dasypus_novemcinctus 533
## (Intercept)-Lynx_rufus 518
## (Intercept)-Didelphis_virginiana 445
## (Intercept)-Sylvilagus_floridanus 539
## (Intercept)-Meleagris_gallopavo 325
## (Intercept)-Sciurus_carolinensis 323
## shrub_cover-Canis_latrans 547
## shrub_cover-Procyon_lotor 1508
## shrub_cover-Dasypus_novemcinctus 379
## shrub_cover-Lynx_rufus 412
## shrub_cover-Didelphis_virginiana 305
## shrub_cover-Sylvilagus_floridanus 362
## shrub_cover-Meleagris_gallopavo 394
## shrub_cover-Sciurus_carolinensis 315
## veg_height-Canis_latrans 752
## veg_height-Procyon_lotor 1479
## veg_height-Dasypus_novemcinctus 1292
## veg_height-Lynx_rufus 684
## veg_height-Didelphis_virginiana 1015
## veg_height-Sylvilagus_floridanus 540
## veg_height-Meleagris_gallopavo 353
## veg_height-Sciurus_carolinensis 739
## week-Canis_latrans 1523
## week-Procyon_lotor 1619
## week-Dasypus_novemcinctus 1824
## week-Lynx_rufus 1013
## week-Didelphis_virginiana 1265
## week-Sylvilagus_floridanus 1010
## week-Meleagris_gallopavo 790
## week-Sciurus_carolinensis 1242
#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): 0.6735
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3643 0.3726 -1.0995 -0.3606 0.3695 1.0042 675
## Veg_shannon_index 0.3644 0.2697 -0.1542 0.3627 0.9209 1.0192 896
## Avg_Cogongrass_Cover 0.3378 0.2941 -0.2439 0.3366 0.9124 1.0213 857
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5729 0.6895 0.0530 0.3694 2.2897 1.0334 823
## Veg_shannon_index 0.2776 0.4286 0.0376 0.1740 1.1284 1.1525 1166
## Avg_Cogongrass_Cover 0.3348 0.4201 0.0388 0.2012 1.4370 1.0092 923
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7806 0.7379 0.0708 0.5632 2.7163 1.0936 268
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7451 0.3312 -3.3574 -2.7652 -2.0273 1.0064 2331
## shrub_cover 0.2668 0.3191 -0.3700 0.2733 0.9022 1.0053 1644
## veg_height 0.0696 0.1984 -0.3282 0.0742 0.4506 1.0002 1327
## week -0.0960 0.1356 -0.3667 -0.0935 0.1682 1.0031 1550
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7991 0.7187 0.1863 0.6067 2.5508 1.0396 1540
## shrub_cover 0.6838 0.5918 0.1317 0.5154 2.2627 1.0220 1195
## veg_height 0.2518 0.2323 0.0547 0.1902 0.7920 1.0186 1931
## week 0.1095 0.0941 0.0250 0.0836 0.3532 1.0052 1559
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0219 0.5303 -0.9817 0.0026
## (Intercept)-Procyon_lotor 0.1295 0.5624 -0.9745 0.1201
## (Intercept)-Dasypus_novemcinctus -0.5385 0.4654 -1.5247 -0.5159
## (Intercept)-Lynx_rufus -0.2365 0.5885 -1.3036 -0.2686
## (Intercept)-Didelphis_virginiana -0.8865 0.5599 -2.0617 -0.8594
## (Intercept)-Sylvilagus_floridanus -0.4015 0.5227 -1.4141 -0.4094
## (Intercept)-Meleagris_gallopavo -0.1810 0.6443 -1.3627 -0.2099
## (Intercept)-Sciurus_carolinensis -0.8940 0.5516 -2.0615 -0.8582
## Veg_shannon_index-Canis_latrans 0.6407 0.3907 -0.0562 0.6197
## Veg_shannon_index-Procyon_lotor 0.4528 0.3590 -0.2046 0.4378
## Veg_shannon_index-Dasypus_novemcinctus 0.1859 0.3434 -0.5135 0.1895
## Veg_shannon_index-Lynx_rufus 0.2172 0.4609 -0.7672 0.2334
## Veg_shannon_index-Didelphis_virginiana 0.4601 0.3798 -0.2451 0.4480
## Veg_shannon_index-Sylvilagus_floridanus 0.4255 0.4063 -0.3463 0.4030
## Veg_shannon_index-Meleagris_gallopavo 0.5063 0.4668 -0.3450 0.4831
## Veg_shannon_index-Sciurus_carolinensis 0.0070 0.4017 -0.8486 0.0344
## Avg_Cogongrass_Cover-Canis_latrans 0.5899 0.3942 -0.1104 0.5644
## Avg_Cogongrass_Cover-Procyon_lotor 0.3725 0.3669 -0.3189 0.3655
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4517 0.3446 -0.2027 0.4451
## Avg_Cogongrass_Cover-Lynx_rufus 0.5842 0.4238 -0.1610 0.5474
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4503 0.3765 -0.3075 0.4418
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0600 0.4606 -1.0915 -0.0248
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0480 0.6186 -1.4074 0.0105
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4344 0.3711 -0.2795 0.4297
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1310 1.0181 666
## (Intercept)-Procyon_lotor 1.2484 1.0587 486
## (Intercept)-Dasypus_novemcinctus 0.3567 1.0066 1101
## (Intercept)-Lynx_rufus 1.0723 1.0244 602
## (Intercept)-Didelphis_virginiana 0.1106 1.0032 883
## (Intercept)-Sylvilagus_floridanus 0.6650 1.0190 911
## (Intercept)-Meleagris_gallopavo 1.1955 1.0189 453
## (Intercept)-Sciurus_carolinensis 0.0860 1.0020 889
## Veg_shannon_index-Canis_latrans 1.5126 1.0135 1285
## Veg_shannon_index-Procyon_lotor 1.2059 1.0202 1326
## Veg_shannon_index-Dasypus_novemcinctus 0.8467 1.0067 1903
## Veg_shannon_index-Lynx_rufus 1.0981 1.0081 1280
## Veg_shannon_index-Didelphis_virginiana 1.2826 1.0089 1462
## Veg_shannon_index-Sylvilagus_floridanus 1.3175 1.0124 1259
## Veg_shannon_index-Meleagris_gallopavo 1.5293 1.0043 973
## Veg_shannon_index-Sciurus_carolinensis 0.7369 1.0075 1407
## Avg_Cogongrass_Cover-Canis_latrans 1.4637 1.0151 1686
## Avg_Cogongrass_Cover-Procyon_lotor 1.1457 1.0182 1625
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1496 1.0037 2275
## Avg_Cogongrass_Cover-Lynx_rufus 1.5092 1.0142 1337
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2499 1.0174 1709
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7221 1.0159 803
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0721 1.0057 548
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1917 1.0084 1457
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7297 0.1865 -3.1205 -2.7192 -2.3857 1.0146
## (Intercept)-Procyon_lotor -2.3037 0.1413 -2.5823 -2.3007 -2.0353 1.0063
## (Intercept)-Dasypus_novemcinctus -1.7775 0.1598 -2.0992 -1.7721 -1.4696 1.0103
## (Intercept)-Lynx_rufus -3.5725 0.3226 -4.1991 -3.5767 -2.9418 1.0842
## (Intercept)-Didelphis_virginiana -2.6281 0.2867 -3.2087 -2.6188 -2.0950 1.0021
## (Intercept)-Sylvilagus_floridanus -3.1824 0.2813 -3.7585 -3.1759 -2.6496 1.0054
## (Intercept)-Meleagris_gallopavo -3.8785 0.4599 -4.8128 -3.8796 -2.9828 1.0112
## (Intercept)-Sciurus_carolinensis -2.6750 0.3074 -3.3051 -2.6630 -2.1119 1.0076
## shrub_cover-Canis_latrans -0.2821 0.2127 -0.7135 -0.2753 0.1190 1.0052
## shrub_cover-Procyon_lotor 0.2482 0.1701 -0.1048 0.2550 0.5667 1.0034
## shrub_cover-Dasypus_novemcinctus 0.8682 0.3039 0.2733 0.8662 1.4745 1.0100
## shrub_cover-Lynx_rufus -0.2068 0.3528 -0.9088 -0.1968 0.4946 1.0176
## shrub_cover-Didelphis_virginiana 0.9983 0.3718 0.3171 0.9799 1.7598 1.0151
## shrub_cover-Sylvilagus_floridanus 0.2960 0.4263 -0.5095 0.2965 1.1629 1.0448
## shrub_cover-Meleagris_gallopavo -0.6411 0.3920 -1.4323 -0.6387 0.1223 1.0151
## shrub_cover-Sciurus_carolinensis 0.8780 0.4090 0.1088 0.8687 1.7135 1.0185
## veg_height-Canis_latrans -0.5724 0.1930 -0.9791 -0.5666 -0.2202 1.0098
## veg_height-Procyon_lotor 0.3396 0.1211 0.1012 0.3390 0.5774 1.0067
## veg_height-Dasypus_novemcinctus 0.2521 0.1377 -0.0051 0.2519 0.5274 1.0007
## veg_height-Lynx_rufus 0.0062 0.2518 -0.4938 0.0066 0.4927 1.0109
## veg_height-Didelphis_virginiana 0.4451 0.2459 -0.0099 0.4381 0.9502 1.0006
## veg_height-Sylvilagus_floridanus 0.1570 0.2554 -0.3359 0.1533 0.6748 1.0216
## veg_height-Meleagris_gallopavo -0.1735 0.3908 -0.9455 -0.1675 0.6059 1.0014
## veg_height-Sciurus_carolinensis 0.0838 0.2181 -0.3275 0.0780 0.5336 1.0040
## week-Canis_latrans 0.0618 0.1354 -0.2053 0.0633 0.3279 1.0003
## week-Procyon_lotor -0.0542 0.1207 -0.3044 -0.0496 0.1707 1.0059
## week-Dasypus_novemcinctus -0.1789 0.1377 -0.4514 -0.1796 0.0878 1.0036
## week-Lynx_rufus -0.0509 0.1912 -0.4406 -0.0489 0.3075 1.0120
## week-Didelphis_virginiana -0.2360 0.2212 -0.7040 -0.2260 0.1621 1.0075
## week-Sylvilagus_floridanus -0.1755 0.2049 -0.6084 -0.1649 0.1991 1.0002
## week-Meleagris_gallopavo -0.2884 0.2413 -0.8176 -0.2711 0.1352 1.0058
## week-Sciurus_carolinensis 0.1307 0.1856 -0.2453 0.1307 0.4788 1.0078
## ESS
## (Intercept)-Canis_latrans 751
## (Intercept)-Procyon_lotor 1331
## (Intercept)-Dasypus_novemcinctus 1465
## (Intercept)-Lynx_rufus 365
## (Intercept)-Didelphis_virginiana 1028
## (Intercept)-Sylvilagus_floridanus 616
## (Intercept)-Meleagris_gallopavo 238
## (Intercept)-Sciurus_carolinensis 760
## shrub_cover-Canis_latrans 950
## shrub_cover-Procyon_lotor 1388
## shrub_cover-Dasypus_novemcinctus 1252
## shrub_cover-Lynx_rufus 492
## shrub_cover-Didelphis_virginiana 636
## shrub_cover-Sylvilagus_floridanus 494
## shrub_cover-Meleagris_gallopavo 327
## shrub_cover-Sciurus_carolinensis 722
## veg_height-Canis_latrans 612
## veg_height-Procyon_lotor 1634
## veg_height-Dasypus_novemcinctus 1994
## veg_height-Lynx_rufus 680
## veg_height-Didelphis_virginiana 1102
## veg_height-Sylvilagus_floridanus 757
## veg_height-Meleagris_gallopavo 487
## veg_height-Sciurus_carolinensis 1081
## week-Canis_latrans 1611
## week-Procyon_lotor 1640
## week-Dasypus_novemcinctus 2085
## week-Lynx_rufus 972
## week-Didelphis_virginiana 1422
## week-Sylvilagus_floridanus 856
## week-Meleagris_gallopavo 737
## week-Sciurus_carolinensis 1871
#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): 0.67
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9727 0.4169 -1.7849 -0.9717 -0.1257 1.0079 511
## Avg_Cogongrass_Cover -0.6229 0.4010 -1.4398 -0.6028 0.1155 1.0122 604
## I(Avg_Cogongrass_Cover^2) 0.7983 0.3778 0.1322 0.7727 1.6257 1.0204 337
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6785 0.8980 0.0563 0.4187 2.9499 1.0158 781
## Avg_Cogongrass_Cover 0.5040 0.7257 0.0454 0.2863 2.2267 1.0029 540
## I(Avg_Cogongrass_Cover^2) 0.5143 1.1178 0.0410 0.2515 2.4310 1.2307 802
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.458 0.5248 0.0444 0.2947 1.8178 1.0745 235
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7362 0.3043 -3.3275 -2.7375 -2.1032 1.0028 1446
## shrub_cover 0.2563 0.3114 -0.3869 0.2579 0.8544 1.0112 1967
## veg_height 0.1008 0.1926 -0.2894 0.1017 0.4883 1.0044 1367
## week -0.0920 0.1390 -0.3826 -0.0865 0.1750 1.0171 1385
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7346 0.7190 0.1545 0.5484 2.4383 1.0063 599
## shrub_cover 0.6582 0.6040 0.1247 0.4835 2.2439 1.0105 969
## veg_height 0.2369 0.2129 0.0555 0.1812 0.7409 1.0093 1824
## week 0.1072 0.1004 0.0249 0.0783 0.3567 1.0143 1957
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6650 0.5689 -1.7433 -0.6772
## (Intercept)-Procyon_lotor -0.4680 0.5651 -1.5471 -0.4866
## (Intercept)-Dasypus_novemcinctus -1.1635 0.5088 -2.2309 -1.1360
## (Intercept)-Lynx_rufus -1.0239 0.6590 -2.2792 -1.0459
## (Intercept)-Didelphis_virginiana -1.4193 0.5718 -2.6956 -1.3947
## (Intercept)-Sylvilagus_floridanus -1.0129 0.5679 -2.1546 -1.0158
## (Intercept)-Meleagris_gallopavo -0.5964 0.7450 -1.8259 -0.6880
## (Intercept)-Sciurus_carolinensis -1.6764 0.6396 -3.0796 -1.6250
## Avg_Cogongrass_Cover-Canis_latrans -0.3093 0.5355 -1.3029 -0.3388
## Avg_Cogongrass_Cover-Procyon_lotor -0.6530 0.5110 -1.6757 -0.6441
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4242 0.4745 -1.3419 -0.4343
## Avg_Cogongrass_Cover-Lynx_rufus -0.5701 0.5857 -1.7794 -0.5479
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3348 0.5333 -1.3463 -0.3563
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1423 0.6833 -2.7778 -1.0460
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0303 0.7678 -2.7881 -0.9362
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6156 0.5166 -1.6916 -0.6073
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2679 0.7556 0.2615 1.0956
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1204 0.6463 0.2174 0.9908
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6625 0.3572 -0.0040 0.6426
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1436 0.5570 0.3055 1.0603
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4840 0.4272 -0.3084 0.4641
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6729 0.4720 -0.1236 0.6359
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3770 0.6809 -0.9367 0.3556
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8325 0.3950 0.1548 0.7978
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5297 1.0058 701
## (Intercept)-Procyon_lotor 0.6671 1.0094 640
## (Intercept)-Dasypus_novemcinctus -0.2120 1.0031 1171
## (Intercept)-Lynx_rufus 0.4127 1.0031 518
## (Intercept)-Didelphis_virginiana -0.4095 1.0003 1013
## (Intercept)-Sylvilagus_floridanus 0.1484 1.0009 785
## (Intercept)-Meleagris_gallopavo 1.1569 1.0437 508
## (Intercept)-Sciurus_carolinensis -0.5731 1.0011 566
## Avg_Cogongrass_Cover-Canis_latrans 0.8642 1.0056 970
## Avg_Cogongrass_Cover-Procyon_lotor 0.3306 1.0105 1000
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5111 1.0024 1172
## Avg_Cogongrass_Cover-Lynx_rufus 0.5677 1.0008 852
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7912 1.0130 1082
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0576 1.0050 432
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1799 1.0263 419
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4101 1.0051 925
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1865 1.0794 241
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7822 1.0642 391
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4227 1.0045 1176
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4788 1.0678 397
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4215 1.0485 627
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7475 1.0166 542
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8777 1.0287 211
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7280 1.0031 792
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7406 0.1760 -3.1021 -2.7381 -2.3997 1.0027
## (Intercept)-Procyon_lotor -2.3169 0.1432 -2.6027 -2.3102 -2.0439 1.0049
## (Intercept)-Dasypus_novemcinctus -1.7778 0.1649 -2.1254 -1.7704 -1.4662 1.0078
## (Intercept)-Lynx_rufus -3.5248 0.3488 -4.2421 -3.5168 -2.8765 1.0047
## (Intercept)-Didelphis_virginiana -2.6296 0.2815 -3.2114 -2.6206 -2.1099 1.0235
## (Intercept)-Sylvilagus_floridanus -3.1659 0.2898 -3.7502 -3.1563 -2.6415 1.0060
## (Intercept)-Meleagris_gallopavo -3.7931 0.4847 -4.7746 -3.8079 -2.8082 1.0085
## (Intercept)-Sciurus_carolinensis -2.6714 0.3067 -3.2880 -2.6566 -2.0945 1.0038
## shrub_cover-Canis_latrans -0.2555 0.2226 -0.7031 -0.2548 0.1769 1.0224
## shrub_cover-Procyon_lotor 0.2407 0.1654 -0.0798 0.2448 0.5525 1.0176
## shrub_cover-Dasypus_novemcinctus 0.8646 0.2980 0.3048 0.8619 1.4699 1.0032
## shrub_cover-Lynx_rufus -0.2150 0.3746 -0.9646 -0.2121 0.4939 1.0194
## shrub_cover-Didelphis_virginiana 0.9922 0.3713 0.2927 0.9817 1.7626 1.0467
## shrub_cover-Sylvilagus_floridanus 0.2324 0.3819 -0.4846 0.2288 1.0202 1.0052
## shrub_cover-Meleagris_gallopavo -0.5805 0.4175 -1.3764 -0.5916 0.2931 1.0173
## shrub_cover-Sciurus_carolinensis 0.8760 0.4001 0.1068 0.8704 1.6558 1.0131
## veg_height-Canis_latrans -0.5592 0.1846 -0.9419 -0.5560 -0.2091 1.0053
## veg_height-Procyon_lotor 0.3533 0.1225 0.1066 0.3531 0.5907 1.0054
## veg_height-Dasypus_novemcinctus 0.2556 0.1357 -0.0075 0.2531 0.5256 1.0020
## veg_height-Lynx_rufus 0.1006 0.2454 -0.3964 0.0985 0.5692 1.0113
## veg_height-Didelphis_virginiana 0.4373 0.2550 -0.0435 0.4249 0.9584 1.0113
## veg_height-Sylvilagus_floridanus 0.1933 0.2565 -0.3193 0.1947 0.7096 1.0222
## veg_height-Meleagris_gallopavo -0.0378 0.3965 -0.8064 -0.0432 0.8150 1.0126
## veg_height-Sciurus_carolinensis 0.0951 0.2145 -0.3242 0.0935 0.5184 1.0036
## week-Canis_latrans 0.0618 0.1349 -0.2155 0.0643 0.3227 1.0011
## week-Procyon_lotor -0.0584 0.1185 -0.2983 -0.0542 0.1608 1.0066
## week-Dasypus_novemcinctus -0.1714 0.1383 -0.4499 -0.1667 0.0907 1.0043
## week-Lynx_rufus -0.0517 0.1946 -0.4538 -0.0441 0.3116 1.0083
## week-Didelphis_virginiana -0.2329 0.2133 -0.6789 -0.2166 0.1506 1.0028
## week-Sylvilagus_floridanus -0.1605 0.2025 -0.5975 -0.1534 0.2084 1.0072
## week-Meleagris_gallopavo -0.2810 0.2378 -0.8130 -0.2635 0.1264 1.0196
## week-Sciurus_carolinensis 0.1262 0.1847 -0.2437 0.1267 0.4774 1.0073
## ESS
## (Intercept)-Canis_latrans 853
## (Intercept)-Procyon_lotor 1159
## (Intercept)-Dasypus_novemcinctus 1370
## (Intercept)-Lynx_rufus 383
## (Intercept)-Didelphis_virginiana 723
## (Intercept)-Sylvilagus_floridanus 543
## (Intercept)-Meleagris_gallopavo 194
## (Intercept)-Sciurus_carolinensis 666
## shrub_cover-Canis_latrans 803
## shrub_cover-Procyon_lotor 1259
## shrub_cover-Dasypus_novemcinctus 1307
## shrub_cover-Lynx_rufus 515
## shrub_cover-Didelphis_virginiana 586
## shrub_cover-Sylvilagus_floridanus 595
## shrub_cover-Meleagris_gallopavo 220
## shrub_cover-Sciurus_carolinensis 778
## veg_height-Canis_latrans 707
## veg_height-Procyon_lotor 1591
## veg_height-Dasypus_novemcinctus 1960
## veg_height-Lynx_rufus 789
## veg_height-Didelphis_virginiana 1081
## veg_height-Sylvilagus_floridanus 594
## veg_height-Meleagris_gallopavo 495
## veg_height-Sciurus_carolinensis 969
## week-Canis_latrans 1557
## week-Procyon_lotor 1598
## week-Dasypus_novemcinctus 1957
## week-Lynx_rufus 1023
## week-Didelphis_virginiana 1307
## week-Sylvilagus_floridanus 1068
## week-Meleagris_gallopavo 685
## week-Sciurus_carolinensis 1785
## 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): 0.7093
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7896 0.7366 -3.1287 -1.8219 -0.2836 1.0046 309
## Cogon_Patch_Size 0.1770 0.7322 -1.3224 0.1755 1.6716 1.0271 388
## Veg_shannon_index 1.0326 0.5170 0.0431 1.0151 2.0955 1.0512 245
## total_shrub_cover -1.1698 0.7085 -2.6867 -1.1245 0.1249 1.0232 264
## Avg_Cogongrass_Cover 0.0103 0.9963 -1.9459 0.0478 1.9987 1.0488 185
## Tree_Density -2.2169 0.8872 -3.9975 -2.2194 -0.3588 1.0331 356
## Avg_Canopy_Cover 1.9460 0.8388 0.3137 1.9242 3.6780 1.0202 663
## I(Avg_Cogongrass_Cover^2) 1.3994 0.6938 0.0308 1.3935 2.7785 1.0343 361
## avg_veg_height -0.0898 0.6130 -1.2787 -0.1132 1.1287 1.0024 223
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1179 5.9921 0.0618 0.7243 12.9070 1.1512 92
## Cogon_Patch_Size 3.4343 9.4995 0.0939 1.3872 18.7413 1.2098 140
## Veg_shannon_index 0.7398 1.2160 0.0483 0.3935 3.5118 1.0094 898
## total_shrub_cover 2.6180 7.5756 0.0832 1.3347 12.0786 1.3081 1248
## Avg_Cogongrass_Cover 1.6915 4.2311 0.0553 0.6025 10.0147 1.1229 652
## Tree_Density 4.5936 9.0784 0.0666 1.6206 27.1312 1.1584 126
## Avg_Canopy_Cover 5.6328 10.2905 0.2483 2.9336 30.0623 1.3245 157
## I(Avg_Cogongrass_Cover^2) 2.6663 4.6744 0.0755 1.2199 12.8004 1.0439 145
## avg_veg_height 0.8292 1.3804 0.0483 0.3991 4.4168 1.0249 464
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3298 3.6699 0.0803 1.1693 12.68 1.2664 71
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7834 0.3055 -3.3756 -2.7911 -2.1447 1.0041 1160
## shrub_cover 0.4314 0.3249 -0.1973 0.4334 1.0674 1.0076 806
## veg_height 0.1427 0.2030 -0.2540 0.1432 0.5442 1.0200 823
## week -0.1011 0.1384 -0.3874 -0.0978 0.1591 1.0034 1405
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6526 0.6267 0.1512 0.4857 2.0987 1.0082 1748
## shrub_cover 0.6907 0.6789 0.1355 0.5240 2.2851 1.0657 873
## veg_height 0.2633 0.2407 0.0585 0.1943 0.8649 1.0237 1426
## week 0.1073 0.0912 0.0249 0.0804 0.3410 1.0021 1905
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4289 1.0661 -3.2569 -1.5219
## (Intercept)-Procyon_lotor -1.1327 1.0959 -2.9873 -1.2141
## (Intercept)-Dasypus_novemcinctus -2.1078 0.9825 -4.1972 -2.0483
## (Intercept)-Lynx_rufus -1.5384 1.2206 -3.6457 -1.6725
## (Intercept)-Didelphis_virginiana -2.6534 1.1902 -5.4985 -2.4904
## (Intercept)-Sylvilagus_floridanus -2.0434 1.1186 -4.5725 -1.9724
## (Intercept)-Meleagris_gallopavo -1.7654 1.1633 -3.9461 -1.8003
## (Intercept)-Sciurus_carolinensis -2.8482 1.3626 -6.2705 -2.5982
## Cogon_Patch_Size-Canis_latrans 1.4415 1.3667 -0.3618 1.1762
## Cogon_Patch_Size-Procyon_lotor -0.4645 0.8944 -2.3086 -0.4364
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1372 0.8623 -1.5601 0.1351
## Cogon_Patch_Size-Lynx_rufus -0.1580 1.4737 -3.3569 -0.0945
## Cogon_Patch_Size-Didelphis_virginiana 1.4364 1.1130 -0.2304 1.2850
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0074 1.7452 -5.0093 -0.7215
## Cogon_Patch_Size-Meleagris_gallopavo 0.7087 1.9439 -1.6620 0.3726
## Cogon_Patch_Size-Sciurus_carolinensis -0.6054 1.4179 -3.7851 -0.4268
## Veg_shannon_index-Canis_latrans 1.3688 0.7040 0.1367 1.3000
## Veg_shannon_index-Procyon_lotor 1.2627 0.6705 0.1122 1.1947
## Veg_shannon_index-Dasypus_novemcinctus 0.6536 0.6144 -0.5571 0.6471
## Veg_shannon_index-Lynx_rufus 1.1138 0.9063 -0.5195 1.0391
## Veg_shannon_index-Didelphis_virginiana 1.1942 0.7304 -0.0745 1.1205
## Veg_shannon_index-Sylvilagus_floridanus 1.1056 0.7386 -0.2227 1.0709
## Veg_shannon_index-Meleagris_gallopavo 1.3703 0.8471 0.0209 1.2711
## Veg_shannon_index-Sciurus_carolinensis 0.4708 0.8119 -1.2861 0.5177
## total_shrub_cover-Canis_latrans 0.1921 0.9484 -1.5075 0.1117
## total_shrub_cover-Procyon_lotor -1.5142 0.7639 -3.1314 -1.4605
## total_shrub_cover-Dasypus_novemcinctus -0.5926 0.8971 -2.5434 -0.5383
## total_shrub_cover-Lynx_rufus -1.8489 1.3661 -5.0967 -1.6947
## total_shrub_cover-Didelphis_virginiana -1.4770 1.1373 -4.1837 -1.3581
## total_shrub_cover-Sylvilagus_floridanus -1.1971 1.3229 -4.1405 -1.0654
## total_shrub_cover-Meleagris_gallopavo -2.6816 1.5288 -6.4751 -2.4463
## total_shrub_cover-Sciurus_carolinensis -1.3344 1.3227 -4.2968 -1.1821
## Avg_Cogongrass_Cover-Canis_latrans 0.1147 1.2602 -2.4307 0.1096
## Avg_Cogongrass_Cover-Procyon_lotor -0.1321 1.2753 -2.7628 -0.0907
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6828 1.3454 -1.7429 0.6026
## Avg_Cogongrass_Cover-Lynx_rufus 0.0417 1.4164 -2.6603 0.0351
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2688 1.3490 -2.3545 0.2739
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6844 1.4840 -4.0939 -0.5286
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3527 1.5246 -3.7265 -0.2546
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1058 1.3313 -2.4949 0.0805
## Tree_Density-Canis_latrans -3.3041 1.5825 -7.1921 -3.0250
## Tree_Density-Procyon_lotor -2.3095 1.1064 -4.8121 -2.2200
## Tree_Density-Dasypus_novemcinctus -4.2019 2.2904 -10.3063 -3.6125
## Tree_Density-Lynx_rufus -0.8393 1.7286 -3.4704 -1.0972
## Tree_Density-Didelphis_virginiana -2.3422 1.4466 -5.5976 -2.2390
## Tree_Density-Sylvilagus_floridanus -2.8422 1.6432 -6.9345 -2.5995
## Tree_Density-Meleagris_gallopavo -2.4296 1.5117 -5.7098 -2.3229
## Tree_Density-Sciurus_carolinensis -2.5458 1.6381 -6.4386 -2.3472
## Avg_Canopy_Cover-Canis_latrans 0.0536 0.7394 -1.4292 0.0723
## Avg_Canopy_Cover-Procyon_lotor 1.6729 0.8723 0.1934 1.6123
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.4335 1.0635 0.8161 2.2764
## Avg_Canopy_Cover-Lynx_rufus 1.2443 1.5690 -1.5195 1.0828
## Avg_Canopy_Cover-Didelphis_virginiana 3.4312 1.6811 1.2310 3.1359
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2961 2.1921 1.3665 3.8319
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6843 1.6127 0.4901 2.3535
## Avg_Canopy_Cover-Sciurus_carolinensis 3.4233 1.9958 1.1018 3.0373
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4216 1.2292 0.6991 2.1977
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4530 1.3981 0.6200 2.2103
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3969 0.7865 -0.0114 1.3508
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6473 1.4039 0.6009 2.3888
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8073 0.8114 -0.8105 0.8031
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0848 0.9450 -0.5958 1.0240
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0898 1.6171 -3.6101 0.2758
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5283 0.8644 0.0098 1.4761
## avg_veg_height-Canis_latrans -0.2747 0.7326 -1.7506 -0.2680
## avg_veg_height-Procyon_lotor 0.0545 0.7293 -1.3838 0.0405
## avg_veg_height-Dasypus_novemcinctus 0.2921 0.7450 -1.0416 0.2478
## avg_veg_height-Lynx_rufus -0.4625 1.0415 -2.9729 -0.3933
## avg_veg_height-Didelphis_virginiana -0.3162 0.8378 -2.0947 -0.3011
## avg_veg_height-Sylvilagus_floridanus -0.2488 0.8346 -2.0493 -0.2189
## avg_veg_height-Meleagris_gallopavo -0.1626 1.0242 -2.2858 -0.1543
## avg_veg_height-Sciurus_carolinensis 0.4015 0.8655 -1.0723 0.3509
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0590 1.0194 192
## (Intercept)-Procyon_lotor 1.1293 1.0108 274
## (Intercept)-Dasypus_novemcinctus -0.3577 1.0354 257
## (Intercept)-Lynx_rufus 1.2460 1.0188 334
## (Intercept)-Didelphis_virginiana -0.7694 1.0527 118
## (Intercept)-Sylvilagus_floridanus -0.0284 1.0737 195
## (Intercept)-Meleagris_gallopavo 0.7237 1.0084 271
## (Intercept)-Sciurus_carolinensis -0.7728 1.0647 124
## Cogon_Patch_Size-Canis_latrans 4.6642 1.0337 221
## Cogon_Patch_Size-Procyon_lotor 1.1994 1.1092 296
## Cogon_Patch_Size-Dasypus_novemcinctus 1.9665 1.0119 513
## Cogon_Patch_Size-Lynx_rufus 2.6963 1.0169 203
## Cogon_Patch_Size-Didelphis_virginiana 4.0511 1.0545 298
## Cogon_Patch_Size-Sylvilagus_floridanus 1.5666 1.0723 113
## Cogon_Patch_Size-Meleagris_gallopavo 4.9310 1.0455 143
## Cogon_Patch_Size-Sciurus_carolinensis 1.7692 1.0835 232
## Veg_shannon_index-Canis_latrans 2.9297 1.0351 414
## Veg_shannon_index-Procyon_lotor 2.7183 1.0576 277
## Veg_shannon_index-Dasypus_novemcinctus 1.8616 1.0442 709
## Veg_shannon_index-Lynx_rufus 3.1647 1.0079 229
## Veg_shannon_index-Didelphis_virginiana 2.8427 1.0122 479
## Veg_shannon_index-Sylvilagus_floridanus 2.6528 1.0451 354
## Veg_shannon_index-Meleagris_gallopavo 3.4032 1.0093 449
## Veg_shannon_index-Sciurus_carolinensis 1.9808 1.0605 513
## total_shrub_cover-Canis_latrans 2.3906 1.0473 311
## total_shrub_cover-Procyon_lotor -0.1532 1.0346 432
## total_shrub_cover-Dasypus_novemcinctus 0.9980 1.0120 399
## total_shrub_cover-Lynx_rufus 0.3928 1.0161 236
## total_shrub_cover-Didelphis_virginiana 0.3519 1.0261 361
## total_shrub_cover-Sylvilagus_floridanus 1.0419 1.0264 280
## total_shrub_cover-Meleagris_gallopavo -0.3545 1.0452 215
## total_shrub_cover-Sciurus_carolinensis 0.8233 1.0259 229
## Avg_Cogongrass_Cover-Canis_latrans 2.6372 1.0249 297
## Avg_Cogongrass_Cover-Procyon_lotor 2.4075 1.0909 148
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.6878 1.0103 231
## Avg_Cogongrass_Cover-Lynx_rufus 2.8499 1.0530 302
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0285 1.0015 257
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8622 1.0372 302
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.3782 1.0570 264
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8175 1.0197 236
## Tree_Density-Canis_latrans -0.8954 1.0070 195
## Tree_Density-Procyon_lotor -0.3137 1.0028 359
## Tree_Density-Dasypus_novemcinctus -1.4814 1.0221 159
## Tree_Density-Lynx_rufus 3.3341 1.0778 137
## Tree_Density-Didelphis_virginiana 0.2767 1.0972 271
## Tree_Density-Sylvilagus_floridanus -0.1545 1.0825 192
## Tree_Density-Meleagris_gallopavo 0.4181 1.0103 406
## Tree_Density-Sciurus_carolinensis 0.3207 1.0743 232
## Avg_Canopy_Cover-Canis_latrans 1.4395 1.0116 583
## Avg_Canopy_Cover-Procyon_lotor 3.5782 1.0605 530
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.0013 1.0277 219
## Avg_Canopy_Cover-Lynx_rufus 4.7644 1.0195 231
## Avg_Canopy_Cover-Didelphis_virginiana 7.5740 1.1483 98
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.9106 1.1548 157
## Avg_Canopy_Cover-Meleagris_gallopavo 6.9085 1.0299 184
## Avg_Canopy_Cover-Sciurus_carolinensis 8.0436 1.1550 95
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4507 1.0417 200
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.8842 1.0449 88
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0723 1.0179 396
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1152 1.0194 200
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4641 1.0849 268
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1299 1.0445 337
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7626 1.1584 110
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.4970 1.0174 270
## avg_veg_height-Canis_latrans 1.1489 1.0043 323
## avg_veg_height-Procyon_lotor 1.5188 1.0049 424
## avg_veg_height-Dasypus_novemcinctus 1.8897 1.0056 479
## avg_veg_height-Lynx_rufus 1.2880 1.0001 321
## avg_veg_height-Didelphis_virginiana 1.2573 1.0051 486
## avg_veg_height-Sylvilagus_floridanus 1.3446 1.0116 253
## avg_veg_height-Meleagris_gallopavo 1.8545 1.0120 236
## avg_veg_height-Sciurus_carolinensis 2.2903 1.0004 421
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7353 0.1844 -3.1048 -2.7279 -2.3997 1.0173
## (Intercept)-Procyon_lotor -2.3233 0.1446 -2.6147 -2.3213 -2.0485 1.0003
## (Intercept)-Dasypus_novemcinctus -1.8480 0.1735 -2.2021 -1.8440 -1.5171 1.0070
## (Intercept)-Lynx_rufus -3.6136 0.3254 -4.2677 -3.6096 -2.9853 1.0221
## (Intercept)-Didelphis_virginiana -2.7149 0.2962 -3.3201 -2.7112 -2.1388 1.0053
## (Intercept)-Sylvilagus_floridanus -3.1746 0.2504 -3.6871 -3.1671 -2.7059 1.0357
## (Intercept)-Meleagris_gallopavo -3.5854 0.5050 -4.5612 -3.5859 -2.5383 1.0928
## (Intercept)-Sciurus_carolinensis -2.8889 0.3178 -3.5519 -2.8739 -2.2798 1.0028
## shrub_cover-Canis_latrans -0.2805 0.2385 -0.7445 -0.2839 0.1878 1.0113
## shrub_cover-Procyon_lotor 0.2868 0.1683 -0.0551 0.2899 0.6019 1.0102
## shrub_cover-Dasypus_novemcinctus 1.0367 0.3121 0.4113 1.0455 1.6421 1.0254
## shrub_cover-Lynx_rufus 0.0324 0.3684 -0.6814 0.0311 0.7532 1.0021
## shrub_cover-Didelphis_virginiana 1.1073 0.3753 0.3941 1.1052 1.8840 1.0091
## shrub_cover-Sylvilagus_floridanus 0.5820 0.3930 -0.1990 0.5775 1.3730 1.0128
## shrub_cover-Meleagris_gallopavo -0.3325 0.4611 -1.2486 -0.3410 0.5692 1.0529
## shrub_cover-Sciurus_carolinensis 1.1492 0.4154 0.3380 1.1434 1.9917 1.0174
## veg_height-Canis_latrans -0.5464 0.1949 -0.9387 -0.5449 -0.1777 1.0036
## veg_height-Procyon_lotor 0.3742 0.1240 0.1264 0.3775 0.6144 1.0104
## veg_height-Dasypus_novemcinctus 0.2973 0.1423 0.0226 0.2941 0.5721 1.0056
## veg_height-Lynx_rufus 0.1856 0.2369 -0.2894 0.1873 0.6271 1.0165
## veg_height-Didelphis_virginiana 0.5279 0.2527 0.0489 0.5234 1.0365 1.0073
## veg_height-Sylvilagus_floridanus 0.1763 0.2482 -0.3172 0.1732 0.6634 1.0058
## veg_height-Meleagris_gallopavo -0.0633 0.4388 -0.9034 -0.0718 0.8608 1.1863
## veg_height-Sciurus_carolinensis 0.1867 0.2215 -0.2555 0.1820 0.6189 1.0199
## week-Canis_latrans 0.0551 0.1349 -0.2164 0.0566 0.3101 1.0036
## week-Procyon_lotor -0.0543 0.1160 -0.2858 -0.0511 0.1636 1.0034
## week-Dasypus_novemcinctus -0.1743 0.1372 -0.4510 -0.1687 0.0816 1.0025
## week-Lynx_rufus -0.0574 0.1914 -0.4387 -0.0527 0.3017 1.0074
## week-Didelphis_virginiana -0.2434 0.2147 -0.7025 -0.2318 0.1322 1.0025
## week-Sylvilagus_floridanus -0.1725 0.2068 -0.6131 -0.1645 0.2030 1.0015
## week-Meleagris_gallopavo -0.2924 0.2295 -0.7668 -0.2742 0.1344 1.0072
## week-Sciurus_carolinensis 0.1206 0.1854 -0.2519 0.1180 0.4906 1.0071
## ESS
## (Intercept)-Canis_latrans 659
## (Intercept)-Procyon_lotor 860
## (Intercept)-Dasypus_novemcinctus 888
## (Intercept)-Lynx_rufus 343
## (Intercept)-Didelphis_virginiana 429
## (Intercept)-Sylvilagus_floridanus 686
## (Intercept)-Meleagris_gallopavo 219
## (Intercept)-Sciurus_carolinensis 456
## shrub_cover-Canis_latrans 384
## shrub_cover-Procyon_lotor 1241
## shrub_cover-Dasypus_novemcinctus 472
## shrub_cover-Lynx_rufus 310
## shrub_cover-Didelphis_virginiana 430
## shrub_cover-Sylvilagus_floridanus 374
## shrub_cover-Meleagris_gallopavo 267
## shrub_cover-Sciurus_carolinensis 375
## veg_height-Canis_latrans 529
## veg_height-Procyon_lotor 1294
## veg_height-Dasypus_novemcinctus 1633
## veg_height-Lynx_rufus 489
## veg_height-Didelphis_virginiana 710
## veg_height-Sylvilagus_floridanus 602
## veg_height-Meleagris_gallopavo 224
## veg_height-Sciurus_carolinensis 877
## week-Canis_latrans 1571
## week-Procyon_lotor 1747
## week-Dasypus_novemcinctus 1915
## week-Lynx_rufus 930
## week-Didelphis_virginiana 1288
## week-Sylvilagus_floridanus 977
## week-Meleagris_gallopavo 839
## week-Sciurus_carolinensis 1481
# 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): 0.4812
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9670 0.6301 -2.2281 -0.9709 0.3016 1.0193 386
## Cogon_Patch_Size -0.5033 0.5990 -1.6478 -0.4994 0.6524 1.0248 684
## Veg_shannon_index 0.9191 0.4045 0.1581 0.8969 1.7486 1.0563 458
## total_shrub_cover -0.5704 0.5288 -1.6631 -0.5563 0.4555 1.0622 899
## Avg_Cogongrass_Cover 1.9745 0.6456 0.7208 1.9614 3.3161 1.0672 260
## Tree_Density -1.8366 0.6608 -3.2547 -1.8162 -0.6129 1.0230 390
## Avg_Canopy_Cover 1.6387 0.5726 0.5368 1.6233 2.8275 1.0135 835
## avg_veg_height -0.4872 0.4430 -1.3816 -0.4865 0.3625 1.0305 436
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4252 3.0000 0.0938 1.6018 10.0368 1.0813 439
## Cogon_Patch_Size 2.2272 3.5736 0.0991 1.2118 10.8516 1.0872 402
## Veg_shannon_index 0.5118 0.7832 0.0471 0.2702 2.5125 1.0109 570
## total_shrub_cover 1.6924 2.9323 0.0843 0.9593 7.9258 1.2691 508
## Avg_Cogongrass_Cover 0.9089 1.5420 0.0518 0.4403 4.5235 1.0166 732
## Tree_Density 1.9311 3.7389 0.0654 0.8387 10.3598 1.0605 429
## Avg_Canopy_Cover 1.9674 3.0856 0.1175 1.1060 8.8168 1.1056 675
## avg_veg_height 0.4335 0.5819 0.0405 0.2361 2.0664 1.0202 1090
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9239 1.8203 0.0922 1.4444 6.7068 1.1258 178
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5803 0.2893 -3.1651 -2.581 -1.9904 1.0032 1836
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6339 0.4805 0.1697 0.5071 1.8122 1.0084 1725
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0058 0.9376 -1.7744 -0.0442
## (Intercept)-Procyon_lotor 0.0932 0.9386 -1.8102 0.1021
## (Intercept)-Dasypus_novemcinctus -1.3546 0.8042 -3.0945 -1.3094
## (Intercept)-Lynx_rufus -0.2045 1.2129 -2.1643 -0.3465
## (Intercept)-Didelphis_virginiana -2.2299 1.0255 -4.5337 -2.1361
## (Intercept)-Sylvilagus_floridanus -1.2579 0.9161 -3.1363 -1.2258
## (Intercept)-Meleagris_gallopavo -1.2565 1.0164 -3.3315 -1.2467
## (Intercept)-Sciurus_carolinensis -2.3223 1.0342 -4.7473 -2.2364
## Cogon_Patch_Size-Canis_latrans 0.5198 0.9348 -0.9243 0.3773
## Cogon_Patch_Size-Procyon_lotor -0.9155 0.6700 -2.2431 -0.8878
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6989 0.5943 -1.9352 -0.6762
## Cogon_Patch_Size-Lynx_rufus -0.6179 1.2176 -2.7577 -0.6880
## Cogon_Patch_Size-Didelphis_virginiana 0.8177 0.9858 -0.6250 0.6960
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6892 1.3958 -5.2026 -1.4164
## Cogon_Patch_Size-Meleagris_gallopavo -0.4039 0.9486 -2.1146 -0.4679
## Cogon_Patch_Size-Sciurus_carolinensis -1.4104 1.0557 -4.0161 -1.2445
## Veg_shannon_index-Canis_latrans 1.1742 0.5514 0.2197 1.1381
## Veg_shannon_index-Procyon_lotor 1.1808 0.5474 0.2406 1.1435
## Veg_shannon_index-Dasypus_novemcinctus 0.6966 0.4655 -0.2056 0.6983
## Veg_shannon_index-Lynx_rufus 0.8257 0.6618 -0.4547 0.8346
## Veg_shannon_index-Didelphis_virginiana 1.0012 0.5699 -0.0280 0.9622
## Veg_shannon_index-Sylvilagus_floridanus 1.0276 0.6295 -0.0916 0.9781
## Veg_shannon_index-Meleagris_gallopavo 1.1772 0.6824 0.0376 1.1147
## Veg_shannon_index-Sciurus_carolinensis 0.4135 0.6022 -0.9494 0.4737
## total_shrub_cover-Canis_latrans 0.1961 0.6846 -0.9451 0.1231
## total_shrub_cover-Procyon_lotor -0.9932 0.5973 -2.2772 -0.9509
## total_shrub_cover-Dasypus_novemcinctus 0.0796 0.5535 -0.9489 0.0592
## total_shrub_cover-Lynx_rufus -1.2450 1.1276 -4.0773 -1.0303
## total_shrub_cover-Didelphis_virginiana -0.6023 0.6878 -2.1048 -0.5675
## total_shrub_cover-Sylvilagus_floridanus -0.2467 0.7829 -1.7498 -0.2482
## total_shrub_cover-Meleagris_gallopavo -2.0857 1.2880 -5.1801 -1.8820
## total_shrub_cover-Sciurus_carolinensis -0.0706 0.6846 -1.3616 -0.0872
## Avg_Cogongrass_Cover-Canis_latrans 2.1970 0.8125 0.7160 2.1374
## Avg_Cogongrass_Cover-Procyon_lotor 2.1181 0.8133 0.5958 2.0978
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4287 0.8677 0.9185 2.3471
## Avg_Cogongrass_Cover-Lynx_rufus 2.3521 0.9195 0.7321 2.2504
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0577 0.7835 0.5857 2.0273
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4518 0.9053 -0.3821 1.4834
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4853 1.0900 -0.9211 1.5915
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2659 0.8073 0.8289 2.2174
## Tree_Density-Canis_latrans -2.3106 1.0286 -4.7708 -2.1784
## Tree_Density-Procyon_lotor -1.5093 0.7022 -2.8127 -1.5255
## Tree_Density-Dasypus_novemcinctus -2.9249 1.2980 -6.0665 -2.6811
## Tree_Density-Lynx_rufus -0.6926 1.0802 -2.4396 -0.8008
## Tree_Density-Didelphis_virginiana -2.0901 0.9492 -4.3877 -2.0089
## Tree_Density-Sylvilagus_floridanus -2.2734 1.1494 -5.1290 -2.1258
## Tree_Density-Meleagris_gallopavo -1.9865 1.1394 -4.3516 -1.9395
## Tree_Density-Sciurus_carolinensis -2.1054 0.9890 -4.4009 -1.9861
## Avg_Canopy_Cover-Canis_latrans 0.3982 0.6427 -0.8943 0.4100
## Avg_Canopy_Cover-Procyon_lotor 1.6527 0.6464 0.4509 1.6079
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8808 0.6285 0.7877 1.8193
## Avg_Canopy_Cover-Lynx_rufus 0.8867 0.9890 -0.9995 0.8758
## Avg_Canopy_Cover-Didelphis_virginiana 2.4025 0.8231 1.0775 2.3035
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8952 1.3097 1.0046 2.6333
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1625 1.1172 0.5496 1.9532
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0481 0.7421 0.8042 1.9686
## avg_veg_height-Canis_latrans -0.6912 0.5531 -1.8644 -0.6528
## avg_veg_height-Procyon_lotor -0.3831 0.5296 -1.4143 -0.3973
## avg_veg_height-Dasypus_novemcinctus -0.2716 0.5507 -1.3295 -0.2877
## avg_veg_height-Lynx_rufus -0.6013 0.6978 -2.0156 -0.5825
## avg_veg_height-Didelphis_virginiana -0.5913 0.6013 -1.7957 -0.5784
## avg_veg_height-Sylvilagus_floridanus -0.6892 0.6309 -2.0349 -0.6644
## avg_veg_height-Meleagris_gallopavo -0.6191 0.7012 -2.1235 -0.5813
## avg_veg_height-Sciurus_carolinensis -0.1717 0.6037 -1.2550 -0.2211
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0023 1.0405 369
## (Intercept)-Procyon_lotor 1.9072 1.0342 291
## (Intercept)-Dasypus_novemcinctus 0.0618 1.0142 517
## (Intercept)-Lynx_rufus 2.6700 1.1012 173
## (Intercept)-Didelphis_virginiana -0.5130 1.0379 402
## (Intercept)-Sylvilagus_floridanus 0.4894 1.0410 577
## (Intercept)-Meleagris_gallopavo 0.7709 1.0414 388
## (Intercept)-Sciurus_carolinensis -0.5314 1.0762 418
## Cogon_Patch_Size-Canis_latrans 2.7942 1.0297 752
## Cogon_Patch_Size-Procyon_lotor 0.3940 1.0602 428
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4043 1.0194 687
## Cogon_Patch_Size-Lynx_rufus 1.9229 1.0600 323
## Cogon_Patch_Size-Didelphis_virginiana 2.7974 1.0468 399
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1859 1.0862 285
## Cogon_Patch_Size-Meleagris_gallopavo 1.6281 1.0067 596
## Cogon_Patch_Size-Sciurus_carolinensis 0.1380 1.0574 441
## Veg_shannon_index-Canis_latrans 2.4075 1.0287 454
## Veg_shannon_index-Procyon_lotor 2.3610 1.0630 418
## Veg_shannon_index-Dasypus_novemcinctus 1.6344 1.0183 878
## Veg_shannon_index-Lynx_rufus 2.0490 1.0317 715
## Veg_shannon_index-Didelphis_virginiana 2.2162 1.0183 867
## Veg_shannon_index-Sylvilagus_floridanus 2.4507 1.0349 566
## Veg_shannon_index-Meleagris_gallopavo 2.7676 1.0434 531
## Veg_shannon_index-Sciurus_carolinensis 1.4531 1.0437 634
## total_shrub_cover-Canis_latrans 1.8146 1.0222 745
## total_shrub_cover-Procyon_lotor 0.0445 1.0107 898
## total_shrub_cover-Dasypus_novemcinctus 1.2331 1.0031 1514
## total_shrub_cover-Lynx_rufus 0.3964 1.1666 247
## total_shrub_cover-Didelphis_virginiana 0.6463 1.0589 840
## total_shrub_cover-Sylvilagus_floridanus 1.3137 1.0047 855
## total_shrub_cover-Meleagris_gallopavo -0.2642 1.1439 280
## total_shrub_cover-Sciurus_carolinensis 1.3511 1.0022 1381
## Avg_Cogongrass_Cover-Canis_latrans 3.9634 1.0216 342
## Avg_Cogongrass_Cover-Procyon_lotor 3.7987 1.0776 317
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.3039 1.0641 337
## Avg_Cogongrass_Cover-Lynx_rufus 4.4825 1.0974 342
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.6834 1.0450 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2302 1.0272 373
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3759 1.0144 378
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.9992 1.0820 354
## Tree_Density-Canis_latrans -0.6618 1.0166 347
## Tree_Density-Procyon_lotor -0.0925 1.0475 640
## Tree_Density-Dasypus_novemcinctus -1.1390 1.0299 319
## Tree_Density-Lynx_rufus 1.7493 1.0458 369
## Tree_Density-Didelphis_virginiana -0.5064 1.0162 516
## Tree_Density-Sylvilagus_floridanus -0.4574 1.0189 523
## Tree_Density-Meleagris_gallopavo 0.1914 1.0336 444
## Tree_Density-Sciurus_carolinensis -0.4740 1.0080 413
## Avg_Canopy_Cover-Canis_latrans 1.6353 1.0009 715
## Avg_Canopy_Cover-Procyon_lotor 3.0043 1.0450 888
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2621 1.0170 431
## Avg_Canopy_Cover-Lynx_rufus 2.8885 1.0057 481
## Avg_Canopy_Cover-Didelphis_virginiana 4.3423 1.0714 271
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.0878 1.0160 374
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9798 1.0358 322
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7338 1.0270 547
## avg_veg_height-Canis_latrans 0.3303 1.0270 816
## avg_veg_height-Procyon_lotor 0.6991 1.0479 668
## avg_veg_height-Dasypus_novemcinctus 0.8606 1.0250 688
## avg_veg_height-Lynx_rufus 0.7417 1.0012 556
## avg_veg_height-Didelphis_virginiana 0.6128 1.0306 727
## avg_veg_height-Sylvilagus_floridanus 0.4562 1.0060 801
## avg_veg_height-Meleagris_gallopavo 0.6929 1.0168 616
## avg_veg_height-Sciurus_carolinensis 1.1526 1.0167 773
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6201 0.1722 -2.9757 -2.6158 -2.2887 1.0007
## (Intercept)-Procyon_lotor -2.2661 0.1277 -2.5238 -2.2666 -2.0207 1.0025
## (Intercept)-Dasypus_novemcinctus -1.5940 0.1345 -1.8626 -1.5892 -1.3393 1.0015
## (Intercept)-Lynx_rufus -3.5059 0.2927 -4.0963 -3.4982 -2.9492 1.0016
## (Intercept)-Didelphis_virginiana -2.3190 0.2443 -2.8175 -2.3071 -1.8750 1.0094
## (Intercept)-Sylvilagus_floridanus -3.1330 0.2722 -3.6900 -3.1293 -2.6209 1.0063
## (Intercept)-Meleagris_gallopavo -3.3220 0.2943 -3.9318 -3.3049 -2.7804 1.0060
## (Intercept)-Sciurus_carolinensis -2.4631 0.2656 -3.0272 -2.4451 -1.9818 1.0022
## ESS
## (Intercept)-Canis_latrans 895
## (Intercept)-Procyon_lotor 1468
## (Intercept)-Dasypus_novemcinctus 2506
## (Intercept)-Lynx_rufus 277
## (Intercept)-Didelphis_virginiana 1250
## (Intercept)-Sylvilagus_floridanus 482
## (Intercept)-Meleagris_gallopavo 431
## (Intercept)-Sciurus_carolinensis 1114
# 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): 0.4642
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5016 0.3970 -1.3014 -0.4997 0.2837 1.0245 687
## Avg_Cogongrass_Cover 0.1487 0.3380 -0.4976 0.1463 0.8117 1.0065 749
## total_shrub_cover -0.4730 0.3468 -1.1870 -0.4595 0.1774 1.0041 1245
## avg_veg_height -0.0096 0.3161 -0.6295 -0.0028 0.6214 1.0118 606
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7287 0.9286 0.0630 0.4767 2.8949 1.0050 856
## Avg_Cogongrass_Cover 0.4090 0.5754 0.0421 0.2458 1.7385 1.0273 1106
## total_shrub_cover 0.7009 1.0060 0.0622 0.4319 2.8086 1.0258 998
## avg_veg_height 0.2727 0.3660 0.0364 0.1679 1.0901 1.0210 1468
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7516 0.7993 0.0615 0.4963 2.8896 1.0323 165
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5569 0.2925 -3.0973 -2.568 -1.9628 1.0042 2816
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6117 0.5347 0.1639 0.4661 1.8959 1.0081 2204
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0087 0.5550 -1.0702 -0.0133
## (Intercept)-Procyon_lotor 0.2205 0.6139 -0.9591 0.2042
## (Intercept)-Dasypus_novemcinctus -0.6708 0.4723 -1.6359 -0.6503
## (Intercept)-Lynx_rufus -0.3633 0.6236 -1.5704 -0.3844
## (Intercept)-Didelphis_virginiana -1.0675 0.5529 -2.1832 -1.0459
## (Intercept)-Sylvilagus_floridanus -0.4385 0.5913 -1.6213 -0.4614
## (Intercept)-Meleagris_gallopavo -0.7068 0.5663 -1.8810 -0.6892
## (Intercept)-Sciurus_carolinensis -1.1404 0.5793 -2.3462 -1.0906
## Avg_Cogongrass_Cover-Canis_latrans 0.4163 0.4625 -0.4162 0.3958
## Avg_Cogongrass_Cover-Procyon_lotor 0.0754 0.4393 -0.8226 0.0727
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2643 0.3988 -0.5291 0.2563
## Avg_Cogongrass_Cover-Lynx_rufus 0.4679 0.4906 -0.4130 0.4325
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3338 0.4399 -0.4790 0.3209
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2515 0.5252 -1.4003 -0.2150
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3598 0.5965 -1.6806 -0.2904
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2296 0.4294 -0.6003 0.2300
## total_shrub_cover-Canis_latrans 0.1090 0.4517 -0.6783 0.0815
## total_shrub_cover-Procyon_lotor -0.8977 0.4827 -2.0313 -0.8494
## total_shrub_cover-Dasypus_novemcinctus -0.0809 0.3844 -0.8073 -0.0963
## total_shrub_cover-Lynx_rufus -0.9381 0.6345 -2.4412 -0.8553
## total_shrub_cover-Didelphis_virginiana -0.2906 0.4335 -1.1469 -0.2959
## total_shrub_cover-Sylvilagus_floridanus -0.4458 0.5453 -1.5922 -0.4120
## total_shrub_cover-Meleagris_gallopavo -1.3095 0.7133 -3.0363 -1.2013
## total_shrub_cover-Sciurus_carolinensis -0.1285 0.4261 -0.9562 -0.1258
## avg_veg_height-Canis_latrans -0.0819 0.4083 -0.9381 -0.0674
## avg_veg_height-Procyon_lotor 0.0720 0.4174 -0.7206 0.0729
## avg_veg_height-Dasypus_novemcinctus 0.1829 0.3987 -0.5583 0.1662
## avg_veg_height-Lynx_rufus -0.0401 0.4899 -0.9821 -0.0348
## avg_veg_height-Didelphis_virginiana -0.0648 0.4228 -0.9368 -0.0610
## avg_veg_height-Sylvilagus_floridanus -0.1566 0.4495 -1.0890 -0.1412
## avg_veg_height-Meleagris_gallopavo -0.2773 0.5002 -1.3616 -0.2476
## avg_veg_height-Sciurus_carolinensis 0.2612 0.4324 -0.5653 0.2437
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0343 1.0347 506
## (Intercept)-Procyon_lotor 1.4816 1.0171 544
## (Intercept)-Dasypus_novemcinctus 0.2268 1.0085 1390
## (Intercept)-Lynx_rufus 0.9382 1.0415 621
## (Intercept)-Didelphis_virginiana -0.0463 1.0130 1058
## (Intercept)-Sylvilagus_floridanus 0.8447 1.0028 736
## (Intercept)-Meleagris_gallopavo 0.3897 1.0038 934
## (Intercept)-Sciurus_carolinensis -0.1416 1.0035 786
## Avg_Cogongrass_Cover-Canis_latrans 1.4141 1.0084 1289
## Avg_Cogongrass_Cover-Procyon_lotor 0.9030 1.0031 1391
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0644 1.0051 1321
## Avg_Cogongrass_Cover-Lynx_rufus 1.5614 1.0143 1098
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2453 1.0094 1157
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6914 1.0052 1101
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6317 1.0049 767
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0770 1.0095 1345
## total_shrub_cover-Canis_latrans 1.1184 1.0033 1527
## total_shrub_cover-Procyon_lotor -0.0850 1.0054 1093
## total_shrub_cover-Dasypus_novemcinctus 0.6985 1.0007 2247
## total_shrub_cover-Lynx_rufus 0.0843 1.0034 700
## total_shrub_cover-Didelphis_virginiana 0.5584 1.0030 2104
## total_shrub_cover-Sylvilagus_floridanus 0.5369 1.0038 818
## total_shrub_cover-Meleagris_gallopavo -0.2355 1.0189 654
## total_shrub_cover-Sciurus_carolinensis 0.7160 1.0026 1753
## avg_veg_height-Canis_latrans 0.7060 1.0037 1052
## avg_veg_height-Procyon_lotor 0.9279 1.0037 1248
## avg_veg_height-Dasypus_novemcinctus 0.9947 1.0034 1203
## avg_veg_height-Lynx_rufus 0.9367 1.0021 917
## avg_veg_height-Didelphis_virginiana 0.7419 1.0052 1070
## avg_veg_height-Sylvilagus_floridanus 0.7057 1.0084 928
## avg_veg_height-Meleagris_gallopavo 0.6036 1.0117 803
## avg_veg_height-Sciurus_carolinensis 1.1339 1.0024 1121
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6102 0.1701 -2.9461 -2.6105 -2.2858 1.0207
## (Intercept)-Procyon_lotor -2.2702 0.1292 -2.5262 -2.2685 -2.0215 1.0005
## (Intercept)-Dasypus_novemcinctus -1.6073 0.1335 -1.8662 -1.6042 -1.3525 1.0033
## (Intercept)-Lynx_rufus -3.3958 0.2770 -3.9630 -3.3838 -2.8931 1.0264
## (Intercept)-Didelphis_virginiana -2.3434 0.2554 -2.8819 -2.3313 -1.8824 1.0066
## (Intercept)-Sylvilagus_floridanus -3.1416 0.2794 -3.7222 -3.1324 -2.6228 1.0022
## (Intercept)-Meleagris_gallopavo -3.2226 0.2924 -3.8191 -3.2076 -2.6849 1.0048
## (Intercept)-Sciurus_carolinensis -2.4666 0.2592 -2.9993 -2.4538 -1.9876 1.0066
## ESS
## (Intercept)-Canis_latrans 936
## (Intercept)-Procyon_lotor 1510
## (Intercept)-Dasypus_novemcinctus 2522
## (Intercept)-Lynx_rufus 517
## (Intercept)-Didelphis_virginiana 1165
## (Intercept)-Sylvilagus_floridanus 431
## (Intercept)-Meleagris_gallopavo 619
## (Intercept)-Sciurus_carolinensis 1229
# 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): 0.4633
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6524 0.4337 -1.5214 -0.6540 0.2117 1.0127 1019
## Tree_Density -0.7613 0.4603 -1.7614 -0.7281 0.0475 1.0017 841
## Avg_Canopy_Cover 0.9876 0.3966 0.2697 0.9615 1.8605 1.0158 984
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0705 1.1464 0.0903 0.7414 4.2370 1.0041 873
## Tree_Density 0.9676 1.5966 0.0539 0.4923 4.8088 1.0027 528
## Avg_Canopy_Cover 0.8242 1.2067 0.0665 0.5000 3.5468 1.0403 596
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6044 0.641 0.0506 0.3831 2.4032 1.0716 225
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5793 0.2931 -3.1334 -2.5882 -1.9642 1.001 2768
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6104 0.4764 0.1586 0.4764 2.0453 1.0068 2222
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0322 0.5943 -1.1469 -0.0469 1.1762
## (Intercept)-Procyon_lotor 0.2378 0.6252 -0.9550 0.2454 1.4821
## (Intercept)-Dasypus_novemcinctus -0.9768 0.5533 -2.1944 -0.9445 0.0006
## (Intercept)-Lynx_rufus -0.2414 0.7342 -1.5127 -0.3039 1.3778
## (Intercept)-Didelphis_virginiana -1.5141 0.6496 -2.8925 -1.4698 -0.3559
## (Intercept)-Sylvilagus_floridanus -0.7182 0.5935 -1.8200 -0.7406 0.5187
## (Intercept)-Meleagris_gallopavo -0.6498 0.6202 -1.8561 -0.6633 0.6193
## (Intercept)-Sciurus_carolinensis -1.5367 0.6406 -2.9008 -1.4889 -0.4148
## Tree_Density-Canis_latrans -0.9153 0.5916 -2.2367 -0.8441 0.0315
## Tree_Density-Procyon_lotor -0.4550 0.4272 -1.3155 -0.4436 0.3776
## Tree_Density-Dasypus_novemcinctus -1.4127 0.9097 -3.6337 -1.2410 -0.1606
## Tree_Density-Lynx_rufus 0.2476 0.7254 -0.8339 0.1361 2.0383
## Tree_Density-Didelphis_virginiana -0.9887 0.7443 -2.8588 -0.8692 0.1542
## Tree_Density-Sylvilagus_floridanus -1.0858 0.7843 -2.9388 -0.9504 0.0673
## Tree_Density-Meleagris_gallopavo -0.8893 0.7405 -2.5875 -0.7966 0.3442
## Tree_Density-Sciurus_carolinensis -0.9326 0.7472 -2.7480 -0.8096 0.1884
## Avg_Canopy_Cover-Canis_latrans 0.0526 0.4681 -0.8752 0.0580 0.9689
## Avg_Canopy_Cover-Procyon_lotor 0.9768 0.4602 0.1478 0.9528 1.9910
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0141 0.4400 0.2070 0.9966 1.9516
## Avg_Canopy_Cover-Lynx_rufus 0.6135 0.6386 -0.5446 0.5767 1.9512
## Avg_Canopy_Cover-Didelphis_virginiana 1.2245 0.5223 0.3296 1.1839 2.4194
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7553 0.8603 0.5761 1.5940 3.9211
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4104 0.7009 0.2848 1.3184 3.0838
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1971 0.4965 0.3756 1.1533 2.3130
## Rhat ESS
## (Intercept)-Canis_latrans 1.0223 697
## (Intercept)-Procyon_lotor 1.0208 481
## (Intercept)-Dasypus_novemcinctus 1.0026 1174
## (Intercept)-Lynx_rufus 1.0115 391
## (Intercept)-Didelphis_virginiana 1.0068 845
## (Intercept)-Sylvilagus_floridanus 1.0015 1138
## (Intercept)-Meleagris_gallopavo 1.0083 872
## (Intercept)-Sciurus_carolinensis 1.0107 914
## Tree_Density-Canis_latrans 1.0041 1211
## Tree_Density-Procyon_lotor 1.0073 1985
## Tree_Density-Dasypus_novemcinctus 1.0095 511
## Tree_Density-Lynx_rufus 1.0184 537
## Tree_Density-Didelphis_virginiana 1.0051 787
## Tree_Density-Sylvilagus_floridanus 1.0038 614
## Tree_Density-Meleagris_gallopavo 1.0031 817
## Tree_Density-Sciurus_carolinensis 1.0003 816
## Avg_Canopy_Cover-Canis_latrans 1.0039 1188
## Avg_Canopy_Cover-Procyon_lotor 1.0106 1437
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0030 1520
## Avg_Canopy_Cover-Lynx_rufus 1.0397 668
## Avg_Canopy_Cover-Didelphis_virginiana 1.0071 1269
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0094 592
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0063 681
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0013 1393
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6213 0.1696 -2.9778 -2.6128 -2.3161 1.0111
## (Intercept)-Procyon_lotor -2.2653 0.1244 -2.5212 -2.2611 -2.0281 1.0026
## (Intercept)-Dasypus_novemcinctus -1.6044 0.1366 -1.8752 -1.6057 -1.3291 1.0061
## (Intercept)-Lynx_rufus -3.4291 0.2895 -3.9778 -3.4225 -2.8592 1.0116
## (Intercept)-Didelphis_virginiana -2.3399 0.2447 -2.8277 -2.3271 -1.8841 1.0031
## (Intercept)-Sylvilagus_floridanus -3.0764 0.2673 -3.6097 -3.0654 -2.5827 1.0072
## (Intercept)-Meleagris_gallopavo -3.2992 0.2922 -3.8838 -3.2920 -2.7702 1.0098
## (Intercept)-Sciurus_carolinensis -2.4715 0.2583 -2.9993 -2.4610 -1.9964 1.0039
## ESS
## (Intercept)-Canis_latrans 997
## (Intercept)-Procyon_lotor 1558
## (Intercept)-Dasypus_novemcinctus 1972
## (Intercept)-Lynx_rufus 332
## (Intercept)-Didelphis_virginiana 1361
## (Intercept)-Sylvilagus_floridanus 626
## (Intercept)-Meleagris_gallopavo 576
## (Intercept)-Sciurus_carolinensis 1075
# 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): 0.4582
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5309 0.4077 -1.3519 -0.5328 0.2861 1.0643 645
## Cogon_Patch_Size -0.0242 0.3911 -0.8497 -0.0126 0.7455 1.0134 1214
## Avg_Cogongrass_Cover 0.1513 0.3067 -0.4336 0.1544 0.7533 1.0035 885
## total_shrub_cover -0.4877 0.3480 -1.2105 -0.4758 0.1670 1.0021 1200
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8146 0.9781 0.0577 0.5169 3.3830 1.0214 610
## Cogon_Patch_Size 0.8162 1.1907 0.0632 0.4494 3.9531 1.0166 834
## Avg_Cogongrass_Cover 0.3920 0.5017 0.0394 0.2414 1.6537 1.0066 1289
## total_shrub_cover 0.6408 0.8142 0.0544 0.3898 2.6634 1.0332 620
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8863 0.8914 0.0608 0.6186 3.1676 1.0151 218
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5617 0.2888 -3.104 -2.57 -1.9653 1.0022 2582
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6044 0.5266 0.1535 0.4602 1.9484 1.0239 1490
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0257 0.5836 -1.0571 -0.0032
## (Intercept)-Procyon_lotor 0.2367 0.6488 -0.9870 0.2356
## (Intercept)-Dasypus_novemcinctus -0.6867 0.5032 -1.7659 -0.6608
## (Intercept)-Lynx_rufus -0.3656 0.6609 -1.5933 -0.3980
## (Intercept)-Didelphis_virginiana -1.1158 0.5912 -2.3632 -1.0638
## (Intercept)-Sylvilagus_floridanus -0.5308 0.5973 -1.6406 -0.5527
## (Intercept)-Meleagris_gallopavo -0.7264 0.6014 -1.9581 -0.7173
## (Intercept)-Sciurus_carolinensis -1.1971 0.6234 -2.5736 -1.1445
## Cogon_Patch_Size-Canis_latrans 0.6916 0.6322 -0.2590 0.5842
## Cogon_Patch_Size-Procyon_lotor -0.1509 0.4547 -1.0414 -0.1466
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0720 0.3998 -0.9088 -0.0673
## Cogon_Patch_Size-Lynx_rufus -0.1028 0.7098 -1.3930 -0.1444
## Cogon_Patch_Size-Didelphis_virginiana 0.6506 0.5270 -0.2127 0.5975
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7264 0.8107 -2.6972 -0.5843
## Cogon_Patch_Size-Meleagris_gallopavo 0.0058 0.5936 -1.1005 -0.0074
## Cogon_Patch_Size-Sciurus_carolinensis -0.5419 0.6198 -2.0906 -0.4556
## Avg_Cogongrass_Cover-Canis_latrans 0.2353 0.3989 -0.5187 0.2217
## Avg_Cogongrass_Cover-Procyon_lotor 0.1604 0.4159 -0.6447 0.1544
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3541 0.3753 -0.3583 0.3403
## Avg_Cogongrass_Cover-Lynx_rufus 0.5054 0.4876 -0.3401 0.4714
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1666 0.4079 -0.6524 0.1739
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1799 0.4849 -1.1999 -0.1482
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4037 0.5934 -1.7097 -0.3581
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4102 0.4022 -0.3216 0.3903
## total_shrub_cover-Canis_latrans 0.0272 0.4593 -0.7895 -0.0033
## total_shrub_cover-Procyon_lotor -0.8898 0.4958 -2.0276 -0.8383
## total_shrub_cover-Dasypus_novemcinctus -0.1161 0.3834 -0.8309 -0.1237
## total_shrub_cover-Lynx_rufus -0.8753 0.6666 -2.4330 -0.7781
## total_shrub_cover-Didelphis_virginiana -0.3876 0.4400 -1.2696 -0.3684
## total_shrub_cover-Sylvilagus_floridanus -0.4029 0.5604 -1.5838 -0.3728
## total_shrub_cover-Meleagris_gallopavo -1.2254 0.6493 -2.7284 -1.1405
## total_shrub_cover-Sciurus_carolinensis -0.1516 0.4379 -0.9936 -0.1676
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2483 1.0163 500
## (Intercept)-Procyon_lotor 1.5231 1.0138 408
## (Intercept)-Dasypus_novemcinctus 0.2348 1.0478 998
## (Intercept)-Lynx_rufus 1.0241 1.0234 509
## (Intercept)-Didelphis_virginiana -0.0502 1.0098 897
## (Intercept)-Sylvilagus_floridanus 0.6421 1.0307 661
## (Intercept)-Meleagris_gallopavo 0.4914 1.0397 789
## (Intercept)-Sciurus_carolinensis -0.0918 1.0169 756
## Cogon_Patch_Size-Canis_latrans 2.1835 1.0121 842
## Cogon_Patch_Size-Procyon_lotor 0.7461 1.0038 1526
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7190 1.0020 2582
## Cogon_Patch_Size-Lynx_rufus 1.5214 1.0237 783
## Cogon_Patch_Size-Didelphis_virginiana 1.7344 1.0023 939
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4628 1.0141 617
## Cogon_Patch_Size-Meleagris_gallopavo 1.1966 1.0264 1224
## Cogon_Patch_Size-Sciurus_carolinensis 0.4067 1.0131 1001
## Avg_Cogongrass_Cover-Canis_latrans 1.1038 1.0030 1428
## Avg_Cogongrass_Cover-Procyon_lotor 1.0167 1.0015 1388
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1392 1.0096 1803
## Avg_Cogongrass_Cover-Lynx_rufus 1.5909 1.0011 1231
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9817 1.0055 1243
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7030 1.0016 989
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6286 1.0048 784
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2530 1.0024 1189
## total_shrub_cover-Canis_latrans 0.9993 1.0012 1389
## total_shrub_cover-Procyon_lotor -0.0576 1.0036 1139
## total_shrub_cover-Dasypus_novemcinctus 0.6552 1.0026 1951
## total_shrub_cover-Lynx_rufus 0.1665 1.0066 554
## total_shrub_cover-Didelphis_virginiana 0.4666 1.0007 1681
## total_shrub_cover-Sylvilagus_floridanus 0.6545 1.0165 533
## total_shrub_cover-Meleagris_gallopavo -0.1956 1.0113 660
## total_shrub_cover-Sciurus_carolinensis 0.7751 1.0066 1851
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5964 0.1652 -2.9374 -2.5883 -2.2808 1.0031
## (Intercept)-Procyon_lotor -2.2782 0.1309 -2.5465 -2.2734 -2.0289 1.0010
## (Intercept)-Dasypus_novemcinctus -1.6086 0.1359 -1.8732 -1.6066 -1.3500 1.0037
## (Intercept)-Lynx_rufus -3.3988 0.2909 -3.9972 -3.3925 -2.8481 1.0130
## (Intercept)-Didelphis_virginiana -2.3458 0.2522 -2.8570 -2.3361 -1.8859 1.0021
## (Intercept)-Sylvilagus_floridanus -3.1570 0.2821 -3.7323 -3.1448 -2.6227 1.0095
## (Intercept)-Meleagris_gallopavo -3.2343 0.2965 -3.8586 -3.2205 -2.6931 1.0050
## (Intercept)-Sciurus_carolinensis -2.4666 0.2596 -2.9997 -2.4519 -1.9829 1.0070
## ESS
## (Intercept)-Canis_latrans 1053
## (Intercept)-Procyon_lotor 1436
## (Intercept)-Dasypus_novemcinctus 2264
## (Intercept)-Lynx_rufus 403
## (Intercept)-Didelphis_virginiana 1359
## (Intercept)-Sylvilagus_floridanus 493
## (Intercept)-Meleagris_gallopavo 496
## (Intercept)-Sciurus_carolinensis 1211
# 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): 0.461
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5487 0.3731 -1.2743 -0.5479 0.1843 1.0004 660
## Veg_shannon_index 0.3751 0.2686 -0.1435 0.3695 0.9302 1.0032 1085
## Avg_Cogongrass_Cover 0.3593 0.2743 -0.1806 0.3572 0.9073 1.0035 928
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6276 0.8278 0.0518 0.3955 2.5021 1.0874 1019
## Veg_shannon_index 0.2750 0.3145 0.0333 0.1722 1.1488 1.0053 1270
## Avg_Cogongrass_Cover 0.3150 0.4347 0.0383 0.2011 1.3010 1.0151 1299
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7986 0.714 0.0711 0.6188 2.6553 1.0224 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.57 0.2845 -3.1159 -2.5783 -1.969 1.0022 2054
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6195 0.5614 0.1562 0.4642 1.994 1.0123 1651
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1332 0.5389 -1.1317 -0.1448
## (Intercept)-Procyon_lotor 0.0556 0.5822 -1.0238 0.0334
## (Intercept)-Dasypus_novemcinctus -0.6951 0.4852 -1.7156 -0.6658
## (Intercept)-Lynx_rufus -0.4399 0.5853 -1.5895 -0.4468
## (Intercept)-Didelphis_virginiana -1.1073 0.5476 -2.3047 -1.0614
## (Intercept)-Sylvilagus_floridanus -0.5309 0.5347 -1.5762 -0.5474
## (Intercept)-Meleagris_gallopavo -0.5806 0.5986 -1.7222 -0.6083
## (Intercept)-Sciurus_carolinensis -1.0978 0.5505 -2.2677 -1.0556
## Veg_shannon_index-Canis_latrans 0.6483 0.3849 -0.0219 0.6244
## Veg_shannon_index-Procyon_lotor 0.4941 0.3743 -0.1848 0.4671
## Veg_shannon_index-Dasypus_novemcinctus 0.2247 0.3350 -0.4450 0.2234
## Veg_shannon_index-Lynx_rufus 0.1954 0.4779 -0.8464 0.2173
## Veg_shannon_index-Didelphis_virginiana 0.4891 0.3742 -0.1799 0.4672
## Veg_shannon_index-Sylvilagus_floridanus 0.4563 0.4057 -0.2756 0.4318
## Veg_shannon_index-Meleagris_gallopavo 0.4873 0.4282 -0.2772 0.4589
## Veg_shannon_index-Sciurus_carolinensis 0.0448 0.3794 -0.7958 0.0623
## Avg_Cogongrass_Cover-Canis_latrans 0.5552 0.3760 -0.1236 0.5312
## Avg_Cogongrass_Cover-Procyon_lotor 0.4378 0.3832 -0.2708 0.4200
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4568 0.3275 -0.1698 0.4473
## Avg_Cogongrass_Cover-Lynx_rufus 0.6189 0.4125 -0.1239 0.5909
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4952 0.3569 -0.2066 0.4948
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0546 0.4579 -1.0654 -0.0262
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0615 0.5152 -1.1925 -0.0166
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4586 0.3563 -0.2211 0.4514
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9823 1.0180 644
## (Intercept)-Procyon_lotor 1.2857 1.0273 501
## (Intercept)-Dasypus_novemcinctus 0.2074 1.0048 1181
## (Intercept)-Lynx_rufus 0.7999 1.0127 743
## (Intercept)-Didelphis_virginiana -0.1316 1.0002 991
## (Intercept)-Sylvilagus_floridanus 0.5922 1.0044 848
## (Intercept)-Meleagris_gallopavo 0.6649 1.0064 623
## (Intercept)-Sciurus_carolinensis -0.0965 1.0519 735
## Veg_shannon_index-Canis_latrans 1.5106 1.0011 1357
## Veg_shannon_index-Procyon_lotor 1.3018 1.0054 1324
## Veg_shannon_index-Dasypus_novemcinctus 0.8881 1.0008 1885
## Veg_shannon_index-Lynx_rufus 1.0599 1.0042 1050
## Veg_shannon_index-Didelphis_virginiana 1.2810 1.0110 1721
## Veg_shannon_index-Sylvilagus_floridanus 1.3460 1.0006 1718
## Veg_shannon_index-Meleagris_gallopavo 1.3996 1.0010 1212
## Veg_shannon_index-Sciurus_carolinensis 0.7365 1.0012 1501
## Avg_Cogongrass_Cover-Canis_latrans 1.3604 1.0014 1600
## Avg_Cogongrass_Cover-Procyon_lotor 1.2785 1.0018 1686
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1381 1.0025 1933
## Avg_Cogongrass_Cover-Lynx_rufus 1.5262 1.0008 1112
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2263 1.0026 1942
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7809 1.0091 1164
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8499 1.0038 835
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1847 1.0035 1658
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5905 0.1642 -2.9231 -2.5862 -2.2875 1.0061
## (Intercept)-Procyon_lotor -2.2779 0.1325 -2.5445 -2.2770 -2.0228 1.0035
## (Intercept)-Dasypus_novemcinctus -1.6090 0.1343 -1.8787 -1.6073 -1.3544 1.0032
## (Intercept)-Lynx_rufus -3.3812 0.2891 -3.9830 -3.3688 -2.8495 1.0298
## (Intercept)-Didelphis_virginiana -2.3479 0.2490 -2.8858 -2.3384 -1.8736 1.0030
## (Intercept)-Sylvilagus_floridanus -3.1344 0.2821 -3.7450 -3.1183 -2.6175 1.0059
## (Intercept)-Meleagris_gallopavo -3.3062 0.3288 -4.0095 -3.2843 -2.7033 1.0016
## (Intercept)-Sciurus_carolinensis -2.4760 0.2665 -3.0465 -2.4650 -1.9860 1.0211
## ESS
## (Intercept)-Canis_latrans 1136
## (Intercept)-Procyon_lotor 1361
## (Intercept)-Dasypus_novemcinctus 2262
## (Intercept)-Lynx_rufus 441
## (Intercept)-Didelphis_virginiana 1300
## (Intercept)-Sylvilagus_floridanus 476
## (Intercept)-Meleagris_gallopavo 377
## (Intercept)-Sciurus_carolinensis 1136
# 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): 0.456
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1369 0.4062 -1.9190 -1.1374 -0.3490 1.0029 593
## Avg_Cogongrass_Cover -0.5523 0.3744 -1.3070 -0.5464 0.1645 1.0143 527
## I(Avg_Cogongrass_Cover^2) 0.7896 0.4223 0.1241 0.7472 1.7826 1.0672 226
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5880 0.7403 0.0494 0.3696 2.6682 1.0107 1064
## Avg_Cogongrass_Cover 0.3656 0.4637 0.0384 0.2143 1.6385 1.0209 967
## I(Avg_Cogongrass_Cover^2) 0.6742 1.2849 0.0435 0.2919 3.7582 1.1790 203
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5187 0.4834 0.0563 0.3586 1.8312 1.0445 322
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5647 0.2916 -3.1032 -2.5747 -1.9601 1.0016 2003
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5974 0.6398 0.1426 0.4485 1.8578 1.0779 1731
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8046 0.5613 -1.8872 -0.7980
## (Intercept)-Procyon_lotor -0.6114 0.6151 -1.7418 -0.6301
## (Intercept)-Dasypus_novemcinctus -1.2561 0.4979 -2.2611 -1.2522
## (Intercept)-Lynx_rufus -1.2396 0.5954 -2.4152 -1.2434
## (Intercept)-Didelphis_virginiana -1.5696 0.5580 -2.7730 -1.5267
## (Intercept)-Sylvilagus_floridanus -1.1245 0.5389 -2.1817 -1.1260
## (Intercept)-Meleagris_gallopavo -0.9689 0.5901 -2.0373 -1.0000
## (Intercept)-Sciurus_carolinensis -1.7950 0.6254 -3.1329 -1.7465
## Avg_Cogongrass_Cover-Canis_latrans -0.3870 0.5037 -1.3313 -0.4047
## Avg_Cogongrass_Cover-Procyon_lotor -0.5515 0.4983 -1.5338 -0.5564
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4059 0.4609 -1.2967 -0.4050
## Avg_Cogongrass_Cover-Lynx_rufus -0.4754 0.5413 -1.5094 -0.4845
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2786 0.5193 -1.2385 -0.3092
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9676 0.5918 -2.2751 -0.9120
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8160 0.5738 -2.0864 -0.7780
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5690 0.5134 -1.6364 -0.5580
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3557 0.9358 0.2254 1.1132
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2233 0.8095 0.2146 1.0234
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6322 0.3361 -0.0111 0.6311
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1599 0.5899 0.2740 1.0690
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4168 0.3906 -0.3659 0.4186
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6986 0.5179 -0.1048 0.6372
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2566 0.8404 -1.0578 0.2167
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7834 0.3842 0.0841 0.7652
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3198 1.0091 625
## (Intercept)-Procyon_lotor 0.6565 1.0025 586
## (Intercept)-Dasypus_novemcinctus -0.2824 1.0004 948
## (Intercept)-Lynx_rufus -0.0197 1.0062 792
## (Intercept)-Didelphis_virginiana -0.5753 1.0022 1070
## (Intercept)-Sylvilagus_floridanus -0.0814 1.0027 902
## (Intercept)-Meleagris_gallopavo 0.2461 1.0193 758
## (Intercept)-Sciurus_carolinensis -0.7022 0.9997 884
## Avg_Cogongrass_Cover-Canis_latrans 0.6290 1.0142 1088
## Avg_Cogongrass_Cover-Procyon_lotor 0.4423 1.0029 876
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5035 1.0024 991
## Avg_Cogongrass_Cover-Lynx_rufus 0.6465 1.0043 808
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8222 1.0080 974
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0508 1.0063 749
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2145 1.0105 738
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4000 1.0044 782
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.8315 1.1558 219
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.4009 1.1484 239
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3163 1.0034 1118
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6316 1.0324 321
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2174 1.0059 992
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8869 1.0133 538
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8806 1.1357 97
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6236 1.0071 727
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6155 0.1692 -2.9578 -2.6134 -2.2933 1.0102
## (Intercept)-Procyon_lotor -2.2812 0.1279 -2.5403 -2.2796 -2.0359 1.0020
## (Intercept)-Dasypus_novemcinctus -1.6104 0.1361 -1.8746 -1.6085 -1.3421 1.0043
## (Intercept)-Lynx_rufus -3.3130 0.2920 -3.9003 -3.3096 -2.7693 1.0096
## (Intercept)-Didelphis_virginiana -2.3549 0.2446 -2.8681 -2.3486 -1.9007 1.0108
## (Intercept)-Sylvilagus_floridanus -3.1089 0.2728 -3.6630 -3.0998 -2.6026 1.0196
## (Intercept)-Meleagris_gallopavo -3.3044 0.3499 -4.0450 -3.2679 -2.6964 1.0346
## (Intercept)-Sciurus_carolinensis -2.4690 0.2569 -2.9985 -2.4535 -1.9999 1.0060
## ESS
## (Intercept)-Canis_latrans 1047
## (Intercept)-Procyon_lotor 1475
## (Intercept)-Dasypus_novemcinctus 2153
## (Intercept)-Lynx_rufus 496
## (Intercept)-Didelphis_virginiana 1363
## (Intercept)-Sylvilagus_floridanus 490
## (Intercept)-Meleagris_gallopavo 296
## (Intercept)-Sciurus_carolinensis 1138
# 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): 0.5278
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0035 0.6865 -3.3733 -2.0086 -0.6104 1.0052 416
## Cogon_Patch_Size -0.0624 0.6362 -1.3374 -0.0586 1.1669 1.0146 697
## Veg_shannon_index 0.8952 0.4467 0.0752 0.8691 1.8404 1.0374 523
## total_shrub_cover -0.7377 0.5520 -1.9134 -0.7187 0.2857 1.0033 1286
## Avg_Cogongrass_Cover 0.3488 0.8912 -1.4689 0.3740 2.0645 1.2187 160
## Tree_Density -2.2113 0.8002 -3.8248 -2.1843 -0.7421 1.0415 289
## Avg_Canopy_Cover 1.6144 0.5816 0.4910 1.5943 2.8136 1.0057 744
## I(Avg_Cogongrass_Cover^2) 1.2109 0.6300 -0.0105 1.2014 2.4863 1.0146 472
## avg_veg_height -0.2025 0.4940 -1.2013 -0.2033 0.7519 1.1463 310
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0701 4.9136 0.0751 1.0571 9.3778 1.1681 772
## Cogon_Patch_Size 2.1945 2.7827 0.1130 1.3214 10.2760 1.0219 453
## Veg_shannon_index 0.6180 0.9715 0.0423 0.3180 3.1110 1.0231 691
## total_shrub_cover 2.1634 4.0822 0.1028 1.3053 9.0109 1.1334 782
## Avg_Cogongrass_Cover 1.1995 2.5503 0.0524 0.5205 5.7389 1.0172 840
## Tree_Density 2.6920 5.6864 0.0678 0.9982 15.9460 1.0498 395
## Avg_Canopy_Cover 2.1582 3.5250 0.0948 1.1417 10.1196 1.0623 434
## I(Avg_Cogongrass_Cover^2) 2.5026 4.2302 0.0850 1.2380 13.6519 1.0318 233
## avg_veg_height 0.4847 0.6779 0.0420 0.2707 2.2872 1.0016 872
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3716 1.6115 0.0569 0.7973 5.6332 1.0982 113
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5694 0.2929 -3.14 -2.5731 -1.9536 1.0081 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6402 0.5723 0.1559 0.4982 1.8951 1.0292 1512
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5643 1.0166 -3.4434 -1.5971
## (Intercept)-Procyon_lotor -1.1580 0.9868 -2.9365 -1.1984
## (Intercept)-Dasypus_novemcinctus -2.3979 0.8767 -4.3538 -2.3516
## (Intercept)-Lynx_rufus -1.6544 1.1054 -3.6144 -1.7345
## (Intercept)-Didelphis_virginiana -2.9635 1.0000 -5.2202 -2.8541
## (Intercept)-Sylvilagus_floridanus -2.1759 0.9826 -4.2763 -2.1503
## (Intercept)-Meleagris_gallopavo -2.0415 1.0305 -4.1316 -2.0167
## (Intercept)-Sciurus_carolinensis -3.3619 1.1917 -6.2046 -3.1815
## Cogon_Patch_Size-Canis_latrans 1.2376 1.1379 -0.3883 1.0464
## Cogon_Patch_Size-Procyon_lotor -0.5276 0.7430 -1.9920 -0.5245
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2636 0.6361 -1.5637 -0.2414
## Cogon_Patch_Size-Lynx_rufus -0.4010 1.3017 -3.1658 -0.3294
## Cogon_Patch_Size-Didelphis_virginiana 1.2701 0.9145 -0.2559 1.1742
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1576 1.2091 -4.0479 -0.9749
## Cogon_Patch_Size-Meleagris_gallopavo 0.1386 0.9635 -1.5847 0.0954
## Cogon_Patch_Size-Sciurus_carolinensis -0.8789 1.0290 -3.2222 -0.7276
## Veg_shannon_index-Canis_latrans 1.2393 0.6695 0.1868 1.1512
## Veg_shannon_index-Procyon_lotor 1.0785 0.5873 0.0410 1.0348
## Veg_shannon_index-Dasypus_novemcinctus 0.6111 0.5234 -0.4326 0.6162
## Veg_shannon_index-Lynx_rufus 0.9068 0.8249 -0.5753 0.8568
## Veg_shannon_index-Didelphis_virginiana 0.9618 0.6022 -0.1448 0.9251
## Veg_shannon_index-Sylvilagus_floridanus 0.9465 0.6314 -0.1735 0.9035
## Veg_shannon_index-Meleagris_gallopavo 1.1713 0.7410 -0.0212 1.0710
## Veg_shannon_index-Sciurus_carolinensis 0.3625 0.6477 -1.0861 0.4206
## total_shrub_cover-Canis_latrans 0.0408 0.6689 -1.1227 -0.0010
## total_shrub_cover-Procyon_lotor -1.2178 0.6705 -2.6977 -1.1663
## total_shrub_cover-Dasypus_novemcinctus 0.0683 0.5595 -0.9985 0.0630
## total_shrub_cover-Lynx_rufus -1.6264 1.1054 -4.2847 -1.4709
## total_shrub_cover-Didelphis_virginiana -0.6504 0.7207 -2.1541 -0.6340
## total_shrub_cover-Sylvilagus_floridanus -0.4293 0.8814 -2.3034 -0.4033
## total_shrub_cover-Meleagris_gallopavo -2.5545 1.3795 -5.7325 -2.3607
## total_shrub_cover-Sciurus_carolinensis -0.0595 0.7052 -1.3598 -0.0756
## Avg_Cogongrass_Cover-Canis_latrans 0.2772 1.1133 -2.0004 0.3077
## Avg_Cogongrass_Cover-Procyon_lotor 0.3542 1.1175 -1.8543 0.3574
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8970 1.1240 -1.2337 0.8386
## Avg_Cogongrass_Cover-Lynx_rufus 0.4479 1.2399 -1.9634 0.4642
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7072 1.1252 -1.4482 0.6821
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2232 1.2628 -2.9711 -0.1040
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0110 1.2359 -2.6213 0.0588
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5142 1.1321 -1.7012 0.5145
## Tree_Density-Canis_latrans -3.0686 1.3952 -6.3255 -2.8292
## Tree_Density-Procyon_lotor -2.1716 0.9649 -4.1181 -2.1604
## Tree_Density-Dasypus_novemcinctus -3.4755 1.4595 -6.9797 -3.2199
## Tree_Density-Lynx_rufus -1.0619 1.6219 -3.5978 -1.2699
## Tree_Density-Didelphis_virginiana -2.3112 1.0509 -4.7297 -2.1905
## Tree_Density-Sylvilagus_floridanus -2.6193 1.2788 -5.3802 -2.4770
## Tree_Density-Meleagris_gallopavo -2.2836 1.2836 -5.0076 -2.2197
## Tree_Density-Sciurus_carolinensis -2.4997 1.1651 -5.2516 -2.3744
## Avg_Canopy_Cover-Canis_latrans 0.3168 0.7106 -1.1082 0.3081
## Avg_Canopy_Cover-Procyon_lotor 1.5764 0.6850 0.3062 1.5512
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8727 0.6862 0.6858 1.8084
## Avg_Canopy_Cover-Lynx_rufus 0.9637 1.0554 -1.0496 0.9815
## Avg_Canopy_Cover-Didelphis_virginiana 2.3246 0.8742 0.9785 2.2032
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0678 1.4948 1.0003 2.7789
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0357 0.9962 0.5248 1.8743
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0364 0.8199 0.7595 1.9407
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3360 1.2323 0.6389 2.0693
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1662 1.1105 0.5102 1.9981
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2347 0.7212 -0.0278 1.1784
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4943 1.4027 0.5843 2.2104
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5672 0.6627 -0.7525 0.5748
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9292 0.8553 -0.5252 0.8634
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.1530 1.2823 -2.8302 -0.0678
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2249 0.7095 -0.0325 1.1734
## avg_veg_height-Canis_latrans -0.4488 0.6250 -1.7947 -0.4211
## avg_veg_height-Procyon_lotor 0.0197 0.6190 -1.1716 0.0014
## avg_veg_height-Dasypus_novemcinctus 0.0640 0.6014 -1.0872 0.0438
## avg_veg_height-Lynx_rufus -0.3889 0.7723 -2.0391 -0.3525
## avg_veg_height-Didelphis_virginiana -0.2808 0.6332 -1.6171 -0.2540
## avg_veg_height-Sylvilagus_floridanus -0.3596 0.6581 -1.7459 -0.3267
## avg_veg_height-Meleagris_gallopavo -0.3092 0.7532 -1.9228 -0.2749
## avg_veg_height-Sciurus_carolinensis 0.0792 0.6366 -1.0946 0.0619
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4114 1.0055 386
## (Intercept)-Procyon_lotor 0.9235 1.0246 363
## (Intercept)-Dasypus_novemcinctus -0.8625 1.0150 528
## (Intercept)-Lynx_rufus 0.8875 1.0045 356
## (Intercept)-Didelphis_virginiana -1.2452 1.0060 492
## (Intercept)-Sylvilagus_floridanus -0.3557 1.0140 470
## (Intercept)-Meleagris_gallopavo -0.0847 1.0090 599
## (Intercept)-Sciurus_carolinensis -1.5134 1.0142 367
## Cogon_Patch_Size-Canis_latrans 4.0098 1.0120 512
## Cogon_Patch_Size-Procyon_lotor 0.8928 1.0011 557
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9619 1.0243 754
## Cogon_Patch_Size-Lynx_rufus 2.0520 1.0046 368
## Cogon_Patch_Size-Didelphis_virginiana 3.2816 1.0194 528
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7317 1.0060 407
## Cogon_Patch_Size-Meleagris_gallopavo 2.2542 1.0175 847
## Cogon_Patch_Size-Sciurus_carolinensis 0.7578 1.0081 520
## Veg_shannon_index-Canis_latrans 2.8290 1.0083 528
## Veg_shannon_index-Procyon_lotor 2.3614 1.0355 427
## Veg_shannon_index-Dasypus_novemcinctus 1.6517 1.0106 985
## Veg_shannon_index-Lynx_rufus 2.6831 1.0916 464
## Veg_shannon_index-Didelphis_virginiana 2.2586 1.0255 942
## Veg_shannon_index-Sylvilagus_floridanus 2.3413 1.0037 665
## Veg_shannon_index-Meleagris_gallopavo 2.9850 1.0263 518
## Veg_shannon_index-Sciurus_carolinensis 1.4695 1.0035 871
## total_shrub_cover-Canis_latrans 1.4780 1.0051 1078
## total_shrub_cover-Procyon_lotor -0.0518 1.0040 864
## total_shrub_cover-Dasypus_novemcinctus 1.1642 1.0094 1506
## total_shrub_cover-Lynx_rufus 0.0640 1.0029 289
## total_shrub_cover-Didelphis_virginiana 0.7484 1.0070 1343
## total_shrub_cover-Sylvilagus_floridanus 1.3082 1.0204 802
## total_shrub_cover-Meleagris_gallopavo -0.4456 1.0103 375
## total_shrub_cover-Sciurus_carolinensis 1.3858 1.0057 1267
## Avg_Cogongrass_Cover-Canis_latrans 2.4498 1.1578 314
## Avg_Cogongrass_Cover-Procyon_lotor 2.5464 1.1166 278
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.2993 1.0836 252
## Avg_Cogongrass_Cover-Lynx_rufus 2.9086 1.1209 210
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0864 1.0909 270
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9526 1.1199 229
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2225 1.1575 255
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8378 1.1157 227
## Tree_Density-Canis_latrans -1.0015 1.0171 267
## Tree_Density-Procyon_lotor -0.2818 1.0260 387
## Tree_Density-Dasypus_novemcinctus -1.3762 1.0135 261
## Tree_Density-Lynx_rufus 2.9410 1.0364 203
## Tree_Density-Didelphis_virginiana -0.5387 1.0165 414
## Tree_Density-Sylvilagus_floridanus -0.5589 1.0392 351
## Tree_Density-Meleagris_gallopavo 0.0067 1.0333 461
## Tree_Density-Sciurus_carolinensis -0.6021 1.0306 474
## Avg_Canopy_Cover-Canis_latrans 1.6784 1.0229 519
## Avg_Canopy_Cover-Procyon_lotor 3.0400 1.0146 516
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3740 1.0114 518
## Avg_Canopy_Cover-Lynx_rufus 3.0680 1.0136 375
## Avg_Canopy_Cover-Didelphis_virginiana 4.4261 1.0028 346
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.6923 1.0210 278
## Avg_Canopy_Cover-Meleagris_gallopavo 4.4496 1.0061 477
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9572 1.0086 563
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.3516 1.0151 183
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9931 1.0011 279
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8074 1.0134 534
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.0085 1.0138 204
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.8703 1.0154 387
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7787 1.0375 443
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.0954 1.0007 182
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.7594 1.0229 660
## avg_veg_height-Canis_latrans 0.6425 1.1092 437
## avg_veg_height-Procyon_lotor 1.3433 1.0783 462
## avg_veg_height-Dasypus_novemcinctus 1.3097 1.0654 465
## avg_veg_height-Lynx_rufus 1.0290 1.0843 497
## avg_veg_height-Didelphis_virginiana 0.9393 1.0972 578
## avg_veg_height-Sylvilagus_floridanus 0.8548 1.0823 539
## avg_veg_height-Meleagris_gallopavo 1.1046 1.1009 461
## avg_veg_height-Sciurus_carolinensis 1.4191 1.0690 381
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6060 0.1646 -2.9379 -2.6025 -2.2908 1.0001
## (Intercept)-Procyon_lotor -2.2703 0.1294 -2.5247 -2.2672 -2.0223 0.9998
## (Intercept)-Dasypus_novemcinctus -1.6040 0.1359 -1.8809 -1.5991 -1.3439 1.0040
## (Intercept)-Lynx_rufus -3.5258 0.3027 -4.1326 -3.5212 -2.9409 1.0023
## (Intercept)-Didelphis_virginiana -2.3250 0.2356 -2.8036 -2.3207 -1.8842 1.0066
## (Intercept)-Sylvilagus_floridanus -3.1140 0.2590 -3.6521 -3.1085 -2.6280 1.0213
## (Intercept)-Meleagris_gallopavo -3.2539 0.2906 -3.8497 -3.2400 -2.7144 1.0326
## (Intercept)-Sciurus_carolinensis -2.4555 0.2580 -3.0084 -2.4375 -1.9999 1.0021
## ESS
## (Intercept)-Canis_latrans 1137
## (Intercept)-Procyon_lotor 1587
## (Intercept)-Dasypus_novemcinctus 2301
## (Intercept)-Lynx_rufus 317
## (Intercept)-Didelphis_virginiana 1429
## (Intercept)-Sylvilagus_floridanus 597
## (Intercept)-Meleagris_gallopavo 473
## (Intercept)-Sciurus_carolinensis 1373
# 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): 0.6785
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3358 0.3647 -1.0732 -0.3349 0.3954 1.0123 708
## Avg_Cogongrass_Cover 0.1892 0.2649 -0.3396 0.1877 0.7085 1.0071 1398
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6144 0.8263 0.0516 0.3633 2.7119 1.0041 718
## Avg_Cogongrass_Cover 0.3367 0.4483 0.0374 0.2020 1.4097 1.0406 988
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5913 0.5836 0.0642 0.419 2.1453 1.0455 338
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7420 0.3277 -3.3714 -2.7495 -2.0793 1.0068 2244
## shrub_cover 0.2587 0.3121 -0.3682 0.2586 0.8944 1.0054 1609
## veg_height 0.0740 0.1955 -0.3178 0.0743 0.4677 1.0008 1451
## week -0.1013 0.1405 -0.3879 -0.0962 0.1710 1.0083 1120
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7599 0.6996 0.1678 0.5641 2.3709 1.0180 718
## shrub_cover 0.7112 0.8000 0.1277 0.5193 2.3531 1.0038 1356
## veg_height 0.2542 0.2688 0.0575 0.1923 0.7989 1.0705 2189
## week 0.1078 0.0951 0.0241 0.0783 0.3836 1.0020 1535
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0834 0.5252 -0.8960 0.0598
## (Intercept)-Procyon_lotor 0.1827 0.5262 -0.8258 0.1754
## (Intercept)-Dasypus_novemcinctus -0.5153 0.4464 -1.4060 -0.5032
## (Intercept)-Lynx_rufus -0.2179 0.5842 -1.3146 -0.2560
## (Intercept)-Didelphis_virginiana -0.8652 0.5072 -1.9594 -0.8369
## (Intercept)-Sylvilagus_floridanus -0.4178 0.4773 -1.3736 -0.4201
## (Intercept)-Meleagris_gallopavo -0.1146 0.7170 -1.3375 -0.1910
## (Intercept)-Sciurus_carolinensis -0.8752 0.5324 -2.0198 -0.8392
## Avg_Cogongrass_Cover-Canis_latrans 0.3995 0.3704 -0.2337 0.3676
## Avg_Cogongrass_Cover-Procyon_lotor 0.2105 0.3341 -0.4455 0.2102
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3478 0.3274 -0.2705 0.3338
## Avg_Cogongrass_Cover-Lynx_rufus 0.4448 0.3881 -0.2428 0.4194
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3053 0.3589 -0.3839 0.2987
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2134 0.4174 -1.1604 -0.1692
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2807 0.5894 -1.5919 -0.2112
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3377 0.3555 -0.3302 0.3253
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1841 1.0096 757
## (Intercept)-Procyon_lotor 1.2444 1.0079 528
## (Intercept)-Dasypus_novemcinctus 0.3636 1.0022 1487
## (Intercept)-Lynx_rufus 1.0857 1.0151 450
## (Intercept)-Didelphis_virginiana 0.0299 1.0189 985
## (Intercept)-Sylvilagus_floridanus 0.5283 1.0200 1310
## (Intercept)-Meleagris_gallopavo 1.5552 1.0200 306
## (Intercept)-Sciurus_carolinensis 0.0826 1.0133 960
## Avg_Cogongrass_Cover-Canis_latrans 1.1971 1.0013 1864
## Avg_Cogongrass_Cover-Procyon_lotor 0.9008 1.0043 2286
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0253 1.0019 2313
## Avg_Cogongrass_Cover-Lynx_rufus 1.2643 1.0080 1678
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0303 1.0078 2137
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5153 1.0038 1162
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6954 1.0366 536
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0807 1.0052 2387
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7463 0.1851 -3.1171 -2.7439 -2.3909 1.0040
## (Intercept)-Procyon_lotor -2.3053 0.1397 -2.5957 -2.3025 -2.0361 1.0080
## (Intercept)-Dasypus_novemcinctus -1.7803 0.1631 -2.1133 -1.7737 -1.4927 1.0157
## (Intercept)-Lynx_rufus -3.5444 0.3476 -4.2431 -3.5323 -2.9122 1.0160
## (Intercept)-Didelphis_virginiana -2.6330 0.2874 -3.2424 -2.6212 -2.1031 1.0152
## (Intercept)-Sylvilagus_floridanus -3.1417 0.2690 -3.6982 -3.1308 -2.6462 1.0591
## (Intercept)-Meleagris_gallopavo -3.8592 0.5115 -4.8595 -3.8572 -2.8472 1.0469
## (Intercept)-Sciurus_carolinensis -2.6723 0.3079 -3.3071 -2.6532 -2.1038 1.0001
## shrub_cover-Canis_latrans -0.2827 0.2203 -0.7036 -0.2831 0.1441 1.0021
## shrub_cover-Procyon_lotor 0.2573 0.1629 -0.0698 0.2568 0.5728 1.0032
## shrub_cover-Dasypus_novemcinctus 0.8663 0.2988 0.2900 0.8603 1.4814 1.0012
## shrub_cover-Lynx_rufus -0.2187 0.3733 -0.9820 -0.2113 0.5119 1.0315
## shrub_cover-Didelphis_virginiana 0.9963 0.3828 0.3383 0.9796 1.7842 1.0078
## shrub_cover-Sylvilagus_floridanus 0.2606 0.4153 -0.5226 0.2481 1.1084 1.0259
## shrub_cover-Meleagris_gallopavo -0.6316 0.4333 -1.4911 -0.6408 0.2617 1.0339
## shrub_cover-Sciurus_carolinensis 0.8786 0.4107 0.0740 0.8682 1.7136 1.0091
## veg_height-Canis_latrans -0.5913 0.1903 -0.9752 -0.5867 -0.2372 1.0025
## veg_height-Procyon_lotor 0.3430 0.1250 0.1002 0.3430 0.5920 1.0018
## veg_height-Dasypus_novemcinctus 0.2565 0.1327 0.0061 0.2550 0.5118 1.0011
## veg_height-Lynx_rufus 0.0154 0.2426 -0.4646 0.0168 0.4790 1.0055
## veg_height-Didelphis_virginiana 0.4577 0.2435 -0.0009 0.4537 0.9634 1.0045
## veg_height-Sylvilagus_floridanus 0.1625 0.2450 -0.3112 0.1612 0.6436 1.0158
## veg_height-Meleagris_gallopavo -0.1326 0.3958 -0.9072 -0.1309 0.6912 1.0277
## veg_height-Sciurus_carolinensis 0.0854 0.2153 -0.3376 0.0815 0.5141 1.0011
## week-Canis_latrans 0.0595 0.1348 -0.2136 0.0605 0.3081 1.0014
## week-Procyon_lotor -0.0603 0.1197 -0.3053 -0.0573 0.1546 1.0037
## week-Dasypus_novemcinctus -0.1784 0.1398 -0.4597 -0.1723 0.0896 1.0015
## week-Lynx_rufus -0.0584 0.1926 -0.4514 -0.0530 0.3065 1.0063
## week-Didelphis_virginiana -0.2328 0.2202 -0.7160 -0.2173 0.1532 1.0066
## week-Sylvilagus_floridanus -0.1838 0.2164 -0.6646 -0.1733 0.2086 1.0169
## week-Meleagris_gallopavo -0.2908 0.2428 -0.8463 -0.2659 0.1201 1.0039
## week-Sciurus_carolinensis 0.1221 0.1831 -0.2370 0.1206 0.4828 1.0065
## ESS
## (Intercept)-Canis_latrans 811
## (Intercept)-Procyon_lotor 1336
## (Intercept)-Dasypus_novemcinctus 1499
## (Intercept)-Lynx_rufus 364
## (Intercept)-Didelphis_virginiana 588
## (Intercept)-Sylvilagus_floridanus 680
## (Intercept)-Meleagris_gallopavo 184
## (Intercept)-Sciurus_carolinensis 801
## shrub_cover-Canis_latrans 711
## shrub_cover-Procyon_lotor 1452
## shrub_cover-Dasypus_novemcinctus 1203
## shrub_cover-Lynx_rufus 496
## shrub_cover-Didelphis_virginiana 621
## shrub_cover-Sylvilagus_floridanus 512
## shrub_cover-Meleagris_gallopavo 244
## shrub_cover-Sciurus_carolinensis 726
## veg_height-Canis_latrans 736
## veg_height-Procyon_lotor 1631
## veg_height-Dasypus_novemcinctus 1803
## veg_height-Lynx_rufus 785
## veg_height-Didelphis_virginiana 1294
## veg_height-Sylvilagus_floridanus 939
## veg_height-Meleagris_gallopavo 389
## veg_height-Sciurus_carolinensis 1148
## week-Canis_latrans 1703
## week-Procyon_lotor 1568
## week-Dasypus_novemcinctus 2108
## week-Lynx_rufus 1042
## week-Didelphis_virginiana 1271
## week-Sylvilagus_floridanus 1005
## week-Meleagris_gallopavo 724
## week-Sciurus_carolinensis 1769
# 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): 0.4513
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5199 0.3486 -1.2196 -0.5186 0.1994 1.0181 773
## Avg_Cogongrass_Cover 0.2142 0.2559 -0.2889 0.2121 0.7277 1.0077 1467
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5731 0.8579 0.0549 0.3648 2.3508 1.0771 1438
## Avg_Cogongrass_Cover 0.2973 0.3728 0.0371 0.1861 1.1898 1.0897 1055
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6349 0.5591 0.0672 0.4619 2.0976 1.1016 194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5552 0.267 -3.0731 -2.5567 -2.0193 0.9999 2486
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5718 0.5199 0.1436 0.4297 1.7637 1.0046 1351
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0758 0.5091 -1.0352 -0.0846
## (Intercept)-Procyon_lotor 0.0810 0.5416 -0.9690 0.0917
## (Intercept)-Dasypus_novemcinctus -0.6553 0.4441 -1.5928 -0.6369
## (Intercept)-Lynx_rufus -0.4239 0.5510 -1.4541 -0.4376
## (Intercept)-Didelphis_virginiana -1.0375 0.5199 -2.1517 -1.0025
## (Intercept)-Sylvilagus_floridanus -0.5015 0.4865 -1.4541 -0.5205
## (Intercept)-Meleagris_gallopavo -0.5607 0.5109 -1.5522 -0.5733
## (Intercept)-Sciurus_carolinensis -1.0555 0.5127 -2.1022 -1.0216
## Avg_Cogongrass_Cover-Canis_latrans 0.3617 0.3456 -0.2700 0.3320
## Avg_Cogongrass_Cover-Procyon_lotor 0.2420 0.3441 -0.3917 0.2269
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3438 0.3169 -0.2484 0.3338
## Avg_Cogongrass_Cover-Lynx_rufus 0.4691 0.3878 -0.2141 0.4412
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3411 0.3591 -0.3461 0.3244
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1674 0.4037 -1.0000 -0.1418
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2213 0.4755 -1.3377 -0.1662
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3449 0.3380 -0.2930 0.3367
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9316 1.0086 694
## (Intercept)-Procyon_lotor 1.1395 1.0262 519
## (Intercept)-Dasypus_novemcinctus 0.1692 1.0084 1132
## (Intercept)-Lynx_rufus 0.7140 1.0447 719
## (Intercept)-Didelphis_virginiana -0.0959 1.0019 1025
## (Intercept)-Sylvilagus_floridanus 0.5153 1.0019 985
## (Intercept)-Meleagris_gallopavo 0.4865 1.0093 812
## (Intercept)-Sciurus_carolinensis -0.1264 1.0059 994
## Avg_Cogongrass_Cover-Canis_latrans 1.1092 1.0016 1944
## Avg_Cogongrass_Cover-Procyon_lotor 0.9815 1.0053 1960
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0112 1.0038 2371
## Avg_Cogongrass_Cover-Lynx_rufus 1.3370 1.0116 1591
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0707 1.0252 1925
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5880 1.0057 1372
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5785 1.0148 1074
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0367 1.0146 2250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5930 0.1657 -2.9330 -2.5851 -2.2867 1.0065
## (Intercept)-Procyon_lotor -2.2721 0.1250 -2.5275 -2.2717 -2.0356 0.9996
## (Intercept)-Dasypus_novemcinctus -1.6061 0.1351 -1.8721 -1.6045 -1.3524 1.0037
## (Intercept)-Lynx_rufus -3.3393 0.2866 -3.8972 -3.3344 -2.7996 1.0173
## (Intercept)-Didelphis_virginiana -2.3502 0.2450 -2.8451 -2.3407 -1.9019 1.0008
## (Intercept)-Sylvilagus_floridanus -3.0781 0.2611 -3.6141 -3.0699 -2.5864 1.0067
## (Intercept)-Meleagris_gallopavo -3.2479 0.3136 -3.9073 -3.2228 -2.7012 1.0148
## (Intercept)-Sciurus_carolinensis -2.4639 0.2596 -3.0109 -2.4497 -1.9882 1.0100
## ESS
## (Intercept)-Canis_latrans 1100
## (Intercept)-Procyon_lotor 1594
## (Intercept)-Dasypus_novemcinctus 2461
## (Intercept)-Lynx_rufus 498
## (Intercept)-Didelphis_virginiana 1349
## (Intercept)-Sylvilagus_floridanus 705
## (Intercept)-Meleagris_gallopavo 411
## (Intercept)-Sciurus_carolinensis 1171
# 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): 0.594
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5176 0.3603 -1.2156 -0.5250 0.2413 1.0063 789
## Avg_Cogongrass_Cover 0.2172 0.2611 -0.2864 0.2206 0.7366 1.0018 1625
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5956 0.7660 0.0577 0.3789 2.5613 1.0044 1031
## Avg_Cogongrass_Cover 0.2954 0.3847 0.0408 0.1862 1.3005 1.0218 1074
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6633 0.6159 0.0762 0.4875 2.2689 1.0107 217
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5817 0.2879 -3.1303 -2.5882 -1.9952 1.0002 2473
## week -0.1001 0.1381 -0.3760 -0.0991 0.1746 1.0005 1537
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5973 0.5451 0.1520 0.4563 1.9307 1.0262 1992
## week 0.1077 0.0969 0.0255 0.0803 0.3557 1.0182 1569
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0828 0.5037 -1.0281 -0.0995
## (Intercept)-Procyon_lotor 0.0869 0.5439 -0.9579 0.0644
## (Intercept)-Dasypus_novemcinctus -0.6634 0.4491 -1.5673 -0.6436
## (Intercept)-Lynx_rufus -0.4137 0.5491 -1.4200 -0.4521
## (Intercept)-Didelphis_virginiana -1.0251 0.5120 -2.1169 -0.9818
## (Intercept)-Sylvilagus_floridanus -0.5071 0.4935 -1.4692 -0.5051
## (Intercept)-Meleagris_gallopavo -0.5720 0.5443 -1.6488 -0.5806
## (Intercept)-Sciurus_carolinensis -1.0848 0.5310 -2.2562 -1.0563
## Avg_Cogongrass_Cover-Canis_latrans 0.3643 0.3432 -0.2801 0.3436
## Avg_Cogongrass_Cover-Procyon_lotor 0.2468 0.3485 -0.3945 0.2369
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3475 0.3173 -0.2396 0.3308
## Avg_Cogongrass_Cover-Lynx_rufus 0.4682 0.3948 -0.2053 0.4376
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3504 0.3394 -0.2692 0.3348
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1717 0.4054 -1.0662 -0.1410
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2184 0.4727 -1.2634 -0.1799
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3460 0.3407 -0.3132 0.3407
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9399 1.0060 956
## (Intercept)-Procyon_lotor 1.1874 1.0025 689
## (Intercept)-Dasypus_novemcinctus 0.2012 1.0028 1265
## (Intercept)-Lynx_rufus 0.7469 1.0025 833
## (Intercept)-Didelphis_virginiana -0.1115 1.0105 1160
## (Intercept)-Sylvilagus_floridanus 0.4867 1.0023 1134
## (Intercept)-Meleagris_gallopavo 0.5207 1.0012 714
## (Intercept)-Sciurus_carolinensis -0.1670 1.0102 1130
## Avg_Cogongrass_Cover-Canis_latrans 1.1044 1.0003 2189
## Avg_Cogongrass_Cover-Procyon_lotor 0.9678 0.9999 2421
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0070 1.0002 1877
## Avg_Cogongrass_Cover-Lynx_rufus 1.3312 1.0026 1594
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0578 1.0010 2009
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5558 1.0062 1489
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5783 1.0035 977
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0462 1.0018 2183
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6105 0.1664 -2.9398 -2.6062 -2.2977 1.0034
## (Intercept)-Procyon_lotor -2.2815 0.1281 -2.5324 -2.2836 -2.0319 1.0006
## (Intercept)-Dasypus_novemcinctus -1.6218 0.1359 -1.9025 -1.6210 -1.3561 1.0022
## (Intercept)-Lynx_rufus -3.3616 0.3014 -3.9737 -3.3460 -2.8043 1.0035
## (Intercept)-Didelphis_virginiana -2.3813 0.2514 -2.8994 -2.3733 -1.9076 1.0075
## (Intercept)-Sylvilagus_floridanus -3.0979 0.2737 -3.6635 -3.0910 -2.5977 1.0053
## (Intercept)-Meleagris_gallopavo -3.3151 0.3140 -3.9334 -3.2930 -2.7419 1.0037
## (Intercept)-Sciurus_carolinensis -2.4788 0.2553 -2.9891 -2.4662 -2.0149 1.0026
## week-Canis_latrans 0.0594 0.1337 -0.2229 0.0647 0.3078 1.0080
## week-Procyon_lotor -0.0618 0.1202 -0.3079 -0.0562 0.1657 1.0017
## week-Dasypus_novemcinctus -0.1754 0.1348 -0.4493 -0.1732 0.0770 1.0009
## week-Lynx_rufus -0.0579 0.1882 -0.4407 -0.0514 0.2912 1.0005
## week-Didelphis_virginiana -0.2339 0.2167 -0.7093 -0.2208 0.1425 1.0041
## week-Sylvilagus_floridanus -0.1599 0.2018 -0.5838 -0.1514 0.2199 1.0010
## week-Meleagris_gallopavo -0.2983 0.2412 -0.8286 -0.2860 0.1387 1.0018
## week-Sciurus_carolinensis 0.1179 0.1826 -0.2447 0.1203 0.4639 1.0092
## ESS
## (Intercept)-Canis_latrans 1083
## (Intercept)-Procyon_lotor 1653
## (Intercept)-Dasypus_novemcinctus 2306
## (Intercept)-Lynx_rufus 393
## (Intercept)-Didelphis_virginiana 1222
## (Intercept)-Sylvilagus_floridanus 655
## (Intercept)-Meleagris_gallopavo 492
## (Intercept)-Sciurus_carolinensis 1374
## week-Canis_latrans 1550
## week-Procyon_lotor 1583
## week-Dasypus_novemcinctus 2218
## week-Lynx_rufus 1151
## week-Didelphis_virginiana 1516
## week-Sylvilagus_floridanus 1127
## week-Meleagris_gallopavo 833
## week-Sciurus_carolinensis 1854
# 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): 0.6175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0561 0.6571 -2.3680 -1.0547 0.2128 1.0062 244
## Cogon_Patch_Size -0.5361 0.6307 -1.8197 -0.5291 0.6495 1.0255 449
## Veg_shannon_index 0.9750 0.4398 0.1604 0.9605 1.8913 1.0538 254
## total_shrub_cover -0.5789 0.5232 -1.6794 -0.5627 0.4308 1.0061 984
## Avg_Cogongrass_Cover 2.1598 0.7306 0.8519 2.1218 3.6858 1.0265 135
## Tree_Density -1.9371 0.7279 -3.4472 -1.8845 -0.5866 1.0070 350
## Avg_Canopy_Cover 1.6892 0.5826 0.5609 1.6728 2.9046 1.0162 656
## avg_veg_height -0.5822 0.4549 -1.5041 -0.5695 0.2835 1.0084 294
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2831 3.4509 0.0831 1.4198 9.7151 1.1212 497
## Cogon_Patch_Size 2.2741 3.4447 0.1156 1.3197 9.3556 1.0931 412
## Veg_shannon_index 0.5124 0.7174 0.0443 0.2681 2.4846 1.0110 657
## total_shrub_cover 1.6469 2.1210 0.0753 0.9919 7.5609 1.0365 403
## Avg_Cogongrass_Cover 0.8759 1.4166 0.0506 0.4237 4.5956 1.0060 539
## Tree_Density 2.7972 5.0302 0.0890 1.2922 14.9299 1.0900 211
## Avg_Canopy_Cover 2.1243 3.6281 0.1137 1.2145 9.9575 1.0901 730
## avg_veg_height 0.4273 0.5720 0.0411 0.2470 1.8508 1.0171 952
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2633 2.3319 0.1063 1.5631 8.4413 1.0566 62
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6041 0.2966 -3.1719 -2.6144 -1.983 1.0023 2358
## week -0.1021 0.1449 -0.4003 -0.1010 0.173 1.0030 1438
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6535 0.5749 0.1715 0.5021 2.1004 1.0005 2021
## week 0.1138 0.1212 0.0256 0.0814 0.3679 1.0232 1848
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1068 1.0121 -2.0985 -0.1099
## (Intercept)-Procyon_lotor -0.0268 0.9943 -2.0258 0.0048
## (Intercept)-Dasypus_novemcinctus -1.4489 0.7840 -3.1449 -1.3839
## (Intercept)-Lynx_rufus -0.3853 1.2533 -2.5277 -0.5206
## (Intercept)-Didelphis_virginiana -2.2058 0.9443 -4.2460 -2.1327
## (Intercept)-Sylvilagus_floridanus -1.2915 0.9172 -3.1907 -1.2561
## (Intercept)-Meleagris_gallopavo -1.3516 0.9934 -3.4456 -1.3404
## (Intercept)-Sciurus_carolinensis -2.2932 0.9875 -4.4970 -2.2112
## Cogon_Patch_Size-Canis_latrans 0.5684 1.0304 -0.9851 0.4036
## Cogon_Patch_Size-Procyon_lotor -1.0053 0.7217 -2.4924 -0.9900
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7968 0.6339 -2.1743 -0.7671
## Cogon_Patch_Size-Lynx_rufus -0.8159 1.3145 -3.4226 -0.8481
## Cogon_Patch_Size-Didelphis_virginiana 0.8362 0.9128 -0.6546 0.7432
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7517 1.2922 -4.8830 -1.5465
## Cogon_Patch_Size-Meleagris_gallopavo -0.4469 1.0472 -2.3763 -0.5138
## Cogon_Patch_Size-Sciurus_carolinensis -1.4774 1.0426 -3.9911 -1.3385
## Veg_shannon_index-Canis_latrans 1.2445 0.5786 0.2345 1.2095
## Veg_shannon_index-Procyon_lotor 1.2429 0.5802 0.2013 1.2016
## Veg_shannon_index-Dasypus_novemcinctus 0.7406 0.5149 -0.2861 0.7385
## Veg_shannon_index-Lynx_rufus 0.9197 0.7109 -0.5467 0.9226
## Veg_shannon_index-Didelphis_virginiana 1.0720 0.5831 0.0152 1.0355
## Veg_shannon_index-Sylvilagus_floridanus 1.0768 0.6441 -0.0937 1.0395
## Veg_shannon_index-Meleagris_gallopavo 1.2237 0.6626 0.1021 1.1673
## Veg_shannon_index-Sciurus_carolinensis 0.4549 0.6554 -1.0448 0.5081
## total_shrub_cover-Canis_latrans 0.1910 0.7176 -0.9899 0.1105
## total_shrub_cover-Procyon_lotor -1.0008 0.6218 -2.3290 -0.9583
## total_shrub_cover-Dasypus_novemcinctus 0.0704 0.5600 -1.0146 0.0580
## total_shrub_cover-Lynx_rufus -1.2217 1.0162 -3.6878 -1.0639
## total_shrub_cover-Didelphis_virginiana -0.6218 0.7169 -2.1080 -0.5886
## total_shrub_cover-Sylvilagus_floridanus -0.2352 0.7869 -1.8356 -0.2688
## total_shrub_cover-Meleagris_gallopavo -2.1075 1.2661 -5.1361 -1.9245
## total_shrub_cover-Sciurus_carolinensis -0.0806 0.6899 -1.4264 -0.0989
## Avg_Cogongrass_Cover-Canis_latrans 2.3994 0.8568 0.8521 2.3526
## Avg_Cogongrass_Cover-Procyon_lotor 2.2863 0.8538 0.7689 2.2506
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6447 0.9402 1.0505 2.5446
## Avg_Cogongrass_Cover-Lynx_rufus 2.4937 0.9308 0.8982 2.3958
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2502 0.8306 0.7605 2.2088
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6641 0.9755 -0.3228 1.6653
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.7153 1.1653 -0.8802 1.7813
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4612 0.8981 0.8493 2.3823
## Tree_Density-Canis_latrans -2.6038 1.2582 -5.5135 -2.3968
## Tree_Density-Procyon_lotor -1.5821 0.7602 -3.0495 -1.5815
## Tree_Density-Dasypus_novemcinctus -3.3879 1.6556 -7.4578 -2.9879
## Tree_Density-Lynx_rufus -0.4681 1.2448 -2.5165 -0.6002
## Tree_Density-Didelphis_virginiana -2.2663 1.0574 -4.6191 -2.1374
## Tree_Density-Sylvilagus_floridanus -2.5462 1.3612 -6.1077 -2.2916
## Tree_Density-Meleagris_gallopavo -2.2061 1.2177 -4.9580 -2.0941
## Tree_Density-Sciurus_carolinensis -2.2973 1.1292 -4.9907 -2.1337
## Avg_Canopy_Cover-Canis_latrans 0.3529 0.6811 -1.0608 0.3577
## Avg_Canopy_Cover-Procyon_lotor 1.7016 0.6781 0.4697 1.6707
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9751 0.6644 0.8603 1.9135
## Avg_Canopy_Cover-Lynx_rufus 0.9158 0.9913 -0.9852 0.9007
## Avg_Canopy_Cover-Didelphis_virginiana 2.5375 0.9085 1.1199 2.4114
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0059 1.3406 1.1032 2.7453
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2673 1.1235 0.6213 2.0632
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1271 0.7706 0.8925 2.0291
## avg_veg_height-Canis_latrans -0.8007 0.5648 -2.0194 -0.7725
## avg_veg_height-Procyon_lotor -0.4621 0.5447 -1.5222 -0.4628
## avg_veg_height-Dasypus_novemcinctus -0.3544 0.5666 -1.4725 -0.3720
## avg_veg_height-Lynx_rufus -0.6884 0.6892 -2.0669 -0.6761
## avg_veg_height-Didelphis_virginiana -0.7001 0.6095 -1.9468 -0.6772
## avg_veg_height-Sylvilagus_floridanus -0.7763 0.6228 -2.1272 -0.7478
## avg_veg_height-Meleagris_gallopavo -0.6937 0.6890 -2.1754 -0.6755
## avg_veg_height-Sciurus_carolinensis -0.2591 0.6249 -1.4390 -0.2806
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0130 1.0220 257
## (Intercept)-Procyon_lotor 1.8711 1.0516 161
## (Intercept)-Dasypus_novemcinctus -0.0627 1.0208 442
## (Intercept)-Lynx_rufus 2.6215 1.0365 185
## (Intercept)-Didelphis_virginiana -0.5638 1.0447 405
## (Intercept)-Sylvilagus_floridanus 0.4860 1.0106 431
## (Intercept)-Meleagris_gallopavo 0.5707 1.0035 422
## (Intercept)-Sciurus_carolinensis -0.6037 1.0238 390
## Cogon_Patch_Size-Canis_latrans 3.0987 1.0035 703
## Cogon_Patch_Size-Procyon_lotor 0.3689 1.0440 193
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3631 1.0165 472
## Cogon_Patch_Size-Lynx_rufus 1.9140 1.0980 277
## Cogon_Patch_Size-Didelphis_virginiana 2.9509 1.0041 511
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2966 1.0376 284
## Cogon_Patch_Size-Meleagris_gallopavo 1.8609 1.0117 398
## Cogon_Patch_Size-Sciurus_carolinensis 0.1989 1.0210 220
## Veg_shannon_index-Canis_latrans 2.5297 1.0353 380
## Veg_shannon_index-Procyon_lotor 2.5575 1.0507 323
## Veg_shannon_index-Dasypus_novemcinctus 1.7453 1.0268 363
## Veg_shannon_index-Lynx_rufus 2.3507 1.0430 373
## Veg_shannon_index-Didelphis_virginiana 2.3221 1.0481 405
## Veg_shannon_index-Sylvilagus_floridanus 2.5886 1.0216 253
## Veg_shannon_index-Meleagris_gallopavo 2.7308 1.0110 631
## Veg_shannon_index-Sciurus_carolinensis 1.5816 1.0171 734
## total_shrub_cover-Canis_latrans 1.8206 1.0083 601
## total_shrub_cover-Procyon_lotor 0.0939 1.0059 950
## total_shrub_cover-Dasypus_novemcinctus 1.2165 1.0086 1063
## total_shrub_cover-Lynx_rufus 0.3574 1.0118 311
## total_shrub_cover-Didelphis_virginiana 0.7240 1.0193 934
## total_shrub_cover-Sylvilagus_floridanus 1.4248 1.0013 821
## total_shrub_cover-Meleagris_gallopavo -0.1806 1.0534 335
## total_shrub_cover-Sciurus_carolinensis 1.3279 1.0017 1151
## Avg_Cogongrass_Cover-Canis_latrans 4.1733 1.0317 229
## Avg_Cogongrass_Cover-Procyon_lotor 4.1144 1.0108 238
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6934 1.0456 141
## Avg_Cogongrass_Cover-Lynx_rufus 4.5694 1.0275 270
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0209 1.0063 236
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5887 1.0106 216
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8697 1.0021 263
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5200 1.0213 210
## Tree_Density-Canis_latrans -0.6808 1.0104 159
## Tree_Density-Procyon_lotor -0.0682 1.0043 553
## Tree_Density-Dasypus_novemcinctus -1.2401 1.0128 173
## Tree_Density-Lynx_rufus 2.3239 1.0187 258
## Tree_Density-Didelphis_virginiana -0.5006 1.0188 460
## Tree_Density-Sylvilagus_floridanus -0.5794 1.0037 205
## Tree_Density-Meleagris_gallopavo -0.0112 1.0361 477
## Tree_Density-Sciurus_carolinensis -0.4846 1.0377 471
## Avg_Canopy_Cover-Canis_latrans 1.6424 1.0075 644
## Avg_Canopy_Cover-Procyon_lotor 3.1526 1.0354 302
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4405 1.0348 348
## Avg_Canopy_Cover-Lynx_rufus 2.9690 1.0107 507
## Avg_Canopy_Cover-Didelphis_virginiana 4.7735 1.0225 210
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.3117 1.0058 295
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9679 1.0092 388
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9042 1.0121 557
## avg_veg_height-Canis_latrans 0.2346 1.0058 358
## avg_veg_height-Procyon_lotor 0.6036 1.0001 520
## avg_veg_height-Dasypus_novemcinctus 0.8021 1.0055 584
## avg_veg_height-Lynx_rufus 0.6246 1.0058 539
## avg_veg_height-Didelphis_virginiana 0.4700 1.0053 476
## avg_veg_height-Sylvilagus_floridanus 0.3843 1.0044 395
## avg_veg_height-Meleagris_gallopavo 0.6106 1.0019 590
## avg_veg_height-Sciurus_carolinensis 1.0649 1.0006 583
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6267 0.1701 -2.9759 -2.6222 -2.3045 1.0121
## (Intercept)-Procyon_lotor -2.2772 0.1253 -2.5359 -2.2756 -2.0305 1.0098
## (Intercept)-Dasypus_novemcinctus -1.6178 0.1340 -1.8799 -1.6160 -1.3552 1.0034
## (Intercept)-Lynx_rufus -3.5078 0.3093 -4.1416 -3.4978 -2.9307 1.0078
## (Intercept)-Didelphis_virginiana -2.3533 0.2481 -2.8647 -2.3449 -1.8900 1.0162
## (Intercept)-Sylvilagus_floridanus -3.1322 0.2519 -3.6333 -3.1213 -2.6612 1.0048
## (Intercept)-Meleagris_gallopavo -3.3766 0.2885 -3.9482 -3.3678 -2.8360 1.0264
## (Intercept)-Sciurus_carolinensis -2.4812 0.2604 -3.0256 -2.4733 -1.9981 1.0013
## week-Canis_latrans 0.0614 0.1322 -0.2055 0.0628 0.3112 0.9998
## week-Procyon_lotor -0.0605 0.1228 -0.3086 -0.0561 0.1742 1.0073
## week-Dasypus_novemcinctus -0.1768 0.1411 -0.4690 -0.1692 0.0887 1.0002
## week-Lynx_rufus -0.0555 0.1913 -0.4459 -0.0497 0.3055 1.0035
## week-Didelphis_virginiana -0.2386 0.2218 -0.7369 -0.2217 0.1539 1.0021
## week-Sylvilagus_floridanus -0.1765 0.2097 -0.6135 -0.1683 0.2124 1.0062
## week-Meleagris_gallopavo -0.3067 0.2457 -0.8615 -0.2834 0.1105 1.0056
## week-Sciurus_carolinensis 0.1147 0.1861 -0.2497 0.1192 0.4788 0.9998
## ESS
## (Intercept)-Canis_latrans 1079
## (Intercept)-Procyon_lotor 1384
## (Intercept)-Dasypus_novemcinctus 2421
## (Intercept)-Lynx_rufus 315
## (Intercept)-Didelphis_virginiana 1374
## (Intercept)-Sylvilagus_floridanus 615
## (Intercept)-Meleagris_gallopavo 517
## (Intercept)-Sciurus_carolinensis 1206
## week-Canis_latrans 1575
## week-Procyon_lotor 1735
## week-Dasypus_novemcinctus 1991
## week-Lynx_rufus 896
## week-Didelphis_virginiana 1565
## week-Sylvilagus_floridanus 1014
## week-Meleagris_gallopavo 743
## week-Sciurus_carolinensis 1712
# 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): 0.6047
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5345 0.3965 -1.3118 -0.5335 0.2608 1.0140 739
## Avg_Cogongrass_Cover 0.1538 0.3280 -0.4648 0.1603 0.8224 1.0088 747
## total_shrub_cover -0.4889 0.3436 -1.2221 -0.4719 0.1579 1.0076 1442
## avg_veg_height -0.0053 0.3094 -0.6261 0.0039 0.5772 1.0062 712
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7122 0.8319 0.0612 0.4751 2.8428 1.0140 1148
## Avg_Cogongrass_Cover 0.3886 0.5247 0.0424 0.2315 1.7311 1.0142 1363
## total_shrub_cover 0.6579 0.7737 0.0602 0.4116 2.7826 1.0361 872
## avg_veg_height 0.2602 0.2893 0.0343 0.1688 1.0634 1.0058 1194
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8052 0.7245 0.0632 0.6026 2.7174 1.0458 200
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5759 0.2836 -3.1248 -2.5779 -1.9754 1.0023 2767
## week -0.0994 0.1351 -0.3715 -0.0989 0.1570 1.0089 1742
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5967 0.5119 0.1639 0.4574 1.8765 1.0041 2002
## week 0.1068 0.1137 0.0247 0.0785 0.3480 1.0257 2010
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0603 0.5526 -1.1130 -0.0669
## (Intercept)-Procyon_lotor 0.1987 0.6014 -0.9776 0.1908
## (Intercept)-Dasypus_novemcinctus -0.6760 0.4814 -1.6918 -0.6650
## (Intercept)-Lynx_rufus -0.3624 0.6225 -1.5657 -0.3766
## (Intercept)-Didelphis_virginiana -1.1114 0.5795 -2.3518 -1.0702
## (Intercept)-Sylvilagus_floridanus -0.4641 0.5662 -1.5590 -0.4657
## (Intercept)-Meleagris_gallopavo -0.7282 0.5892 -1.9483 -0.7149
## (Intercept)-Sciurus_carolinensis -1.1413 0.5733 -2.3400 -1.1153
## Avg_Cogongrass_Cover-Canis_latrans 0.4013 0.4310 -0.3613 0.3750
## Avg_Cogongrass_Cover-Procyon_lotor 0.0703 0.4192 -0.7679 0.0697
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2622 0.3976 -0.4977 0.2688
## Avg_Cogongrass_Cover-Lynx_rufus 0.4435 0.5030 -0.4418 0.4143
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3505 0.4367 -0.4649 0.3334
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2480 0.5060 -1.3659 -0.2099
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3261 0.5682 -1.5476 -0.2776
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2468 0.4075 -0.5319 0.2488
## total_shrub_cover-Canis_latrans 0.0898 0.4386 -0.7048 0.0590
## total_shrub_cover-Procyon_lotor -0.9148 0.4664 -1.9418 -0.8705
## total_shrub_cover-Dasypus_novemcinctus -0.0894 0.3860 -0.7849 -0.1009
## total_shrub_cover-Lynx_rufus -0.9231 0.6342 -2.3842 -0.8398
## total_shrub_cover-Didelphis_virginiana -0.2936 0.4263 -1.1372 -0.2928
## total_shrub_cover-Sylvilagus_floridanus -0.4226 0.4993 -1.5006 -0.4127
## total_shrub_cover-Meleagris_gallopavo -1.2614 0.6426 -2.7656 -1.1732
## total_shrub_cover-Sciurus_carolinensis -0.1553 0.4393 -0.9973 -0.1648
## avg_veg_height-Canis_latrans -0.0851 0.3962 -0.8844 -0.0767
## avg_veg_height-Procyon_lotor 0.0882 0.4087 -0.6776 0.0794
## avg_veg_height-Dasypus_novemcinctus 0.1808 0.3892 -0.5498 0.1673
## avg_veg_height-Lynx_rufus -0.0211 0.4898 -0.9900 -0.0242
## avg_veg_height-Didelphis_virginiana -0.0481 0.4168 -0.9071 -0.0417
## avg_veg_height-Sylvilagus_floridanus -0.1499 0.4340 -1.0411 -0.1401
## avg_veg_height-Meleagris_gallopavo -0.2506 0.5056 -1.3475 -0.2237
## avg_veg_height-Sciurus_carolinensis 0.2570 0.4265 -0.5204 0.2384
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0852 1.0219 777
## (Intercept)-Procyon_lotor 1.4422 1.0281 441
## (Intercept)-Dasypus_novemcinctus 0.2280 1.0016 1259
## (Intercept)-Lynx_rufus 0.9571 1.0247 695
## (Intercept)-Didelphis_virginiana -0.0561 1.0045 1004
## (Intercept)-Sylvilagus_floridanus 0.6808 1.0041 845
## (Intercept)-Meleagris_gallopavo 0.3805 1.0076 845
## (Intercept)-Sciurus_carolinensis -0.0851 1.0055 868
## Avg_Cogongrass_Cover-Canis_latrans 1.3143 1.0084 1148
## Avg_Cogongrass_Cover-Procyon_lotor 0.9133 1.0078 1312
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0521 1.0056 1603
## Avg_Cogongrass_Cover-Lynx_rufus 1.5396 1.0051 1180
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2755 1.0037 1463
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6493 1.0098 1144
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6366 1.0010 922
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0730 1.0087 1390
## total_shrub_cover-Canis_latrans 1.0180 1.0026 1312
## total_shrub_cover-Procyon_lotor -0.0892 1.0056 1414
## total_shrub_cover-Dasypus_novemcinctus 0.7202 1.0002 1680
## total_shrub_cover-Lynx_rufus 0.1153 1.0141 892
## total_shrub_cover-Didelphis_virginiana 0.5774 1.0013 2205
## total_shrub_cover-Sylvilagus_floridanus 0.5353 1.0084 1300
## total_shrub_cover-Meleagris_gallopavo -0.2588 1.0071 733
## total_shrub_cover-Sciurus_carolinensis 0.7423 1.0091 2100
## avg_veg_height-Canis_latrans 0.6795 1.0143 1096
## avg_veg_height-Procyon_lotor 0.9134 1.0131 1345
## avg_veg_height-Dasypus_novemcinctus 0.9680 1.0028 1519
## avg_veg_height-Lynx_rufus 0.9336 1.0095 1013
## avg_veg_height-Didelphis_virginiana 0.7493 1.0033 1297
## avg_veg_height-Sylvilagus_floridanus 0.6570 1.0061 1351
## avg_veg_height-Meleagris_gallopavo 0.6470 1.0005 852
## avg_veg_height-Sciurus_carolinensis 1.1479 1.0016 1250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6165 0.1702 -2.9477 -2.6097 -2.3036 1.0237
## (Intercept)-Procyon_lotor -2.2803 0.1283 -2.5462 -2.2776 -2.0429 1.0038
## (Intercept)-Dasypus_novemcinctus -1.6247 0.1360 -1.9011 -1.6236 -1.3701 1.0082
## (Intercept)-Lynx_rufus -3.4127 0.2779 -3.9756 -3.4115 -2.8920 1.0182
## (Intercept)-Didelphis_virginiana -2.3772 0.2470 -2.8830 -2.3742 -1.9124 1.0062
## (Intercept)-Sylvilagus_floridanus -3.1730 0.2849 -3.7693 -3.1676 -2.6309 1.0077
## (Intercept)-Meleagris_gallopavo -3.2601 0.2978 -3.8850 -3.2425 -2.6930 1.0180
## (Intercept)-Sciurus_carolinensis -2.4973 0.2646 -3.0481 -2.4839 -2.0154 1.0014
## week-Canis_latrans 0.0581 0.1316 -0.2073 0.0576 0.3196 1.0016
## week-Procyon_lotor -0.0595 0.1165 -0.2928 -0.0564 0.1587 1.0043
## week-Dasypus_novemcinctus -0.1712 0.1384 -0.4610 -0.1669 0.0816 1.0012
## week-Lynx_rufus -0.0517 0.1884 -0.4263 -0.0436 0.3012 1.0009
## week-Didelphis_virginiana -0.2370 0.2211 -0.7142 -0.2261 0.1734 1.0014
## week-Sylvilagus_floridanus -0.1580 0.2037 -0.6018 -0.1452 0.2012 1.0089
## week-Meleagris_gallopavo -0.2967 0.2393 -0.8020 -0.2817 0.1126 1.0181
## week-Sciurus_carolinensis 0.1178 0.1831 -0.2334 0.1192 0.4829 1.0033
## ESS
## (Intercept)-Canis_latrans 1053
## (Intercept)-Procyon_lotor 1556
## (Intercept)-Dasypus_novemcinctus 2239
## (Intercept)-Lynx_rufus 493
## (Intercept)-Didelphis_virginiana 1330
## (Intercept)-Sylvilagus_floridanus 628
## (Intercept)-Meleagris_gallopavo 542
## (Intercept)-Sciurus_carolinensis 920
## week-Canis_latrans 1949
## week-Procyon_lotor 1481
## week-Dasypus_novemcinctus 1813
## week-Lynx_rufus 1003
## week-Didelphis_virginiana 1501
## week-Sylvilagus_floridanus 904
## week-Meleagris_gallopavo 918
## week-Sciurus_carolinensis 2098
# 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): 0.6027
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3802 0.3318 -1.0104 -0.3967 0.3102 1.0051 1684
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6785 0.7573 0.1077 0.4903 2.4921 1.0956 1158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5727 0.2777 -3.0946 -2.5787 -2.0107 1.0060 2458
## week -0.1029 0.1348 -0.3890 -0.0983 0.1567 1.0041 1404
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5851 0.4832 0.1464 0.4548 1.8054 1.0135 1808
## week 0.1077 0.0957 0.0237 0.0793 0.3665 1.0081 1652
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1402 0.3711 -0.5403 0.1247 0.9266 1.0069
## (Intercept)-Procyon_lotor 0.4659 0.3790 -0.2392 0.4618 1.2658 1.0004
## (Intercept)-Dasypus_novemcinctus -0.5835 0.3382 -1.2455 -0.5819 0.0653 1.0001
## (Intercept)-Lynx_rufus -0.0981 0.5992 -0.9257 -0.1795 1.0811 1.1702
## (Intercept)-Didelphis_virginiana -1.1337 0.4148 -1.9866 -1.1107 -0.3872 0.9998
## (Intercept)-Sylvilagus_floridanus -0.3949 0.4137 -1.1761 -0.4162 0.4840 1.0021
## (Intercept)-Meleagris_gallopavo -0.4235 0.4566 -1.2640 -0.4395 0.5458 1.0140
## (Intercept)-Sciurus_carolinensis -1.1071 0.4026 -1.9391 -1.0872 -0.3797 1.0030
## ESS
## (Intercept)-Canis_latrans 1777
## (Intercept)-Procyon_lotor 1873
## (Intercept)-Dasypus_novemcinctus 2842
## (Intercept)-Lynx_rufus 303
## (Intercept)-Didelphis_virginiana 2054
## (Intercept)-Sylvilagus_floridanus 1343
## (Intercept)-Meleagris_gallopavo 1079
## (Intercept)-Sciurus_carolinensis 2090
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6060 0.1652 -2.9448 -2.6026 -2.3014 1.0169
## (Intercept)-Procyon_lotor -2.2670 0.1273 -2.5136 -2.2683 -2.0250 1.0095
## (Intercept)-Dasypus_novemcinctus -1.6179 0.1373 -1.8922 -1.6157 -1.3506 1.0011
## (Intercept)-Lynx_rufus -3.3527 0.2961 -3.9818 -3.3314 -2.8302 1.0395
## (Intercept)-Didelphis_virginiana -2.3675 0.2411 -2.8689 -2.3613 -1.9308 1.0080
## (Intercept)-Sylvilagus_floridanus -3.1103 0.2807 -3.6805 -3.1011 -2.5800 1.0175
## (Intercept)-Meleagris_gallopavo -3.3196 0.3234 -3.9846 -3.3036 -2.7325 1.0317
## (Intercept)-Sciurus_carolinensis -2.4920 0.2650 -3.0358 -2.4828 -2.0063 1.0022
## week-Canis_latrans 0.0543 0.1351 -0.2123 0.0581 0.3041 0.9996
## week-Procyon_lotor -0.0567 0.1206 -0.3049 -0.0503 0.1669 0.9999
## week-Dasypus_novemcinctus -0.1747 0.1331 -0.4478 -0.1715 0.0750 1.0022
## week-Lynx_rufus -0.0616 0.1921 -0.4513 -0.0577 0.2945 1.0020
## week-Didelphis_virginiana -0.2372 0.2172 -0.6867 -0.2193 0.1546 1.0029
## week-Sylvilagus_floridanus -0.1745 0.2062 -0.6101 -0.1570 0.1958 1.0057
## week-Meleagris_gallopavo -0.2870 0.2402 -0.8107 -0.2693 0.1294 1.0041
## week-Sciurus_carolinensis 0.1194 0.1877 -0.2597 0.1210 0.4815 1.0069
## ESS
## (Intercept)-Canis_latrans 1045
## (Intercept)-Procyon_lotor 1586
## (Intercept)-Dasypus_novemcinctus 2265
## (Intercept)-Lynx_rufus 352
## (Intercept)-Didelphis_virginiana 1402
## (Intercept)-Sylvilagus_floridanus 635
## (Intercept)-Meleagris_gallopavo 473
## (Intercept)-Sciurus_carolinensis 1185
## week-Canis_latrans 1553
## week-Procyon_lotor 1731
## week-Dasypus_novemcinctus 2139
## week-Lynx_rufus 1067
## week-Didelphis_virginiana 1521
## week-Sylvilagus_floridanus 1001
## week-Meleagris_gallopavo 752
## week-Sciurus_carolinensis 1865
#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): 0.5997
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5643 0.3641 -1.2893 -0.5610 0.1327 1.0111 744
## Veg_shannon_index 0.3562 0.2720 -0.1594 0.3524 0.9144 1.0077 998
## Avg_Cogongrass_Cover 0.3417 0.2868 -0.2099 0.3361 0.9142 1.0115 905
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5905 0.8079 0.0532 0.3724 2.2491 1.0194 1032
## Veg_shannon_index 0.2614 0.2982 0.0352 0.1680 1.0661 1.0047 1430
## Avg_Cogongrass_Cover 0.3230 0.4015 0.0382 0.2044 1.3229 1.0150 1111
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7707 0.6979 0.0716 0.583 2.5016 1.0084 281
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5759 0.2934 -3.1319 -2.5847 -1.9606 1.0104 2490
## week -0.1017 0.1438 -0.3906 -0.1009 0.1821 1.0012 1559
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6294 0.6222 0.1618 0.4697 2.2478 1.0082 1721
## week 0.1128 0.1485 0.0248 0.0817 0.3765 1.1126 1563
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1387 0.5494 -1.1647 -0.1646
## (Intercept)-Procyon_lotor 0.0299 0.5802 -1.0935 0.0021
## (Intercept)-Dasypus_novemcinctus -0.7038 0.4567 -1.6018 -0.6878
## (Intercept)-Lynx_rufus -0.4375 0.5614 -1.4882 -0.4409
## (Intercept)-Didelphis_virginiana -1.0929 0.5317 -2.2226 -1.0532
## (Intercept)-Sylvilagus_floridanus -0.5627 0.5384 -1.6056 -0.5826
## (Intercept)-Meleagris_gallopavo -0.6313 0.5284 -1.6948 -0.6322
## (Intercept)-Sciurus_carolinensis -1.0799 0.5262 -2.1994 -1.0336
## Veg_shannon_index-Canis_latrans 0.6313 0.3746 -0.0654 0.6081
## Veg_shannon_index-Procyon_lotor 0.4724 0.3850 -0.2205 0.4460
## Veg_shannon_index-Dasypus_novemcinctus 0.2050 0.3336 -0.4621 0.2035
## Veg_shannon_index-Lynx_rufus 0.1899 0.4442 -0.7186 0.1977
## Veg_shannon_index-Didelphis_virginiana 0.4674 0.3760 -0.2176 0.4520
## Veg_shannon_index-Sylvilagus_floridanus 0.4321 0.4058 -0.3429 0.4217
## Veg_shannon_index-Meleagris_gallopavo 0.4433 0.4277 -0.2949 0.4225
## Veg_shannon_index-Sciurus_carolinensis 0.0357 0.3769 -0.7684 0.0576
## Avg_Cogongrass_Cover-Canis_latrans 0.5444 0.3894 -0.1595 0.5164
## Avg_Cogongrass_Cover-Procyon_lotor 0.4011 0.3859 -0.2981 0.3913
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4421 0.3422 -0.2153 0.4379
## Avg_Cogongrass_Cover-Lynx_rufus 0.6141 0.4269 -0.1157 0.5720
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4791 0.3652 -0.2073 0.4581
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0726 0.4498 -1.0564 -0.0444
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0948 0.5143 -1.2115 -0.0581
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4240 0.3610 -0.2778 0.4262
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9767 1.0043 582
## (Intercept)-Procyon_lotor 1.2087 1.0016 487
## (Intercept)-Dasypus_novemcinctus 0.1819 1.0135 1377
## (Intercept)-Lynx_rufus 0.7319 1.0132 772
## (Intercept)-Didelphis_virginiana -0.1536 1.0389 977
## (Intercept)-Sylvilagus_floridanus 0.5812 1.0093 818
## (Intercept)-Meleagris_gallopavo 0.4169 1.0074 838
## (Intercept)-Sciurus_carolinensis -0.1525 1.0067 919
## Veg_shannon_index-Canis_latrans 1.4704 1.0054 1372
## Veg_shannon_index-Procyon_lotor 1.3316 1.0017 1346
## Veg_shannon_index-Dasypus_novemcinctus 0.8552 1.0088 2027
## Veg_shannon_index-Lynx_rufus 1.0520 1.0050 1012
## Veg_shannon_index-Didelphis_virginiana 1.2867 1.0071 1615
## Veg_shannon_index-Sylvilagus_floridanus 1.3023 1.0053 1515
## Veg_shannon_index-Meleagris_gallopavo 1.3378 1.0074 1283
## Veg_shannon_index-Sciurus_carolinensis 0.7383 1.0022 1604
## Avg_Cogongrass_Cover-Canis_latrans 1.3881 1.0045 1746
## Avg_Cogongrass_Cover-Procyon_lotor 1.2405 1.0165 1482
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1418 1.0076 2022
## Avg_Cogongrass_Cover-Lynx_rufus 1.5888 1.0131 1314
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2360 1.0059 1715
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7310 1.0030 1223
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8152 1.0039 913
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1593 1.0112 1699
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6022 0.1706 -2.9596 -2.5919 -2.2875 1.0034
## (Intercept)-Procyon_lotor -2.2822 0.1313 -2.5487 -2.2787 -2.0374 1.0226
## (Intercept)-Dasypus_novemcinctus -1.6206 0.1356 -1.8968 -1.6159 -1.3678 1.0016
## (Intercept)-Lynx_rufus -3.3729 0.2926 -3.9437 -3.3734 -2.8090 1.0003
## (Intercept)-Didelphis_virginiana -2.3798 0.2461 -2.8946 -2.3695 -1.9302 1.0182
## (Intercept)-Sylvilagus_floridanus -3.1434 0.2933 -3.7554 -3.1226 -2.6255 1.0229
## (Intercept)-Meleagris_gallopavo -3.3169 0.3114 -3.9754 -3.3024 -2.7455 1.0221
## (Intercept)-Sciurus_carolinensis -2.4846 0.2576 -3.0182 -2.4783 -1.9996 1.0001
## week-Canis_latrans 0.0592 0.1339 -0.2050 0.0635 0.3155 1.0020
## week-Procyon_lotor -0.0578 0.1179 -0.2936 -0.0561 0.1710 0.9997
## week-Dasypus_novemcinctus -0.1770 0.1417 -0.4659 -0.1731 0.0826 1.0054
## week-Lynx_rufus -0.0578 0.1944 -0.4752 -0.0527 0.3111 1.0164
## week-Didelphis_virginiana -0.2362 0.2161 -0.7027 -0.2223 0.1548 1.0142
## week-Sylvilagus_floridanus -0.1930 0.2100 -0.6325 -0.1850 0.1875 1.0040
## week-Meleagris_gallopavo -0.2987 0.2472 -0.8399 -0.2791 0.1061 1.0019
## week-Sciurus_carolinensis 0.1186 0.1834 -0.2475 0.1255 0.4693 1.0022
## ESS
## (Intercept)-Canis_latrans 1090
## (Intercept)-Procyon_lotor 1357
## (Intercept)-Dasypus_novemcinctus 2282
## (Intercept)-Lynx_rufus 484
## (Intercept)-Didelphis_virginiana 1342
## (Intercept)-Sylvilagus_floridanus 447
## (Intercept)-Meleagris_gallopavo 481
## (Intercept)-Sciurus_carolinensis 1295
## week-Canis_latrans 1635
## week-Procyon_lotor 1823
## week-Dasypus_novemcinctus 1943
## week-Lynx_rufus 973
## week-Didelphis_virginiana 1497
## week-Sylvilagus_floridanus 861
## week-Meleagris_gallopavo 777
## week-Sciurus_carolinensis 1865
# 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): 0.5988
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5496 0.3976 -1.3563 -0.5499 0.2658 1.0148 747
## Cogon_Patch_Size -0.0412 0.3926 -0.8555 -0.0401 0.7201 1.0063 1098
## Avg_Cogongrass_Cover 0.1577 0.3239 -0.4948 0.1648 0.7961 1.0066 1315
## total_shrub_cover -0.4713 0.3524 -1.1964 -0.4588 0.1976 1.0087 1398
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6938 0.8268 0.0563 0.4361 2.9028 1.0080 806
## Cogon_Patch_Size 0.7325 0.9239 0.0582 0.4320 3.2483 1.0070 867
## Avg_Cogongrass_Cover 0.4346 0.6468 0.0415 0.2546 1.8223 1.0225 1081
## total_shrub_cover 0.6241 0.8167 0.0604 0.3882 2.5524 1.0144 767
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8907 0.8109 0.072 0.6659 3.0488 1.0427 293
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5743 0.2844 -3.1124 -2.5807 -1.9942 1.0055 2612
## week -0.0987 0.1370 -0.3769 -0.0946 0.1709 1.0018 1673
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5867 0.5338 0.1529 0.4510 1.8082 1.0440 2025
## week 0.1105 0.1115 0.0243 0.0788 0.3758 1.0054 1949
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0297 0.5784 -1.1059 -0.0490
## (Intercept)-Procyon_lotor 0.1347 0.6054 -0.9748 0.1247
## (Intercept)-Dasypus_novemcinctus -0.7009 0.4941 -1.7328 -0.6839
## (Intercept)-Lynx_rufus -0.4079 0.6260 -1.5832 -0.4244
## (Intercept)-Didelphis_virginiana -1.0764 0.5835 -2.2702 -1.0488
## (Intercept)-Sylvilagus_floridanus -0.5444 0.5714 -1.6270 -0.5556
## (Intercept)-Meleagris_gallopavo -0.7198 0.5862 -1.9330 -0.6975
## (Intercept)-Sciurus_carolinensis -1.1336 0.5835 -2.3443 -1.1033
## Cogon_Patch_Size-Canis_latrans 0.6464 0.6094 -0.2952 0.5549
## Cogon_Patch_Size-Procyon_lotor -0.1657 0.4490 -1.0496 -0.1689
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0789 0.4049 -0.9276 -0.0661
## Cogon_Patch_Size-Lynx_rufus -0.1624 0.7025 -1.4656 -0.1869
## Cogon_Patch_Size-Didelphis_virginiana 0.6089 0.5028 -0.2387 0.5663
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6636 0.7131 -2.3427 -0.5698
## Cogon_Patch_Size-Meleagris_gallopavo 0.0049 0.5956 -1.1516 -0.0059
## Cogon_Patch_Size-Sciurus_carolinensis -0.5381 0.6019 -1.9398 -0.4565
## Avg_Cogongrass_Cover-Canis_latrans 0.2437 0.4006 -0.4994 0.2376
## Avg_Cogongrass_Cover-Procyon_lotor 0.1693 0.4330 -0.6337 0.1468
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3542 0.3773 -0.3646 0.3486
## Avg_Cogongrass_Cover-Lynx_rufus 0.5156 0.4900 -0.3207 0.4727
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1725 0.4154 -0.6466 0.1732
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2159 0.5064 -1.3339 -0.1837
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4316 0.6084 -1.8144 -0.3665
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4362 0.4112 -0.3144 0.4139
## total_shrub_cover-Canis_latrans 0.0362 0.4350 -0.7640 0.0160
## total_shrub_cover-Procyon_lotor -0.8792 0.4910 -1.9813 -0.8393
## total_shrub_cover-Dasypus_novemcinctus -0.0990 0.3774 -0.8255 -0.1039
## total_shrub_cover-Lynx_rufus -0.8507 0.6497 -2.3590 -0.7631
## total_shrub_cover-Didelphis_virginiana -0.3620 0.4369 -1.2231 -0.3583
## total_shrub_cover-Sylvilagus_floridanus -0.3718 0.5221 -1.4649 -0.3583
## total_shrub_cover-Meleagris_gallopavo -1.2392 0.6701 -2.7797 -1.1518
## total_shrub_cover-Sciurus_carolinensis -0.1463 0.4439 -1.0119 -0.1574
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2093 1.0162 660
## (Intercept)-Procyon_lotor 1.3499 1.0086 691
## (Intercept)-Dasypus_novemcinctus 0.2340 1.0185 1023
## (Intercept)-Lynx_rufus 0.9014 1.0379 587
## (Intercept)-Didelphis_virginiana 0.0031 1.0151 752
## (Intercept)-Sylvilagus_floridanus 0.6644 1.0100 961
## (Intercept)-Meleagris_gallopavo 0.3883 1.0023 904
## (Intercept)-Sciurus_carolinensis -0.0614 1.0055 857
## Cogon_Patch_Size-Canis_latrans 2.1009 1.0035 962
## Cogon_Patch_Size-Procyon_lotor 0.7210 1.0127 1889
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7063 1.0017 2037
## Cogon_Patch_Size-Lynx_rufus 1.3482 1.0201 782
## Cogon_Patch_Size-Didelphis_virginiana 1.6953 1.0070 1079
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4975 1.0044 756
## Cogon_Patch_Size-Meleagris_gallopavo 1.2658 1.0040 1111
## Cogon_Patch_Size-Sciurus_carolinensis 0.4327 1.0061 981
## Avg_Cogongrass_Cover-Canis_latrans 1.0612 1.0029 1724
## Avg_Cogongrass_Cover-Procyon_lotor 1.1040 1.0070 1531
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1096 1.0026 1813
## Avg_Cogongrass_Cover-Lynx_rufus 1.6251 1.0104 1202
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0132 1.0012 1834
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6666 1.0017 1287
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5793 1.0057 861
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2996 1.0029 1449
## total_shrub_cover-Canis_latrans 0.9190 1.0041 1388
## total_shrub_cover-Procyon_lotor -0.0311 1.0084 1345
## total_shrub_cover-Dasypus_novemcinctus 0.6658 1.0007 1739
## total_shrub_cover-Lynx_rufus 0.1793 1.0235 637
## total_shrub_cover-Didelphis_virginiana 0.4575 1.0022 1989
## total_shrub_cover-Sylvilagus_floridanus 0.5994 1.0028 975
## total_shrub_cover-Meleagris_gallopavo -0.1864 1.0011 616
## total_shrub_cover-Sciurus_carolinensis 0.7697 1.0024 1757
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6029 0.1727 -2.9562 -2.5998 -2.2800 1.0035
## (Intercept)-Procyon_lotor -2.2861 0.1293 -2.5351 -2.2852 -2.0329 1.0073
## (Intercept)-Dasypus_novemcinctus -1.6213 0.1381 -1.8822 -1.6181 -1.3562 1.0010
## (Intercept)-Lynx_rufus -3.3900 0.2825 -3.9625 -3.3779 -2.8707 1.0392
## (Intercept)-Didelphis_virginiana -2.3770 0.2533 -2.9110 -2.3677 -1.9132 1.0022
## (Intercept)-Sylvilagus_floridanus -3.1563 0.2823 -3.7512 -3.1443 -2.6131 1.0074
## (Intercept)-Meleagris_gallopavo -3.2810 0.3045 -3.9026 -3.2687 -2.7103 1.0063
## (Intercept)-Sciurus_carolinensis -2.4794 0.2508 -3.0038 -2.4681 -2.0184 1.0173
## week-Canis_latrans 0.0614 0.1327 -0.2069 0.0662 0.3094 1.0103
## week-Procyon_lotor -0.0598 0.1171 -0.2974 -0.0546 0.1649 1.0001
## week-Dasypus_novemcinctus -0.1771 0.1377 -0.4608 -0.1745 0.0761 1.0016
## week-Lynx_rufus -0.0546 0.1929 -0.4432 -0.0488 0.3197 0.9999
## week-Didelphis_virginiana -0.2319 0.2189 -0.6937 -0.2186 0.1734 1.0028
## week-Sylvilagus_floridanus -0.1568 0.2062 -0.6029 -0.1423 0.2095 1.0030
## week-Meleagris_gallopavo -0.2947 0.2473 -0.8688 -0.2634 0.1256 1.0006
## week-Sciurus_carolinensis 0.1221 0.1793 -0.2376 0.1219 0.4680 1.0036
## ESS
## (Intercept)-Canis_latrans 1013
## (Intercept)-Procyon_lotor 1432
## (Intercept)-Dasypus_novemcinctus 2216
## (Intercept)-Lynx_rufus 423
## (Intercept)-Didelphis_virginiana 1288
## (Intercept)-Sylvilagus_floridanus 515
## (Intercept)-Meleagris_gallopavo 553
## (Intercept)-Sciurus_carolinensis 1098
## week-Canis_latrans 1549
## week-Procyon_lotor 1808
## week-Dasypus_novemcinctus 2090
## week-Lynx_rufus 1101
## week-Didelphis_virginiana 1445
## week-Sylvilagus_floridanus 1011
## week-Meleagris_gallopavo 792
## week-Sciurus_carolinensis 1964
#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): 0.6098
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6116 0.4626 -1.5003 -0.6330 0.3368 1.0156 794
## Tree_Density -0.7646 0.4594 -1.7649 -0.7333 0.0606 1.0142 767
## Avg_Canopy_Cover 0.9859 0.3807 0.2416 0.9731 1.7989 1.0144 1039
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2427 1.6666 0.0902 0.7778 5.4312 1.0598 474
## Tree_Density 0.9953 1.6837 0.0553 0.4934 4.9890 1.0270 754
## Avg_Canopy_Cover 0.7146 0.8577 0.0620 0.4526 2.7151 1.0234 758
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4704 0.5047 0.052 0.3032 1.836 1.005 213
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5715 0.2931 -3.1333 -2.5840 -1.9538 1.0047 2636
## week -0.1073 0.1494 -0.4268 -0.1037 0.1812 1.0152 1280
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6279 0.5765 0.1630 0.4859 1.9150 1.0646 2025
## week 0.1114 0.1075 0.0241 0.0807 0.3893 1.0008 1377
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0052 0.5486 -1.0597 -0.0083 1.1199
## (Intercept)-Procyon_lotor 0.2953 0.6092 -0.9312 0.3009 1.4582
## (Intercept)-Dasypus_novemcinctus -0.9411 0.5377 -2.1182 -0.8969 0.0310
## (Intercept)-Lynx_rufus -0.0858 0.9628 -1.4503 -0.2269 2.4525
## (Intercept)-Didelphis_virginiana -1.5128 0.6182 -2.8063 -1.4805 -0.3666
## (Intercept)-Sylvilagus_floridanus -0.7389 0.5782 -1.9099 -0.7238 0.3890
## (Intercept)-Meleagris_gallopavo -0.6300 0.6153 -1.8490 -0.6430 0.6314
## (Intercept)-Sciurus_carolinensis -1.5280 0.6475 -2.8685 -1.4967 -0.3431
## Tree_Density-Canis_latrans -0.9178 0.5840 -2.2873 -0.8452 0.0052
## Tree_Density-Procyon_lotor -0.4553 0.4171 -1.3010 -0.4485 0.3650
## Tree_Density-Dasypus_novemcinctus -1.3673 0.9006 -3.6340 -1.1527 -0.1796
## Tree_Density-Lynx_rufus 0.2111 0.7594 -0.9421 0.0898 2.0834
## Tree_Density-Didelphis_virginiana -0.9794 0.7041 -2.6721 -0.8690 0.0885
## Tree_Density-Sylvilagus_floridanus -1.0686 0.7801 -3.0014 -0.9492 0.0975
## Tree_Density-Meleagris_gallopavo -0.9231 0.7357 -2.6325 -0.8274 0.2511
## Tree_Density-Sciurus_carolinensis -0.8987 0.7215 -2.6023 -0.7784 0.2291
## Avg_Canopy_Cover-Canis_latrans 0.0835 0.4571 -0.8435 0.0898 0.9456
## Avg_Canopy_Cover-Procyon_lotor 0.9587 0.4468 0.1326 0.9296 1.8950
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0004 0.4249 0.2230 0.9841 1.8879
## Avg_Canopy_Cover-Lynx_rufus 0.6812 0.6449 -0.5095 0.6556 2.1136
## Avg_Canopy_Cover-Didelphis_virginiana 1.2104 0.4865 0.3904 1.1747 2.2921
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6873 0.7522 0.5692 1.5582 3.5141
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3563 0.6443 0.3004 1.2736 2.8859
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1683 0.4774 0.3185 1.1344 2.1980
## Rhat ESS
## (Intercept)-Canis_latrans 1.0115 679
## (Intercept)-Procyon_lotor 1.0115 464
## (Intercept)-Dasypus_novemcinctus 1.0241 1007
## (Intercept)-Lynx_rufus 1.0364 165
## (Intercept)-Didelphis_virginiana 1.0226 740
## (Intercept)-Sylvilagus_floridanus 1.0021 1080
## (Intercept)-Meleagris_gallopavo 1.0066 705
## (Intercept)-Sciurus_carolinensis 1.0086 814
## Tree_Density-Canis_latrans 1.0086 939
## Tree_Density-Procyon_lotor 0.9999 2479
## Tree_Density-Dasypus_novemcinctus 1.0461 470
## Tree_Density-Lynx_rufus 1.0212 631
## Tree_Density-Didelphis_virginiana 1.0096 852
## Tree_Density-Sylvilagus_floridanus 1.0051 650
## Tree_Density-Meleagris_gallopavo 1.0251 718
## Tree_Density-Sciurus_carolinensis 1.0112 865
## Avg_Canopy_Cover-Canis_latrans 1.0041 1310
## Avg_Canopy_Cover-Procyon_lotor 1.0035 1388
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0041 1762
## Avg_Canopy_Cover-Lynx_rufus 1.0144 534
## Avg_Canopy_Cover-Didelphis_virginiana 1.0005 1306
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0078 582
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0040 833
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0027 1322
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6269 0.1750 -2.9855 -2.6191 -2.2965 1.0265
## (Intercept)-Procyon_lotor -2.2685 0.1295 -2.5356 -2.2667 -2.0223 1.0075
## (Intercept)-Dasypus_novemcinctus -1.6162 0.1363 -1.8775 -1.6153 -1.3576 1.0007
## (Intercept)-Lynx_rufus -3.4777 0.3174 -4.1459 -3.4599 -2.9182 1.0217
## (Intercept)-Didelphis_virginiana -2.3553 0.2432 -2.8504 -2.3445 -1.9053 1.0131
## (Intercept)-Sylvilagus_floridanus -3.0842 0.2610 -3.6107 -3.0725 -2.5952 1.0042
## (Intercept)-Meleagris_gallopavo -3.3441 0.3059 -3.9741 -3.3318 -2.7662 1.0067
## (Intercept)-Sciurus_carolinensis -2.4655 0.2520 -2.9950 -2.4593 -1.9965 1.0104
## week-Canis_latrans 0.0610 0.1295 -0.2081 0.0631 0.3053 1.0031
## week-Procyon_lotor -0.0561 0.1200 -0.3052 -0.0514 0.1739 1.0164
## week-Dasypus_novemcinctus -0.1786 0.1383 -0.4749 -0.1732 0.0711 1.0017
## week-Lynx_rufus -0.0586 0.1882 -0.4601 -0.0462 0.2904 1.0330
## week-Didelphis_virginiana -0.2308 0.2164 -0.7083 -0.2149 0.1634 1.0053
## week-Sylvilagus_floridanus -0.1696 0.2049 -0.6175 -0.1562 0.1945 1.0185
## week-Meleagris_gallopavo -0.3021 0.2515 -0.8656 -0.2789 0.1283 1.0006
## week-Sciurus_carolinensis 0.1191 0.1883 -0.2513 0.1216 0.4924 1.0017
## ESS
## (Intercept)-Canis_latrans 958
## (Intercept)-Procyon_lotor 1427
## (Intercept)-Dasypus_novemcinctus 2271
## (Intercept)-Lynx_rufus 286
## (Intercept)-Didelphis_virginiana 1423
## (Intercept)-Sylvilagus_floridanus 704
## (Intercept)-Meleagris_gallopavo 482
## (Intercept)-Sciurus_carolinensis 1241
## week-Canis_latrans 1527
## week-Procyon_lotor 1841
## week-Dasypus_novemcinctus 2028
## week-Lynx_rufus 902
## week-Didelphis_virginiana 1303
## week-Sylvilagus_floridanus 1047
## week-Meleagris_gallopavo 626
## week-Sciurus_carolinensis 1695
# 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): 0.603
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1264 0.3974 -1.9126 -1.1254 -0.3360 1.0153 717
## Avg_Cogongrass_Cover -0.5563 0.3611 -1.2903 -0.5607 0.1482 1.0011 745
## I(Avg_Cogongrass_Cover^2) 0.7783 0.3909 0.1281 0.7411 1.6438 1.0147 646
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5816 0.6994 0.0533 0.3885 2.3038 1.0271 1215
## Avg_Cogongrass_Cover 0.3624 0.4357 0.0416 0.2219 1.4988 1.0068 1170
## I(Avg_Cogongrass_Cover^2) 0.6299 1.0878 0.0431 0.2734 3.8777 1.0768 327
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5175 0.515 0.0523 0.3599 1.9917 1.0471 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5813 0.2847 -3.1455 -2.5873 -1.9747 1.0147 2207
## week -0.0969 0.1362 -0.3819 -0.0944 0.1636 1.0012 1231
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5856 0.5466 0.1512 0.4491 1.7527 1.0209 1727
## week 0.1053 0.1020 0.0237 0.0768 0.3488 1.0153 1521
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7744 0.5504 -1.8722 -0.7728
## (Intercept)-Procyon_lotor -0.5741 0.5858 -1.6706 -0.5880
## (Intercept)-Dasypus_novemcinctus -1.2403 0.4990 -2.2151 -1.2456
## (Intercept)-Lynx_rufus -1.2320 0.5894 -2.3810 -1.2385
## (Intercept)-Didelphis_virginiana -1.5787 0.5670 -2.8529 -1.5452
## (Intercept)-Sylvilagus_floridanus -1.1169 0.5514 -2.2272 -1.1122
## (Intercept)-Meleagris_gallopavo -0.9957 0.5601 -2.0778 -0.9989
## (Intercept)-Sciurus_carolinensis -1.7731 0.6075 -3.0800 -1.7183
## Avg_Cogongrass_Cover-Canis_latrans -0.3863 0.4908 -1.3008 -0.3976
## Avg_Cogongrass_Cover-Procyon_lotor -0.5318 0.5039 -1.5098 -0.5375
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4355 0.4762 -1.3889 -0.4336
## Avg_Cogongrass_Cover-Lynx_rufus -0.4876 0.5073 -1.4823 -0.4966
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2963 0.5110 -1.2727 -0.3135
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9841 0.5584 -2.1991 -0.9265
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8403 0.5458 -2.0674 -0.8099
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5718 0.4970 -1.6113 -0.5691
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3240 0.8756 0.2419 1.0795
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2158 0.8056 0.2445 1.0342
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6416 0.3530 -0.0301 0.6353
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1057 0.5043 0.2756 1.0407
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4552 0.4236 -0.2719 0.4308
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6741 0.4926 -0.1130 0.6215
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1501 0.5797 -1.1550 0.1878
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7732 0.3681 0.1122 0.7501
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3241 1.0117 796
## (Intercept)-Procyon_lotor 0.6544 1.0221 628
## (Intercept)-Dasypus_novemcinctus -0.2703 1.0044 1214
## (Intercept)-Lynx_rufus -0.0778 1.0147 878
## (Intercept)-Didelphis_virginiana -0.5808 1.0029 840
## (Intercept)-Sylvilagus_floridanus -0.0269 1.0186 873
## (Intercept)-Meleagris_gallopavo 0.1213 1.0105 838
## (Intercept)-Sciurus_carolinensis -0.7129 1.0007 752
## Avg_Cogongrass_Cover-Canis_latrans 0.6021 1.0007 1234
## Avg_Cogongrass_Cover-Procyon_lotor 0.5117 1.0008 1082
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5090 1.0010 1179
## Avg_Cogongrass_Cover-Lynx_rufus 0.5654 1.0050 1153
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7339 1.0056 1093
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0015 1.0074 879
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1504 1.0054 1006
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4071 1.0026 1145
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.6313 1.0544 242
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.4467 1.0856 298
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3381 1.0027 1271
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2785 1.0370 610
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3115 1.0085 647
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6887 1.0184 497
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.2703 1.0182 594
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5448 1.0007 1078
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6307 0.1663 -2.9577 -2.6298 -2.3123 1.0004
## (Intercept)-Procyon_lotor -2.2869 0.1273 -2.5410 -2.2850 -2.0444 1.0052
## (Intercept)-Dasypus_novemcinctus -1.6243 0.1407 -1.9047 -1.6217 -1.3504 1.0056
## (Intercept)-Lynx_rufus -3.3237 0.2844 -3.9005 -3.3111 -2.7959 1.0271
## (Intercept)-Didelphis_virginiana -2.3850 0.2523 -2.9160 -2.3763 -1.9229 1.0047
## (Intercept)-Sylvilagus_floridanus -3.1306 0.2905 -3.7420 -3.1161 -2.5996 1.0143
## (Intercept)-Meleagris_gallopavo -3.3000 0.3133 -3.9496 -3.2844 -2.7389 1.0160
## (Intercept)-Sciurus_carolinensis -2.4876 0.2651 -3.0259 -2.4749 -1.9763 1.0028
## week-Canis_latrans 0.0587 0.1329 -0.2129 0.0590 0.3103 1.0068
## week-Procyon_lotor -0.0557 0.1190 -0.2981 -0.0542 0.1713 1.0123
## week-Dasypus_novemcinctus -0.1706 0.1373 -0.4576 -0.1659 0.0808 1.0017
## week-Lynx_rufus -0.0499 0.1972 -0.4560 -0.0498 0.3222 1.0063
## week-Didelphis_virginiana -0.2220 0.2158 -0.6832 -0.2078 0.1540 1.0049
## week-Sylvilagus_floridanus -0.1532 0.2025 -0.5866 -0.1435 0.2167 1.0031
## week-Meleagris_gallopavo -0.2838 0.2363 -0.8073 -0.2674 0.1396 1.0122
## week-Sciurus_carolinensis 0.1193 0.1758 -0.2233 0.1208 0.4761 1.0030
## ESS
## (Intercept)-Canis_latrans 1093
## (Intercept)-Procyon_lotor 1454
## (Intercept)-Dasypus_novemcinctus 2206
## (Intercept)-Lynx_rufus 570
## (Intercept)-Didelphis_virginiana 1247
## (Intercept)-Sylvilagus_floridanus 458
## (Intercept)-Meleagris_gallopavo 476
## (Intercept)-Sciurus_carolinensis 1428
## week-Canis_latrans 1579
## week-Procyon_lotor 1617
## week-Dasypus_novemcinctus 2301
## week-Lynx_rufus 1026
## week-Didelphis_virginiana 1456
## week-Sylvilagus_floridanus 915
## week-Meleagris_gallopavo 878
## week-Sciurus_carolinensis 1738
# 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): 0.6237
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0518 0.6880 -3.4341 -2.0506 -0.6458 1.0284 389
## Cogon_Patch_Size -0.0386 0.6656 -1.4337 -0.0450 1.3349 1.0126 603
## Veg_shannon_index 0.9237 0.4515 0.0712 0.9090 1.8794 1.0323 459
## total_shrub_cover -0.7467 0.5920 -1.9429 -0.7431 0.3603 1.0418 840
## Avg_Cogongrass_Cover 0.4074 0.9362 -1.4267 0.4192 2.2112 1.0232 214
## Tree_Density -2.3361 0.8413 -4.0131 -2.3330 -0.6447 1.0247 224
## Avg_Canopy_Cover 1.7664 0.6453 0.6107 1.7306 3.1765 1.0189 558
## I(Avg_Cogongrass_Cover^2) 1.2650 0.6781 0.0162 1.2405 2.6469 1.0124 614
## avg_veg_height -0.2090 0.4798 -1.1977 -0.1835 0.7138 1.0167 366
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0100 2.7812 0.0757 1.0789 9.7398 1.1193 346
## Cogon_Patch_Size 2.6358 4.3527 0.1204 1.5005 12.1056 1.3029 185
## Veg_shannon_index 0.5879 0.8166 0.0426 0.3190 2.8193 1.0776 671
## total_shrub_cover 2.3582 3.2099 0.1203 1.3996 9.3060 1.0342 514
## Avg_Cogongrass_Cover 1.2854 2.2339 0.0534 0.5310 7.1613 1.0764 553
## Tree_Density 2.4637 5.0103 0.0668 0.9730 15.1788 1.2438 208
## Avg_Canopy_Cover 2.3143 3.3133 0.1116 1.3517 10.5126 1.0813 349
## I(Avg_Cogongrass_Cover^2) 2.7910 4.2337 0.0824 1.3490 14.2066 1.0515 179
## avg_veg_height 0.4626 0.6642 0.0409 0.2638 2.1874 1.0466 745
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8724 2.2552 0.0793 1.1707 8.1555 1.1874 105
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6057 0.2939 -3.1553 -2.6137 -2.0013 1.0035 2736
## week -0.0984 0.1386 -0.3830 -0.0941 0.1667 1.0007 1208
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6565 0.5053 0.1845 0.5176 1.9099 1.0012 1849
## week 0.1075 0.0998 0.0246 0.0799 0.3568 1.0061 1673
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.6018 0.9724 -3.4500 -1.6390
## (Intercept)-Procyon_lotor -1.2872 1.0163 -3.2006 -1.3262
## (Intercept)-Dasypus_novemcinctus -2.3985 0.9194 -4.4723 -2.3252
## (Intercept)-Lynx_rufus -1.6902 1.2406 -3.8646 -1.7850
## (Intercept)-Didelphis_virginiana -3.0488 1.0710 -5.4908 -2.9155
## (Intercept)-Sylvilagus_floridanus -2.2016 0.9810 -4.3465 -2.1538
## (Intercept)-Meleagris_gallopavo -2.0353 1.0410 -4.1705 -2.0424
## (Intercept)-Sciurus_carolinensis -3.4412 1.2325 -6.4369 -3.2423
## Cogon_Patch_Size-Canis_latrans 1.3145 1.1915 -0.4226 1.1193
## Cogon_Patch_Size-Procyon_lotor -0.5572 0.8551 -2.3172 -0.5237
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2809 0.7011 -1.7398 -0.2525
## Cogon_Patch_Size-Lynx_rufus -0.3632 1.4129 -3.6045 -0.2943
## Cogon_Patch_Size-Didelphis_virginiana 1.4314 0.9982 -0.1691 1.3114
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2676 1.4245 -4.7646 -1.0217
## Cogon_Patch_Size-Meleagris_gallopavo 0.1086 1.0210 -1.7640 0.0529
## Cogon_Patch_Size-Sciurus_carolinensis -0.8640 1.0720 -3.3665 -0.7294
## Veg_shannon_index-Canis_latrans 1.2911 0.6619 0.1661 1.2207
## Veg_shannon_index-Procyon_lotor 1.1360 0.6129 0.0431 1.1041
## Veg_shannon_index-Dasypus_novemcinctus 0.6555 0.5295 -0.4126 0.6564
## Veg_shannon_index-Lynx_rufus 0.9698 0.8228 -0.5322 0.9274
## Veg_shannon_index-Didelphis_virginiana 1.0009 0.6228 -0.1730 0.9709
## Veg_shannon_index-Sylvilagus_floridanus 0.9985 0.6647 -0.2916 0.9725
## Veg_shannon_index-Meleagris_gallopavo 1.1927 0.7195 -0.0583 1.1229
## Veg_shannon_index-Sciurus_carolinensis 0.4063 0.6800 -1.0459 0.4299
## total_shrub_cover-Canis_latrans 0.0416 0.7068 -1.2808 0.0157
## total_shrub_cover-Procyon_lotor -1.2468 0.6887 -2.7356 -1.1907
## total_shrub_cover-Dasypus_novemcinctus 0.0871 0.6032 -1.0897 0.0905
## total_shrub_cover-Lynx_rufus -1.7201 1.2291 -4.6445 -1.5085
## total_shrub_cover-Didelphis_virginiana -0.7180 0.7801 -2.3205 -0.6759
## total_shrub_cover-Sylvilagus_floridanus -0.4473 0.9557 -2.4478 -0.4223
## total_shrub_cover-Meleagris_gallopavo -2.6252 1.4209 -5.8610 -2.4469
## total_shrub_cover-Sciurus_carolinensis -0.0216 0.7686 -1.4739 -0.0486
## Avg_Cogongrass_Cover-Canis_latrans 0.2824 1.1665 -2.0417 0.3020
## Avg_Cogongrass_Cover-Procyon_lotor 0.4334 1.1678 -1.8446 0.4416
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9647 1.2610 -1.2984 0.8887
## Avg_Cogongrass_Cover-Lynx_rufus 0.5210 1.2900 -2.0036 0.5204
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7174 1.2205 -1.5241 0.6593
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1766 1.2441 -2.7929 -0.1106
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0232 1.2205 -2.6078 0.0673
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5931 1.2089 -1.6900 0.5578
## Tree_Density-Canis_latrans -3.1283 1.3432 -6.3778 -2.9070
## Tree_Density-Procyon_lotor -2.3165 1.0323 -4.5751 -2.2913
## Tree_Density-Dasypus_novemcinctus -3.5999 1.5105 -7.4035 -3.2963
## Tree_Density-Lynx_rufus -1.3239 1.5332 -3.8255 -1.4776
## Tree_Density-Didelphis_virginiana -2.4637 1.0727 -4.9678 -2.3663
## Tree_Density-Sylvilagus_floridanus -2.7208 1.2624 -5.5322 -2.6207
## Tree_Density-Meleagris_gallopavo -2.3753 1.2308 -4.8249 -2.3742
## Tree_Density-Sciurus_carolinensis -2.6825 1.1677 -5.2981 -2.5841
## Avg_Canopy_Cover-Canis_latrans 0.2917 0.7162 -1.1591 0.2895
## Avg_Canopy_Cover-Procyon_lotor 1.6792 0.7591 0.3828 1.6336
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0254 0.7565 0.7969 1.9420
## Avg_Canopy_Cover-Lynx_rufus 1.1725 1.2334 -1.1188 1.1259
## Avg_Canopy_Cover-Didelphis_virginiana 2.4989 0.9180 1.0891 2.3785
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3030 1.5631 1.0161 3.0297
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2235 1.1323 0.5787 2.0528
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2027 0.9309 0.8180 2.0739
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4037 1.2694 0.6548 2.1730
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2764 1.2544 0.5273 2.0590
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2644 0.7453 -0.0490 1.1929
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6529 1.4998 0.5462 2.3725
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6508 0.7519 -0.8444 0.6459
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9716 0.8532 -0.5403 0.9019
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.1825 1.4650 -3.4856 0.0367
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2933 0.7753 -0.0536 1.2054
## avg_veg_height-Canis_latrans -0.4743 0.6331 -1.8257 -0.4265
## avg_veg_height-Procyon_lotor -0.0126 0.6095 -1.1725 -0.0236
## avg_veg_height-Dasypus_novemcinctus 0.0543 0.5977 -1.0573 0.0258
## avg_veg_height-Lynx_rufus -0.3531 0.7184 -1.9105 -0.2989
## avg_veg_height-Didelphis_virginiana -0.3176 0.6506 -1.6979 -0.2815
## avg_veg_height-Sylvilagus_floridanus -0.3633 0.6771 -1.8161 -0.3067
## avg_veg_height-Meleagris_gallopavo -0.2817 0.7389 -1.8909 -0.2407
## avg_veg_height-Sciurus_carolinensis 0.0691 0.6346 -1.1473 0.0308
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3943 1.0200 422
## (Intercept)-Procyon_lotor 0.7579 1.0410 363
## (Intercept)-Dasypus_novemcinctus -0.7510 1.0711 515
## (Intercept)-Lynx_rufus 1.0648 1.0561 322
## (Intercept)-Didelphis_virginiana -1.2382 1.0715 462
## (Intercept)-Sylvilagus_floridanus -0.3086 1.0171 689
## (Intercept)-Meleagris_gallopavo 0.0979 1.0336 553
## (Intercept)-Sciurus_carolinensis -1.5966 1.1062 287
## Cogon_Patch_Size-Canis_latrans 3.9993 1.1134 332
## Cogon_Patch_Size-Procyon_lotor 1.0957 1.0342 457
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0357 1.0160 455
## Cogon_Patch_Size-Lynx_rufus 2.2670 1.0355 220
## Cogon_Patch_Size-Didelphis_virginiana 3.7264 1.0910 405
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8934 1.0596 238
## Cogon_Patch_Size-Meleagris_gallopavo 2.3795 1.0282 592
## Cogon_Patch_Size-Sciurus_carolinensis 0.8198 1.0655 386
## Veg_shannon_index-Canis_latrans 2.8325 1.0692 403
## Veg_shannon_index-Procyon_lotor 2.5297 1.0687 349
## Veg_shannon_index-Dasypus_novemcinctus 1.7007 1.0129 678
## Veg_shannon_index-Lynx_rufus 2.7958 1.0220 489
## Veg_shannon_index-Didelphis_virginiana 2.3470 1.0555 799
## Veg_shannon_index-Sylvilagus_floridanus 2.3927 1.0237 673
## Veg_shannon_index-Meleagris_gallopavo 2.7630 1.0134 651
## Veg_shannon_index-Sciurus_carolinensis 1.6180 1.0124 666
## total_shrub_cover-Canis_latrans 1.5493 1.0055 824
## total_shrub_cover-Procyon_lotor -0.0232 1.0207 895
## total_shrub_cover-Dasypus_novemcinctus 1.2604 1.0027 1356
## total_shrub_cover-Lynx_rufus 0.1338 1.0595 292
## total_shrub_cover-Didelphis_virginiana 0.7087 1.0298 709
## total_shrub_cover-Sylvilagus_floridanus 1.3853 1.0491 707
## total_shrub_cover-Meleagris_gallopavo -0.4952 1.0478 339
## total_shrub_cover-Sciurus_carolinensis 1.5642 1.0114 1053
## Avg_Cogongrass_Cover-Canis_latrans 2.4490 1.0251 388
## Avg_Cogongrass_Cover-Procyon_lotor 2.7639 1.0190 267
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.8108 1.0413 269
## Avg_Cogongrass_Cover-Lynx_rufus 3.1753 1.0380 257
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2437 1.0180 300
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0392 1.0252 353
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2143 1.0162 321
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.1644 1.0082 269
## Tree_Density-Canis_latrans -1.0622 1.0813 356
## Tree_Density-Procyon_lotor -0.3206 1.0524 342
## Tree_Density-Dasypus_novemcinctus -1.5347 1.1414 183
## Tree_Density-Lynx_rufus 2.1236 1.0257 218
## Tree_Density-Didelphis_virginiana -0.6012 1.0543 441
## Tree_Density-Sylvilagus_floridanus -0.5811 1.0331 394
## Tree_Density-Meleagris_gallopavo 0.0999 1.0156 582
## Tree_Density-Sciurus_carolinensis -0.6580 1.0640 403
## Avg_Canopy_Cover-Canis_latrans 1.7424 1.0417 579
## Avg_Canopy_Cover-Procyon_lotor 3.3436 1.0423 560
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7448 1.1093 213
## Avg_Canopy_Cover-Lynx_rufus 3.7526 1.0359 324
## Avg_Canopy_Cover-Didelphis_virginiana 4.7188 1.1435 143
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1924 1.0883 218
## Avg_Canopy_Cover-Meleagris_gallopavo 4.7488 1.0627 412
## Avg_Canopy_Cover-Sciurus_carolinensis 4.4447 1.1002 436
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5930 1.0825 145
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.4702 1.0749 222
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8859 1.0350 400
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.3124 1.0103 210
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.1519 1.0445 455
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8539 1.0236 369
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.0908 1.0310 160
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.0108 1.0374 583
## avg_veg_height-Canis_latrans 0.6908 1.0152 487
## avg_veg_height-Procyon_lotor 1.2535 1.0178 519
## avg_veg_height-Dasypus_novemcinctus 1.3111 1.0145 463
## avg_veg_height-Lynx_rufus 0.9492 1.0143 498
## avg_veg_height-Didelphis_virginiana 0.8672 1.0100 591
## avg_veg_height-Sylvilagus_floridanus 0.8383 1.0132 542
## avg_veg_height-Meleagris_gallopavo 1.1213 1.0210 429
## avg_veg_height-Sciurus_carolinensis 1.4038 1.0089 551
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6126 0.1638 -2.9511 -2.6093 -2.3047 1.0057
## (Intercept)-Procyon_lotor -2.2791 0.1273 -2.5327 -2.2770 -2.0466 1.0016
## (Intercept)-Dasypus_novemcinctus -1.6178 0.1370 -1.8934 -1.6141 -1.3590 1.0009
## (Intercept)-Lynx_rufus -3.5551 0.2785 -4.1162 -3.5563 -3.0047 1.0052
## (Intercept)-Didelphis_virginiana -2.3491 0.2419 -2.8541 -2.3394 -1.9022 1.0033
## (Intercept)-Sylvilagus_floridanus -3.1756 0.2640 -3.7232 -3.1674 -2.6781 1.0135
## (Intercept)-Meleagris_gallopavo -3.3156 0.2922 -3.9135 -3.3087 -2.7788 1.0030
## (Intercept)-Sciurus_carolinensis -2.5011 0.2682 -3.0494 -2.4857 -1.9950 1.0156
## week-Canis_latrans 0.0637 0.1289 -0.1898 0.0658 0.3133 1.0067
## week-Procyon_lotor -0.0615 0.1181 -0.3089 -0.0570 0.1602 1.0088
## week-Dasypus_novemcinctus -0.1742 0.1375 -0.4605 -0.1670 0.0761 1.0041
## week-Lynx_rufus -0.0479 0.1993 -0.4591 -0.0440 0.3216 1.0123
## week-Didelphis_virginiana -0.2270 0.2175 -0.6921 -0.2086 0.1679 1.0007
## week-Sylvilagus_floridanus -0.1662 0.2109 -0.6144 -0.1560 0.2114 0.9996
## week-Meleagris_gallopavo -0.2847 0.2427 -0.8157 -0.2621 0.1390 1.0109
## week-Sciurus_carolinensis 0.1146 0.1861 -0.2601 0.1191 0.4739 1.0043
## ESS
## (Intercept)-Canis_latrans 921
## (Intercept)-Procyon_lotor 1394
## (Intercept)-Dasypus_novemcinctus 2266
## (Intercept)-Lynx_rufus 395
## (Intercept)-Didelphis_virginiana 1429
## (Intercept)-Sylvilagus_floridanus 542
## (Intercept)-Meleagris_gallopavo 515
## (Intercept)-Sciurus_carolinensis 1158
## week-Canis_latrans 1602
## week-Procyon_lotor 1627
## week-Dasypus_novemcinctus 2100
## week-Lynx_rufus 853
## week-Didelphis_virginiana 1563
## week-Sylvilagus_floridanus 961
## week-Meleagris_gallopavo 814
## week-Sciurus_carolinensis 1782
# 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): 0.5015
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3082 0.3558 -0.9830 -0.3112 0.4453 1.0116 787
## Avg_Cogongrass_Cover 0.2062 0.2803 -0.3644 0.2114 0.7389 1.0067 870
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6190 0.8776 0.0540 0.3870 2.5186 1.0421 1031
## Avg_Cogongrass_Cover 0.3343 0.4357 0.0371 0.2042 1.3923 1.0270 859
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5738 0.5749 0.0554 0.3872 2.1908 1.0419 252
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7260 0.3232 -3.3575 -2.7288 -2.0384 1.0020 2203
## shrub_cover 0.2563 0.3200 -0.3763 0.2493 0.8820 1.0221 1292
## veg_height 0.0678 0.1995 -0.3463 0.0717 0.4397 1.0009 1450
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7550 0.6627 0.1680 0.5731 2.5180 1.0284 1143
## shrub_cover 0.7070 0.6303 0.1432 0.5279 2.3513 1.0307 980
## veg_height 0.2597 0.2401 0.0548 0.1933 0.8566 1.0034 1911
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1309 0.5155 -0.7945 0.1040
## (Intercept)-Procyon_lotor 0.2316 0.5257 -0.7585 0.2217
## (Intercept)-Dasypus_novemcinctus -0.4923 0.4460 -1.4096 -0.4862
## (Intercept)-Lynx_rufus -0.2126 0.5768 -1.2675 -0.2417
## (Intercept)-Didelphis_virginiana -0.8559 0.5160 -1.9694 -0.8258
## (Intercept)-Sylvilagus_floridanus -0.3828 0.4669 -1.2978 -0.3893
## (Intercept)-Meleagris_gallopavo -0.0858 0.6605 -1.1896 -0.1483
## (Intercept)-Sciurus_carolinensis -0.8666 0.5168 -1.9922 -0.8335
## Avg_Cogongrass_Cover-Canis_latrans 0.4326 0.3811 -0.2427 0.4018
## Avg_Cogongrass_Cover-Procyon_lotor 0.2289 0.3478 -0.4202 0.2262
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3725 0.3325 -0.2683 0.3631
## Avg_Cogongrass_Cover-Lynx_rufus 0.4567 0.3898 -0.2342 0.4232
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3166 0.3774 -0.4365 0.3101
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1970 0.4426 -1.1407 -0.1553
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2515 0.5974 -1.5594 -0.2076
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3560 0.3578 -0.2974 0.3458
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2064 1.0148 791
## (Intercept)-Procyon_lotor 1.2897 1.0061 627
## (Intercept)-Dasypus_novemcinctus 0.3478 1.0092 1395
## (Intercept)-Lynx_rufus 1.0323 1.0220 707
## (Intercept)-Didelphis_virginiana 0.0345 1.0105 975
## (Intercept)-Sylvilagus_floridanus 0.5310 1.0097 1286
## (Intercept)-Meleagris_gallopavo 1.4581 1.0976 417
## (Intercept)-Sciurus_carolinensis 0.0541 1.0168 1141
## Avg_Cogongrass_Cover-Canis_latrans 1.2605 0.9999 1563
## Avg_Cogongrass_Cover-Procyon_lotor 0.9335 1.0034 1936
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0580 1.0017 1695
## Avg_Cogongrass_Cover-Lynx_rufus 1.3043 1.0020 1382
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1282 1.0006 1174
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5921 1.0053 939
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7477 1.0078 558
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1084 1.0034 1813
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7322 0.1847 -3.1178 -2.7249 -2.3911 1.0030
## (Intercept)-Procyon_lotor -2.2947 0.1416 -2.5929 -2.2896 -2.0331 0.9998
## (Intercept)-Dasypus_novemcinctus -1.7704 0.1621 -2.0930 -1.7658 -1.4597 1.0039
## (Intercept)-Lynx_rufus -3.4958 0.3249 -4.1489 -3.4809 -2.8887 1.0343
## (Intercept)-Didelphis_virginiana -2.5839 0.2813 -3.1591 -2.5702 -2.0532 1.0024
## (Intercept)-Sylvilagus_floridanus -3.1053 0.2817 -3.6884 -3.0964 -2.5924 0.9996
## (Intercept)-Meleagris_gallopavo -3.8609 0.4654 -4.7508 -3.8741 -2.9215 1.1090
## (Intercept)-Sciurus_carolinensis -2.6634 0.3159 -3.3645 -2.6455 -2.0867 1.0246
## shrub_cover-Canis_latrans -0.2845 0.2200 -0.7222 -0.2863 0.1448 1.0226
## shrub_cover-Procyon_lotor 0.2579 0.1630 -0.0584 0.2555 0.5710 1.0021
## shrub_cover-Dasypus_novemcinctus 0.8784 0.3062 0.3052 0.8672 1.5087 1.0083
## shrub_cover-Lynx_rufus -0.1906 0.3508 -0.8959 -0.2002 0.4966 1.1074
## shrub_cover-Didelphis_virginiana 0.9936 0.3907 0.2750 0.9837 1.8049 1.0075
## shrub_cover-Sylvilagus_floridanus 0.2542 0.4242 -0.5034 0.2348 1.1136 1.0529
## shrub_cover-Meleagris_gallopavo -0.6551 0.4143 -1.4489 -0.6654 0.1924 1.0573
## shrub_cover-Sciurus_carolinensis 0.8809 0.4235 0.0641 0.8813 1.7263 1.0219
## veg_height-Canis_latrans -0.5928 0.1959 -0.9949 -0.5798 -0.2266 1.0038
## veg_height-Procyon_lotor 0.3415 0.1203 0.1063 0.3431 0.5770 1.0141
## veg_height-Dasypus_novemcinctus 0.2578 0.1340 -0.0086 0.2565 0.5203 1.0016
## veg_height-Lynx_rufus 0.0200 0.2471 -0.4855 0.0273 0.5066 1.0061
## veg_height-Didelphis_virginiana 0.4442 0.2482 -0.0243 0.4392 0.9275 1.0029
## veg_height-Sylvilagus_floridanus 0.1657 0.2478 -0.3245 0.1628 0.6460 1.0034
## veg_height-Meleagris_gallopavo -0.1601 0.4001 -0.9159 -0.1570 0.6489 1.0195
## veg_height-Sciurus_carolinensis 0.0904 0.2235 -0.3241 0.0843 0.5524 1.0117
## ESS
## (Intercept)-Canis_latrans 712
## (Intercept)-Procyon_lotor 1328
## (Intercept)-Dasypus_novemcinctus 1501
## (Intercept)-Lynx_rufus 376
## (Intercept)-Didelphis_virginiana 853
## (Intercept)-Sylvilagus_floridanus 614
## (Intercept)-Meleagris_gallopavo 214
## (Intercept)-Sciurus_carolinensis 664
## shrub_cover-Canis_latrans 859
## shrub_cover-Procyon_lotor 1483
## shrub_cover-Dasypus_novemcinctus 1236
## shrub_cover-Lynx_rufus 465
## shrub_cover-Didelphis_virginiana 626
## shrub_cover-Sylvilagus_floridanus 532
## shrub_cover-Meleagris_gallopavo 256
## shrub_cover-Sciurus_carolinensis 617
## veg_height-Canis_latrans 693
## veg_height-Procyon_lotor 1758
## veg_height-Dasypus_novemcinctus 1894
## veg_height-Lynx_rufus 662
## veg_height-Didelphis_virginiana 1133
## veg_height-Sylvilagus_floridanus 901
## veg_height-Meleagris_gallopavo 465
## veg_height-Sciurus_carolinensis 1000
# 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): 0.5305
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7666 0.6849 -2.0529 -0.7850 0.6307 1.0322 265
## Cogon_Patch_Size -0.4139 0.6692 -1.7334 -0.4050 0.9610 1.0233 393
## Veg_shannon_index 1.0256 0.4941 0.1150 1.0043 2.0667 1.0243 313
## total_shrub_cover -0.8779 0.6613 -2.2987 -0.8374 0.3505 1.0222 421
## Avg_Cogongrass_Cover 1.9394 0.7517 0.5099 1.9129 3.4964 1.0175 192
## Tree_Density -1.8551 0.8670 -3.5781 -1.8704 -0.1379 1.0297 475
## Avg_Canopy_Cover 1.8635 0.7631 0.4494 1.8244 3.4025 1.0167 670
## avg_veg_height -0.3722 0.5220 -1.3658 -0.3868 0.7062 1.0606 247
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2645 2.9204 0.0751 1.3239 10.7163 1.0768 289
## Cogon_Patch_Size 2.4483 3.7822 0.0821 1.2888 12.0289 1.0729 183
## Veg_shannon_index 0.7759 1.2595 0.0507 0.4088 3.6208 1.0440 706
## total_shrub_cover 2.5699 3.9580 0.1190 1.2981 13.6594 1.1707 262
## Avg_Cogongrass_Cover 1.0473 2.2178 0.0461 0.4267 6.0274 1.1581 331
## Tree_Density 5.2273 11.6272 0.0826 2.0850 30.3084 1.1148 307
## Avg_Canopy_Cover 4.4135 6.2449 0.1797 2.4536 21.3987 1.1364 277
## avg_veg_height 0.4847 0.8449 0.0411 0.2459 2.4090 1.0614 704
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1326 3.5049 0.1383 2.1367 11.667 1.273 89
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7506 0.3163 -3.3397 -2.7599 -2.0565 1.0299 1668
## shrub_cover 0.4008 0.3272 -0.2522 0.3937 1.0845 1.0153 1393
## veg_height 0.0863 0.2053 -0.3406 0.0884 0.5014 1.0062 1524
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6988 0.8117 0.1587 0.5034 2.4835 1.0414 1970
## shrub_cover 0.7073 0.6249 0.1484 0.5358 2.3191 1.0469 893
## veg_height 0.2686 0.2413 0.0585 0.2034 0.9194 1.0026 1571
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0837 1.0521 -1.8083 0.0278
## (Intercept)-Procyon_lotor 0.0923 1.0372 -1.8195 0.0822
## (Intercept)-Dasypus_novemcinctus -1.2143 0.9164 -3.1924 -1.1413
## (Intercept)-Lynx_rufus -0.1398 1.4324 -2.4492 -0.3466
## (Intercept)-Didelphis_virginiana -1.8207 1.0975 -4.2273 -1.7282
## (Intercept)-Sylvilagus_floridanus -0.9392 0.9913 -2.9671 -0.9142
## (Intercept)-Meleagris_gallopavo -0.8680 1.2159 -3.1762 -0.8963
## (Intercept)-Sciurus_carolinensis -1.8094 1.1104 -4.3047 -1.7353
## Cogon_Patch_Size-Canis_latrans 0.5481 1.0548 -1.0444 0.3855
## Cogon_Patch_Size-Procyon_lotor -0.9850 0.7735 -2.6459 -0.9402
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4119 0.8387 -2.0429 -0.4265
## Cogon_Patch_Size-Lynx_rufus -0.7265 1.3096 -3.4508 -0.7034
## Cogon_Patch_Size-Didelphis_virginiana 0.8566 1.0405 -0.7285 0.7056
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4826 1.4102 -4.7930 -1.2530
## Cogon_Patch_Size-Meleagris_gallopavo -0.1323 1.2197 -2.2299 -0.2316
## Cogon_Patch_Size-Sciurus_carolinensis -1.2320 1.1943 -4.0525 -1.0632
## Veg_shannon_index-Canis_latrans 1.3712 0.6941 0.1657 1.3094
## Veg_shannon_index-Procyon_lotor 1.3677 0.6360 0.2607 1.3202
## Veg_shannon_index-Dasypus_novemcinctus 0.6834 0.5893 -0.5489 0.6974
## Veg_shannon_index-Lynx_rufus 0.9320 0.8444 -0.8095 0.9347
## Veg_shannon_index-Didelphis_virginiana 1.2053 0.7029 -0.0113 1.1412
## Veg_shannon_index-Sylvilagus_floridanus 1.1619 0.7342 -0.1470 1.1117
## Veg_shannon_index-Meleagris_gallopavo 1.3830 0.8281 0.0028 1.2884
## Veg_shannon_index-Sciurus_carolinensis 0.3626 0.7884 -1.3671 0.4246
## total_shrub_cover-Canis_latrans 0.6513 1.0893 -0.9442 0.4502
## total_shrub_cover-Procyon_lotor -1.1864 0.6714 -2.6457 -1.1436
## total_shrub_cover-Dasypus_novemcinctus -0.4320 0.9003 -2.4052 -0.3605
## total_shrub_cover-Lynx_rufus -1.5774 1.3740 -4.8572 -1.3900
## total_shrub_cover-Didelphis_virginiana -1.2573 1.0885 -3.9639 -1.0938
## total_shrub_cover-Sylvilagus_floridanus -0.9415 1.2474 -3.9447 -0.8455
## total_shrub_cover-Meleagris_gallopavo -2.2341 1.4729 -5.7053 -2.0082
## total_shrub_cover-Sciurus_carolinensis -1.0041 1.2207 -3.8964 -0.8269
## Avg_Cogongrass_Cover-Canis_latrans 2.2608 0.9602 0.5131 2.1882
## Avg_Cogongrass_Cover-Procyon_lotor 2.0753 0.8920 0.4554 2.0423
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4690 1.0538 0.6982 2.3427
## Avg_Cogongrass_Cover-Lynx_rufus 2.2436 1.0124 0.4707 2.1473
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0099 0.9406 0.2386 1.9788
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3417 1.0851 -1.0709 1.4272
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5671 1.2306 -1.2512 1.6963
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1858 1.0317 0.4061 2.1160
## Tree_Density-Canis_latrans -3.0174 1.6320 -7.0084 -2.6958
## Tree_Density-Procyon_lotor -1.5224 0.7984 -3.1372 -1.5269
## Tree_Density-Dasypus_novemcinctus -3.9920 2.2200 -9.8481 -3.3982
## Tree_Density-Lynx_rufus -0.2218 1.4792 -2.4917 -0.3628
## Tree_Density-Didelphis_virginiana -2.1099 1.3336 -4.9401 -2.0336
## Tree_Density-Sylvilagus_floridanus -2.6235 1.5840 -6.4723 -2.3944
## Tree_Density-Meleagris_gallopavo -2.2693 1.7471 -6.0040 -2.1590
## Tree_Density-Sciurus_carolinensis -2.1362 1.6391 -5.8291 -2.0475
## Avg_Canopy_Cover-Canis_latrans 0.1062 0.6759 -1.2272 0.1109
## Avg_Canopy_Cover-Procyon_lotor 1.7839 0.8050 0.4061 1.7241
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3078 0.9047 0.9025 2.1689
## Avg_Canopy_Cover-Lynx_rufus 0.7273 1.2535 -1.6938 0.6761
## Avg_Canopy_Cover-Didelphis_virginiana 3.2207 1.3968 1.2541 2.9590
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9419 1.8138 1.2621 3.6269
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6323 1.5426 0.4272 2.3424
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0372 1.4228 1.0793 2.7753
## avg_veg_height-Canis_latrans -0.4259 0.6395 -1.6741 -0.4435
## avg_veg_height-Procyon_lotor -0.3428 0.6092 -1.5624 -0.3540
## avg_veg_height-Dasypus_novemcinctus -0.1411 0.6430 -1.3748 -0.1455
## avg_veg_height-Lynx_rufus -0.4918 0.7462 -2.0239 -0.4934
## avg_veg_height-Didelphis_virginiana -0.5288 0.6989 -1.9979 -0.5013
## avg_veg_height-Sylvilagus_floridanus -0.6002 0.7363 -2.1254 -0.5703
## avg_veg_height-Meleagris_gallopavo -0.4106 0.8193 -2.0976 -0.3993
## avg_veg_height-Sciurus_carolinensis -0.0432 0.6910 -1.3230 -0.0895
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2684 1.0386 254
## (Intercept)-Procyon_lotor 2.1640 1.1182 230
## (Intercept)-Dasypus_novemcinctus 0.4470 1.0082 421
## (Intercept)-Lynx_rufus 3.2428 1.1990 150
## (Intercept)-Didelphis_virginiana 0.0958 1.0380 370
## (Intercept)-Sylvilagus_floridanus 1.0350 1.0078 473
## (Intercept)-Meleagris_gallopavo 1.6667 1.0173 244
## (Intercept)-Sciurus_carolinensis 0.1294 1.0195 274
## Cogon_Patch_Size-Canis_latrans 3.1081 1.0603 620
## Cogon_Patch_Size-Procyon_lotor 0.4467 1.0449 248
## Cogon_Patch_Size-Dasypus_novemcinctus 1.3450 1.0079 622
## Cogon_Patch_Size-Lynx_rufus 1.9410 1.0557 216
## Cogon_Patch_Size-Didelphis_virginiana 3.3582 1.0543 239
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6614 1.0078 271
## Cogon_Patch_Size-Meleagris_gallopavo 2.5878 1.0347 261
## Cogon_Patch_Size-Sciurus_carolinensis 0.6409 1.0378 297
## Veg_shannon_index-Canis_latrans 2.9796 1.0478 343
## Veg_shannon_index-Procyon_lotor 2.6964 1.0734 370
## Veg_shannon_index-Dasypus_novemcinctus 1.8459 1.0150 455
## Veg_shannon_index-Lynx_rufus 2.7091 1.0218 341
## Veg_shannon_index-Didelphis_virginiana 2.7223 1.0390 591
## Veg_shannon_index-Sylvilagus_floridanus 2.8316 1.0190 453
## Veg_shannon_index-Meleagris_gallopavo 3.1744 1.0130 507
## Veg_shannon_index-Sciurus_carolinensis 1.8018 1.0138 510
## total_shrub_cover-Canis_latrans 3.3984 1.2130 208
## total_shrub_cover-Procyon_lotor -0.0096 1.0205 1074
## total_shrub_cover-Dasypus_novemcinctus 1.1297 1.0584 366
## total_shrub_cover-Lynx_rufus 0.5951 1.0338 180
## total_shrub_cover-Didelphis_virginiana 0.3702 1.1044 227
## total_shrub_cover-Sylvilagus_floridanus 1.3163 1.0674 296
## total_shrub_cover-Meleagris_gallopavo -0.1036 1.0427 236
## total_shrub_cover-Sciurus_carolinensis 0.9368 1.1073 319
## Avg_Cogongrass_Cover-Canis_latrans 4.1889 1.0296 254
## Avg_Cogongrass_Cover-Procyon_lotor 3.9919 1.0256 256
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8083 1.0407 267
## Avg_Cogongrass_Cover-Lynx_rufus 4.5614 1.0289 333
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8390 1.0152 344
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2934 1.0183 308
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6707 1.0187 258
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3018 1.0302 277
## Tree_Density-Canis_latrans -0.7470 1.0834 263
## Tree_Density-Procyon_lotor 0.0843 1.0187 524
## Tree_Density-Dasypus_novemcinctus -1.2847 1.1749 130
## Tree_Density-Lynx_rufus 3.0695 1.1371 193
## Tree_Density-Didelphis_virginiana 0.5033 1.0346 407
## Tree_Density-Sylvilagus_floridanus 0.0327 1.0712 327
## Tree_Density-Meleagris_gallopavo 0.9614 1.0600 236
## Tree_Density-Sciurus_carolinensis 1.0771 1.0150 378
## Avg_Canopy_Cover-Canis_latrans 1.4310 1.0268 549
## Avg_Canopy_Cover-Procyon_lotor 3.5880 1.0282 613
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.4832 1.1139 278
## Avg_Canopy_Cover-Lynx_rufus 3.3267 1.0110 263
## Avg_Canopy_Cover-Didelphis_virginiana 6.7997 1.0973 180
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.3133 1.0522 233
## Avg_Canopy_Cover-Meleagris_gallopavo 6.5108 1.0545 172
## Avg_Canopy_Cover-Sciurus_carolinensis 6.6829 1.1634 213
## avg_veg_height-Canis_latrans 0.8654 1.0588 416
## avg_veg_height-Procyon_lotor 0.8572 1.0680 449
## avg_veg_height-Dasypus_novemcinctus 1.1310 1.0215 442
## avg_veg_height-Lynx_rufus 0.9501 1.0272 415
## avg_veg_height-Didelphis_virginiana 0.7994 1.0198 470
## avg_veg_height-Sylvilagus_floridanus 0.7189 1.0325 378
## avg_veg_height-Meleagris_gallopavo 1.1943 1.0502 349
## avg_veg_height-Sciurus_carolinensis 1.4352 1.0651 396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7506 0.1827 -3.1315 -2.7442 -2.4117 1.0035
## (Intercept)-Procyon_lotor -2.2972 0.1400 -2.5809 -2.2907 -2.0305 1.0049
## (Intercept)-Dasypus_novemcinctus -1.7991 0.1668 -2.1330 -1.7957 -1.4864 1.0054
## (Intercept)-Lynx_rufus -3.5401 0.3549 -4.2775 -3.5150 -2.9184 1.1184
## (Intercept)-Didelphis_virginiana -2.6405 0.2930 -3.2262 -2.6348 -2.0800 1.0158
## (Intercept)-Sylvilagus_floridanus -3.1644 0.2523 -3.6632 -3.1579 -2.6954 1.0305
## (Intercept)-Meleagris_gallopavo -3.6820 0.4349 -4.6385 -3.6589 -2.8952 1.0281
## (Intercept)-Sciurus_carolinensis -2.8127 0.3167 -3.4567 -2.8032 -2.2033 1.0925
## shrub_cover-Canis_latrans -0.3570 0.2226 -0.7689 -0.3601 0.0975 1.0756
## shrub_cover-Procyon_lotor 0.2891 0.1575 -0.0264 0.2898 0.5943 0.9996
## shrub_cover-Dasypus_novemcinctus 0.9825 0.3068 0.3956 0.9769 1.5766 1.0327
## shrub_cover-Lynx_rufus 0.0778 0.3723 -0.6887 0.0951 0.7812 1.0161
## shrub_cover-Didelphis_virginiana 1.0616 0.3863 0.3611 1.0432 1.8619 1.0095
## shrub_cover-Sylvilagus_floridanus 0.5545 0.3997 -0.2312 0.5558 1.3525 1.0370
## shrub_cover-Meleagris_gallopavo -0.4369 0.4140 -1.3103 -0.4266 0.3570 1.0467
## shrub_cover-Sciurus_carolinensis 1.0747 0.4169 0.2629 1.0817 1.8753 1.1778
## veg_height-Canis_latrans -0.5945 0.1876 -0.9744 -0.5903 -0.2401 1.0016
## veg_height-Procyon_lotor 0.3555 0.1240 0.1187 0.3546 0.5984 1.0124
## veg_height-Dasypus_novemcinctus 0.2759 0.1371 0.0085 0.2739 0.5449 1.0049
## veg_height-Lynx_rufus 0.0926 0.2448 -0.4090 0.1001 0.5465 1.0087
## veg_height-Didelphis_virginiana 0.5012 0.2440 0.0481 0.4898 1.0028 1.0129
## veg_height-Sylvilagus_floridanus 0.1645 0.2422 -0.3152 0.1634 0.6356 1.0040
## veg_height-Meleagris_gallopavo -0.2150 0.3596 -0.9340 -0.2024 0.4662 1.0287
## veg_height-Sciurus_carolinensis 0.1675 0.2317 -0.2779 0.1663 0.6308 1.0584
## ESS
## (Intercept)-Canis_latrans 707
## (Intercept)-Procyon_lotor 1222
## (Intercept)-Dasypus_novemcinctus 954
## (Intercept)-Lynx_rufus 289
## (Intercept)-Didelphis_virginiana 597
## (Intercept)-Sylvilagus_floridanus 724
## (Intercept)-Meleagris_gallopavo 297
## (Intercept)-Sciurus_carolinensis 473
## shrub_cover-Canis_latrans 672
## shrub_cover-Procyon_lotor 1405
## shrub_cover-Dasypus_novemcinctus 575
## shrub_cover-Lynx_rufus 333
## shrub_cover-Didelphis_virginiana 564
## shrub_cover-Sylvilagus_floridanus 407
## shrub_cover-Meleagris_gallopavo 335
## shrub_cover-Sciurus_carolinensis 351
## veg_height-Canis_latrans 771
## veg_height-Procyon_lotor 1454
## veg_height-Dasypus_novemcinctus 1652
## veg_height-Lynx_rufus 617
## veg_height-Didelphis_virginiana 985
## veg_height-Sylvilagus_floridanus 951
## veg_height-Meleagris_gallopavo 477
## veg_height-Sciurus_carolinensis 803
# 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): 0.541
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1676 0.4059 -0.9577 -0.1719 0.6141 1.0048 411
## Avg_Cogongrass_Cover 0.0260 0.3698 -0.6925 0.0151 0.7461 1.0022 750
## total_shrub_cover -0.8828 0.4474 -1.8706 -0.8578 -0.0571 1.0310 463
## avg_veg_height 0.0872 0.3968 -0.7323 0.0852 0.8456 1.0062 497
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5770 0.7247 0.0517 0.3693 2.2792 1.0184 892
## Avg_Cogongrass_Cover 0.5122 0.7419 0.0437 0.2760 2.3515 1.0117 656
## total_shrub_cover 0.9639 1.4052 0.0672 0.5675 4.0333 1.0856 447
## avg_veg_height 0.4143 0.6412 0.0390 0.2197 2.1150 1.0108 526
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9556 0.844 0.0765 0.7172 3.0452 1.0171 248
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7552 0.2953 -3.3217 -2.756 -2.1671 1.0036 1311
## shrub_cover 0.5176 0.3287 -0.1181 0.512 1.1771 1.0155 902
## veg_height 0.0959 0.2046 -0.3146 0.096 0.4932 1.0018 1457
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5655 0.5474 0.1268 0.4217 1.8476 1.0583 1386
## shrub_cover 0.7330 0.6606 0.1427 0.5617 2.4138 1.0019 771
## veg_height 0.2621 0.2229 0.0575 0.1981 0.8590 1.0081 1267
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2219 0.5750 -0.8545 0.2040
## (Intercept)-Procyon_lotor 0.3620 0.5940 -0.7310 0.3410
## (Intercept)-Dasypus_novemcinctus -0.3145 0.5296 -1.3305 -0.3232
## (Intercept)-Lynx_rufus -0.1378 0.5917 -1.3077 -0.1498
## (Intercept)-Didelphis_virginiana -0.5689 0.6157 -1.8531 -0.5395
## (Intercept)-Sylvilagus_floridanus -0.0278 0.6101 -1.1668 -0.0622
## (Intercept)-Meleagris_gallopavo -0.3301 0.6448 -1.6194 -0.3312
## (Intercept)-Sciurus_carolinensis -0.5528 0.6482 -1.8795 -0.5236
## Avg_Cogongrass_Cover-Canis_latrans 0.3649 0.5189 -0.5386 0.3230
## Avg_Cogongrass_Cover-Procyon_lotor -0.0602 0.4788 -1.0570 -0.0440
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1706 0.4669 -0.7178 0.1581
## Avg_Cogongrass_Cover-Lynx_rufus 0.3851 0.5616 -0.5677 0.3283
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1555 0.5240 -0.8545 0.1557
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4236 0.5987 -1.7771 -0.3546
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4701 0.6939 -2.1104 -0.3931
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0369 0.4952 -0.9856 0.0375
## total_shrub_cover-Canis_latrans 0.1284 0.6165 -0.9298 0.0779
## total_shrub_cover-Procyon_lotor -1.2067 0.5697 -2.5216 -1.1291
## total_shrub_cover-Dasypus_novemcinctus -0.5043 0.6119 -1.9957 -0.4401
## total_shrub_cover-Lynx_rufus -1.2727 0.7451 -2.9709 -1.2037
## total_shrub_cover-Didelphis_virginiana -0.8994 0.6256 -2.2973 -0.8515
## total_shrub_cover-Sylvilagus_floridanus -1.2267 0.8070 -3.0283 -1.1398
## total_shrub_cover-Meleagris_gallopavo -1.4681 0.7873 -3.2720 -1.3662
## total_shrub_cover-Sciurus_carolinensis -0.9645 0.7614 -2.6635 -0.8668
## avg_veg_height-Canis_latrans 0.0850 0.4774 -0.8252 0.0910
## avg_veg_height-Procyon_lotor 0.1254 0.4712 -0.8033 0.1211
## avg_veg_height-Dasypus_novemcinctus 0.3182 0.4692 -0.5739 0.3005
## avg_veg_height-Lynx_rufus 0.0011 0.6162 -1.3044 0.0198
## avg_veg_height-Didelphis_virginiana -0.0191 0.5198 -1.0755 -0.0013
## avg_veg_height-Sylvilagus_floridanus 0.0173 0.5362 -1.0605 0.0191
## avg_veg_height-Meleagris_gallopavo -0.2948 0.7649 -2.1801 -0.1942
## avg_veg_height-Sciurus_carolinensis 0.4801 0.5577 -0.4731 0.4243
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4261 1.0015 645
## (Intercept)-Procyon_lotor 1.5882 1.0029 514
## (Intercept)-Dasypus_novemcinctus 0.7712 1.0068 660
## (Intercept)-Lynx_rufus 1.0559 1.0028 699
## (Intercept)-Didelphis_virginiana 0.5684 1.0114 451
## (Intercept)-Sylvilagus_floridanus 1.3132 1.0029 491
## (Intercept)-Meleagris_gallopavo 0.9879 1.0073 459
## (Intercept)-Sciurus_carolinensis 0.6224 1.0041 461
## Avg_Cogongrass_Cover-Canis_latrans 1.4900 1.0028 923
## Avg_Cogongrass_Cover-Procyon_lotor 0.8471 1.0024 1096
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1659 1.0120 1189
## Avg_Cogongrass_Cover-Lynx_rufus 1.6283 1.0113 841
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2406 1.0016 1016
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5753 1.0091 829
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6614 1.0281 483
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0002 1.0040 988
## total_shrub_cover-Canis_latrans 1.4971 1.0443 515
## total_shrub_cover-Procyon_lotor -0.2817 1.0046 669
## total_shrub_cover-Dasypus_novemcinctus 0.5392 1.0196 357
## total_shrub_cover-Lynx_rufus 0.0404 1.0248 443
## total_shrub_cover-Didelphis_virginiana 0.2091 1.0133 503
## total_shrub_cover-Sylvilagus_floridanus 0.1005 1.0241 341
## total_shrub_cover-Meleagris_gallopavo -0.2076 1.0459 423
## total_shrub_cover-Sciurus_carolinensis 0.2515 1.0100 324
## avg_veg_height-Canis_latrans 1.0266 1.0043 730
## avg_veg_height-Procyon_lotor 1.0703 1.0047 904
## avg_veg_height-Dasypus_novemcinctus 1.3144 1.0050 945
## avg_veg_height-Lynx_rufus 1.1525 1.0014 523
## avg_veg_height-Didelphis_virginiana 0.9675 1.0043 1023
## avg_veg_height-Sylvilagus_floridanus 1.0555 1.0008 822
## avg_veg_height-Meleagris_gallopavo 0.9383 1.0136 405
## avg_veg_height-Sciurus_carolinensis 1.7256 1.0018 714
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7676 0.1891 -3.1540 -2.7613 -2.4272 1.0164
## (Intercept)-Procyon_lotor -2.3051 0.1364 -2.5829 -2.3015 -2.0466 1.0082
## (Intercept)-Dasypus_novemcinctus -1.8497 0.1795 -2.2094 -1.8469 -1.5061 1.0106
## (Intercept)-Lynx_rufus -3.4146 0.3120 -4.0575 -3.4073 -2.8454 1.0194
## (Intercept)-Didelphis_virginiana -2.7413 0.2972 -3.3474 -2.7379 -2.1709 1.0366
## (Intercept)-Sylvilagus_floridanus -3.2243 0.2705 -3.7867 -3.2125 -2.7130 1.0167
## (Intercept)-Meleagris_gallopavo -3.4609 0.5137 -4.5264 -3.4413 -2.5238 1.0096
## (Intercept)-Sciurus_carolinensis -2.8402 0.3280 -3.5336 -2.8343 -2.2163 1.0005
## shrub_cover-Canis_latrans -0.2786 0.2489 -0.7627 -0.2725 0.2053 1.0100
## shrub_cover-Procyon_lotor 0.3211 0.1592 0.0079 0.3233 0.6295 1.0051
## shrub_cover-Dasypus_novemcinctus 1.0779 0.3465 0.4131 1.0788 1.7530 1.0029
## shrub_cover-Lynx_rufus 0.2056 0.3679 -0.5805 0.2346 0.8622 1.0295
## shrub_cover-Didelphis_virginiana 1.2317 0.3885 0.4865 1.2287 1.9977 1.0127
## shrub_cover-Sylvilagus_floridanus 0.7430 0.4271 -0.2028 0.7691 1.5384 1.0278
## shrub_cover-Meleagris_gallopavo -0.2504 0.4827 -1.2477 -0.2356 0.6007 1.0227
## shrub_cover-Sciurus_carolinensis 1.2114 0.4419 0.3439 1.2105 2.0837 1.0180
## veg_height-Canis_latrans -0.5916 0.1946 -0.9885 -0.5915 -0.2210 1.0067
## veg_height-Procyon_lotor 0.3510 0.1198 0.1156 0.3507 0.5850 1.0025
## veg_height-Dasypus_novemcinctus 0.2760 0.1440 0.0033 0.2740 0.5605 1.0000
## veg_height-Lynx_rufus 0.0736 0.2463 -0.4220 0.0807 0.5342 1.0120
## veg_height-Didelphis_virginiana 0.4539 0.2638 -0.0442 0.4464 1.0072 1.0542
## veg_height-Sylvilagus_floridanus 0.0796 0.2461 -0.3906 0.0772 0.5871 1.0165
## veg_height-Meleagris_gallopavo -0.0148 0.4735 -0.9488 -0.0185 0.9349 1.0219
## veg_height-Sciurus_carolinensis 0.1242 0.2319 -0.3113 0.1238 0.5864 1.0138
## ESS
## (Intercept)-Canis_latrans 685
## (Intercept)-Procyon_lotor 1496
## (Intercept)-Dasypus_novemcinctus 656
## (Intercept)-Lynx_rufus 433
## (Intercept)-Didelphis_virginiana 357
## (Intercept)-Sylvilagus_floridanus 424
## (Intercept)-Meleagris_gallopavo 269
## (Intercept)-Sciurus_carolinensis 345
## shrub_cover-Canis_latrans 572
## shrub_cover-Procyon_lotor 1461
## shrub_cover-Dasypus_novemcinctus 346
## shrub_cover-Lynx_rufus 419
## shrub_cover-Didelphis_virginiana 347
## shrub_cover-Sylvilagus_floridanus 318
## shrub_cover-Meleagris_gallopavo 335
## shrub_cover-Sciurus_carolinensis 311
## veg_height-Canis_latrans 684
## veg_height-Procyon_lotor 1580
## veg_height-Dasypus_novemcinctus 1814
## veg_height-Lynx_rufus 641
## veg_height-Didelphis_virginiana 703
## veg_height-Sylvilagus_floridanus 648
## veg_height-Meleagris_gallopavo 350
## veg_height-Sciurus_carolinensis 868
# 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): 0.497
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2171 0.3352 -0.8913 -0.2167 0.4572 1.0121 1071
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6783 0.6917 0.1057 0.4814 2.4859 1.0027 1436
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7380 0.3236 -3.3464 -2.7506 -2.0535 1.0098 1451
## shrub_cover 0.2423 0.3192 -0.4097 0.2508 0.8707 1.0044 1938
## veg_height 0.0709 0.2001 -0.3337 0.0724 0.4752 1.0078 1585
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7813 0.7096 0.1770 0.5838 2.6691 1.0200 906
## shrub_cover 0.7277 0.6530 0.1327 0.5452 2.4813 1.0014 1035
## veg_height 0.2573 0.2427 0.0575 0.1952 0.7812 1.0309 1735
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2448 0.3824 -0.4730 0.2215 1.0402 1.0049
## (Intercept)-Procyon_lotor 0.5198 0.3818 -0.1545 0.5010 1.3277 1.0010
## (Intercept)-Dasypus_novemcinctus -0.4864 0.3430 -1.1599 -0.4880 0.1769 1.0073
## (Intercept)-Lynx_rufus 0.0609 0.4919 -0.7797 0.0233 1.1279 1.0094
## (Intercept)-Didelphis_virginiana -0.9374 0.4421 -1.8876 -0.9271 -0.1281 1.0017
## (Intercept)-Sylvilagus_floridanus -0.3116 0.4071 -1.1054 -0.3290 0.5013 1.0014
## (Intercept)-Meleagris_gallopavo 0.1213 0.6694 -0.9556 0.0375 1.7282 1.0031
## (Intercept)-Sciurus_carolinensis -0.9219 0.4395 -1.8116 -0.9035 -0.0722 1.0091
## ESS
## (Intercept)-Canis_latrans 1995
## (Intercept)-Procyon_lotor 1791
## (Intercept)-Dasypus_novemcinctus 2399
## (Intercept)-Lynx_rufus 742
## (Intercept)-Didelphis_virginiana 1631
## (Intercept)-Sylvilagus_floridanus 1445
## (Intercept)-Meleagris_gallopavo 389
## (Intercept)-Sciurus_carolinensis 1605
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7247 0.1799 -3.0831 -2.7199 -2.3786 1.0151
## (Intercept)-Procyon_lotor -2.2925 0.1406 -2.5798 -2.2891 -2.0344 1.0047
## (Intercept)-Dasypus_novemcinctus -1.7632 0.1567 -2.0901 -1.7594 -1.4703 1.0008
## (Intercept)-Lynx_rufus -3.5599 0.3218 -4.1960 -3.5596 -2.9439 1.0272
## (Intercept)-Didelphis_virginiana -2.6065 0.2904 -3.1947 -2.5954 -2.0658 1.0046
## (Intercept)-Sylvilagus_floridanus -3.1353 0.2860 -3.7409 -3.1144 -2.6329 1.0069
## (Intercept)-Meleagris_gallopavo -3.8962 0.4923 -4.8754 -3.9057 -2.9529 1.0032
## (Intercept)-Sciurus_carolinensis -2.6706 0.3026 -3.3115 -2.6599 -2.1132 1.0063
## shrub_cover-Canis_latrans -0.3043 0.2166 -0.7178 -0.3063 0.1305 1.0150
## shrub_cover-Procyon_lotor 0.2482 0.1608 -0.0778 0.2488 0.5592 1.0027
## shrub_cover-Dasypus_novemcinctus 0.8604 0.2967 0.2939 0.8558 1.4447 1.0008
## shrub_cover-Lynx_rufus -0.2752 0.3800 -1.0075 -0.2771 0.4827 1.0272
## shrub_cover-Didelphis_virginiana 0.9946 0.3885 0.2799 0.9828 1.7938 1.0006
## shrub_cover-Sylvilagus_floridanus 0.2413 0.4201 -0.5534 0.2367 1.0807 1.0092
## shrub_cover-Meleagris_gallopavo -0.6698 0.4236 -1.4823 -0.6693 0.1855 1.0035
## shrub_cover-Sciurus_carolinensis 0.8805 0.4083 0.1250 0.8699 1.7170 1.0036
## veg_height-Canis_latrans -0.5783 0.1836 -0.9437 -0.5741 -0.2231 1.0116
## veg_height-Procyon_lotor 0.3413 0.1239 0.1041 0.3403 0.5865 1.0098
## veg_height-Dasypus_novemcinctus 0.2549 0.1364 -0.0179 0.2598 0.5202 1.0156
## veg_height-Lynx_rufus 0.0396 0.2434 -0.4541 0.0440 0.5201 1.0043
## veg_height-Didelphis_virginiana 0.4661 0.2532 -0.0012 0.4532 1.0020 1.0083
## veg_height-Sylvilagus_floridanus 0.1545 0.2433 -0.3197 0.1479 0.6476 1.0137
## veg_height-Meleagris_gallopavo -0.2110 0.3790 -0.9576 -0.2199 0.5433 1.0041
## veg_height-Sciurus_carolinensis 0.0899 0.2213 -0.3395 0.0832 0.5348 1.0097
## ESS
## (Intercept)-Canis_latrans 821
## (Intercept)-Procyon_lotor 1413
## (Intercept)-Dasypus_novemcinctus 1839
## (Intercept)-Lynx_rufus 365
## (Intercept)-Didelphis_virginiana 774
## (Intercept)-Sylvilagus_floridanus 566
## (Intercept)-Meleagris_gallopavo 200
## (Intercept)-Sciurus_carolinensis 744
## shrub_cover-Canis_latrans 843
## shrub_cover-Procyon_lotor 1107
## shrub_cover-Dasypus_novemcinctus 1293
## shrub_cover-Lynx_rufus 440
## shrub_cover-Didelphis_virginiana 714
## shrub_cover-Sylvilagus_floridanus 500
## shrub_cover-Meleagris_gallopavo 297
## shrub_cover-Sciurus_carolinensis 834
## veg_height-Canis_latrans 792
## veg_height-Procyon_lotor 1636
## veg_height-Dasypus_novemcinctus 1973
## veg_height-Lynx_rufus 793
## veg_height-Didelphis_virginiana 981
## veg_height-Sylvilagus_floridanus 873
## veg_height-Meleagris_gallopavo 440
## veg_height-Sciurus_carolinensis 1048
#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): 0.5015
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3730 0.3790 -1.1195 -0.3755 0.3624 1.0154 622
## Veg_shannon_index 0.3668 0.2815 -0.1721 0.3686 0.9015 1.0051 867
## Avg_Cogongrass_Cover 0.3388 0.3011 -0.2645 0.3467 0.9064 1.0055 819
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6070 0.7475 0.0531 0.3925 2.3249 1.0060 1027
## Veg_shannon_index 0.3066 0.4181 0.0386 0.1828 1.2849 1.0242 837
## Avg_Cogongrass_Cover 0.3726 0.5154 0.0390 0.2065 1.6437 1.0143 915
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7822 0.8053 0.0665 0.5451 2.7834 1.0132 257
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7188 0.3281 -3.3665 -2.7189 -2.0650 1.0171 1749
## shrub_cover 0.2487 0.3072 -0.3767 0.2522 0.8731 1.0033 1842
## veg_height 0.0714 0.2009 -0.3376 0.0702 0.4649 1.0107 1431
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7947 0.7818 0.1589 0.5876 2.7374 1.0474 853
## shrub_cover 0.6821 0.5742 0.1269 0.5193 2.2092 1.0066 957
## veg_height 0.2550 0.2321 0.0571 0.1895 0.8112 1.0268 1574
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0529 0.5528 -0.9490 0.0275
## (Intercept)-Procyon_lotor 0.1495 0.5601 -0.9483 0.1440
## (Intercept)-Dasypus_novemcinctus -0.5406 0.4802 -1.4966 -0.5245
## (Intercept)-Lynx_rufus -0.2173 0.6001 -1.3754 -0.2573
## (Intercept)-Didelphis_virginiana -0.9246 0.5667 -2.0638 -0.8873
## (Intercept)-Sylvilagus_floridanus -0.4368 0.5223 -1.4924 -0.4400
## (Intercept)-Meleagris_gallopavo -0.1814 0.6518 -1.4209 -0.2136
## (Intercept)-Sciurus_carolinensis -0.9238 0.5559 -2.1127 -0.8850
## Veg_shannon_index-Canis_latrans 0.6499 0.4095 -0.0676 0.6272
## Veg_shannon_index-Procyon_lotor 0.4651 0.3735 -0.2443 0.4461
## Veg_shannon_index-Dasypus_novemcinctus 0.1911 0.3522 -0.5702 0.2042
## Veg_shannon_index-Lynx_rufus 0.2125 0.4701 -0.7478 0.2204
## Veg_shannon_index-Didelphis_virginiana 0.4789 0.3772 -0.2442 0.4672
## Veg_shannon_index-Sylvilagus_floridanus 0.4516 0.4193 -0.2807 0.4281
## Veg_shannon_index-Meleagris_gallopavo 0.5337 0.4992 -0.3517 0.4888
## Veg_shannon_index-Sciurus_carolinensis -0.0086 0.4090 -0.9199 0.0125
## Avg_Cogongrass_Cover-Canis_latrans 0.6141 0.4207 -0.1051 0.5831
## Avg_Cogongrass_Cover-Procyon_lotor 0.3763 0.3705 -0.3362 0.3644
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4618 0.3381 -0.1636 0.4433
## Avg_Cogongrass_Cover-Lynx_rufus 0.6078 0.4453 -0.1702 0.5743
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4655 0.3898 -0.3019 0.4579
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0963 0.4793 -1.1874 -0.0605
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0686 0.6232 -1.5256 -0.0034
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4274 0.3737 -0.2929 0.4159
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2505 1.0105 890
## (Intercept)-Procyon_lotor 1.2944 1.0027 523
## (Intercept)-Dasypus_novemcinctus 0.3792 1.0165 1075
## (Intercept)-Lynx_rufus 1.0594 1.0097 565
## (Intercept)-Didelphis_virginiana 0.0751 1.0328 813
## (Intercept)-Sylvilagus_floridanus 0.6138 1.0105 960
## (Intercept)-Meleagris_gallopavo 1.2330 1.0102 280
## (Intercept)-Sciurus_carolinensis 0.0913 1.0039 792
## Veg_shannon_index-Canis_latrans 1.5426 1.0099 1249
## Veg_shannon_index-Procyon_lotor 1.2463 1.0076 1374
## Veg_shannon_index-Dasypus_novemcinctus 0.8656 1.0014 1842
## Veg_shannon_index-Lynx_rufus 1.1121 1.0038 1098
## Veg_shannon_index-Didelphis_virginiana 1.2583 1.0029 1733
## Veg_shannon_index-Sylvilagus_floridanus 1.3342 1.0031 984
## Veg_shannon_index-Meleagris_gallopavo 1.6465 1.0013 858
## Veg_shannon_index-Sciurus_carolinensis 0.7275 1.0015 1398
## Avg_Cogongrass_Cover-Canis_latrans 1.5151 1.0004 1236
## Avg_Cogongrass_Cover-Procyon_lotor 1.1796 1.0103 1665
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1686 1.0025 2039
## Avg_Cogongrass_Cover-Lynx_rufus 1.5962 1.0042 1268
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2660 1.0003 1553
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7296 1.0180 911
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0324 1.0042 604
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1968 1.0047 1280
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7280 0.1829 -3.1032 -2.7239 -2.3806 1.0029
## (Intercept)-Procyon_lotor -2.2908 0.1416 -2.5779 -2.2888 -2.0289 1.0068
## (Intercept)-Dasypus_novemcinctus -1.7631 0.1612 -2.0980 -1.7594 -1.4604 1.0012
## (Intercept)-Lynx_rufus -3.5298 0.3384 -4.2103 -3.5193 -2.8919 1.0532
## (Intercept)-Didelphis_virginiana -2.5855 0.2820 -3.1571 -2.5735 -2.0502 1.0009
## (Intercept)-Sylvilagus_floridanus -3.1350 0.2860 -3.7561 -3.1178 -2.6227 1.0372
## (Intercept)-Meleagris_gallopavo -3.8315 0.4832 -4.7956 -3.8387 -2.8772 1.0092
## (Intercept)-Sciurus_carolinensis -2.6432 0.3184 -3.3105 -2.6307 -2.0566 1.0099
## shrub_cover-Canis_latrans -0.2818 0.2159 -0.7016 -0.2819 0.1596 1.0142
## shrub_cover-Procyon_lotor 0.2474 0.1669 -0.0850 0.2476 0.5786 1.0014
## shrub_cover-Dasypus_novemcinctus 0.8706 0.3042 0.3031 0.8595 1.5209 1.0002
## shrub_cover-Lynx_rufus -0.2365 0.3635 -0.9837 -0.2362 0.4611 1.0324
## shrub_cover-Didelphis_virginiana 0.9730 0.3751 0.2750 0.9565 1.7479 1.0033
## shrub_cover-Sylvilagus_floridanus 0.2321 0.4118 -0.5558 0.2255 1.0725 1.0022
## shrub_cover-Meleagris_gallopavo -0.6472 0.4196 -1.4837 -0.6459 0.1655 1.0190
## shrub_cover-Sciurus_carolinensis 0.8626 0.4079 0.0741 0.8526 1.6906 1.0056
## veg_height-Canis_latrans -0.5818 0.1848 -0.9634 -0.5718 -0.2352 1.0152
## veg_height-Procyon_lotor 0.3414 0.1214 0.1072 0.3428 0.5755 1.0107
## veg_height-Dasypus_novemcinctus 0.2553 0.1343 0.0044 0.2507 0.5228 1.0027
## veg_height-Lynx_rufus -0.0015 0.2474 -0.5031 0.0007 0.4788 1.0329
## veg_height-Didelphis_virginiana 0.4565 0.2473 -0.0112 0.4527 0.9476 1.0048
## veg_height-Sylvilagus_floridanus 0.1621 0.2470 -0.3426 0.1647 0.6298 1.0042
## veg_height-Meleagris_gallopavo -0.1723 0.4015 -1.0017 -0.1654 0.6176 1.0173
## veg_height-Sciurus_carolinensis 0.0825 0.2139 -0.3156 0.0807 0.5224 1.0530
## ESS
## (Intercept)-Canis_latrans 849
## (Intercept)-Procyon_lotor 1355
## (Intercept)-Dasypus_novemcinctus 1270
## (Intercept)-Lynx_rufus 349
## (Intercept)-Didelphis_virginiana 702
## (Intercept)-Sylvilagus_floridanus 511
## (Intercept)-Meleagris_gallopavo 176
## (Intercept)-Sciurus_carolinensis 745
## shrub_cover-Canis_latrans 980
## shrub_cover-Procyon_lotor 1290
## shrub_cover-Dasypus_novemcinctus 1221
## shrub_cover-Lynx_rufus 384
## shrub_cover-Didelphis_virginiana 687
## shrub_cover-Sylvilagus_floridanus 526
## shrub_cover-Meleagris_gallopavo 219
## shrub_cover-Sciurus_carolinensis 747
## veg_height-Canis_latrans 729
## veg_height-Procyon_lotor 1736
## veg_height-Dasypus_novemcinctus 2060
## veg_height-Lynx_rufus 756
## veg_height-Didelphis_virginiana 1034
## veg_height-Sylvilagus_floridanus 858
## veg_height-Meleagris_gallopavo 369
## veg_height-Sciurus_carolinensis 1129
# 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): 0.5282
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2689 0.4374 -1.1174 -0.2682 0.6134 1.0166 423
## Cogon_Patch_Size -0.0186 0.4059 -0.8427 -0.0048 0.7815 1.0081 1045
## Avg_Cogongrass_Cover 0.1066 0.3611 -0.6511 0.1130 0.7911 1.0109 668
## total_shrub_cover -0.8575 0.4737 -1.9247 -0.8209 0.0034 1.0327 332
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6457 0.8552 0.0529 0.3649 2.9936 1.0061 627
## Cogon_Patch_Size 0.7847 1.1104 0.0585 0.4391 3.5468 1.0497 840
## Avg_Cogongrass_Cover 0.4809 0.6956 0.0410 0.2702 2.0624 1.0075 765
## total_shrub_cover 0.8859 1.2835 0.0596 0.4774 4.3604 1.0783 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2684 1.289 0.1074 0.9266 4.5217 1.1908 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7468 0.2768 -3.3024 -2.7432 -2.1782 1.0133 1113
## shrub_cover 0.4906 0.3074 -0.1315 0.4874 1.1042 1.0049 991
## veg_height 0.0783 0.2033 -0.3163 0.0775 0.4738 1.0019 1372
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5818 0.5523 0.1282 0.4284 1.9420 1.0107 1137
## shrub_cover 0.6793 0.6528 0.1262 0.4898 2.3204 1.0253 689
## veg_height 0.2521 0.2148 0.0547 0.1896 0.8405 1.0033 1187
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1587 0.6099 -0.9322 0.1186
## (Intercept)-Procyon_lotor 0.2756 0.6335 -0.8784 0.2505
## (Intercept)-Dasypus_novemcinctus -0.4005 0.5738 -1.5291 -0.3996
## (Intercept)-Lynx_rufus -0.2160 0.6378 -1.4196 -0.2292
## (Intercept)-Didelphis_virginiana -0.6580 0.6009 -1.9018 -0.6388
## (Intercept)-Sylvilagus_floridanus -0.1387 0.6662 -1.3821 -0.1632
## (Intercept)-Meleagris_gallopavo -0.4225 0.6789 -1.7145 -0.4316
## (Intercept)-Sciurus_carolinensis -0.7086 0.6694 -2.1522 -0.6721
## Cogon_Patch_Size-Canis_latrans 0.6651 0.6565 -0.3265 0.5659
## Cogon_Patch_Size-Procyon_lotor -0.1551 0.4568 -1.1184 -0.1405
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0095 0.4485 -0.8551 0.0052
## Cogon_Patch_Size-Lynx_rufus -0.0765 0.7005 -1.4308 -0.1010
## Cogon_Patch_Size-Didelphis_virginiana 0.5796 0.4883 -0.2963 0.5395
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6634 0.7938 -2.6190 -0.5297
## Cogon_Patch_Size-Meleagris_gallopavo 0.0554 0.6522 -1.1335 0.0313
## Cogon_Patch_Size-Sciurus_carolinensis -0.5233 0.6841 -2.1955 -0.4322
## Avg_Cogongrass_Cover-Canis_latrans 0.2932 0.4477 -0.5312 0.2682
## Avg_Cogongrass_Cover-Procyon_lotor 0.0818 0.4512 -0.8156 0.0925
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3292 0.4260 -0.4359 0.3137
## Avg_Cogongrass_Cover-Lynx_rufus 0.4841 0.5416 -0.4653 0.4336
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0826 0.4687 -0.8988 0.1081
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2406 0.5698 -1.4829 -0.2034
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4509 0.7260 -2.0333 -0.3611
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3498 0.4836 -0.5306 0.3268
## total_shrub_cover-Canis_latrans -0.0003 0.6102 -1.0944 -0.0541
## total_shrub_cover-Procyon_lotor -1.1495 0.5633 -2.3850 -1.0935
## total_shrub_cover-Dasypus_novemcinctus -0.4866 0.5699 -1.7085 -0.4527
## total_shrub_cover-Lynx_rufus -1.2507 0.7981 -3.1395 -1.1144
## total_shrub_cover-Didelphis_virginiana -0.8810 0.6290 -2.3199 -0.8196
## total_shrub_cover-Sylvilagus_floridanus -1.1997 0.8737 -3.3678 -1.0422
## total_shrub_cover-Meleagris_gallopavo -1.4446 0.8038 -3.3580 -1.3399
## total_shrub_cover-Sciurus_carolinensis -0.8048 0.6947 -2.3831 -0.7508
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4406 1.0012 602
## (Intercept)-Procyon_lotor 1.6269 1.0041 555
## (Intercept)-Dasypus_novemcinctus 0.8034 1.0211 655
## (Intercept)-Lynx_rufus 1.0986 1.0250 520
## (Intercept)-Didelphis_virginiana 0.5123 1.0231 578
## (Intercept)-Sylvilagus_floridanus 1.2726 1.0030 433
## (Intercept)-Meleagris_gallopavo 0.9812 1.0267 426
## (Intercept)-Sciurus_carolinensis 0.5147 1.0220 446
## Cogon_Patch_Size-Canis_latrans 2.2855 1.0034 1057
## Cogon_Patch_Size-Procyon_lotor 0.7604 1.0041 1170
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9015 1.0021 1677
## Cogon_Patch_Size-Lynx_rufus 1.4471 1.0060 827
## Cogon_Patch_Size-Didelphis_virginiana 1.6724 1.0010 1265
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5250 1.0208 640
## Cogon_Patch_Size-Meleagris_gallopavo 1.4484 1.0049 680
## Cogon_Patch_Size-Sciurus_carolinensis 0.5288 1.0126 698
## Avg_Cogongrass_Cover-Canis_latrans 1.2636 1.0084 1191
## Avg_Cogongrass_Cover-Procyon_lotor 0.9882 1.0035 1319
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2587 1.0080 1293
## Avg_Cogongrass_Cover-Lynx_rufus 1.7300 1.0024 1007
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9789 1.0084 1111
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7619 1.0068 643
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7856 1.0048 473
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.4041 1.0067 819
## total_shrub_cover-Canis_latrans 1.2682 1.0108 668
## total_shrub_cover-Procyon_lotor -0.2200 1.0125 547
## total_shrub_cover-Dasypus_novemcinctus 0.5026 1.0093 555
## total_shrub_cover-Lynx_rufus -0.0070 1.0276 288
## total_shrub_cover-Didelphis_virginiana 0.2085 1.0241 499
## total_shrub_cover-Sylvilagus_floridanus 0.1561 1.0335 257
## total_shrub_cover-Meleagris_gallopavo -0.1854 1.0166 377
## total_shrub_cover-Sciurus_carolinensis 0.3788 1.0441 499
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7400 0.1851 -3.1234 -2.7322 -2.3942 1.0095
## (Intercept)-Procyon_lotor -2.3096 0.1363 -2.5895 -2.3108 -2.0515 1.0138
## (Intercept)-Dasypus_novemcinctus -1.8307 0.1730 -2.1896 -1.8271 -1.4985 1.0119
## (Intercept)-Lynx_rufus -3.4153 0.3228 -4.0928 -3.4010 -2.8456 1.0075
## (Intercept)-Didelphis_virginiana -2.6723 0.2938 -3.2589 -2.6621 -2.1244 1.0282
## (Intercept)-Sylvilagus_floridanus -3.2246 0.2623 -3.7392 -3.2183 -2.7300 1.0066
## (Intercept)-Meleagris_gallopavo -3.4530 0.4975 -4.4565 -3.4230 -2.5404 1.0289
## (Intercept)-Sciurus_carolinensis -2.8026 0.3267 -3.4443 -2.7975 -2.2088 1.0731
## shrub_cover-Canis_latrans -0.2586 0.2365 -0.7318 -0.2547 0.1908 1.0048
## shrub_cover-Procyon_lotor 0.3201 0.1604 0.0009 0.3218 0.6288 1.0017
## shrub_cover-Dasypus_novemcinctus 1.0329 0.3259 0.4147 1.0352 1.6770 1.0282
## shrub_cover-Lynx_rufus 0.1662 0.3485 -0.5182 0.1713 0.8161 1.0136
## shrub_cover-Didelphis_virginiana 1.1575 0.4082 0.4341 1.1301 2.0385 1.0420
## shrub_cover-Sylvilagus_floridanus 0.7361 0.3978 -0.0650 0.7463 1.4777 1.0227
## shrub_cover-Meleagris_gallopavo -0.2470 0.4649 -1.1807 -0.2319 0.6050 1.0187
## shrub_cover-Sciurus_carolinensis 1.1303 0.4186 0.3201 1.1380 1.9450 1.0294
## veg_height-Canis_latrans -0.5778 0.1916 -0.9881 -0.5656 -0.2184 1.0219
## veg_height-Procyon_lotor 0.3492 0.1266 0.0983 0.3481 0.5974 1.0066
## veg_height-Dasypus_novemcinctus 0.2782 0.1410 0.0200 0.2785 0.5612 1.0001
## veg_height-Lynx_rufus 0.0602 0.2399 -0.4213 0.0619 0.5305 1.0011
## veg_height-Didelphis_virginiana 0.4421 0.2538 -0.0309 0.4337 0.9594 1.0014
## veg_height-Sylvilagus_floridanus 0.0693 0.2491 -0.4113 0.0647 0.5673 1.0118
## veg_height-Meleagris_gallopavo -0.1037 0.4304 -0.9547 -0.1031 0.7528 1.0083
## veg_height-Sciurus_carolinensis 0.1328 0.2297 -0.3122 0.1257 0.5956 1.0533
## ESS
## (Intercept)-Canis_latrans 653
## (Intercept)-Procyon_lotor 1133
## (Intercept)-Dasypus_novemcinctus 869
## (Intercept)-Lynx_rufus 424
## (Intercept)-Didelphis_virginiana 516
## (Intercept)-Sylvilagus_floridanus 469
## (Intercept)-Meleagris_gallopavo 314
## (Intercept)-Sciurus_carolinensis 421
## shrub_cover-Canis_latrans 685
## shrub_cover-Procyon_lotor 1242
## shrub_cover-Dasypus_novemcinctus 572
## shrub_cover-Lynx_rufus 555
## shrub_cover-Didelphis_virginiana 411
## shrub_cover-Sylvilagus_floridanus 324
## shrub_cover-Meleagris_gallopavo 296
## shrub_cover-Sciurus_carolinensis 356
## veg_height-Canis_latrans 745
## veg_height-Procyon_lotor 1638
## veg_height-Dasypus_novemcinctus 1364
## veg_height-Lynx_rufus 832
## veg_height-Didelphis_virginiana 888
## veg_height-Sylvilagus_floridanus 628
## veg_height-Meleagris_gallopavo 376
## veg_height-Sciurus_carolinensis 762
#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): 0.5078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4216 0.4468 -1.3073 -0.4266 0.4535 1.0055 756
## Tree_Density -0.7668 0.4760 -1.8289 -0.7288 0.0841 1.0079 789
## Avg_Canopy_Cover 1.1692 0.4907 0.3130 1.1248 2.2212 1.0218 562
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1033 1.5262 0.0744 0.7004 4.5563 1.0701 509
## Tree_Density 1.1236 1.8620 0.0586 0.5519 5.8012 1.0047 616
## Avg_Canopy_Cover 1.3660 1.8700 0.1067 0.8182 5.8529 1.0220 649
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7 0.7886 0.0513 0.43 2.8942 1.0977 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7622 0.3227 -3.4013 -2.7732 -2.0924 1.0062 2507
## shrub_cover 0.2800 0.3228 -0.3749 0.2771 0.9153 1.0038 1608
## veg_height 0.1110 0.1986 -0.2919 0.1132 0.5014 1.0019 1753
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7702 0.7378 0.1769 0.5826 2.6160 1.0207 1225
## shrub_cover 0.7407 0.6983 0.1458 0.5622 2.4889 1.0136 1132
## veg_height 0.2591 0.2119 0.0583 0.1999 0.8734 1.0057 1518
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0921 0.5924 -1.0228 0.0653 1.2974
## (Intercept)-Procyon_lotor 0.3521 0.6439 -0.8752 0.3388 1.6403
## (Intercept)-Dasypus_novemcinctus -0.7893 0.5799 -2.0120 -0.7478 0.2518
## (Intercept)-Lynx_rufus 0.0787 0.8616 -1.4272 -0.0324 2.0114
## (Intercept)-Didelphis_virginiana -1.1891 0.6731 -2.6241 -1.1349 -0.0139
## (Intercept)-Sylvilagus_floridanus -0.5956 0.6370 -1.9135 -0.5678 0.5897
## (Intercept)-Meleagris_gallopavo -0.1675 0.7602 -1.4971 -0.2378 1.4797
## (Intercept)-Sciurus_carolinensis -1.2546 0.6904 -2.7512 -1.2080 -0.0846
## Tree_Density-Canis_latrans -0.9698 0.6239 -2.3942 -0.8523 0.0262
## Tree_Density-Procyon_lotor -0.4697 0.4270 -1.3312 -0.4652 0.3644
## Tree_Density-Dasypus_novemcinctus -1.4604 0.9891 -4.0604 -1.2346 -0.1860
## Tree_Density-Lynx_rufus 0.2915 0.8181 -0.9330 0.1527 2.4078
## Tree_Density-Didelphis_virginiana -0.9741 0.7939 -2.8229 -0.8605 0.2385
## Tree_Density-Sylvilagus_floridanus -1.0885 0.8302 -3.1098 -0.9472 0.1623
## Tree_Density-Meleagris_gallopavo -1.0387 0.8395 -3.0588 -0.9245 0.2589
## Tree_Density-Sciurus_carolinensis -0.8320 0.7549 -2.5637 -0.7437 0.3944
## Avg_Canopy_Cover-Canis_latrans -0.1055 0.4850 -1.0962 -0.1036 0.8534
## Avg_Canopy_Cover-Procyon_lotor 1.0372 0.4983 0.1743 0.9958 2.0956
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1469 0.5075 0.2528 1.1067 2.2327
## Avg_Canopy_Cover-Lynx_rufus 0.7678 0.7851 -0.5969 0.7304 2.4304
## Avg_Canopy_Cover-Didelphis_virginiana 1.6249 0.7745 0.4566 1.4927 3.4540
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.2122 1.0325 0.7125 2.0483 4.7265
## Avg_Canopy_Cover-Meleagris_gallopavo 1.6562 0.8995 0.3284 1.5234 3.7759
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5535 0.7136 0.4336 1.4547 3.2617
## Rhat ESS
## (Intercept)-Canis_latrans 1.0143 658
## (Intercept)-Procyon_lotor 1.0169 365
## (Intercept)-Dasypus_novemcinctus 1.0167 812
## (Intercept)-Lynx_rufus 1.0532 327
## (Intercept)-Didelphis_virginiana 1.0133 672
## (Intercept)-Sylvilagus_floridanus 1.0086 999
## (Intercept)-Meleagris_gallopavo 1.0060 439
## (Intercept)-Sciurus_carolinensis 1.0167 598
## Tree_Density-Canis_latrans 1.0006 946
## Tree_Density-Procyon_lotor 1.0019 1400
## Tree_Density-Dasypus_novemcinctus 1.0197 446
## Tree_Density-Lynx_rufus 1.0288 480
## Tree_Density-Didelphis_virginiana 1.0096 867
## Tree_Density-Sylvilagus_floridanus 1.0036 491
## Tree_Density-Meleagris_gallopavo 1.0368 581
## Tree_Density-Sciurus_carolinensis 1.0018 910
## Avg_Canopy_Cover-Canis_latrans 1.0055 1119
## Avg_Canopy_Cover-Procyon_lotor 1.0047 1203
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0031 1289
## Avg_Canopy_Cover-Lynx_rufus 1.0186 498
## Avg_Canopy_Cover-Didelphis_virginiana 1.0504 317
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0101 350
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0321 417
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0119 649
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7516 0.1851 -3.1282 -2.7445 -2.4130 1.0018
## (Intercept)-Procyon_lotor -2.2943 0.1421 -2.5839 -2.2914 -2.0204 1.0122
## (Intercept)-Dasypus_novemcinctus -1.7775 0.1578 -2.0972 -1.7755 -1.4804 1.0006
## (Intercept)-Lynx_rufus -3.6574 0.3415 -4.3491 -3.6486 -2.9757 1.0623
## (Intercept)-Didelphis_virginiana -2.6749 0.2848 -3.2515 -2.6702 -2.1318 1.0034
## (Intercept)-Sylvilagus_floridanus -3.0905 0.2542 -3.6289 -3.0794 -2.6201 1.0187
## (Intercept)-Meleagris_gallopavo -3.8191 0.4352 -4.6784 -3.8109 -3.0120 1.0158
## (Intercept)-Sciurus_carolinensis -2.7541 0.3113 -3.3641 -2.7430 -2.1739 0.9998
## shrub_cover-Canis_latrans -0.3030 0.2218 -0.7329 -0.3018 0.1406 1.0330
## shrub_cover-Procyon_lotor 0.2585 0.1614 -0.0701 0.2616 0.5598 1.0039
## shrub_cover-Dasypus_novemcinctus 0.8896 0.2934 0.3176 0.8903 1.4722 1.0040
## shrub_cover-Lynx_rufus -0.2546 0.3433 -0.9368 -0.2528 0.4569 1.0193
## shrub_cover-Didelphis_virginiana 1.0528 0.3679 0.3957 1.0362 1.8124 1.0062
## shrub_cover-Sylvilagus_floridanus 0.4174 0.3769 -0.3465 0.4249 1.1302 1.0062
## shrub_cover-Meleagris_gallopavo -0.6359 0.4010 -1.4186 -0.6329 0.1044 1.0077
## shrub_cover-Sciurus_carolinensis 0.9766 0.4291 0.1656 0.9679 1.8329 1.0120
## veg_height-Canis_latrans -0.5858 0.1888 -0.9736 -0.5742 -0.2493 1.0123
## veg_height-Procyon_lotor 0.3481 0.1208 0.1164 0.3460 0.5884 1.0090
## veg_height-Dasypus_novemcinctus 0.2709 0.1376 0.0005 0.2684 0.5401 1.0069
## veg_height-Lynx_rufus 0.1033 0.2417 -0.3930 0.1110 0.5607 1.0009
## veg_height-Didelphis_virginiana 0.5253 0.2549 0.0500 0.5165 1.0602 1.0029
## veg_height-Sylvilagus_floridanus 0.1734 0.2350 -0.3096 0.1817 0.6247 1.0028
## veg_height-Meleagris_gallopavo -0.1352 0.3525 -0.8639 -0.1225 0.5536 1.0146
## veg_height-Sciurus_carolinensis 0.1528 0.2211 -0.2565 0.1470 0.6008 1.0030
## ESS
## (Intercept)-Canis_latrans 702
## (Intercept)-Procyon_lotor 1211
## (Intercept)-Dasypus_novemcinctus 1429
## (Intercept)-Lynx_rufus 284
## (Intercept)-Didelphis_virginiana 595
## (Intercept)-Sylvilagus_floridanus 768
## (Intercept)-Meleagris_gallopavo 271
## (Intercept)-Sciurus_carolinensis 459
## shrub_cover-Canis_latrans 774
## shrub_cover-Procyon_lotor 1579
## shrub_cover-Dasypus_novemcinctus 1256
## shrub_cover-Lynx_rufus 489
## shrub_cover-Didelphis_virginiana 726
## shrub_cover-Sylvilagus_floridanus 588
## shrub_cover-Meleagris_gallopavo 367
## shrub_cover-Sciurus_carolinensis 507
## veg_height-Canis_latrans 840
## veg_height-Procyon_lotor 1645
## veg_height-Dasypus_novemcinctus 1965
## veg_height-Lynx_rufus 704
## veg_height-Didelphis_virginiana 948
## veg_height-Sylvilagus_floridanus 999
## veg_height-Meleagris_gallopavo 651
## veg_height-Sciurus_carolinensis 928
# 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): 0.5055
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0018 0.4173 -1.8674 -0.9969 -0.2090 1.0266 568
## Avg_Cogongrass_Cover -0.6140 0.3903 -1.4140 -0.6008 0.1462 1.0201 694
## I(Avg_Cogongrass_Cover^2) 0.8043 0.3759 0.1604 0.7656 1.6549 1.0088 509
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6012 0.7269 0.0573 0.3834 2.3615 1.0033 1146
## Avg_Cogongrass_Cover 0.5165 0.8370 0.0454 0.2824 2.4695 1.0332 870
## I(Avg_Cogongrass_Cover^2) 0.5436 0.8583 0.0412 0.2584 2.8563 1.0310 293
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4263 0.4678 0.0405 0.2702 1.7017 1.0388 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7254 0.3113 -3.3368 -2.7295 -2.0786 1.0038 1705
## shrub_cover 0.2805 0.3085 -0.3426 0.2784 0.8995 1.0033 1534
## veg_height 0.0953 0.1929 -0.2940 0.0999 0.4925 1.0011 1299
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6896 0.7348 0.1460 0.5103 2.2958 1.0825 1252
## shrub_cover 0.6744 0.6332 0.1252 0.4939 2.1948 1.0312 1054
## veg_height 0.2445 0.2234 0.0556 0.1864 0.7834 1.0131 1779
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6936 0.5560 -1.7651 -0.7030
## (Intercept)-Procyon_lotor -0.4892 0.5651 -1.5416 -0.4975
## (Intercept)-Dasypus_novemcinctus -1.1646 0.5161 -2.1849 -1.1463
## (Intercept)-Lynx_rufus -1.0702 0.6182 -2.3074 -1.0724
## (Intercept)-Didelphis_virginiana -1.4333 0.5664 -2.6806 -1.3845
## (Intercept)-Sylvilagus_floridanus -1.0524 0.5436 -2.1689 -1.0495
## (Intercept)-Meleagris_gallopavo -0.6781 0.6780 -1.8410 -0.7128
## (Intercept)-Sciurus_carolinensis -1.6784 0.6283 -3.0762 -1.6342
## Avg_Cogongrass_Cover-Canis_latrans -0.3198 0.5336 -1.3100 -0.3371
## Avg_Cogongrass_Cover-Procyon_lotor -0.6552 0.5079 -1.6653 -0.6455
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4501 0.4879 -1.3857 -0.4632
## Avg_Cogongrass_Cover-Lynx_rufus -0.5666 0.5746 -1.7601 -0.5515
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3180 0.5495 -1.3187 -0.3482
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1198 0.6219 -2.5451 -1.0458
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9976 0.7073 -2.6519 -0.9302
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6126 0.5333 -1.6550 -0.6140
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2958 0.7806 0.2534 1.1172
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1387 0.7044 0.2615 0.9982
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6732 0.3588 0.0117 0.6524
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1521 0.5623 0.2922 1.0685
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4747 0.4076 -0.2990 0.4693
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6930 0.4352 -0.0632 0.6597
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3257 0.6257 -0.9139 0.3150
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8234 0.3892 0.1113 0.7911
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4263 1.0058 770
## (Intercept)-Procyon_lotor 0.6615 1.0032 693
## (Intercept)-Dasypus_novemcinctus -0.1996 1.0025 1167
## (Intercept)-Lynx_rufus 0.1659 1.0137 589
## (Intercept)-Didelphis_virginiana -0.4247 1.0103 862
## (Intercept)-Sylvilagus_floridanus 0.0028 1.0064 890
## (Intercept)-Meleagris_gallopavo 0.7892 1.0834 557
## (Intercept)-Sciurus_carolinensis -0.5610 1.0009 850
## Avg_Cogongrass_Cover-Canis_latrans 0.8378 1.0270 928
## Avg_Cogongrass_Cover-Procyon_lotor 0.3388 1.0002 1117
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5606 1.0026 1213
## Avg_Cogongrass_Cover-Lynx_rufus 0.5587 1.0330 997
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8774 1.0175 944
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1042 1.0036 814
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2099 1.0075 482
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4262 1.0000 973
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3076 1.0184 286
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0809 1.0200 288
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4182 1.0019 1203
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4922 1.0083 461
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3344 1.0065 744
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6825 1.0084 667
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.6680 1.0320 397
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6299 1.0005 913
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7351 0.1720 -3.0832 -2.7321 -2.4167 1.0145
## (Intercept)-Procyon_lotor -2.3121 0.1438 -2.6078 -2.3108 -2.0477 1.0093
## (Intercept)-Dasypus_novemcinctus -1.7770 0.1612 -2.0984 -1.7765 -1.4755 1.0026
## (Intercept)-Lynx_rufus -3.4646 0.3291 -4.1239 -3.4563 -2.8272 1.0029
## (Intercept)-Didelphis_virginiana -2.6014 0.2924 -3.1904 -2.5909 -2.0666 1.0297
## (Intercept)-Sylvilagus_floridanus -3.1263 0.2687 -3.6756 -3.1202 -2.6155 0.9997
## (Intercept)-Meleagris_gallopavo -3.7031 0.4957 -4.6839 -3.7166 -2.7008 1.0575
## (Intercept)-Sciurus_carolinensis -2.6737 0.3214 -3.3471 -2.6563 -2.0974 1.0056
## shrub_cover-Canis_latrans -0.2523 0.2141 -0.6546 -0.2584 0.1730 1.0013
## shrub_cover-Procyon_lotor 0.2461 0.1635 -0.0876 0.2502 0.5552 1.0034
## shrub_cover-Dasypus_novemcinctus 0.8646 0.2960 0.3048 0.8604 1.4557 1.0118
## shrub_cover-Lynx_rufus -0.1769 0.3792 -0.9663 -0.1669 0.5407 1.0231
## shrub_cover-Didelphis_virginiana 1.0161 0.3860 0.3423 0.9870 1.8554 1.0206
## shrub_cover-Sylvilagus_floridanus 0.2524 0.4171 -0.5051 0.2267 1.1074 1.0028
## shrub_cover-Meleagris_gallopavo -0.5383 0.4336 -1.3842 -0.5468 0.3649 1.0578
## shrub_cover-Sciurus_carolinensis 0.8777 0.4004 0.1192 0.8677 1.6892 1.0291
## veg_height-Canis_latrans -0.5716 0.1916 -0.9556 -0.5660 -0.2033 1.0053
## veg_height-Procyon_lotor 0.3555 0.1227 0.1186 0.3521 0.5995 1.0057
## veg_height-Dasypus_novemcinctus 0.2626 0.1376 -0.0073 0.2613 0.5345 1.0012
## veg_height-Lynx_rufus 0.0752 0.2398 -0.4203 0.0828 0.5257 1.0014
## veg_height-Didelphis_virginiana 0.4330 0.2633 -0.0554 0.4268 0.9846 1.0046
## veg_height-Sylvilagus_floridanus 0.1791 0.2525 -0.3411 0.1871 0.6588 1.0319
## veg_height-Meleagris_gallopavo -0.0470 0.4025 -0.8560 -0.0422 0.7522 1.0092
## veg_height-Sciurus_carolinensis 0.1134 0.2233 -0.3093 0.1074 0.5827 1.0062
## ESS
## (Intercept)-Canis_latrans 970
## (Intercept)-Procyon_lotor 1171
## (Intercept)-Dasypus_novemcinctus 1377
## (Intercept)-Lynx_rufus 434
## (Intercept)-Didelphis_virginiana 622
## (Intercept)-Sylvilagus_floridanus 701
## (Intercept)-Meleagris_gallopavo 201
## (Intercept)-Sciurus_carolinensis 746
## shrub_cover-Canis_latrans 928
## shrub_cover-Procyon_lotor 1251
## shrub_cover-Dasypus_novemcinctus 1058
## shrub_cover-Lynx_rufus 445
## shrub_cover-Didelphis_virginiana 557
## shrub_cover-Sylvilagus_floridanus 447
## shrub_cover-Meleagris_gallopavo 219
## shrub_cover-Sciurus_carolinensis 759
## veg_height-Canis_latrans 828
## veg_height-Procyon_lotor 1313
## veg_height-Dasypus_novemcinctus 1880
## veg_height-Lynx_rufus 837
## veg_height-Didelphis_virginiana 691
## veg_height-Sylvilagus_floridanus 732
## veg_height-Meleagris_gallopavo 332
## veg_height-Sciurus_carolinensis 913
# 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): 0.5333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8539 0.7938 -3.5283 -1.8340 -0.4161 1.0652 235
## Cogon_Patch_Size 0.1500 0.7316 -1.2533 0.1281 1.6161 1.0184 490
## Veg_shannon_index 0.9829 0.5382 -0.0092 0.9821 2.0589 1.0281 292
## total_shrub_cover -1.1562 0.7195 -2.7081 -1.1289 0.1915 1.0207 295
## Avg_Cogongrass_Cover -0.0459 1.0221 -2.0538 -0.0487 2.0267 1.0158 160
## Tree_Density -2.2789 0.9141 -4.1055 -2.2842 -0.5226 1.1483 237
## Avg_Canopy_Cover 1.9429 0.8097 0.3340 1.9310 3.5627 1.0063 484
## I(Avg_Cogongrass_Cover^2) 1.4137 0.6880 0.0965 1.4058 2.7900 1.0233 530
## avg_veg_height -0.0525 0.5842 -1.2471 -0.0418 1.0969 1.1733 242
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9225 3.2906 0.0672 0.8846 10.8719 1.0524 333
## Cogon_Patch_Size 2.9005 4.4251 0.1060 1.5617 13.6843 1.1048 199
## Veg_shannon_index 0.7428 1.1187 0.0460 0.3611 3.8947 1.0104 530
## total_shrub_cover 2.9503 4.9316 0.1217 1.5543 14.6490 1.2624 166
## Avg_Cogongrass_Cover 1.3725 2.8204 0.0530 0.5436 7.2065 1.0399 452
## Tree_Density 3.8983 9.4430 0.0655 1.3861 22.4058 1.3951 170
## Avg_Canopy_Cover 4.8873 7.4049 0.1922 2.6419 23.6344 1.0738 253
## I(Avg_Cogongrass_Cover^2) 2.9421 5.8928 0.0774 1.2164 16.9148 1.5128 159
## avg_veg_height 0.7183 1.3094 0.0441 0.3511 3.4675 1.0479 513
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0694 2.7442 0.0733 1.0246 9.5688 1.2479 48
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7581 0.2872 -3.3123 -2.7584 -2.1547 1.0016 2028
## shrub_cover 0.4359 0.3136 -0.2093 0.4360 1.0402 1.0102 1352
## veg_height 0.1299 0.2013 -0.2720 0.1302 0.5351 1.0221 1201
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6292 0.5952 0.1437 0.4714 1.9852 1.0697 1154
## shrub_cover 0.6622 0.5841 0.1324 0.4963 2.1496 1.0000 1006
## veg_height 0.2586 0.2157 0.0566 0.1994 0.8304 1.0069 1166
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4907 1.0608 -3.4886 -1.5019
## (Intercept)-Procyon_lotor -1.2082 1.0474 -3.2710 -1.2064
## (Intercept)-Dasypus_novemcinctus -2.1953 1.0071 -4.3651 -2.1195
## (Intercept)-Lynx_rufus -1.5763 1.3596 -4.0366 -1.6469
## (Intercept)-Didelphis_virginiana -2.7148 1.2194 -5.6534 -2.5477
## (Intercept)-Sylvilagus_floridanus -1.9629 1.1264 -4.3346 -1.9108
## (Intercept)-Meleagris_gallopavo -1.8804 1.0950 -4.0948 -1.8804
## (Intercept)-Sciurus_carolinensis -3.0455 1.4131 -6.3525 -2.8359
## Cogon_Patch_Size-Canis_latrans 1.5079 1.3562 -0.4534 1.2582
## Cogon_Patch_Size-Procyon_lotor -0.5036 0.8457 -2.1977 -0.5096
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1584 0.9012 -1.5719 0.1294
## Cogon_Patch_Size-Lynx_rufus -0.1136 1.4595 -3.0825 -0.1183
## Cogon_Patch_Size-Didelphis_virginiana 1.4366 1.0874 -0.2733 1.3254
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0676 1.5031 -4.6679 -0.8512
## Cogon_Patch_Size-Meleagris_gallopavo 0.3951 1.2166 -1.6184 0.2580
## Cogon_Patch_Size-Sciurus_carolinensis -0.7257 1.1900 -3.4632 -0.5947
## Veg_shannon_index-Canis_latrans 1.3431 0.7557 0.1380 1.2575
## Veg_shannon_index-Procyon_lotor 1.2086 0.6691 0.0336 1.1624
## Veg_shannon_index-Dasypus_novemcinctus 0.6377 0.6156 -0.6098 0.6409
## Veg_shannon_index-Lynx_rufus 1.0130 0.9488 -0.7555 0.9860
## Veg_shannon_index-Didelphis_virginiana 1.1783 0.7480 -0.1452 1.1194
## Veg_shannon_index-Sylvilagus_floridanus 1.0299 0.7683 -0.3880 1.0130
## Veg_shannon_index-Meleagris_gallopavo 1.2999 0.8344 -0.0771 1.2219
## Veg_shannon_index-Sciurus_carolinensis 0.4543 0.8347 -1.5086 0.5227
## total_shrub_cover-Canis_latrans 0.2524 0.9161 -1.3320 0.1639
## total_shrub_cover-Procyon_lotor -1.5706 0.7804 -3.3423 -1.5149
## total_shrub_cover-Dasypus_novemcinctus -0.5340 0.9608 -2.6650 -0.4483
## total_shrub_cover-Lynx_rufus -2.0125 1.5126 -5.5850 -1.7732
## total_shrub_cover-Didelphis_virginiana -1.5489 1.2897 -4.5477 -1.3461
## total_shrub_cover-Sylvilagus_floridanus -1.2863 1.4068 -4.5640 -1.1309
## total_shrub_cover-Meleagris_gallopavo -2.7638 1.5816 -6.7433 -2.5031
## total_shrub_cover-Sciurus_carolinensis -1.1155 1.1971 -3.8618 -0.9920
## Avg_Cogongrass_Cover-Canis_latrans 0.0132 1.2467 -2.3945 -0.0050
## Avg_Cogongrass_Cover-Procyon_lotor -0.1296 1.2589 -2.5783 -0.1377
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5597 1.3545 -1.8017 0.4449
## Avg_Cogongrass_Cover-Lynx_rufus 0.0076 1.3455 -2.5672 -0.0334
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0834 1.2910 -2.4925 0.0630
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6638 1.4411 -3.8622 -0.5739
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2971 1.4054 -3.2679 -0.2571
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0556 1.3275 -2.6359 0.0301
## Tree_Density-Canis_latrans -3.1740 1.4714 -6.8882 -2.9725
## Tree_Density-Procyon_lotor -2.4040 1.0847 -4.6921 -2.3424
## Tree_Density-Dasypus_novemcinctus -4.0478 2.0680 -9.4222 -3.5878
## Tree_Density-Lynx_rufus -1.0243 1.8257 -3.9640 -1.2136
## Tree_Density-Didelphis_virginiana -2.3908 1.4450 -5.2803 -2.3235
## Tree_Density-Sylvilagus_floridanus -2.8450 1.5998 -6.5349 -2.7133
## Tree_Density-Meleagris_gallopavo -2.3572 1.4379 -5.3028 -2.3241
## Tree_Density-Sciurus_carolinensis -2.5595 1.5052 -5.9181 -2.4811
## Avg_Canopy_Cover-Canis_latrans 0.1250 0.7291 -1.3355 0.1131
## Avg_Canopy_Cover-Procyon_lotor 1.6687 0.8539 0.1435 1.6149
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3700 0.9744 0.8085 2.2491
## Avg_Canopy_Cover-Lynx_rufus 1.2519 1.5077 -1.5097 1.1748
## Avg_Canopy_Cover-Didelphis_virginiana 3.3507 1.5963 1.1498 3.0796
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2160 2.0120 1.2380 3.8726
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5091 1.3313 0.5116 2.2816
## Avg_Canopy_Cover-Sciurus_carolinensis 3.2611 1.6971 1.0015 2.9049
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3934 1.2431 0.6378 2.1498
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5275 1.5167 0.6854 2.2766
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3874 0.7881 0.0424 1.3408
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7394 1.5166 0.5950 2.4449
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8153 0.8124 -0.7060 0.8207
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0969 0.9145 -0.5383 1.0427
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1605 1.5689 -3.5683 0.2698
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.4949 0.8100 0.0931 1.4304
## avg_veg_height-Canis_latrans -0.2008 0.6943 -1.5789 -0.1981
## avg_veg_height-Procyon_lotor 0.0673 0.6945 -1.3240 0.0628
## avg_veg_height-Dasypus_novemcinctus 0.3221 0.6992 -0.9803 0.2672
## avg_veg_height-Lynx_rufus -0.4147 0.9915 -2.7186 -0.3078
## avg_veg_height-Didelphis_virginiana -0.2450 0.7860 -1.9370 -0.1816
## avg_veg_height-Sylvilagus_floridanus -0.1976 0.8125 -1.9916 -0.1470
## avg_veg_height-Meleagris_gallopavo -0.1071 0.9884 -2.1590 -0.0770
## avg_veg_height-Sciurus_carolinensis 0.3679 0.8142 -1.0847 0.3259
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6977 1.0818 340
## (Intercept)-Procyon_lotor 0.8522 1.0421 252
## (Intercept)-Dasypus_novemcinctus -0.4860 1.0794 279
## (Intercept)-Lynx_rufus 1.2091 1.1734 197
## (Intercept)-Didelphis_virginiana -0.7370 1.0434 196
## (Intercept)-Sylvilagus_floridanus 0.1536 1.0527 258
## (Intercept)-Meleagris_gallopavo 0.3730 1.0767 370
## (Intercept)-Sciurus_carolinensis -0.9559 1.0206 218
## Cogon_Patch_Size-Canis_latrans 4.7809 1.0561 330
## Cogon_Patch_Size-Procyon_lotor 1.1421 1.0057 327
## Cogon_Patch_Size-Dasypus_novemcinctus 2.0296 1.0192 516
## Cogon_Patch_Size-Lynx_rufus 2.7745 1.0216 288
## Cogon_Patch_Size-Didelphis_virginiana 3.9928 1.0747 247
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2899 1.0235 281
## Cogon_Patch_Size-Meleagris_gallopavo 3.2769 1.0652 412
## Cogon_Patch_Size-Sciurus_carolinensis 1.3740 1.0152 321
## Veg_shannon_index-Canis_latrans 3.1209 1.0097 405
## Veg_shannon_index-Procyon_lotor 2.5994 1.0276 240
## Veg_shannon_index-Dasypus_novemcinctus 1.8112 1.0267 537
## Veg_shannon_index-Lynx_rufus 3.0806 1.0258 339
## Veg_shannon_index-Didelphis_virginiana 2.8632 1.0297 402
## Veg_shannon_index-Sylvilagus_floridanus 2.6409 1.0453 349
## Veg_shannon_index-Meleagris_gallopavo 3.1445 1.0398 291
## Veg_shannon_index-Sciurus_carolinensis 1.8975 1.0201 567
## total_shrub_cover-Canis_latrans 2.3969 1.0926 344
## total_shrub_cover-Procyon_lotor -0.2120 1.0681 463
## total_shrub_cover-Dasypus_novemcinctus 1.0828 1.0064 360
## total_shrub_cover-Lynx_rufus 0.1706 1.1904 180
## total_shrub_cover-Didelphis_virginiana 0.3888 1.1936 208
## total_shrub_cover-Sylvilagus_floridanus 1.0021 1.0472 253
## total_shrub_cover-Meleagris_gallopavo -0.3686 1.1875 171
## total_shrub_cover-Sciurus_carolinensis 0.9467 1.0079 337
## Avg_Cogongrass_Cover-Canis_latrans 2.5389 1.0193 192
## Avg_Cogongrass_Cover-Procyon_lotor 2.4699 1.0061 201
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.6110 1.0202 199
## Avg_Cogongrass_Cover-Lynx_rufus 2.7810 1.0112 268
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7489 1.0453 179
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8888 1.0129 243
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.3529 1.0121 193
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7590 1.0075 244
## Tree_Density-Canis_latrans -0.9084 1.0387 192
## Tree_Density-Procyon_lotor -0.4720 1.1127 289
## Tree_Density-Dasypus_novemcinctus -1.3759 1.0841 167
## Tree_Density-Lynx_rufus 2.9612 1.2294 137
## Tree_Density-Didelphis_virginiana 0.1571 1.0438 305
## Tree_Density-Sylvilagus_floridanus -0.0936 1.0353 322
## Tree_Density-Meleagris_gallopavo 0.5064 1.0175 374
## Tree_Density-Sciurus_carolinensis 0.2574 1.0187 313
## Avg_Canopy_Cover-Canis_latrans 1.5774 1.0206 445
## Avg_Canopy_Cover-Procyon_lotor 3.4326 1.0041 495
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.5253 1.0553 169
## Avg_Canopy_Cover-Lynx_rufus 4.4949 1.0277 219
## Avg_Canopy_Cover-Didelphis_virginiana 7.2154 1.0635 198
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.8036 1.0611 195
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5861 1.0300 310
## Avg_Canopy_Cover-Sciurus_carolinensis 7.8610 1.0143 220
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5282 1.1650 275
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0640 1.5583 138
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1074 1.0291 380
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.7428 1.3690 171
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4361 1.0503 312
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0707 1.0457 351
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7283 1.4085 155
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2912 1.0105 470
## avg_veg_height-Canis_latrans 1.1607 1.0610 371
## avg_veg_height-Procyon_lotor 1.4710 1.0494 460
## avg_veg_height-Dasypus_novemcinctus 1.7704 1.0417 480
## avg_veg_height-Lynx_rufus 1.2974 1.1476 280
## avg_veg_height-Didelphis_virginiana 1.1864 1.0430 353
## avg_veg_height-Sylvilagus_floridanus 1.3118 1.1068 368
## avg_veg_height-Meleagris_gallopavo 1.7381 1.1945 306
## avg_veg_height-Sciurus_carolinensis 2.1025 1.0918 327
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7186 0.1804 -3.1035 -2.7129 -2.3907 1.0017
## (Intercept)-Procyon_lotor -2.3148 0.1454 -2.6086 -2.3106 -2.0407 1.0225
## (Intercept)-Dasypus_novemcinctus -1.8292 0.1757 -2.1935 -1.8177 -1.5010 1.0096
## (Intercept)-Lynx_rufus -3.5735 0.3147 -4.2080 -3.5622 -2.9867 1.0904
## (Intercept)-Didelphis_virginiana -2.6927 0.2977 -3.2820 -2.6817 -2.1369 1.0236
## (Intercept)-Sylvilagus_floridanus -3.1585 0.2528 -3.6696 -3.1524 -2.6858 1.0446
## (Intercept)-Meleagris_gallopavo -3.5359 0.4657 -4.4500 -3.5226 -2.6343 1.1665
## (Intercept)-Sciurus_carolinensis -2.8338 0.3020 -3.4424 -2.8351 -2.2566 1.0051
## shrub_cover-Canis_latrans -0.2855 0.2311 -0.7349 -0.2878 0.1806 1.0396
## shrub_cover-Procyon_lotor 0.2854 0.1553 -0.0239 0.2846 0.5895 1.0092
## shrub_cover-Dasypus_novemcinctus 1.0238 0.3191 0.3891 1.0302 1.6223 1.0032
## shrub_cover-Lynx_rufus 0.0545 0.3633 -0.6643 0.0573 0.7628 1.0525
## shrub_cover-Didelphis_virginiana 1.1282 0.3774 0.4404 1.1160 1.8795 1.0259
## shrub_cover-Sylvilagus_floridanus 0.6023 0.3777 -0.1418 0.5976 1.3438 1.0073
## shrub_cover-Meleagris_gallopavo -0.3304 0.4249 -1.1568 -0.3253 0.5077 1.0752
## shrub_cover-Sciurus_carolinensis 1.0964 0.4065 0.3227 1.0944 1.8978 1.0088
## veg_height-Canis_latrans -0.5530 0.1876 -0.9221 -0.5512 -0.1812 1.0142
## veg_height-Procyon_lotor 0.3719 0.1226 0.1392 0.3705 0.6119 1.0027
## veg_height-Dasypus_novemcinctus 0.2922 0.1438 0.0119 0.2911 0.5815 1.0047
## veg_height-Lynx_rufus 0.1722 0.2229 -0.2839 0.1799 0.5800 1.0061
## veg_height-Didelphis_virginiana 0.5164 0.2425 0.0636 0.5036 1.0092 1.0046
## veg_height-Sylvilagus_floridanus 0.1711 0.2581 -0.3486 0.1723 0.6708 1.0080
## veg_height-Meleagris_gallopavo -0.1104 0.4170 -0.9332 -0.1093 0.7262 1.1978
## veg_height-Sciurus_carolinensis 0.1766 0.2223 -0.2589 0.1757 0.6299 1.0103
## ESS
## (Intercept)-Canis_latrans 831
## (Intercept)-Procyon_lotor 932
## (Intercept)-Dasypus_novemcinctus 734
## (Intercept)-Lynx_rufus 365
## (Intercept)-Didelphis_virginiana 501
## (Intercept)-Sylvilagus_floridanus 648
## (Intercept)-Meleagris_gallopavo 233
## (Intercept)-Sciurus_carolinensis 454
## shrub_cover-Canis_latrans 500
## shrub_cover-Procyon_lotor 1245
## shrub_cover-Dasypus_novemcinctus 517
## shrub_cover-Lynx_rufus 399
## shrub_cover-Didelphis_virginiana 383
## shrub_cover-Sylvilagus_floridanus 474
## shrub_cover-Meleagris_gallopavo 316
## shrub_cover-Sciurus_carolinensis 451
## veg_height-Canis_latrans 636
## veg_height-Procyon_lotor 1183
## veg_height-Dasypus_novemcinctus 1457
## veg_height-Lynx_rufus 608
## veg_height-Didelphis_virginiana 904
## veg_height-Sylvilagus_floridanus 584
## veg_height-Meleagris_gallopavo 162
## veg_height-Sciurus_carolinensis 933
#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): 0.613
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3876 0.3318 -1.0277 -0.3991 0.3055 1.0026 1707
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6473 0.5728 0.1113 0.476 2.1559 1.0025 1728
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4241 0.2859 -2.9794 -2.4272 -1.8367 1.0006 2372
## week 0.1863 0.2052 -0.2020 0.1848 0.6075 1.0081 1214
## I(week^2) -0.2222 0.1272 -0.4874 -0.2170 0.0086 1.0030 1057
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5804 0.4852 0.1453 0.4467 1.7844 1.0202 1325
## week 0.2034 0.2422 0.0326 0.1367 0.7766 1.0425 913
## I(week^2) 0.0866 0.0945 0.0201 0.0628 0.3025 1.0581 1055
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1461 0.3717 -0.5433 0.1235 0.9230 1.0006
## (Intercept)-Procyon_lotor 0.4577 0.3773 -0.2535 0.4516 1.2283 1.0015
## (Intercept)-Dasypus_novemcinctus -0.5949 0.3351 -1.2577 -0.5871 0.0760 1.0008
## (Intercept)-Lynx_rufus -0.1389 0.4811 -1.0036 -0.1656 0.9091 1.0116
## (Intercept)-Didelphis_virginiana -1.1314 0.4034 -1.9522 -1.1132 -0.3947 1.0011
## (Intercept)-Sylvilagus_floridanus -0.4099 0.4251 -1.2034 -0.4267 0.4743 1.0038
## (Intercept)-Meleagris_gallopavo -0.4290 0.4594 -1.2479 -0.4513 0.5264 1.0031
## (Intercept)-Sciurus_carolinensis -1.0941 0.3969 -1.9052 -1.0729 -0.3505 1.0008
## ESS
## (Intercept)-Canis_latrans 1699
## (Intercept)-Procyon_lotor 1568
## (Intercept)-Dasypus_novemcinctus 2765
## (Intercept)-Lynx_rufus 890
## (Intercept)-Didelphis_virginiana 2188
## (Intercept)-Sylvilagus_floridanus 1294
## (Intercept)-Meleagris_gallopavo 979
## (Intercept)-Sciurus_carolinensis 1988
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4500 0.1860 -2.8248 -2.4456 -2.1044 1.0022
## (Intercept)-Procyon_lotor -2.1704 0.1461 -2.4565 -2.1695 -1.8814 1.0031
## (Intercept)-Dasypus_novemcinctus -1.4919 0.1557 -1.8035 -1.4909 -1.1920 1.0019
## (Intercept)-Lynx_rufus -3.2009 0.3093 -3.8256 -3.2012 -2.6225 1.0229
## (Intercept)-Didelphis_virginiana -2.1614 0.2657 -2.6980 -2.1514 -1.6582 1.0120
## (Intercept)-Sylvilagus_floridanus -2.9780 0.2970 -3.5844 -2.9683 -2.4179 1.0137
## (Intercept)-Meleagris_gallopavo -3.1186 0.3451 -3.8239 -3.1006 -2.4903 1.0020
## (Intercept)-Sciurus_carolinensis -2.3447 0.2726 -2.9089 -2.3363 -1.8369 1.0094
## week-Canis_latrans 0.4518 0.2457 0.0032 0.4435 0.9513 1.0023
## week-Procyon_lotor 0.1621 0.1978 -0.2383 0.1635 0.5631 1.0112
## week-Dasypus_novemcinctus 0.0642 0.2115 -0.3552 0.0625 0.4824 1.0104
## week-Lynx_rufus 0.2816 0.3077 -0.3006 0.2690 0.9287 1.0152
## week-Didelphis_virginiana 0.0306 0.3154 -0.6316 0.0446 0.6223 1.0019
## week-Sylvilagus_floridanus 0.0600 0.2952 -0.5232 0.0628 0.6434 1.0079
## week-Meleagris_gallopavo -0.0784 0.3523 -0.8398 -0.0554 0.5499 1.0087
## week-Sciurus_carolinensis 0.5452 0.3391 -0.0537 0.5151 1.2781 1.0034
## I(week^2)-Canis_latrans -0.1964 0.1041 -0.4071 -0.1947 -0.0034 1.0022
## I(week^2)-Procyon_lotor -0.1113 0.0871 -0.2841 -0.1105 0.0561 1.0152
## I(week^2)-Dasypus_novemcinctus -0.1524 0.1009 -0.3569 -0.1504 0.0428 1.0020
## I(week^2)-Lynx_rufus -0.2014 0.1435 -0.5085 -0.1980 0.0656 1.0069
## I(week^2)-Didelphis_virginiana -0.3746 0.2317 -0.9377 -0.3426 -0.0159 1.0123
## I(week^2)-Sylvilagus_floridanus -0.1661 0.1527 -0.4678 -0.1600 0.1131 1.0220
## I(week^2)-Meleagris_gallopavo -0.3968 0.2579 -1.0141 -0.3549 -0.0077 1.0411
## I(week^2)-Sciurus_carolinensis -0.1952 0.1412 -0.4924 -0.1886 0.0644 1.0057
## ESS
## (Intercept)-Canis_latrans 918
## (Intercept)-Procyon_lotor 1571
## (Intercept)-Dasypus_novemcinctus 2464
## (Intercept)-Lynx_rufus 521
## (Intercept)-Didelphis_virginiana 1537
## (Intercept)-Sylvilagus_floridanus 646
## (Intercept)-Meleagris_gallopavo 497
## (Intercept)-Sciurus_carolinensis 1266
## week-Canis_latrans 1271
## week-Procyon_lotor 1736
## week-Dasypus_novemcinctus 1963
## week-Lynx_rufus 931
## week-Didelphis_virginiana 1114
## week-Sylvilagus_floridanus 1241
## week-Meleagris_gallopavo 625
## week-Sciurus_carolinensis 1077
## I(week^2)-Canis_latrans 1305
## I(week^2)-Procyon_lotor 1618
## I(week^2)-Dasypus_novemcinctus 1834
## I(week^2)-Lynx_rufus 804
## I(week^2)-Didelphis_virginiana 442
## I(week^2)-Sylvilagus_floridanus 821
## I(week^2)-Meleagris_gallopavo 243
## I(week^2)-Sciurus_carolinensis 1276
#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): 0.6365
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9771 0.6248 -2.1920 -0.9970 0.2798 1.0092 464
## Cogon_Patch_Size -0.5171 0.6141 -1.8482 -0.5057 0.6741 1.0093 510
## Veg_shannon_index 0.9218 0.4369 0.1145 0.9001 1.8525 1.0197 170
## total_shrub_cover -0.5810 0.5109 -1.6666 -0.5531 0.3937 1.0041 661
## Avg_Cogongrass_Cover 1.9819 0.7020 0.6948 1.9521 3.4099 1.0122 177
## Tree_Density -1.8521 0.6956 -3.2817 -1.8242 -0.5479 1.0079 330
## Avg_Canopy_Cover 1.6311 0.5883 0.5118 1.6042 2.8349 1.0097 597
## avg_veg_height -0.5030 0.4798 -1.4649 -0.4992 0.4181 1.0229 297
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2415 2.9081 0.1224 1.4687 8.8043 1.0217 615
## Cogon_Patch_Size 1.9434 2.6771 0.1101 1.1387 8.5287 1.0160 381
## Veg_shannon_index 0.5007 0.8044 0.0436 0.2648 2.3011 1.0439 176
## total_shrub_cover 1.6272 2.4870 0.0755 0.8890 7.5952 1.0596 448
## Avg_Cogongrass_Cover 0.9499 1.5108 0.0486 0.4528 5.0788 1.0110 447
## Tree_Density 2.2138 3.8080 0.0720 1.0436 11.0477 1.0048 229
## Avg_Canopy_Cover 2.4200 5.3742 0.1058 1.1798 12.5300 1.1745 162
## avg_veg_height 0.4199 0.5875 0.0435 0.2505 1.7632 1.0610 1108
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0574 2.59 0.0678 1.2136 9.338 1.0873 50
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4495 0.2958 -3.0295 -2.4571 -1.8375 1.0021 2197
## week 0.1945 0.2006 -0.1962 0.1903 0.5882 1.0029 1219
## I(week^2) -0.2163 0.1252 -0.4755 -0.2104 0.0168 1.0104 848
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6527 0.5810 0.1686 0.5008 2.0509 1.0073 1948
## week 0.1911 0.2126 0.0340 0.1360 0.6739 1.0087 1413
## I(week^2) 0.0816 0.0871 0.0203 0.0592 0.2718 1.0617 1116
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0541 0.8626 -1.6590 -0.0664
## (Intercept)-Procyon_lotor 0.1708 0.8866 -1.6114 0.1960
## (Intercept)-Dasypus_novemcinctus -1.2985 0.7282 -2.8704 -1.2719
## (Intercept)-Lynx_rufus -0.2676 1.1973 -2.3010 -0.4152
## (Intercept)-Didelphis_virginiana -2.1927 0.9472 -4.1703 -2.1325
## (Intercept)-Sylvilagus_floridanus -1.1908 0.8790 -3.0106 -1.1843
## (Intercept)-Meleagris_gallopavo -1.3116 0.9889 -3.3991 -1.2660
## (Intercept)-Sciurus_carolinensis -2.2769 0.9957 -4.3861 -2.2236
## Cogon_Patch_Size-Canis_latrans 0.4566 0.9224 -1.0257 0.3426
## Cogon_Patch_Size-Procyon_lotor -0.9373 0.7406 -2.4689 -0.8729
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7069 0.6088 -1.9481 -0.6785
## Cogon_Patch_Size-Lynx_rufus -0.6974 1.1887 -3.0990 -0.6923
## Cogon_Patch_Size-Didelphis_virginiana 0.7779 0.8740 -0.6225 0.6982
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5750 1.1855 -4.5766 -1.3782
## Cogon_Patch_Size-Meleagris_gallopavo -0.3914 0.9810 -2.2093 -0.4466
## Cogon_Patch_Size-Sciurus_carolinensis -1.3456 0.9741 -3.7055 -1.2038
## Veg_shannon_index-Canis_latrans 1.2070 0.5994 0.2083 1.1487
## Veg_shannon_index-Procyon_lotor 1.1816 0.6348 0.1699 1.1020
## Veg_shannon_index-Dasypus_novemcinctus 0.7119 0.4947 -0.2539 0.7064
## Veg_shannon_index-Lynx_rufus 0.8914 0.7050 -0.4237 0.8696
## Veg_shannon_index-Didelphis_virginiana 1.0136 0.5737 -0.0381 0.9699
## Veg_shannon_index-Sylvilagus_floridanus 1.0192 0.6215 -0.1074 0.9741
## Veg_shannon_index-Meleagris_gallopavo 1.1597 0.6809 0.0351 1.0975
## Veg_shannon_index-Sciurus_carolinensis 0.4132 0.6126 -0.9430 0.4563
## total_shrub_cover-Canis_latrans 0.1688 0.6669 -0.9038 0.1023
## total_shrub_cover-Procyon_lotor -0.9872 0.5963 -2.2022 -0.9539
## total_shrub_cover-Dasypus_novemcinctus 0.0737 0.5683 -0.9545 0.0547
## total_shrub_cover-Lynx_rufus -1.2224 1.0474 -3.8207 -1.0352
## total_shrub_cover-Didelphis_virginiana -0.6145 0.6981 -2.0724 -0.5749
## total_shrub_cover-Sylvilagus_floridanus -0.2947 0.8085 -1.9180 -0.3114
## total_shrub_cover-Meleagris_gallopavo -2.0605 1.2378 -4.9462 -1.8898
## total_shrub_cover-Sciurus_carolinensis -0.0941 0.6880 -1.3853 -0.1342
## Avg_Cogongrass_Cover-Canis_latrans 2.2208 0.8728 0.7072 2.1440
## Avg_Cogongrass_Cover-Procyon_lotor 2.1477 0.9006 0.5310 2.0836
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4693 0.9144 0.8954 2.4129
## Avg_Cogongrass_Cover-Lynx_rufus 2.3907 1.0297 0.6951 2.2924
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0987 0.8750 0.5141 2.0590
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4435 0.9320 -0.4311 1.4650
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4908 1.1564 -1.1479 1.5828
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2950 0.8958 0.7256 2.2409
## Tree_Density-Canis_latrans -2.3738 1.1337 -5.1008 -2.2039
## Tree_Density-Procyon_lotor -1.4921 0.7580 -3.0013 -1.4933
## Tree_Density-Dasypus_novemcinctus -3.0569 1.3849 -6.4995 -2.7736
## Tree_Density-Lynx_rufus -0.5671 1.2064 -2.5436 -0.6832
## Tree_Density-Didelphis_virginiana -2.1085 0.9920 -4.4471 -2.0249
## Tree_Density-Sylvilagus_floridanus -2.3576 1.2381 -5.4203 -2.1653
## Tree_Density-Meleagris_gallopavo -2.0783 1.1781 -4.7907 -1.9996
## Tree_Density-Sciurus_carolinensis -2.2137 1.0764 -4.7517 -2.0707
## Avg_Canopy_Cover-Canis_latrans 0.3480 0.6549 -0.9868 0.3522
## Avg_Canopy_Cover-Procyon_lotor 1.6503 0.6868 0.4574 1.5989
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9032 0.6881 0.7779 1.8325
## Avg_Canopy_Cover-Lynx_rufus 0.8271 0.9894 -1.1915 0.8448
## Avg_Canopy_Cover-Didelphis_virginiana 2.4296 0.8784 1.0613 2.3104
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1057 1.7769 1.0738 2.7027
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2209 1.2483 0.5437 1.9697
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0607 0.7609 0.8455 1.9685
## avg_veg_height-Canis_latrans -0.6875 0.5670 -1.8830 -0.6690
## avg_veg_height-Procyon_lotor -0.3828 0.5455 -1.4366 -0.3928
## avg_veg_height-Dasypus_novemcinctus -0.2645 0.5677 -1.3712 -0.2803
## avg_veg_height-Lynx_rufus -0.6120 0.6861 -1.9962 -0.5927
## avg_veg_height-Didelphis_virginiana -0.6089 0.6273 -1.8715 -0.5810
## avg_veg_height-Sylvilagus_floridanus -0.7066 0.6598 -2.0457 -0.6931
## avg_veg_height-Meleagris_gallopavo -0.6651 0.7317 -2.2712 -0.6335
## avg_veg_height-Sciurus_carolinensis -0.1942 0.6272 -1.3736 -0.2120
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6529 1.0094 451
## (Intercept)-Procyon_lotor 1.8016 1.0095 202
## (Intercept)-Dasypus_novemcinctus 0.0178 1.0012 781
## (Intercept)-Lynx_rufus 2.4879 1.0750 174
## (Intercept)-Didelphis_virginiana -0.4310 1.0231 454
## (Intercept)-Sylvilagus_floridanus 0.5430 1.0027 782
## (Intercept)-Meleagris_gallopavo 0.5358 1.0075 486
## (Intercept)-Sciurus_carolinensis -0.4934 1.0329 421
## Cogon_Patch_Size-Canis_latrans 2.6578 1.0143 795
## Cogon_Patch_Size-Procyon_lotor 0.3360 1.0421 146
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4484 1.0158 421
## Cogon_Patch_Size-Lynx_rufus 1.7131 1.0299 312
## Cogon_Patch_Size-Didelphis_virginiana 2.7026 1.0087 635
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1815 1.0005 287
## Cogon_Patch_Size-Meleagris_gallopavo 1.7733 1.0152 570
## Cogon_Patch_Size-Sciurus_carolinensis 0.1455 1.0052 407
## Veg_shannon_index-Canis_latrans 2.5608 1.0258 192
## Veg_shannon_index-Procyon_lotor 2.5884 1.0275 116
## Veg_shannon_index-Dasypus_novemcinctus 1.7130 1.0066 398
## Veg_shannon_index-Lynx_rufus 2.3442 1.0457 220
## Veg_shannon_index-Didelphis_virginiana 2.2081 1.0023 291
## Veg_shannon_index-Sylvilagus_floridanus 2.4191 1.0444 322
## Veg_shannon_index-Meleagris_gallopavo 2.6263 1.0079 536
## Veg_shannon_index-Sciurus_carolinensis 1.4894 1.0023 768
## total_shrub_cover-Canis_latrans 1.6992 1.0182 665
## total_shrub_cover-Procyon_lotor 0.0823 1.0021 1158
## total_shrub_cover-Dasypus_novemcinctus 1.2405 1.0141 574
## total_shrub_cover-Lynx_rufus 0.3495 1.0507 297
## total_shrub_cover-Didelphis_virginiana 0.6810 1.0044 1056
## total_shrub_cover-Sylvilagus_floridanus 1.3442 1.0094 827
## total_shrub_cover-Meleagris_gallopavo -0.1751 1.0135 244
## total_shrub_cover-Sciurus_carolinensis 1.3746 1.0224 1099
## Avg_Cogongrass_Cover-Canis_latrans 4.1013 1.0264 282
## Avg_Cogongrass_Cover-Procyon_lotor 4.0599 1.0243 206
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5041 1.0235 234
## Avg_Cogongrass_Cover-Lynx_rufus 4.6788 1.0152 225
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9478 1.0140 265
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2875 1.0055 387
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5679 1.0054 333
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1654 1.0152 211
## Tree_Density-Canis_latrans -0.6436 1.0642 230
## Tree_Density-Procyon_lotor 0.0556 1.0017 517
## Tree_Density-Dasypus_novemcinctus -1.1214 1.0221 260
## Tree_Density-Lynx_rufus 2.1782 1.0186 269
## Tree_Density-Didelphis_virginiana -0.4410 1.0149 435
## Tree_Density-Sylvilagus_floridanus -0.3726 1.0178 395
## Tree_Density-Meleagris_gallopavo 0.0344 1.0025 373
## Tree_Density-Sciurus_carolinensis -0.4481 1.0036 380
## Avg_Canopy_Cover-Canis_latrans 1.6195 1.0039 732
## Avg_Canopy_Cover-Procyon_lotor 3.1703 1.0022 252
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4768 1.0152 265
## Avg_Canopy_Cover-Lynx_rufus 2.8262 1.0114 381
## Avg_Canopy_Cover-Didelphis_virginiana 4.4921 1.0017 180
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.7392 1.0660 110
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3340 1.0556 122
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8962 1.0020 574
## avg_veg_height-Canis_latrans 0.3788 1.0108 459
## avg_veg_height-Procyon_lotor 0.7045 1.0231 504
## avg_veg_height-Dasypus_novemcinctus 0.9107 1.0113 626
## avg_veg_height-Lynx_rufus 0.7162 1.0206 565
## avg_veg_height-Didelphis_virginiana 0.5790 1.0114 481
## avg_veg_height-Sylvilagus_floridanus 0.5307 1.0051 537
## avg_veg_height-Meleagris_gallopavo 0.6564 1.0067 480
## avg_veg_height-Sciurus_carolinensis 1.1297 1.0090 551
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4663 0.1868 -2.8491 -2.4589 -2.1124 1.0005
## (Intercept)-Procyon_lotor -2.1761 0.1464 -2.4752 -2.1735 -1.8974 1.0049
## (Intercept)-Dasypus_novemcinctus -1.4899 0.1585 -1.8095 -1.4868 -1.1940 1.0036
## (Intercept)-Lynx_rufus -3.3475 0.3152 -3.9507 -3.3485 -2.7399 1.0147
## (Intercept)-Didelphis_virginiana -2.1457 0.2611 -2.6697 -2.1396 -1.6410 1.0081
## (Intercept)-Sylvilagus_floridanus -3.0177 0.2865 -3.5896 -3.0170 -2.4784 1.0517
## (Intercept)-Meleagris_gallopavo -3.1934 0.3164 -3.8696 -3.1798 -2.6227 1.0012
## (Intercept)-Sciurus_carolinensis -2.3351 0.2758 -2.9112 -2.3225 -1.8324 1.0030
## week-Canis_latrans 0.4513 0.2483 -0.0119 0.4415 0.9645 1.0009
## week-Procyon_lotor 0.1636 0.1961 -0.2249 0.1590 0.5560 1.0067
## week-Dasypus_novemcinctus 0.0642 0.2102 -0.3487 0.0658 0.4583 1.0081
## week-Lynx_rufus 0.2913 0.3020 -0.2881 0.2791 0.9093 1.0029
## week-Didelphis_virginiana 0.0544 0.3081 -0.5822 0.0633 0.6344 0.9998
## week-Sylvilagus_floridanus 0.0572 0.2997 -0.5570 0.0690 0.6286 1.0044
## week-Meleagris_gallopavo -0.0950 0.3435 -0.8568 -0.0699 0.5238 1.0054
## week-Sciurus_carolinensis 0.5406 0.3337 -0.0498 0.5147 1.2651 1.0064
## I(week^2)-Canis_latrans -0.1968 0.1035 -0.4121 -0.1942 -0.0008 1.0037
## I(week^2)-Procyon_lotor -0.1102 0.0866 -0.2780 -0.1094 0.0603 1.0086
## I(week^2)-Dasypus_novemcinctus -0.1527 0.0994 -0.3556 -0.1523 0.0405 1.0177
## I(week^2)-Lynx_rufus -0.2094 0.1517 -0.5255 -0.2016 0.0703 1.0197
## I(week^2)-Didelphis_virginiana -0.3537 0.2139 -0.8772 -0.3291 -0.0141 1.0178
## I(week^2)-Sylvilagus_floridanus -0.1652 0.1552 -0.4863 -0.1581 0.1285 1.0164
## I(week^2)-Meleagris_gallopavo -0.3791 0.2418 -0.9874 -0.3511 0.0085 1.0060
## I(week^2)-Sciurus_carolinensis -0.1873 0.1390 -0.4727 -0.1813 0.0640 1.0024
## ESS
## (Intercept)-Canis_latrans 1029
## (Intercept)-Procyon_lotor 1626
## (Intercept)-Dasypus_novemcinctus 2241
## (Intercept)-Lynx_rufus 394
## (Intercept)-Didelphis_virginiana 1740
## (Intercept)-Sylvilagus_floridanus 348
## (Intercept)-Meleagris_gallopavo 639
## (Intercept)-Sciurus_carolinensis 1245
## week-Canis_latrans 1132
## week-Procyon_lotor 1747
## week-Dasypus_novemcinctus 1828
## week-Lynx_rufus 948
## week-Didelphis_virginiana 1440
## week-Sylvilagus_floridanus 1157
## week-Meleagris_gallopavo 666
## week-Sciurus_carolinensis 1241
## I(week^2)-Canis_latrans 1060
## I(week^2)-Procyon_lotor 1522
## I(week^2)-Dasypus_novemcinctus 1760
## I(week^2)-Lynx_rufus 637
## I(week^2)-Didelphis_virginiana 544
## I(week^2)-Sylvilagus_floridanus 606
## I(week^2)-Meleagris_gallopavo 275
## I(week^2)-Sciurus_carolinensis 1403
#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): 0.6227
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5360 0.3941 -1.3259 -0.5295 0.2575 1.0167 625
## Avg_Cogongrass_Cover 0.1376 0.3400 -0.5439 0.1324 0.8219 1.0091 637
## total_shrub_cover -0.4799 0.3461 -1.2065 -0.4728 0.1912 1.0085 1422
## avg_veg_height 0.0124 0.3249 -0.6470 0.0203 0.6319 1.0237 582
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7280 0.9062 0.0598 0.4680 2.9634 1.0230 1030
## Avg_Cogongrass_Cover 0.3837 0.4594 0.0414 0.2424 1.5417 1.0093 1130
## total_shrub_cover 0.6816 0.8828 0.0657 0.4290 2.7567 1.0252 943
## avg_veg_height 0.2693 0.3315 0.0351 0.1702 1.1406 1.0120 1459
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8706 0.805 0.096 0.6526 2.8859 1.025 279
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4389 0.2866 -2.9790 -2.4518 -1.8387 0.9999 2022
## week 0.1773 0.2074 -0.2414 0.1746 0.5906 1.0136 1146
## I(week^2) -0.2225 0.1243 -0.4810 -0.2143 0.0022 1.0270 822
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5918 0.5089 0.1461 0.4534 1.8858 1.0174 1867
## week 0.2090 0.2379 0.0357 0.1444 0.7564 1.0640 1497
## I(week^2) 0.0825 0.0840 0.0202 0.0590 0.2877 1.0887 604
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0556 0.5775 -1.1546 -0.0656
## (Intercept)-Procyon_lotor 0.1571 0.6285 -1.0730 0.1430
## (Intercept)-Dasypus_novemcinctus -0.6791 0.4959 -1.6696 -0.6833
## (Intercept)-Lynx_rufus -0.3858 0.6577 -1.6329 -0.4226
## (Intercept)-Didelphis_virginiana -1.0936 0.5594 -2.2491 -1.0585
## (Intercept)-Sylvilagus_floridanus -0.4620 0.5657 -1.5094 -0.4824
## (Intercept)-Meleagris_gallopavo -0.7292 0.5661 -1.8352 -0.7130
## (Intercept)-Sciurus_carolinensis -1.1575 0.5741 -2.3576 -1.1253
## Avg_Cogongrass_Cover-Canis_latrans 0.3990 0.4445 -0.4024 0.3775
## Avg_Cogongrass_Cover-Procyon_lotor 0.0573 0.4433 -0.8365 0.0612
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2475 0.4077 -0.5671 0.2451
## Avg_Cogongrass_Cover-Lynx_rufus 0.4529 0.4945 -0.4155 0.4216
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3367 0.4392 -0.4879 0.3221
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2628 0.5091 -1.3451 -0.2332
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3520 0.5844 -1.6852 -0.2966
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2364 0.4284 -0.5853 0.2283
## total_shrub_cover-Canis_latrans 0.1204 0.4592 -0.6751 0.0879
## total_shrub_cover-Procyon_lotor -0.9099 0.4893 -1.9770 -0.8685
## total_shrub_cover-Dasypus_novemcinctus -0.0881 0.3832 -0.8234 -0.1015
## total_shrub_cover-Lynx_rufus -0.9234 0.6161 -2.3173 -0.8611
## total_shrub_cover-Didelphis_virginiana -0.3120 0.4276 -1.1543 -0.3175
## total_shrub_cover-Sylvilagus_floridanus -0.4262 0.5178 -1.5173 -0.4095
## total_shrub_cover-Meleagris_gallopavo -1.2803 0.6612 -2.7315 -1.1950
## total_shrub_cover-Sciurus_carolinensis -0.1421 0.4218 -0.9520 -0.1468
## avg_veg_height-Canis_latrans -0.0775 0.4132 -0.9157 -0.0685
## avg_veg_height-Procyon_lotor 0.0934 0.4255 -0.7110 0.0844
## avg_veg_height-Dasypus_novemcinctus 0.1853 0.4012 -0.5881 0.1762
## avg_veg_height-Lynx_rufus -0.0113 0.4925 -1.0034 -0.0161
## avg_veg_height-Didelphis_virginiana -0.0367 0.4352 -0.9473 -0.0198
## avg_veg_height-Sylvilagus_floridanus -0.1152 0.4389 -1.0212 -0.1082
## avg_veg_height-Meleagris_gallopavo -0.2386 0.5128 -1.3613 -0.1970
## avg_veg_height-Sciurus_carolinensis 0.2710 0.4309 -0.5287 0.2532
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1005 1.0126 530
## (Intercept)-Procyon_lotor 1.4192 1.0483 348
## (Intercept)-Dasypus_novemcinctus 0.3046 1.0055 1167
## (Intercept)-Lynx_rufus 0.9879 1.0667 529
## (Intercept)-Didelphis_virginiana -0.1026 1.0004 1133
## (Intercept)-Sylvilagus_floridanus 0.7370 1.0217 696
## (Intercept)-Meleagris_gallopavo 0.3859 1.0036 1124
## (Intercept)-Sciurus_carolinensis -0.1620 1.0147 913
## Avg_Cogongrass_Cover-Canis_latrans 1.3383 1.0072 1082
## Avg_Cogongrass_Cover-Procyon_lotor 0.9247 1.0030 1247
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0522 1.0037 1468
## Avg_Cogongrass_Cover-Lynx_rufus 1.5538 1.0156 961
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2588 1.0056 1072
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6512 1.0137 943
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6690 1.0070 841
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1269 1.0045 1153
## total_shrub_cover-Canis_latrans 1.1408 1.0004 1466
## total_shrub_cover-Procyon_lotor -0.0769 1.0039 1100
## total_shrub_cover-Dasypus_novemcinctus 0.6759 1.0011 2133
## total_shrub_cover-Lynx_rufus 0.0925 1.0103 764
## total_shrub_cover-Didelphis_virginiana 0.4809 1.0035 2163
## total_shrub_cover-Sylvilagus_floridanus 0.5386 1.0311 1125
## total_shrub_cover-Meleagris_gallopavo -0.2254 1.0128 676
## total_shrub_cover-Sciurus_carolinensis 0.7276 1.0063 1761
## avg_veg_height-Canis_latrans 0.7165 1.0049 1015
## avg_veg_height-Procyon_lotor 1.0025 1.0012 1056
## avg_veg_height-Dasypus_novemcinctus 0.9880 1.0009 1200
## avg_veg_height-Lynx_rufus 0.9090 1.0025 915
## avg_veg_height-Didelphis_virginiana 0.7706 1.0049 1009
## avg_veg_height-Sylvilagus_floridanus 0.7365 1.0223 1020
## avg_veg_height-Meleagris_gallopavo 0.6414 1.0106 874
## avg_veg_height-Sciurus_carolinensis 1.1797 0.9998 1206
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4579 0.1838 -2.8300 -2.4520 -2.1120 1.0136
## (Intercept)-Procyon_lotor -2.1824 0.1473 -2.4923 -2.1784 -1.9026 1.0086
## (Intercept)-Dasypus_novemcinctus -1.4989 0.1550 -1.7996 -1.4971 -1.2051 1.0028
## (Intercept)-Lynx_rufus -3.2663 0.2979 -3.8592 -3.2653 -2.6821 1.0295
## (Intercept)-Didelphis_virginiana -2.1886 0.2693 -2.7269 -2.1766 -1.6993 1.0013
## (Intercept)-Sylvilagus_floridanus -3.0282 0.3130 -3.6892 -3.0145 -2.4499 1.0204
## (Intercept)-Meleagris_gallopavo -3.0683 0.3111 -3.7194 -3.0577 -2.5072 1.0076
## (Intercept)-Sciurus_carolinensis -2.3387 0.2748 -2.8951 -2.3217 -1.8305 1.0148
## week-Canis_latrans 0.4551 0.2470 0.0029 0.4448 0.9628 1.0007
## week-Procyon_lotor 0.1574 0.2007 -0.2352 0.1537 0.5542 1.0039
## week-Dasypus_novemcinctus 0.0585 0.2156 -0.3658 0.0538 0.4789 1.0015
## week-Lynx_rufus 0.2883 0.3135 -0.3056 0.2775 0.9475 1.0205
## week-Didelphis_virginiana 0.0048 0.3190 -0.6464 0.0174 0.6195 1.0114
## week-Sylvilagus_floridanus 0.0502 0.2960 -0.5544 0.0570 0.6187 1.0133
## week-Meleagris_gallopavo -0.1087 0.3514 -0.8653 -0.0868 0.5256 1.0226
## week-Sciurus_carolinensis 0.5354 0.3342 -0.0734 0.5206 1.2657 1.0060
## I(week^2)-Canis_latrans -0.1925 0.1046 -0.4121 -0.1880 -0.0005 1.0021
## I(week^2)-Procyon_lotor -0.1123 0.0877 -0.2851 -0.1117 0.0600 1.0006
## I(week^2)-Dasypus_novemcinctus -0.1505 0.1019 -0.3601 -0.1488 0.0496 1.0008
## I(week^2)-Lynx_rufus -0.2093 0.1437 -0.5059 -0.2040 0.0553 1.0051
## I(week^2)-Didelphis_virginiana -0.3826 0.2232 -0.9122 -0.3570 -0.0227 1.0125
## I(week^2)-Sylvilagus_floridanus -0.1635 0.1496 -0.4757 -0.1634 0.1208 1.0024
## I(week^2)-Meleagris_gallopavo -0.3785 0.2382 -0.9617 -0.3452 -0.0016 1.1432
## I(week^2)-Sciurus_carolinensis -0.1952 0.1385 -0.4883 -0.1885 0.0613 1.0074
## ESS
## (Intercept)-Canis_latrans 1150
## (Intercept)-Procyon_lotor 1614
## (Intercept)-Dasypus_novemcinctus 2260
## (Intercept)-Lynx_rufus 553
## (Intercept)-Didelphis_virginiana 1190
## (Intercept)-Sylvilagus_floridanus 562
## (Intercept)-Meleagris_gallopavo 702
## (Intercept)-Sciurus_carolinensis 1138
## week-Canis_latrans 1333
## week-Procyon_lotor 1672
## week-Dasypus_novemcinctus 2004
## week-Lynx_rufus 1041
## week-Didelphis_virginiana 1122
## week-Sylvilagus_floridanus 945
## week-Meleagris_gallopavo 474
## week-Sciurus_carolinensis 1285
## I(week^2)-Canis_latrans 1341
## I(week^2)-Procyon_lotor 1557
## I(week^2)-Dasypus_novemcinctus 1625
## I(week^2)-Lynx_rufus 924
## I(week^2)-Didelphis_virginiana 408
## I(week^2)-Sylvilagus_floridanus 709
## I(week^2)-Meleagris_gallopavo 254
## I(week^2)-Sciurus_carolinensis 1589
#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): 0.6188
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6540 0.4608 -1.5749 -0.6535 0.2537 1.0219 829
## Tree_Density -0.7771 0.4570 -1.7869 -0.7414 0.0629 1.0104 657
## Avg_Canopy_Cover 0.9881 0.3736 0.3059 0.9741 1.7877 1.0034 925
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1086 1.2631 0.0841 0.7389 4.2749 1.0219 691
## Tree_Density 0.9865 1.5148 0.0557 0.5159 4.9602 1.0435 570
## Avg_Canopy_Cover 0.7605 0.9954 0.0627 0.4453 3.4952 1.0142 571
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6734 0.7772 0.0502 0.4174 2.7544 1.0326 124
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4498 0.2875 -3.0259 -2.4520 -1.8601 1.0012 2565
## week 0.1875 0.2117 -0.2318 0.1877 0.6003 1.0030 1232
## I(week^2) -0.2273 0.1353 -0.5144 -0.2161 0.0132 1.0465 465
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6151 0.5052 0.1451 0.4732 1.9451 1.0102 2229
## week 0.2066 0.2132 0.0329 0.1415 0.7986 1.0124 934
## I(week^2) 0.0901 0.1008 0.0198 0.0615 0.3607 1.1591 280
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0740 0.6052 -1.2655 -0.0736 1.1576
## (Intercept)-Procyon_lotor 0.2227 0.6647 -1.1243 0.2467 1.5016
## (Intercept)-Dasypus_novemcinctus -0.9863 0.5571 -2.1845 -0.9534 0.0388
## (Intercept)-Lynx_rufus -0.2400 0.7967 -1.6025 -0.3335 1.5776
## (Intercept)-Didelphis_virginiana -1.5028 0.6435 -2.9033 -1.4577 -0.3476
## (Intercept)-Sylvilagus_floridanus -0.7617 0.5991 -2.0009 -0.7607 0.3863
## (Intercept)-Meleagris_gallopavo -0.6665 0.6317 -1.9020 -0.6681 0.6410
## (Intercept)-Sciurus_carolinensis -1.5628 0.6992 -3.0439 -1.5136 -0.3840
## Tree_Density-Canis_latrans -0.9717 0.6093 -2.4277 -0.8946 0.0206
## Tree_Density-Procyon_lotor -0.4689 0.4329 -1.3391 -0.4550 0.3797
## Tree_Density-Dasypus_novemcinctus -1.3970 0.8909 -3.6870 -1.2099 -0.1677
## Tree_Density-Lynx_rufus 0.2308 0.7426 -0.8978 0.1270 2.0116
## Tree_Density-Didelphis_virginiana -1.0297 0.7991 -2.9853 -0.8808 0.1485
## Tree_Density-Sylvilagus_floridanus -1.1085 0.7756 -3.0191 -0.9810 0.0741
## Tree_Density-Meleagris_gallopavo -0.9374 0.7394 -2.6374 -0.8507 0.3106
## Tree_Density-Sciurus_carolinensis -0.8805 0.7046 -2.6256 -0.7947 0.2586
## Avg_Canopy_Cover-Canis_latrans 0.0665 0.4889 -0.9207 0.0816 1.0247
## Avg_Canopy_Cover-Procyon_lotor 0.9802 0.4396 0.1945 0.9513 1.8969
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0101 0.4257 0.2284 0.9964 1.9063
## Avg_Canopy_Cover-Lynx_rufus 0.6640 0.6302 -0.4811 0.6351 1.9959
## Avg_Canopy_Cover-Didelphis_virginiana 1.2129 0.5037 0.3559 1.1658 2.3615
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7017 0.7711 0.5804 1.5643 3.6012
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3965 0.6859 0.3189 1.2947 3.0282
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1855 0.4971 0.3068 1.1467 2.2692
## Rhat ESS
## (Intercept)-Canis_latrans 1.0076 672
## (Intercept)-Procyon_lotor 1.0034 364
## (Intercept)-Dasypus_novemcinctus 1.0136 909
## (Intercept)-Lynx_rufus 1.0285 337
## (Intercept)-Didelphis_virginiana 1.0014 790
## (Intercept)-Sylvilagus_floridanus 1.0078 954
## (Intercept)-Meleagris_gallopavo 1.0398 800
## (Intercept)-Sciurus_carolinensis 1.0154 611
## Tree_Density-Canis_latrans 1.0094 951
## Tree_Density-Procyon_lotor 1.0013 2346
## Tree_Density-Dasypus_novemcinctus 1.0211 459
## Tree_Density-Lynx_rufus 1.0381 448
## Tree_Density-Didelphis_virginiana 1.0137 698
## Tree_Density-Sylvilagus_floridanus 1.0189 681
## Tree_Density-Meleagris_gallopavo 1.0227 796
## Tree_Density-Sciurus_carolinensis 1.0116 945
## Avg_Canopy_Cover-Canis_latrans 1.0034 1050
## Avg_Canopy_Cover-Procyon_lotor 1.0052 1606
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0016 1371
## Avg_Canopy_Cover-Lynx_rufus 1.0063 621
## Avg_Canopy_Cover-Didelphis_virginiana 1.0019 912
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0004 551
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0094 625
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0017 1110
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4735 0.1901 -2.8517 -2.4728 -2.1157 1.0023
## (Intercept)-Procyon_lotor -2.1758 0.1440 -2.4623 -2.1713 -1.9065 1.0056
## (Intercept)-Dasypus_novemcinctus -1.4943 0.1589 -1.8205 -1.4905 -1.1874 1.0037
## (Intercept)-Lynx_rufus -3.2788 0.3214 -3.9345 -3.2748 -2.6704 1.0103
## (Intercept)-Didelphis_virginiana -2.1676 0.2634 -2.7054 -2.1564 -1.6902 1.0008
## (Intercept)-Sylvilagus_floridanus -2.9797 0.2866 -3.5607 -2.9664 -2.4419 1.0077
## (Intercept)-Meleagris_gallopavo -3.1686 0.3217 -3.8177 -3.1562 -2.5789 1.0166
## (Intercept)-Sciurus_carolinensis -2.3284 0.2718 -2.8809 -2.3184 -1.8265 1.0005
## week-Canis_latrans 0.4463 0.2468 -0.0133 0.4369 0.9576 1.0023
## week-Procyon_lotor 0.1601 0.1993 -0.2279 0.1572 0.5537 1.0088
## week-Dasypus_novemcinctus 0.0564 0.2160 -0.3532 0.0622 0.4735 1.0017
## week-Lynx_rufus 0.2888 0.3016 -0.2577 0.2757 0.9168 1.0012
## week-Didelphis_virginiana 0.0370 0.3114 -0.6191 0.0419 0.6295 0.9998
## week-Sylvilagus_floridanus 0.0399 0.3046 -0.5753 0.0428 0.6124 1.0028
## week-Meleagris_gallopavo -0.0961 0.3666 -0.8930 -0.0643 0.5557 1.0316
## week-Sciurus_carolinensis 0.5488 0.3409 -0.0494 0.5227 1.2664 1.0002
## I(week^2)-Canis_latrans -0.1940 0.1060 -0.4046 -0.1905 0.0117 1.0092
## I(week^2)-Procyon_lotor -0.1122 0.0891 -0.2921 -0.1114 0.0585 1.0124
## I(week^2)-Dasypus_novemcinctus -0.1499 0.1004 -0.3483 -0.1495 0.0430 1.0020
## I(week^2)-Lynx_rufus -0.2104 0.1458 -0.5161 -0.2022 0.0563 1.0109
## I(week^2)-Didelphis_virginiana -0.3744 0.2290 -0.9384 -0.3444 -0.0244 1.0376
## I(week^2)-Sylvilagus_floridanus -0.1646 0.1534 -0.4909 -0.1534 0.1143 1.0122
## I(week^2)-Meleagris_gallopavo -0.3980 0.2884 -1.1556 -0.3524 0.0123 1.1809
## I(week^2)-Sciurus_carolinensis -0.1996 0.1424 -0.5027 -0.1902 0.0601 1.0007
## ESS
## (Intercept)-Canis_latrans 1172
## (Intercept)-Procyon_lotor 1871
## (Intercept)-Dasypus_novemcinctus 2346
## (Intercept)-Lynx_rufus 395
## (Intercept)-Didelphis_virginiana 1403
## (Intercept)-Sylvilagus_floridanus 705
## (Intercept)-Meleagris_gallopavo 608
## (Intercept)-Sciurus_carolinensis 1257
## week-Canis_latrans 1204
## week-Procyon_lotor 1664
## week-Dasypus_novemcinctus 1473
## week-Lynx_rufus 963
## week-Didelphis_virginiana 1410
## week-Sylvilagus_floridanus 1320
## week-Meleagris_gallopavo 421
## week-Sciurus_carolinensis 963
## I(week^2)-Canis_latrans 1305
## I(week^2)-Procyon_lotor 1516
## I(week^2)-Dasypus_novemcinctus 1541
## I(week^2)-Lynx_rufus 795
## I(week^2)-Didelphis_virginiana 383
## I(week^2)-Sylvilagus_floridanus 827
## I(week^2)-Meleagris_gallopavo 118
## I(week^2)-Sciurus_carolinensis 1303
#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): 0.617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5255 0.3969 -1.3046 -0.5385 0.3212 1.0021 1001
## Cogon_Patch_Size -0.0351 0.3937 -0.8397 -0.0277 0.7436 1.0026 1473
## Avg_Cogongrass_Cover 0.1444 0.3059 -0.4813 0.1496 0.7242 1.0003 768
## total_shrub_cover -0.4779 0.3366 -1.1410 -0.4699 0.1837 1.0023 1247
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8029 1.0008 0.0645 0.5133 3.4380 1.0060 858
## Cogon_Patch_Size 0.8296 1.2317 0.0645 0.4523 3.7179 1.0193 773
## Avg_Cogongrass_Cover 0.4089 0.4889 0.0419 0.2598 1.6425 1.0087 1038
## total_shrub_cover 0.6238 0.7415 0.0597 0.3993 2.5942 1.0658 890
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8998 0.7993 0.0805 0.6757 3.0121 1.1428 260
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4406 0.2849 -2.9843 -2.4429 -1.8532 1.0032 2319
## week 0.1835 0.2053 -0.2351 0.1873 0.5919 1.0097 1293
## I(week^2) -0.2221 0.1177 -0.4649 -0.2202 0.0069 1.0019 1151
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6070 0.5379 0.1555 0.4640 1.9909 1.0012 1710
## week 0.2094 0.2272 0.0337 0.1469 0.7727 1.0088 1534
## I(week^2) 0.0776 0.0659 0.0201 0.0575 0.2568 1.0236 1429
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0234 0.5961 -1.0353 -0.0077
## (Intercept)-Procyon_lotor 0.1694 0.6310 -1.0000 0.1610
## (Intercept)-Dasypus_novemcinctus -0.7028 0.4893 -1.7333 -0.6837
## (Intercept)-Lynx_rufus -0.3614 0.6442 -1.5462 -0.3962
## (Intercept)-Didelphis_virginiana -1.1000 0.5804 -2.3179 -1.0646
## (Intercept)-Sylvilagus_floridanus -0.5592 0.5924 -1.7069 -0.5568
## (Intercept)-Meleagris_gallopavo -0.7044 0.5674 -1.8675 -0.6922
## (Intercept)-Sciurus_carolinensis -1.1942 0.6103 -2.5538 -1.1596
## Cogon_Patch_Size-Canis_latrans 0.7019 0.6624 -0.2644 0.5937
## Cogon_Patch_Size-Procyon_lotor -0.1478 0.4371 -1.0194 -0.1454
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0742 0.4163 -0.9670 -0.0581
## Cogon_Patch_Size-Lynx_rufus -0.1126 0.7062 -1.4238 -0.1396
## Cogon_Patch_Size-Didelphis_virginiana 0.6433 0.4972 -0.2155 0.5923
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7140 0.8043 -2.6390 -0.5862
## Cogon_Patch_Size-Meleagris_gallopavo -0.0324 0.5740 -1.1960 -0.0237
## Cogon_Patch_Size-Sciurus_carolinensis -0.5576 0.6355 -2.0500 -0.4781
## Avg_Cogongrass_Cover-Canis_latrans 0.2443 0.4028 -0.4956 0.2278
## Avg_Cogongrass_Cover-Procyon_lotor 0.1505 0.4192 -0.6610 0.1473
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3515 0.3824 -0.3619 0.3414
## Avg_Cogongrass_Cover-Lynx_rufus 0.5042 0.4896 -0.3214 0.4654
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1588 0.4242 -0.7016 0.1498
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1937 0.4812 -1.2312 -0.1564
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4218 0.6001 -1.8560 -0.3698
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4310 0.4187 -0.3530 0.4144
## total_shrub_cover-Canis_latrans 0.0358 0.4497 -0.7767 0.0180
## total_shrub_cover-Procyon_lotor -0.8700 0.4829 -1.9210 -0.8224
## total_shrub_cover-Dasypus_novemcinctus -0.1034 0.3909 -0.8491 -0.1199
## total_shrub_cover-Lynx_rufus -0.8616 0.6530 -2.3608 -0.7816
## total_shrub_cover-Didelphis_virginiana -0.3771 0.4338 -1.2587 -0.3653
## total_shrub_cover-Sylvilagus_floridanus -0.3819 0.5118 -1.4227 -0.3617
## total_shrub_cover-Meleagris_gallopavo -1.2416 0.6608 -2.7728 -1.1398
## total_shrub_cover-Sciurus_carolinensis -0.1392 0.4425 -0.9968 -0.1486
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2730 1.0077 679
## (Intercept)-Procyon_lotor 1.4274 1.0155 575
## (Intercept)-Dasypus_novemcinctus 0.1948 1.0053 1043
## (Intercept)-Lynx_rufus 1.0986 1.0013 711
## (Intercept)-Didelphis_virginiana -0.0396 1.0012 901
## (Intercept)-Sylvilagus_floridanus 0.7056 1.0164 921
## (Intercept)-Meleagris_gallopavo 0.3895 1.0035 996
## (Intercept)-Sciurus_carolinensis -0.0985 1.0049 851
## Cogon_Patch_Size-Canis_latrans 2.3644 1.0079 784
## Cogon_Patch_Size-Procyon_lotor 0.7149 1.0045 1871
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6732 1.0022 2276
## Cogon_Patch_Size-Lynx_rufus 1.4024 1.0114 1105
## Cogon_Patch_Size-Didelphis_virginiana 1.7381 1.0163 1006
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4634 1.0081 713
## Cogon_Patch_Size-Meleagris_gallopavo 1.1274 1.0003 1394
## Cogon_Patch_Size-Sciurus_carolinensis 0.4232 1.0178 895
## Avg_Cogongrass_Cover-Canis_latrans 1.1274 1.0036 1595
## Avg_Cogongrass_Cover-Procyon_lotor 1.0224 1.0020 1880
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1406 1.0020 1831
## Avg_Cogongrass_Cover-Lynx_rufus 1.5996 1.0016 1376
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9885 1.0010 1563
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6718 1.0075 1053
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5818 1.0020 683
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3183 1.0080 1596
## total_shrub_cover-Canis_latrans 0.9903 1.0065 1559
## total_shrub_cover-Procyon_lotor -0.0519 1.0027 1348
## total_shrub_cover-Dasypus_novemcinctus 0.7188 1.0025 2356
## total_shrub_cover-Lynx_rufus 0.1729 1.0365 785
## total_shrub_cover-Didelphis_virginiana 0.4768 1.0038 2165
## total_shrub_cover-Sylvilagus_floridanus 0.6270 1.0051 1141
## total_shrub_cover-Meleagris_gallopavo -0.2525 1.0101 611
## total_shrub_cover-Sciurus_carolinensis 0.7719 1.0009 1841
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4440 0.1809 -2.8060 -2.4398 -2.0966 1.0139
## (Intercept)-Procyon_lotor -2.1892 0.1483 -2.4806 -2.1859 -1.9011 1.0031
## (Intercept)-Dasypus_novemcinctus -1.4922 0.1583 -1.8033 -1.4906 -1.1919 1.0041
## (Intercept)-Lynx_rufus -3.2575 0.2922 -3.8323 -3.2589 -2.7059 1.0161
## (Intercept)-Didelphis_virginiana -2.1717 0.2606 -2.6893 -2.1753 -1.6684 1.0029
## (Intercept)-Sylvilagus_floridanus -3.0476 0.3062 -3.6658 -3.0303 -2.4915 1.0346
## (Intercept)-Meleagris_gallopavo -3.1004 0.3144 -3.7346 -3.0864 -2.5155 1.0079
## (Intercept)-Sciurus_carolinensis -2.3339 0.2842 -2.9161 -2.3248 -1.8048 1.0140
## week-Canis_latrans 0.4554 0.2443 0.0009 0.4465 0.9674 1.0010
## week-Procyon_lotor 0.1536 0.1976 -0.2435 0.1547 0.5509 1.0003
## week-Dasypus_novemcinctus 0.0578 0.2098 -0.3715 0.0573 0.4696 1.0047
## week-Lynx_rufus 0.2986 0.2997 -0.2865 0.2947 0.9266 1.0029
## week-Didelphis_virginiana 0.0289 0.3208 -0.6245 0.0349 0.6477 1.0023
## week-Sylvilagus_floridanus 0.0483 0.2892 -0.5342 0.0529 0.6108 1.0213
## week-Meleagris_gallopavo -0.1088 0.3526 -0.8641 -0.0892 0.5363 1.0315
## week-Sciurus_carolinensis 0.5526 0.3387 -0.0429 0.5274 1.3176 1.0031
## I(week^2)-Canis_latrans -0.1961 0.1021 -0.4059 -0.1935 -0.0051 0.9997
## I(week^2)-Procyon_lotor -0.1096 0.0866 -0.2767 -0.1103 0.0579 1.0056
## I(week^2)-Dasypus_novemcinctus -0.1550 0.0996 -0.3499 -0.1528 0.0368 1.0025
## I(week^2)-Lynx_rufus -0.2110 0.1371 -0.4972 -0.2079 0.0499 1.0050
## I(week^2)-Didelphis_virginiana -0.3640 0.2049 -0.8418 -0.3433 -0.0146 1.0021
## I(week^2)-Sylvilagus_floridanus -0.1667 0.1485 -0.4787 -0.1636 0.1048 1.0035
## I(week^2)-Meleagris_gallopavo -0.3787 0.2317 -0.9130 -0.3547 -0.0054 1.0405
## I(week^2)-Sciurus_carolinensis -0.2006 0.1394 -0.4923 -0.1914 0.0543 1.0169
## ESS
## (Intercept)-Canis_latrans 1399
## (Intercept)-Procyon_lotor 1664
## (Intercept)-Dasypus_novemcinctus 2265
## (Intercept)-Lynx_rufus 554
## (Intercept)-Didelphis_virginiana 1418
## (Intercept)-Sylvilagus_floridanus 583
## (Intercept)-Meleagris_gallopavo 594
## (Intercept)-Sciurus_carolinensis 1127
## week-Canis_latrans 1341
## week-Procyon_lotor 1739
## week-Dasypus_novemcinctus 1746
## week-Lynx_rufus 831
## week-Didelphis_virginiana 1288
## week-Sylvilagus_floridanus 1163
## week-Meleagris_gallopavo 689
## week-Sciurus_carolinensis 1220
## I(week^2)-Canis_latrans 1512
## I(week^2)-Procyon_lotor 1679
## I(week^2)-Dasypus_novemcinctus 1518
## I(week^2)-Lynx_rufus 796
## I(week^2)-Didelphis_virginiana 582
## I(week^2)-Sylvilagus_floridanus 838
## I(week^2)-Meleagris_gallopavo 268
## I(week^2)-Sciurus_carolinensis 1297
#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): 0.622
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5467 0.3535 -1.2551 -0.5504 0.1480 1.0093 689
## Veg_shannon_index 0.3696 0.2629 -0.1634 0.3696 0.8809 1.0091 1261
## Avg_Cogongrass_Cover 0.3516 0.2737 -0.2111 0.3504 0.8897 1.0008 1069
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5565 0.6142 0.0509 0.3623 2.3047 1.0159 754
## Veg_shannon_index 0.2685 0.3458 0.0350 0.1723 1.0705 1.0371 1517
## Avg_Cogongrass_Cover 0.3075 0.3927 0.0403 0.1961 1.1965 1.0325 1399
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.82 0.7385 0.0858 0.6245 2.7466 1.0597 199
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4387 0.2927 -2.9832 -2.4439 -1.8563 1.0036 2396
## week 0.1776 0.1996 -0.2188 0.1772 0.5658 1.0001 1007
## I(week^2) -0.2203 0.1202 -0.4727 -0.2142 0.0116 1.0042 834
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6203 0.6507 0.1547 0.4686 1.8624 1.0276 2226
## week 0.1992 0.2344 0.0343 0.1373 0.7110 1.0231 1246
## I(week^2) 0.0832 0.0927 0.0187 0.0598 0.2964 1.0130 774
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1581 0.5191 -1.1303 -0.1837
## (Intercept)-Procyon_lotor 0.0217 0.5650 -1.0273 0.0190
## (Intercept)-Dasypus_novemcinctus -0.6633 0.4680 -1.6636 -0.6482
## (Intercept)-Lynx_rufus -0.4423 0.5734 -1.5305 -0.4567
## (Intercept)-Didelphis_virginiana -1.0754 0.5369 -2.2421 -1.0252
## (Intercept)-Sylvilagus_floridanus -0.5395 0.5435 -1.5708 -0.5570
## (Intercept)-Meleagris_gallopavo -0.5332 0.5465 -1.5724 -0.5536
## (Intercept)-Sciurus_carolinensis -1.0618 0.5377 -2.2051 -1.0303
## Veg_shannon_index-Canis_latrans 0.6448 0.3696 -0.0143 0.6216
## Veg_shannon_index-Procyon_lotor 0.4893 0.3898 -0.2342 0.4639
## Veg_shannon_index-Dasypus_novemcinctus 0.2139 0.3305 -0.4389 0.2202
## Veg_shannon_index-Lynx_rufus 0.2070 0.4479 -0.7641 0.2253
## Veg_shannon_index-Didelphis_virginiana 0.4765 0.3711 -0.2283 0.4581
## Veg_shannon_index-Sylvilagus_floridanus 0.4663 0.4154 -0.3029 0.4498
## Veg_shannon_index-Meleagris_gallopavo 0.4685 0.4296 -0.3377 0.4454
## Veg_shannon_index-Sciurus_carolinensis 0.0482 0.3797 -0.7582 0.0667
## Avg_Cogongrass_Cover-Canis_latrans 0.5379 0.3620 -0.1230 0.5184
## Avg_Cogongrass_Cover-Procyon_lotor 0.4164 0.3772 -0.2615 0.3964
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4586 0.3292 -0.1662 0.4571
## Avg_Cogongrass_Cover-Lynx_rufus 0.5964 0.4034 -0.1223 0.5691
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4847 0.3670 -0.2105 0.4766
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0278 0.4434 -1.0066 0.0015
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0519 0.4879 -1.0995 -0.0093
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4312 0.3579 -0.2441 0.4189
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9207 1.0189 681
## (Intercept)-Procyon_lotor 1.1771 1.0097 503
## (Intercept)-Dasypus_novemcinctus 0.2584 1.0075 1313
## (Intercept)-Lynx_rufus 0.7236 1.0171 550
## (Intercept)-Didelphis_virginiana -0.1353 1.0099 834
## (Intercept)-Sylvilagus_floridanus 0.5899 1.0022 830
## (Intercept)-Meleagris_gallopavo 0.6099 1.0086 730
## (Intercept)-Sciurus_carolinensis -0.1111 1.0132 950
## Veg_shannon_index-Canis_latrans 1.4539 1.0040 1571
## Veg_shannon_index-Procyon_lotor 1.3004 1.0001 1362
## Veg_shannon_index-Dasypus_novemcinctus 0.8530 1.0025 1939
## Veg_shannon_index-Lynx_rufus 1.0255 1.0012 1191
## Veg_shannon_index-Didelphis_virginiana 1.2580 1.0017 1982
## Veg_shannon_index-Sylvilagus_floridanus 1.3276 1.0108 1402
## Veg_shannon_index-Meleagris_gallopavo 1.3659 1.0081 1347
## Veg_shannon_index-Sciurus_carolinensis 0.7388 1.0003 1585
## Avg_Cogongrass_Cover-Canis_latrans 1.3376 1.0051 1880
## Avg_Cogongrass_Cover-Procyon_lotor 1.2211 1.0067 1418
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1416 1.0012 1839
## Avg_Cogongrass_Cover-Lynx_rufus 1.4817 1.0028 1231
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2608 1.0005 1883
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7661 1.0011 1184
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7952 1.0051 963
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1834 1.0011 1551
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4331 0.1839 -2.8071 -2.4274 -2.0928 1.0043
## (Intercept)-Procyon_lotor -2.1876 0.1438 -2.4848 -2.1837 -1.9246 0.9997
## (Intercept)-Dasypus_novemcinctus -1.4935 0.1569 -1.8192 -1.4880 -1.1926 1.0111
## (Intercept)-Lynx_rufus -3.2386 0.3130 -3.8913 -3.2313 -2.6636 1.0196
## (Intercept)-Didelphis_virginiana -2.1803 0.2630 -2.7066 -2.1788 -1.6762 1.0009
## (Intercept)-Sylvilagus_floridanus -3.0204 0.3090 -3.6666 -3.0061 -2.4626 1.0072
## (Intercept)-Meleagris_gallopavo -3.1735 0.3361 -3.8629 -3.1560 -2.5688 1.0003
## (Intercept)-Sciurus_carolinensis -2.3462 0.2834 -2.9409 -2.3362 -1.8270 1.0025
## week-Canis_latrans 0.4583 0.2422 0.0185 0.4475 0.9641 1.0041
## week-Procyon_lotor 0.1563 0.1964 -0.2263 0.1613 0.5352 1.0004
## week-Dasypus_novemcinctus 0.0594 0.2085 -0.3717 0.0581 0.4583 1.0096
## week-Lynx_rufus 0.2630 0.3062 -0.3129 0.2612 0.8738 1.0049
## week-Didelphis_virginiana 0.0362 0.3154 -0.6307 0.0568 0.6322 1.0113
## week-Sylvilagus_floridanus 0.0444 0.2896 -0.5361 0.0530 0.5851 1.0042
## week-Meleagris_gallopavo -0.0862 0.3492 -0.8590 -0.0532 0.5166 1.0062
## week-Sciurus_carolinensis 0.5356 0.3301 -0.0404 0.5118 1.2535 1.0025
## I(week^2)-Canis_latrans -0.1959 0.1030 -0.4063 -0.1931 0.0033 1.0012
## I(week^2)-Procyon_lotor -0.1082 0.0862 -0.2799 -0.1083 0.0587 1.0022
## I(week^2)-Dasypus_novemcinctus -0.1516 0.0997 -0.3493 -0.1500 0.0423 1.0073
## I(week^2)-Lynx_rufus -0.2098 0.1534 -0.5327 -0.1996 0.0691 1.0012
## I(week^2)-Didelphis_virginiana -0.3694 0.2303 -0.9528 -0.3387 -0.0152 1.0089
## I(week^2)-Sylvilagus_floridanus -0.1698 0.1496 -0.4837 -0.1614 0.1058 1.0216
## I(week^2)-Meleagris_gallopavo -0.3740 0.2389 -0.9517 -0.3440 -0.0019 1.0280
## I(week^2)-Sciurus_carolinensis -0.1940 0.1389 -0.4876 -0.1890 0.0586 1.0062
## ESS
## (Intercept)-Canis_latrans 1273
## (Intercept)-Procyon_lotor 1631
## (Intercept)-Dasypus_novemcinctus 2413
## (Intercept)-Lynx_rufus 459
## (Intercept)-Didelphis_virginiana 1421
## (Intercept)-Sylvilagus_floridanus 556
## (Intercept)-Meleagris_gallopavo 450
## (Intercept)-Sciurus_carolinensis 1216
## week-Canis_latrans 1360
## week-Procyon_lotor 1676
## week-Dasypus_novemcinctus 1776
## week-Lynx_rufus 1007
## week-Didelphis_virginiana 1293
## week-Sylvilagus_floridanus 1166
## week-Meleagris_gallopavo 406
## week-Sciurus_carolinensis 1014
## I(week^2)-Canis_latrans 1447
## I(week^2)-Procyon_lotor 1587
## I(week^2)-Dasypus_novemcinctus 1791
## I(week^2)-Lynx_rufus 732
## I(week^2)-Didelphis_virginiana 420
## I(week^2)-Sylvilagus_floridanus 767
## I(week^2)-Meleagris_gallopavo 204
## I(week^2)-Sciurus_carolinensis 1315
#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): 0.6158
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5251 0.3616 -1.2444 -0.5183 0.1846 1.0029 668
## Avg_Cogongrass_Cover 0.2231 0.2524 -0.3056 0.2261 0.7036 1.0075 1389
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5926 0.7534 0.0534 0.3819 2.3946 1.0267 1138
## Avg_Cogongrass_Cover 0.2976 0.4091 0.0371 0.1850 1.2224 1.0235 1554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6797 0.6136 0.0662 0.5032 2.3686 1.1052 167
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4297 0.2827 -2.9699 -2.4330 -1.8249 1.0057 2490
## week 0.1824 0.2083 -0.2443 0.1879 0.5776 1.0080 1247
## I(week^2) -0.2256 0.1256 -0.4872 -0.2195 0.0021 1.0062 737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5786 0.5107 0.1463 0.4480 1.7597 1.0112 1910
## week 0.2075 0.2248 0.0348 0.1394 0.7785 1.0069 1330
## I(week^2) 0.0846 0.1171 0.0198 0.0605 0.2972 1.1136 934
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0766 0.4964 -1.0278 -0.0824
## (Intercept)-Procyon_lotor 0.0598 0.5555 -1.0508 0.0476
## (Intercept)-Dasypus_novemcinctus -0.6400 0.4533 -1.5577 -0.6422
## (Intercept)-Lynx_rufus -0.4351 0.5702 -1.5229 -0.4588
## (Intercept)-Didelphis_virginiana -1.0531 0.5320 -2.1741 -1.0164
## (Intercept)-Sylvilagus_floridanus -0.5387 0.4850 -1.4915 -0.5315
## (Intercept)-Meleagris_gallopavo -0.5603 0.5375 -1.6093 -0.5724
## (Intercept)-Sciurus_carolinensis -1.0813 0.5393 -2.2155 -1.0413
## Avg_Cogongrass_Cover-Canis_latrans 0.3697 0.3450 -0.2799 0.3503
## Avg_Cogongrass_Cover-Procyon_lotor 0.2398 0.3398 -0.4010 0.2287
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3550 0.3200 -0.2539 0.3463
## Avg_Cogongrass_Cover-Lynx_rufus 0.4745 0.3729 -0.1938 0.4564
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3484 0.3369 -0.2804 0.3353
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1538 0.4150 -1.0907 -0.1178
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2243 0.4792 -1.3408 -0.1688
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3608 0.3479 -0.3059 0.3532
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9110 1.0021 600
## (Intercept)-Procyon_lotor 1.1363 1.0160 353
## (Intercept)-Dasypus_novemcinctus 0.2552 1.0047 1134
## (Intercept)-Lynx_rufus 0.7382 1.0185 644
## (Intercept)-Didelphis_virginiana -0.1011 1.0018 940
## (Intercept)-Sylvilagus_floridanus 0.4116 1.0078 901
## (Intercept)-Meleagris_gallopavo 0.5428 1.0087 958
## (Intercept)-Sciurus_carolinensis -0.1122 1.0067 877
## Avg_Cogongrass_Cover-Canis_latrans 1.0914 1.0005 1973
## Avg_Cogongrass_Cover-Procyon_lotor 0.9302 1.0007 1962
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0130 1.0026 2482
## Avg_Cogongrass_Cover-Lynx_rufus 1.2870 1.0084 1979
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0525 1.0132 2135
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5728 1.0006 1360
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5701 1.0010 1113
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0894 1.0156 1773
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4528 0.1818 -2.8222 -2.4503 -2.1003 1.0029
## (Intercept)-Procyon_lotor -2.1789 0.1453 -2.4751 -2.1755 -1.9004 1.0027
## (Intercept)-Dasypus_novemcinctus -1.4973 0.1601 -1.8141 -1.4983 -1.1793 1.0001
## (Intercept)-Lynx_rufus -3.2044 0.3171 -3.8561 -3.1947 -2.6177 1.0591
## (Intercept)-Didelphis_virginiana -2.1733 0.2685 -2.7252 -2.1628 -1.6838 1.0072
## (Intercept)-Sylvilagus_floridanus -2.9595 0.2818 -3.5570 -2.9468 -2.4588 1.0102
## (Intercept)-Meleagris_gallopavo -3.1350 0.3410 -3.8546 -3.1101 -2.5185 1.0139
## (Intercept)-Sciurus_carolinensis -2.3341 0.2762 -2.9286 -2.3233 -1.8394 1.0015
## week-Canis_latrans 0.4500 0.2431 0.0090 0.4387 0.9476 1.0202
## week-Procyon_lotor 0.1634 0.1958 -0.2188 0.1626 0.5607 1.0001
## week-Dasypus_novemcinctus 0.0558 0.2123 -0.3788 0.0589 0.4645 1.0009
## week-Lynx_rufus 0.2807 0.3011 -0.3075 0.2705 0.8887 1.0025
## week-Didelphis_virginiana 0.0277 0.3201 -0.6575 0.0383 0.6326 1.0118
## week-Sylvilagus_floridanus 0.0715 0.2821 -0.5285 0.0821 0.6058 1.0112
## week-Meleagris_gallopavo -0.0959 0.3695 -0.9295 -0.0578 0.5551 1.0147
## week-Sciurus_carolinensis 0.5370 0.3375 -0.0735 0.5118 1.2666 1.0017
## I(week^2)-Canis_latrans -0.1955 0.1041 -0.4079 -0.1927 -0.0003 1.0169
## I(week^2)-Procyon_lotor -0.1140 0.0886 -0.2904 -0.1120 0.0595 1.0026
## I(week^2)-Dasypus_novemcinctus -0.1507 0.1000 -0.3527 -0.1499 0.0414 1.0013
## I(week^2)-Lynx_rufus -0.2104 0.1509 -0.5215 -0.2037 0.0711 1.0018
## I(week^2)-Didelphis_virginiana -0.3716 0.2184 -0.8668 -0.3482 -0.0290 1.0304
## I(week^2)-Sylvilagus_floridanus -0.1695 0.1434 -0.4650 -0.1660 0.1017 1.0031
## I(week^2)-Meleagris_gallopavo -0.3955 0.2515 -1.0046 -0.3604 0.0078 1.0148
## I(week^2)-Sciurus_carolinensis -0.1930 0.1381 -0.4770 -0.1891 0.0632 1.0003
## ESS
## (Intercept)-Canis_latrans 1297
## (Intercept)-Procyon_lotor 1744
## (Intercept)-Dasypus_novemcinctus 2283
## (Intercept)-Lynx_rufus 484
## (Intercept)-Didelphis_virginiana 1152
## (Intercept)-Sylvilagus_floridanus 781
## (Intercept)-Meleagris_gallopavo 468
## (Intercept)-Sciurus_carolinensis 1386
## week-Canis_latrans 1383
## week-Procyon_lotor 1547
## week-Dasypus_novemcinctus 2160
## week-Lynx_rufus 1443
## week-Didelphis_virginiana 1161
## week-Sylvilagus_floridanus 1129
## week-Meleagris_gallopavo 610
## week-Sciurus_carolinensis 1215
## I(week^2)-Canis_latrans 1411
## I(week^2)-Procyon_lotor 1677
## I(week^2)-Dasypus_novemcinctus 1836
## I(week^2)-Lynx_rufus 731
## I(week^2)-Didelphis_virginiana 442
## I(week^2)-Sylvilagus_floridanus 758
## I(week^2)-Meleagris_gallopavo 199
## I(week^2)-Sciurus_carolinensis 1624
# 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): 0.6167
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1204 0.3970 -1.9424 -1.1093 -0.3344 1.0047 569
## Avg_Cogongrass_Cover -0.5591 0.3711 -1.2896 -0.5572 0.1486 1.0003 594
## I(Avg_Cogongrass_Cover^2) 0.7587 0.3618 0.1130 0.7338 1.5304 1.0021 427
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5728 0.6357 0.0499 0.3726 2.3394 1.0210 978
## Avg_Cogongrass_Cover 0.3817 0.5102 0.0434 0.2328 1.6176 1.0522 1160
## I(Avg_Cogongrass_Cover^2) 0.4953 0.8156 0.0407 0.2521 2.4693 1.0345 435
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4673 0.452 0.0449 0.3244 1.6623 1.0229 235
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4275 0.2826 -2.9826 -2.4308 -1.8256 1.0003 2301
## week 0.1908 0.2005 -0.1927 0.1896 0.6026 1.0086 1132
## I(week^2) -0.2199 0.1198 -0.4599 -0.2181 0.0081 1.0074 804
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5711 0.4582 0.1432 0.4479 1.8508 1.0005 2015
## week 0.2001 0.2104 0.0327 0.1341 0.7438 1.0135 1394
## I(week^2) 0.0782 0.0681 0.0199 0.0584 0.2620 1.0065 1226
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7672 0.5430 -1.8136 -0.7762
## (Intercept)-Procyon_lotor -0.5716 0.5663 -1.6847 -0.5806
## (Intercept)-Dasypus_novemcinctus -1.2419 0.4920 -2.2124 -1.2320
## (Intercept)-Lynx_rufus -1.2074 0.5853 -2.3768 -1.1962
## (Intercept)-Didelphis_virginiana -1.5622 0.5410 -2.6843 -1.5444
## (Intercept)-Sylvilagus_floridanus -1.0984 0.5427 -2.1971 -1.1028
## (Intercept)-Meleagris_gallopavo -0.9768 0.5642 -2.0358 -0.9904
## (Intercept)-Sciurus_carolinensis -1.7922 0.6090 -3.0815 -1.7581
## Avg_Cogongrass_Cover-Canis_latrans -0.3958 0.4949 -1.3527 -0.4028
## Avg_Cogongrass_Cover-Procyon_lotor -0.5567 0.4809 -1.5074 -0.5517
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4213 0.4650 -1.3182 -0.4191
## Avg_Cogongrass_Cover-Lynx_rufus -0.4677 0.5332 -1.5418 -0.4711
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3065 0.5201 -1.2764 -0.3180
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0065 0.5837 -2.3220 -0.9616
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8586 0.5585 -2.0414 -0.8237
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5644 0.5028 -1.5839 -0.5620
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2260 0.7612 0.2210 1.0557
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1237 0.6306 0.2479 1.0011
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6387 0.3310 -0.0096 0.6297
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1148 0.5434 0.2853 1.0491
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4332 0.3710 -0.3049 0.4247
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6608 0.4483 -0.1164 0.6246
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2138 0.5640 -0.9164 0.2375
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7796 0.3602 0.1348 0.7650
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3237 1.0035 657
## (Intercept)-Procyon_lotor 0.5441 1.0023 524
## (Intercept)-Dasypus_novemcinctus -0.3003 1.0125 1113
## (Intercept)-Lynx_rufus -0.0303 1.0077 642
## (Intercept)-Didelphis_virginiana -0.5598 1.0064 863
## (Intercept)-Sylvilagus_floridanus -0.0438 1.0036 970
## (Intercept)-Meleagris_gallopavo 0.1938 1.0055 896
## (Intercept)-Sciurus_carolinensis -0.7209 1.0005 790
## Avg_Cogongrass_Cover-Canis_latrans 0.6465 1.0038 1090
## Avg_Cogongrass_Cover-Procyon_lotor 0.3544 1.0077 1005
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4995 1.0013 970
## Avg_Cogongrass_Cover-Lynx_rufus 0.5938 1.0008 672
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7830 1.0006 1100
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0069 1.0035 812
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1141 1.0017 844
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3846 1.0019 927
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1762 1.0016 302
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7866 1.0012 326
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2955 1.0028 1082
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3586 1.0047 331
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1656 1.0031 954
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6253 1.0258 590
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.2449 1.0047 388
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5350 1.0059 724
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4747 0.1789 -2.8456 -2.4695 -2.1317 1.0221
## (Intercept)-Procyon_lotor -2.1797 0.1454 -2.4709 -2.1770 -1.9036 1.0053
## (Intercept)-Dasypus_novemcinctus -1.4990 0.1612 -1.8137 -1.5004 -1.1846 1.0125
## (Intercept)-Lynx_rufus -3.1686 0.3031 -3.8058 -3.1553 -2.6131 1.0049
## (Intercept)-Didelphis_virginiana -2.1920 0.2633 -2.7265 -2.1878 -1.7039 1.0162
## (Intercept)-Sylvilagus_floridanus -3.0138 0.3008 -3.6236 -3.0058 -2.4720 1.0209
## (Intercept)-Meleagris_gallopavo -3.1078 0.3332 -3.7820 -3.0921 -2.5012 1.0047
## (Intercept)-Sciurus_carolinensis -2.3298 0.2729 -2.8842 -2.3200 -1.8126 1.0011
## week-Canis_latrans 0.4435 0.2452 -0.0097 0.4383 0.9622 1.0003
## week-Procyon_lotor 0.1668 0.1941 -0.2150 0.1702 0.5461 1.0023
## week-Dasypus_novemcinctus 0.0601 0.2124 -0.3643 0.0587 0.4679 1.0021
## week-Lynx_rufus 0.2812 0.2931 -0.2945 0.2766 0.8715 1.0000
## week-Didelphis_virginiana 0.0487 0.3048 -0.5626 0.0589 0.6281 1.0090
## week-Sylvilagus_floridanus 0.0669 0.2894 -0.5275 0.0775 0.6020 1.0131
## week-Meleagris_gallopavo -0.0932 0.3517 -0.8429 -0.0729 0.5293 1.0180
## week-Sciurus_carolinensis 0.5489 0.3264 -0.0520 0.5360 1.2546 1.0001
## I(week^2)-Canis_latrans -0.1930 0.1050 -0.4029 -0.1907 0.0062 1.0131
## I(week^2)-Procyon_lotor -0.1149 0.0870 -0.2861 -0.1137 0.0576 1.0047
## I(week^2)-Dasypus_novemcinctus -0.1511 0.1003 -0.3550 -0.1505 0.0418 1.0026
## I(week^2)-Lynx_rufus -0.2021 0.1449 -0.5036 -0.1973 0.0733 1.0077
## I(week^2)-Didelphis_virginiana -0.3548 0.1949 -0.7714 -0.3383 -0.0162 1.0521
## I(week^2)-Sylvilagus_floridanus -0.1661 0.1456 -0.4597 -0.1617 0.1217 1.0147
## I(week^2)-Meleagris_gallopavo -0.3850 0.2174 -0.8771 -0.3640 -0.0147 1.0000
## I(week^2)-Sciurus_carolinensis -0.1987 0.1366 -0.4797 -0.1944 0.0566 1.0005
## ESS
## (Intercept)-Canis_latrans 1175
## (Intercept)-Procyon_lotor 1673
## (Intercept)-Dasypus_novemcinctus 2062
## (Intercept)-Lynx_rufus 523
## (Intercept)-Didelphis_virginiana 1276
## (Intercept)-Sylvilagus_floridanus 657
## (Intercept)-Meleagris_gallopavo 519
## (Intercept)-Sciurus_carolinensis 1322
## week-Canis_latrans 974
## week-Procyon_lotor 1511
## week-Dasypus_novemcinctus 1796
## week-Lynx_rufus 1014
## week-Didelphis_virginiana 1080
## week-Sylvilagus_floridanus 1117
## week-Meleagris_gallopavo 606
## week-Sciurus_carolinensis 1265
## I(week^2)-Canis_latrans 1167
## I(week^2)-Procyon_lotor 1527
## I(week^2)-Dasypus_novemcinctus 1512
## I(week^2)-Lynx_rufus 795
## I(week^2)-Didelphis_virginiana 533
## I(week^2)-Sylvilagus_floridanus 692
## I(week^2)-Meleagris_gallopavo 407
## I(week^2)-Sciurus_carolinensis 1368
# 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): 0.6357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0513 0.6814 -3.4263 -2.0563 -0.6956 1.0097 390
## Cogon_Patch_Size -0.0565 0.6934 -1.5237 -0.0345 1.2904 1.0399 611
## Veg_shannon_index 0.9033 0.4453 0.0812 0.8860 1.8630 1.0236 396
## total_shrub_cover -0.7165 0.5605 -1.8918 -0.6927 0.3673 1.0057 1421
## Avg_Cogongrass_Cover 0.3270 1.0126 -1.7207 0.3358 2.3869 1.1418 144
## Tree_Density -2.2754 0.8212 -3.8761 -2.2610 -0.6789 1.0551 330
## Avg_Canopy_Cover 1.6817 0.6299 0.4994 1.6671 2.9539 1.0151 417
## I(Avg_Cogongrass_Cover^2) 1.2682 0.6712 -0.0326 1.2632 2.6719 1.0144 401
## avg_veg_height -0.1675 0.4968 -1.1410 -0.1588 0.7993 1.1239 247
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0063 2.9697 0.0648 1.0609 10.1579 1.0495 432
## Cogon_Patch_Size 2.8218 4.2647 0.1428 1.5608 12.4075 1.0094 589
## Veg_shannon_index 0.5936 0.8818 0.0465 0.3281 2.6863 1.0975 801
## total_shrub_cover 2.2499 3.0805 0.1203 1.3561 10.0399 1.0841 304
## Avg_Cogongrass_Cover 1.0880 1.8575 0.0473 0.4719 6.0191 1.0325 666
## Tree_Density 2.4714 4.6173 0.0677 1.0154 13.9230 1.1095 325
## Avg_Canopy_Cover 2.2924 2.9806 0.1227 1.3164 9.9419 1.0134 254
## I(Avg_Cogongrass_Cover^2) 2.5044 4.7151 0.0873 1.1993 13.9181 1.1518 130
## avg_veg_height 0.4578 0.7768 0.0409 0.2454 2.0451 1.0073 1106
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7355 2.0809 0.0673 1.0309 7.4308 1.011 101
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4473 0.3009 -3.0197 -2.4519 -1.8143 1.0055 2512
## week 0.1856 0.2020 -0.2182 0.1815 0.5832 1.0055 1352
## I(week^2) -0.2225 0.1175 -0.4741 -0.2139 -0.0090 1.0137 1099
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6524 0.5244 0.1726 0.5095 2.0787 1.0083 1572
## week 0.2027 0.2174 0.0339 0.1384 0.7590 1.0228 1013
## I(week^2) 0.0802 0.0803 0.0194 0.0583 0.2631 1.0511 534
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5683 0.9759 -3.3405 -1.5950
## (Intercept)-Procyon_lotor -1.2791 1.0196 -3.3497 -1.2781
## (Intercept)-Dasypus_novemcinctus -2.4431 0.8714 -4.3386 -2.3886
## (Intercept)-Lynx_rufus -1.7563 1.1436 -3.8964 -1.8303
## (Intercept)-Didelphis_virginiana -3.0458 1.0773 -5.5265 -2.9127
## (Intercept)-Sylvilagus_floridanus -2.2060 0.9838 -4.2455 -2.1773
## (Intercept)-Meleagris_gallopavo -2.0808 0.9736 -4.0337 -2.0636
## (Intercept)-Sciurus_carolinensis -3.4272 1.2657 -6.3777 -3.2454
## Cogon_Patch_Size-Canis_latrans 1.3531 1.2285 -0.4399 1.1448
## Cogon_Patch_Size-Procyon_lotor -0.5670 0.8039 -2.2443 -0.5390
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2565 0.7088 -1.7359 -0.2422
## Cogon_Patch_Size-Lynx_rufus -0.3849 1.3576 -3.2286 -0.3312
## Cogon_Patch_Size-Didelphis_virginiana 1.4203 0.9706 -0.2015 1.3057
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2952 1.4068 -4.7028 -1.0705
## Cogon_Patch_Size-Meleagris_gallopavo 0.1308 1.0962 -1.9430 0.0842
## Cogon_Patch_Size-Sciurus_carolinensis -0.8938 1.0793 -3.4588 -0.7661
## Veg_shannon_index-Canis_latrans 1.2508 0.6531 0.1415 1.1970
## Veg_shannon_index-Procyon_lotor 1.0883 0.5880 0.0180 1.0360
## Veg_shannon_index-Dasypus_novemcinctus 0.6398 0.5302 -0.4566 0.6439
## Veg_shannon_index-Lynx_rufus 0.8560 0.8188 -0.7765 0.8559
## Veg_shannon_index-Didelphis_virginiana 0.9713 0.6050 -0.1740 0.9431
## Veg_shannon_index-Sylvilagus_floridanus 0.9613 0.6541 -0.2554 0.9271
## Veg_shannon_index-Meleagris_gallopavo 1.2067 0.7144 -0.0181 1.1242
## Veg_shannon_index-Sciurus_carolinensis 0.3715 0.6842 -1.0951 0.4117
## total_shrub_cover-Canis_latrans 0.0481 0.7040 -1.2265 -0.0108
## total_shrub_cover-Procyon_lotor -1.2489 0.6900 -2.7669 -1.1838
## total_shrub_cover-Dasypus_novemcinctus 0.1025 0.5713 -0.9681 0.0952
## total_shrub_cover-Lynx_rufus -1.6084 1.1963 -4.5544 -1.4188
## total_shrub_cover-Didelphis_virginiana -0.6999 0.7481 -2.2797 -0.6764
## total_shrub_cover-Sylvilagus_floridanus -0.4255 0.9229 -2.3994 -0.3976
## total_shrub_cover-Meleagris_gallopavo -2.6048 1.4118 -5.8607 -2.3927
## total_shrub_cover-Sciurus_carolinensis -0.0110 0.7349 -1.3983 -0.0238
## Avg_Cogongrass_Cover-Canis_latrans 0.1887 1.2527 -2.3112 0.2018
## Avg_Cogongrass_Cover-Procyon_lotor 0.3226 1.1647 -1.9143 0.3076
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8642 1.2601 -1.5153 0.8130
## Avg_Cogongrass_Cover-Lynx_rufus 0.4285 1.2725 -2.0865 0.4064
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6190 1.2382 -1.7416 0.5962
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1981 1.3142 -3.0129 -0.1378
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0319 1.3242 -2.8083 0.0792
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5018 1.2458 -1.8989 0.4944
## Tree_Density-Canis_latrans -3.0063 1.2621 -5.9648 -2.8512
## Tree_Density-Procyon_lotor -2.2302 0.9830 -4.2882 -2.1952
## Tree_Density-Dasypus_novemcinctus -3.5368 1.5147 -7.0366 -3.2608
## Tree_Density-Lynx_rufus -1.1857 1.5249 -3.7218 -1.3325
## Tree_Density-Didelphis_virginiana -2.3684 1.0488 -4.6387 -2.3138
## Tree_Density-Sylvilagus_floridanus -2.7315 1.3685 -5.9352 -2.5706
## Tree_Density-Meleagris_gallopavo -2.3354 1.2371 -4.9587 -2.3236
## Tree_Density-Sciurus_carolinensis -2.5660 1.2083 -5.2810 -2.4468
## Avg_Canopy_Cover-Canis_latrans 0.2572 0.7063 -1.1523 0.2377
## Avg_Canopy_Cover-Procyon_lotor 1.6326 0.7333 0.3391 1.5990
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9550 0.7284 0.7375 1.8701
## Avg_Canopy_Cover-Lynx_rufus 1.0097 1.1754 -1.1842 0.9713
## Avg_Canopy_Cover-Didelphis_virginiana 2.4939 0.9461 1.0528 2.3498
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2286 1.5625 1.0268 2.9307
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1439 1.1185 0.5102 1.9557
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1242 0.8635 0.7650 2.0064
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3821 1.2955 0.6563 2.1569
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2015 1.1872 0.5059 2.0139
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2754 0.7225 -0.0319 1.2245
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5101 1.4197 0.5719 2.2883
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6451 0.7076 -0.7342 0.6432
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0104 0.9179 -0.4726 0.9213
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.0126 1.4518 -3.2975 0.1064
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3063 0.7466 -0.0186 1.2688
## avg_veg_height-Canis_latrans -0.4404 0.6289 -1.7741 -0.4027
## avg_veg_height-Procyon_lotor 0.0091 0.6122 -1.1406 -0.0110
## avg_veg_height-Dasypus_novemcinctus 0.0954 0.6037 -1.0599 0.0913
## avg_veg_height-Lynx_rufus -0.3197 0.7944 -1.9726 -0.2816
## avg_veg_height-Didelphis_virginiana -0.2630 0.6612 -1.6543 -0.2251
## avg_veg_height-Sylvilagus_floridanus -0.3174 0.6761 -1.7668 -0.2985
## avg_veg_height-Meleagris_gallopavo -0.2345 0.7558 -1.8609 -0.2083
## avg_veg_height-Sciurus_carolinensis 0.0863 0.6277 -1.1042 0.0596
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4890 1.0396 435
## (Intercept)-Procyon_lotor 0.6834 1.0508 331
## (Intercept)-Dasypus_novemcinctus -0.9276 1.0060 405
## (Intercept)-Lynx_rufus 0.7827 1.0306 336
## (Intercept)-Didelphis_virginiana -1.2794 1.0117 437
## (Intercept)-Sylvilagus_floridanus -0.3081 1.0078 653
## (Intercept)-Meleagris_gallopavo -0.1297 1.0080 523
## (Intercept)-Sciurus_carolinensis -1.4436 1.0130 337
## Cogon_Patch_Size-Canis_latrans 4.4232 1.0175 458
## Cogon_Patch_Size-Procyon_lotor 0.9002 1.0239 335
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0833 1.0525 283
## Cogon_Patch_Size-Lynx_rufus 2.2627 1.0855 374
## Cogon_Patch_Size-Didelphis_virginiana 3.6881 1.0077 363
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9297 1.0325 317
## Cogon_Patch_Size-Meleagris_gallopavo 2.4345 1.0497 514
## Cogon_Patch_Size-Sciurus_carolinensis 0.7836 1.0134 578
## Veg_shannon_index-Canis_latrans 2.6962 1.0189 796
## Veg_shannon_index-Procyon_lotor 2.3620 1.0099 592
## Veg_shannon_index-Dasypus_novemcinctus 1.6377 1.0124 693
## Veg_shannon_index-Lynx_rufus 2.4520 1.0644 498
## Veg_shannon_index-Didelphis_virginiana 2.2619 1.0036 872
## Veg_shannon_index-Sylvilagus_floridanus 2.3993 1.0010 764
## Veg_shannon_index-Meleagris_gallopavo 2.9218 1.0014 639
## Veg_shannon_index-Sciurus_carolinensis 1.6220 1.0358 737
## total_shrub_cover-Canis_latrans 1.5673 1.0037 985
## total_shrub_cover-Procyon_lotor -0.0595 1.0056 1102
## total_shrub_cover-Dasypus_novemcinctus 1.2843 1.0006 1351
## total_shrub_cover-Lynx_rufus 0.1662 1.0594 289
## total_shrub_cover-Didelphis_virginiana 0.7397 1.0048 1399
## total_shrub_cover-Sylvilagus_floridanus 1.3438 1.0103 688
## total_shrub_cover-Meleagris_gallopavo -0.5184 1.0689 303
## total_shrub_cover-Sciurus_carolinensis 1.4389 1.0013 1297
## Avg_Cogongrass_Cover-Canis_latrans 2.6568 1.1036 235
## Avg_Cogongrass_Cover-Procyon_lotor 2.7777 1.0747 185
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5992 1.1182 188
## Avg_Cogongrass_Cover-Lynx_rufus 3.0491 1.0926 252
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2725 1.0786 258
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.2738 1.0777 275
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.5950 1.0920 207
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.1227 1.1091 198
## Tree_Density-Canis_latrans -0.9944 1.0769 240
## Tree_Density-Procyon_lotor -0.3895 1.0171 358
## Tree_Density-Dasypus_novemcinctus -1.3691 1.0877 204
## Tree_Density-Lynx_rufus 2.1972 1.0815 271
## Tree_Density-Didelphis_virginiana -0.5020 1.0325 376
## Tree_Density-Sylvilagus_floridanus -0.4677 1.1008 278
## Tree_Density-Meleagris_gallopavo 0.0371 1.0439 516
## Tree_Density-Sciurus_carolinensis -0.5759 1.0796 285
## Avg_Canopy_Cover-Canis_latrans 1.6630 1.0423 470
## Avg_Canopy_Cover-Procyon_lotor 3.1997 1.0081 622
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6146 1.0280 392
## Avg_Canopy_Cover-Lynx_rufus 3.5523 1.0213 310
## Avg_Canopy_Cover-Didelphis_virginiana 4.6907 1.0119 220
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.0562 1.0043 222
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9272 1.0359 263
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2444 1.0080 399
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5696 1.0985 129
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.1114 1.0230 191
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9034 1.0086 513
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6716 1.0749 208
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0890 1.0048 364
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1894 1.0162 324
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.4052 1.0362 149
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.9352 1.0049 571
## avg_veg_height-Canis_latrans 0.7311 1.0607 348
## avg_veg_height-Procyon_lotor 1.2840 1.0629 346
## avg_veg_height-Dasypus_novemcinctus 1.2826 1.0428 570
## avg_veg_height-Lynx_rufus 1.1060 1.0979 334
## avg_veg_height-Didelphis_virginiana 0.9427 1.0681 432
## avg_veg_height-Sylvilagus_floridanus 0.9601 1.0552 433
## avg_veg_height-Meleagris_gallopavo 1.1997 1.0732 425
## avg_veg_height-Sciurus_carolinensis 1.3783 1.0357 420
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4593 0.1879 -2.8434 -2.4522 -2.1124 1.0065
## (Intercept)-Procyon_lotor -2.1731 0.1456 -2.4581 -2.1715 -1.8928 1.0038
## (Intercept)-Dasypus_novemcinctus -1.4902 0.1512 -1.7838 -1.4894 -1.1932 1.0083
## (Intercept)-Lynx_rufus -3.3617 0.2899 -3.9436 -3.3645 -2.8013 1.0128
## (Intercept)-Didelphis_virginiana -2.1524 0.2607 -2.6888 -2.1397 -1.6579 1.0053
## (Intercept)-Sylvilagus_floridanus -3.0446 0.2825 -3.6240 -3.0320 -2.5076 1.0207
## (Intercept)-Meleagris_gallopavo -3.1258 0.3249 -3.8149 -3.1109 -2.5314 1.0392
## (Intercept)-Sciurus_carolinensis -2.3597 0.2906 -2.9801 -2.3440 -1.8306 1.0008
## week-Canis_latrans 0.4396 0.2499 -0.0178 0.4305 0.9605 1.0035
## week-Procyon_lotor 0.1691 0.1942 -0.2033 0.1634 0.5565 0.9996
## week-Dasypus_novemcinctus 0.0640 0.2054 -0.3322 0.0625 0.4705 1.0035
## week-Lynx_rufus 0.2647 0.3037 -0.2897 0.2534 0.8950 1.0058
## week-Didelphis_virginiana 0.0247 0.3153 -0.6159 0.0339 0.6203 1.0083
## week-Sylvilagus_floridanus 0.0463 0.2855 -0.5275 0.0488 0.5866 1.0066
## week-Meleagris_gallopavo -0.1017 0.3516 -0.8775 -0.0789 0.5127 1.0077
## week-Sciurus_carolinensis 0.5444 0.3332 -0.0407 0.5188 1.2946 1.0030
## I(week^2)-Canis_latrans -0.1886 0.1044 -0.3981 -0.1851 0.0144 1.0104
## I(week^2)-Procyon_lotor -0.1168 0.0870 -0.2858 -0.1160 0.0523 1.0003
## I(week^2)-Dasypus_novemcinctus -0.1535 0.0972 -0.3482 -0.1508 0.0331 1.0068
## I(week^2)-Lynx_rufus -0.2050 0.1439 -0.5054 -0.1979 0.0543 1.0185
## I(week^2)-Didelphis_virginiana -0.3780 0.2169 -0.8816 -0.3520 -0.0332 1.0123
## I(week^2)-Sylvilagus_floridanus -0.1628 0.1458 -0.4684 -0.1592 0.1054 1.0096
## I(week^2)-Meleagris_gallopavo -0.3632 0.2397 -0.9457 -0.3309 0.0091 1.0129
## I(week^2)-Sciurus_carolinensis -0.1925 0.1380 -0.4804 -0.1882 0.0559 1.0009
## ESS
## (Intercept)-Canis_latrans 1092
## (Intercept)-Procyon_lotor 1773
## (Intercept)-Dasypus_novemcinctus 2416
## (Intercept)-Lynx_rufus 521
## (Intercept)-Didelphis_virginiana 1709
## (Intercept)-Sylvilagus_floridanus 654
## (Intercept)-Meleagris_gallopavo 483
## (Intercept)-Sciurus_carolinensis 1066
## week-Canis_latrans 921
## week-Procyon_lotor 1817
## week-Dasypus_novemcinctus 2065
## week-Lynx_rufus 978
## week-Didelphis_virginiana 1246
## week-Sylvilagus_floridanus 1226
## week-Meleagris_gallopavo 630
## week-Sciurus_carolinensis 1272
## I(week^2)-Canis_latrans 1171
## I(week^2)-Procyon_lotor 1757
## I(week^2)-Dasypus_novemcinctus 1898
## I(week^2)-Lynx_rufus 782
## I(week^2)-Didelphis_virginiana 477
## I(week^2)-Sylvilagus_floridanus 631
## I(week^2)-Meleagris_gallopavo 215
## I(week^2)-Sciurus_carolinensis 1268
#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): 0.7063
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2082 0.34 -0.8608 -0.2218 0.4786 1.0034 1320
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6781 0.7134 0.0975 0.4738 2.556 1.0159 1336
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6052 0.3242 -3.1995 -2.6205 -1.9694 1.0026 1974
## shrub_cover 0.2524 0.3373 -0.4195 0.2565 0.9285 1.0051 1006
## veg_height 0.0606 0.2059 -0.3586 0.0679 0.4427 1.0035 1381
## week 0.1795 0.2024 -0.2264 0.1763 0.5799 1.0047 1147
## I(week^2) -0.2228 0.1265 -0.5055 -0.2140 0.0039 1.0329 800
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7866 0.7376 0.1762 0.5850 2.6453 1.0379 952
## shrub_cover 0.7323 0.6697 0.1580 0.5622 2.2609 1.0262 1385
## veg_height 0.2743 0.2531 0.0577 0.2044 0.9177 1.0033 1249
## week 0.1917 0.1949 0.0323 0.1333 0.7147 1.0094 1157
## I(week^2) 0.0850 0.1039 0.0197 0.0595 0.3014 1.1180 324
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2412 0.3834 -0.4710 0.2221 1.0634 1.0008
## (Intercept)-Procyon_lotor 0.5208 0.3856 -0.1879 0.5036 1.3242 1.0033
## (Intercept)-Dasypus_novemcinctus -0.4859 0.3428 -1.1871 -0.4765 0.1628 1.0049
## (Intercept)-Lynx_rufus 0.1072 0.5351 -0.7856 0.0467 1.3667 1.0105
## (Intercept)-Didelphis_virginiana -0.9244 0.4293 -1.8250 -0.9156 -0.1167 1.0029
## (Intercept)-Sylvilagus_floridanus -0.3438 0.4037 -1.1356 -0.3489 0.4554 1.0038
## (Intercept)-Meleagris_gallopavo 0.0647 0.5813 -0.9247 0.0135 1.2937 1.0146
## (Intercept)-Sciurus_carolinensis -0.9169 0.4268 -1.7881 -0.9097 -0.1170 1.0040
## ESS
## (Intercept)-Canis_latrans 1844
## (Intercept)-Procyon_lotor 1763
## (Intercept)-Dasypus_novemcinctus 2468
## (Intercept)-Lynx_rufus 494
## (Intercept)-Didelphis_virginiana 1770
## (Intercept)-Sylvilagus_floridanus 1361
## (Intercept)-Meleagris_gallopavo 519
## (Intercept)-Sciurus_carolinensis 1396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5805 0.2017 -3.0050 -2.5694 -2.2101 1.0047
## (Intercept)-Procyon_lotor -2.1923 0.1577 -2.5112 -2.1854 -1.8926 1.0003
## (Intercept)-Dasypus_novemcinctus -1.6506 0.1804 -2.0050 -1.6468 -1.3057 1.0032
## (Intercept)-Lynx_rufus -3.4511 0.3475 -4.1628 -3.4410 -2.8025 1.0305
## (Intercept)-Didelphis_virginiana -2.4290 0.2918 -3.0167 -2.4242 -1.8819 1.0118
## (Intercept)-Sylvilagus_floridanus -3.0271 0.2838 -3.6076 -3.0190 -2.4916 1.0143
## (Intercept)-Meleagris_gallopavo -3.7334 0.4634 -4.6568 -3.7314 -2.8213 1.0002
## (Intercept)-Sciurus_carolinensis -2.5381 0.3192 -3.1998 -2.5202 -1.9599 1.0015
## shrub_cover-Canis_latrans -0.2891 0.2212 -0.7258 -0.2845 0.1397 0.9999
## shrub_cover-Procyon_lotor 0.2601 0.1598 -0.0530 0.2607 0.5661 1.0016
## shrub_cover-Dasypus_novemcinctus 0.8845 0.3001 0.3104 0.8780 1.4942 1.0134
## shrub_cover-Lynx_rufus -0.2899 0.3778 -1.0087 -0.2928 0.4749 1.0483
## shrub_cover-Didelphis_virginiana 1.0107 0.3772 0.3219 0.9969 1.7709 1.0174
## shrub_cover-Sylvilagus_floridanus 0.2648 0.4399 -0.5302 0.2528 1.1876 1.0026
## shrub_cover-Meleagris_gallopavo -0.6841 0.4031 -1.4794 -0.6855 0.1256 1.0231
## shrub_cover-Sciurus_carolinensis 0.8965 0.4107 0.0973 0.8907 1.7139 1.0393
## veg_height-Canis_latrans -0.5809 0.1903 -0.9658 -0.5720 -0.2378 1.0027
## veg_height-Procyon_lotor 0.3438 0.1221 0.1160 0.3416 0.5885 0.9997
## veg_height-Dasypus_novemcinctus 0.2581 0.1358 -0.0054 0.2571 0.5306 1.0013
## veg_height-Lynx_rufus 0.0251 0.2467 -0.4907 0.0377 0.4782 1.0140
## veg_height-Didelphis_virginiana 0.4824 0.2468 0.0232 0.4746 0.9817 1.0020
## veg_height-Sylvilagus_floridanus 0.1430 0.2389 -0.3327 0.1442 0.6151 1.0049
## veg_height-Meleagris_gallopavo -0.2431 0.3774 -1.0255 -0.2300 0.4501 1.0067
## veg_height-Sciurus_carolinensis 0.0957 0.2186 -0.3303 0.0966 0.5171 1.0029
## week-Canis_latrans 0.4421 0.2450 0.0036 0.4313 0.9546 1.0027
## week-Procyon_lotor 0.1597 0.1953 -0.2183 0.1598 0.5321 1.0020
## week-Dasypus_novemcinctus 0.0644 0.2132 -0.3620 0.0653 0.4896 1.0009
## week-Lynx_rufus 0.2546 0.2943 -0.3168 0.2524 0.8475 1.0258
## week-Didelphis_virginiana 0.0434 0.3112 -0.5930 0.0549 0.6477 1.0131
## week-Sylvilagus_floridanus 0.0496 0.2942 -0.5642 0.0514 0.6197 1.0213
## week-Meleagris_gallopavo -0.0749 0.3407 -0.8231 -0.0554 0.5313 1.0387
## week-Sciurus_carolinensis 0.5329 0.3378 -0.0643 0.5039 1.2853 1.0084
## I(week^2)-Canis_latrans -0.1940 0.1037 -0.4054 -0.1900 0.0046 1.0033
## I(week^2)-Procyon_lotor -0.1135 0.0879 -0.2861 -0.1133 0.0556 1.0002
## I(week^2)-Dasypus_novemcinctus -0.1539 0.1004 -0.3606 -0.1509 0.0327 1.0010
## I(week^2)-Lynx_rufus -0.2056 0.1518 -0.5336 -0.1973 0.0606 1.0165
## I(week^2)-Didelphis_virginiana -0.3770 0.2285 -0.9376 -0.3447 -0.0260 1.0579
## I(week^2)-Sylvilagus_floridanus -0.1574 0.1517 -0.4759 -0.1542 0.1328 1.0179
## I(week^2)-Meleagris_gallopavo -0.3784 0.2558 -0.9819 -0.3366 -0.0037 1.0712
## I(week^2)-Sciurus_carolinensis -0.1950 0.1386 -0.4816 -0.1903 0.0543 1.0043
## ESS
## (Intercept)-Canis_latrans 850
## (Intercept)-Procyon_lotor 1544
## (Intercept)-Dasypus_novemcinctus 1785
## (Intercept)-Lynx_rufus 375
## (Intercept)-Didelphis_virginiana 943
## (Intercept)-Sylvilagus_floridanus 777
## (Intercept)-Meleagris_gallopavo 256
## (Intercept)-Sciurus_carolinensis 788
## shrub_cover-Canis_latrans 944
## shrub_cover-Procyon_lotor 1524
## shrub_cover-Dasypus_novemcinctus 1207
## shrub_cover-Lynx_rufus 408
## shrub_cover-Didelphis_virginiana 760
## shrub_cover-Sylvilagus_floridanus 539
## shrub_cover-Meleagris_gallopavo 293
## shrub_cover-Sciurus_carolinensis 780
## veg_height-Canis_latrans 730
## veg_height-Procyon_lotor 1505
## veg_height-Dasypus_novemcinctus 1951
## veg_height-Lynx_rufus 778
## veg_height-Didelphis_virginiana 1207
## veg_height-Sylvilagus_floridanus 972
## veg_height-Meleagris_gallopavo 496
## veg_height-Sciurus_carolinensis 1043
## week-Canis_latrans 1128
## week-Procyon_lotor 1701
## week-Dasypus_novemcinctus 2068
## week-Lynx_rufus 1137
## week-Didelphis_virginiana 1136
## week-Sylvilagus_floridanus 1196
## week-Meleagris_gallopavo 558
## week-Sciurus_carolinensis 1070
## I(week^2)-Canis_latrans 1259
## I(week^2)-Procyon_lotor 1674
## I(week^2)-Dasypus_novemcinctus 1634
## I(week^2)-Lynx_rufus 620
## I(week^2)-Didelphis_virginiana 352
## I(week^2)-Sylvilagus_floridanus 536
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1367
#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): 0.7348
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6764 0.7197 -2.0782 -0.7103 0.8668 1.0345 320
## Cogon_Patch_Size -0.3930 0.6765 -1.7710 -0.3801 0.9308 1.0050 600
## Veg_shannon_index 1.0149 0.5136 0.1265 0.9742 2.1630 1.0169 148
## total_shrub_cover -0.8781 0.6348 -2.2965 -0.8309 0.2583 1.0245 441
## Avg_Cogongrass_Cover 1.9156 0.7498 0.5103 1.8958 3.4522 1.0259 162
## Tree_Density -1.8577 0.8483 -3.6105 -1.8476 -0.1640 1.0188 394
## Avg_Canopy_Cover 1.8664 0.7901 0.3016 1.8559 3.4186 1.0262 540
## avg_veg_height -0.3867 0.5157 -1.4394 -0.3905 0.6375 1.0161 316
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5491 3.5042 0.0823 1.3884 12.1478 1.0838 135
## Cogon_Patch_Size 2.6811 5.1185 0.0796 1.2355 14.5760 1.0834 149
## Veg_shannon_index 0.6562 1.0918 0.0514 0.3462 2.9879 1.0480 751
## total_shrub_cover 2.0480 4.8127 0.0681 1.0003 9.4193 1.1765 526
## Avg_Cogongrass_Cover 1.2893 2.4847 0.0471 0.5257 6.9405 1.1737 300
## Tree_Density 4.5214 9.1403 0.0810 1.7130 26.4098 1.3059 91
## Avg_Canopy_Cover 5.2872 10.8533 0.2242 2.5666 28.2109 1.0992 135
## avg_veg_height 0.6180 1.2671 0.0436 0.3074 2.9977 1.1323 599
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8299 3.8646 0.0868 1.5723 12.8304 1.1344 49
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6143 0.3078 -3.2101 -2.6165 -1.9857 1.0050 1466
## shrub_cover 0.4111 0.3290 -0.2532 0.4115 1.0849 1.0052 1518
## veg_height 0.0948 0.2018 -0.3011 0.0936 0.5018 1.0136 1315
## week 0.1815 0.2018 -0.2197 0.1828 0.5696 1.0033 1066
## I(week^2) -0.2248 0.1240 -0.4822 -0.2201 0.0061 1.0084 906
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6745 0.6138 0.1446 0.5127 2.2120 1.0131 1136
## shrub_cover 0.7281 0.7242 0.1354 0.5464 2.4693 1.0292 818
## veg_height 0.2694 0.2288 0.0605 0.2059 0.8514 1.0165 2011
## week 0.1985 0.2027 0.0343 0.1375 0.7172 1.0047 1280
## I(week^2) 0.0845 0.0804 0.0195 0.0606 0.3015 1.0391 832
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1609 0.9635 -1.6322 0.1369
## (Intercept)-Procyon_lotor 0.2855 1.0683 -1.7087 0.2920
## (Intercept)-Dasypus_novemcinctus -1.1250 0.8305 -2.8330 -1.1017
## (Intercept)-Lynx_rufus 0.0871 1.5894 -2.1761 -0.2366
## (Intercept)-Didelphis_virginiana -1.8615 1.1371 -4.3519 -1.7596
## (Intercept)-Sylvilagus_floridanus -0.9170 1.0707 -3.0016 -0.9330
## (Intercept)-Meleagris_gallopavo -0.7574 1.2556 -3.2668 -0.8021
## (Intercept)-Sciurus_carolinensis -1.8325 1.1373 -4.2563 -1.7363
## Cogon_Patch_Size-Canis_latrans 0.5688 1.1178 -1.0532 0.4014
## Cogon_Patch_Size-Procyon_lotor -0.9280 0.7802 -2.6614 -0.8755
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4438 0.8396 -2.0789 -0.4769
## Cogon_Patch_Size-Lynx_rufus -0.6511 1.3977 -3.4019 -0.6415
## Cogon_Patch_Size-Didelphis_virginiana 0.8463 1.0175 -0.7142 0.7155
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6697 1.7417 -5.7120 -1.2794
## Cogon_Patch_Size-Meleagris_gallopavo -0.0892 1.1944 -2.0337 -0.2282
## Cogon_Patch_Size-Sciurus_carolinensis -1.2409 1.2313 -4.2800 -1.0634
## Veg_shannon_index-Canis_latrans 1.3246 0.6745 0.1771 1.2515
## Veg_shannon_index-Procyon_lotor 1.3114 0.6367 0.2030 1.2689
## Veg_shannon_index-Dasypus_novemcinctus 0.7221 0.5871 -0.4333 0.7114
## Veg_shannon_index-Lynx_rufus 0.9394 0.7934 -0.6780 0.9288
## Veg_shannon_index-Didelphis_virginiana 1.2017 0.6767 0.0042 1.1433
## Veg_shannon_index-Sylvilagus_floridanus 1.1314 0.7159 -0.1166 1.0746
## Veg_shannon_index-Meleagris_gallopavo 1.3428 0.7946 0.0030 1.2546
## Veg_shannon_index-Sciurus_carolinensis 0.4408 0.7816 -1.2842 0.4959
## total_shrub_cover-Canis_latrans 0.4354 0.9601 -0.9854 0.2977
## total_shrub_cover-Procyon_lotor -1.1560 0.6698 -2.6660 -1.0976
## total_shrub_cover-Dasypus_novemcinctus -0.4272 0.8527 -2.3730 -0.3704
## total_shrub_cover-Lynx_rufus -1.4452 1.2763 -4.4526 -1.2486
## total_shrub_cover-Didelphis_virginiana -1.2170 1.0566 -3.8590 -1.0433
## total_shrub_cover-Sylvilagus_floridanus -0.9612 1.1990 -3.6185 -0.8658
## total_shrub_cover-Meleagris_gallopavo -2.0018 1.2765 -5.1434 -1.7792
## total_shrub_cover-Sciurus_carolinensis -0.9967 1.1921 -3.9774 -0.8056
## Avg_Cogongrass_Cover-Canis_latrans 2.2898 0.9946 0.6664 2.1715
## Avg_Cogongrass_Cover-Procyon_lotor 2.0809 0.9433 0.4212 2.0040
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5082 1.1064 0.7522 2.3791
## Avg_Cogongrass_Cover-Lynx_rufus 2.2702 1.0422 0.5137 2.1654
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9651 0.9565 0.1540 1.9248
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3124 1.0394 -0.9182 1.3645
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5908 1.3939 -1.5378 1.6872
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2029 0.9753 0.4786 2.1197
## Tree_Density-Canis_latrans -2.8668 1.7204 -7.2725 -2.5043
## Tree_Density-Procyon_lotor -1.4930 0.8405 -3.2013 -1.4949
## Tree_Density-Dasypus_novemcinctus -3.8138 2.1123 -9.2827 -3.3018
## Tree_Density-Lynx_rufus -0.2604 1.7073 -2.8061 -0.4846
## Tree_Density-Didelphis_virginiana -2.1695 1.3434 -5.1632 -2.0581
## Tree_Density-Sylvilagus_floridanus -2.5168 1.6406 -6.4639 -2.2564
## Tree_Density-Meleagris_gallopavo -2.1838 1.5311 -5.3489 -2.1219
## Tree_Density-Sciurus_carolinensis -2.1803 1.5152 -5.5717 -2.0561
## Avg_Canopy_Cover-Canis_latrans 0.1436 0.6690 -1.1717 0.1317
## Avg_Canopy_Cover-Procyon_lotor 1.7588 0.7925 0.3908 1.6888
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3366 0.9456 0.8589 2.2027
## Avg_Canopy_Cover-Lynx_rufus 0.7939 1.2444 -1.4593 0.7139
## Avg_Canopy_Cover-Didelphis_virginiana 3.4088 1.6724 1.3052 3.0449
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1472 2.3870 1.2196 3.6721
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6801 1.5225 0.4667 2.4203
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0365 1.5178 1.0046 2.6882
## avg_veg_height-Canis_latrans -0.4812 0.6261 -1.7265 -0.4775
## avg_veg_height-Procyon_lotor -0.3759 0.6068 -1.6041 -0.3638
## avg_veg_height-Dasypus_novemcinctus -0.1211 0.6497 -1.3472 -0.1349
## avg_veg_height-Lynx_rufus -0.5783 0.8208 -2.3463 -0.5305
## avg_veg_height-Didelphis_virginiana -0.6107 0.7386 -2.2044 -0.5807
## avg_veg_height-Sylvilagus_floridanus -0.5942 0.7046 -2.0623 -0.5706
## avg_veg_height-Meleagris_gallopavo -0.3925 0.8427 -2.1517 -0.3858
## avg_veg_height-Sciurus_carolinensis 0.0346 0.7552 -1.2533 -0.0402
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1036 1.0195 275
## (Intercept)-Procyon_lotor 2.3228 1.0146 179
## (Intercept)-Dasypus_novemcinctus 0.3770 1.0048 471
## (Intercept)-Lynx_rufus 4.1807 1.1044 74
## (Intercept)-Didelphis_virginiana 0.1569 1.0171 338
## (Intercept)-Sylvilagus_floridanus 1.2362 1.0226 463
## (Intercept)-Meleagris_gallopavo 1.8856 1.0163 305
## (Intercept)-Sciurus_carolinensis 0.1450 1.0407 353
## Cogon_Patch_Size-Canis_latrans 3.1693 1.0206 320
## Cogon_Patch_Size-Procyon_lotor 0.5277 1.0207 169
## Cogon_Patch_Size-Dasypus_novemcinctus 1.3630 1.0065 543
## Cogon_Patch_Size-Lynx_rufus 2.2371 1.0185 222
## Cogon_Patch_Size-Didelphis_virginiana 3.1608 1.0094 402
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4625 1.0556 132
## Cogon_Patch_Size-Meleagris_gallopavo 2.7359 1.0027 440
## Cogon_Patch_Size-Sciurus_carolinensis 0.6812 1.0019 327
## Veg_shannon_index-Canis_latrans 2.8113 1.0249 195
## Veg_shannon_index-Procyon_lotor 2.7412 1.0252 125
## Veg_shannon_index-Dasypus_novemcinctus 1.9115 1.0052 441
## Veg_shannon_index-Lynx_rufus 2.5433 1.0041 250
## Veg_shannon_index-Didelphis_virginiana 2.7036 1.0469 385
## Veg_shannon_index-Sylvilagus_floridanus 2.7845 1.0223 315
## Veg_shannon_index-Meleagris_gallopavo 3.2178 1.0329 255
## Veg_shannon_index-Sciurus_carolinensis 1.9319 1.0046 377
## total_shrub_cover-Canis_latrans 2.7383 1.0187 314
## total_shrub_cover-Procyon_lotor -0.0089 1.0178 503
## total_shrub_cover-Dasypus_novemcinctus 1.1030 1.0071 506
## total_shrub_cover-Lynx_rufus 0.4807 1.1171 178
## total_shrub_cover-Didelphis_virginiana 0.3617 1.0277 324
## total_shrub_cover-Sylvilagus_floridanus 1.1031 1.0696 263
## total_shrub_cover-Meleagris_gallopavo -0.1306 1.0267 242
## total_shrub_cover-Sciurus_carolinensis 0.8606 1.0453 232
## Avg_Cogongrass_Cover-Canis_latrans 4.5143 1.0495 267
## Avg_Cogongrass_Cover-Procyon_lotor 4.1344 1.0326 183
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.1480 1.0661 173
## Avg_Cogongrass_Cover-Lynx_rufus 4.6727 1.0569 274
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0068 1.0190 244
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2342 1.0004 362
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.0805 1.0601 173
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3741 1.0277 214
## Tree_Density-Canis_latrans -0.6659 1.1846 75
## Tree_Density-Procyon_lotor 0.1617 1.0078 444
## Tree_Density-Dasypus_novemcinctus -1.1816 1.1357 132
## Tree_Density-Lynx_rufus 3.4114 1.1400 140
## Tree_Density-Didelphis_virginiana 0.2341 1.0112 351
## Tree_Density-Sylvilagus_floridanus 0.1694 1.0132 306
## Tree_Density-Meleagris_gallopavo 0.7192 1.0378 282
## Tree_Density-Sciurus_carolinensis 0.6736 1.0113 380
## Avg_Canopy_Cover-Canis_latrans 1.4885 1.0129 741
## Avg_Canopy_Cover-Procyon_lotor 3.4185 1.0059 282
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.5492 1.0278 298
## Avg_Canopy_Cover-Lynx_rufus 3.5539 1.0312 253
## Avg_Canopy_Cover-Didelphis_virginiana 7.7928 1.0496 159
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.4492 1.0617 108
## Avg_Canopy_Cover-Meleagris_gallopavo 6.4694 1.0184 225
## Avg_Canopy_Cover-Sciurus_carolinensis 6.8916 1.0505 152
## avg_veg_height-Canis_latrans 0.7514 1.0092 619
## avg_veg_height-Procyon_lotor 0.7922 1.0113 577
## avg_veg_height-Dasypus_novemcinctus 1.2553 1.0046 533
## avg_veg_height-Lynx_rufus 0.8892 1.0156 359
## avg_veg_height-Didelphis_virginiana 0.7057 1.0209 593
## avg_veg_height-Sylvilagus_floridanus 0.7445 1.0087 582
## avg_veg_height-Meleagris_gallopavo 1.2155 1.0148 451
## avg_veg_height-Sciurus_carolinensis 1.7441 1.0182 542
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5931 0.1916 -2.9867 -2.5904 -2.2284 1.0008
## (Intercept)-Procyon_lotor -2.2093 0.1617 -2.5407 -2.2034 -1.9060 1.0140
## (Intercept)-Dasypus_novemcinctus -1.7012 0.1894 -2.0929 -1.6976 -1.3358 1.0130
## (Intercept)-Lynx_rufus -3.4224 0.3807 -4.2214 -3.3931 -2.7558 1.0397
## (Intercept)-Didelphis_virginiana -2.4716 0.3116 -3.1057 -2.4617 -1.8813 1.0073
## (Intercept)-Sylvilagus_floridanus -3.0437 0.2673 -3.6147 -3.0398 -2.5317 1.0366
## (Intercept)-Meleagris_gallopavo -3.5401 0.4448 -4.4447 -3.5240 -2.6986 1.0196
## (Intercept)-Sciurus_carolinensis -2.6698 0.3351 -3.3622 -2.6668 -2.0263 1.0103
## shrub_cover-Canis_latrans -0.3482 0.2355 -0.8077 -0.3508 0.1075 1.0095
## shrub_cover-Procyon_lotor 0.2808 0.1675 -0.0629 0.2840 0.5820 1.0008
## shrub_cover-Dasypus_novemcinctus 1.0031 0.3143 0.4040 0.9988 1.6514 1.0081
## shrub_cover-Lynx_rufus 0.0670 0.3858 -0.7170 0.0830 0.7687 1.0624
## shrub_cover-Didelphis_virginiana 1.1102 0.3788 0.4097 1.0952 1.8878 1.0039
## shrub_cover-Sylvilagus_floridanus 0.5748 0.3852 -0.2023 0.5839 1.3025 1.0235
## shrub_cover-Meleagris_gallopavo -0.4500 0.4102 -1.2634 -0.4461 0.3488 1.0371
## shrub_cover-Sciurus_carolinensis 1.0896 0.4263 0.2600 1.0863 1.9433 1.0014
## veg_height-Canis_latrans -0.5914 0.1812 -0.9522 -0.5888 -0.2518 1.0035
## veg_height-Procyon_lotor 0.3538 0.1259 0.0947 0.3540 0.5979 1.0086
## veg_height-Dasypus_novemcinctus 0.2812 0.1415 0.0119 0.2777 0.5599 1.0139
## veg_height-Lynx_rufus 0.0816 0.2526 -0.4303 0.0836 0.5643 1.0082
## veg_height-Didelphis_virginiana 0.4968 0.2536 0.0335 0.4871 0.9991 1.0151
## veg_height-Sylvilagus_floridanus 0.1776 0.2409 -0.2829 0.1735 0.6566 1.0263
## veg_height-Meleagris_gallopavo -0.2289 0.3645 -0.9603 -0.2268 0.5040 1.0057
## veg_height-Sciurus_carolinensis 0.1568 0.2280 -0.2715 0.1527 0.6148 1.0046
## week-Canis_latrans 0.4548 0.2450 -0.0123 0.4485 0.9426 1.0022
## week-Procyon_lotor 0.1614 0.2013 -0.2419 0.1643 0.5558 1.0023
## week-Dasypus_novemcinctus 0.0608 0.2169 -0.3538 0.0595 0.4902 1.0010
## week-Lynx_rufus 0.2756 0.2943 -0.2971 0.2733 0.8720 1.0000
## week-Didelphis_virginiana 0.0485 0.3193 -0.6369 0.0598 0.6437 1.0011
## week-Sylvilagus_floridanus 0.0506 0.2956 -0.5656 0.0661 0.5979 1.0079
## week-Meleagris_gallopavo -0.0845 0.3642 -0.9090 -0.0564 0.5844 1.0028
## week-Sciurus_carolinensis 0.5386 0.3341 -0.0614 0.5140 1.2848 1.0048
## I(week^2)-Canis_latrans -0.1968 0.1051 -0.4177 -0.1939 0.0086 1.0022
## I(week^2)-Procyon_lotor -0.1123 0.0890 -0.2853 -0.1120 0.0675 1.0009
## I(week^2)-Dasypus_novemcinctus -0.1557 0.1029 -0.3633 -0.1522 0.0401 1.0040
## I(week^2)-Lynx_rufus -0.2107 0.1486 -0.5237 -0.2039 0.0686 1.0064
## I(week^2)-Didelphis_virginiana -0.3796 0.2036 -0.8414 -0.3597 -0.0341 1.0340
## I(week^2)-Sylvilagus_floridanus -0.1658 0.1472 -0.4714 -0.1598 0.0987 1.0031
## I(week^2)-Meleagris_gallopavo -0.3954 0.2548 -0.9695 -0.3596 -0.0003 1.0203
## I(week^2)-Sciurus_carolinensis -0.1999 0.1407 -0.4929 -0.1915 0.0534 0.9997
## ESS
## (Intercept)-Canis_latrans 862
## (Intercept)-Procyon_lotor 1250
## (Intercept)-Dasypus_novemcinctus 1066
## (Intercept)-Lynx_rufus 170
## (Intercept)-Didelphis_virginiana 529
## (Intercept)-Sylvilagus_floridanus 642
## (Intercept)-Meleagris_gallopavo 279
## (Intercept)-Sciurus_carolinensis 443
## shrub_cover-Canis_latrans 640
## shrub_cover-Procyon_lotor 1113
## shrub_cover-Dasypus_novemcinctus 591
## shrub_cover-Lynx_rufus 302
## shrub_cover-Didelphis_virginiana 481
## shrub_cover-Sylvilagus_floridanus 463
## shrub_cover-Meleagris_gallopavo 339
## shrub_cover-Sciurus_carolinensis 322
## veg_height-Canis_latrans 825
## veg_height-Procyon_lotor 1420
## veg_height-Dasypus_novemcinctus 1800
## veg_height-Lynx_rufus 645
## veg_height-Didelphis_virginiana 905
## veg_height-Sylvilagus_floridanus 824
## veg_height-Meleagris_gallopavo 402
## veg_height-Sciurus_carolinensis 806
## week-Canis_latrans 1229
## week-Procyon_lotor 1457
## week-Dasypus_novemcinctus 1575
## week-Lynx_rufus 757
## week-Didelphis_virginiana 939
## week-Sylvilagus_floridanus 1000
## week-Meleagris_gallopavo 539
## week-Sciurus_carolinensis 939
## I(week^2)-Canis_latrans 1333
## I(week^2)-Procyon_lotor 1344
## I(week^2)-Dasypus_novemcinctus 1705
## I(week^2)-Lynx_rufus 650
## I(week^2)-Didelphis_virginiana 547
## I(week^2)-Sylvilagus_floridanus 824
## I(week^2)-Meleagris_gallopavo 227
## I(week^2)-Sciurus_carolinensis 1191
#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): 0.7553
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0987 0.4607 -0.9944 -0.1043 0.8257 1.1353 277
## Avg_Cogongrass_Cover -0.0097 0.3924 -0.7742 -0.0045 0.7938 1.0018 632
## total_shrub_cover -0.9537 0.5040 -2.0256 -0.9229 -0.0681 1.0106 397
## avg_veg_height 0.1727 0.4096 -0.6366 0.1701 0.9828 1.0463 368
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6064 0.8228 0.0476 0.3633 2.6028 1.0134 704
## Avg_Cogongrass_Cover 0.5012 0.7050 0.0443 0.2836 2.3118 1.0266 813
## total_shrub_cover 1.1235 1.6565 0.0808 0.6560 4.9637 1.0470 350
## avg_veg_height 0.3856 0.5608 0.0387 0.2230 1.6747 1.0310 584
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2649 1.4831 0.0633 0.7853 5.1259 1.0704 157
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6504 0.3027 -3.2418 -2.6495 -2.0452 1.0152 622
## shrub_cover 0.5348 0.3459 -0.1681 0.5322 1.2263 1.0056 993
## veg_height 0.0706 0.2152 -0.3661 0.0735 0.4982 1.0089 1178
## week 0.1983 0.2073 -0.2269 0.2018 0.6125 1.0092 894
## I(week^2) -0.2235 0.1201 -0.4746 -0.2213 0.0066 1.0122 1084
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5865 0.5508 0.1175 0.4322 1.9470 1.0037 1290
## shrub_cover 0.8370 0.7495 0.1513 0.6342 2.7724 1.0395 436
## veg_height 0.2797 0.2976 0.0593 0.2091 0.8925 1.0167 1834
## week 0.1937 0.1915 0.0347 0.1347 0.7346 1.0014 1200
## I(week^2) 0.0815 0.0799 0.0190 0.0588 0.2854 1.0113 635
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2508 0.5991 -0.8709 0.2281
## (Intercept)-Procyon_lotor 0.4024 0.6339 -0.7896 0.3892
## (Intercept)-Dasypus_novemcinctus -0.2210 0.6280 -1.4170 -0.2460
## (Intercept)-Lynx_rufus -0.0725 0.6551 -1.3341 -0.0942
## (Intercept)-Didelphis_virginiana -0.5012 0.6665 -1.9366 -0.4838
## (Intercept)-Sylvilagus_floridanus 0.0387 0.6822 -1.2009 0.0119
## (Intercept)-Meleagris_gallopavo -0.2895 0.7143 -1.7314 -0.2719
## (Intercept)-Sciurus_carolinensis -0.4581 0.6848 -1.8738 -0.4437
## Avg_Cogongrass_Cover-Canis_latrans 0.3412 0.5305 -0.5692 0.2903
## Avg_Cogongrass_Cover-Procyon_lotor -0.1075 0.4933 -1.1210 -0.0947
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1341 0.4795 -0.7798 0.1306
## Avg_Cogongrass_Cover-Lynx_rufus 0.3680 0.5902 -0.6262 0.3023
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1469 0.5236 -0.7925 0.1187
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4619 0.6021 -1.7823 -0.4027
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4606 0.6856 -2.0572 -0.3750
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0073 0.5319 -1.0920 0.0035
## total_shrub_cover-Canis_latrans 0.1627 0.6715 -1.0107 0.1212
## total_shrub_cover-Procyon_lotor -1.2797 0.6164 -2.7065 -1.2050
## total_shrub_cover-Dasypus_novemcinctus -0.5947 0.7117 -2.2896 -0.4793
## total_shrub_cover-Lynx_rufus -1.3938 0.8577 -3.4010 -1.2876
## total_shrub_cover-Didelphis_virginiana -0.9978 0.6913 -2.5850 -0.9227
## total_shrub_cover-Sylvilagus_floridanus -1.3562 0.8846 -3.3732 -1.2545
## total_shrub_cover-Meleagris_gallopavo -1.5470 0.8244 -3.4093 -1.4562
## total_shrub_cover-Sciurus_carolinensis -1.0818 0.7991 -2.9776 -0.9979
## avg_veg_height-Canis_latrans 0.1603 0.5014 -0.8181 0.1561
## avg_veg_height-Procyon_lotor 0.1857 0.4943 -0.8279 0.1918
## avg_veg_height-Dasypus_novemcinctus 0.3995 0.4933 -0.5140 0.3809
## avg_veg_height-Lynx_rufus 0.0963 0.6194 -1.1447 0.1057
## avg_veg_height-Didelphis_virginiana 0.0372 0.5456 -1.0782 0.0495
## avg_veg_height-Sylvilagus_floridanus 0.1016 0.5565 -1.0523 0.0999
## avg_veg_height-Meleagris_gallopavo -0.0952 0.7334 -1.6800 -0.0620
## avg_veg_height-Sciurus_carolinensis 0.5558 0.5818 -0.4628 0.5075
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5081 1.0496 404
## (Intercept)-Procyon_lotor 1.6899 1.0746 477
## (Intercept)-Dasypus_novemcinctus 1.1036 1.0304 437
## (Intercept)-Lynx_rufus 1.2679 1.0466 483
## (Intercept)-Didelphis_virginiana 0.7586 1.0385 419
## (Intercept)-Sylvilagus_floridanus 1.4620 1.0713 340
## (Intercept)-Meleagris_gallopavo 1.0338 1.0772 364
## (Intercept)-Sciurus_carolinensis 0.8779 1.0696 412
## Avg_Cogongrass_Cover-Canis_latrans 1.4861 1.0066 962
## Avg_Cogongrass_Cover-Procyon_lotor 0.8327 1.0157 918
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0889 1.0036 1127
## Avg_Cogongrass_Cover-Lynx_rufus 1.7023 1.0241 840
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2292 1.0034 1080
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5626 1.0005 716
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7702 1.0152 582
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0257 1.0046 981
## total_shrub_cover-Canis_latrans 1.6550 1.0030 421
## total_shrub_cover-Procyon_lotor -0.2976 1.0150 499
## total_shrub_cover-Dasypus_novemcinctus 0.5449 1.0214 299
## total_shrub_cover-Lynx_rufus -0.0027 1.0038 351
## total_shrub_cover-Didelphis_virginiana 0.1763 1.0153 415
## total_shrub_cover-Sylvilagus_floridanus 0.0813 1.0605 284
## total_shrub_cover-Meleagris_gallopavo -0.1842 1.0080 385
## total_shrub_cover-Sciurus_carolinensis 0.2244 1.0374 376
## avg_veg_height-Canis_latrans 1.1248 1.0226 697
## avg_veg_height-Procyon_lotor 1.1113 1.0229 704
## avg_veg_height-Dasypus_novemcinctus 1.4487 1.0207 685
## avg_veg_height-Lynx_rufus 1.3227 1.0230 493
## avg_veg_height-Didelphis_virginiana 1.0712 1.0294 612
## avg_veg_height-Sylvilagus_floridanus 1.2244 1.0281 570
## avg_veg_height-Meleagris_gallopavo 1.2434 1.0379 358
## avg_veg_height-Sciurus_carolinensis 1.8070 1.0165 677
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6312 0.2069 -3.0472 -2.6278 -2.2486 1.0030
## (Intercept)-Procyon_lotor -2.2116 0.1568 -2.5267 -2.2088 -1.9078 1.0021
## (Intercept)-Dasypus_novemcinctus -1.7583 0.2026 -2.1586 -1.7542 -1.3781 1.0026
## (Intercept)-Lynx_rufus -3.2821 0.3342 -3.9762 -3.2730 -2.6703 1.0248
## (Intercept)-Didelphis_virginiana -2.6046 0.3246 -3.2637 -2.5993 -1.9770 1.0079
## (Intercept)-Sylvilagus_floridanus -3.1336 0.2827 -3.7026 -3.1277 -2.6067 1.0290
## (Intercept)-Meleagris_gallopavo -3.3970 0.5265 -4.5165 -3.3820 -2.4487 1.0429
## (Intercept)-Sciurus_carolinensis -2.7468 0.3414 -3.4378 -2.7406 -2.1304 1.0154
## shrub_cover-Canis_latrans -0.2776 0.2460 -0.7401 -0.2819 0.2249 1.0260
## shrub_cover-Procyon_lotor 0.3293 0.1604 0.0000 0.3306 0.6324 1.0054
## shrub_cover-Dasypus_novemcinctus 1.1600 0.3709 0.4528 1.1552 1.8629 1.0263
## shrub_cover-Lynx_rufus 0.1853 0.3758 -0.6054 0.2093 0.8670 1.0201
## shrub_cover-Didelphis_virginiana 1.3313 0.4279 0.5350 1.3161 2.2520 1.0269
## shrub_cover-Sylvilagus_floridanus 0.7773 0.4372 -0.2076 0.7924 1.5996 1.0546
## shrub_cover-Meleagris_gallopavo -0.3155 0.4855 -1.3409 -0.3133 0.6102 1.0362
## shrub_cover-Sciurus_carolinensis 1.2912 0.4224 0.4601 1.2876 2.1238 1.0408
## veg_height-Canis_latrans -0.6068 0.1965 -1.0100 -0.6012 -0.2334 1.0208
## veg_height-Procyon_lotor 0.3492 0.1227 0.1077 0.3510 0.5859 1.0073
## veg_height-Dasypus_novemcinctus 0.2846 0.1442 0.0009 0.2827 0.5713 0.9999
## veg_height-Lynx_rufus 0.0460 0.2505 -0.4439 0.0486 0.5177 1.0074
## veg_height-Didelphis_virginiana 0.4595 0.2624 -0.0250 0.4473 1.0080 1.0061
## veg_height-Sylvilagus_floridanus 0.0698 0.2504 -0.4026 0.0642 0.5583 1.0190
## veg_height-Meleagris_gallopavo -0.1317 0.4807 -1.0504 -0.1452 0.8435 1.0279
## veg_height-Sciurus_carolinensis 0.1244 0.2309 -0.3005 0.1168 0.6044 1.0014
## week-Canis_latrans 0.4652 0.2438 0.0038 0.4599 0.9514 1.0089
## week-Procyon_lotor 0.1639 0.1952 -0.2280 0.1657 0.5324 1.0065
## week-Dasypus_novemcinctus 0.0660 0.2190 -0.3541 0.0668 0.4907 1.0037
## week-Lynx_rufus 0.2809 0.3057 -0.3189 0.2772 0.8898 1.0087
## week-Didelphis_virginiana 0.0714 0.3198 -0.5532 0.0796 0.6809 1.0045
## week-Sylvilagus_floridanus 0.0798 0.2939 -0.4983 0.0791 0.6564 1.0054
## week-Meleagris_gallopavo -0.0776 0.3529 -0.8537 -0.0526 0.5755 1.0109
## week-Sciurus_carolinensis 0.5497 0.3219 -0.0606 0.5340 1.2439 1.0052
## I(week^2)-Canis_latrans -0.1987 0.1036 -0.4127 -0.1951 -0.0066 1.0139
## I(week^2)-Procyon_lotor -0.1139 0.0878 -0.2846 -0.1125 0.0619 1.0067
## I(week^2)-Dasypus_novemcinctus -0.1521 0.1034 -0.3600 -0.1521 0.0477 1.0137
## I(week^2)-Lynx_rufus -0.2102 0.1448 -0.5028 -0.2088 0.0685 1.0060
## I(week^2)-Didelphis_virginiana -0.3799 0.2215 -0.8796 -0.3540 -0.0282 1.0027
## I(week^2)-Sylvilagus_floridanus -0.1594 0.1459 -0.4516 -0.1579 0.1130 1.0357
## I(week^2)-Meleagris_gallopavo -0.3725 0.2280 -0.8919 -0.3499 0.0166 1.0157
## I(week^2)-Sciurus_carolinensis -0.2071 0.1374 -0.4896 -0.2004 0.0537 1.0083
## ESS
## (Intercept)-Canis_latrans 581
## (Intercept)-Procyon_lotor 1532
## (Intercept)-Dasypus_novemcinctus 710
## (Intercept)-Lynx_rufus 546
## (Intercept)-Didelphis_virginiana 499
## (Intercept)-Sylvilagus_floridanus 611
## (Intercept)-Meleagris_gallopavo 274
## (Intercept)-Sciurus_carolinensis 345
## shrub_cover-Canis_latrans 614
## shrub_cover-Procyon_lotor 1430
## shrub_cover-Dasypus_novemcinctus 356
## shrub_cover-Lynx_rufus 482
## shrub_cover-Didelphis_virginiana 277
## shrub_cover-Sylvilagus_floridanus 251
## shrub_cover-Meleagris_gallopavo 362
## shrub_cover-Sciurus_carolinensis 352
## veg_height-Canis_latrans 490
## veg_height-Procyon_lotor 1607
## veg_height-Dasypus_novemcinctus 1711
## veg_height-Lynx_rufus 697
## veg_height-Didelphis_virginiana 756
## veg_height-Sylvilagus_floridanus 612
## veg_height-Meleagris_gallopavo 332
## veg_height-Sciurus_carolinensis 620
## week-Canis_latrans 1243
## week-Procyon_lotor 1661
## week-Dasypus_novemcinctus 1825
## week-Lynx_rufus 949
## week-Didelphis_virginiana 997
## week-Sylvilagus_floridanus 788
## week-Meleagris_gallopavo 565
## week-Sciurus_carolinensis 1166
## I(week^2)-Canis_latrans 1017
## I(week^2)-Procyon_lotor 1447
## I(week^2)-Dasypus_novemcinctus 1593
## I(week^2)-Lynx_rufus 794
## I(week^2)-Didelphis_virginiana 321
## I(week^2)-Sylvilagus_floridanus 637
## I(week^2)-Meleagris_gallopavo 289
## I(week^2)-Sciurus_carolinensis 855
#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): 0.714
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3976 0.4903 -1.3531 -0.4091 0.6334 1.0006 520
## Tree_Density -0.8061 0.4638 -1.8090 -0.7784 0.0568 1.0113 754
## Avg_Canopy_Cover 1.1422 0.4582 0.3081 1.1227 2.1289 1.0117 612
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3186 2.1600 0.0979 0.7838 5.5139 1.0609 417
## Tree_Density 1.0393 1.9912 0.0538 0.5161 4.8885 1.2033 568
## Avg_Canopy_Cover 1.2226 1.5053 0.0909 0.7800 5.1781 1.0431 612
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6473 0.7394 0.0475 0.3829 2.7205 1.0545 144
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6335 0.3323 -3.2588 -2.6443 -1.9021 1.0053 2019
## shrub_cover 0.2975 0.3295 -0.3512 0.2924 0.9526 1.0030 1881
## veg_height 0.0936 0.1970 -0.3041 0.0953 0.4791 1.0000 1973
## week 0.1908 0.2122 -0.2215 0.1935 0.6063 1.0077 1087
## I(week^2) -0.2173 0.1201 -0.4657 -0.2120 0.0025 1.0043 1199
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7517 0.6456 0.1781 0.5813 2.3941 1.0071 1189
## shrub_cover 0.7327 0.6285 0.1511 0.5666 2.3727 1.0078 1412
## veg_height 0.2728 0.2768 0.0605 0.2045 0.9248 1.0043 1451
## week 0.2110 0.2534 0.0367 0.1465 0.8076 1.0244 1280
## I(week^2) 0.0812 0.0810 0.0193 0.0592 0.2957 1.0087 632
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1155 0.6067 -1.0377 0.0975 1.3789
## (Intercept)-Procyon_lotor 0.4054 0.6321 -0.8708 0.4059 1.6492
## (Intercept)-Dasypus_novemcinctus -0.8199 0.5718 -2.0155 -0.8036 0.2439
## (Intercept)-Lynx_rufus 0.2438 1.1221 -1.3464 0.0539 3.2840
## (Intercept)-Didelphis_virginiana -1.2646 0.6768 -2.7286 -1.2302 -0.0674
## (Intercept)-Sylvilagus_floridanus -0.5928 0.6205 -1.8215 -0.5890 0.6094
## (Intercept)-Meleagris_gallopavo -0.1495 0.9156 -1.6208 -0.2625 1.9265
## (Intercept)-Sciurus_carolinensis -1.3387 0.7026 -2.9075 -1.2915 -0.0857
## Tree_Density-Canis_latrans -0.9838 0.6144 -2.4192 -0.9089 0.0282
## Tree_Density-Procyon_lotor -0.4927 0.4327 -1.3476 -0.4915 0.3613
## Tree_Density-Dasypus_novemcinctus -1.4550 0.9254 -3.7489 -1.2460 -0.2069
## Tree_Density-Lynx_rufus 0.1730 0.7711 -1.0345 0.0685 2.1496
## Tree_Density-Didelphis_virginiana -1.0124 0.7902 -3.0315 -0.8881 0.2124
## Tree_Density-Sylvilagus_floridanus -1.1547 0.8133 -3.1428 -1.0310 0.1097
## Tree_Density-Meleagris_gallopavo -1.0460 0.8127 -3.0556 -0.9299 0.2944
## Tree_Density-Sciurus_carolinensis -0.9019 0.7831 -2.7234 -0.8190 0.3878
## Avg_Canopy_Cover-Canis_latrans -0.0633 0.4931 -1.0140 -0.0746 0.9128
## Avg_Canopy_Cover-Procyon_lotor 1.0526 0.5052 0.1529 1.0190 2.1391
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1270 0.4772 0.2613 1.0933 2.1243
## Avg_Canopy_Cover-Lynx_rufus 0.7723 0.7355 -0.5340 0.7307 2.3627
## Avg_Canopy_Cover-Didelphis_virginiana 1.5885 0.7225 0.4732 1.4750 3.3404
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1815 1.0246 0.7299 1.9898 4.7372
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5393 0.7771 0.2996 1.4472 3.2787
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5126 0.6609 0.4230 1.4234 3.0460
## Rhat ESS
## (Intercept)-Canis_latrans 1.0060 625
## (Intercept)-Procyon_lotor 1.0069 472
## (Intercept)-Dasypus_novemcinctus 1.0155 975
## (Intercept)-Lynx_rufus 1.0013 187
## (Intercept)-Didelphis_virginiana 1.0010 754
## (Intercept)-Sylvilagus_floridanus 1.0021 883
## (Intercept)-Meleagris_gallopavo 1.0490 205
## (Intercept)-Sciurus_carolinensis 1.0077 591
## Tree_Density-Canis_latrans 1.0151 873
## Tree_Density-Procyon_lotor 1.0002 1908
## Tree_Density-Dasypus_novemcinctus 1.0753 443
## Tree_Density-Lynx_rufus 1.0289 461
## Tree_Density-Didelphis_virginiana 1.0601 721
## Tree_Density-Sylvilagus_floridanus 1.0422 560
## Tree_Density-Meleagris_gallopavo 1.0129 727
## Tree_Density-Sciurus_carolinensis 1.0061 891
## Avg_Canopy_Cover-Canis_latrans 1.0043 1100
## Avg_Canopy_Cover-Procyon_lotor 1.0069 1539
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0051 1754
## Avg_Canopy_Cover-Lynx_rufus 1.0003 560
## Avg_Canopy_Cover-Didelphis_virginiana 1.0501 288
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0461 431
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0132 579
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0118 919
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6036 0.2023 -3.0096 -2.5969 -2.2188 1.0032
## (Intercept)-Procyon_lotor -2.2122 0.1570 -2.5243 -2.2081 -1.9203 1.0129
## (Intercept)-Dasypus_novemcinctus -1.6663 0.1862 -2.0348 -1.6636 -1.3086 1.0009
## (Intercept)-Lynx_rufus -3.5311 0.3757 -4.2908 -3.5315 -2.8144 1.0597
## (Intercept)-Didelphis_virginiana -2.4753 0.2992 -3.0947 -2.4721 -1.8888 1.0018
## (Intercept)-Sylvilagus_floridanus -3.0169 0.2783 -3.5833 -3.0030 -2.4859 1.0016
## (Intercept)-Meleagris_gallopavo -3.6820 0.4545 -4.6022 -3.6609 -2.8527 1.0221
## (Intercept)-Sciurus_carolinensis -2.6103 0.3384 -3.2968 -2.6010 -1.9667 1.0088
## shrub_cover-Canis_latrans -0.2896 0.2254 -0.7323 -0.2912 0.1547 1.0080
## shrub_cover-Procyon_lotor 0.2576 0.1650 -0.0873 0.2612 0.5720 1.0020
## shrub_cover-Dasypus_novemcinctus 0.9167 0.3032 0.3332 0.9188 1.5228 1.0005
## shrub_cover-Lynx_rufus -0.2644 0.3540 -0.9815 -0.2582 0.3989 1.0597
## shrub_cover-Didelphis_virginiana 1.0547 0.3565 0.3855 1.0371 1.7827 1.0040
## shrub_cover-Sylvilagus_floridanus 0.4323 0.3965 -0.3383 0.4308 1.2302 1.0082
## shrub_cover-Meleagris_gallopavo -0.6451 0.3895 -1.4357 -0.6433 0.1146 1.0142
## shrub_cover-Sciurus_carolinensis 0.9759 0.4182 0.1904 0.9662 1.8160 1.0000
## veg_height-Canis_latrans -0.5857 0.1868 -0.9698 -0.5779 -0.2352 1.0192
## veg_height-Procyon_lotor 0.3511 0.1234 0.1078 0.3522 0.5955 1.0020
## veg_height-Dasypus_novemcinctus 0.2776 0.1343 0.0236 0.2720 0.5424 1.0094
## veg_height-Lynx_rufus 0.0889 0.2443 -0.3913 0.0955 0.5559 1.0066
## veg_height-Didelphis_virginiana 0.5319 0.2431 0.0641 0.5242 1.0260 1.0175
## veg_height-Sylvilagus_floridanus 0.1790 0.2441 -0.2931 0.1825 0.6563 1.0351
## veg_height-Meleagris_gallopavo -0.1758 0.3683 -0.9275 -0.1608 0.5336 1.0085
## veg_height-Sciurus_carolinensis 0.1399 0.2262 -0.2920 0.1420 0.5795 1.0039
## week-Canis_latrans 0.4494 0.2468 -0.0134 0.4377 0.9729 1.0044
## week-Procyon_lotor 0.1628 0.2013 -0.2240 0.1646 0.5618 1.0015
## week-Dasypus_novemcinctus 0.0633 0.2140 -0.3615 0.0676 0.4790 1.0090
## week-Lynx_rufus 0.2873 0.3088 -0.2985 0.2718 0.9312 1.0006
## week-Didelphis_virginiana 0.0481 0.3156 -0.5969 0.0533 0.6382 1.0006
## week-Sylvilagus_floridanus 0.0452 0.3008 -0.5691 0.0579 0.6102 1.0187
## week-Meleagris_gallopavo -0.0881 0.3469 -0.8292 -0.0623 0.5503 1.0618
## week-Sciurus_carolinensis 0.5479 0.3428 -0.0696 0.5181 1.2770 1.0162
## I(week^2)-Canis_latrans -0.1919 0.1031 -0.4014 -0.1904 0.0051 1.0034
## I(week^2)-Procyon_lotor -0.1114 0.0881 -0.2872 -0.1095 0.0583 1.0119
## I(week^2)-Dasypus_novemcinctus -0.1547 0.1010 -0.3474 -0.1515 0.0403 1.0012
## I(week^2)-Lynx_rufus -0.2038 0.1493 -0.5029 -0.1970 0.0801 1.0051
## I(week^2)-Didelphis_virginiana -0.3625 0.2000 -0.8029 -0.3446 -0.0267 1.0037
## I(week^2)-Sylvilagus_floridanus -0.1655 0.1519 -0.4840 -0.1586 0.1173 1.0008
## I(week^2)-Meleagris_gallopavo -0.3638 0.2337 -0.8930 -0.3321 0.0007 1.0082
## I(week^2)-Sciurus_carolinensis -0.1961 0.1422 -0.4928 -0.1905 0.0662 1.0046
## ESS
## (Intercept)-Canis_latrans 605
## (Intercept)-Procyon_lotor 1487
## (Intercept)-Dasypus_novemcinctus 1483
## (Intercept)-Lynx_rufus 238
## (Intercept)-Didelphis_virginiana 737
## (Intercept)-Sylvilagus_floridanus 861
## (Intercept)-Meleagris_gallopavo 274
## (Intercept)-Sciurus_carolinensis 620
## shrub_cover-Canis_latrans 803
## shrub_cover-Procyon_lotor 1061
## shrub_cover-Dasypus_novemcinctus 1147
## shrub_cover-Lynx_rufus 379
## shrub_cover-Didelphis_virginiana 704
## shrub_cover-Sylvilagus_floridanus 652
## shrub_cover-Meleagris_gallopavo 297
## shrub_cover-Sciurus_carolinensis 563
## veg_height-Canis_latrans 640
## veg_height-Procyon_lotor 1702
## veg_height-Dasypus_novemcinctus 1922
## veg_height-Lynx_rufus 763
## veg_height-Didelphis_virginiana 1056
## veg_height-Sylvilagus_floridanus 1013
## veg_height-Meleagris_gallopavo 579
## veg_height-Sciurus_carolinensis 911
## week-Canis_latrans 1139
## week-Procyon_lotor 1583
## week-Dasypus_novemcinctus 2078
## week-Lynx_rufus 923
## week-Didelphis_virginiana 1083
## week-Sylvilagus_floridanus 1024
## week-Meleagris_gallopavo 647
## week-Sciurus_carolinensis 1211
## I(week^2)-Canis_latrans 1335
## I(week^2)-Procyon_lotor 1467
## I(week^2)-Dasypus_novemcinctus 1819
## I(week^2)-Lynx_rufus 584
## I(week^2)-Didelphis_virginiana 548
## I(week^2)-Sylvilagus_floridanus 787
## I(week^2)-Meleagris_gallopavo 211
## I(week^2)-Sciurus_carolinensis 1355
#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): 0.7297
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1978 0.4450 -1.0670 -0.2115 0.7326 1.0552 403
## Cogon_Patch_Size 0.0364 0.4060 -0.7730 0.0508 0.7641 1.0052 863
## Avg_Cogongrass_Cover 0.0878 0.3542 -0.6255 0.0900 0.7711 1.0016 718
## total_shrub_cover -0.9314 0.4693 -1.9863 -0.8997 -0.0572 1.0265 390
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6726 0.8984 0.0478 0.3794 3.1913 1.0005 662
## Cogon_Patch_Size 0.7389 1.1159 0.0579 0.4223 3.4931 1.0369 943
## Avg_Cogongrass_Cover 0.4819 0.8369 0.0417 0.2763 2.1973 1.0924 856
## total_shrub_cover 0.9335 1.3598 0.0658 0.5252 4.4789 1.0138 496
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2269 1.1466 0.0966 0.8935 4.2834 1.1056 198
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6154 0.2824 -3.1817 -2.6218 -2.0503 1.0059 1129
## shrub_cover 0.5278 0.3098 -0.1253 0.5295 1.1331 1.0064 1188
## veg_height 0.0861 0.1985 -0.3055 0.0837 0.4833 1.0163 1152
## week 0.1904 0.2135 -0.2400 0.1881 0.6121 1.0017 1120
## I(week^2) -0.2203 0.1213 -0.4710 -0.2125 0.0023 1.0354 404
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5548 0.4885 0.1198 0.4174 1.8517 1.0267 1297
## shrub_cover 0.6771 0.5762 0.1409 0.5249 2.1603 1.0391 891
## veg_height 0.2427 0.2269 0.0546 0.1817 0.7943 1.0062 1426
## week 0.2035 0.2226 0.0343 0.1402 0.7311 1.0052 978
## I(week^2) 0.0777 0.0749 0.0192 0.0576 0.2673 1.0898 586
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2121 0.6089 -0.8775 0.1765
## (Intercept)-Procyon_lotor 0.3305 0.6374 -0.8819 0.3047
## (Intercept)-Dasypus_novemcinctus -0.3162 0.5643 -1.4359 -0.3181
## (Intercept)-Lynx_rufus -0.1762 0.6553 -1.4687 -0.1966
## (Intercept)-Didelphis_virginiana -0.6386 0.6378 -1.9738 -0.6050
## (Intercept)-Sylvilagus_floridanus -0.0836 0.6528 -1.3201 -0.1125
## (Intercept)-Meleagris_gallopavo -0.3796 0.6710 -1.7734 -0.3930
## (Intercept)-Sciurus_carolinensis -0.6317 0.6719 -1.9848 -0.6160
## Cogon_Patch_Size-Canis_latrans 0.6862 0.6469 -0.3260 0.5985
## Cogon_Patch_Size-Procyon_lotor -0.1128 0.4602 -1.0555 -0.0976
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0577 0.4459 -0.8238 0.0507
## Cogon_Patch_Size-Lynx_rufus -0.0033 0.7200 -1.3465 -0.0410
## Cogon_Patch_Size-Didelphis_virginiana 0.5994 0.4960 -0.2732 0.5708
## Cogon_Patch_Size-Sylvilagus_floridanus -0.5715 0.7627 -2.4491 -0.4505
## Cogon_Patch_Size-Meleagris_gallopavo 0.1011 0.6314 -1.1462 0.0990
## Cogon_Patch_Size-Sciurus_carolinensis -0.4631 0.6752 -2.0491 -0.3704
## Avg_Cogongrass_Cover-Canis_latrans 0.2768 0.4668 -0.5544 0.2449
## Avg_Cogongrass_Cover-Procyon_lotor 0.0453 0.4557 -0.8588 0.0463
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2931 0.4330 -0.5059 0.2841
## Avg_Cogongrass_Cover-Lynx_rufus 0.4468 0.5284 -0.4660 0.3982
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0575 0.4871 -0.8858 0.0533
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2760 0.5845 -1.5252 -0.2376
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4565 0.7392 -2.0535 -0.3925
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3071 0.4818 -0.6058 0.2887
## total_shrub_cover-Canis_latrans -0.0200 0.5951 -1.0666 -0.0646
## total_shrub_cover-Procyon_lotor -1.2334 0.5743 -2.5595 -1.1824
## total_shrub_cover-Dasypus_novemcinctus -0.5639 0.6124 -1.9268 -0.5093
## total_shrub_cover-Lynx_rufus -1.3755 0.8405 -3.3571 -1.2732
## total_shrub_cover-Didelphis_virginiana -0.9333 0.6248 -2.3906 -0.8754
## total_shrub_cover-Sylvilagus_floridanus -1.2688 0.8590 -3.2107 -1.1381
## total_shrub_cover-Meleagris_gallopavo -1.5073 0.7753 -3.3275 -1.3991
## total_shrub_cover-Sciurus_carolinensis -0.9252 0.7483 -2.6467 -0.8517
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5506 1.0543 614
## (Intercept)-Procyon_lotor 1.6517 1.0452 539
## (Intercept)-Dasypus_novemcinctus 0.8678 1.0184 448
## (Intercept)-Lynx_rufus 1.1408 1.0203 489
## (Intercept)-Didelphis_virginiana 0.5554 1.0679 453
## (Intercept)-Sylvilagus_floridanus 1.2910 1.0147 401
## (Intercept)-Meleagris_gallopavo 0.9688 1.0429 557
## (Intercept)-Sciurus_carolinensis 0.7158 1.0311 500
## Cogon_Patch_Size-Canis_latrans 2.1707 1.0262 901
## Cogon_Patch_Size-Procyon_lotor 0.7768 1.0042 1005
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9126 1.0043 1366
## Cogon_Patch_Size-Lynx_rufus 1.5278 1.0081 567
## Cogon_Patch_Size-Didelphis_virginiana 1.6679 1.0261 1063
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5878 1.0117 785
## Cogon_Patch_Size-Meleagris_gallopavo 1.3651 1.0102 1015
## Cogon_Patch_Size-Sciurus_carolinensis 0.6258 1.0026 781
## Avg_Cogongrass_Cover-Canis_latrans 1.2580 1.0012 1011
## Avg_Cogongrass_Cover-Procyon_lotor 0.9679 1.0022 1036
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2331 1.0110 989
## Avg_Cogongrass_Cover-Lynx_rufus 1.6899 1.0050 1143
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0292 1.0074 942
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7867 1.0028 831
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8260 1.0385 551
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2854 1.0010 895
## total_shrub_cover-Canis_latrans 1.2936 1.0163 587
## total_shrub_cover-Procyon_lotor -0.2524 1.0080 574
## total_shrub_cover-Dasypus_novemcinctus 0.4864 1.0137 367
## total_shrub_cover-Lynx_rufus -0.0112 1.0215 406
## total_shrub_cover-Didelphis_virginiana 0.1498 1.0291 506
## total_shrub_cover-Sylvilagus_floridanus 0.0829 1.0379 324
## total_shrub_cover-Meleagris_gallopavo -0.3030 1.0249 464
## total_shrub_cover-Sciurus_carolinensis 0.3256 1.0287 339
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5811 0.1976 -2.9823 -2.5783 -2.2094 1.0246
## (Intercept)-Procyon_lotor -2.2141 0.1550 -2.5393 -2.2123 -1.9277 1.0012
## (Intercept)-Dasypus_novemcinctus -1.7307 0.1953 -2.1111 -1.7284 -1.3546 1.0045
## (Intercept)-Lynx_rufus -3.2709 0.3313 -3.9764 -3.2476 -2.6788 1.0152
## (Intercept)-Didelphis_virginiana -2.5180 0.2900 -3.1093 -2.5129 -1.9613 1.0369
## (Intercept)-Sylvilagus_floridanus -3.1329 0.2858 -3.7020 -3.1270 -2.5748 1.0008
## (Intercept)-Meleagris_gallopavo -3.3030 0.4925 -4.2771 -3.2935 -2.3738 1.0700
## (Intercept)-Sciurus_carolinensis -2.7176 0.3511 -3.4437 -2.7085 -2.0463 1.0166
## shrub_cover-Canis_latrans -0.2634 0.2273 -0.7163 -0.2652 0.1909 1.0004
## shrub_cover-Procyon_lotor 0.3231 0.1625 0.0001 0.3249 0.6326 1.0041
## shrub_cover-Dasypus_novemcinctus 1.0756 0.3468 0.4344 1.0665 1.7662 1.0076
## shrub_cover-Lynx_rufus 0.2376 0.3584 -0.5063 0.2455 0.8705 1.0141
## shrub_cover-Didelphis_virginiana 1.2056 0.3834 0.4964 1.1935 1.9823 1.0254
## shrub_cover-Sylvilagus_floridanus 0.8029 0.3992 -0.0105 0.8202 1.5798 1.0193
## shrub_cover-Meleagris_gallopavo -0.2184 0.4384 -1.0940 -0.2171 0.6162 1.0395
## shrub_cover-Sciurus_carolinensis 1.1978 0.4290 0.3413 1.1915 2.0427 1.0429
## veg_height-Canis_latrans -0.5724 0.1888 -0.9560 -0.5656 -0.2170 1.0523
## veg_height-Procyon_lotor 0.3509 0.1204 0.1189 0.3505 0.5950 1.0033
## veg_height-Dasypus_novemcinctus 0.2822 0.1423 0.0011 0.2812 0.5727 1.0012
## veg_height-Lynx_rufus 0.0813 0.2276 -0.3710 0.0857 0.5243 1.0158
## veg_height-Didelphis_virginiana 0.4420 0.2488 -0.0254 0.4344 0.9556 1.0118
## veg_height-Sylvilagus_floridanus 0.0736 0.2297 -0.3718 0.0717 0.5144 1.0019
## veg_height-Meleagris_gallopavo -0.0765 0.4250 -0.9385 -0.0814 0.7917 1.1011
## veg_height-Sciurus_carolinensis 0.1419 0.2333 -0.2946 0.1332 0.6024 1.0189
## week-Canis_latrans 0.4564 0.2496 -0.0028 0.4441 0.9654 1.0048
## week-Procyon_lotor 0.1543 0.1965 -0.2206 0.1499 0.5440 1.0005
## week-Dasypus_novemcinctus 0.0568 0.2108 -0.3576 0.0548 0.4657 1.0011
## week-Lynx_rufus 0.2866 0.3094 -0.2976 0.2716 0.9432 1.0068
## week-Didelphis_virginiana 0.0580 0.3123 -0.6074 0.0548 0.6668 1.0047
## week-Sylvilagus_floridanus 0.0564 0.2938 -0.5326 0.0625 0.6135 1.0108
## week-Meleagris_gallopavo -0.1021 0.3559 -0.9194 -0.0766 0.5290 1.0145
## week-Sciurus_carolinensis 0.5485 0.3300 -0.0420 0.5258 1.2511 1.0002
## I(week^2)-Canis_latrans -0.2008 0.1061 -0.4196 -0.1974 -0.0065 1.0012
## I(week^2)-Procyon_lotor -0.1107 0.0864 -0.2868 -0.1090 0.0541 1.0050
## I(week^2)-Dasypus_novemcinctus -0.1504 0.0989 -0.3487 -0.1488 0.0380 1.0045
## I(week^2)-Lynx_rufus -0.2040 0.1410 -0.4995 -0.1994 0.0595 1.0457
## I(week^2)-Didelphis_virginiana -0.3626 0.2151 -0.8766 -0.3340 -0.0243 1.0520
## I(week^2)-Sylvilagus_floridanus -0.1728 0.1510 -0.4841 -0.1641 0.1080 1.0277
## I(week^2)-Meleagris_gallopavo -0.3755 0.2478 -0.9628 -0.3399 0.0187 1.0478
## I(week^2)-Sciurus_carolinensis -0.1999 0.1377 -0.4793 -0.1934 0.0526 1.0021
## ESS
## (Intercept)-Canis_latrans 1011
## (Intercept)-Procyon_lotor 1379
## (Intercept)-Dasypus_novemcinctus 712
## (Intercept)-Lynx_rufus 381
## (Intercept)-Didelphis_virginiana 696
## (Intercept)-Sylvilagus_floridanus 534
## (Intercept)-Meleagris_gallopavo 341
## (Intercept)-Sciurus_carolinensis 361
## shrub_cover-Canis_latrans 722
## shrub_cover-Procyon_lotor 1554
## shrub_cover-Dasypus_novemcinctus 479
## shrub_cover-Lynx_rufus 516
## shrub_cover-Didelphis_virginiana 432
## shrub_cover-Sylvilagus_floridanus 321
## shrub_cover-Meleagris_gallopavo 483
## shrub_cover-Sciurus_carolinensis 299
## veg_height-Canis_latrans 752
## veg_height-Procyon_lotor 1691
## veg_height-Dasypus_novemcinctus 1183
## veg_height-Lynx_rufus 699
## veg_height-Didelphis_virginiana 903
## veg_height-Sylvilagus_floridanus 837
## veg_height-Meleagris_gallopavo 415
## veg_height-Sciurus_carolinensis 575
## week-Canis_latrans 1118
## week-Procyon_lotor 1739
## week-Dasypus_novemcinctus 2092
## week-Lynx_rufus 864
## week-Didelphis_virginiana 1117
## week-Sylvilagus_floridanus 1086
## week-Meleagris_gallopavo 697
## week-Sciurus_carolinensis 1050
## I(week^2)-Canis_latrans 1155
## I(week^2)-Procyon_lotor 1762
## I(week^2)-Dasypus_novemcinctus 1531
## I(week^2)-Lynx_rufus 695
## I(week^2)-Didelphis_virginiana 421
## I(week^2)-Sylvilagus_floridanus 635
## I(week^2)-Meleagris_gallopavo 231
## I(week^2)-Sciurus_carolinensis 1145
#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): 0.7062
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3684 0.3757 -1.1162 -0.3742 0.3888 1.0101 492
## Veg_shannon_index 0.3552 0.2904 -0.1995 0.3495 0.9289 1.0070 875
## Avg_Cogongrass_Cover 0.3074 0.3019 -0.2700 0.3079 0.8834 1.0019 847
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6009 0.7144 0.0500 0.3970 2.3813 1.0191 784
## Veg_shannon_index 0.3045 0.4089 0.0372 0.1873 1.2012 1.0291 1355
## Avg_Cogongrass_Cover 0.3834 0.5890 0.0386 0.2255 1.7472 1.0332 725
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7598 0.6838 0.0711 0.5767 2.6217 1.0094 258
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6065 0.3164 -3.2309 -2.6114 -1.9527 1.0011 2093
## shrub_cover 0.2550 0.3127 -0.3823 0.2528 0.9018 1.0031 1450
## veg_height 0.0705 0.1984 -0.3088 0.0706 0.4635 1.0087 1911
## week 0.1960 0.1995 -0.1816 0.1948 0.6111 1.0201 1167
## I(week^2) -0.2191 0.1194 -0.4658 -0.2151 0.0063 1.0059 972
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7488 0.7187 0.1687 0.5532 2.3538 1.0320 813
## shrub_cover 0.6977 0.6232 0.1351 0.5302 2.2766 1.0045 1001
## veg_height 0.2460 0.2121 0.0531 0.1885 0.7588 1.0021 1449
## week 0.2101 0.2407 0.0344 0.1423 0.7777 1.0369 1026
## I(week^2) 0.0784 0.0732 0.0200 0.0579 0.2618 1.0701 472
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0126 0.5140 -0.9512 0.0039
## (Intercept)-Procyon_lotor 0.1474 0.5387 -0.8211 0.1273
## (Intercept)-Dasypus_novemcinctus -0.5574 0.4849 -1.5671 -0.5438
## (Intercept)-Lynx_rufus -0.2425 0.6312 -1.3861 -0.2599
## (Intercept)-Didelphis_virginiana -0.8988 0.5401 -2.0575 -0.8529
## (Intercept)-Sylvilagus_floridanus -0.4349 0.5368 -1.5609 -0.4342
## (Intercept)-Meleagris_gallopavo -0.1422 0.6938 -1.3029 -0.2025
## (Intercept)-Sciurus_carolinensis -0.8918 0.5407 -2.0239 -0.8553
## Veg_shannon_index-Canis_latrans 0.6389 0.4043 -0.0693 0.6067
## Veg_shannon_index-Procyon_lotor 0.4629 0.3770 -0.2260 0.4486
## Veg_shannon_index-Dasypus_novemcinctus 0.1872 0.3396 -0.4990 0.1935
## Veg_shannon_index-Lynx_rufus 0.2200 0.4768 -0.7896 0.2199
## Veg_shannon_index-Didelphis_virginiana 0.4874 0.3943 -0.2564 0.4660
## Veg_shannon_index-Sylvilagus_floridanus 0.4260 0.4210 -0.3477 0.4020
## Veg_shannon_index-Meleagris_gallopavo 0.5191 0.5071 -0.3520 0.4780
## Veg_shannon_index-Sciurus_carolinensis -0.0226 0.4127 -0.9288 0.0033
## Avg_Cogongrass_Cover-Canis_latrans 0.5982 0.4145 -0.1339 0.5720
## Avg_Cogongrass_Cover-Procyon_lotor 0.3575 0.3794 -0.3848 0.3433
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4481 0.3584 -0.2305 0.4438
## Avg_Cogongrass_Cover-Lynx_rufus 0.5853 0.4454 -0.1851 0.5481
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4318 0.3803 -0.2985 0.4178
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1231 0.4768 -1.1752 -0.0916
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1177 0.6264 -1.5645 -0.0658
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4056 0.3765 -0.3229 0.4062
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0761 1.0091 674
## (Intercept)-Procyon_lotor 1.2537 1.0224 670
## (Intercept)-Dasypus_novemcinctus 0.3280 1.0021 1075
## (Intercept)-Lynx_rufus 1.1422 1.0380 494
## (Intercept)-Didelphis_virginiana 0.0575 1.0050 1034
## (Intercept)-Sylvilagus_floridanus 0.5931 1.0045 750
## (Intercept)-Meleagris_gallopavo 1.4374 1.0133 383
## (Intercept)-Sciurus_carolinensis 0.0984 1.0058 986
## Veg_shannon_index-Canis_latrans 1.4816 0.9996 1144
## Veg_shannon_index-Procyon_lotor 1.2556 1.0006 860
## Veg_shannon_index-Dasypus_novemcinctus 0.8605 1.0031 1513
## Veg_shannon_index-Lynx_rufus 1.1283 1.0007 904
## Veg_shannon_index-Didelphis_virginiana 1.3359 1.0085 1695
## Veg_shannon_index-Sylvilagus_floridanus 1.3569 1.0026 1047
## Veg_shannon_index-Meleagris_gallopavo 1.6568 1.0050 915
## Veg_shannon_index-Sciurus_carolinensis 0.7016 1.0010 1493
## Avg_Cogongrass_Cover-Canis_latrans 1.4921 1.0001 1583
## Avg_Cogongrass_Cover-Procyon_lotor 1.1540 1.0021 1671
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1823 1.0059 1840
## Avg_Cogongrass_Cover-Lynx_rufus 1.5795 1.0046 1191
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2308 1.0013 1477
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7202 1.0082 899
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9562 1.0046 537
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1596 1.0043 1811
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5589 0.1927 -2.9482 -2.5563 -2.2088 1.0024
## (Intercept)-Procyon_lotor -2.2068 0.1601 -2.5332 -2.2027 -1.9048 1.0075
## (Intercept)-Dasypus_novemcinctus -1.6441 0.1814 -2.0087 -1.6411 -1.3026 0.9999
## (Intercept)-Lynx_rufus -3.3942 0.3541 -4.1221 -3.3801 -2.7446 1.0368
## (Intercept)-Didelphis_virginiana -2.4293 0.3061 -3.0646 -2.4167 -1.8682 1.0051
## (Intercept)-Sylvilagus_floridanus -3.0580 0.3017 -3.6683 -3.0429 -2.5109 1.0033
## (Intercept)-Meleagris_gallopavo -3.7140 0.4797 -4.7155 -3.6941 -2.7963 1.0125
## (Intercept)-Sciurus_carolinensis -2.5253 0.3204 -3.1816 -2.5193 -1.9147 1.0188
## shrub_cover-Canis_latrans -0.2752 0.2159 -0.6889 -0.2722 0.1396 1.0240
## shrub_cover-Procyon_lotor 0.2463 0.1734 -0.1123 0.2516 0.5666 1.0021
## shrub_cover-Dasypus_novemcinctus 0.8806 0.3018 0.3126 0.8725 1.4745 1.0027
## shrub_cover-Lynx_rufus -0.1980 0.3762 -0.9523 -0.1986 0.5343 1.0190
## shrub_cover-Didelphis_virginiana 1.0232 0.3777 0.3273 1.0099 1.7995 1.0092
## shrub_cover-Sylvilagus_floridanus 0.2546 0.4220 -0.5260 0.2383 1.1247 1.0053
## shrub_cover-Meleagris_gallopavo -0.6693 0.4154 -1.5331 -0.6521 0.1435 1.0247
## shrub_cover-Sciurus_carolinensis 0.8838 0.4115 0.0930 0.8739 1.7006 1.0229
## veg_height-Canis_latrans -0.5732 0.1865 -0.9485 -0.5668 -0.2261 1.0004
## veg_height-Procyon_lotor 0.3415 0.1265 0.0944 0.3405 0.5880 1.0061
## veg_height-Dasypus_novemcinctus 0.2558 0.1352 0.0022 0.2557 0.5324 1.0024
## veg_height-Lynx_rufus 0.0217 0.2410 -0.4592 0.0170 0.4951 1.0074
## veg_height-Didelphis_virginiana 0.4589 0.2514 -0.0042 0.4501 0.9921 1.0037
## veg_height-Sylvilagus_floridanus 0.1664 0.2495 -0.3393 0.1704 0.6564 1.0016
## veg_height-Meleagris_gallopavo -0.1608 0.3824 -0.9167 -0.1705 0.6078 1.0153
## veg_height-Sciurus_carolinensis 0.0906 0.2140 -0.3343 0.0857 0.5133 1.0089
## week-Canis_latrans 0.4643 0.2468 -0.0037 0.4557 0.9715 1.0046
## week-Procyon_lotor 0.1668 0.1974 -0.2012 0.1595 0.5604 1.0088
## week-Dasypus_novemcinctus 0.0661 0.2115 -0.3569 0.0710 0.4787 1.0023
## week-Lynx_rufus 0.2923 0.2987 -0.2700 0.2797 0.9141 1.0139
## week-Didelphis_virginiana 0.0478 0.3135 -0.6049 0.0573 0.6303 1.0252
## week-Sylvilagus_floridanus 0.0575 0.2943 -0.5333 0.0637 0.6234 1.0198
## week-Meleagris_gallopavo -0.0872 0.3541 -0.8254 -0.0595 0.5401 1.0625
## week-Sciurus_carolinensis 0.5565 0.3561 -0.0452 0.5220 1.3622 1.0036
## I(week^2)-Canis_latrans -0.2015 0.1034 -0.4040 -0.2018 -0.0028 1.0018
## I(week^2)-Procyon_lotor -0.1152 0.0873 -0.2883 -0.1136 0.0546 1.0002
## I(week^2)-Dasypus_novemcinctus -0.1584 0.1024 -0.3623 -0.1585 0.0346 1.0031
## I(week^2)-Lynx_rufus -0.1990 0.1400 -0.4884 -0.1933 0.0700 1.0042
## I(week^2)-Didelphis_virginiana -0.3631 0.2006 -0.8213 -0.3400 -0.0192 1.0260
## I(week^2)-Sylvilagus_floridanus -0.1541 0.1488 -0.4615 -0.1491 0.1295 1.0022
## I(week^2)-Meleagris_gallopavo -0.3734 0.2378 -0.9118 -0.3453 -0.0129 1.0572
## I(week^2)-Sciurus_carolinensis -0.2004 0.1450 -0.4976 -0.1953 0.0702 1.0058
## ESS
## (Intercept)-Canis_latrans 1000
## (Intercept)-Procyon_lotor 1285
## (Intercept)-Dasypus_novemcinctus 1561
## (Intercept)-Lynx_rufus 370
## (Intercept)-Didelphis_virginiana 901
## (Intercept)-Sylvilagus_floridanus 505
## (Intercept)-Meleagris_gallopavo 164
## (Intercept)-Sciurus_carolinensis 949
## shrub_cover-Canis_latrans 819
## shrub_cover-Procyon_lotor 1133
## shrub_cover-Dasypus_novemcinctus 1360
## shrub_cover-Lynx_rufus 391
## shrub_cover-Didelphis_virginiana 668
## shrub_cover-Sylvilagus_floridanus 440
## shrub_cover-Meleagris_gallopavo 207
## shrub_cover-Sciurus_carolinensis 758
## veg_height-Canis_latrans 695
## veg_height-Procyon_lotor 1515
## veg_height-Dasypus_novemcinctus 1904
## veg_height-Lynx_rufus 791
## veg_height-Didelphis_virginiana 1122
## veg_height-Sylvilagus_floridanus 766
## veg_height-Meleagris_gallopavo 474
## veg_height-Sciurus_carolinensis 1141
## week-Canis_latrans 1284
## week-Procyon_lotor 1566
## week-Dasypus_novemcinctus 2077
## week-Lynx_rufus 956
## week-Didelphis_virginiana 1284
## week-Sylvilagus_floridanus 1091
## week-Meleagris_gallopavo 690
## week-Sciurus_carolinensis 949
## I(week^2)-Canis_latrans 1193
## I(week^2)-Procyon_lotor 1514
## I(week^2)-Dasypus_novemcinctus 1611
## I(week^2)-Lynx_rufus 765
## I(week^2)-Didelphis_virginiana 535
## I(week^2)-Sylvilagus_floridanus 845
## I(week^2)-Meleagris_gallopavo 184
## I(week^2)-Sciurus_carolinensis 988
#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): 0.6978
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3555 0.3752 -1.0836 -0.3535 0.4061 1.0030 599
## Avg_Cogongrass_Cover 0.2277 0.2704 -0.3066 0.2254 0.7498 1.0087 1009
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5477 0.6151 0.0506 0.3466 2.2368 1.0054 896
## Avg_Cogongrass_Cover 0.3192 0.3637 0.0398 0.2059 1.2390 1.0064 1144
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7088 0.8021 0.062 0.4859 2.5682 1.1012 172
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5711 0.3157 -3.1796 -2.5777 -1.9013 1.0173 1694
## shrub_cover 0.2815 0.3175 -0.3521 0.2755 0.9322 1.0037 1384
## veg_height 0.0669 0.1980 -0.3294 0.0719 0.4613 1.0049 1389
## week 0.1834 0.2064 -0.2110 0.1761 0.6019 1.0097 1173
## I(week^2) -0.2275 0.1292 -0.5018 -0.2220 0.0135 1.0204 882
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7038 0.6075 0.1493 0.5248 2.3005 1.0151 924
## shrub_cover 0.6694 0.6849 0.1262 0.4900 2.3735 1.0214 1148
## veg_height 0.2621 0.2357 0.0573 0.1932 0.8526 1.0428 1420
## week 0.2100 0.2631 0.0326 0.1448 0.7918 1.0148 1383
## I(week^2) 0.0890 0.0909 0.0203 0.0620 0.3148 1.0430 618
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0300 0.5364 -0.9737 0.0131
## (Intercept)-Procyon_lotor 0.1435 0.5576 -0.9718 0.1434
## (Intercept)-Dasypus_novemcinctus -0.5274 0.4511 -1.4399 -0.5067
## (Intercept)-Lynx_rufus -0.2686 0.5855 -1.4019 -0.2868
## (Intercept)-Didelphis_virginiana -0.8619 0.5303 -1.9996 -0.8223
## (Intercept)-Sylvilagus_floridanus -0.4165 0.4953 -1.4001 -0.4259
## (Intercept)-Meleagris_gallopavo -0.1926 0.6507 -1.3133 -0.2484
## (Intercept)-Sciurus_carolinensis -0.8838 0.5268 -2.0329 -0.8523
## Avg_Cogongrass_Cover-Canis_latrans 0.4386 0.3834 -0.2382 0.4126
## Avg_Cogongrass_Cover-Procyon_lotor 0.2318 0.3453 -0.4178 0.2227
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3656 0.3371 -0.2527 0.3460
## Avg_Cogongrass_Cover-Lynx_rufus 0.4682 0.3986 -0.2433 0.4423
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3280 0.3609 -0.3602 0.3175
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1884 0.4246 -1.1443 -0.1557
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1842 0.5559 -1.3528 -0.1341
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3699 0.3568 -0.3089 0.3614
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1404 0.9999 546
## (Intercept)-Procyon_lotor 1.2798 1.0177 417
## (Intercept)-Dasypus_novemcinctus 0.3121 1.0051 1073
## (Intercept)-Lynx_rufus 1.0253 1.0036 416
## (Intercept)-Didelphis_virginiana 0.1075 1.0023 922
## (Intercept)-Sylvilagus_floridanus 0.5575 1.0015 914
## (Intercept)-Meleagris_gallopavo 1.3046 1.0188 426
## (Intercept)-Sciurus_carolinensis 0.1140 1.0128 875
## Avg_Cogongrass_Cover-Canis_latrans 1.2656 1.0070 1726
## Avg_Cogongrass_Cover-Procyon_lotor 0.9650 1.0134 1538
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0519 1.0003 2021
## Avg_Cogongrass_Cover-Lynx_rufus 1.3290 1.0067 1456
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0597 1.0028 1847
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5632 1.0004 1141
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8089 1.0230 736
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1163 1.0015 1571
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5953 0.2012 -2.9936 -2.5928 -2.2078 1.0007
## (Intercept)-Procyon_lotor -2.2021 0.1578 -2.5247 -2.1973 -1.9001 1.0002
## (Intercept)-Dasypus_novemcinctus -1.6496 0.1786 -2.0194 -1.6406 -1.3082 1.0002
## (Intercept)-Lynx_rufus -3.3466 0.3537 -4.0600 -3.3385 -2.6824 1.0059
## (Intercept)-Didelphis_virginiana -2.4066 0.2994 -3.0164 -2.3946 -1.8535 0.9999
## (Intercept)-Sylvilagus_floridanus -3.0111 0.3042 -3.6483 -2.9981 -2.4712 1.0096
## (Intercept)-Meleagris_gallopavo -3.6061 0.4964 -4.5612 -3.6121 -2.6295 1.0565
## (Intercept)-Sciurus_carolinensis -2.5102 0.3185 -3.1660 -2.5008 -1.9037 1.0534
## shrub_cover-Canis_latrans -0.2768 0.2196 -0.7114 -0.2768 0.1564 1.0012
## shrub_cover-Procyon_lotor 0.2589 0.1667 -0.0772 0.2601 0.5682 1.0101
## shrub_cover-Dasypus_novemcinctus 0.8641 0.2990 0.3110 0.8577 1.4516 1.0079
## shrub_cover-Lynx_rufus -0.1765 0.3700 -0.8664 -0.1871 0.5598 1.0307
## shrub_cover-Didelphis_virginiana 0.9791 0.3720 0.3119 0.9536 1.7672 1.0024
## shrub_cover-Sylvilagus_floridanus 0.2652 0.3945 -0.5116 0.2575 1.0474 1.0082
## shrub_cover-Meleagris_gallopavo -0.5851 0.4095 -1.3623 -0.5939 0.2474 1.0282
## shrub_cover-Sciurus_carolinensis 0.8832 0.4139 0.0944 0.8722 1.6925 1.0112
## veg_height-Canis_latrans -0.5935 0.1933 -0.9917 -0.5915 -0.2350 1.0244
## veg_height-Procyon_lotor 0.3432 0.1224 0.1113 0.3429 0.5827 1.0002
## veg_height-Dasypus_novemcinctus 0.2581 0.1331 -0.0082 0.2556 0.5227 1.0148
## veg_height-Lynx_rufus 0.0096 0.2484 -0.4873 0.0129 0.4727 1.0614
## veg_height-Didelphis_virginiana 0.4610 0.2550 -0.0053 0.4489 0.9986 1.0101
## veg_height-Sylvilagus_floridanus 0.1595 0.2435 -0.2939 0.1519 0.6519 1.0040
## veg_height-Meleagris_gallopavo -0.1845 0.4073 -0.9888 -0.1807 0.5990 1.0466
## veg_height-Sciurus_carolinensis 0.0760 0.2183 -0.3615 0.0755 0.5123 1.0181
## week-Canis_latrans 0.4605 0.2590 -0.0208 0.4486 0.9979 1.0082
## week-Procyon_lotor 0.1519 0.1933 -0.2145 0.1486 0.5526 1.0016
## week-Dasypus_novemcinctus 0.0600 0.2100 -0.3547 0.0606 0.4732 1.0112
## week-Lynx_rufus 0.2658 0.3035 -0.3060 0.2642 0.8839 1.0030
## week-Didelphis_virginiana 0.0356 0.3215 -0.6087 0.0430 0.6430 1.0176
## week-Sylvilagus_floridanus 0.0587 0.2955 -0.5281 0.0623 0.6357 1.0142
## week-Meleagris_gallopavo -0.0857 0.3476 -0.8226 -0.0627 0.5443 1.0105
## week-Sciurus_carolinensis 0.5576 0.3369 -0.0484 0.5418 1.3018 1.0008
## I(week^2)-Canis_latrans -0.2024 0.1090 -0.4293 -0.2003 0.0019 1.0094
## I(week^2)-Procyon_lotor -0.1107 0.0892 -0.2883 -0.1094 0.0624 1.0003
## I(week^2)-Dasypus_novemcinctus -0.1544 0.0994 -0.3515 -0.1535 0.0390 1.0010
## I(week^2)-Lynx_rufus -0.2111 0.1521 -0.5319 -0.2040 0.0632 1.0058
## I(week^2)-Didelphis_virginiana -0.3960 0.2428 -0.9917 -0.3636 -0.0196 1.0272
## I(week^2)-Sylvilagus_floridanus -0.1539 0.1468 -0.4573 -0.1533 0.1231 1.0043
## I(week^2)-Meleagris_gallopavo -0.3929 0.2610 -1.0396 -0.3538 0.0107 1.0646
## I(week^2)-Sciurus_carolinensis -0.1993 0.1418 -0.4872 -0.1938 0.0633 1.0004
## ESS
## (Intercept)-Canis_latrans 883
## (Intercept)-Procyon_lotor 1650
## (Intercept)-Dasypus_novemcinctus 1648
## (Intercept)-Lynx_rufus 350
## (Intercept)-Didelphis_virginiana 767
## (Intercept)-Sylvilagus_floridanus 658
## (Intercept)-Meleagris_gallopavo 257
## (Intercept)-Sciurus_carolinensis 780
## shrub_cover-Canis_latrans 725
## shrub_cover-Procyon_lotor 1393
## shrub_cover-Dasypus_novemcinctus 1328
## shrub_cover-Lynx_rufus 431
## shrub_cover-Didelphis_virginiana 637
## shrub_cover-Sylvilagus_floridanus 592
## shrub_cover-Meleagris_gallopavo 310
## shrub_cover-Sciurus_carolinensis 632
## veg_height-Canis_latrans 733
## veg_height-Procyon_lotor 1588
## veg_height-Dasypus_novemcinctus 1960
## veg_height-Lynx_rufus 784
## veg_height-Didelphis_virginiana 1031
## veg_height-Sylvilagus_floridanus 858
## veg_height-Meleagris_gallopavo 388
## veg_height-Sciurus_carolinensis 1285
## week-Canis_latrans 1003
## week-Procyon_lotor 1630
## week-Dasypus_novemcinctus 2103
## week-Lynx_rufus 1211
## week-Didelphis_virginiana 1049
## week-Sylvilagus_floridanus 1292
## week-Meleagris_gallopavo 393
## week-Sciurus_carolinensis 1225
## I(week^2)-Canis_latrans 1098
## I(week^2)-Procyon_lotor 1556
## I(week^2)-Dasypus_novemcinctus 1815
## I(week^2)-Lynx_rufus 639
## I(week^2)-Didelphis_virginiana 360
## I(week^2)-Sylvilagus_floridanus 892
## I(week^2)-Meleagris_gallopavo 194
## I(week^2)-Sciurus_carolinensis 1218
# 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): 0.699
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0423 0.4209 -1.8490 -1.0563 -0.1352 1.0203 611
## Avg_Cogongrass_Cover -0.6371 0.4046 -1.4409 -0.6257 0.1343 1.0031 492
## I(Avg_Cogongrass_Cover^2) 0.8225 0.3649 0.1791 0.8041 1.6002 1.0244 439
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6682 0.8890 0.0536 0.3832 3.0751 1.0222 647
## Avg_Cogongrass_Cover 0.4827 0.7384 0.0419 0.2590 2.1467 1.0761 826
## I(Avg_Cogongrass_Cover^2) 0.4794 0.8726 0.0379 0.2388 2.5045 1.1047 488
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5445 0.5628 0.0519 0.3488 2.1421 1.0212 227
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5966 0.2972 -3.1720 -2.5944 -2.0053 1.0101 1556
## shrub_cover 0.2626 0.3006 -0.3541 0.2650 0.8547 1.0022 1614
## veg_height 0.0900 0.1904 -0.3021 0.0953 0.4517 1.0004 1552
## week 0.1894 0.2018 -0.2003 0.1865 0.5732 1.0027 1196
## I(week^2) -0.2220 0.1196 -0.4837 -0.2166 -0.0030 0.9999 963
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6736 0.5625 0.1443 0.5326 2.1083 1.0075 714
## shrub_cover 0.6537 0.6282 0.1233 0.4866 2.1222 1.0182 775
## veg_height 0.2450 0.2298 0.0542 0.1856 0.7522 1.0544 1562
## week 0.1902 0.2039 0.0328 0.1316 0.7212 1.0194 1274
## I(week^2) 0.0811 0.0775 0.0190 0.0593 0.2798 1.0078 881
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7250 0.5634 -1.8027 -0.7315
## (Intercept)-Procyon_lotor -0.5372 0.5823 -1.6684 -0.5469
## (Intercept)-Dasypus_novemcinctus -1.2142 0.5136 -2.2934 -1.1955
## (Intercept)-Lynx_rufus -1.1088 0.6211 -2.3852 -1.1004
## (Intercept)-Didelphis_virginiana -1.4566 0.5639 -2.6579 -1.4255
## (Intercept)-Sylvilagus_floridanus -1.0979 0.5505 -2.1829 -1.0736
## (Intercept)-Meleagris_gallopavo -0.6835 0.7472 -1.8798 -0.7513
## (Intercept)-Sciurus_carolinensis -1.7201 0.6573 -3.1495 -1.6569
## Avg_Cogongrass_Cover-Canis_latrans -0.3440 0.5409 -1.3220 -0.3728
## Avg_Cogongrass_Cover-Procyon_lotor -0.6663 0.5144 -1.6747 -0.6666
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4643 0.4849 -1.4370 -0.4668
## Avg_Cogongrass_Cover-Lynx_rufus -0.5939 0.5865 -1.8218 -0.5782
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3799 0.5465 -1.4086 -0.4056
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1423 0.6661 -2.6135 -1.0699
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9945 0.7084 -2.6178 -0.9100
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6303 0.5279 -1.6473 -0.6322
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2639 0.7235 0.2174 1.1236
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1240 0.6792 0.2348 1.0167
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6958 0.3572 0.0389 0.6910
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1587 0.5555 0.2943 1.0833
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5169 0.4122 -0.2702 0.5023
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6947 0.4437 -0.0790 0.6616
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4017 0.6077 -0.8018 0.3798
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8490 0.4009 0.1443 0.8210
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4541 1.0031 825
## (Intercept)-Procyon_lotor 0.6477 0.9997 628
## (Intercept)-Dasypus_novemcinctus -0.2322 0.9997 898
## (Intercept)-Lynx_rufus 0.0995 1.0229 672
## (Intercept)-Didelphis_virginiana -0.4042 1.0014 741
## (Intercept)-Sylvilagus_floridanus -0.0073 1.0080 920
## (Intercept)-Meleagris_gallopavo 1.0052 1.0682 370
## (Intercept)-Sciurus_carolinensis -0.5965 1.0159 811
## Avg_Cogongrass_Cover-Canis_latrans 0.8342 1.0132 907
## Avg_Cogongrass_Cover-Procyon_lotor 0.3494 1.0006 1040
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5286 1.0065 924
## Avg_Cogongrass_Cover-Lynx_rufus 0.5415 1.0143 765
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7631 1.0171 856
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0588 1.0195 498
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1785 1.0291 516
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3826 1.0055 871
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0199 1.0548 413
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0472 1.1284 317
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4470 1.0040 909
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5251 1.0911 589
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3793 1.0143 504
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6696 1.0011 633
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.6686 1.0021 408
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7128 1.0079 934
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5800 0.1952 -2.9725 -2.5789 -2.2015 1.0278
## (Intercept)-Procyon_lotor -2.2230 0.1612 -2.5485 -2.2203 -1.9123 1.0051
## (Intercept)-Dasypus_novemcinctus -1.6519 0.1766 -2.0009 -1.6498 -1.3180 1.0130
## (Intercept)-Lynx_rufus -3.3483 0.3523 -4.0807 -3.3351 -2.7128 1.0405
## (Intercept)-Didelphis_virginiana -2.4221 0.2907 -3.0197 -2.4163 -1.8741 1.0089
## (Intercept)-Sylvilagus_floridanus -3.0240 0.2923 -3.6361 -3.0190 -2.4821 1.0186
## (Intercept)-Meleagris_gallopavo -3.6006 0.5057 -4.5840 -3.6176 -2.5652 1.0958
## (Intercept)-Sciurus_carolinensis -2.5077 0.3051 -3.1423 -2.4916 -1.9385 1.0114
## shrub_cover-Canis_latrans -0.2599 0.2238 -0.6997 -0.2605 0.1631 1.0105
## shrub_cover-Procyon_lotor 0.2331 0.1666 -0.1002 0.2368 0.5482 1.0012
## shrub_cover-Dasypus_novemcinctus 0.8712 0.3030 0.3019 0.8645 1.4625 1.0082
## shrub_cover-Lynx_rufus -0.1623 0.3768 -0.8799 -0.1678 0.5967 1.0405
## shrub_cover-Didelphis_virginiana 1.0091 0.3825 0.3185 0.9892 1.8289 1.0397
## shrub_cover-Sylvilagus_floridanus 0.2483 0.3931 -0.4716 0.2308 1.0547 1.0019
## shrub_cover-Meleagris_gallopavo -0.5798 0.4396 -1.4377 -0.5775 0.2992 1.1419
## shrub_cover-Sciurus_carolinensis 0.8347 0.3900 0.0776 0.8287 1.6200 1.0018
## veg_height-Canis_latrans -0.5672 0.1871 -0.9348 -0.5673 -0.2033 1.0060
## veg_height-Procyon_lotor 0.3460 0.1212 0.1083 0.3465 0.5774 1.0012
## veg_height-Dasypus_novemcinctus 0.2628 0.1360 -0.0034 0.2625 0.5247 1.0014
## veg_height-Lynx_rufus 0.0868 0.2443 -0.4131 0.0932 0.5627 1.0069
## veg_height-Didelphis_virginiana 0.4321 0.2580 -0.0501 0.4189 0.9803 1.0302
## veg_height-Sylvilagus_floridanus 0.1804 0.2459 -0.3111 0.1767 0.6560 1.0150
## veg_height-Meleagris_gallopavo -0.0957 0.4076 -0.9083 -0.0997 0.7121 1.0084
## veg_height-Sciurus_carolinensis 0.0898 0.2148 -0.3306 0.0831 0.5095 1.0213
## week-Canis_latrans 0.4520 0.2442 0.0114 0.4406 0.9605 1.0095
## week-Procyon_lotor 0.1602 0.1943 -0.2088 0.1595 0.5461 1.0049
## week-Dasypus_novemcinctus 0.0685 0.2116 -0.3447 0.0658 0.4917 1.0105
## week-Lynx_rufus 0.2781 0.2931 -0.2847 0.2702 0.8726 1.0022
## week-Didelphis_virginiana 0.0538 0.3132 -0.6215 0.0639 0.6309 1.0125
## week-Sylvilagus_floridanus 0.0512 0.2915 -0.5445 0.0584 0.6056 1.0030
## week-Meleagris_gallopavo -0.0784 0.3363 -0.8197 -0.0548 0.5120 1.0169
## week-Sciurus_carolinensis 0.5335 0.3322 -0.0491 0.5058 1.2373 1.0024
## I(week^2)-Canis_latrans -0.1941 0.1010 -0.4002 -0.1893 -0.0072 1.0026
## I(week^2)-Procyon_lotor -0.1141 0.0850 -0.2824 -0.1130 0.0471 1.0040
## I(week^2)-Dasypus_novemcinctus -0.1559 0.1010 -0.3613 -0.1538 0.0314 1.0288
## I(week^2)-Lynx_rufus -0.1968 0.1415 -0.4826 -0.1936 0.0680 1.0179
## I(week^2)-Didelphis_virginiana -0.3795 0.2164 -0.8592 -0.3563 -0.0379 1.0088
## I(week^2)-Sylvilagus_floridanus -0.1709 0.1500 -0.4811 -0.1646 0.1018 1.0133
## I(week^2)-Meleagris_gallopavo -0.3699 0.2246 -0.8854 -0.3384 -0.0011 1.0029
## I(week^2)-Sciurus_carolinensis -0.1970 0.1388 -0.4941 -0.1879 0.0587 1.0049
## ESS
## (Intercept)-Canis_latrans 967
## (Intercept)-Procyon_lotor 1334
## (Intercept)-Dasypus_novemcinctus 1635
## (Intercept)-Lynx_rufus 357
## (Intercept)-Didelphis_virginiana 718
## (Intercept)-Sylvilagus_floridanus 657
## (Intercept)-Meleagris_gallopavo 176
## (Intercept)-Sciurus_carolinensis 936
## shrub_cover-Canis_latrans 888
## shrub_cover-Procyon_lotor 1318
## shrub_cover-Dasypus_novemcinctus 1267
## shrub_cover-Lynx_rufus 504
## shrub_cover-Didelphis_virginiana 586
## shrub_cover-Sylvilagus_floridanus 581
## shrub_cover-Meleagris_gallopavo 209
## shrub_cover-Sciurus_carolinensis 915
## veg_height-Canis_latrans 751
## veg_height-Procyon_lotor 1461
## veg_height-Dasypus_novemcinctus 1918
## veg_height-Lynx_rufus 857
## veg_height-Didelphis_virginiana 792
## veg_height-Sylvilagus_floridanus 739
## veg_height-Meleagris_gallopavo 340
## veg_height-Sciurus_carolinensis 1147
## week-Canis_latrans 1232
## week-Procyon_lotor 1790
## week-Dasypus_novemcinctus 1818
## week-Lynx_rufus 1198
## week-Didelphis_virginiana 1298
## week-Sylvilagus_floridanus 1243
## week-Meleagris_gallopavo 481
## week-Sciurus_carolinensis 1192
## I(week^2)-Canis_latrans 1179
## I(week^2)-Procyon_lotor 1520
## I(week^2)-Dasypus_novemcinctus 1767
## I(week^2)-Lynx_rufus 865
## I(week^2)-Didelphis_virginiana 518
## I(week^2)-Sylvilagus_floridanus 747
## I(week^2)-Meleagris_gallopavo 228
## I(week^2)-Sciurus_carolinensis 1227
# 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): 0.7402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8426 0.7256 -3.3647 -1.8164 -0.4788 1.0599 212
## Cogon_Patch_Size 0.1816 0.7249 -1.2491 0.1845 1.6279 1.0098 466
## Veg_shannon_index 1.0277 0.5606 0.0419 0.9797 2.2960 1.1707 266
## total_shrub_cover -1.2203 0.7275 -2.7558 -1.1914 0.1461 1.1243 285
## Avg_Cogongrass_Cover -0.0976 0.9767 -2.0057 -0.1184 1.8606 1.1362 172
## Tree_Density -2.1959 0.9321 -3.9168 -2.2452 -0.3033 1.0397 218
## Avg_Canopy_Cover 1.9176 0.8822 0.1757 1.9081 3.7656 1.0313 751
## I(Avg_Cogongrass_Cover^2) 1.4719 0.6984 0.0901 1.4422 2.8555 1.0011 283
## avg_veg_height 0.0161 0.5865 -1.0672 -0.0027 1.2299 1.0624 316
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8173 3.0834 0.0585 0.7941 10.4586 1.0218 233
## Cogon_Patch_Size 2.9735 4.6104 0.1191 1.5443 14.1790 1.0383 356
## Veg_shannon_index 0.8256 1.6883 0.0449 0.3532 4.6446 1.3022 182
## total_shrub_cover 2.8908 4.4705 0.0970 1.4138 14.5800 1.0259 228
## Avg_Cogongrass_Cover 1.6080 3.7210 0.0564 0.5684 8.8952 1.4056 354
## Tree_Density 5.3004 13.3553 0.0724 1.7905 33.5698 1.8763 108
## Avg_Canopy_Cover 7.3016 11.0499 0.3123 3.7448 35.9709 1.4323 89
## I(Avg_Cogongrass_Cover^2) 2.5312 4.4767 0.0664 1.0935 13.7667 1.0579 166
## avg_veg_height 0.7626 1.4842 0.0470 0.3660 4.0373 1.0418 680
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6313 3.8477 0.0667 1.3127 13.2462 1.3492 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6353 0.3086 -3.2528 -2.6404 -2.0091 1.0014 1500
## shrub_cover 0.4520 0.3239 -0.1997 0.4545 1.0746 1.0068 1166
## veg_height 0.1319 0.2029 -0.2735 0.1355 0.5327 1.0062 1245
## week 0.1802 0.2132 -0.2486 0.1783 0.5907 1.0001 1193
## I(week^2) -0.2228 0.1224 -0.4817 -0.2199 0.0129 1.0088 1033
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6593 0.6393 0.1400 0.4834 2.2292 1.0093 1114
## shrub_cover 0.7037 0.5791 0.1537 0.5487 2.3013 1.0069 1531
## veg_height 0.2587 0.2525 0.0572 0.1941 0.8215 1.0050 1478
## week 0.2183 0.2590 0.0331 0.1415 0.8826 1.0163 1334
## I(week^2) 0.0844 0.0879 0.0204 0.0591 0.2969 1.0018 898
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5051 1.0013 -3.4760 -1.5081
## (Intercept)-Procyon_lotor -1.2207 1.0230 -3.2328 -1.2350
## (Intercept)-Dasypus_novemcinctus -2.1642 0.9961 -4.2748 -2.1123
## (Intercept)-Lynx_rufus -1.5895 1.1941 -3.8660 -1.6457
## (Intercept)-Didelphis_virginiana -2.6726 1.1745 -5.3701 -2.5156
## (Intercept)-Sylvilagus_floridanus -1.9414 1.0556 -4.2258 -1.8929
## (Intercept)-Meleagris_gallopavo -1.8269 1.3035 -4.3361 -1.8365
## (Intercept)-Sciurus_carolinensis -2.8723 1.3430 -6.0907 -2.6591
## Cogon_Patch_Size-Canis_latrans 1.5701 1.3934 -0.4465 1.3151
## Cogon_Patch_Size-Procyon_lotor -0.4735 0.8503 -2.2225 -0.4284
## Cogon_Patch_Size-Dasypus_novemcinctus 0.2255 0.9325 -1.5277 0.1953
## Cogon_Patch_Size-Lynx_rufus -0.0686 1.3764 -2.5761 -0.0913
## Cogon_Patch_Size-Didelphis_virginiana 1.5539 1.1283 -0.1972 1.4163
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9975 1.4897 -4.6302 -0.7771
## Cogon_Patch_Size-Meleagris_gallopavo 0.3490 1.1910 -1.7163 0.2738
## Cogon_Patch_Size-Sciurus_carolinensis -0.6316 1.3593 -3.8158 -0.5305
## Veg_shannon_index-Canis_latrans 1.3964 0.8810 0.0703 1.2663
## Veg_shannon_index-Procyon_lotor 1.2985 0.7190 0.1118 1.2129
## Veg_shannon_index-Dasypus_novemcinctus 0.6926 0.6626 -0.6091 0.6718
## Veg_shannon_index-Lynx_rufus 0.9440 0.8985 -0.9003 0.9242
## Veg_shannon_index-Didelphis_virginiana 1.2553 0.7958 -0.0676 1.1645
## Veg_shannon_index-Sylvilagus_floridanus 1.1487 0.8220 -0.3058 1.0693
## Veg_shannon_index-Meleagris_gallopavo 1.3545 0.8701 -0.0459 1.2221
## Veg_shannon_index-Sciurus_carolinensis 0.4871 0.9764 -1.4853 0.5685
## total_shrub_cover-Canis_latrans 0.2269 1.0665 -1.5509 0.1029
## total_shrub_cover-Procyon_lotor -1.5672 0.7518 -3.2211 -1.4899
## total_shrub_cover-Dasypus_novemcinctus -0.7036 0.9979 -2.9107 -0.6356
## total_shrub_cover-Lynx_rufus -2.1184 1.4840 -5.5721 -1.8556
## total_shrub_cover-Didelphis_virginiana -1.6527 1.2649 -4.7810 -1.4353
## total_shrub_cover-Sylvilagus_floridanus -1.3631 1.3584 -4.6236 -1.2185
## total_shrub_cover-Meleagris_gallopavo -2.7359 1.6027 -6.5792 -2.4767
## total_shrub_cover-Sciurus_carolinensis -1.3139 1.2851 -4.3543 -1.1772
## Avg_Cogongrass_Cover-Canis_latrans -0.0173 1.2788 -2.4547 -0.0696
## Avg_Cogongrass_Cover-Procyon_lotor -0.2186 1.2526 -2.6028 -0.2103
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5431 1.4037 -1.8450 0.4207
## Avg_Cogongrass_Cover-Lynx_rufus -0.0045 1.3476 -2.5340 -0.0818
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0489 1.3027 -2.3752 0.0302
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7028 1.3410 -3.4829 -0.6226
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4065 1.4885 -3.6961 -0.3706
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0099 1.3328 -2.5445 -0.0447
## Tree_Density-Canis_latrans -3.2554 1.7920 -7.8204 -2.9255
## Tree_Density-Procyon_lotor -2.3363 1.1383 -4.6304 -2.2755
## Tree_Density-Dasypus_novemcinctus -4.4319 2.6479 -11.5034 -3.7298
## Tree_Density-Lynx_rufus -0.9187 1.6158 -3.5413 -1.0673
## Tree_Density-Didelphis_virginiana -2.2496 1.4922 -5.3618 -2.2665
## Tree_Density-Sylvilagus_floridanus -2.7843 1.6283 -6.6444 -2.6144
## Tree_Density-Meleagris_gallopavo -2.3672 1.4353 -5.4491 -2.3568
## Tree_Density-Sciurus_carolinensis -2.5488 1.9132 -6.4080 -2.4427
## Avg_Canopy_Cover-Canis_latrans -0.0376 0.7632 -1.6468 -0.0060
## Avg_Canopy_Cover-Procyon_lotor 1.6719 0.8848 0.1471 1.5998
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5808 1.1824 0.8434 2.3715
## Avg_Canopy_Cover-Lynx_rufus 0.8038 1.5432 -2.2490 0.7152
## Avg_Canopy_Cover-Didelphis_virginiana 3.6295 1.7165 1.2825 3.2851
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7933 2.5991 1.3510 4.2548
## Avg_Canopy_Cover-Meleagris_gallopavo 2.8455 1.7333 0.4341 2.5076
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7722 2.1763 1.0585 3.2372
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4822 1.3114 0.6908 2.2006
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3843 1.1509 0.7204 2.2140
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4743 0.7850 0.0959 1.4154
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6609 1.4042 0.6684 2.3928
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9209 0.8270 -0.5751 0.8937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2289 0.9814 -0.4732 1.1499
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2642 1.7547 -3.9652 0.4445
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6093 0.9047 0.0183 1.5537
## avg_veg_height-Canis_latrans -0.1789 0.7012 -1.6012 -0.1811
## avg_veg_height-Procyon_lotor 0.1219 0.6944 -1.2103 0.1001
## avg_veg_height-Dasypus_novemcinctus 0.3956 0.7436 -0.8763 0.3328
## avg_veg_height-Lynx_rufus -0.2799 0.9484 -2.4262 -0.2386
## avg_veg_height-Didelphis_virginiana -0.1731 0.8224 -1.8785 -0.1721
## avg_veg_height-Sylvilagus_floridanus -0.1315 0.8040 -1.8216 -0.1064
## avg_veg_height-Meleagris_gallopavo -0.0377 0.9682 -2.0756 -0.0105
## avg_veg_height-Sciurus_carolinensis 0.4475 0.8447 -0.9604 0.3531
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5151 1.0495 143
## (Intercept)-Procyon_lotor 0.7894 1.0681 159
## (Intercept)-Dasypus_novemcinctus -0.4404 1.0325 334
## (Intercept)-Lynx_rufus 1.0038 1.0361 189
## (Intercept)-Didelphis_virginiana -0.7748 1.0344 239
## (Intercept)-Sylvilagus_floridanus 0.1072 1.0335 434
## (Intercept)-Meleagris_gallopavo 0.5456 1.1027 171
## (Intercept)-Sciurus_carolinensis -0.8784 1.0143 221
## Cogon_Patch_Size-Canis_latrans 5.1109 1.0105 354
## Cogon_Patch_Size-Procyon_lotor 1.0889 1.0713 370
## Cogon_Patch_Size-Dasypus_novemcinctus 2.2195 1.0152 426
## Cogon_Patch_Size-Lynx_rufus 2.7845 1.0157 400
## Cogon_Patch_Size-Didelphis_virginiana 4.2871 1.0425 272
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3097 1.0175 320
## Cogon_Patch_Size-Meleagris_gallopavo 2.9260 1.0326 404
## Cogon_Patch_Size-Sciurus_carolinensis 1.8868 1.0105 389
## Veg_shannon_index-Canis_latrans 3.5244 1.3090 194
## Veg_shannon_index-Procyon_lotor 2.9140 1.1665 206
## Veg_shannon_index-Dasypus_novemcinctus 2.0628 1.0300 396
## Veg_shannon_index-Lynx_rufus 2.7606 1.0475 394
## Veg_shannon_index-Didelphis_virginiana 3.1308 1.1662 319
## Veg_shannon_index-Sylvilagus_floridanus 3.0987 1.1571 264
## Veg_shannon_index-Meleagris_gallopavo 3.4019 1.2015 344
## Veg_shannon_index-Sciurus_carolinensis 2.1401 1.0504 263
## total_shrub_cover-Canis_latrans 2.8330 1.0382 277
## total_shrub_cover-Procyon_lotor -0.2915 1.1116 573
## total_shrub_cover-Dasypus_novemcinctus 1.0430 1.1310 170
## total_shrub_cover-Lynx_rufus -0.0244 1.0513 200
## total_shrub_cover-Didelphis_virginiana 0.2427 1.0790 165
## total_shrub_cover-Sylvilagus_floridanus 0.9083 1.0331 286
## total_shrub_cover-Meleagris_gallopavo -0.4276 1.0545 182
## total_shrub_cover-Sciurus_carolinensis 0.8732 1.0513 284
## Avg_Cogongrass_Cover-Canis_latrans 2.4727 1.0944 276
## Avg_Cogongrass_Cover-Procyon_lotor 2.2874 1.0581 250
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5927 1.1909 266
## Avg_Cogongrass_Cover-Lynx_rufus 2.8241 1.0825 286
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7401 1.1624 226
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7494 1.0686 328
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2678 1.0646 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6937 1.1200 298
## Tree_Density-Canis_latrans -0.7463 1.3500 153
## Tree_Density-Procyon_lotor -0.3051 1.0940 299
## Tree_Density-Dasypus_novemcinctus -1.4839 1.6099 81
## Tree_Density-Lynx_rufus 2.6643 1.0185 226
## Tree_Density-Didelphis_virginiana 0.9396 1.0353 357
## Tree_Density-Sylvilagus_floridanus 0.0874 1.1652 229
## Tree_Density-Meleagris_gallopavo 0.4388 1.0311 459
## Tree_Density-Sciurus_carolinensis 0.5901 1.2076 160
## Avg_Canopy_Cover-Canis_latrans 1.4031 1.0957 333
## Avg_Canopy_Cover-Procyon_lotor 3.6444 1.0315 370
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.3397 1.2214 152
## Avg_Canopy_Cover-Lynx_rufus 4.1138 1.0348 229
## Avg_Canopy_Cover-Didelphis_virginiana 8.0300 1.2355 128
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.3224 1.4188 125
## Avg_Canopy_Cover-Meleagris_gallopavo 7.4098 1.1435 190
## Avg_Canopy_Cover-Sciurus_carolinensis 9.2917 1.3946 154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8821 1.1220 216
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.3068 1.0496 251
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1883 1.0129 313
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.0481 1.0847 228
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5541 1.0250 226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3581 1.0130 240
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.0445 1.0171 110
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5370 1.0115 387
## avg_veg_height-Canis_latrans 1.2407 1.0551 453
## avg_veg_height-Procyon_lotor 1.6057 1.0487 420
## avg_veg_height-Dasypus_novemcinctus 1.9808 1.0243 372
## avg_veg_height-Lynx_rufus 1.5367 1.0313 384
## avg_veg_height-Didelphis_virginiana 1.4393 1.0307 474
## avg_veg_height-Sylvilagus_floridanus 1.4566 1.0347 346
## avg_veg_height-Meleagris_gallopavo 1.8896 1.0112 356
## avg_veg_height-Sciurus_carolinensis 2.4305 1.0202 396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5665 0.1964 -2.9667 -2.5596 -2.1966 1.0148
## (Intercept)-Procyon_lotor -2.2220 0.1603 -2.5492 -2.2178 -1.9222 1.0003
## (Intercept)-Dasypus_novemcinctus -1.7267 0.1927 -2.1130 -1.7197 -1.3563 1.0040
## (Intercept)-Lynx_rufus -3.4367 0.3411 -4.1434 -3.4161 -2.8252 1.0088
## (Intercept)-Didelphis_virginiana -2.5279 0.3085 -3.1539 -2.5193 -1.9489 1.0044
## (Intercept)-Sylvilagus_floridanus -3.0759 0.2725 -3.6303 -3.0747 -2.5567 1.0013
## (Intercept)-Meleagris_gallopavo -3.4379 0.4833 -4.4746 -3.4131 -2.5187 1.0068
## (Intercept)-Sciurus_carolinensis -2.7497 0.3236 -3.4010 -2.7428 -2.1494 1.0407
## shrub_cover-Canis_latrans -0.2946 0.2349 -0.7379 -0.2967 0.1786 1.0113
## shrub_cover-Procyon_lotor 0.2871 0.1655 -0.0505 0.2897 0.6013 1.0094
## shrub_cover-Dasypus_novemcinctus 1.0645 0.3200 0.4387 1.0702 1.6917 1.0257
## shrub_cover-Lynx_rufus 0.0971 0.3628 -0.6490 0.0982 0.7712 1.0073
## shrub_cover-Didelphis_virginiana 1.1610 0.3764 0.4487 1.1542 1.9136 1.0219
## shrub_cover-Sylvilagus_floridanus 0.6502 0.3990 -0.1371 0.6460 1.4442 1.0102
## shrub_cover-Meleagris_gallopavo -0.3588 0.4370 -1.2529 -0.3527 0.4889 1.0043
## shrub_cover-Sciurus_carolinensis 1.1505 0.3988 0.3577 1.1638 1.9452 1.0236
## veg_height-Canis_latrans -0.5532 0.1886 -0.9258 -0.5496 -0.1925 1.0004
## veg_height-Procyon_lotor 0.3678 0.1240 0.1302 0.3696 0.6119 1.0060
## veg_height-Dasypus_novemcinctus 0.3014 0.1388 0.0362 0.2964 0.5856 1.0005
## veg_height-Lynx_rufus 0.1483 0.2430 -0.3453 0.1518 0.6168 1.0331
## veg_height-Didelphis_virginiana 0.5205 0.2499 0.0418 0.5121 1.0244 1.0118
## veg_height-Sylvilagus_floridanus 0.1563 0.2460 -0.3356 0.1553 0.6297 1.0098
## veg_height-Meleagris_gallopavo -0.0766 0.4041 -0.8718 -0.0808 0.7663 1.0087
## veg_height-Sciurus_carolinensis 0.2055 0.2277 -0.2233 0.2021 0.6629 1.0204
## week-Canis_latrans 0.4538 0.2530 -0.0022 0.4376 0.9826 1.0079
## week-Procyon_lotor 0.1566 0.2007 -0.2326 0.1575 0.5551 1.0033
## week-Dasypus_novemcinctus 0.0687 0.2159 -0.3649 0.0714 0.4870 1.0062
## week-Lynx_rufus 0.2756 0.3044 -0.3161 0.2622 0.8882 1.0004
## week-Didelphis_virginiana 0.0472 0.3143 -0.6078 0.0570 0.6316 1.0093
## week-Sylvilagus_floridanus 0.0393 0.3090 -0.5960 0.0538 0.6161 1.0150
## week-Meleagris_gallopavo -0.1136 0.3574 -0.9206 -0.0844 0.5169 1.0114
## week-Sciurus_carolinensis 0.5536 0.3524 -0.0613 0.5224 1.3104 1.0162
## I(week^2)-Canis_latrans -0.1975 0.1074 -0.4199 -0.1954 0.0017 1.0001
## I(week^2)-Procyon_lotor -0.1096 0.0885 -0.2840 -0.1095 0.0631 1.0059
## I(week^2)-Dasypus_novemcinctus -0.1590 0.1035 -0.3639 -0.1568 0.0459 1.0033
## I(week^2)-Lynx_rufus -0.2106 0.1412 -0.5050 -0.2055 0.0523 1.0015
## I(week^2)-Didelphis_virginiana -0.3725 0.2211 -0.9149 -0.3459 -0.0282 1.0099
## I(week^2)-Sylvilagus_floridanus -0.1681 0.1490 -0.4776 -0.1615 0.1022 1.0139
## I(week^2)-Meleagris_gallopavo -0.3847 0.2363 -0.9426 -0.3528 -0.0092 1.0308
## I(week^2)-Sciurus_carolinensis -0.2002 0.1440 -0.4876 -0.1952 0.0646 1.0095
## ESS
## (Intercept)-Canis_latrans 978
## (Intercept)-Procyon_lotor 1415
## (Intercept)-Dasypus_novemcinctus 804
## (Intercept)-Lynx_rufus 339
## (Intercept)-Didelphis_virginiana 486
## (Intercept)-Sylvilagus_floridanus 722
## (Intercept)-Meleagris_gallopavo 245
## (Intercept)-Sciurus_carolinensis 546
## shrub_cover-Canis_latrans 655
## shrub_cover-Procyon_lotor 1418
## shrub_cover-Dasypus_novemcinctus 299
## shrub_cover-Lynx_rufus 356
## shrub_cover-Didelphis_virginiana 365
## shrub_cover-Sylvilagus_floridanus 263
## shrub_cover-Meleagris_gallopavo 363
## shrub_cover-Sciurus_carolinensis 459
## veg_height-Canis_latrans 706
## veg_height-Procyon_lotor 1511
## veg_height-Dasypus_novemcinctus 1871
## veg_height-Lynx_rufus 557
## veg_height-Didelphis_virginiana 955
## veg_height-Sylvilagus_floridanus 700
## veg_height-Meleagris_gallopavo 290
## veg_height-Sciurus_carolinensis 676
## week-Canis_latrans 1049
## week-Procyon_lotor 1527
## week-Dasypus_novemcinctus 2089
## week-Lynx_rufus 914
## week-Didelphis_virginiana 1060
## week-Sylvilagus_floridanus 1032
## week-Meleagris_gallopavo 609
## week-Sciurus_carolinensis 972
## I(week^2)-Canis_latrans 1169
## I(week^2)-Procyon_lotor 1621
## I(week^2)-Dasypus_novemcinctus 1556
## I(week^2)-Lynx_rufus 782
## I(week^2)-Didelphis_virginiana 413
## I(week^2)-Sylvilagus_floridanus 633
## I(week^2)-Meleagris_gallopavo 306
## I(week^2)-Sciurus_carolinensis 1007
waicOcc(ms_full_full_T10, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -965.26596 98.64504 2127.82199
waicOcc(ms_full_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1003.94830 94.59413 2197.08487
waicOcc(ms_full_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -996.60684 79.68375 2152.58118
waicOcc(ms_full_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1002.45838 92.21533 2189.34743
waicOcc(ms_full_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1011.97905 81.91792 2187.79395
waicOcc(ms_full_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1016.65534 76.28158 2185.87384
waicOcc(ms_full_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1027.46758 65.22177 2185.37870
waicOcc(ms_full_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1011.2787 80.0422 2182.6419
waicOcc(ms_full_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -960.5943 101.1670 2123.5225
waicOcc(ms_null_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1066.22378 28.39953 2189.24663
waicOcc(ms_null_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1003.755 61.527 2130.564
waicOcc(ms_null_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1045.34156 48.87038 2188.42388
waicOcc(ms_null_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1037.05311 42.82742 2159.76107
waicOcc(ms_null_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1041.93989 49.84896 2183.57770
waicOcc(ms_null_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1049.86538 44.74693 2189.22461
waicOcc(ms_null_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1054.55063 39.40444 2187.91014
waicOcc(ms_null_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1048.11176 43.08162 2182.38677
waicOcc(ms_null_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -998.34395 61.33387 2119.35565
waicOcc(ms_week_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -998.64241 68.47496 2134.23473
waicOcc(ms_week_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1041.0238 55.2594 2192.5664
waicOcc(ms_week_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1062.82472 33.99855 2193.64654
waicOcc(ms_week_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1046.58280 50.86752 2194.90064
waicOcc(ms_week_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1037.79526 56.29858 2188.18768
waicOcc(ms_week_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1034.37170 48.26226 2165.26791
waicOcc(ms_week_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1050.44062 46.22532 2193.33187
waicOcc(ms_week_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1044.48324 49.87848 2188.72344
waicOcc(ms_week_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -993.55574 69.12671 2125.36489
waicOcc(ms_cover_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -968.09083 91.46748 2119.11662
waicOcc(ms_cover_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1008.87569 85.10948 2187.97035
waicOcc(ms_cover_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1031.06573 58.81608 2179.76361
waicOcc(ms_cover_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1015.77229 75.70812 2182.96081
waicOcc(ms_cover_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1006.6527 82.1894 2177.6841
waicOcc(ms_cover_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1000.57221 72.08855 2145.32152
waicOcc(ms_cover_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1020.35967 71.09747 2182.91429
waicOcc(ms_cover_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1015.19613 73.62561 2177.64348
waicOcc(ms_cover_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -965.02672 92.70713 2115.46768
waicOcc(ms_weekQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -993.4475 75.9925 2138.8800
waicOcc(ms_weekQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1034.35563 63.77695 2196.26517
waicOcc(ms_weekQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1056.76609 41.44597 2196.42412
waicOcc(ms_weekQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1040.00390 58.63028 2197.26836
waicOcc(ms_weekQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1031.27458 63.90056 2190.35028
waicOcc(ms_weekQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1026.94588 57.23364 2168.35905
waicOcc(ms_weekQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1043.79707 54.23875 2196.07164
waicOcc(ms_weekQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1038.40503 56.93949 2190.68904
waicOcc(ms_weekQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -987.43443 75.51408 2125.89702
waicOcc(ms_fullQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -958.4475 107.9931 2132.8811
waicOcc(ms_fullQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -996.8976 102.8264 2199.4480
waicOcc(ms_fullQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1020.77392 73.53228 2188.61240
waicOcc(ms_fullQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1005.26193 90.83702 2192.19790
waicOcc(ms_fullQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -995.82191 98.51073 2188.66529
waicOcc(ms_fullQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -990.19441 87.52715 2155.44311
waicOcc(ms_fullQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1009.75914 84.68883 2188.89594
waicOcc(ms_fullQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1004.13112 89.05752 2186.37729
waicOcc(ms_fullQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -953.6821 108.4959 2124.3560
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: 0.3303
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.621
## Procyon_lotor Bayesian p-value: 0.0803
## Dasypus_novemcinctus Bayesian p-value: 0
## Lynx_rufus Bayesian p-value: 0.3353
## Didelphis_virginiana Bayesian p-value: 0.37
## Sylvilagus_floridanus Bayesian p-value: 0.4017
## Meleagris_gallopavo Bayesian p-value: 0.5703
## Sciurus_carolinensis Bayesian p-value: 0.264
## 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): 0.7402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8426 0.7256 -3.3647 -1.8164 -0.4788 1.0599 212
## Cogon_Patch_Size 0.1816 0.7249 -1.2491 0.1845 1.6279 1.0098 466
## Veg_shannon_index 1.0277 0.5606 0.0419 0.9797 2.2960 1.1707 266
## total_shrub_cover -1.2203 0.7275 -2.7558 -1.1914 0.1461 1.1243 285
## Avg_Cogongrass_Cover -0.0976 0.9767 -2.0057 -0.1184 1.8606 1.1362 172
## Tree_Density -2.1959 0.9321 -3.9168 -2.2452 -0.3033 1.0397 218
## Avg_Canopy_Cover 1.9176 0.8822 0.1757 1.9081 3.7656 1.0313 751
## I(Avg_Cogongrass_Cover^2) 1.4719 0.6984 0.0901 1.4422 2.8555 1.0011 283
## avg_veg_height 0.0161 0.5865 -1.0672 -0.0027 1.2299 1.0624 316
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8173 3.0834 0.0585 0.7941 10.4586 1.0218 233
## Cogon_Patch_Size 2.9735 4.6104 0.1191 1.5443 14.1790 1.0383 356
## Veg_shannon_index 0.8256 1.6883 0.0449 0.3532 4.6446 1.3022 182
## total_shrub_cover 2.8908 4.4705 0.0970 1.4138 14.5800 1.0259 228
## Avg_Cogongrass_Cover 1.6080 3.7210 0.0564 0.5684 8.8952 1.4056 354
## Tree_Density 5.3004 13.3553 0.0724 1.7905 33.5698 1.8763 108
## Avg_Canopy_Cover 7.3016 11.0499 0.3123 3.7448 35.9709 1.4323 89
## I(Avg_Cogongrass_Cover^2) 2.5312 4.4767 0.0664 1.0935 13.7667 1.0579 166
## avg_veg_height 0.7626 1.4842 0.0470 0.3660 4.0373 1.0418 680
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6313 3.8477 0.0667 1.3127 13.2462 1.3492 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6353 0.3086 -3.2528 -2.6404 -2.0091 1.0014 1500
## shrub_cover 0.4520 0.3239 -0.1997 0.4545 1.0746 1.0068 1166
## veg_height 0.1319 0.2029 -0.2735 0.1355 0.5327 1.0062 1245
## week 0.1802 0.2132 -0.2486 0.1783 0.5907 1.0001 1193
## I(week^2) -0.2228 0.1224 -0.4817 -0.2199 0.0129 1.0088 1033
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6593 0.6393 0.1400 0.4834 2.2292 1.0093 1114
## shrub_cover 0.7037 0.5791 0.1537 0.5487 2.3013 1.0069 1531
## veg_height 0.2587 0.2525 0.0572 0.1941 0.8215 1.0050 1478
## week 0.2183 0.2590 0.0331 0.1415 0.8826 1.0163 1334
## I(week^2) 0.0844 0.0879 0.0204 0.0591 0.2969 1.0018 898
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5051 1.0013 -3.4760 -1.5081
## (Intercept)-Procyon_lotor -1.2207 1.0230 -3.2328 -1.2350
## (Intercept)-Dasypus_novemcinctus -2.1642 0.9961 -4.2748 -2.1123
## (Intercept)-Lynx_rufus -1.5895 1.1941 -3.8660 -1.6457
## (Intercept)-Didelphis_virginiana -2.6726 1.1745 -5.3701 -2.5156
## (Intercept)-Sylvilagus_floridanus -1.9414 1.0556 -4.2258 -1.8929
## (Intercept)-Meleagris_gallopavo -1.8269 1.3035 -4.3361 -1.8365
## (Intercept)-Sciurus_carolinensis -2.8723 1.3430 -6.0907 -2.6591
## Cogon_Patch_Size-Canis_latrans 1.5701 1.3934 -0.4465 1.3151
## Cogon_Patch_Size-Procyon_lotor -0.4735 0.8503 -2.2225 -0.4284
## Cogon_Patch_Size-Dasypus_novemcinctus 0.2255 0.9325 -1.5277 0.1953
## Cogon_Patch_Size-Lynx_rufus -0.0686 1.3764 -2.5761 -0.0913
## Cogon_Patch_Size-Didelphis_virginiana 1.5539 1.1283 -0.1972 1.4163
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9975 1.4897 -4.6302 -0.7771
## Cogon_Patch_Size-Meleagris_gallopavo 0.3490 1.1910 -1.7163 0.2738
## Cogon_Patch_Size-Sciurus_carolinensis -0.6316 1.3593 -3.8158 -0.5305
## Veg_shannon_index-Canis_latrans 1.3964 0.8810 0.0703 1.2663
## Veg_shannon_index-Procyon_lotor 1.2985 0.7190 0.1118 1.2129
## Veg_shannon_index-Dasypus_novemcinctus 0.6926 0.6626 -0.6091 0.6718
## Veg_shannon_index-Lynx_rufus 0.9440 0.8985 -0.9003 0.9242
## Veg_shannon_index-Didelphis_virginiana 1.2553 0.7958 -0.0676 1.1645
## Veg_shannon_index-Sylvilagus_floridanus 1.1487 0.8220 -0.3058 1.0693
## Veg_shannon_index-Meleagris_gallopavo 1.3545 0.8701 -0.0459 1.2221
## Veg_shannon_index-Sciurus_carolinensis 0.4871 0.9764 -1.4853 0.5685
## total_shrub_cover-Canis_latrans 0.2269 1.0665 -1.5509 0.1029
## total_shrub_cover-Procyon_lotor -1.5672 0.7518 -3.2211 -1.4899
## total_shrub_cover-Dasypus_novemcinctus -0.7036 0.9979 -2.9107 -0.6356
## total_shrub_cover-Lynx_rufus -2.1184 1.4840 -5.5721 -1.8556
## total_shrub_cover-Didelphis_virginiana -1.6527 1.2649 -4.7810 -1.4353
## total_shrub_cover-Sylvilagus_floridanus -1.3631 1.3584 -4.6236 -1.2185
## total_shrub_cover-Meleagris_gallopavo -2.7359 1.6027 -6.5792 -2.4767
## total_shrub_cover-Sciurus_carolinensis -1.3139 1.2851 -4.3543 -1.1772
## Avg_Cogongrass_Cover-Canis_latrans -0.0173 1.2788 -2.4547 -0.0696
## Avg_Cogongrass_Cover-Procyon_lotor -0.2186 1.2526 -2.6028 -0.2103
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5431 1.4037 -1.8450 0.4207
## Avg_Cogongrass_Cover-Lynx_rufus -0.0045 1.3476 -2.5340 -0.0818
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0489 1.3027 -2.3752 0.0302
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7028 1.3410 -3.4829 -0.6226
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4065 1.4885 -3.6961 -0.3706
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0099 1.3328 -2.5445 -0.0447
## Tree_Density-Canis_latrans -3.2554 1.7920 -7.8204 -2.9255
## Tree_Density-Procyon_lotor -2.3363 1.1383 -4.6304 -2.2755
## Tree_Density-Dasypus_novemcinctus -4.4319 2.6479 -11.5034 -3.7298
## Tree_Density-Lynx_rufus -0.9187 1.6158 -3.5413 -1.0673
## Tree_Density-Didelphis_virginiana -2.2496 1.4922 -5.3618 -2.2665
## Tree_Density-Sylvilagus_floridanus -2.7843 1.6283 -6.6444 -2.6144
## Tree_Density-Meleagris_gallopavo -2.3672 1.4353 -5.4491 -2.3568
## Tree_Density-Sciurus_carolinensis -2.5488 1.9132 -6.4080 -2.4427
## Avg_Canopy_Cover-Canis_latrans -0.0376 0.7632 -1.6468 -0.0060
## Avg_Canopy_Cover-Procyon_lotor 1.6719 0.8848 0.1471 1.5998
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5808 1.1824 0.8434 2.3715
## Avg_Canopy_Cover-Lynx_rufus 0.8038 1.5432 -2.2490 0.7152
## Avg_Canopy_Cover-Didelphis_virginiana 3.6295 1.7165 1.2825 3.2851
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7933 2.5991 1.3510 4.2548
## Avg_Canopy_Cover-Meleagris_gallopavo 2.8455 1.7333 0.4341 2.5076
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7722 2.1763 1.0585 3.2372
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4822 1.3114 0.6908 2.2006
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3843 1.1509 0.7204 2.2140
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4743 0.7850 0.0959 1.4154
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6609 1.4042 0.6684 2.3928
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9209 0.8270 -0.5751 0.8937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2289 0.9814 -0.4732 1.1499
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2642 1.7547 -3.9652 0.4445
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6093 0.9047 0.0183 1.5537
## avg_veg_height-Canis_latrans -0.1789 0.7012 -1.6012 -0.1811
## avg_veg_height-Procyon_lotor 0.1219 0.6944 -1.2103 0.1001
## avg_veg_height-Dasypus_novemcinctus 0.3956 0.7436 -0.8763 0.3328
## avg_veg_height-Lynx_rufus -0.2799 0.9484 -2.4262 -0.2386
## avg_veg_height-Didelphis_virginiana -0.1731 0.8224 -1.8785 -0.1721
## avg_veg_height-Sylvilagus_floridanus -0.1315 0.8040 -1.8216 -0.1064
## avg_veg_height-Meleagris_gallopavo -0.0377 0.9682 -2.0756 -0.0105
## avg_veg_height-Sciurus_carolinensis 0.4475 0.8447 -0.9604 0.3531
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5151 1.0495 143
## (Intercept)-Procyon_lotor 0.7894 1.0681 159
## (Intercept)-Dasypus_novemcinctus -0.4404 1.0325 334
## (Intercept)-Lynx_rufus 1.0038 1.0361 189
## (Intercept)-Didelphis_virginiana -0.7748 1.0344 239
## (Intercept)-Sylvilagus_floridanus 0.1072 1.0335 434
## (Intercept)-Meleagris_gallopavo 0.5456 1.1027 171
## (Intercept)-Sciurus_carolinensis -0.8784 1.0143 221
## Cogon_Patch_Size-Canis_latrans 5.1109 1.0105 354
## Cogon_Patch_Size-Procyon_lotor 1.0889 1.0713 370
## Cogon_Patch_Size-Dasypus_novemcinctus 2.2195 1.0152 426
## Cogon_Patch_Size-Lynx_rufus 2.7845 1.0157 400
## Cogon_Patch_Size-Didelphis_virginiana 4.2871 1.0425 272
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3097 1.0175 320
## Cogon_Patch_Size-Meleagris_gallopavo 2.9260 1.0326 404
## Cogon_Patch_Size-Sciurus_carolinensis 1.8868 1.0105 389
## Veg_shannon_index-Canis_latrans 3.5244 1.3090 194
## Veg_shannon_index-Procyon_lotor 2.9140 1.1665 206
## Veg_shannon_index-Dasypus_novemcinctus 2.0628 1.0300 396
## Veg_shannon_index-Lynx_rufus 2.7606 1.0475 394
## Veg_shannon_index-Didelphis_virginiana 3.1308 1.1662 319
## Veg_shannon_index-Sylvilagus_floridanus 3.0987 1.1571 264
## Veg_shannon_index-Meleagris_gallopavo 3.4019 1.2015 344
## Veg_shannon_index-Sciurus_carolinensis 2.1401 1.0504 263
## total_shrub_cover-Canis_latrans 2.8330 1.0382 277
## total_shrub_cover-Procyon_lotor -0.2915 1.1116 573
## total_shrub_cover-Dasypus_novemcinctus 1.0430 1.1310 170
## total_shrub_cover-Lynx_rufus -0.0244 1.0513 200
## total_shrub_cover-Didelphis_virginiana 0.2427 1.0790 165
## total_shrub_cover-Sylvilagus_floridanus 0.9083 1.0331 286
## total_shrub_cover-Meleagris_gallopavo -0.4276 1.0545 182
## total_shrub_cover-Sciurus_carolinensis 0.8732 1.0513 284
## Avg_Cogongrass_Cover-Canis_latrans 2.4727 1.0944 276
## Avg_Cogongrass_Cover-Procyon_lotor 2.2874 1.0581 250
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5927 1.1909 266
## Avg_Cogongrass_Cover-Lynx_rufus 2.8241 1.0825 286
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7401 1.1624 226
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7494 1.0686 328
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2678 1.0646 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6937 1.1200 298
## Tree_Density-Canis_latrans -0.7463 1.3500 153
## Tree_Density-Procyon_lotor -0.3051 1.0940 299
## Tree_Density-Dasypus_novemcinctus -1.4839 1.6099 81
## Tree_Density-Lynx_rufus 2.6643 1.0185 226
## Tree_Density-Didelphis_virginiana 0.9396 1.0353 357
## Tree_Density-Sylvilagus_floridanus 0.0874 1.1652 229
## Tree_Density-Meleagris_gallopavo 0.4388 1.0311 459
## Tree_Density-Sciurus_carolinensis 0.5901 1.2076 160
## Avg_Canopy_Cover-Canis_latrans 1.4031 1.0957 333
## Avg_Canopy_Cover-Procyon_lotor 3.6444 1.0315 370
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.3397 1.2214 152
## Avg_Canopy_Cover-Lynx_rufus 4.1138 1.0348 229
## Avg_Canopy_Cover-Didelphis_virginiana 8.0300 1.2355 128
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.3224 1.4188 125
## Avg_Canopy_Cover-Meleagris_gallopavo 7.4098 1.1435 190
## Avg_Canopy_Cover-Sciurus_carolinensis 9.2917 1.3946 154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8821 1.1220 216
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.3068 1.0496 251
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1883 1.0129 313
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.0481 1.0847 228
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5541 1.0250 226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3581 1.0130 240
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.0445 1.0171 110
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5370 1.0115 387
## avg_veg_height-Canis_latrans 1.2407 1.0551 453
## avg_veg_height-Procyon_lotor 1.6057 1.0487 420
## avg_veg_height-Dasypus_novemcinctus 1.9808 1.0243 372
## avg_veg_height-Lynx_rufus 1.5367 1.0313 384
## avg_veg_height-Didelphis_virginiana 1.4393 1.0307 474
## avg_veg_height-Sylvilagus_floridanus 1.4566 1.0347 346
## avg_veg_height-Meleagris_gallopavo 1.8896 1.0112 356
## avg_veg_height-Sciurus_carolinensis 2.4305 1.0202 396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5665 0.1964 -2.9667 -2.5596 -2.1966 1.0148
## (Intercept)-Procyon_lotor -2.2220 0.1603 -2.5492 -2.2178 -1.9222 1.0003
## (Intercept)-Dasypus_novemcinctus -1.7267 0.1927 -2.1130 -1.7197 -1.3563 1.0040
## (Intercept)-Lynx_rufus -3.4367 0.3411 -4.1434 -3.4161 -2.8252 1.0088
## (Intercept)-Didelphis_virginiana -2.5279 0.3085 -3.1539 -2.5193 -1.9489 1.0044
## (Intercept)-Sylvilagus_floridanus -3.0759 0.2725 -3.6303 -3.0747 -2.5567 1.0013
## (Intercept)-Meleagris_gallopavo -3.4379 0.4833 -4.4746 -3.4131 -2.5187 1.0068
## (Intercept)-Sciurus_carolinensis -2.7497 0.3236 -3.4010 -2.7428 -2.1494 1.0407
## shrub_cover-Canis_latrans -0.2946 0.2349 -0.7379 -0.2967 0.1786 1.0113
## shrub_cover-Procyon_lotor 0.2871 0.1655 -0.0505 0.2897 0.6013 1.0094
## shrub_cover-Dasypus_novemcinctus 1.0645 0.3200 0.4387 1.0702 1.6917 1.0257
## shrub_cover-Lynx_rufus 0.0971 0.3628 -0.6490 0.0982 0.7712 1.0073
## shrub_cover-Didelphis_virginiana 1.1610 0.3764 0.4487 1.1542 1.9136 1.0219
## shrub_cover-Sylvilagus_floridanus 0.6502 0.3990 -0.1371 0.6460 1.4442 1.0102
## shrub_cover-Meleagris_gallopavo -0.3588 0.4370 -1.2529 -0.3527 0.4889 1.0043
## shrub_cover-Sciurus_carolinensis 1.1505 0.3988 0.3577 1.1638 1.9452 1.0236
## veg_height-Canis_latrans -0.5532 0.1886 -0.9258 -0.5496 -0.1925 1.0004
## veg_height-Procyon_lotor 0.3678 0.1240 0.1302 0.3696 0.6119 1.0060
## veg_height-Dasypus_novemcinctus 0.3014 0.1388 0.0362 0.2964 0.5856 1.0005
## veg_height-Lynx_rufus 0.1483 0.2430 -0.3453 0.1518 0.6168 1.0331
## veg_height-Didelphis_virginiana 0.5205 0.2499 0.0418 0.5121 1.0244 1.0118
## veg_height-Sylvilagus_floridanus 0.1563 0.2460 -0.3356 0.1553 0.6297 1.0098
## veg_height-Meleagris_gallopavo -0.0766 0.4041 -0.8718 -0.0808 0.7663 1.0087
## veg_height-Sciurus_carolinensis 0.2055 0.2277 -0.2233 0.2021 0.6629 1.0204
## week-Canis_latrans 0.4538 0.2530 -0.0022 0.4376 0.9826 1.0079
## week-Procyon_lotor 0.1566 0.2007 -0.2326 0.1575 0.5551 1.0033
## week-Dasypus_novemcinctus 0.0687 0.2159 -0.3649 0.0714 0.4870 1.0062
## week-Lynx_rufus 0.2756 0.3044 -0.3161 0.2622 0.8882 1.0004
## week-Didelphis_virginiana 0.0472 0.3143 -0.6078 0.0570 0.6316 1.0093
## week-Sylvilagus_floridanus 0.0393 0.3090 -0.5960 0.0538 0.6161 1.0150
## week-Meleagris_gallopavo -0.1136 0.3574 -0.9206 -0.0844 0.5169 1.0114
## week-Sciurus_carolinensis 0.5536 0.3524 -0.0613 0.5224 1.3104 1.0162
## I(week^2)-Canis_latrans -0.1975 0.1074 -0.4199 -0.1954 0.0017 1.0001
## I(week^2)-Procyon_lotor -0.1096 0.0885 -0.2840 -0.1095 0.0631 1.0059
## I(week^2)-Dasypus_novemcinctus -0.1590 0.1035 -0.3639 -0.1568 0.0459 1.0033
## I(week^2)-Lynx_rufus -0.2106 0.1412 -0.5050 -0.2055 0.0523 1.0015
## I(week^2)-Didelphis_virginiana -0.3725 0.2211 -0.9149 -0.3459 -0.0282 1.0099
## I(week^2)-Sylvilagus_floridanus -0.1681 0.1490 -0.4776 -0.1615 0.1022 1.0139
## I(week^2)-Meleagris_gallopavo -0.3847 0.2363 -0.9426 -0.3528 -0.0092 1.0308
## I(week^2)-Sciurus_carolinensis -0.2002 0.1440 -0.4876 -0.1952 0.0646 1.0095
## ESS
## (Intercept)-Canis_latrans 978
## (Intercept)-Procyon_lotor 1415
## (Intercept)-Dasypus_novemcinctus 804
## (Intercept)-Lynx_rufus 339
## (Intercept)-Didelphis_virginiana 486
## (Intercept)-Sylvilagus_floridanus 722
## (Intercept)-Meleagris_gallopavo 245
## (Intercept)-Sciurus_carolinensis 546
## shrub_cover-Canis_latrans 655
## shrub_cover-Procyon_lotor 1418
## shrub_cover-Dasypus_novemcinctus 299
## shrub_cover-Lynx_rufus 356
## shrub_cover-Didelphis_virginiana 365
## shrub_cover-Sylvilagus_floridanus 263
## shrub_cover-Meleagris_gallopavo 363
## shrub_cover-Sciurus_carolinensis 459
## veg_height-Canis_latrans 706
## veg_height-Procyon_lotor 1511
## veg_height-Dasypus_novemcinctus 1871
## veg_height-Lynx_rufus 557
## veg_height-Didelphis_virginiana 955
## veg_height-Sylvilagus_floridanus 700
## veg_height-Meleagris_gallopavo 290
## veg_height-Sciurus_carolinensis 676
## week-Canis_latrans 1049
## week-Procyon_lotor 1527
## week-Dasypus_novemcinctus 2089
## week-Lynx_rufus 914
## week-Didelphis_virginiana 1060
## week-Sylvilagus_floridanus 1032
## week-Meleagris_gallopavo 609
## week-Sciurus_carolinensis 972
## I(week^2)-Canis_latrans 1169
## I(week^2)-Procyon_lotor 1621
## I(week^2)-Dasypus_novemcinctus 1556
## I(week^2)-Lynx_rufus 782
## I(week^2)-Didelphis_virginiana 413
## I(week^2)-Sylvilagus_floridanus 633
## I(week^2)-Meleagris_gallopavo 306
## I(week^2)-Sciurus_carolinensis 1007
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] -1.65 -1.75 -1.78 -1.54 -1.82 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Lynx_rufus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.002666667
MCMCplot(ms_fullQ_fullQ_T10$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] 0.0403 0.0298 0.2022 0.0766 0.0969 ...
## $ z.0.samples : int [1:3000, 1:8, 1:100] 0 0 0 0 0 0 0 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T10, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:8, 1:100] 0.0403 0.0298 0.2022 0.0766 0.0969 ...
psi.0.quants <- apply(out.pred$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] 0.000891 0.119146 0.90786 0.001017 0.119811 ...
## - 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 0.119 0.12 0.12 0.118 0.121 ...
## $ psi.low : num 0.000891 0.001017 0.00107 0.001051 0.001158 ...
## $ psi.high : num 0.908 0.89 0.893 0.894 0.894 ...
## $ 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): 0.7402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8426 0.7256 -3.3647 -1.8164 -0.4788 1.0599 212
## Cogon_Patch_Size 0.1816 0.7249 -1.2491 0.1845 1.6279 1.0098 466
## Veg_shannon_index 1.0277 0.5606 0.0419 0.9797 2.2960 1.1707 266
## total_shrub_cover -1.2203 0.7275 -2.7558 -1.1914 0.1461 1.1243 285
## Avg_Cogongrass_Cover -0.0976 0.9767 -2.0057 -0.1184 1.8606 1.1362 172
## Tree_Density -2.1959 0.9321 -3.9168 -2.2452 -0.3033 1.0397 218
## Avg_Canopy_Cover 1.9176 0.8822 0.1757 1.9081 3.7656 1.0313 751
## I(Avg_Cogongrass_Cover^2) 1.4719 0.6984 0.0901 1.4422 2.8555 1.0011 283
## avg_veg_height 0.0161 0.5865 -1.0672 -0.0027 1.2299 1.0624 316
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8173 3.0834 0.0585 0.7941 10.4586 1.0218 233
## Cogon_Patch_Size 2.9735 4.6104 0.1191 1.5443 14.1790 1.0383 356
## Veg_shannon_index 0.8256 1.6883 0.0449 0.3532 4.6446 1.3022 182
## total_shrub_cover 2.8908 4.4705 0.0970 1.4138 14.5800 1.0259 228
## Avg_Cogongrass_Cover 1.6080 3.7210 0.0564 0.5684 8.8952 1.4056 354
## Tree_Density 5.3004 13.3553 0.0724 1.7905 33.5698 1.8763 108
## Avg_Canopy_Cover 7.3016 11.0499 0.3123 3.7448 35.9709 1.4323 89
## I(Avg_Cogongrass_Cover^2) 2.5312 4.4767 0.0664 1.0935 13.7667 1.0579 166
## avg_veg_height 0.7626 1.4842 0.0470 0.3660 4.0373 1.0418 680
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6313 3.8477 0.0667 1.3127 13.2462 1.3492 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6353 0.3086 -3.2528 -2.6404 -2.0091 1.0014 1500
## shrub_cover 0.4520 0.3239 -0.1997 0.4545 1.0746 1.0068 1166
## veg_height 0.1319 0.2029 -0.2735 0.1355 0.5327 1.0062 1245
## week 0.1802 0.2132 -0.2486 0.1783 0.5907 1.0001 1193
## I(week^2) -0.2228 0.1224 -0.4817 -0.2199 0.0129 1.0088 1033
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6593 0.6393 0.1400 0.4834 2.2292 1.0093 1114
## shrub_cover 0.7037 0.5791 0.1537 0.5487 2.3013 1.0069 1531
## veg_height 0.2587 0.2525 0.0572 0.1941 0.8215 1.0050 1478
## week 0.2183 0.2590 0.0331 0.1415 0.8826 1.0163 1334
## I(week^2) 0.0844 0.0879 0.0204 0.0591 0.2969 1.0018 898
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5051 1.0013 -3.4760 -1.5081
## (Intercept)-Procyon_lotor -1.2207 1.0230 -3.2328 -1.2350
## (Intercept)-Dasypus_novemcinctus -2.1642 0.9961 -4.2748 -2.1123
## (Intercept)-Lynx_rufus -1.5895 1.1941 -3.8660 -1.6457
## (Intercept)-Didelphis_virginiana -2.6726 1.1745 -5.3701 -2.5156
## (Intercept)-Sylvilagus_floridanus -1.9414 1.0556 -4.2258 -1.8929
## (Intercept)-Meleagris_gallopavo -1.8269 1.3035 -4.3361 -1.8365
## (Intercept)-Sciurus_carolinensis -2.8723 1.3430 -6.0907 -2.6591
## Cogon_Patch_Size-Canis_latrans 1.5701 1.3934 -0.4465 1.3151
## Cogon_Patch_Size-Procyon_lotor -0.4735 0.8503 -2.2225 -0.4284
## Cogon_Patch_Size-Dasypus_novemcinctus 0.2255 0.9325 -1.5277 0.1953
## Cogon_Patch_Size-Lynx_rufus -0.0686 1.3764 -2.5761 -0.0913
## Cogon_Patch_Size-Didelphis_virginiana 1.5539 1.1283 -0.1972 1.4163
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9975 1.4897 -4.6302 -0.7771
## Cogon_Patch_Size-Meleagris_gallopavo 0.3490 1.1910 -1.7163 0.2738
## Cogon_Patch_Size-Sciurus_carolinensis -0.6316 1.3593 -3.8158 -0.5305
## Veg_shannon_index-Canis_latrans 1.3964 0.8810 0.0703 1.2663
## Veg_shannon_index-Procyon_lotor 1.2985 0.7190 0.1118 1.2129
## Veg_shannon_index-Dasypus_novemcinctus 0.6926 0.6626 -0.6091 0.6718
## Veg_shannon_index-Lynx_rufus 0.9440 0.8985 -0.9003 0.9242
## Veg_shannon_index-Didelphis_virginiana 1.2553 0.7958 -0.0676 1.1645
## Veg_shannon_index-Sylvilagus_floridanus 1.1487 0.8220 -0.3058 1.0693
## Veg_shannon_index-Meleagris_gallopavo 1.3545 0.8701 -0.0459 1.2221
## Veg_shannon_index-Sciurus_carolinensis 0.4871 0.9764 -1.4853 0.5685
## total_shrub_cover-Canis_latrans 0.2269 1.0665 -1.5509 0.1029
## total_shrub_cover-Procyon_lotor -1.5672 0.7518 -3.2211 -1.4899
## total_shrub_cover-Dasypus_novemcinctus -0.7036 0.9979 -2.9107 -0.6356
## total_shrub_cover-Lynx_rufus -2.1184 1.4840 -5.5721 -1.8556
## total_shrub_cover-Didelphis_virginiana -1.6527 1.2649 -4.7810 -1.4353
## total_shrub_cover-Sylvilagus_floridanus -1.3631 1.3584 -4.6236 -1.2185
## total_shrub_cover-Meleagris_gallopavo -2.7359 1.6027 -6.5792 -2.4767
## total_shrub_cover-Sciurus_carolinensis -1.3139 1.2851 -4.3543 -1.1772
## Avg_Cogongrass_Cover-Canis_latrans -0.0173 1.2788 -2.4547 -0.0696
## Avg_Cogongrass_Cover-Procyon_lotor -0.2186 1.2526 -2.6028 -0.2103
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5431 1.4037 -1.8450 0.4207
## Avg_Cogongrass_Cover-Lynx_rufus -0.0045 1.3476 -2.5340 -0.0818
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0489 1.3027 -2.3752 0.0302
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7028 1.3410 -3.4829 -0.6226
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4065 1.4885 -3.6961 -0.3706
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0099 1.3328 -2.5445 -0.0447
## Tree_Density-Canis_latrans -3.2554 1.7920 -7.8204 -2.9255
## Tree_Density-Procyon_lotor -2.3363 1.1383 -4.6304 -2.2755
## Tree_Density-Dasypus_novemcinctus -4.4319 2.6479 -11.5034 -3.7298
## Tree_Density-Lynx_rufus -0.9187 1.6158 -3.5413 -1.0673
## Tree_Density-Didelphis_virginiana -2.2496 1.4922 -5.3618 -2.2665
## Tree_Density-Sylvilagus_floridanus -2.7843 1.6283 -6.6444 -2.6144
## Tree_Density-Meleagris_gallopavo -2.3672 1.4353 -5.4491 -2.3568
## Tree_Density-Sciurus_carolinensis -2.5488 1.9132 -6.4080 -2.4427
## Avg_Canopy_Cover-Canis_latrans -0.0376 0.7632 -1.6468 -0.0060
## Avg_Canopy_Cover-Procyon_lotor 1.6719 0.8848 0.1471 1.5998
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5808 1.1824 0.8434 2.3715
## Avg_Canopy_Cover-Lynx_rufus 0.8038 1.5432 -2.2490 0.7152
## Avg_Canopy_Cover-Didelphis_virginiana 3.6295 1.7165 1.2825 3.2851
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7933 2.5991 1.3510 4.2548
## Avg_Canopy_Cover-Meleagris_gallopavo 2.8455 1.7333 0.4341 2.5076
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7722 2.1763 1.0585 3.2372
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4822 1.3114 0.6908 2.2006
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3843 1.1509 0.7204 2.2140
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4743 0.7850 0.0959 1.4154
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6609 1.4042 0.6684 2.3928
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9209 0.8270 -0.5751 0.8937
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2289 0.9814 -0.4732 1.1499
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2642 1.7547 -3.9652 0.4445
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6093 0.9047 0.0183 1.5537
## avg_veg_height-Canis_latrans -0.1789 0.7012 -1.6012 -0.1811
## avg_veg_height-Procyon_lotor 0.1219 0.6944 -1.2103 0.1001
## avg_veg_height-Dasypus_novemcinctus 0.3956 0.7436 -0.8763 0.3328
## avg_veg_height-Lynx_rufus -0.2799 0.9484 -2.4262 -0.2386
## avg_veg_height-Didelphis_virginiana -0.1731 0.8224 -1.8785 -0.1721
## avg_veg_height-Sylvilagus_floridanus -0.1315 0.8040 -1.8216 -0.1064
## avg_veg_height-Meleagris_gallopavo -0.0377 0.9682 -2.0756 -0.0105
## avg_veg_height-Sciurus_carolinensis 0.4475 0.8447 -0.9604 0.3531
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5151 1.0495 143
## (Intercept)-Procyon_lotor 0.7894 1.0681 159
## (Intercept)-Dasypus_novemcinctus -0.4404 1.0325 334
## (Intercept)-Lynx_rufus 1.0038 1.0361 189
## (Intercept)-Didelphis_virginiana -0.7748 1.0344 239
## (Intercept)-Sylvilagus_floridanus 0.1072 1.0335 434
## (Intercept)-Meleagris_gallopavo 0.5456 1.1027 171
## (Intercept)-Sciurus_carolinensis -0.8784 1.0143 221
## Cogon_Patch_Size-Canis_latrans 5.1109 1.0105 354
## Cogon_Patch_Size-Procyon_lotor 1.0889 1.0713 370
## Cogon_Patch_Size-Dasypus_novemcinctus 2.2195 1.0152 426
## Cogon_Patch_Size-Lynx_rufus 2.7845 1.0157 400
## Cogon_Patch_Size-Didelphis_virginiana 4.2871 1.0425 272
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3097 1.0175 320
## Cogon_Patch_Size-Meleagris_gallopavo 2.9260 1.0326 404
## Cogon_Patch_Size-Sciurus_carolinensis 1.8868 1.0105 389
## Veg_shannon_index-Canis_latrans 3.5244 1.3090 194
## Veg_shannon_index-Procyon_lotor 2.9140 1.1665 206
## Veg_shannon_index-Dasypus_novemcinctus 2.0628 1.0300 396
## Veg_shannon_index-Lynx_rufus 2.7606 1.0475 394
## Veg_shannon_index-Didelphis_virginiana 3.1308 1.1662 319
## Veg_shannon_index-Sylvilagus_floridanus 3.0987 1.1571 264
## Veg_shannon_index-Meleagris_gallopavo 3.4019 1.2015 344
## Veg_shannon_index-Sciurus_carolinensis 2.1401 1.0504 263
## total_shrub_cover-Canis_latrans 2.8330 1.0382 277
## total_shrub_cover-Procyon_lotor -0.2915 1.1116 573
## total_shrub_cover-Dasypus_novemcinctus 1.0430 1.1310 170
## total_shrub_cover-Lynx_rufus -0.0244 1.0513 200
## total_shrub_cover-Didelphis_virginiana 0.2427 1.0790 165
## total_shrub_cover-Sylvilagus_floridanus 0.9083 1.0331 286
## total_shrub_cover-Meleagris_gallopavo -0.4276 1.0545 182
## total_shrub_cover-Sciurus_carolinensis 0.8732 1.0513 284
## Avg_Cogongrass_Cover-Canis_latrans 2.4727 1.0944 276
## Avg_Cogongrass_Cover-Procyon_lotor 2.2874 1.0581 250
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5927 1.1909 266
## Avg_Cogongrass_Cover-Lynx_rufus 2.8241 1.0825 286
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7401 1.1624 226
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7494 1.0686 328
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2678 1.0646 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6937 1.1200 298
## Tree_Density-Canis_latrans -0.7463 1.3500 153
## Tree_Density-Procyon_lotor -0.3051 1.0940 299
## Tree_Density-Dasypus_novemcinctus -1.4839 1.6099 81
## Tree_Density-Lynx_rufus 2.6643 1.0185 226
## Tree_Density-Didelphis_virginiana 0.9396 1.0353 357
## Tree_Density-Sylvilagus_floridanus 0.0874 1.1652 229
## Tree_Density-Meleagris_gallopavo 0.4388 1.0311 459
## Tree_Density-Sciurus_carolinensis 0.5901 1.2076 160
## Avg_Canopy_Cover-Canis_latrans 1.4031 1.0957 333
## Avg_Canopy_Cover-Procyon_lotor 3.6444 1.0315 370
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.3397 1.2214 152
## Avg_Canopy_Cover-Lynx_rufus 4.1138 1.0348 229
## Avg_Canopy_Cover-Didelphis_virginiana 8.0300 1.2355 128
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.3224 1.4188 125
## Avg_Canopy_Cover-Meleagris_gallopavo 7.4098 1.1435 190
## Avg_Canopy_Cover-Sciurus_carolinensis 9.2917 1.3946 154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8821 1.1220 216
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.3068 1.0496 251
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1883 1.0129 313
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.0481 1.0847 228
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5541 1.0250 226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3581 1.0130 240
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.0445 1.0171 110
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5370 1.0115 387
## avg_veg_height-Canis_latrans 1.2407 1.0551 453
## avg_veg_height-Procyon_lotor 1.6057 1.0487 420
## avg_veg_height-Dasypus_novemcinctus 1.9808 1.0243 372
## avg_veg_height-Lynx_rufus 1.5367 1.0313 384
## avg_veg_height-Didelphis_virginiana 1.4393 1.0307 474
## avg_veg_height-Sylvilagus_floridanus 1.4566 1.0347 346
## avg_veg_height-Meleagris_gallopavo 1.8896 1.0112 356
## avg_veg_height-Sciurus_carolinensis 2.4305 1.0202 396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5665 0.1964 -2.9667 -2.5596 -2.1966 1.0148
## (Intercept)-Procyon_lotor -2.2220 0.1603 -2.5492 -2.2178 -1.9222 1.0003
## (Intercept)-Dasypus_novemcinctus -1.7267 0.1927 -2.1130 -1.7197 -1.3563 1.0040
## (Intercept)-Lynx_rufus -3.4367 0.3411 -4.1434 -3.4161 -2.8252 1.0088
## (Intercept)-Didelphis_virginiana -2.5279 0.3085 -3.1539 -2.5193 -1.9489 1.0044
## (Intercept)-Sylvilagus_floridanus -3.0759 0.2725 -3.6303 -3.0747 -2.5567 1.0013
## (Intercept)-Meleagris_gallopavo -3.4379 0.4833 -4.4746 -3.4131 -2.5187 1.0068
## (Intercept)-Sciurus_carolinensis -2.7497 0.3236 -3.4010 -2.7428 -2.1494 1.0407
## shrub_cover-Canis_latrans -0.2946 0.2349 -0.7379 -0.2967 0.1786 1.0113
## shrub_cover-Procyon_lotor 0.2871 0.1655 -0.0505 0.2897 0.6013 1.0094
## shrub_cover-Dasypus_novemcinctus 1.0645 0.3200 0.4387 1.0702 1.6917 1.0257
## shrub_cover-Lynx_rufus 0.0971 0.3628 -0.6490 0.0982 0.7712 1.0073
## shrub_cover-Didelphis_virginiana 1.1610 0.3764 0.4487 1.1542 1.9136 1.0219
## shrub_cover-Sylvilagus_floridanus 0.6502 0.3990 -0.1371 0.6460 1.4442 1.0102
## shrub_cover-Meleagris_gallopavo -0.3588 0.4370 -1.2529 -0.3527 0.4889 1.0043
## shrub_cover-Sciurus_carolinensis 1.1505 0.3988 0.3577 1.1638 1.9452 1.0236
## veg_height-Canis_latrans -0.5532 0.1886 -0.9258 -0.5496 -0.1925 1.0004
## veg_height-Procyon_lotor 0.3678 0.1240 0.1302 0.3696 0.6119 1.0060
## veg_height-Dasypus_novemcinctus 0.3014 0.1388 0.0362 0.2964 0.5856 1.0005
## veg_height-Lynx_rufus 0.1483 0.2430 -0.3453 0.1518 0.6168 1.0331
## veg_height-Didelphis_virginiana 0.5205 0.2499 0.0418 0.5121 1.0244 1.0118
## veg_height-Sylvilagus_floridanus 0.1563 0.2460 -0.3356 0.1553 0.6297 1.0098
## veg_height-Meleagris_gallopavo -0.0766 0.4041 -0.8718 -0.0808 0.7663 1.0087
## veg_height-Sciurus_carolinensis 0.2055 0.2277 -0.2233 0.2021 0.6629 1.0204
## week-Canis_latrans 0.4538 0.2530 -0.0022 0.4376 0.9826 1.0079
## week-Procyon_lotor 0.1566 0.2007 -0.2326 0.1575 0.5551 1.0033
## week-Dasypus_novemcinctus 0.0687 0.2159 -0.3649 0.0714 0.4870 1.0062
## week-Lynx_rufus 0.2756 0.3044 -0.3161 0.2622 0.8882 1.0004
## week-Didelphis_virginiana 0.0472 0.3143 -0.6078 0.0570 0.6316 1.0093
## week-Sylvilagus_floridanus 0.0393 0.3090 -0.5960 0.0538 0.6161 1.0150
## week-Meleagris_gallopavo -0.1136 0.3574 -0.9206 -0.0844 0.5169 1.0114
## week-Sciurus_carolinensis 0.5536 0.3524 -0.0613 0.5224 1.3104 1.0162
## I(week^2)-Canis_latrans -0.1975 0.1074 -0.4199 -0.1954 0.0017 1.0001
## I(week^2)-Procyon_lotor -0.1096 0.0885 -0.2840 -0.1095 0.0631 1.0059
## I(week^2)-Dasypus_novemcinctus -0.1590 0.1035 -0.3639 -0.1568 0.0459 1.0033
## I(week^2)-Lynx_rufus -0.2106 0.1412 -0.5050 -0.2055 0.0523 1.0015
## I(week^2)-Didelphis_virginiana -0.3725 0.2211 -0.9149 -0.3459 -0.0282 1.0099
## I(week^2)-Sylvilagus_floridanus -0.1681 0.1490 -0.4776 -0.1615 0.1022 1.0139
## I(week^2)-Meleagris_gallopavo -0.3847 0.2363 -0.9426 -0.3528 -0.0092 1.0308
## I(week^2)-Sciurus_carolinensis -0.2002 0.1440 -0.4876 -0.1952 0.0646 1.0095
## ESS
## (Intercept)-Canis_latrans 978
## (Intercept)-Procyon_lotor 1415
## (Intercept)-Dasypus_novemcinctus 804
## (Intercept)-Lynx_rufus 339
## (Intercept)-Didelphis_virginiana 486
## (Intercept)-Sylvilagus_floridanus 722
## (Intercept)-Meleagris_gallopavo 245
## (Intercept)-Sciurus_carolinensis 546
## shrub_cover-Canis_latrans 655
## shrub_cover-Procyon_lotor 1418
## shrub_cover-Dasypus_novemcinctus 299
## shrub_cover-Lynx_rufus 356
## shrub_cover-Didelphis_virginiana 365
## shrub_cover-Sylvilagus_floridanus 263
## shrub_cover-Meleagris_gallopavo 363
## shrub_cover-Sciurus_carolinensis 459
## veg_height-Canis_latrans 706
## veg_height-Procyon_lotor 1511
## veg_height-Dasypus_novemcinctus 1871
## veg_height-Lynx_rufus 557
## veg_height-Didelphis_virginiana 955
## veg_height-Sylvilagus_floridanus 700
## veg_height-Meleagris_gallopavo 290
## veg_height-Sciurus_carolinensis 676
## week-Canis_latrans 1049
## week-Procyon_lotor 1527
## week-Dasypus_novemcinctus 2089
## week-Lynx_rufus 914
## week-Didelphis_virginiana 1060
## week-Sylvilagus_floridanus 1032
## week-Meleagris_gallopavo 609
## week-Sciurus_carolinensis 972
## I(week^2)-Canis_latrans 1169
## I(week^2)-Procyon_lotor 1621
## I(week^2)-Dasypus_novemcinctus 1556
## I(week^2)-Lynx_rufus 782
## I(week^2)-Didelphis_virginiana 413
## I(week^2)-Sylvilagus_floridanus 633
## I(week^2)-Meleagris_gallopavo 306
## I(week^2)-Sciurus_carolinensis 1007
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] -1.65 -1.75 -1.78 -1.54 -1.82 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Lynx_rufus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.002666667
MCMCplot(ms_fullQ_fullQ_T10$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 %in% c("Odocoileus_virginianus", "Dasypus_novemcinctus",
"Procyon_lotor", "Sciurus_niger")) %>%
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$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 2
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$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, 1:2] 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 [1:2] "Canis_latrans" "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 [1:2, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:2] "Canis_latrans" "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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1477
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0339 0.7518 -1.5215 0.0348 1.6042 1.0035 2134
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6087 27.9569 0.0516 0.493 19.9948 1.1529 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2112 1.1137 -3.6496 -2.5297 0.8932 1.0114 1566
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.7871 98.733 0.06 0.8167 63.9871 1.191 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2762 0.4057 -0.4565 0.2487 1.1266 1.0111
## (Intercept)-Sylvilagus_floridanus -0.1953 0.5130 -1.1298 -0.2183 0.8668 1.0055
## ESS
## (Intercept)-Canis_latrans 1590
## (Intercept)-Sylvilagus_floridanus 682
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6288 0.1809 -3.0225 -2.6179 -2.2869 1.0023
## (Intercept)-Sylvilagus_floridanus -3.1878 0.3202 -3.8509 -3.1703 -2.6123 1.0091
## ESS
## (Intercept)-Canis_latrans 979
## (Intercept)-Sylvilagus_floridanus 405
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2115
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6791 1.3532 -2.0634 0.6931 3.2475 1.0100 440
## Cogon_Patch_Size 0.1441 1.3969 -2.7277 0.1359 2.9218 1.0084 711
## Veg_shannon_index 1.1758 1.2856 -1.6413 1.2404 3.4853 1.0120 805
## total_shrub_cover 0.5670 1.6025 -2.6376 0.5723 3.8237 1.0528 269
## Avg_Cogongrass_Cover 0.6742 1.4787 -2.3023 0.6833 3.5748 1.0382 911
## Tree_Density -1.2470 1.7157 -4.3617 -1.3492 2.2257 1.0768 405
## Avg_Canopy_Cover 0.4495 1.6125 -2.7957 0.4948 3.4909 1.0126 3000
## avg_veg_height -0.2067 1.2069 -2.5327 -0.2504 2.2283 1.0018 783
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 29.9227 214.5883 0.0704 1.9406 170.0797 1.0774 2356
## Cogon_Patch_Size 80.8818 1596.4107 0.0785 4.7736 384.5630 1.3260 3000
## Veg_shannon_index 44.8969 392.8236 0.0560 1.3813 270.5991 1.4179 167
## total_shrub_cover 195.5317 3450.6594 0.0895 7.8775 620.3681 1.2454 3000
## Avg_Cogongrass_Cover 247.7544 8459.1537 0.0801 4.1658 650.5554 1.3548 3000
## Tree_Density 545.8488 4151.4497 0.0830 14.2335 3810.2456 1.2088 366
## Avg_Canopy_Cover 1086.3185 11583.3939 1.6650 93.6131 5423.3378 1.4768 720
## avg_veg_height 24.6752 680.9476 0.0592 0.8438 63.8565 1.2459 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.6563 27.0409 0.0674 1.3373 51.8756 1.4054 129
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3282 1.1681 -3.7339 -2.6863 0.9108 1.0092 1419
## shrub_cover -0.0839 0.8205 -1.7675 -0.1075 1.6973 1.0251 1786
## veg_height -0.2209 0.7719 -1.7561 -0.2539 1.5680 1.0076 2383
## week -0.0592 0.6318 -1.4602 -0.0445 1.3176 1.0028 2644
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 27.6673 677.4722 0.0517 0.7213 62.6192 1.2368 2716
## shrub_cover 7.5349 168.7131 0.0672 0.7569 24.8855 1.3040 3000
## veg_height 12.3316 398.9707 0.0794 0.7331 21.5051 1.2880 3000
## week 2.3154 18.5271 0.0455 0.3613 13.0767 1.2416 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.9982 2.0140 -0.9364 1.7048
## (Intercept)-Sylvilagus_floridanus -0.0090 2.6722 -6.3356 0.2743
## Cogon_Patch_Size-Canis_latrans 1.9211 2.3652 -1.5883 1.4954
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0482 3.7638 -12.0877 -1.2465
## Veg_shannon_index-Canis_latrans 2.2155 3.1204 -0.7223 1.7414
## Veg_shannon_index-Sylvilagus_floridanus 2.5927 3.5846 -0.7603 1.9073
## total_shrub_cover-Canis_latrans 2.8029 2.4461 -0.7225 2.4136
## total_shrub_cover-Sylvilagus_floridanus -1.6124 6.4253 -17.9426 -0.3727
## Avg_Cogongrass_Cover-Canis_latrans 3.6193 4.4208 -0.6444 2.4958
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7604 4.2273 -12.2270 -0.0457
## Tree_Density-Canis_latrans -6.6499 7.1162 -28.5389 -4.2268
## Tree_Density-Sylvilagus_floridanus -7.5475 10.4358 -41.0052 -3.8258
## Avg_Canopy_Cover-Canis_latrans -0.5529 1.2203 -2.8671 -0.4501
## Avg_Canopy_Cover-Sylvilagus_floridanus 16.2887 14.3848 1.7831 11.7497
## avg_veg_height-Canis_latrans -0.3464 1.5601 -4.0651 -0.2562
## avg_veg_height-Sylvilagus_floridanus -0.3774 1.6704 -3.9969 -0.2922
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 7.2088 1.0191 178
## (Intercept)-Sylvilagus_floridanus 3.9597 1.1399 136
## Cogon_Patch_Size-Canis_latrans 7.6360 1.0395 283
## Cogon_Patch_Size-Sylvilagus_floridanus 3.3148 1.0375 118
## Veg_shannon_index-Canis_latrans 9.7629 1.9371 46
## Veg_shannon_index-Sylvilagus_floridanus 11.6566 1.8909 31
## total_shrub_cover-Canis_latrans 8.9919 1.0402 88
## total_shrub_cover-Sylvilagus_floridanus 7.9809 1.2491 39
## Avg_Cogongrass_Cover-Canis_latrans 19.0794 1.9005 51
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.2304 2.0232 51
## Tree_Density-Canis_latrans -0.9134 2.0274 36
## Tree_Density-Sylvilagus_floridanus 1.4626 3.7582 22
## Avg_Canopy_Cover-Canis_latrans 1.2530 1.0944 168
## Avg_Canopy_Cover-Sylvilagus_floridanus 56.4929 2.7473 17
## avg_veg_height-Canis_latrans 2.3290 1.1243 141
## avg_veg_height-Sylvilagus_floridanus 2.7803 1.0166 554
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8649 0.1938 -3.2561 -2.8630 -2.4993 1.0183
## (Intercept)-Sylvilagus_floridanus -3.3233 0.2650 -3.8671 -3.3146 -2.8379 1.0112
## shrub_cover-Canis_latrans -0.5205 0.2197 -0.9405 -0.5201 -0.0566 1.0085
## shrub_cover-Sylvilagus_floridanus 0.3558 0.4942 -0.5570 0.3297 1.3299 1.2247
## veg_height-Canis_latrans -0.7244 0.1882 -1.1012 -0.7217 -0.3666 1.0052
## veg_height-Sylvilagus_floridanus 0.1759 0.2972 -0.4161 0.1814 0.7463 1.0197
## week-Canis_latrans 0.0906 0.1411 -0.2094 0.0957 0.3505 1.0005
## week-Sylvilagus_floridanus -0.2100 0.2739 -0.8147 -0.1836 0.2603 1.0063
## ESS
## (Intercept)-Canis_latrans 441
## (Intercept)-Sylvilagus_floridanus 557
## shrub_cover-Canis_latrans 332
## shrub_cover-Sylvilagus_floridanus 94
## veg_height-Canis_latrans 681
## veg_height-Sylvilagus_floridanus 336
## week-Canis_latrans 1616
## week-Sylvilagus_floridanus 580
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.187
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0731 0.7454 -1.4453 0.0658 1.6 1.0049 2100
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.872 28.953 0.0545 0.5323 22.7384 1.1128 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3232 1.0894 -3.6926 -2.6401 0.6232 1.0022 1529
## shrub_cover -0.1169 0.6884 -1.4729 -0.1436 1.3899 1.0033 2306
## veg_height -0.2479 0.7408 -1.6902 -0.2827 1.4369 1.0012 2768
## week -0.0440 0.6245 -1.3306 -0.0401 1.3811 1.0092 2492
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.7548 46.7422 0.0505 0.6728 49.5028 1.0928 2417
## shrub_cover 3.3795 25.4142 0.0518 0.4702 17.5776 1.1419 3000
## veg_height 17.5788 692.6554 0.0685 0.6581 23.6559 1.3198 3000
## week 2.5037 17.8021 0.0444 0.3501 13.6694 1.0944 2123
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3693 0.4176 -0.3614 0.3426 1.2589 1.0059
## (Intercept)-Sylvilagus_floridanus -0.1845 0.5666 -1.1044 -0.2188 0.9225 1.0432
## ESS
## (Intercept)-Canis_latrans 1585
## (Intercept)-Sylvilagus_floridanus 399
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8138 0.1917 -3.1829 -2.8111 -2.4477 1.0017
## (Intercept)-Sylvilagus_floridanus -3.2177 0.3106 -3.8929 -3.1987 -2.6561 1.0043
## shrub_cover-Canis_latrans -0.3748 0.2244 -0.8188 -0.3692 0.0509 1.0128
## shrub_cover-Sylvilagus_floridanus 0.1249 0.4518 -0.6922 0.0893 1.0766 1.0061
## veg_height-Canis_latrans -0.7017 0.1926 -1.0851 -0.6924 -0.3338 1.0124
## veg_height-Sylvilagus_floridanus 0.1014 0.2846 -0.4606 0.1098 0.6301 1.0045
## week-Canis_latrans 0.0933 0.1429 -0.1988 0.0982 0.3639 1.0020
## week-Sylvilagus_floridanus -0.2045 0.2628 -0.7764 -0.1856 0.2592 1.0018
## ESS
## (Intercept)-Canis_latrans 732
## (Intercept)-Sylvilagus_floridanus 433
## shrub_cover-Canis_latrans 624
## shrub_cover-Sylvilagus_floridanus 387
## veg_height-Canis_latrans 678
## veg_height-Sylvilagus_floridanus 621
## week-Canis_latrans 1332
## week-Sylvilagus_floridanus 697
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2282
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9048 1.1831 -1.4092 0.8818 3.2223 1.0258 378
## Avg_Cogongrass_Cover -0.0373 1.1770 -2.3841 -0.0165 2.2985 1.0157 585
## total_shrub_cover -0.0411 1.4339 -2.8956 -0.0264 2.7617 1.0009 195
## avg_veg_height 0.2650 1.0910 -1.9558 0.2540 2.4430 1.0271 244
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.1374 72.3366 0.0593 0.9358 95.9111 1.1160 369
## Avg_Cogongrass_Cover 16.5837 74.8157 0.0825 2.3875 115.4081 1.0116 1249
## total_shrub_cover 65.1302 421.4610 0.1049 8.7429 336.9211 1.0822 1089
## avg_veg_height 14.6766 476.9661 0.0541 0.7138 33.4325 1.3214 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.2299 19.6786 0.0684 1.3374 47.9003 1.29 102
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3961 1.1674 -3.8260 -2.7530 0.7834 1.0043 1551
## shrub_cover 0.0392 0.8718 -1.7552 0.0247 1.8549 1.0025 536
## veg_height -0.2884 0.7408 -1.7341 -0.3228 1.4315 1.0078 3000
## week -0.0372 0.6066 -1.3103 -0.0319 1.1855 1.0021 2422
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.4657 72.8629 0.0545 0.7387 70.5511 1.0889 2671
## shrub_cover 5.4473 32.6015 0.0761 1.0380 30.0247 1.0108 3000
## veg_height 4.8671 42.4857 0.0708 0.6461 21.1413 1.0362 3000
## week 3.0218 30.8853 0.0415 0.3358 13.1889 1.0881 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.6523 1.7339 -0.5622 1.3139
## (Intercept)-Sylvilagus_floridanus 1.3143 1.6997 -1.2989 1.1494
## Avg_Cogongrass_Cover-Canis_latrans 1.0414 1.5214 -1.0416 0.7834
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2984 1.6842 -5.3245 -1.0321
## total_shrub_cover-Canis_latrans 1.7016 1.4278 -0.4363 1.4916
## total_shrub_cover-Sylvilagus_floridanus -3.0223 3.8321 -12.3198 -2.8231
## avg_veg_height-Canis_latrans 0.3369 1.0099 -1.6020 0.2759
## avg_veg_height-Sylvilagus_floridanus 0.3774 1.3333 -2.2079 0.3162
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.3012 1.1228 65
## (Intercept)-Sylvilagus_floridanus 4.9118 1.0361 159
## Avg_Cogongrass_Cover-Canis_latrans 4.8063 1.1122 261
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2229 1.0197 169
## total_shrub_cover-Canis_latrans 5.1344 1.1090 166
## total_shrub_cover-Sylvilagus_floridanus 3.4561 1.0909 55
## avg_veg_height-Canis_latrans 2.4413 1.0303 388
## avg_veg_height-Sylvilagus_floridanus 3.1430 1.0351 103
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.9209 0.2079 -3.3499 -2.9123 -2.5235 1.0531
## (Intercept)-Sylvilagus_floridanus -3.4282 0.2916 -4.0364 -3.4167 -2.8754 1.0129
## shrub_cover-Canis_latrans -0.5175 0.2391 -0.9645 -0.5290 -0.0275 1.0815
## shrub_cover-Sylvilagus_floridanus 0.6490 0.6777 -0.7877 0.7989 1.6962 1.0370
## veg_height-Canis_latrans -0.7511 0.1980 -1.1600 -0.7471 -0.3827 1.0385
## veg_height-Sylvilagus_floridanus 0.0157 0.3103 -0.5613 0.0121 0.6192 1.0226
## week-Canis_latrans 0.0864 0.1422 -0.1977 0.0871 0.3522 1.0013
## week-Sylvilagus_floridanus -0.1854 0.2560 -0.7555 -0.1622 0.2618 1.0032
## ESS
## (Intercept)-Canis_latrans 454
## (Intercept)-Sylvilagus_floridanus 319
## shrub_cover-Canis_latrans 326
## shrub_cover-Sylvilagus_floridanus 32
## veg_height-Canis_latrans 593
## veg_height-Sylvilagus_floridanus 213
## week-Canis_latrans 1282
## week-Sylvilagus_floridanus 586
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1925
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0296 0.8917 -1.9276 0.0023 1.7141 1.0083 1134
## Tree_Density -1.0483 1.0212 -2.9911 -1.0674 1.1757 1.0051 1320
## Avg_Canopy_Cover 0.5789 1.2992 -2.1975 0.6073 3.1285 1.0010 2534
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.8265 34.6221 0.0530 0.6905 28.1718 1.1140 3000
## Tree_Density 11.8374 172.8947 0.0579 0.6726 52.9329 1.3638 573
## Avg_Canopy_Cover 46.7509 319.4539 0.4645 8.0389 262.7529 1.1044 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2955 5.648 0.0453 0.3794 6.7792 1.5666 201
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3083 1.1318 -3.7090 -2.6496 0.7729 1.0079 1818
## shrub_cover -0.0042 0.7537 -1.5474 -0.0128 1.6170 1.0090 2150
## veg_height -0.2282 0.7236 -1.7241 -0.2473 1.3194 1.0066 2857
## week -0.0703 0.6359 -1.4172 -0.0650 1.2273 1.0031 2403
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.2751 52.8194 0.0467 0.6625 53.0230 1.0950 3000
## shrub_cover 3.5585 23.2596 0.0642 0.6798 20.0351 1.1634 3000
## veg_height 3.8325 23.2454 0.0722 0.6317 23.0321 1.0855 3000
## week 2.6848 35.8752 0.0406 0.3561 14.0984 1.2868 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2652 0.6917 -1.0684 0.2540 1.6258
## (Intercept)-Sylvilagus_floridanus -0.3534 0.8707 -2.0283 -0.3577 1.3512
## Tree_Density-Canis_latrans -1.3012 0.8213 -3.0818 -1.2077 0.0337
## Tree_Density-Sylvilagus_floridanus -1.7154 1.6957 -5.0855 -1.4583 0.3473
## Avg_Canopy_Cover-Canis_latrans -0.4908 0.5848 -1.7335 -0.4479 0.5199
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0265 2.5970 0.9373 3.4990 10.5935
## Rhat ESS
## (Intercept)-Canis_latrans 1.0242 638
## (Intercept)-Sylvilagus_floridanus 1.0241 516
## Tree_Density-Canis_latrans 1.0243 641
## Tree_Density-Sylvilagus_floridanus 1.3905 100
## Avg_Canopy_Cover-Canis_latrans 1.0355 275
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1900 112
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8118 0.1916 -3.1989 -2.8060 -2.4457 1.0053
## (Intercept)-Sylvilagus_floridanus -3.1967 0.2778 -3.7771 -3.1782 -2.6801 1.0091
## shrub_cover-Canis_latrans -0.3653 0.2253 -0.7908 -0.3667 0.0799 1.0203
## shrub_cover-Sylvilagus_floridanus 0.4337 0.4094 -0.3350 0.4267 1.2601 1.0314
## veg_height-Canis_latrans -0.6974 0.1835 -1.0680 -0.6954 -0.3586 1.0018
## veg_height-Sylvilagus_floridanus 0.1395 0.2794 -0.4093 0.1333 0.7015 1.0064
## week-Canis_latrans 0.0855 0.1431 -0.2012 0.0905 0.3593 1.0034
## week-Sylvilagus_floridanus -0.2206 0.2680 -0.8204 -0.1985 0.2421 1.0073
## ESS
## (Intercept)-Canis_latrans 723
## (Intercept)-Sylvilagus_floridanus 482
## shrub_cover-Canis_latrans 805
## shrub_cover-Sylvilagus_floridanus 506
## veg_height-Canis_latrans 745
## veg_height-Sylvilagus_floridanus 764
## week-Canis_latrans 1555
## week-Sylvilagus_floridanus 664
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2215
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7793 1.2558 -1.7652 0.7679 3.2792 1.0199 266
## Cogon_Patch_Size 0.0432 1.3304 -2.6838 0.0345 2.6769 1.0050 1015
## Avg_Cogongrass_Cover 0.0901 1.0030 -1.9561 0.0723 2.1194 1.0156 978
## total_shrub_cover 0.0524 1.4025 -2.8081 0.0566 2.7756 1.0478 199
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 75.3316 1892.6772 0.0609 1.1511 177.1408 1.3154 2229
## Cogon_Patch_Size 263.2375 4923.9905 0.1144 7.0329 1062.7418 1.3188 2370
## Avg_Cogongrass_Cover 19.7627 240.5497 0.0605 0.8697 76.3675 1.2453 355
## total_shrub_cover 74.4925 583.4359 0.0981 4.9245 386.4529 1.0990 1453
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.6268 10.1829 0.0586 0.9012 26.3624 1.5781 59
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3908 1.1611 -3.8128 -2.7309 0.6876 1.0022 1559
## shrub_cover -0.0087 0.8586 -1.7937 -0.0466 1.7450 1.0278 528
## veg_height -0.2746 0.7195 -1.6770 -0.3135 1.3108 1.0004 2496
## week -0.0477 0.6039 -1.3150 -0.0530 1.2498 1.0100 2652
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 29.6806 748.9274 0.0586 0.8220 65.9098 1.1132 3000
## shrub_cover 5.1313 26.3793 0.0704 0.9461 34.7294 1.1425 2342
## veg_height 3.3944 22.3836 0.0688 0.6122 20.3608 1.2172 3000
## week 2.4854 18.7297 0.0433 0.3458 14.1691 1.2017 1405
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.9006 2.5575 -0.6384 1.3932
## (Intercept)-Sylvilagus_floridanus 1.0887 2.4208 -2.3534 0.7801
## Cogon_Patch_Size-Canis_latrans 2.3845 2.9816 -0.5439 1.6330
## Cogon_Patch_Size-Sylvilagus_floridanus -3.6216 6.8805 -28.5091 -1.5925
## Avg_Cogongrass_Cover-Canis_latrans 0.7547 2.1851 -0.8119 0.3931
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1162 2.0867 -3.2508 -0.2382
## total_shrub_cover-Canis_latrans 1.5591 2.0045 -0.6023 1.1020
## total_shrub_cover-Sylvilagus_floridanus -2.4843 3.8738 -12.9708 -1.8389
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 9.1150 1.7264 28
## (Intercept)-Sylvilagus_floridanus 7.6179 1.1170 63
## Cogon_Patch_Size-Canis_latrans 9.2701 1.1487 162
## Cogon_Patch_Size-Sylvilagus_floridanus 1.5207 1.5707 22
## Avg_Cogongrass_Cover-Canis_latrans 3.5685 1.9156 76
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.6298 1.3038 94
## total_shrub_cover-Canis_latrans 6.4024 1.5433 25
## total_shrub_cover-Sylvilagus_floridanus 3.0872 1.2629 34
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8751 0.2063 -3.2941 -2.8635 -2.4811 1.0237
## (Intercept)-Sylvilagus_floridanus -3.4535 0.3157 -4.1307 -3.4381 -2.8498 1.1451
## shrub_cover-Canis_latrans -0.5018 0.2474 -0.9707 -0.5104 0.0032 1.0123
## shrub_cover-Sylvilagus_floridanus 0.5201 0.6682 -0.8275 0.6301 1.6000 1.1657
## veg_height-Canis_latrans -0.7137 0.2035 -1.1237 -0.7069 -0.3225 1.0250
## veg_height-Sylvilagus_floridanus 0.0471 0.3056 -0.5060 0.0379 0.6556 1.0609
## week-Canis_latrans 0.0827 0.1382 -0.1910 0.0862 0.3433 1.0110
## week-Sylvilagus_floridanus -0.2094 0.2598 -0.7937 -0.1918 0.2557 1.0345
## ESS
## (Intercept)-Canis_latrans 452
## (Intercept)-Sylvilagus_floridanus 213
## shrub_cover-Canis_latrans 362
## shrub_cover-Sylvilagus_floridanus 46
## veg_height-Canis_latrans 569
## veg_height-Sylvilagus_floridanus 353
## week-Canis_latrans 1199
## week-Sylvilagus_floridanus 506
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1962
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1992 0.9322 -1.6184 0.1682 2.2000 1.0424 769
## Veg_shannon_index 0.7787 0.8965 -1.1299 0.7933 2.5386 1.0209 945
## Avg_Cogongrass_Cover 0.2291 1.0164 -1.9264 0.2420 2.2496 1.0110 1407
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.5294 50.3046 0.0511 0.7016 33.8137 1.1004 2200
## Veg_shannon_index 5.5374 46.8224 0.0531 0.6360 32.8825 1.0561 3000
## Avg_Cogongrass_Cover 8.6540 37.7435 0.0864 1.5871 58.0559 1.0012 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3242 3.7225 0.0532 0.497 7.856 1.1609 393
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2677 1.1770 -3.6843 -2.6178 0.9204 1.0036 1532
## shrub_cover -0.1419 0.6696 -1.4797 -0.1672 1.3116 1.0002 1718
## veg_height -0.2211 0.7244 -1.6755 -0.2513 1.4499 1.0020 2541
## week -0.0540 0.6066 -1.3637 -0.0437 1.2081 1.0031 2740
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.6098 116.3729 0.0551 0.7764 61.1783 1.0020 3000
## shrub_cover 3.6713 30.7662 0.0461 0.4220 16.5697 1.0845 3000
## veg_height 3.8244 27.6783 0.0725 0.7153 22.5786 1.1408 3000
## week 2.8695 27.6562 0.0418 0.3379 14.7348 1.1744 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5515 0.7958 -0.8908 0.5056
## (Intercept)-Sylvilagus_floridanus -0.0564 0.9158 -1.6621 -0.1083
## Veg_shannon_index-Canis_latrans 1.1478 0.6567 0.0349 1.0824
## Veg_shannon_index-Sylvilagus_floridanus 0.8204 0.8306 -0.4594 0.7167
## Avg_Cogongrass_Cover-Canis_latrans 1.2395 0.9428 -0.0860 1.0790
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3987 0.7827 -1.9522 -0.3891
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2156 1.0710 414
## (Intercept)-Sylvilagus_floridanus 1.9102 1.0809 317
## Veg_shannon_index-Canis_latrans 2.6435 1.0221 607
## Veg_shannon_index-Sylvilagus_floridanus 2.8042 1.1104 248
## Avg_Cogongrass_Cover-Canis_latrans 3.4460 1.0286 381
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2111 1.0593 579
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8113 0.1881 -3.1826 -2.8133 -2.4483 1.0243
## (Intercept)-Sylvilagus_floridanus -3.2914 0.3463 -4.0258 -3.2776 -2.6663 1.0311
## shrub_cover-Canis_latrans -0.3380 0.2203 -0.7599 -0.3376 0.1073 1.0084
## shrub_cover-Sylvilagus_floridanus 0.0257 0.4159 -0.7044 -0.0140 0.9389 1.0318
## veg_height-Canis_latrans -0.7126 0.1892 -1.0805 -0.7113 -0.3487 1.0175
## veg_height-Sylvilagus_floridanus 0.1622 0.2996 -0.4352 0.1649 0.7424 1.0172
## week-Canis_latrans 0.0884 0.1426 -0.2148 0.0929 0.3485 1.0050
## week-Sylvilagus_floridanus -0.2056 0.2681 -0.7953 -0.1871 0.2609 1.0218
## ESS
## (Intercept)-Canis_latrans 669
## (Intercept)-Sylvilagus_floridanus 279
## shrub_cover-Canis_latrans 810
## shrub_cover-Sylvilagus_floridanus 410
## veg_height-Canis_latrans 600
## veg_height-Sylvilagus_floridanus 488
## week-Canis_latrans 1424
## week-Sylvilagus_floridanus 658
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1968
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6612 1.0159 -2.5478 -0.6612 1.5258 1.0060 820
## Avg_Cogongrass_Cover -0.5153 1.1858 -2.9142 -0.5553 1.9721 1.0018 1874
## I(Avg_Cogongrass_Cover^2) 1.1328 1.2374 -1.5386 1.1686 3.4334 1.0031 1227
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7482 340.5603 0.0511 0.6869 31.3105 1.3055 3000
## Avg_Cogongrass_Cover 49.8003 715.6257 0.1011 3.2061 147.9620 1.1144 2320
## I(Avg_Cogongrass_Cover^2) 29.0727 275.0286 0.0653 1.6530 177.5966 1.0719 1536
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5432 4.5927 0.0516 0.4936 9.9904 1.1297 297
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2894 1.1318 -3.7462 -2.6144 0.7179 1.0019 1371
## shrub_cover -0.1106 0.6871 -1.5027 -0.1174 1.3359 1.0121 1503
## veg_height -0.1799 0.7539 -1.6335 -0.2182 1.5017 1.0010 2662
## week -0.0593 0.6074 -1.3840 -0.0377 1.2032 1.0031 3435
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7519 98.6458 0.0552 0.8143 70.5741 1.1051 3000
## shrub_cover 2.8006 22.3494 0.0465 0.4457 15.4751 1.1743 2319
## veg_height 8.0658 144.5101 0.0770 0.7395 25.1869 1.1084 3000
## week 2.1910 15.4960 0.0403 0.3636 11.2954 1.0699 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6588 0.9915 -2.6320 -0.6382
## (Intercept)-Sylvilagus_floridanus -1.0771 1.0736 -3.2107 -1.0736
## Avg_Cogongrass_Cover-Canis_latrans 0.3379 1.0373 -1.4210 0.2649
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.4762 2.2081 -8.1056 -2.0432
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6954 1.8158 0.1862 2.3465
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7274 2.1556 -0.1640 1.2400
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2737 1.0058 435
## (Intercept)-Sylvilagus_floridanus 1.0829 1.0223 368
## Avg_Cogongrass_Cover-Canis_latrans 2.6620 1.0167 679
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2300 1.0533 112
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.1484 1.0070 181
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 8.5709 1.6796 39
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8298 0.1957 -3.2358 -2.8168 -2.4720 1.0020
## (Intercept)-Sylvilagus_floridanus -3.3286 0.3552 -4.1378 -3.2917 -2.7376 1.0280
## shrub_cover-Canis_latrans -0.3127 0.2230 -0.7505 -0.3116 0.1143 1.0004
## shrub_cover-Sylvilagus_floridanus 0.0938 0.3964 -0.6068 0.0560 0.9770 1.0086
## veg_height-Canis_latrans -0.7152 0.1980 -1.1356 -0.7128 -0.3322 1.0044
## veg_height-Sylvilagus_floridanus 0.2016 0.2982 -0.3867 0.1991 0.7856 1.0164
## week-Canis_latrans 0.0913 0.1417 -0.1909 0.0957 0.3689 1.0003
## week-Sylvilagus_floridanus -0.2141 0.2639 -0.7920 -0.1933 0.2331 1.0119
## ESS
## (Intercept)-Canis_latrans 748
## (Intercept)-Sylvilagus_floridanus 151
## shrub_cover-Canis_latrans 872
## shrub_cover-Sylvilagus_floridanus 361
## veg_height-Canis_latrans 645
## veg_height-Sylvilagus_floridanus 335
## week-Canis_latrans 1546
## week-Sylvilagus_floridanus 626
## 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2053
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2680 1.6023 -3.3592 -0.3095 2.8881 1.1390 425
## Cogon_Patch_Size 0.2283 1.5255 -2.8130 0.2487 3.0881 1.0019 923
## Veg_shannon_index 0.8471 1.5970 -2.8025 1.0610 3.6560 1.5391 259
## total_shrub_cover 0.2796 1.5789 -2.9239 0.3024 3.3592 1.0700 231
## Avg_Cogongrass_Cover -0.2764 1.5703 -3.3564 -0.2712 2.7898 1.0518 750
## Tree_Density -0.7973 1.6665 -4.0050 -0.8286 2.6161 1.1400 505
## Avg_Canopy_Cover 0.2648 1.6374 -2.9027 0.2941 3.4643 1.0369 2622
## I(Avg_Cogongrass_Cover^2) 0.8885 1.5935 -2.3970 0.9849 3.8034 1.4214 192
## avg_veg_height -0.4697 1.3523 -3.2321 -0.4297 2.2156 1.0385 647
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept) 612.6679 18186.4244 0.0721 3.8299 2036.3589
## Cogon_Patch_Size 1538.4140 31708.3883 0.1067 36.9171 5103.5446
## Veg_shannon_index 46.2674 320.3202 0.0611 1.7696 284.9728
## total_shrub_cover 68.0113 588.2844 0.0695 3.6611 460.1344
## Avg_Cogongrass_Cover 171.3002 3934.3188 0.0657 2.1203 647.4031
## Tree_Density 862.2811 20731.4885 0.1122 29.6324 3762.7076
## Avg_Canopy_Cover 17895.0465 113941.3737 4.6100 479.3221 120628.6362
## I(Avg_Cogongrass_Cover^2) 5652.7333 75690.1322 0.0855 14.4871 22515.9535
## avg_veg_height 130.9792 1881.0983 0.0610 1.5641 802.7693
## Rhat ESS
## (Intercept) 1.3463 3000
## Cogon_Patch_Size 1.2893 3000
## Veg_shannon_index 1.2702 777
## total_shrub_cover 1.2583 1041
## Avg_Cogongrass_Cover 1.4237 859
## Tree_Density 1.3369 3000
## Avg_Canopy_Cover 2.5996 659
## I(Avg_Cogongrass_Cover^2) 1.7551 1973
## avg_veg_height 1.6498 485
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1221.628 3102.267 0.0693 4.121 8700.142 7.3115 23
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3395 1.1266 -3.7197 -2.6868 0.7141 1.0125 1666
## shrub_cover 0.0045 0.7778 -1.6351 -0.0260 1.6343 1.0299 2041
## veg_height -0.2317 0.7341 -1.6740 -0.2633 1.3324 1.0069 3000
## week -0.0416 0.6477 -1.3565 -0.0499 1.3252 1.0042 2490
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.2215 271.2735 0.0533 0.7125 41.1137 1.2988 3000
## shrub_cover 5.2202 56.5164 0.0717 0.7564 26.8242 1.2948 3000
## veg_height 4.8652 47.1024 0.0675 0.6014 24.6947 1.1980 1699
## week 3.0908 38.9079 0.0441 0.3651 14.2898 1.2883 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans 0.6256 5.6457 -8.8425
## (Intercept)-Sylvilagus_floridanus -0.8516 10.1204 -20.3482
## Cogon_Patch_Size-Canis_latrans 8.6463 12.0798 -1.8872
## Cogon_Patch_Size-Sylvilagus_floridanus -5.0264 9.8813 -33.4151
## Veg_shannon_index-Canis_latrans 1.8510 3.6064 -4.5330
## Veg_shannon_index-Sylvilagus_floridanus 1.7387 3.4410 -4.9938
## total_shrub_cover-Canis_latrans 1.9302 3.7887 -3.7504
## total_shrub_cover-Sylvilagus_floridanus -0.8240 3.7123 -8.9809
## Avg_Cogongrass_Cover-Canis_latrans -0.6279 4.7894 -11.9442
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.8658 6.3632 -14.8610
## Tree_Density-Canis_latrans -7.9469 8.5138 -33.3204
## Tree_Density-Sylvilagus_floridanus -6.1963 9.9454 -33.2914
## Avg_Canopy_Cover-Canis_latrans -8.6711 15.2873 -54.5020
## Avg_Canopy_Cover-Sylvilagus_floridanus 59.0604 63.4249 3.4758
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 22.2338 34.4717 0.4019
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0933 9.6845 -12.0735
## avg_veg_height-Canis_latrans -2.0511 3.9824 -14.5754
## avg_veg_height-Sylvilagus_floridanus -1.8354 6.0876 -20.4406
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.0513 17.3308 1.3003 77
## (Intercept)-Sylvilagus_floridanus -1.0410 29.0286 2.2272 30
## Cogon_Patch_Size-Canis_latrans 4.7946 47.5526 1.1635 34
## Cogon_Patch_Size-Sylvilagus_floridanus -2.1551 9.5997 1.3617 51
## Veg_shannon_index-Canis_latrans 1.6696 11.1916 1.5215 75
## Veg_shannon_index-Sylvilagus_floridanus 1.5927 9.6628 1.4729 209
## total_shrub_cover-Canis_latrans 1.4680 13.6560 1.3008 53
## total_shrub_cover-Sylvilagus_floridanus -0.3349 4.9147 1.1538 87
## Avg_Cogongrass_Cover-Canis_latrans -0.2308 6.0303 1.4372 75
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9222 4.5080 1.4629 113
## Tree_Density-Canis_latrans -5.3407 1.4697 1.2865 47
## Tree_Density-Sylvilagus_floridanus -3.2605 8.1450 1.1578 32
## Avg_Canopy_Cover-Canis_latrans -1.1650 1.1993 11.7898 6
## Avg_Canopy_Cover-Sylvilagus_floridanus 23.6777 203.8466 17.2814 5
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3741 134.5441 12.3889 5
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9195 28.3135 3.9271 39
## avg_veg_height-Canis_latrans -0.8804 1.9220 4.9194 44
## avg_veg_height-Sylvilagus_floridanus -0.7526 6.0083 2.3096 46
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8626 0.2020 -3.2683 -2.8568 -2.4767 1.4553
## (Intercept)-Sylvilagus_floridanus -3.2826 0.2674 -3.8142 -3.2795 -2.7710 1.1054
## shrub_cover-Canis_latrans -0.4196 0.2391 -0.8699 -0.4204 0.0654 1.1846
## shrub_cover-Sylvilagus_floridanus 0.4862 0.4589 -0.3435 0.4676 1.4401 1.4282
## veg_height-Canis_latrans -0.6446 0.1967 -1.0331 -0.6423 -0.2669 1.0528
## veg_height-Sylvilagus_floridanus 0.1163 0.2813 -0.4402 0.1202 0.6590 1.1207
## week-Canis_latrans 0.0913 0.1417 -0.2014 0.0970 0.3598 1.0060
## week-Sylvilagus_floridanus -0.1997 0.2626 -0.7709 -0.1701 0.2500 1.0146
## ESS
## (Intercept)-Canis_latrans 122
## (Intercept)-Sylvilagus_floridanus 301
## shrub_cover-Canis_latrans 214
## shrub_cover-Sylvilagus_floridanus 124
## veg_height-Canis_latrans 558
## veg_height-Sylvilagus_floridanus 293
## week-Canis_latrans 1405
## week-Sylvilagus_floridanus 642
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1588
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4347 1.2915 -2.0805 0.4066 3.0504 1.0461 303
## Cogon_Patch_Size 0.0629 1.3808 -2.6339 0.0356 2.7857 1.0015 1007
## Veg_shannon_index 1.2753 1.2394 -1.3121 1.2925 3.7294 1.0188 1150
## total_shrub_cover 0.3775 1.1679 -2.0702 0.3985 2.5894 1.0217 685
## Avg_Cogongrass_Cover 0.9298 1.4011 -2.0386 0.9459 3.7171 1.0033 900
## Tree_Density -1.3778 1.5693 -4.1784 -1.4953 1.9457 1.0048 888
## Avg_Canopy_Cover 0.5013 1.6027 -2.7453 0.5688 3.5099 1.0008 2305
## avg_veg_height -0.5434 1.1479 -2.7362 -0.5571 1.8283 1.0053 776
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.7039 125.9659 0.0688 1.8084 127.4299 1.0826 1846
## Cogon_Patch_Size 106.3448 1047.4836 0.0981 6.6921 598.1513 1.1678 2191
## Veg_shannon_index 16.0445 140.4340 0.0561 1.1482 94.3247 1.2009 1349
## total_shrub_cover 19.6376 314.2231 0.0558 1.0285 97.6444 1.3176 2130
## Avg_Cogongrass_Cover 59.8786 862.6469 0.0704 2.4498 186.9863 1.2619 3000
## Tree_Density 99.8084 652.9808 0.0826 5.4446 737.1660 1.1503 348
## Avg_Canopy_Cover 780.5080 19004.0524 1.2215 55.0331 2744.8467 1.3180 3000
## avg_veg_height 8.5557 71.5880 0.0572 0.8180 58.2396 1.0880 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.4483 13.2337 0.054 0.842 39.3668 1.3387 73
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2687 1.1191 -3.677 -2.5973 0.6637 1.0098 1664
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.0264 63.2441 0.0605 0.8384 61.4446 1.0743 2792
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.4438 1.7455 -1.0437 1.1738
## (Intercept)-Sylvilagus_floridanus -0.2516 2.3827 -5.7411 -0.1729
## Cogon_Patch_Size-Canis_latrans 2.2127 2.8151 -1.1175 1.4923
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8220 4.3661 -14.8854 -1.8244
## Veg_shannon_index-Canis_latrans 2.0045 1.6518 -0.1547 1.7142
## Veg_shannon_index-Sylvilagus_floridanus 2.0413 1.6353 -0.3926 1.7537
## total_shrub_cover-Canis_latrans 0.8462 1.2089 -1.1500 0.7004
## total_shrub_cover-Sylvilagus_floridanus 0.1164 2.0963 -5.2088 0.3474
## Avg_Cogongrass_Cover-Canis_latrans 2.8024 2.2881 -0.2690 2.3783
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6892 2.0450 -3.9388 0.7829
## Tree_Density-Canis_latrans -4.1409 3.3250 -13.5946 -3.1536
## Tree_Density-Sylvilagus_floridanus -3.8619 4.6381 -14.8334 -2.9374
## Avg_Canopy_Cover-Canis_latrans -0.3837 0.9782 -2.6368 -0.2773
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.5517 8.5265 1.7136 9.1565
## avg_veg_height-Canis_latrans -0.8223 1.1424 -3.1883 -0.7947
## avg_veg_height-Sylvilagus_floridanus -0.6482 1.6033 -3.7286 -0.6542
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.1117 1.0531 146
## (Intercept)-Sylvilagus_floridanus 4.2132 1.0207 99
## Cogon_Patch_Size-Canis_latrans 9.9632 1.1294 168
## Cogon_Patch_Size-Sylvilagus_floridanus 2.7302 1.1803 82
## Veg_shannon_index-Canis_latrans 6.1919 1.0623 152
## Veg_shannon_index-Sylvilagus_floridanus 6.2186 1.0118 247
## total_shrub_cover-Canis_latrans 3.7423 1.0102 340
## total_shrub_cover-Sylvilagus_floridanus 3.3773 1.1447 170
## Avg_Cogongrass_Cover-Canis_latrans 9.0301 1.0301 158
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.5534 1.0440 301
## Tree_Density-Canis_latrans -0.4611 1.0182 123
## Tree_Density-Sylvilagus_floridanus 1.5420 1.5765 52
## Avg_Canopy_Cover-Canis_latrans 1.2873 1.0168 384
## Avg_Canopy_Cover-Sylvilagus_floridanus 35.9157 1.0744 45
## avg_veg_height-Canis_latrans 1.4038 1.0215 471
## avg_veg_height-Sylvilagus_floridanus 2.3644 1.0548 305
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7014 0.1823 -3.0643 -2.6965 -2.3762 1.0073
## (Intercept)-Sylvilagus_floridanus -3.2974 0.2735 -3.8551 -3.2932 -2.7722 1.0034
## ESS
## (Intercept)-Canis_latrans 717
## (Intercept)-Sylvilagus_floridanus 332
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.155
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2928 0.9708 -1.6717 0.2545 2.3877 1.0045 499
## Avg_Cogongrass_Cover 0.1902 0.9869 -1.8363 0.2000 2.1904 1.0055 1664
## total_shrub_cover 0.0655 0.9402 -1.9001 0.0680 1.9924 1.0134 774
## avg_veg_height -0.2862 0.8652 -2.0114 -0.2703 1.5502 1.0063 1142
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9055 58.8015 0.0519 0.6527 44.8432 1.1117 3000
## Avg_Cogongrass_Cover 7.0682 37.6445 0.0710 1.1775 47.9308 1.0934 3000
## total_shrub_cover 6.5287 42.0196 0.0540 0.7656 42.5169 1.1043 767
## avg_veg_height 5.0486 39.2810 0.0497 0.4971 27.7218 1.1507 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8444 7.8498 0.0639 0.7625 21.8151 1.217 108
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2587 1.1612 -3.7599 -2.584 0.9332 1.0211 1791
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.2365 158.4209 0.0635 0.8045 60.7828 1.2554 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5705 0.9676 -0.9025 0.4668
## (Intercept)-Sylvilagus_floridanus 0.2628 1.1802 -1.5843 0.1055
## Avg_Cogongrass_Cover-Canis_latrans 0.8971 0.9043 -0.5207 0.8006
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4085 0.9283 -2.5158 -0.3646
## total_shrub_cover-Canis_latrans 0.4938 0.7472 -0.6185 0.3922
## total_shrub_cover-Sylvilagus_floridanus -0.3825 1.2906 -3.5525 -0.2419
## avg_veg_height-Canis_latrans -0.3412 0.7411 -1.7255 -0.3449
## avg_veg_height-Sylvilagus_floridanus -0.3004 0.9209 -2.2216 -0.2685
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9699 1.0651 203
## (Intercept)-Sylvilagus_floridanus 3.1581 1.0340 132
## Avg_Cogongrass_Cover-Canis_latrans 2.9566 1.0219 430
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2465 1.0203 498
## total_shrub_cover-Canis_latrans 2.3398 1.0212 434
## total_shrub_cover-Sylvilagus_floridanus 1.8480 1.0470 157
## avg_veg_height-Canis_latrans 1.1398 1.0256 545
## avg_veg_height-Sylvilagus_floridanus 1.4304 1.0513 291
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6689 0.1848 -3.0516 -2.6612 -2.3357 1.0169
## (Intercept)-Sylvilagus_floridanus -3.3248 0.3539 -4.0484 -3.3137 -2.6793 1.0118
## ESS
## (Intercept)-Canis_latrans 594
## (Intercept)-Sylvilagus_floridanus 205
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1457
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0533 0.9482 -1.7659 -0.1026 2.0562 1.0031 844
## Tree_Density -1.0727 1.0237 -3.0455 -1.1161 1.1537 1.0018 1237
## Avg_Canopy_Cover 0.5651 1.3027 -2.1998 0.5722 3.1028 1.0001 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5649 49.7191 0.0538 0.7069 38.2676 1.1748 1218
## Tree_Density 10.4650 76.7407 0.0585 0.7611 75.0394 1.1060 978
## Avg_Canopy_Cover 61.6319 460.9433 0.3355 7.3627 399.3764 1.1279 1151
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9847 7.2266 0.044 0.3865 15.092 1.5567 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2311 1.0815 -3.5572 -2.5361 0.895 1.0138 1796
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.4763 71.0868 0.0558 0.682 47.8511 1.1198 2284
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3352 1.1352 -1.0839 0.1572 3.1549
## (Intercept)-Sylvilagus_floridanus -0.3404 1.0446 -2.0479 -0.4229 2.0140
## Tree_Density-Canis_latrans -1.2594 0.8062 -3.0596 -1.1793 0.0276
## Tree_Density-Sylvilagus_floridanus -1.8943 2.1556 -5.6849 -1.5408 0.1968
## Avg_Canopy_Cover-Canis_latrans -0.4653 0.7283 -2.1980 -0.3765 0.6014
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9213 3.3195 0.7553 3.2266 11.4413
## Rhat ESS
## (Intercept)-Canis_latrans 1.2000 117
## (Intercept)-Sylvilagus_floridanus 1.0158 366
## Tree_Density-Canis_latrans 1.0042 597
## Tree_Density-Sylvilagus_floridanus 1.2537 113
## Avg_Canopy_Cover-Canis_latrans 1.1068 155
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.2599 72
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6633 0.1873 -3.0451 -2.6607 -2.3211 1.0008
## (Intercept)-Sylvilagus_floridanus -3.1587 0.2789 -3.7171 -3.1521 -2.6335 1.0033
## ESS
## (Intercept)-Canis_latrans 565
## (Intercept)-Sylvilagus_floridanus 520
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1602
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2994 1.0106 -1.6852 0.2861 2.3715 1.0100 512
## Cogon_Patch_Size 0.1027 1.3107 -2.6510 0.0869 2.8429 1.0061 1441
## Avg_Cogongrass_Cover -0.0189 0.8505 -1.7489 -0.0372 1.7745 1.0053 1105
## total_shrub_cover 0.0598 0.9870 -2.1468 0.1001 1.9875 1.0036 675
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.2485 28.6857 0.0634 0.8982 46.1890 1.0672 2110
## Cogon_Patch_Size 102.6027 1820.7944 0.1583 7.0148 384.9183 1.1692 3000
## Avg_Cogongrass_Cover 7.1495 154.5319 0.0490 0.5425 28.5075 1.2917 3000
## total_shrub_cover 7.0203 43.0422 0.0513 0.7562 48.2383 1.0353 1400
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1902 6.2622 0.0547 0.6717 15.4393 1.4196 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1922 1.1482 -3.7186 -2.5076 0.6563 1.004 1680
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.1877 94.5852 0.0701 0.9533 61.8614 1.1153 2726
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8113 0.9351 -0.7590 0.7256
## (Intercept)-Sylvilagus_floridanus -0.0794 1.2518 -2.5871 -0.0890
## Cogon_Patch_Size-Canis_latrans 2.2931 1.8963 -0.1077 1.8590
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4996 3.4769 -11.6944 -1.6815
## Avg_Cogongrass_Cover-Canis_latrans 0.1160 0.6013 -0.9978 0.1041
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1838 0.9666 -2.0320 -0.2216
## total_shrub_cover-Canis_latrans 0.3579 0.7293 -0.8811 0.3006
## total_shrub_cover-Sylvilagus_floridanus -0.3079 1.3903 -3.6534 -0.1864
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9558 1.0139 377
## (Intercept)-Sylvilagus_floridanus 2.4211 1.0312 210
## Cogon_Patch_Size-Canis_latrans 7.1001 1.0961 287
## Cogon_Patch_Size-Sylvilagus_floridanus 1.8398 1.1797 88
## Avg_Cogongrass_Cover-Canis_latrans 1.3060 1.0239 1012
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9150 1.0580 443
## total_shrub_cover-Canis_latrans 2.0205 1.0443 481
## total_shrub_cover-Sylvilagus_floridanus 2.4325 1.0488 134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6318 0.1839 -3.0148 -2.6233 -2.2986 1.0046
## (Intercept)-Sylvilagus_floridanus -3.3686 0.3452 -4.0529 -3.3639 -2.7398 1.0388
## ESS
## (Intercept)-Canis_latrans 727
## (Intercept)-Sylvilagus_floridanus 168
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1453
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1189 0.9258 -1.6767 0.0915 2.0803 1.0181 984
## Veg_shannon_index 0.7431 0.8680 -1.1773 0.7789 2.4003 1.0068 1516
## Avg_Cogongrass_Cover 0.2290 0.9298 -1.8110 0.2443 1.9889 1.0041 1357
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.9997 52.0067 0.0518 0.6340 30.6962 1.1140 1685
## Veg_shannon_index 4.4236 43.6410 0.0519 0.5160 22.3159 1.1721 2086
## Avg_Cogongrass_Cover 7.6642 66.0221 0.0685 0.9816 43.7966 1.2628 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1001 1.9889 0.0484 0.4247 6.647 1.1866 345
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1818 1.2019 -3.6929 -2.5295 1.1042 1.0152 1610
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.2968 107.494 0.0607 0.8852 58.4458 1.0425 2780
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3427 0.7316 -0.9913 0.3049
## (Intercept)-Sylvilagus_floridanus -0.0556 0.9983 -1.6054 -0.1614
## Veg_shannon_index-Canis_latrans 1.0403 0.5957 0.0648 0.9830
## Veg_shannon_index-Sylvilagus_floridanus 0.8332 0.8082 -0.5180 0.7478
## Avg_Cogongrass_Cover-Canis_latrans 0.8675 0.6996 -0.2117 0.7863
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2504 0.7880 -1.7154 -0.2480
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8823 1.0208 495
## (Intercept)-Sylvilagus_floridanus 2.0267 1.2087 239
## Veg_shannon_index-Canis_latrans 2.2812 1.0153 625
## Veg_shannon_index-Sylvilagus_floridanus 2.6633 1.0117 344
## Avg_Cogongrass_Cover-Canis_latrans 2.4357 1.0259 568
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2806 1.0304 642
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6266 0.1753 -2.9921 -2.6208 -2.3064 1.0054
## (Intercept)-Sylvilagus_floridanus -3.2809 0.3311 -3.9685 -3.2604 -2.6695 1.1161
## ESS
## (Intercept)-Canis_latrans 815
## (Intercept)-Sylvilagus_floridanus 256
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1538
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7890 1.0039 -2.7367 -0.8078 1.4452 1.0166 978
## Avg_Cogongrass_Cover -0.5879 1.1654 -2.8872 -0.6341 1.8811 1.0089 2279
## I(Avg_Cogongrass_Cover^2) 1.2403 1.3372 -1.7711 1.2747 3.7088 1.0062 1029
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.8601 30.0199 0.0513 0.6512 38.7768 1.0894 2686
## Avg_Cogongrass_Cover 32.9648 282.9565 0.0827 2.7138 166.0882 1.1642 1205
## I(Avg_Cogongrass_Cover^2) 28.9902 273.2663 0.0662 1.9889 180.5982 1.2221 2770
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.734 6.5229 0.0518 0.4781 8.8185 1.6067 174
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2358 1.1747 -3.7399 -2.5734 0.9655 1.0119 1306
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.5912 112.8751 0.0634 0.9005 66.5089 1.2021 2374
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9029 0.9246 -2.8350 -0.8548
## (Intercept)-Sylvilagus_floridanus -1.1468 1.0441 -3.3058 -1.1218
## Avg_Cogongrass_Cover-Canis_latrans -0.0228 0.9879 -1.7745 -0.0827
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.5786 2.4971 -10.0533 -1.9693
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9222 1.9635 0.2869 2.5769
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8658 2.0124 -0.1308 1.3759
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7674 1.0300 454
## (Intercept)-Sylvilagus_floridanus 0.8952 1.0554 426
## Avg_Cogongrass_Cover-Canis_latrans 2.1118 1.0146 531
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1572 1.2505 83
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.8086 1.0088 189
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 7.4167 1.0268 70
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6873 0.1734 -3.0499 -2.6813 -2.3713 1.0064
## (Intercept)-Sylvilagus_floridanus -3.3379 0.3372 -4.0059 -3.3289 -2.7150 1.0020
## ESS
## (Intercept)-Canis_latrans 825
## (Intercept)-Sylvilagus_floridanus 211
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1602
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5760 1.5276 -3.3458 -0.6778 2.6694 1.2580 551
## Cogon_Patch_Size 0.2331 1.5948 -2.9433 0.2742 3.3763 1.0572 1219
## Veg_shannon_index 0.9452 1.5524 -2.4060 1.0711 3.7567 1.4505 260
## total_shrub_cover 0.0566 1.4257 -2.8360 0.0763 2.8581 1.1065 396
## Avg_Cogongrass_Cover -0.2496 1.5424 -3.1453 -0.2727 2.8382 1.0810 497
## Tree_Density -0.8220 1.7785 -4.2431 -0.8546 2.8673 1.3853 211
## Avg_Canopy_Cover 0.3420 1.6000 -2.7637 0.3847 3.4373 1.0591 1279
## I(Avg_Cogongrass_Cover^2) 0.8204 1.6088 -2.3390 0.8825 3.8047 1.3131 209
## avg_veg_height -0.4705 1.3765 -3.0026 -0.5359 2.4497 1.1091 858
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept) 116.4444 1307.1418 0.0672 2.5301 667.0350
## Cogon_Patch_Size 7913.0421 128607.1769 0.1921 54.9652 31658.4850
## Veg_shannon_index 4256.0241 32819.0608 0.0744 7.1253 29435.6178
## total_shrub_cover 424.7153 5523.2949 0.0661 2.0440 2427.7889
## Avg_Cogongrass_Cover 531.4011 4295.7451 0.0729 2.8134 3902.3348
## Tree_Density 35264.3755 391054.5219 0.1298 84.6876 193531.5460
## Avg_Canopy_Cover 162385.7374 1749425.5070 1.2401 281.1105 944600.9932
## I(Avg_Cogongrass_Cover^2) 4438.6102 67248.2383 0.1176 35.2204 17447.2227
## avg_veg_height 33.5955 202.8587 0.0617 1.3034 312.6447
## Rhat ESS
## (Intercept) 1.2508 2225
## Cogon_Patch_Size 1.5974 3000
## Veg_shannon_index 2.4473 431
## total_shrub_cover 1.7637 1454
## Avg_Cogongrass_Cover 2.0672 361
## Tree_Density 1.9332 1889
## Avg_Canopy_Cover 1.9597 3000
## I(Avg_Cogongrass_Cover^2) 1.6432 2391
## avg_veg_height 1.8654 240
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 85.947 451.9349 0.0612 1.4324 962.3444 3.0531 60
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2747 1.1318 -3.79 -2.5866 0.6736 1.0079 1681
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.24 46.1769 0.0614 0.8419 57.1732 1.1352 2836
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.7139 5.0872 -10.1867
## (Intercept)-Sylvilagus_floridanus -1.1967 4.8942 -10.2681
## Cogon_Patch_Size-Canis_latrans 21.9077 34.1725 -0.4910
## Cogon_Patch_Size-Sylvilagus_floridanus -15.4807 25.0894 -85.4350
## Veg_shannon_index-Canis_latrans 19.7568 27.2276 0.1355
## Veg_shannon_index-Sylvilagus_floridanus 18.8165 28.6769 -0.6660
## total_shrub_cover-Canis_latrans -1.1278 5.1260 -15.7616
## total_shrub_cover-Sylvilagus_floridanus -2.8419 11.4758 -44.4748
## Avg_Cogongrass_Cover-Canis_latrans 2.4206 11.0845 -10.1117
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -3.8218 12.6529 -47.0786
## Tree_Density-Canis_latrans -42.4934 57.4725 -187.6229
## Tree_Density-Sylvilagus_floridanus -52.6197 75.8490 -222.2867
## Avg_Canopy_Cover-Canis_latrans -5.0355 8.2023 -27.8015
## Avg_Canopy_Cover-Sylvilagus_floridanus 142.0220 202.7344 1.9436
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 17.0025 20.8662 0.9336
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 7.7172 16.0138 -9.8957
## avg_veg_height-Canis_latrans -1.1333 2.9162 -7.9958
## avg_veg_height-Sylvilagus_floridanus -0.7358 4.2108 -9.0000
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.9742 10.9346 1.9180 125
## (Intercept)-Sylvilagus_floridanus -1.4224 10.6100 1.7270 98
## Cogon_Patch_Size-Canis_latrans 5.8137 126.7382 9.3097 10
## Cogon_Patch_Size-Sylvilagus_floridanus -4.2077 3.6124 7.4728 19
## Veg_shannon_index-Canis_latrans 3.1511 85.9512 19.9673 4
## Veg_shannon_index-Sylvilagus_floridanus 2.7042 97.4445 15.9463 6
## total_shrub_cover-Canis_latrans -0.1027 6.1745 3.9247 23
## total_shrub_cover-Sylvilagus_floridanus -0.2124 7.6075 4.1727 36
## Avg_Cogongrass_Cover-Canis_latrans -0.1710 40.0293 4.0181 24
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9039 8.3402 2.2979 59
## Tree_Density-Canis_latrans -7.5282 -0.7745 19.1248 5
## Tree_Density-Sylvilagus_floridanus -6.4420 1.1492 20.6020 2
## Avg_Canopy_Cover-Canis_latrans -1.2964 1.6110 9.8030 17
## Avg_Canopy_Cover-Sylvilagus_floridanus 16.8108 617.5807 21.6002 2
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4845 72.2963 9.4633 9
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1686 54.0574 5.4995 11
## avg_veg_height-Canis_latrans -0.9330 3.5692 1.2971 283
## avg_veg_height-Sylvilagus_floridanus -0.5500 5.1284 1.2362 273
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7288 0.1741 -3.0696 -2.7326 -2.4009 1.3827
## (Intercept)-Sylvilagus_floridanus -3.3059 0.2483 -3.8037 -3.3029 -2.8317 1.0261
## ESS
## (Intercept)-Canis_latrans 189
## (Intercept)-Sylvilagus_floridanus 375
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1403 0.8859 -1.7501 0.1436 1.9095 1.0020 1289
## Avg_Cogongrass_Cover 0.0070 0.8801 -1.8216 0.0117 1.7880 0.9996 2348
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.4393 29.0495 0.0531 0.7247 25.9105 1.1653 2652
## Avg_Cogongrass_Cover 5.6682 30.1161 0.0677 1.1006 35.6325 1.1049 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0128 1.7955 0.044 0.4315 5.7733 1.0284 502
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3758 1.0543 -3.6917 -2.6746 0.6061 1.0074 1713
## shrub_cover -0.1109 0.6788 -1.5125 -0.1279 1.3253 1.0119 1571
## veg_height -0.2061 0.7261 -1.6114 -0.2316 1.3985 1.0071 2204
## week -0.0518 0.6124 -1.3782 -0.0476 1.1901 1.0093 2602
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.6576 74.6250 0.0477 0.5960 47.8149 1.2634 3000
## shrub_cover 2.7842 23.5430 0.0466 0.4299 16.1476 1.1279 3000
## veg_height 3.6120 17.2708 0.0791 0.7171 25.9446 1.0031 2461
## week 2.5145 25.3742 0.0441 0.3359 11.1861 1.2205 1189
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5150 0.7019 -0.7735 0.4681
## (Intercept)-Sylvilagus_floridanus -0.1824 0.7369 -1.5777 -0.2163
## Avg_Cogongrass_Cover-Canis_latrans 0.6321 0.6144 -0.3373 0.5566
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6133 0.6352 -1.9516 -0.5749
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1335 1.0139 611
## (Intercept)-Sylvilagus_floridanus 1.3311 1.0077 435
## Avg_Cogongrass_Cover-Canis_latrans 1.9808 1.0080 942
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5377 0.9997 871
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8259 0.1985 -3.2280 -2.8238 -2.4625 1.0020
## (Intercept)-Sylvilagus_floridanus -3.2044 0.3082 -3.8712 -3.1791 -2.6494 1.0317
## shrub_cover-Canis_latrans -0.3254 0.2244 -0.7609 -0.3156 0.0950 1.0068
## shrub_cover-Sylvilagus_floridanus 0.0853 0.4255 -0.6727 0.0587 0.9865 1.0063
## veg_height-Canis_latrans -0.7093 0.1911 -1.1086 -0.7016 -0.3566 1.0060
## veg_height-Sylvilagus_floridanus 0.1906 0.2864 -0.3587 0.1963 0.7616 1.0176
## week-Canis_latrans 0.0865 0.1419 -0.2076 0.0888 0.3552 1.0149
## week-Sylvilagus_floridanus -0.2171 0.2556 -0.7648 -0.2020 0.2332 1.0261
## ESS
## (Intercept)-Canis_latrans 695
## (Intercept)-Sylvilagus_floridanus 430
## shrub_cover-Canis_latrans 851
## shrub_cover-Sylvilagus_floridanus 446
## veg_height-Canis_latrans 786
## veg_height-Sylvilagus_floridanus 648
## week-Canis_latrans 1485
## week-Sylvilagus_floridanus 757
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1417
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0326 0.8870 -1.6840 0.0094 1.8438 1.0019 1197
## Avg_Cogongrass_Cover -0.0502 0.8319 -1.8533 -0.0343 1.6709 1.0028 2439
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.4812 26.2380 0.0530 0.6238 30.1454 1.3165 808
## Avg_Cogongrass_Cover 3.7482 14.6158 0.0607 0.7813 25.1226 1.0459 2483
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2498 2.9652 0.0479 0.4626 7.3745 1.3116 316
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2027 1.1063 -3.6212 -2.5077 0.8948 1.0001 1564
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.0202 78.8943 0.0592 0.8283 55.8465 1.0469 1752
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2621 0.6931 -1.0810 0.2587
## (Intercept)-Sylvilagus_floridanus -0.1230 1.1250 -1.6740 -0.2417
## Avg_Cogongrass_Cover-Canis_latrans 0.4071 0.4897 -0.4401 0.3655
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5244 0.6128 -1.8101 -0.4884
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6645 1.0241 535
## (Intercept)-Sylvilagus_floridanus 1.9316 1.1278 164
## Avg_Cogongrass_Cover-Canis_latrans 1.4759 1.0140 1738
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5459 1.0098 1152
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6316 0.1801 -3.0087 -2.6253 -2.2954 1.0267
## (Intercept)-Sylvilagus_floridanus -3.1954 0.3415 -3.9652 -3.1661 -2.6157 1.0650
## ESS
## (Intercept)-Canis_latrans 878
## (Intercept)-Sylvilagus_floridanus 277
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1755
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0104 0.8562 -1.8150 0.0116 1.7107 1.0031 934
## Avg_Cogongrass_Cover 0.0076 0.8169 -1.6962 0.0057 1.7134 1.0006 2285
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6645 18.3416 0.0508 0.6149 26.9798 1.1506 2814
## Avg_Cogongrass_Cover 5.5535 37.7446 0.0576 0.7673 32.3851 1.1653 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2764 3.6255 0.0528 0.4734 6.8105 1.1729 331
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2183 1.1191 -3.6077 -2.5331 0.8049 1.0015 1751
## week -0.0536 0.6154 -1.3644 -0.0390 1.2373 1.0019 2653
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.5724 70.4143 0.0559 0.7504 55.637 1.0637 2815
## week 3.2089 47.8297 0.0415 0.3569 12.907 1.1428 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2827 0.6760 -0.9471 0.2589
## (Intercept)-Sylvilagus_floridanus -0.2841 0.7948 -1.7241 -0.2538
## Avg_Cogongrass_Cover-Canis_latrans 0.4466 0.4927 -0.3907 0.4030
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4893 0.5679 -1.7201 -0.4479
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7491 1.0102 505
## (Intercept)-Sylvilagus_floridanus 1.1304 1.0402 284
## Avg_Cogongrass_Cover-Canis_latrans 1.5389 1.0039 1492
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5348 1.0029 1338
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6540 0.1786 -3.0291 -2.6447 -2.3187 1.0010
## (Intercept)-Sylvilagus_floridanus -3.2025 0.2968 -3.8467 -3.1880 -2.6740 1.0023
## week-Canis_latrans 0.0882 0.1405 -0.2014 0.0928 0.3478 1.0068
## week-Sylvilagus_floridanus -0.2092 0.2607 -0.7779 -0.1911 0.2500 1.0239
## ESS
## (Intercept)-Canis_latrans 882
## (Intercept)-Sylvilagus_floridanus 395
## week-Canis_latrans 1355
## week-Sylvilagus_floridanus 731
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1963
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3227 1.3741 -2.3943 0.3059 3.1088 1.0238 610
## Cogon_Patch_Size 0.1223 1.3982 -2.7162 0.1033 2.8810 1.0037 642
## Veg_shannon_index 1.2728 1.3919 -1.7055 1.2788 4.0086 1.0321 648
## total_shrub_cover 0.4482 1.3835 -2.3747 0.4558 3.1480 1.0285 448
## Avg_Cogongrass_Cover 0.8142 1.4876 -2.2103 0.8628 3.5041 1.0590 993
## Tree_Density -1.2127 1.6288 -4.2248 -1.3211 2.3143 1.1144 519
## Avg_Canopy_Cover 0.3484 1.6531 -3.0530 0.3603 3.5964 1.0360 2651
## avg_veg_height -0.6283 1.3011 -3.2787 -0.6042 1.9470 1.0588 938
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 107.0151 1580.5657 0.0690 2.4672 519.0006 1.4934
## Cogon_Patch_Size 156.1174 1188.1309 0.0919 6.8584 878.1994 1.1340
## Veg_shannon_index 31.8873 393.0994 0.0625 1.5025 174.3091 1.2530
## total_shrub_cover 20.2346 206.1585 0.0644 1.1650 98.8605 1.3009
## Avg_Cogongrass_Cover 150.6647 1651.6344 0.0817 5.8199 769.8747 1.1851
## Tree_Density 1139.3392 17717.1337 0.0870 8.7063 4859.9821 1.3922
## Avg_Canopy_Cover 2341.8088 18239.3207 1.2007 142.8240 13387.3225 1.2429
## avg_veg_height 99.7891 777.8775 0.0622 1.7095 685.0007 1.9214
## ESS
## (Intercept) 3000
## Cogon_Patch_Size 226
## Veg_shannon_index 3000
## total_shrub_cover 1960
## Avg_Cogongrass_Cover 1849
## Tree_Density 1233
## Avg_Canopy_Cover 1110
## avg_veg_height 213
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 229.7663 632.09 0.0748 2.2167 1910.117 6.4533 31
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2483 1.2193 -3.7725 -2.6136 0.9666 1.0015 1769
## week -0.0360 0.6517 -1.3397 -0.0479 1.3374 1.0004 2646
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.9583 109.7910 0.0634 0.8416 58.0288 1.2554 3000
## week 3.2540 21.3675 0.0461 0.3773 16.5854 1.0392 2458
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.3388 4.1976 -2.4392 1.1945
## (Intercept)-Sylvilagus_floridanus 0.4271 4.1548 -6.9150 0.0913
## Cogon_Patch_Size-Canis_latrans 2.6395 3.9253 -1.1593 1.5722
## Cogon_Patch_Size-Sylvilagus_floridanus -3.6204 6.3918 -23.0586 -1.7288
## Veg_shannon_index-Canis_latrans 2.2578 1.8996 -0.5718 1.9175
## Veg_shannon_index-Sylvilagus_floridanus 2.5426 2.7511 -1.1677 1.9921
## total_shrub_cover-Canis_latrans 1.0587 1.8633 -1.9885 0.7990
## total_shrub_cover-Sylvilagus_floridanus 0.3355 2.2152 -4.6330 0.3898
## Avg_Cogongrass_Cover-Canis_latrans 4.7025 5.2303 -0.0305 3.0630
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2539 3.8814 -11.3444 0.4786
## Tree_Density-Canis_latrans -5.1657 4.7097 -17.6246 -3.5179
## Tree_Density-Sylvilagus_floridanus -8.7132 13.7645 -53.0518 -3.3225
## Avg_Canopy_Cover-Canis_latrans -2.8803 4.8514 -17.0862 -0.7528
## Avg_Canopy_Cover-Sylvilagus_floridanus 22.7374 21.4159 1.4761 12.9892
## avg_veg_height-Canis_latrans -3.0934 5.3735 -19.8965 -1.2423
## avg_veg_height-Sylvilagus_floridanus -1.0077 3.8306 -9.9761 -0.7708
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 16.5802 2.5006 24
## (Intercept)-Sylvilagus_floridanus 11.3684 1.4975 65
## Cogon_Patch_Size-Canis_latrans 15.1040 1.2312 59
## Cogon_Patch_Size-Sylvilagus_floridanus 2.7855 1.2921 33
## Veg_shannon_index-Canis_latrans 6.9151 1.1675 138
## Veg_shannon_index-Sylvilagus_floridanus 10.3751 1.5013 108
## total_shrub_cover-Canis_latrans 5.8878 1.2671 144
## total_shrub_cover-Sylvilagus_floridanus 4.6621 1.0772 230
## Avg_Cogongrass_Cover-Canis_latrans 19.3283 3.3604 27
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 5.2818 1.5517 157
## Tree_Density-Canis_latrans -0.4472 1.4239 94
## Tree_Density-Sylvilagus_floridanus 1.3227 2.8390 13
## Avg_Canopy_Cover-Canis_latrans 1.0949 8.7790 15
## Avg_Canopy_Cover-Sylvilagus_floridanus 71.7599 3.9191 12
## avg_veg_height-Canis_latrans 1.0489 6.5939 13
## avg_veg_height-Sylvilagus_floridanus 5.9481 1.5259 207
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7255 0.1801 -3.0785 -2.7240 -2.3803 1.1820
## (Intercept)-Sylvilagus_floridanus -3.3572 0.2740 -3.9242 -3.3457 -2.8315 1.0407
## week-Canis_latrans 0.0898 0.1398 -0.1943 0.0943 0.3533 1.0097
## week-Sylvilagus_floridanus -0.2166 0.2677 -0.8031 -0.1965 0.2418 1.0121
## ESS
## (Intercept)-Canis_latrans 436
## (Intercept)-Sylvilagus_floridanus 492
## week-Canis_latrans 1491
## week-Sylvilagus_floridanus 532
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3073 0.9830 -1.5749 0.2625 2.3577 1.0159 260
## Avg_Cogongrass_Cover 0.1298 1.0237 -2.0237 0.1678 2.1236 1.0051 1378
## total_shrub_cover 0.0402 0.9930 -2.0355 0.0226 2.2389 1.0170 490
## avg_veg_height -0.2902 0.8746 -1.9709 -0.3077 1.4861 1.0015 1556
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.4879 58.7339 0.0481 0.5897 27.7418 1.1566 3000
## Avg_Cogongrass_Cover 10.5615 68.3110 0.0715 1.3739 67.3175 1.0101 3000
## total_shrub_cover 37.8011 1576.4867 0.0601 0.9040 76.1464 1.3224 3000
## avg_veg_height 4.3552 34.7468 0.0505 0.5616 26.4368 1.1336 2833
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2919 9.1704 0.0675 0.919 21.2638 1.6105 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2232 1.1916 -3.7554 -2.5700 0.8913 0.9998 1751
## week -0.0501 0.6344 -1.4076 -0.0427 1.2931 1.0050 2827
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.5466 131.0044 0.0681 0.9804 58.7015 1.0817 2541
## week 2.3287 11.6239 0.0440 0.3590 15.7106 1.0116 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5003 0.8881 -1.1538 0.4667
## (Intercept)-Sylvilagus_floridanus 0.2734 1.1989 -1.6937 0.1127
## Avg_Cogongrass_Cover-Canis_latrans 0.9560 0.9551 -0.5101 0.8201
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5524 1.0599 -3.0660 -0.4519
## total_shrub_cover-Canis_latrans 0.5235 0.7972 -0.6587 0.4020
## total_shrub_cover-Sylvilagus_floridanus -0.5745 1.5878 -4.7159 -0.3410
## avg_veg_height-Canis_latrans -0.3719 0.7236 -1.8130 -0.3559
## avg_veg_height-Sylvilagus_floridanus -0.3042 0.9002 -2.0450 -0.2937
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4008 1.0307 245
## (Intercept)-Sylvilagus_floridanus 2.9067 1.0238 182
## Avg_Cogongrass_Cover-Canis_latrans 3.3409 1.0329 396
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2718 1.0496 407
## total_shrub_cover-Canis_latrans 2.5388 1.0642 339
## total_shrub_cover-Sylvilagus_floridanus 2.1871 1.1030 102
## avg_veg_height-Canis_latrans 1.0391 1.0075 779
## avg_veg_height-Sylvilagus_floridanus 1.4851 1.0239 555
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6811 0.1896 -3.0914 -2.6743 -2.3366 1.0105
## (Intercept)-Sylvilagus_floridanus -3.3935 0.3599 -4.0974 -3.3882 -2.7321 1.0050
## week-Canis_latrans 0.0851 0.1410 -0.2023 0.0888 0.3404 1.0096
## week-Sylvilagus_floridanus -0.1957 0.2635 -0.7674 -0.1794 0.2627 1.0237
## ESS
## (Intercept)-Canis_latrans 496
## (Intercept)-Sylvilagus_floridanus 176
## week-Canis_latrans 1396
## week-Sylvilagus_floridanus 628
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1777
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0058 0.7452 -1.466 -0.0029 1.4856 1.0017 2277
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7314 30.2077 0.0493 0.5181 19.748 1.0366 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1809 1.1012 -3.6359 -2.4767 0.7187 1.0117 1799
## week -0.0582 0.6170 -1.4170 -0.0459 1.2273 1.0008 2770
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7831 143.3019 0.0561 0.8530 68.6358 1.1612 3000
## week 2.1216 9.9144 0.0452 0.3386 15.5454 1.0624 2480
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2592 0.4085 -0.4867 0.2388 1.1327 1.0040
## (Intercept)-Sylvilagus_floridanus -0.2315 0.4930 -1.1303 -0.2527 0.7869 1.0016
## ESS
## (Intercept)-Canis_latrans 1659
## (Intercept)-Sylvilagus_floridanus 675
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6320 0.1776 -2.9947 -2.6286 -2.2944 1.0212
## (Intercept)-Sylvilagus_floridanus -3.2009 0.3139 -3.8563 -3.1732 -2.6493 1.0041
## week-Canis_latrans 0.0919 0.1378 -0.1859 0.0961 0.3505 1.0076
## week-Sylvilagus_floridanus -0.2109 0.2682 -0.8194 -0.1817 0.2336 1.0032
## ESS
## (Intercept)-Canis_latrans 973
## (Intercept)-Sylvilagus_floridanus 412
## week-Canis_latrans 1411
## week-Sylvilagus_floridanus 737
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1835
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0927 0.8949 -1.6629 0.0694 2.0030 1.0401 658
## Veg_shannon_index 0.7510 0.8255 -1.0899 0.7804 2.3044 1.0001 1676
## Avg_Cogongrass_Cover 0.2028 0.9450 -1.8546 0.2093 2.1383 1.0011 1626
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.7048 107.9425 0.0537 0.6144 23.9579 1.3379 3000
## Veg_shannon_index 4.2896 31.1393 0.0506 0.5589 20.6923 1.0716 3000
## Avg_Cogongrass_Cover 7.2130 59.0103 0.0624 0.9899 39.7175 1.1479 2781
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1638 2.3771 0.0501 0.455 6.7193 1.0632 614
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1857 1.1681 -3.6813 -2.5287 1.0338 1.0042 1769
## week -0.0457 0.5947 -1.2891 -0.0379 1.2653 1.0002 2525
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.0058 136.7928 0.0604 0.8825 66.5537 1.2394 3000
## week 2.4645 19.4788 0.0408 0.3450 14.4574 1.2190 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3313 0.7014 -0.9642 0.3050
## (Intercept)-Sylvilagus_floridanus -0.1068 0.8890 -1.6586 -0.1760
## Veg_shannon_index-Canis_latrans 1.0540 0.5875 0.0942 0.9926
## Veg_shannon_index-Sylvilagus_floridanus 0.7631 0.7372 -0.5884 0.7250
## Avg_Cogongrass_Cover-Canis_latrans 0.8665 0.6867 -0.1774 0.7809
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2870 0.7529 -1.7458 -0.2879
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8305 1.0396 554
## (Intercept)-Sylvilagus_floridanus 1.9530 1.0652 209
## Veg_shannon_index-Canis_latrans 2.3416 1.0365 538
## Veg_shannon_index-Sylvilagus_floridanus 2.2955 1.0051 600
## Avg_Cogongrass_Cover-Canis_latrans 2.4217 1.0201 509
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2544 1.0058 631
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6355 0.1764 -2.9863 -2.6312 -2.3083 1.0137
## (Intercept)-Sylvilagus_floridanus -3.2859 0.3459 -3.9751 -3.2727 -2.6744 1.0437
## week-Canis_latrans 0.0880 0.1423 -0.2083 0.0918 0.3510 1.0004
## week-Sylvilagus_floridanus -0.1957 0.2531 -0.7609 -0.1777 0.2384 1.0056
## ESS
## (Intercept)-Canis_latrans 948
## (Intercept)-Sylvilagus_floridanus 286
## week-Canis_latrans 1390
## week-Sylvilagus_floridanus 681
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1927
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3666 1.0940 -1.8227 0.3553 2.5785 1.0518 501
## Cogon_Patch_Size 0.0375 1.3127 -2.5635 0.0342 2.7209 1.0093 2131
## Avg_Cogongrass_Cover -0.0272 0.8526 -1.7621 -0.0304 1.7816 1.0221 1426
## total_shrub_cover 0.0728 1.0053 -1.9936 0.0715 2.0440 1.0095 766
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.8520 505.7559 0.0574 0.9144 46.3938 1.3007 3000
## Cogon_Patch_Size 66.8929 490.1576 0.1410 7.3763 327.6602 1.0862 1367
## Avg_Cogongrass_Cover 5.6945 43.2341 0.0523 0.5866 31.3355 1.0005 2780
## total_shrub_cover 8.6592 50.1565 0.0567 0.7975 60.9795 1.1512 877
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0075 3.9322 0.0569 0.7781 11.1456 1.1391 234
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1721 1.1753 -3.7208 -2.4777 0.8728 1.0012 1809
## week -0.0534 0.6060 -1.3084 -0.0526 1.2941 1.0014 2561
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.8678 197.5258 0.0709 1.1423 78.8023 1.3232 3000
## week 2.2741 16.3532 0.0443 0.3406 14.2802 1.0995 2734
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8495 0.9821 -0.8395 0.7450
## (Intercept)-Sylvilagus_floridanus 0.1036 1.3797 -2.4713 0.0163
## Cogon_Patch_Size-Canis_latrans 2.2009 1.8545 -0.1571 1.7298
## Cogon_Patch_Size-Sylvilagus_floridanus -2.5567 3.1726 -11.6334 -1.7386
## Avg_Cogongrass_Cover-Canis_latrans 0.1588 0.6041 -0.9056 0.1227
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2461 0.9823 -2.4176 -0.2148
## total_shrub_cover-Canis_latrans 0.4261 0.7888 -0.8304 0.3387
## total_shrub_cover-Sylvilagus_floridanus -0.4224 1.6921 -4.9306 -0.1968
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.0495 1.0298 384
## (Intercept)-Sylvilagus_floridanus 3.0892 1.1382 145
## Cogon_Patch_Size-Canis_latrans 7.0424 1.0039 355
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2237 1.3263 105
## Avg_Cogongrass_Cover-Canis_latrans 1.3952 1.0220 614
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6510 1.1016 371
## total_shrub_cover-Canis_latrans 2.4062 1.0349 353
## total_shrub_cover-Sylvilagus_floridanus 2.3881 1.0276 100
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6510 0.1799 -2.9965 -2.6500 -2.3149 1.0148
## (Intercept)-Sylvilagus_floridanus -3.4580 0.3705 -4.1705 -3.4591 -2.7595 1.1397
## week-Canis_latrans 0.0821 0.1452 -0.2150 0.0873 0.3603 1.0029
## week-Sylvilagus_floridanus -0.2163 0.2565 -0.7560 -0.1964 0.2347 1.0312
## ESS
## (Intercept)-Canis_latrans 736
## (Intercept)-Sylvilagus_floridanus 162
## week-Canis_latrans 1492
## week-Sylvilagus_floridanus 594
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1778
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0960 0.9455 -2.0391 -0.1104 1.8032 1.0059 887
## Tree_Density -1.0627 1.0371 -3.0856 -1.0763 1.1853 1.0017 1295
## Avg_Canopy_Cover 0.5943 1.2657 -2.0378 0.6292 3.0679 1.0019 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5381 27.3114 0.0540 0.7356 30.3299 1.1998 2836
## Tree_Density 8.7913 77.1798 0.0521 0.7589 52.5056 1.0791 3000
## Avg_Canopy_Cover 111.2627 2709.2006 0.3055 7.3361 301.9328 1.3203 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3547 3.4282 0.0467 0.4143 9.3048 1.115 362
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2618 1.1311 -3.6462 -2.5899 0.8903 1.0072 1567
## week -0.0447 0.6231 -1.3721 -0.0369 1.2884 1.0012 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.0652 40.8809 0.0551 0.7396 54.2388 1.0655 2619
## week 4.8341 95.1395 0.0455 0.3517 14.8015 1.3191 1373
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1909 0.7414 -1.2586 0.1634 1.7433
## (Intercept)-Sylvilagus_floridanus -0.4312 0.9154 -2.1631 -0.4538 1.5043
## Tree_Density-Canis_latrans -1.2403 0.7790 -2.9702 -1.1573 0.0271
## Tree_Density-Sylvilagus_floridanus -1.7244 1.4106 -5.2837 -1.5102 0.3469
## Avg_Canopy_Cover-Canis_latrans -0.4329 0.5919 -1.7324 -0.3841 0.6034
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7960 2.4082 0.6957 3.3192 10.2149
## Rhat ESS
## (Intercept)-Canis_latrans 1.0212 494
## (Intercept)-Sylvilagus_floridanus 1.0032 499
## Tree_Density-Canis_latrans 1.0068 807
## Tree_Density-Sylvilagus_floridanus 1.0151 339
## Avg_Canopy_Cover-Canis_latrans 1.0085 662
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1689 137
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6767 0.1880 -3.0554 -2.6683 -2.3211 1.0009
## (Intercept)-Sylvilagus_floridanus -3.1964 0.2689 -3.7216 -3.1937 -2.6837 1.0099
## week-Canis_latrans 0.0859 0.1433 -0.2175 0.0888 0.3439 1.0020
## week-Sylvilagus_floridanus -0.2025 0.2532 -0.7772 -0.1814 0.2382 1.0033
## ESS
## (Intercept)-Canis_latrans 726
## (Intercept)-Sylvilagus_floridanus 515
## week-Canis_latrans 1436
## week-Sylvilagus_floridanus 691
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1885
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8327 0.9950 -2.6467 -0.8866 1.3777 1.0041 796
## Avg_Cogongrass_Cover -0.6343 1.1421 -2.8579 -0.6707 1.8133 1.0315 1593
## I(Avg_Cogongrass_Cover^2) 1.3272 1.3370 -1.5563 1.3628 3.9594 1.0285 621
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.7226 70.2448 0.0515 0.6073 33.4828 1.1399 3000
## Avg_Cogongrass_Cover 29.7373 293.9681 0.0838 2.2017 146.3801 1.1265 3000
## I(Avg_Cogongrass_Cover^2) 24.3923 227.5806 0.0617 1.6303 129.8848 1.1331 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9382 1.9966 0.0453 0.4121 4.8067 1.0835 563
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1960 1.2005 -3.8137 -2.5524 0.9156 1.0118 1831
## week -0.0419 0.6183 -1.4301 -0.0431 1.2480 1.0037 2716
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.1046 166.3306 0.0668 1.0406 65.8487 1.2569 3000
## week 3.3272 53.9280 0.0430 0.3244 13.5942 1.2305 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9144 0.8538 -2.6605 -0.8879
## (Intercept)-Sylvilagus_floridanus -1.2280 1.0130 -3.3570 -1.1839
## Avg_Cogongrass_Cover-Canis_latrans -0.0977 0.9812 -1.9092 -0.1636
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.3680 2.2254 -7.5965 -1.8903
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.8548 1.8374 0.3120 2.5489
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7338 1.4521 -0.1792 1.4188
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7732 1.0226 477
## (Intercept)-Sylvilagus_floridanus 0.8043 1.0085 528
## Avg_Cogongrass_Cover-Canis_latrans 2.0346 1.0126 784
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2931 1.2514 115
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3548 1.1453 174
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 5.2345 1.2741 122
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6827 0.1800 -3.0451 -2.6789 -2.3291 1.0119
## (Intercept)-Sylvilagus_floridanus -3.3779 0.3366 -4.0551 -3.3796 -2.7358 1.1047
## week-Canis_latrans 0.0866 0.1406 -0.2042 0.0872 0.3586 1.0026
## week-Sylvilagus_floridanus -0.2141 0.2599 -0.7568 -0.2054 0.2559 1.0056
## ESS
## (Intercept)-Canis_latrans 755
## (Intercept)-Sylvilagus_floridanus 207
## week-Canis_latrans 1444
## week-Sylvilagus_floridanus 664
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1913
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7382 1.5204 -3.4801 -0.7946 2.3954 1.0843 433
## Cogon_Patch_Size 0.2171 1.5449 -2.8677 0.2476 3.1702 1.0004 1112
## Veg_shannon_index 1.2760 1.4257 -1.7686 1.3171 4.0985 1.0235 716
## total_shrub_cover 0.1299 1.3252 -2.4343 0.0979 2.9420 1.0148 512
## Avg_Cogongrass_Cover -0.3028 1.5624 -3.3801 -0.3309 2.6895 1.1055 499
## Tree_Density -1.0920 1.7254 -4.3793 -1.1523 2.3355 1.0589 546
## Avg_Canopy_Cover 0.3579 1.5819 -2.8490 0.4000 3.3162 1.0199 2789
## I(Avg_Cogongrass_Cover^2) 1.1534 1.5226 -2.1063 1.2626 4.0394 1.0106 1017
## avg_veg_height -0.6826 1.2967 -3.3432 -0.6871 1.9210 1.0443 634
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 69.8847 615.4806 0.0766 2.8702 383.1194 1.0806
## Cogon_Patch_Size 365.5184 1667.3426 0.1642 35.7879 2741.1885 1.0349
## Veg_shannon_index 64.4538 347.6769 0.0650 2.3897 524.8160 1.2386
## total_shrub_cover 34.4481 276.2488 0.0605 1.1051 173.1941 1.4230
## Avg_Cogongrass_Cover 142.9345 5178.0852 0.0647 1.8789 313.0551 1.3405
## Tree_Density 1029.2383 11992.8392 0.1383 43.9739 4483.6787 1.1411
## Avg_Canopy_Cover 1699.8719 16754.8416 1.0839 139.6592 9370.5590 1.3906
## I(Avg_Cogongrass_Cover^2) 112.2475 1376.6610 0.0987 7.9103 551.2268 1.1321
## avg_veg_height 13.8012 127.2228 0.0580 1.0782 77.3591 1.1576
## ESS
## (Intercept) 679
## Cogon_Patch_Size 396
## Veg_shannon_index 207
## total_shrub_cover 517
## Avg_Cogongrass_Cover 2636
## Tree_Density 3000
## Avg_Canopy_Cover 3000
## I(Avg_Cogongrass_Cover^2) 2373
## avg_veg_height 2790
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.3235 24.921 0.0526 0.8143 52.537 1.5008 115
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2613 1.1332 -3.7514 -2.5856 0.8644 1.0006 1776
## week -0.0492 0.6222 -1.3704 -0.0512 1.2620 1.0017 2645
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 27.3432 906.0836 0.0622 0.8784 63.8265 1.3182 3000
## week 2.8557 25.1520 0.0411 0.3668 15.3851 1.1698 2826
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.8920 2.7192 -6.2730
## (Intercept)-Sylvilagus_floridanus -2.9332 4.2150 -14.4831
## Cogon_Patch_Size-Canis_latrans 6.8062 6.4377 -0.2697
## Cogon_Patch_Size-Sylvilagus_floridanus -5.4504 7.5453 -25.9535
## Veg_shannon_index-Canis_latrans 3.5013 3.4047 0.0886
## Veg_shannon_index-Sylvilagus_floridanus 3.2220 3.6681 -0.7400
## total_shrub_cover-Canis_latrans 0.2982 1.8241 -2.8312
## total_shrub_cover-Sylvilagus_floridanus -0.1658 3.7940 -12.4771
## Avg_Cogongrass_Cover-Canis_latrans -0.0718 3.4126 -5.9613
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3172 3.8620 -10.8115
## Tree_Density-Canis_latrans -8.6545 7.6326 -30.4576
## Tree_Density-Sylvilagus_floridanus -8.5236 9.9661 -39.4720
## Avg_Canopy_Cover-Canis_latrans -0.8271 1.6446 -5.2645
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.3468 16.4093 1.2732
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.0100 3.5673 0.7982
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9308 2.8491 -2.8771
## avg_veg_height-Canis_latrans -1.3043 1.3559 -4.3203
## avg_veg_height-Sylvilagus_floridanus -0.6140 2.1804 -4.6182
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -1.0213 5.3382 1.2011 202
## (Intercept)-Sylvilagus_floridanus -2.1092 3.5361 1.0323 74
## Cogon_Patch_Size-Canis_latrans 4.7953 24.2484 1.1254 80
## Cogon_Patch_Size-Sylvilagus_floridanus -3.2677 3.3661 1.1123 41
## Veg_shannon_index-Canis_latrans 2.4896 13.5394 2.2206 19
## Veg_shannon_index-Sylvilagus_floridanus 2.1906 13.2843 1.6716 36
## total_shrub_cover-Canis_latrans 0.1662 4.2469 1.1070 148
## total_shrub_cover-Sylvilagus_floridanus 0.0727 5.7997 1.3238 64
## Avg_Cogongrass_Cover-Canis_latrans -0.4440 8.9572 1.5937 61
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7733 3.6660 1.0774 283
## Tree_Density-Canis_latrans -6.1294 -0.9654 1.5496 37
## Tree_Density-Sylvilagus_floridanus -4.9383 0.7172 1.7614 33
## Avg_Canopy_Cover-Canis_latrans -0.5605 1.5462 1.5212 119
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.7956 61.0533 1.5252 18
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.9291 14.5505 1.0662 90
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6220 9.6363 1.1326 164
## avg_veg_height-Canis_latrans -1.1437 1.0260 1.0763 279
## avg_veg_height-Sylvilagus_floridanus -0.7189 4.3275 1.0495 353
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6983 0.1797 -3.0607 -2.6978 -2.3476 1.1586
## (Intercept)-Sylvilagus_floridanus -3.3351 0.2781 -3.9435 -3.3236 -2.8119 1.0432
## week-Canis_latrans 0.0893 0.1463 -0.2051 0.0937 0.3617 1.0012
## week-Sylvilagus_floridanus -0.2141 0.2687 -0.8075 -0.1923 0.2497 1.0066
## ESS
## (Intercept)-Canis_latrans 210
## (Intercept)-Sylvilagus_floridanus 322
## week-Canis_latrans 1368
## week-Sylvilagus_floridanus 614
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.145
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1056 0.8553 -1.6060 0.1025 1.8189 1.0109 1505
## Avg_Cogongrass_Cover 0.0452 0.9217 -1.8128 0.0301 1.9386 1.0010 2441
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 222.0872 11961.4550 0.0535 0.6510 27.7706 1.3234 3000
## Avg_Cogongrass_Cover 7.3223 47.2168 0.0701 1.0312 49.4783 1.2354 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1046 3.3801 0.0517 0.4691 5.801 1.256 796
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3443 1.0677 -3.6719 -2.6430 0.5956 1.0011 1499
## shrub_cover -0.0941 0.6883 -1.4645 -0.1303 1.3570 1.0007 2071
## veg_height -0.1974 0.7563 -1.6584 -0.2396 1.5147 1.0016 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.7532 522.9757 0.0454 0.6218 57.9706 1.2764 3000
## shrub_cover 3.2738 19.9669 0.0477 0.4801 16.5073 1.1105 1925
## veg_height 7.7785 145.2547 0.0863 0.7825 24.3069 1.2700 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4480 0.7074 -0.8480 0.4136
## (Intercept)-Sylvilagus_floridanus -0.1866 0.6837 -1.5300 -0.1857
## Avg_Cogongrass_Cover-Canis_latrans 0.6576 0.6411 -0.3218 0.5751
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5769 0.6250 -2.0028 -0.5436
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9504 1.0116 559
## (Intercept)-Sylvilagus_floridanus 1.2067 1.0292 680
## Avg_Cogongrass_Cover-Canis_latrans 2.1075 1.0023 932
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5777 1.0046 1131
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8235 0.2003 -3.2369 -2.8200 -2.4442 1.0258
## (Intercept)-Sylvilagus_floridanus -3.1865 0.2969 -3.8139 -3.1700 -2.6524 1.0164
## shrub_cover-Canis_latrans -0.3335 0.2210 -0.7609 -0.3372 0.0998 1.0024
## shrub_cover-Sylvilagus_floridanus 0.1339 0.4385 -0.6545 0.0921 1.0705 1.0050
## veg_height-Canis_latrans -0.7245 0.1982 -1.1365 -0.7184 -0.3485 1.0172
## veg_height-Sylvilagus_floridanus 0.1927 0.2918 -0.3771 0.1994 0.7438 1.0095
## ESS
## (Intercept)-Canis_latrans 582
## (Intercept)-Sylvilagus_floridanus 521
## shrub_cover-Canis_latrans 818
## shrub_cover-Sylvilagus_floridanus 446
## veg_height-Canis_latrans 652
## veg_height-Sylvilagus_floridanus 527
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1642
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5945 1.3446 -2.1079 0.6040 3.1622 1.0256 357
## Cogon_Patch_Size 0.1941 1.4093 -2.5286 0.2114 2.9741 1.0142 789
## Veg_shannon_index 1.2254 1.2511 -1.2878 1.2581 3.6293 1.0103 886
## total_shrub_cover 0.7957 1.5924 -2.4245 0.7799 3.8758 1.0138 309
## Avg_Cogongrass_Cover 0.8299 1.3894 -2.0447 0.8641 3.4489 1.0106 859
## Tree_Density -1.2146 1.6246 -4.1926 -1.2450 2.0879 1.0038 576
## Avg_Canopy_Cover 0.4797 1.6342 -2.7885 0.5211 3.5969 1.0043 2235
## avg_veg_height -0.2878 1.2303 -2.7340 -0.2765 2.1308 1.0043 599
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 42.0195 485.6368 0.0691 2.4520 225.8517 1.3886 719
## Cogon_Patch_Size 659.2675 24640.1065 0.0799 4.9850 743.2189 1.3435 1989
## Veg_shannon_index 30.1170 487.8969 0.0622 1.1306 100.6672 1.2016 1884
## total_shrub_cover 64.0406 323.5699 0.0957 5.6181 460.0375 1.0850 756
## Avg_Cogongrass_Cover 36.5355 174.4368 0.0679 2.9735 293.6199 1.0704 1178
## Tree_Density 252.0939 1348.9909 0.0918 13.5931 2023.3487 1.1307 153
## Avg_Canopy_Cover 519.6406 3292.8737 1.2291 57.7037 3296.8806 1.0386 607
## avg_veg_height 9.7560 74.5593 0.0611 1.0061 61.4824 1.1079 2403
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 8.0904 22.2343 0.0582 1.6986 58.0598 1.065 101
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4619 1.0581 -3.7632 -2.7429 0.5583 1.0084 1553
## shrub_cover -0.1490 0.7577 -1.6198 -0.2072 1.5107 1.0077 680
## veg_height -0.2168 0.7646 -1.7426 -0.2450 1.5993 1.0004 2630
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.1168 86.7966 0.0527 0.5329 43.4975 1.2083 2806
## shrub_cover 3.2312 16.6913 0.0579 0.6099 19.1622 1.0147 3000
## veg_height 4.6010 28.2918 0.0783 0.7741 26.2483 1.0678 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.1717 2.3800 -0.8529 1.7366
## (Intercept)-Sylvilagus_floridanus -0.1883 3.2550 -5.1296 0.1273
## Cogon_Patch_Size-Canis_latrans 2.5229 3.4807 -1.3041 1.6380
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3875 7.7073 -12.1223 -1.2104
## Veg_shannon_index-Canis_latrans 1.7499 1.4106 -0.6775 1.6657
## Veg_shannon_index-Sylvilagus_floridanus 2.0644 1.9123 -0.6069 1.7534
## total_shrub_cover-Canis_latrans 3.1008 2.6200 -0.2139 2.5360
## total_shrub_cover-Sylvilagus_floridanus 0.8568 4.6432 -8.0088 0.7439
## Avg_Cogongrass_Cover-Canis_latrans 3.1374 3.0673 -0.5498 2.5019
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5184 2.2885 -4.5556 0.6861
## Tree_Density-Canis_latrans -6.4295 6.3182 -24.2738 -4.2604
## Tree_Density-Sylvilagus_floridanus -5.4418 6.6658 -28.6975 -3.5439
## Avg_Canopy_Cover-Canis_latrans -0.4233 0.9513 -2.3188 -0.4188
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.8568 9.7744 1.6014 9.3628
## avg_veg_height-Canis_latrans -0.2896 1.3590 -3.1008 -0.2745
## avg_veg_height-Sylvilagus_floridanus -0.6591 1.8622 -4.9627 -0.5200
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 7.8475 1.1179 100
## (Intercept)-Sylvilagus_floridanus 4.1222 1.2530 122
## Cogon_Patch_Size-Canis_latrans 12.4035 1.1720 101
## Cogon_Patch_Size-Sylvilagus_floridanus 2.9821 1.4280 91
## Veg_shannon_index-Canis_latrans 4.8730 1.0056 339
## Veg_shannon_index-Sylvilagus_floridanus 6.6546 1.1329 210
## total_shrub_cover-Canis_latrans 10.3786 1.0417 41
## total_shrub_cover-Sylvilagus_floridanus 12.3803 1.1115 42
## Avg_Cogongrass_Cover-Canis_latrans 11.1415 1.0865 174
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.6360 1.0425 271
## Tree_Density-Canis_latrans -0.6824 1.0376 45
## Tree_Density-Sylvilagus_floridanus 1.3841 1.1126 45
## Avg_Canopy_Cover-Canis_latrans 1.4189 1.0038 592
## Avg_Canopy_Cover-Sylvilagus_floridanus 44.0322 1.1890 32
## avg_veg_height-Canis_latrans 2.3245 1.0009 428
## avg_veg_height-Sylvilagus_floridanus 2.6830 1.0278 297
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8550 0.1922 -3.2402 -2.8509 -2.4930 1.0164
## (Intercept)-Sylvilagus_floridanus -3.2622 0.2668 -3.8133 -3.2507 -2.7649 1.0965
## shrub_cover-Canis_latrans -0.5320 0.2078 -0.9289 -0.5349 -0.1236 1.0036
## shrub_cover-Sylvilagus_floridanus 0.1842 0.5350 -0.7878 0.1534 1.2344 1.1232
## veg_height-Canis_latrans -0.7344 0.1908 -1.1070 -0.7319 -0.3855 1.0046
## veg_height-Sylvilagus_floridanus 0.1856 0.2761 -0.3749 0.1930 0.7023 1.0074
## ESS
## (Intercept)-Canis_latrans 578
## (Intercept)-Sylvilagus_floridanus 271
## shrub_cover-Canis_latrans 626
## shrub_cover-Sylvilagus_floridanus 58
## veg_height-Canis_latrans 644
## veg_height-Sylvilagus_floridanus 574
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1832
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9053 1.2047 -1.6660 0.9315 3.2800 1.0084 513
## Avg_Cogongrass_Cover -0.0558 1.1920 -2.5074 -0.0287 2.3824 1.0872 413
## total_shrub_cover 0.1389 1.5288 -3.0150 0.1211 3.0721 1.1543 135
## avg_veg_height 0.1982 1.1246 -2.0399 0.1737 2.3898 1.1066 262
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.4333 505.3698 0.0558 0.9380 91.0962 1.3090 3000
## Avg_Cogongrass_Cover 18.9735 113.4707 0.0662 1.8311 114.8858 1.1639 348
## total_shrub_cover 75.2455 796.3692 0.0951 6.6222 378.8994 1.1801 3000
## avg_veg_height 5.6618 33.6825 0.0566 0.7766 37.3975 1.1316 2146
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.6909 45.8977 0.0781 1.4887 101.9937 2.8097 32
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3830 1.1889 -3.7993 -2.7448 0.8073 1.0048 1482
## shrub_cover -0.0166 0.8615 -1.6777 -0.0587 1.8934 1.0636 255
## veg_height -0.2892 0.7494 -1.7623 -0.3235 1.4227 1.0220 2155
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.5794 66.5515 0.0562 0.7294 59.5881 1.0405 2754
## shrub_cover 4.1857 17.9339 0.0598 0.8473 28.8365 1.0060 2173
## veg_height 5.3628 89.8404 0.0700 0.6355 20.2749 1.2720 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.5992 1.5828 -0.7586 1.3683
## (Intercept)-Sylvilagus_floridanus 1.3317 1.7313 -1.3022 1.1440
## Avg_Cogongrass_Cover-Canis_latrans 0.9820 1.8537 -1.6802 0.7161
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1202 1.6906 -5.3867 -0.8616
## total_shrub_cover-Canis_latrans 1.5036 1.9396 -2.8187 1.4366
## total_shrub_cover-Sylvilagus_floridanus -2.6370 4.3237 -12.6652 -2.2337
## avg_veg_height-Canis_latrans 0.4080 1.2042 -1.4873 0.2960
## avg_veg_height-Sylvilagus_floridanus 0.2024 1.4712 -2.4961 0.1547
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.4550 1.3232 123
## (Intercept)-Sylvilagus_floridanus 5.8456 1.0617 131
## Avg_Cogongrass_Cover-Canis_latrans 4.8669 1.1295 107
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4524 1.2913 111
## total_shrub_cover-Canis_latrans 5.2132 1.1378 72
## total_shrub_cover-Sylvilagus_floridanus 3.8905 1.6800 22
## avg_veg_height-Canis_latrans 2.8023 1.2800 166
## avg_veg_height-Sylvilagus_floridanus 3.1741 1.1293 99
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.9226 0.2067 -3.3654 -2.9128 -2.5397 1.1083
## (Intercept)-Sylvilagus_floridanus -3.3980 0.2731 -3.9428 -3.3992 -2.8584 1.0440
## shrub_cover-Canis_latrans -0.4913 0.2742 -0.9792 -0.5165 0.1077 1.0719
## shrub_cover-Sylvilagus_floridanus 0.4999 0.7504 -0.9061 0.6642 1.7054 1.4293
## veg_height-Canis_latrans -0.7474 0.1957 -1.1497 -0.7437 -0.3708 1.0052
## veg_height-Sylvilagus_floridanus 0.0578 0.3192 -0.5518 0.0572 0.6879 1.2253
## ESS
## (Intercept)-Canis_latrans 357
## (Intercept)-Sylvilagus_floridanus 308
## shrub_cover-Canis_latrans 215
## shrub_cover-Sylvilagus_floridanus 22
## veg_height-Canis_latrans 546
## veg_height-Sylvilagus_floridanus 158
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1435
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0599 0.7408 -1.4474 0.0625 1.5448 1.0041 2308
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1811 18.1588 0.0457 0.5352 19.1183 1.0592 2177
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3296 1.0966 -3.6687 -2.6528 0.7872 1.0053 1854
## shrub_cover -0.0857 0.6943 -1.5009 -0.1105 1.3642 1.0059 2147
## veg_height -0.2560 0.7390 -1.6973 -0.2861 1.3608 1.0019 2688
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.5226 153.0901 0.0484 0.5746 46.3332 1.3126 3000
## shrub_cover 2.9109 20.1316 0.0506 0.4922 16.0637 1.1378 3000
## veg_height 3.8360 38.1249 0.0709 0.6444 18.1893 1.2169 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3671 0.4148 -0.3921 0.3443 1.2128 1.0023
## (Intercept)-Sylvilagus_floridanus -0.2040 0.4767 -1.1117 -0.2161 0.7746 1.0014
## ESS
## (Intercept)-Canis_latrans 1514
## (Intercept)-Sylvilagus_floridanus 1018
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7993 0.1879 -3.1782 -2.7899 -2.4516 1.0212
## (Intercept)-Sylvilagus_floridanus -3.1778 0.2858 -3.7590 -3.1666 -2.6496 1.0239
## shrub_cover-Canis_latrans -0.3585 0.2207 -0.8066 -0.3556 0.0738 1.0084
## shrub_cover-Sylvilagus_floridanus 0.1457 0.4617 -0.6226 0.0955 1.1655 1.0181
## veg_height-Canis_latrans -0.6985 0.1934 -1.0877 -0.6952 -0.3315 1.0311
## veg_height-Sylvilagus_floridanus 0.0899 0.2862 -0.4729 0.0866 0.6554 1.0075
## ESS
## (Intercept)-Canis_latrans 669
## (Intercept)-Sylvilagus_floridanus 544
## shrub_cover-Canis_latrans 1007
## shrub_cover-Sylvilagus_floridanus 407
## veg_height-Canis_latrans 699
## veg_height-Sylvilagus_floridanus 641
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1503
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1779 0.8962 -1.6473 0.1470 1.9958 1.0109 920
## Veg_shannon_index 0.7167 0.8581 -1.2160 0.7502 2.3723 1.0118 1466
## Avg_Cogongrass_Cover 0.2239 0.9666 -1.8109 0.2602 2.1552 1.0016 2277
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.7729 60.1794 0.0561 0.6499 23.5394 1.1652 3000
## Veg_shannon_index 3.9079 25.6406 0.0497 0.5840 24.1215 1.2726 2758
## Avg_Cogongrass_Cover 600.8828 31965.7332 0.0848 1.5590 45.9818 1.3226 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4261 3.112 0.0505 0.5552 8.2584 1.0533 399
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2908 1.1279 -3.7055 -2.6180 0.7834 1.0017 1768
## shrub_cover -0.1409 0.6631 -1.5047 -0.1586 1.2636 1.0033 2319
## veg_height -0.2568 0.7845 -1.8245 -0.2680 1.5336 1.0108 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.5788 62.3459 0.0521 0.7218 60.4605 1.1192 3000
## shrub_cover 3.1829 36.6816 0.0451 0.4041 16.3085 1.2321 3000
## veg_height 4.8063 45.0528 0.0820 0.7367 24.2627 1.2241 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5299 0.7804 -0.9131 0.4797
## (Intercept)-Sylvilagus_floridanus -0.1137 0.8315 -1.6707 -0.1440
## Veg_shannon_index-Canis_latrans 1.0873 0.6397 -0.0354 1.0329
## Veg_shannon_index-Sylvilagus_floridanus 0.7117 0.7378 -0.5548 0.6455
## Avg_Cogongrass_Cover-Canis_latrans 1.1768 0.8244 -0.0685 1.0374
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4370 0.7207 -1.8819 -0.4139
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2644 1.0167 477
## (Intercept)-Sylvilagus_floridanus 1.7103 1.0350 397
## Veg_shannon_index-Canis_latrans 2.4878 1.0117 868
## Veg_shannon_index-Sylvilagus_floridanus 2.4117 1.0294 483
## Avg_Cogongrass_Cover-Canis_latrans 3.1130 1.0039 708
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9394 1.0184 790
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8056 0.1897 -3.2036 -2.8029 -2.4524 1.0072
## (Intercept)-Sylvilagus_floridanus -3.2464 0.3226 -3.8954 -3.2283 -2.6455 1.0198
## shrub_cover-Canis_latrans -0.3459 0.2182 -0.7792 -0.3500 0.1024 1.0016
## shrub_cover-Sylvilagus_floridanus 0.0616 0.4204 -0.6724 0.0252 0.9854 1.0051
## veg_height-Canis_latrans -0.7126 0.1935 -1.1010 -0.7105 -0.3376 1.0178
## veg_height-Sylvilagus_floridanus 0.1642 0.2883 -0.4012 0.1656 0.7110 1.0015
## ESS
## (Intercept)-Canis_latrans 765
## (Intercept)-Sylvilagus_floridanus 304
## shrub_cover-Canis_latrans 915
## shrub_cover-Sylvilagus_floridanus 426
## veg_height-Canis_latrans 803
## veg_height-Sylvilagus_floridanus 616
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1773
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7112 1.2892 -1.9228 0.7190 3.2598 1.0184 316
## Cogon_Patch_Size 0.0339 1.3493 -2.7185 0.0232 2.6371 1.0154 1532
## Avg_Cogongrass_Cover 0.1022 1.0003 -2.0007 0.1268 2.0627 1.0010 805
## total_shrub_cover -0.1052 1.3372 -2.7468 -0.1286 2.5717 1.0276 400
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 26.2873 302.2165 0.0648 1.3938 117.0860 1.2629 1023
## Cogon_Patch_Size 131.2429 800.6300 0.1477 9.4476 803.3890 1.2260 273
## Avg_Cogongrass_Cover 7.8689 63.8592 0.0560 0.8621 43.3300 1.1465 3000
## total_shrub_cover 45.4098 346.5928 0.0959 5.6230 284.2329 1.2008 2633
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.3548 53.0543 0.0696 1.3281 67.8348 2.3721 50
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3856 1.1529 -3.8311 -2.7294 0.8038 1.0042 1743
## shrub_cover 0.0324 0.8637 -1.7429 0.0311 1.8243 1.0007 1965
## veg_height -0.2795 0.7117 -1.6781 -0.3158 1.3019 1.0009 2741
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.0685 97.1349 0.0550 0.7221 55.5073 1.0528 2331
## shrub_cover 5.3916 38.3893 0.0896 0.9944 27.9390 1.0888 3000
## veg_height 3.8498 40.3784 0.0631 0.5445 20.8806 1.2009 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.7101 1.8624 -1.0276 1.3880
## (Intercept)-Sylvilagus_floridanus 0.7421 2.1052 -2.8770 0.5048
## Cogon_Patch_Size-Canis_latrans 2.9098 3.6082 -0.3800 1.8662
## Cogon_Patch_Size-Sylvilagus_floridanus -3.8318 5.2576 -19.3666 -2.1231
## Avg_Cogongrass_Cover-Canis_latrans 0.4744 0.7925 -0.8438 0.3945
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2143 1.4723 -3.4113 -0.1501
## total_shrub_cover-Canis_latrans 1.4310 1.4706 -0.6728 1.1619
## total_shrub_cover-Sylvilagus_floridanus -2.7074 3.0810 -10.1511 -2.1667
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.6078 1.1190 122
## (Intercept)-Sylvilagus_floridanus 5.7660 1.1428 124
## Cogon_Patch_Size-Canis_latrans 14.7383 1.2055 68
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1894 1.6199 52
## Avg_Cogongrass_Cover-Canis_latrans 2.2657 1.0215 763
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4788 1.0191 181
## total_shrub_cover-Canis_latrans 4.9919 1.0878 173
## total_shrub_cover-Sylvilagus_floridanus 2.0596 1.0341 68
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8505 0.1961 -3.2328 -2.8543 -2.4624 1.0154
## (Intercept)-Sylvilagus_floridanus -3.3938 0.2964 -4.0050 -3.3886 -2.8396 1.0665
## shrub_cover-Canis_latrans -0.4963 0.2469 -0.9657 -0.5055 0.0133 1.0026
## shrub_cover-Sylvilagus_floridanus 0.6917 0.5365 -0.4954 0.7505 1.6218 1.0213
## veg_height-Canis_latrans -0.7075 0.1931 -1.1012 -0.7032 -0.3547 1.0013
## veg_height-Sylvilagus_floridanus 0.0046 0.2854 -0.5553 0.0075 0.5929 1.0112
## ESS
## (Intercept)-Canis_latrans 536
## (Intercept)-Sylvilagus_floridanus 265
## shrub_cover-Canis_latrans 353
## shrub_cover-Sylvilagus_floridanus 100
## veg_height-Canis_latrans 700
## veg_height-Sylvilagus_floridanus 324
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1487
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0023 0.9878 -1.9855 -0.0070 2.1043 1.0164 522
## Tree_Density -1.1687 1.1282 -3.3507 -1.2195 1.2586 1.0069 962
## Avg_Canopy_Cover 0.5385 1.3835 -2.4246 0.5342 3.2938 1.0061 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.8030 113.6922 0.0570 0.7442 48.1794 1.3989 1063
## Tree_Density 54.2438 2363.6523 0.0554 0.8375 60.9356 1.3189 3000
## Avg_Canopy_Cover 149.5002 3481.0563 0.6069 11.3599 502.9953 1.2996 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 8.1444 39.675 0.0467 0.449 89.7366 3.2038 14
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3954 1.0662 -3.6952 -2.7048 0.5288 1.0027 1506
## shrub_cover 0.0413 0.7676 -1.5522 0.0251 1.6840 1.0111 2306
## veg_height -0.2269 0.7618 -1.7508 -0.2457 1.4747 1.0030 2757
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.1092 716.6564 0.0487 0.5420 43.9625 1.2927 3000
## shrub_cover 4.0618 24.3516 0.0675 0.6815 23.4955 1.0686 3000
## veg_height 5.6468 86.6062 0.0753 0.6841 26.1427 1.2777 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4663 1.2505 -1.1496 0.2868 4.5350
## (Intercept)-Sylvilagus_floridanus -0.3868 1.1158 -2.6799 -0.3533 1.7460
## Tree_Density-Canis_latrans -1.4336 0.9049 -3.6195 -1.3084 0.0318
## Tree_Density-Sylvilagus_floridanus -1.8935 1.6857 -5.9608 -1.6155 0.4417
## Avg_Canopy_Cover-Canis_latrans -0.7345 1.1201 -4.3794 -0.5137 0.4463
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.0844 4.0614 1.0070 4.0851 17.6176
## Rhat ESS
## (Intercept)-Canis_latrans 1.4773 49
## (Intercept)-Sylvilagus_floridanus 1.0541 269
## Tree_Density-Canis_latrans 1.0753 662
## Tree_Density-Sylvilagus_floridanus 1.0153 252
## Avg_Canopy_Cover-Canis_latrans 2.3966 12
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5957 40
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8403 0.2042 -3.2241 -2.8385 -2.4588 1.0421
## (Intercept)-Sylvilagus_floridanus -3.1559 0.2591 -3.7064 -3.1477 -2.6737 1.0040
## shrub_cover-Canis_latrans -0.3597 0.2415 -0.8317 -0.3654 0.1208 1.0341
## shrub_cover-Sylvilagus_floridanus 0.4577 0.4363 -0.3822 0.4550 1.3134 1.0289
## veg_height-Canis_latrans -0.7119 0.1971 -1.1228 -0.7019 -0.3481 1.0060
## veg_height-Sylvilagus_floridanus 0.1304 0.2747 -0.4209 0.1283 0.6664 1.0119
## ESS
## (Intercept)-Canis_latrans 482
## (Intercept)-Sylvilagus_floridanus 756
## shrub_cover-Canis_latrans 658
## shrub_cover-Sylvilagus_floridanus 289
## veg_height-Canis_latrans 628
## veg_height-Sylvilagus_floridanus 807
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1513
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6810 0.9859 -2.5738 -0.7196 1.4544 1.0161 745
## Avg_Cogongrass_Cover -0.5274 1.1798 -2.7064 -0.5676 2.0372 1.0014 1756
## I(Avg_Cogongrass_Cover^2) 1.1098 1.1539 -1.5166 1.1477 3.2321 1.0092 1420
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.7857 19.0879 0.0547 0.6933 38.6231 1.0465 2777
## Avg_Cogongrass_Cover 18.3257 99.7320 0.0941 2.6231 129.8513 1.0675 1469
## I(Avg_Cogongrass_Cover^2) 27.1274 581.1716 0.0610 1.1897 92.6503 1.1548 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1957 2.7552 0.0488 0.3984 7.2617 1.1352 445
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2517 1.1740 -3.6849 -2.6167 0.8889 1.0035 1381
## shrub_cover -0.1105 0.6501 -1.5063 -0.1185 1.3219 1.0035 2379
## veg_height -0.1981 0.7566 -1.7369 -0.2276 1.4398 1.0008 2078
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.0908 94.6432 0.0553 0.7636 70.5608 1.1754 2077
## shrub_cover 3.0472 53.2158 0.0473 0.4091 13.4976 1.2925 3000
## veg_height 3.9527 32.4795 0.0790 0.6743 21.5545 1.1392 2230
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6277 0.9462 -2.4139 -0.6529
## (Intercept)-Sylvilagus_floridanus -1.1228 1.0300 -3.1498 -1.0658
## Avg_Cogongrass_Cover-Canis_latrans 0.2002 0.9893 -1.4434 0.0949
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.1591 1.5806 -6.2272 -1.8936
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3426 1.4943 0.1968 2.0946
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3547 1.3633 -0.1001 1.1226
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3099 1.0296 467
## (Intercept)-Sylvilagus_floridanus 0.7460 1.0295 494
## Avg_Cogongrass_Cover-Canis_latrans 2.4651 0.9999 719
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1667 1.0151 298
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.9560 1.0104 229
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.8855 1.1370 213
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8121 0.1875 -3.1889 -2.8075 -2.4672 1.0071
## (Intercept)-Sylvilagus_floridanus -3.2695 0.3268 -3.9358 -3.2551 -2.6888 1.0018
## shrub_cover-Canis_latrans -0.3076 0.2224 -0.7504 -0.3102 0.1390 1.0048
## shrub_cover-Sylvilagus_floridanus 0.0982 0.3963 -0.6151 0.0672 0.9848 1.0115
## veg_height-Canis_latrans -0.7067 0.1928 -1.1003 -0.6988 -0.3508 0.9999
## veg_height-Sylvilagus_floridanus 0.1974 0.3027 -0.3741 0.1975 0.7833 1.0004
## ESS
## (Intercept)-Canis_latrans 735
## (Intercept)-Sylvilagus_floridanus 289
## shrub_cover-Canis_latrans 677
## shrub_cover-Sylvilagus_floridanus 399
## veg_height-Canis_latrans 850
## veg_height-Sylvilagus_floridanus 422
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1587
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3880 1.4846 -3.1569 -0.4484 2.5865 1.0543 396
## Cogon_Patch_Size 0.2153 1.5569 -2.8339 0.2141 3.2230 1.0188 768
## Veg_shannon_index 1.3251 1.3659 -1.6595 1.4219 3.8145 1.0314 897
## total_shrub_cover 0.4336 1.5101 -2.6369 0.4350 3.3746 1.0613 376
## Avg_Cogongrass_Cover -0.2216 1.5287 -3.2361 -0.2110 2.7623 1.0335 515
## Tree_Density -1.0357 1.6379 -4.0715 -1.1250 2.3597 1.0188 969
## Avg_Canopy_Cover 0.4220 1.6034 -2.7117 0.4390 3.4765 1.0192 3000
## I(Avg_Cogongrass_Cover^2) 1.1935 1.4359 -1.9996 1.2878 3.7828 1.0554 770
## avg_veg_height -0.3158 1.2688 -2.9030 -0.3006 2.1918 1.0121 447
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept) 41.2276 622.0854 0.0689 1.5343 196.0013
## Cogon_Patch_Size 690.6120 14857.0924 0.1932 34.2728 2571.5276
## Veg_shannon_index 92.9249 1347.6048 0.0639 1.5973 555.6561
## total_shrub_cover 68.5037 720.1381 0.0796 3.6829 398.1460
## Avg_Cogongrass_Cover 32.3829 301.1010 0.0679 1.6140 191.0771
## Tree_Density 350.8323 2679.2227 0.1097 22.3719 2041.2909
## Avg_Canopy_Cover 1642.0228 11061.1103 2.2413 127.0146 10288.9692
## I(Avg_Cogongrass_Cover^2) 40.9927 232.5976 0.0780 3.2152 258.9104
## avg_veg_height 8.9301 45.2177 0.0614 0.9305 59.3989
## Rhat ESS
## (Intercept) 1.3207 3000
## Cogon_Patch_Size 1.3694 3000
## Veg_shannon_index 1.5322 374
## total_shrub_cover 1.1011 2546
## Avg_Cogongrass_Cover 1.1106 1788
## Tree_Density 1.1209 1634
## Avg_Canopy_Cover 1.3057 581
## I(Avg_Cogongrass_Cover^2) 1.0914 1577
## avg_veg_height 1.1198 1180
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.0199 12.0333 0.0602 1.1078 36.7054 1.044 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2788 1.1874 -3.7325 -2.6302 0.9797 1.0110 1576
## shrub_cover -0.0495 0.7570 -1.5453 -0.0655 1.5454 1.0438 1227
## veg_height -0.2073 0.7422 -1.6574 -0.2504 1.3509 1.0014 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 27.0744 821.3146 0.0534 0.7296 63.7202 1.3127 3000
## shrub_cover 5.2178 51.1136 0.0633 0.6466 30.2120 1.1106 3000
## veg_height 3.4182 31.9042 0.0707 0.6507 20.5000 1.2555 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.3191 1.9877 -4.2932
## (Intercept)-Sylvilagus_floridanus -1.2247 3.4990 -9.0651
## Cogon_Patch_Size-Canis_latrans 5.8234 5.3002 -0.0759
## Cogon_Patch_Size-Sylvilagus_floridanus -5.9354 8.6259 -31.2935
## Veg_shannon_index-Canis_latrans 2.4019 1.6034 -0.1424
## Veg_shannon_index-Sylvilagus_floridanus 3.7052 6.0723 -0.4780
## total_shrub_cover-Canis_latrans 1.8924 2.0207 -1.4492
## total_shrub_cover-Sylvilagus_floridanus -1.1223 4.4314 -13.3385
## Avg_Cogongrass_Cover-Canis_latrans 0.0975 2.7220 -4.7767
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1421 3.0091 -8.4228
## Tree_Density-Canis_latrans -5.8953 4.4940 -17.9997
## Tree_Density-Sylvilagus_floridanus -6.3787 7.3225 -29.1672
## Avg_Canopy_Cover-Canis_latrans -0.5047 1.0497 -2.7232
## Avg_Canopy_Cover-Sylvilagus_floridanus 20.3439 20.8818 2.4854
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.6689 2.6956 0.3855
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4600 2.3860 -3.2226
## avg_veg_height-Canis_latrans -0.5384 1.3460 -3.3598
## avg_veg_height-Sylvilagus_floridanus -0.5129 2.2783 -4.6720
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3216 3.6352 1.0043 210
## (Intercept)-Sylvilagus_floridanus -1.0897 4.7045 1.3310 90
## Cogon_Patch_Size-Canis_latrans 4.3140 20.1586 1.3052 109
## Cogon_Patch_Size-Sylvilagus_floridanus -3.1646 3.8228 2.5431 49
## Veg_shannon_index-Canis_latrans 2.1583 6.4280 1.0638 234
## Veg_shannon_index-Sylvilagus_floridanus 2.1988 20.9809 3.1876 24
## total_shrub_cover-Canis_latrans 1.6191 6.6291 1.0143 113
## total_shrub_cover-Sylvilagus_floridanus -0.2366 5.7897 1.1442 56
## Avg_Cogongrass_Cover-Canis_latrans -0.0029 6.1242 1.0682 259
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7586 3.6431 1.1887 450
## Tree_Density-Canis_latrans -4.7273 -0.5555 1.1024 108
## Tree_Density-Sylvilagus_floridanus -4.0945 1.6204 1.3677 36
## Avg_Canopy_Cover-Canis_latrans -0.4472 1.4094 1.0059 508
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.2784 86.5216 4.7108 10
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9810 11.3415 1.2995 109
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3834 6.4468 1.0836 183
## avg_veg_height-Canis_latrans -0.4728 1.9425 1.0482 219
## avg_veg_height-Sylvilagus_floridanus -0.3920 3.1548 1.2229 266
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7977 0.1843 -3.1741 -2.7929 -2.4390 1.0061
## (Intercept)-Sylvilagus_floridanus -3.2757 0.2644 -3.8234 -3.2631 -2.7777 1.1115
## shrub_cover-Canis_latrans -0.4466 0.2520 -0.9173 -0.4567 0.0933 1.0040
## shrub_cover-Sylvilagus_floridanus 0.3637 0.4814 -0.4898 0.3188 1.3792 1.3270
## veg_height-Canis_latrans -0.6606 0.1925 -1.0344 -0.6567 -0.3079 1.0028
## veg_height-Sylvilagus_floridanus 0.1444 0.2989 -0.4299 0.1386 0.7320 1.0362
## ESS
## (Intercept)-Canis_latrans 734
## (Intercept)-Sylvilagus_floridanus 403
## shrub_cover-Canis_latrans 371
## shrub_cover-Sylvilagus_floridanus 95
## veg_height-Canis_latrans 619
## veg_height-Sylvilagus_floridanus 510
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1785
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0095 0.7375 -1.5715 0.0187 1.4866 1.0032 1835
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.5819 62.7434 0.0485 0.509 20.2393 1.1115 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0536 1.1585 -3.5638 -2.3589 0.9686 1.0000 1671
## week 0.2104 0.7343 -1.4086 0.2364 1.5780 1.0032 2364
## I(week^2) -0.1545 0.5785 -1.3574 -0.1677 1.0395 1.0073 2481
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.2217 297.2764 0.0632 0.8844 60.7678 1.1187 3000
## week 4.0738 30.6516 0.0537 0.5098 21.8568 1.1305 1851
## I(week^2) 2.0070 10.8596 0.0379 0.3026 11.9220 1.0926 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2685 0.4032 -0.4641 0.2452 1.0795 1.0003
## (Intercept)-Sylvilagus_floridanus -0.1926 0.5307 -1.1327 -0.2374 1.0126 1.0053
## ESS
## (Intercept)-Canis_latrans 1737
## (Intercept)-Sylvilagus_floridanus 570
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4540 0.1971 -2.8533 -2.4475 -2.0924 1.0009
## (Intercept)-Sylvilagus_floridanus -3.1247 0.3775 -3.9147 -3.0965 -2.4583 1.0102
## week-Canis_latrans 0.5491 0.2917 -0.0036 0.5460 1.1440 0.9998
## week-Sylvilagus_floridanus -0.0345 0.4100 -0.8530 -0.0327 0.7354 1.0064
## I(week^2)-Canis_latrans -0.2272 0.1211 -0.4697 -0.2281 0.0086 1.0000
## I(week^2)-Sylvilagus_floridanus -0.1309 0.1939 -0.5295 -0.1266 0.2432 1.0008
## ESS
## (Intercept)-Canis_latrans 1060
## (Intercept)-Sylvilagus_floridanus 416
## week-Canis_latrans 1109
## week-Sylvilagus_floridanus 834
## I(week^2)-Canis_latrans 1073
## I(week^2)-Sylvilagus_floridanus 695
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1962
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2623 1.2853 -2.2975 0.2565 2.8399 1.0093 482
## Cogon_Patch_Size 0.0429 1.3815 -2.7268 0.0056 2.7500 1.0026 1062
## Veg_shannon_index 1.2318 1.2150 -1.3204 1.2702 3.7373 1.0110 802
## total_shrub_cover 0.4387 1.1920 -1.9948 0.4289 2.8071 1.0222 546
## Avg_Cogongrass_Cover 0.9409 1.3392 -1.9082 0.9843 3.4442 1.0032 931
## Tree_Density -1.4246 1.6028 -4.2252 -1.5228 2.0923 1.0111 482
## Avg_Canopy_Cover 0.5639 1.5212 -2.5206 0.6104 3.5022 1.0042 2073
## avg_veg_height -0.5100 1.1733 -2.8011 -0.5110 1.8529 1.0281 597
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.2692 229.7860 0.0636 1.7860 113.0025 1.1630 971
## Cogon_Patch_Size 57.9506 559.1106 0.0931 5.2003 306.8892 1.3979 2447
## Veg_shannon_index 35.7598 453.4666 0.0600 0.9985 202.9003 1.4419 277
## total_shrub_cover 12.4622 99.2320 0.0558 0.9771 66.5270 1.0193 2652
## Avg_Cogongrass_Cover 55.3962 607.8054 0.0692 2.6121 370.8096 1.5236 541
## Tree_Density 289.0483 3378.3700 0.0695 4.1421 1720.6966 1.2421 581
## Avg_Canopy_Cover 1041.8896 22120.5101 0.6110 42.2174 4532.2067 1.3342 3000
## avg_veg_height 12.6143 136.7237 0.0567 0.8346 66.9574 1.4175 860
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.8112 24.7372 0.056 0.7487 61.5925 1.2268 132
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1979 1.0940 -3.6278 -2.4777 0.8634 1.0005 1635
## week 0.2048 0.7051 -1.3109 0.2329 1.6726 1.0060 2496
## I(week^2) -0.1786 0.5894 -1.4388 -0.1720 1.0231 1.0005 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.7068 37.1257 0.0669 0.8462 53.5419 1.0005 2577
## week 4.1335 46.2883 0.0558 0.5373 20.9375 1.3005 2101
## I(week^2) 2.3480 16.8853 0.0371 0.2940 13.0454 1.0200 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.3741 1.9715 -1.3402 1.0431
## (Intercept)-Sylvilagus_floridanus -0.4735 2.1099 -4.7461 -0.3755
## Cogon_Patch_Size-Canis_latrans 1.7099 2.0716 -1.0930 1.3143
## Cogon_Patch_Size-Sylvilagus_floridanus -2.1663 3.4759 -11.3761 -1.5389
## Veg_shannon_index-Canis_latrans 2.2910 2.7601 -0.0785 1.6570
## Veg_shannon_index-Sylvilagus_floridanus 2.1566 2.3568 -0.5076 1.6731
## total_shrub_cover-Canis_latrans 0.8226 1.3513 -1.2823 0.6340
## total_shrub_cover-Sylvilagus_floridanus 0.3775 1.8158 -3.4762 0.3982
## Avg_Cogongrass_Cover-Canis_latrans 3.3279 3.7690 -0.0996 2.3663
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.3662 2.9511 -7.3117 0.6804
## Tree_Density-Canis_latrans -4.6705 5.2577 -21.3954 -3.1078
## Tree_Density-Sylvilagus_floridanus -5.8007 8.2209 -33.1985 -3.1014
## Avg_Canopy_Cover-Canis_latrans -0.4926 1.1557 -3.5796 -0.3080
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.3782 12.6034 1.1864 7.7022
## avg_veg_height-Canis_latrans -0.9643 1.5547 -4.5160 -0.8126
## avg_veg_height-Sylvilagus_floridanus -0.4789 1.9488 -3.9875 -0.5280
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.8279 1.3492 117
## (Intercept)-Sylvilagus_floridanus 3.4547 1.0674 182
## Cogon_Patch_Size-Canis_latrans 6.9189 1.0240 427
## Cogon_Patch_Size-Sylvilagus_floridanus 2.8537 1.0524 145
## Veg_shannon_index-Canis_latrans 11.2495 2.1462 29
## Veg_shannon_index-Sylvilagus_floridanus 8.4904 1.2974 64
## total_shrub_cover-Canis_latrans 3.9777 1.0240 260
## total_shrub_cover-Sylvilagus_floridanus 4.0445 1.0060 360
## Avg_Cogongrass_Cover-Canis_latrans 14.3728 1.6341 40
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.6608 1.0820 193
## Tree_Density-Canis_latrans -0.4869 1.7308 24
## Tree_Density-Sylvilagus_floridanus 0.7810 1.0871 22
## Avg_Canopy_Cover-Canis_latrans 1.2475 1.1296 156
## Avg_Canopy_Cover-Sylvilagus_floridanus 51.4057 1.1675 18
## avg_veg_height-Canis_latrans 1.3951 1.1513 98
## avg_veg_height-Sylvilagus_floridanus 3.1144 1.0722 190
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5356 0.1982 -2.9257 -2.5305 -2.1573 1.0140
## (Intercept)-Sylvilagus_floridanus -3.2155 0.3201 -3.8639 -3.2074 -2.6146 1.0094
## week-Canis_latrans 0.5299 0.2919 -0.0176 0.5185 1.1178 1.0071
## week-Sylvilagus_floridanus -0.0456 0.3967 -0.8447 -0.0550 0.7377 1.0104
## I(week^2)-Canis_latrans -0.2189 0.1210 -0.4696 -0.2157 0.0082 1.0091
## I(week^2)-Sylvilagus_floridanus -0.1384 0.1931 -0.5437 -0.1346 0.2200 1.0019
## ESS
## (Intercept)-Canis_latrans 708
## (Intercept)-Sylvilagus_floridanus 319
## week-Canis_latrans 1075
## week-Sylvilagus_floridanus 692
## I(week^2)-Canis_latrans 1148
## I(week^2)-Sylvilagus_floridanus 568
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1975
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2365 0.9845 -1.7540 0.1876 2.3445 1.0193 565
## Avg_Cogongrass_Cover 0.1455 0.9848 -1.8924 0.1616 2.0888 1.0125 1028
## total_shrub_cover 0.0595 0.9365 -1.9331 0.0742 1.9525 1.0035 1144
## avg_veg_height -0.2629 0.8831 -2.1038 -0.2496 1.5007 1.0080 905
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.2406 65.7557 0.0528 0.6774 41.7887 1.0919 1184
## Avg_Cogongrass_Cover 6.9098 28.7889 0.0714 1.2555 40.8927 1.0182 1747
## total_shrub_cover 5.5584 28.7096 0.0551 0.7987 38.0604 1.0579 1359
## avg_veg_height 4.7742 46.1551 0.0483 0.5577 24.1203 1.2731 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0761 4.537 0.067 0.7815 12.7288 1.0311 407
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0769 1.1628 -3.6183 -2.3890 0.9886 1.0028 1890
## week 0.1788 0.7434 -1.5410 0.2201 1.5594 1.0034 1912
## I(week^2) -0.1418 0.5804 -1.3617 -0.1479 1.1001 1.0071 2687
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.7951 419.3677 0.0748 1.1580 66.3269 1.3333 3000
## week 3.5010 27.4419 0.0571 0.5188 17.7217 1.1669 3000
## I(week^2) 1.9827 10.1008 0.0403 0.3131 13.2821 1.0564 2447
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4715 0.8282 -0.9994 0.3868
## (Intercept)-Sylvilagus_floridanus 0.2788 1.3188 -1.7306 0.0749
## Avg_Cogongrass_Cover-Canis_latrans 0.8686 0.8271 -0.4900 0.7808
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4629 0.9998 -2.7184 -0.3924
## total_shrub_cover-Canis_latrans 0.4892 0.6621 -0.6118 0.4122
## total_shrub_cover-Sylvilagus_floridanus -0.3801 1.2160 -3.9556 -0.2554
## avg_veg_height-Canis_latrans -0.3537 0.6796 -1.7818 -0.3388
## avg_veg_height-Sylvilagus_floridanus -0.2685 0.8754 -2.0459 -0.2655
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4473 1.0252 466
## (Intercept)-Sylvilagus_floridanus 3.6869 1.1352 122
## Avg_Cogongrass_Cover-Canis_latrans 2.8490 1.0312 490
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3262 1.0124 340
## total_shrub_cover-Canis_latrans 2.0491 1.0140 486
## total_shrub_cover-Sylvilagus_floridanus 1.7161 1.0118 210
## avg_veg_height-Canis_latrans 0.9683 1.0107 677
## avg_veg_height-Sylvilagus_floridanus 1.4637 1.0129 470
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5049 0.2045 -2.9129 -2.4991 -2.1238 1.0087
## (Intercept)-Sylvilagus_floridanus -3.3140 0.4060 -4.1401 -3.3001 -2.5623 1.0335
## week-Canis_latrans 0.5331 0.2949 -0.0273 0.5299 1.1364 1.0083
## week-Sylvilagus_floridanus -0.0482 0.3916 -0.8322 -0.0490 0.7166 1.0390
## I(week^2)-Canis_latrans -0.2191 0.1218 -0.4556 -0.2173 0.0181 1.0037
## I(week^2)-Sylvilagus_floridanus -0.1394 0.2028 -0.5776 -0.1211 0.2231 1.0319
## ESS
## (Intercept)-Canis_latrans 915
## (Intercept)-Sylvilagus_floridanus 235
## week-Canis_latrans 1096
## week-Sylvilagus_floridanus 790
## I(week^2)-Canis_latrans 1139
## I(week^2)-Sylvilagus_floridanus 371
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0417 0.8867 -1.7853 -0.0458 1.7769 1.0032 1284
## Tree_Density -1.0213 1.0588 -3.0252 -1.0605 1.3960 1.0012 1309
## Avg_Canopy_Cover 0.5627 1.2700 -2.0662 0.5791 3.0118 1.0043 1679
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.3650 257.7234 0.0556 0.6655 31.0655 1.2585 3000
## Tree_Density 56.2345 2438.7055 0.0552 0.7947 56.0519 1.3144 3000
## Avg_Canopy_Cover 66.8241 859.6883 0.2581 6.6538 261.6633 1.1506 2068
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1263 3.5258 0.0471 0.3297 7.5114 1.0392 360
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1122 1.1360 -3.5657 -2.4213 0.9518 1.0082 1817
## week 0.2075 0.6875 -1.2629 0.2354 1.5591 1.0002 2340
## I(week^2) -0.1429 0.6037 -1.3759 -0.1661 1.0965 1.0046 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.9447 68.0579 0.0606 0.8474 59.3905 1.1708 2322
## week 3.8614 53.6019 0.0533 0.4995 15.6283 1.1992 3000
## I(week^2) 13.5309 386.6422 0.0381 0.2986 13.5300 1.3572 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2287 0.7217 -1.0378 0.1774 1.7881
## (Intercept)-Sylvilagus_floridanus -0.3452 0.8974 -2.0368 -0.3737 1.6289
## Tree_Density-Canis_latrans -1.1938 0.7951 -2.9008 -1.1018 0.0520
## Tree_Density-Sylvilagus_floridanus -1.6552 1.3133 -4.7224 -1.4801 0.3983
## Avg_Canopy_Cover-Canis_latrans -0.3826 0.5781 -1.5973 -0.3455 0.5755
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6598 2.4402 0.6394 3.0896 10.5422
## Rhat ESS
## (Intercept)-Canis_latrans 1.0308 603
## (Intercept)-Sylvilagus_floridanus 1.0140 434
## Tree_Density-Canis_latrans 1.0056 768
## Tree_Density-Sylvilagus_floridanus 1.0147 363
## Avg_Canopy_Cover-Canis_latrans 1.0120 466
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0128 160
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4925 0.1978 -2.8791 -2.4859 -2.1187 1.0270
## (Intercept)-Sylvilagus_floridanus -3.1312 0.3239 -3.8115 -3.1203 -2.5510 1.0230
## week-Canis_latrans 0.5376 0.2895 -0.0205 0.5285 1.1099 0.9998
## week-Sylvilagus_floridanus -0.0283 0.3977 -0.8069 -0.0157 0.7571 1.0060
## I(week^2)-Canis_latrans -0.2179 0.1200 -0.4604 -0.2163 0.0109 1.0038
## I(week^2)-Sylvilagus_floridanus -0.1253 0.1856 -0.5100 -0.1188 0.2161 1.0123
## ESS
## (Intercept)-Canis_latrans 1037
## (Intercept)-Sylvilagus_floridanus 531
## week-Canis_latrans 1151
## week-Sylvilagus_floridanus 846
## I(week^2)-Canis_latrans 1184
## I(week^2)-Sylvilagus_floridanus 638
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.201
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3641 1.1197 -1.9659 0.3617 2.5968 1.0487 518
## Cogon_Patch_Size 0.0232 1.3614 -2.7603 0.0381 2.7059 1.0146 1675
## Avg_Cogongrass_Cover -0.0387 0.8900 -1.8050 -0.0330 1.8512 1.0101 1132
## total_shrub_cover 0.0556 0.9887 -2.0717 0.0749 1.9772 1.0122 852
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.8978 84.6103 0.0594 0.9694 44.3946 1.0762 3000
## Cogon_Patch_Size 97.7713 1079.5809 0.1900 9.4087 472.0580 1.3019 3000
## Avg_Cogongrass_Cover 7.1493 104.0684 0.0494 0.5324 30.3785 1.3180 3000
## total_shrub_cover 17.3567 312.0769 0.0553 0.8436 64.9405 1.2064 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0812 4.3362 0.0546 0.7565 11.7794 1.1733 276
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0022 1.2277 -3.6635 -2.3315 1.2157 1.0039 1667
## week 0.2108 0.7319 -1.3763 0.2361 1.6554 1.0044 2449
## I(week^2) -0.1574 0.5860 -1.3599 -0.1731 1.0912 1.0054 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.3202 168.9007 0.0864 1.3735 64.0603 1.2980 1582
## week 3.3229 21.9023 0.0527 0.5517 20.9160 1.1403 3000
## I(week^2) 1.8732 13.2626 0.0356 0.2972 11.7954 1.1594 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.9379 1.0398 -0.8425 0.8326
## (Intercept)-Sylvilagus_floridanus 0.0527 1.3687 -2.4664 -0.0592
## Cogon_Patch_Size-Canis_latrans 2.4019 2.0080 -0.1120 1.9314
## Cogon_Patch_Size-Sylvilagus_floridanus -3.0643 3.4059 -12.3528 -2.1113
## Avg_Cogongrass_Cover-Canis_latrans 0.1186 0.6106 -1.0407 0.0872
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1755 0.9875 -2.0691 -0.2033
## total_shrub_cover-Canis_latrans 0.4152 0.7480 -0.7589 0.3285
## total_shrub_cover-Sylvilagus_floridanus -0.5309 2.0059 -5.7368 -0.2078
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.3026 1.0544 325
## (Intercept)-Sylvilagus_floridanus 3.2518 1.1039 215
## Cogon_Patch_Size-Canis_latrans 7.7553 1.0294 326
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8825 1.3613 97
## Avg_Cogongrass_Cover-Canis_latrans 1.3969 1.0226 1008
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9131 1.0283 528
## total_shrub_cover-Canis_latrans 2.2116 1.0603 528
## total_shrub_cover-Sylvilagus_floridanus 2.0979 1.4193 88
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4654 0.1982 -2.8672 -2.4620 -2.0915 1.0091
## (Intercept)-Sylvilagus_floridanus -3.3713 0.3987 -4.1587 -3.3676 -2.6309 1.0893
## week-Canis_latrans 0.5521 0.3000 -0.0253 0.5467 1.1536 1.0070
## week-Sylvilagus_floridanus -0.0464 0.3888 -0.8481 -0.0350 0.6858 1.0082
## I(week^2)-Canis_latrans -0.2265 0.1226 -0.4688 -0.2228 0.0094 1.0016
## I(week^2)-Sylvilagus_floridanus -0.1285 0.1927 -0.5232 -0.1217 0.2403 1.0220
## ESS
## (Intercept)-Canis_latrans 777
## (Intercept)-Sylvilagus_floridanus 169
## week-Canis_latrans 1183
## week-Sylvilagus_floridanus 805
## I(week^2)-Canis_latrans 1197
## I(week^2)-Sylvilagus_floridanus 531
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1843
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0978 0.8951 -1.7083 0.0855 1.9003 1.0080 698
## Veg_shannon_index 0.7305 0.8144 -1.0777 0.7355 2.3785 1.0041 1525
## Avg_Cogongrass_Cover 0.1924 0.8902 -1.5947 0.2017 1.9875 1.0001 2051
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8312 25.3598 0.0524 0.6170 23.7059 1.0946 3000
## Veg_shannon_index 6.0178 102.4837 0.0506 0.5323 22.9820 1.2096 3000
## Avg_Cogongrass_Cover 49.4316 2253.6396 0.0685 1.0080 37.3530 1.3224 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4097 3.6567 0.0494 0.4964 8.8091 1.0515 635
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0713 1.1305 -3.5018 -2.3930 1.0148 1.0010 1481
## week 0.2198 0.7302 -1.3317 0.2236 1.8005 1.0053 2209
## I(week^2) -0.1564 0.5796 -1.2850 -0.1740 1.1226 1.0012 2035
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.9035 240.1009 0.0673 0.9073 57.6679 1.2938 3000
## week 3.4018 29.4120 0.0571 0.5314 22.3683 1.2876 3000
## I(week^2) 4.0315 109.1188 0.0375 0.2788 14.4405 1.3254 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3241 0.7528 -0.9876 0.2746
## (Intercept)-Sylvilagus_floridanus -0.1234 0.8554 -1.6728 -0.1540
## Veg_shannon_index-Canis_latrans 1.0257 0.5841 0.0080 0.9741
## Veg_shannon_index-Sylvilagus_floridanus 0.7736 0.7168 -0.4440 0.7109
## Avg_Cogongrass_Cover-Canis_latrans 0.8353 0.6854 -0.2319 0.7355
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3132 0.7005 -1.7097 -0.3095
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9648 1.0060 414
## (Intercept)-Sylvilagus_floridanus 1.8153 1.0055 304
## Veg_shannon_index-Canis_latrans 2.2953 1.0029 938
## Veg_shannon_index-Sylvilagus_floridanus 2.3929 1.0098 520
## Avg_Cogongrass_Cover-Canis_latrans 2.3947 1.0035 748
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0933 1.0047 826
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4728 0.1967 -2.8869 -2.4664 -2.0945 1.0069
## (Intercept)-Sylvilagus_floridanus -3.1802 0.3663 -3.9200 -3.1719 -2.5108 1.0023
## week-Canis_latrans 0.5237 0.3027 -0.0337 0.5075 1.1477 1.0021
## week-Sylvilagus_floridanus -0.0252 0.3903 -0.8340 -0.0053 0.6870 0.9999
## I(week^2)-Canis_latrans -0.2156 0.1248 -0.4690 -0.2121 0.0163 1.0005
## I(week^2)-Sylvilagus_floridanus -0.1401 0.1884 -0.5239 -0.1332 0.2273 1.0054
## ESS
## (Intercept)-Canis_latrans 1028
## (Intercept)-Sylvilagus_floridanus 365
## week-Canis_latrans 899
## week-Sylvilagus_floridanus 803
## I(week^2)-Canis_latrans 1054
## I(week^2)-Sylvilagus_floridanus 605
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1843
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0390 0.8486 -1.6559 0.0155 1.8440 1.0017 836
## Avg_Cogongrass_Cover -0.0573 0.8032 -1.8176 -0.0591 1.6591 1.0001 2331
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0320 32.1965 0.0513 0.5967 24.0162 1.1691 3000
## Avg_Cogongrass_Cover 9.3255 210.3395 0.0634 0.7999 31.5613 1.2812 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2298 2.7335 0.0524 0.4897 6.819 1.1314 603
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1067 1.0839 -3.5256 -2.4023 0.7322 1.0017 1701
## week 0.2219 0.7001 -1.3745 0.2340 1.5709 1.0009 2248
## I(week^2) -0.1482 0.6009 -1.4620 -0.1539 1.1609 1.0022 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9672 107.5911 0.0599 0.8303 51.0474 1.1559 3000
## week 5.3273 117.2070 0.0527 0.5209 20.1579 1.2904 3000
## I(week^2) 3.4514 81.2862 0.0393 0.2998 12.2069 1.3010 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2678 0.6439 -1.0370 0.2493
## (Intercept)-Sylvilagus_floridanus -0.1930 0.8484 -1.7911 -0.2418
## Avg_Cogongrass_Cover-Canis_latrans 0.4369 0.4972 -0.4482 0.3934
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5013 0.5956 -1.7371 -0.4609
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5608 1.0245 666
## (Intercept)-Sylvilagus_floridanus 1.4929 1.0173 266
## Avg_Cogongrass_Cover-Canis_latrans 1.5001 1.0082 1617
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5187 1.0076 996
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4728 0.1970 -2.8799 -2.4709 -2.1020 1.0228
## (Intercept)-Sylvilagus_floridanus -3.1439 0.3631 -3.9266 -3.1260 -2.5008 1.0041
## week-Canis_latrans 0.5493 0.2951 0.0042 0.5368 1.1473 1.0004
## week-Sylvilagus_floridanus -0.0392 0.4012 -0.8303 -0.0290 0.7275 1.0076
## I(week^2)-Canis_latrans -0.2227 0.1205 -0.4595 -0.2212 0.0087 1.0013
## I(week^2)-Sylvilagus_floridanus -0.1378 0.1941 -0.5884 -0.1278 0.2194 1.0014
## ESS
## (Intercept)-Canis_latrans 961
## (Intercept)-Sylvilagus_floridanus 474
## week-Canis_latrans 1038
## week-Sylvilagus_floridanus 844
## I(week^2)-Canis_latrans 1149
## I(week^2)-Sylvilagus_floridanus 674
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.195
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8288 1.0109 -2.6371 -0.8910 1.4411 1.0026 963
## Avg_Cogongrass_Cover -0.5538 1.1861 -2.6839 -0.6066 1.9708 1.0041 1337
## I(Avg_Cogongrass_Cover^2) 1.2292 1.3406 -1.8120 1.2824 3.7245 1.0115 730
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.3045 91.0112 0.0526 0.6799 35.3289 1.2022 3000
## Avg_Cogongrass_Cover 137.3860 5624.7181 0.0752 2.5756 215.7039 1.3252 3000
## I(Avg_Cogongrass_Cover^2) 85.8631 2603.2855 0.0712 2.4583 323.6891 1.3122 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2463 2.6425 0.0487 0.4652 7.3958 1.0941 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0801 1.2031 -3.6893 -2.4199 1.0657 1.0111 1904
## week 0.2026 0.7188 -1.4218 0.2247 1.6683 1.0059 2726
## I(week^2) -0.1324 0.6163 -1.3562 -0.1554 1.2025 1.0007 2740
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.3328 141.5769 0.0754 1.1886 80.5372 1.1669 3000
## week 4.4411 47.1407 0.0532 0.5139 21.1647 1.1791 3000
## I(week^2) 2.0422 10.8746 0.0381 0.3067 13.3540 1.0019 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9636 0.9392 -2.8376 -0.9562
## (Intercept)-Sylvilagus_floridanus -1.1728 1.0232 -3.1889 -1.1573
## Avg_Cogongrass_Cover-Canis_latrans 0.0428 1.0292 -1.7790 -0.0243
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.6678 3.1429 -13.0426 -1.9045
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2879 2.1966 0.4004 2.8104
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.3880 2.9059 -0.0882 1.4823
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8426 1.0123 460
## (Intercept)-Sylvilagus_floridanus 0.7846 1.0080 582
## Avg_Cogongrass_Cover-Canis_latrans 2.3139 1.0105 274
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4980 1.1566 38
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.9429 1.0125 146
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 11.2757 1.2416 37
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5150 0.1970 -2.9170 -2.5062 -2.1431 1.0122
## (Intercept)-Sylvilagus_floridanus -3.3385 0.3960 -4.1169 -3.3407 -2.6084 1.0403
## week-Canis_latrans 0.5264 0.2903 -0.0287 0.5282 1.0814 1.0085
## week-Sylvilagus_floridanus -0.0563 0.3934 -0.8362 -0.0494 0.6829 1.0092
## I(week^2)-Canis_latrans -0.2182 0.1187 -0.4592 -0.2153 0.0052 1.0050
## I(week^2)-Sylvilagus_floridanus -0.1240 0.1975 -0.5504 -0.1126 0.2343 1.0588
## ESS
## (Intercept)-Canis_latrans 1064
## (Intercept)-Sylvilagus_floridanus 207
## week-Canis_latrans 1078
## week-Sylvilagus_floridanus 832
## I(week^2)-Canis_latrans 1088
## I(week^2)-Sylvilagus_floridanus 573
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1967
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6285 1.5444 -3.4549 -0.6788 2.4382 1.1229 405
## Cogon_Patch_Size 0.2272 1.5740 -3.0409 0.2564 3.2579 1.0052 1139
## Veg_shannon_index 1.1334 1.5181 -2.0507 1.2210 3.9198 1.0404 778
## total_shrub_cover 0.0905 1.3755 -2.6704 0.0596 2.8293 1.0613 403
## Avg_Cogongrass_Cover -0.4265 1.5627 -3.5031 -0.4164 2.6782 1.0100 725
## Tree_Density -0.9655 1.7290 -4.1839 -1.0486 2.5300 1.0924 299
## Avg_Canopy_Cover 0.2731 1.6150 -2.8811 0.2839 3.4121 1.0125 2001
## I(Avg_Cogongrass_Cover^2) 0.9520 1.6066 -2.3765 1.0453 3.9054 1.0262 649
## avg_veg_height -0.7009 1.4044 -3.3815 -0.7148 2.1435 1.0717 483
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept) 134.9380 2261.4875 0.0664 2.4808 764.5998
## Cogon_Patch_Size 681.1685 5953.5118 0.1480 40.2935 3820.0901
## Veg_shannon_index 295.1838 1939.8247 0.0733 3.7821 2252.7028
## total_shrub_cover 133.2025 6449.5233 0.0594 0.9534 101.3314
## Avg_Cogongrass_Cover 174.4472 5776.3753 0.0694 1.9831 396.5250
## Tree_Density 773.3941 7650.8307 0.0841 20.1312 4762.9067
## Avg_Canopy_Cover 2237.9604 18684.6175 2.6048 252.0547 10702.4661
## I(Avg_Cogongrass_Cover^2) 1016.7796 18739.6333 0.1134 17.7834 6150.6298
## avg_veg_height 33.0449 320.4932 0.0678 1.5520 172.2763
## Rhat ESS
## (Intercept) 1.3810 2308
## Cogon_Patch_Size 1.1169 917
## Veg_shannon_index 2.0625 127
## total_shrub_cover 1.3230 3000
## Avg_Cogongrass_Cover 1.3050 3000
## Tree_Density 1.2357 1557
## Avg_Canopy_Cover 1.1486 2669
## I(Avg_Cogongrass_Cover^2) 1.4638 3000
## avg_veg_height 1.1145 1843
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 12.544 32.3381 0.0644 1.5864 96.8558 1.009 104
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1287 1.1363 -3.6650 -2.4428 0.8878 1.0124 1740
## week 0.1825 0.7397 -1.5323 0.2122 1.6257 1.0123 2755
## I(week^2) -0.1377 0.6133 -1.4299 -0.1536 1.2566 1.0067 2790
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.6481 177.4369 0.0647 0.9932 66.7529 1.2425 3000
## week 5.0667 95.1225 0.0541 0.5123 22.6208 1.2914 3000
## I(week^2) 2.9033 25.5402 0.0380 0.3060 16.6401 1.1219 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -1.6131 3.6109 -12.5361
## (Intercept)-Sylvilagus_floridanus -2.3641 5.2285 -15.7671
## Cogon_Patch_Size-Canis_latrans 7.7893 8.7926 -0.2619
## Cogon_Patch_Size-Sylvilagus_floridanus -5.9342 7.8005 -27.1196
## Veg_shannon_index-Canis_latrans 5.8860 10.5989 -0.0343
## Veg_shannon_index-Sylvilagus_floridanus 4.1597 5.0396 -0.7702
## total_shrub_cover-Canis_latrans 0.1548 1.9394 -4.3548
## total_shrub_cover-Sylvilagus_floridanus -0.0177 2.3219 -4.1734
## Avg_Cogongrass_Cover-Canis_latrans 0.0203 4.3282 -6.3245
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5622 3.8147 -11.8332
## Tree_Density-Canis_latrans -7.8659 9.5173 -35.4624
## Tree_Density-Sylvilagus_floridanus -8.0880 11.6694 -42.0494
## Avg_Canopy_Cover-Canis_latrans -0.8365 3.1455 -6.2539
## Avg_Canopy_Cover-Sylvilagus_floridanus 23.7278 17.4739 2.6879
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 11.3793 14.9411 0.9879
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7287 4.0257 -2.5457
## avg_veg_height-Canis_latrans -1.5752 2.0275 -6.1210
## avg_veg_height-Sylvilagus_floridanus -0.8414 2.8754 -6.7885
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -1.0259 3.3514 1.2926 111
## (Intercept)-Sylvilagus_floridanus -1.6800 5.3750 1.6249 81
## Cogon_Patch_Size-Canis_latrans 4.9672 34.3442 1.7009 35
## Cogon_Patch_Size-Sylvilagus_floridanus -3.5438 3.0791 1.5201 56
## Veg_shannon_index-Canis_latrans 2.7160 35.5169 2.8368 41
## Veg_shannon_index-Sylvilagus_floridanus 2.6192 19.7308 2.3054 27
## total_shrub_cover-Canis_latrans 0.1807 3.7109 1.0952 156
## total_shrub_cover-Sylvilagus_floridanus 0.0497 3.9553 1.0631 161
## Avg_Cogongrass_Cover-Canis_latrans -0.4762 12.9910 1.1925 53
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0036 3.4087 1.0796 240
## Tree_Density-Canis_latrans -4.4220 0.8623 2.0509 32
## Tree_Density-Sylvilagus_floridanus -3.8144 4.4117 2.6299 13
## Avg_Canopy_Cover-Canis_latrans -0.7642 8.3693 1.4559 64
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.3207 65.1342 2.6644 19
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.3597 61.8424 3.5856 15
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8732 14.5448 1.6364 89
## avg_veg_height-Canis_latrans -1.3527 1.8919 1.0732 201
## avg_veg_height-Sylvilagus_floridanus -0.8034 4.9938 1.1357 133
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5467 0.2112 -2.9830 -2.5366 -2.1440 1.2013
## (Intercept)-Sylvilagus_floridanus -3.2623 0.3183 -3.9153 -3.2449 -2.6851 1.0254
## week-Canis_latrans 0.5295 0.3005 -0.0492 0.5295 1.1309 1.0271
## week-Sylvilagus_floridanus -0.0426 0.4049 -0.8499 -0.0414 0.7061 1.0189
## I(week^2)-Canis_latrans -0.2153 0.1226 -0.4605 -0.2157 0.0170 1.0168
## I(week^2)-Sylvilagus_floridanus -0.1260 0.1989 -0.5524 -0.1151 0.2486 1.0142
## ESS
## (Intercept)-Canis_latrans 120
## (Intercept)-Sylvilagus_floridanus 356
## week-Canis_latrans 962
## week-Sylvilagus_floridanus 806
## I(week^2)-Canis_latrans 1024
## I(week^2)-Sylvilagus_floridanus 541
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0753 0.7482 -1.5385 0.0715 1.6121 1.0008 2364
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9647 32.5676 0.0527 0.5407 20.8663 1.1282 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2085 1.0915 -3.6253 -2.5149 0.7104 1.0142 1696
## shrub_cover -0.0933 0.7181 -1.4928 -0.1200 1.4950 1.0036 1857
## veg_height -0.2331 0.7310 -1.6812 -0.2618 1.4173 1.0013 2338
## week 0.2071 0.7173 -1.3478 0.2299 1.6487 1.0080 2514
## I(week^2) -0.1552 0.5627 -1.3250 -0.1727 1.0799 1.0018 2529
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.0248 120.5443 0.0552 0.7059 51.8701 1.2660 3000
## shrub_cover 3.9742 54.0052 0.0514 0.4837 17.5586 1.1099 3000
## veg_height 4.6384 58.4320 0.0673 0.6544 22.0087 1.2234 3000
## week 4.5433 55.8700 0.0505 0.5225 20.8722 1.2516 3000
## I(week^2) 1.9527 11.4900 0.0400 0.2903 12.8573 1.0224 2609
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3663 0.4179 -0.3979 0.3513 1.2644 1.0045
## (Intercept)-Sylvilagus_floridanus -0.2041 0.4791 -1.1331 -0.2149 0.7747 1.0035
## ESS
## (Intercept)-Canis_latrans 1905
## (Intercept)-Sylvilagus_floridanus 897
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6255 0.2057 -3.0441 -2.6246 -2.2401 1.0444
## (Intercept)-Sylvilagus_floridanus -3.1079 0.3372 -3.8305 -3.0921 -2.5015 1.0115
## shrub_cover-Canis_latrans -0.3557 0.2327 -0.8038 -0.3538 0.1074 1.0189
## shrub_cover-Sylvilagus_floridanus 0.1578 0.4631 -0.6238 0.1190 1.1829 1.0344
## veg_height-Canis_latrans -0.7009 0.1934 -1.0962 -0.6941 -0.3447 1.0124
## veg_height-Sylvilagus_floridanus 0.0825 0.2777 -0.4694 0.0798 0.6272 1.0326
## week-Canis_latrans 0.5724 0.2941 0.0280 0.5634 1.1907 1.0074
## week-Sylvilagus_floridanus -0.0248 0.3902 -0.7809 -0.0265 0.7297 1.0212
## I(week^2)-Canis_latrans -0.2308 0.1208 -0.4706 -0.2301 -0.0002 1.0007
## I(week^2)-Sylvilagus_floridanus -0.1339 0.1952 -0.5420 -0.1248 0.2216 1.0206
## ESS
## (Intercept)-Canis_latrans 769
## (Intercept)-Sylvilagus_floridanus 564
## shrub_cover-Canis_latrans 767
## shrub_cover-Sylvilagus_floridanus 398
## veg_height-Canis_latrans 800
## veg_height-Sylvilagus_floridanus 699
## week-Canis_latrans 1074
## week-Sylvilagus_floridanus 1008
## I(week^2)-Canis_latrans 1353
## I(week^2)-Sylvilagus_floridanus 625
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2147
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5020 1.3959 -2.2354 0.5095 3.2874 1.0081 355
## Cogon_Patch_Size 0.0350 1.4634 -2.7906 -0.0023 3.0224 1.0166 718
## Veg_shannon_index 1.2145 1.2807 -1.4324 1.2213 3.7530 1.0131 698
## total_shrub_cover 0.5972 1.5686 -2.6776 0.6122 3.5783 1.0501 298
## Avg_Cogongrass_Cover 0.7308 1.4068 -2.1827 0.7838 3.3327 1.0245 1198
## Tree_Density -1.0953 1.6756 -4.2095 -1.1685 2.2634 1.0678 366
## Avg_Canopy_Cover 0.3647 1.5573 -2.7616 0.4191 3.3557 1.0040 2421
## avg_veg_height -0.1853 1.2738 -2.7096 -0.2184 2.4360 1.0197 616
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.4945 654.5570 0.0837 2.6578 282.0365 1.2183 1364
## Cogon_Patch_Size 514.9045 11487.2283 0.0877 9.0247 2025.5858 1.3396 1287
## Veg_shannon_index 212.1177 10244.2378 0.0571 1.2354 161.3989 1.3220 3000
## total_shrub_cover 359.5233 4135.5720 0.0834 7.0731 2084.9739 1.1921 359
## Avg_Cogongrass_Cover 66.4690 499.8541 0.0758 3.8153 444.4491 1.1408 1911
## Tree_Density 912.7204 7802.9846 0.1060 20.8603 4206.6769 1.1512 251
## Avg_Canopy_Cover 1421.8953 14798.7926 0.7023 91.7891 7808.1048 1.1444 3000
## avg_veg_height 15.7814 185.4895 0.0567 1.0087 63.0723 1.1198 2684
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 40.1276 230.0829 0.0671 2.212 335.4204 2.0144 102
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2629 1.0954 -3.6551 -2.5541 0.7053 1.0144 1808
## shrub_cover -0.0577 0.8034 -1.6326 -0.1166 1.7268 1.0117 1086
## veg_height -0.2522 0.7431 -1.8102 -0.2561 1.3817 1.0037 3000
## week 0.2224 0.7343 -1.3685 0.2574 1.7110 1.0009 2125
## I(week^2) -0.1530 0.6004 -1.4279 -0.1611 1.1862 1.0035 2556
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.9002 326.6221 0.0563 0.7257 53.4261 1.3287 3000
## shrub_cover 5.5327 58.4297 0.0607 0.7589 28.3045 1.2812 3000
## veg_height 4.8145 37.9584 0.0788 0.6919 24.8683 1.0988 1850
## week 4.2717 34.9920 0.0546 0.5401 20.3692 1.3088 3000
## I(week^2) 3.4871 100.8919 0.0405 0.3071 12.6399 1.3252 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.2543 2.9321 -1.3237 1.6376
## (Intercept)-Sylvilagus_floridanus -0.3059 3.1950 -9.6487 -0.0040
## Cogon_Patch_Size-Canis_latrans 4.0749 7.0219 -1.2039 1.9946
## Cogon_Patch_Size-Sylvilagus_floridanus -4.8769 8.0299 -29.8958 -2.0292
## Veg_shannon_index-Canis_latrans 1.8811 1.5863 -0.6875 1.7054
## Veg_shannon_index-Sylvilagus_floridanus 2.3136 2.4678 -0.7881 1.7948
## total_shrub_cover-Canis_latrans 4.1186 4.6483 -0.2344 2.6067
## total_shrub_cover-Sylvilagus_floridanus -3.1630 9.8990 -37.3768 -0.2185
## Avg_Cogongrass_Cover-Canis_latrans 3.1717 3.3533 -0.6732 2.3964
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2739 3.8956 -12.8276 0.3442
## Tree_Density-Canis_latrans -8.5583 10.3184 -42.5453 -4.8798
## Tree_Density-Sylvilagus_floridanus -6.4648 9.9955 -39.9721 -3.5973
## Avg_Canopy_Cover-Canis_latrans -0.5857 1.1403 -3.3183 -0.4796
## Avg_Canopy_Cover-Sylvilagus_floridanus 17.1558 16.0542 1.2552 11.1430
## avg_veg_height-Canis_latrans -0.2394 1.4204 -3.0072 -0.2316
## avg_veg_height-Sylvilagus_floridanus -0.3661 1.9444 -4.0416 -0.3927
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 9.8801 1.4029 84
## (Intercept)-Sylvilagus_floridanus 5.1084 1.0579 95
## Cogon_Patch_Size-Canis_latrans 23.4925 1.2508 55
## Cogon_Patch_Size-Sylvilagus_floridanus 2.6960 1.6548 32
## Veg_shannon_index-Canis_latrans 5.1317 1.0695 251
## Veg_shannon_index-Sylvilagus_floridanus 9.1001 1.2037 92
## total_shrub_cover-Canis_latrans 18.3065 1.5826 25
## total_shrub_cover-Sylvilagus_floridanus 6.5698 2.4386 10
## Avg_Cogongrass_Cover-Canis_latrans 12.1121 1.1122 138
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.7179 1.6668 50
## Tree_Density-Canis_latrans -0.7231 1.7522 16
## Tree_Density-Sylvilagus_floridanus 2.6293 2.0129 20
## Avg_Canopy_Cover-Canis_latrans 1.3356 1.0531 193
## Avg_Canopy_Cover-Sylvilagus_floridanus 61.1380 1.5075 10
## avg_veg_height-Canis_latrans 2.5638 1.0155 392
## avg_veg_height-Sylvilagus_floridanus 3.4344 1.0597 396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6803 0.2040 -3.1000 -2.6782 -2.2994 1.0303
## (Intercept)-Sylvilagus_floridanus -3.2020 0.3023 -3.8383 -3.1920 -2.6574 1.0046
## shrub_cover-Canis_latrans -0.5290 0.2074 -0.9240 -0.5313 -0.1231 1.0155
## shrub_cover-Sylvilagus_floridanus 0.3516 0.5378 -0.6347 0.3255 1.3962 1.0961
## veg_height-Canis_latrans -0.7311 0.1942 -1.1174 -0.7271 -0.3583 1.0381
## veg_height-Sylvilagus_floridanus 0.1590 0.2944 -0.4242 0.1712 0.7148 1.0449
## week-Canis_latrans 0.5592 0.2963 0.0056 0.5516 1.1622 0.9997
## week-Sylvilagus_floridanus -0.0300 0.4018 -0.8309 -0.0405 0.7497 1.0067
## I(week^2)-Canis_latrans -0.2271 0.1214 -0.4696 -0.2259 0.0005 1.0005
## I(week^2)-Sylvilagus_floridanus -0.1456 0.2006 -0.5590 -0.1401 0.2229 1.0073
## ESS
## (Intercept)-Canis_latrans 870
## (Intercept)-Sylvilagus_floridanus 657
## shrub_cover-Canis_latrans 588
## shrub_cover-Sylvilagus_floridanus 94
## veg_height-Canis_latrans 714
## veg_height-Sylvilagus_floridanus 412
## week-Canis_latrans 1007
## week-Sylvilagus_floridanus 709
## I(week^2)-Canis_latrans 1081
## I(week^2)-Sylvilagus_floridanus 435
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2383
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9105 1.3379 -1.7606 0.8965 3.5775 1.0320 338
## Avg_Cogongrass_Cover -0.0475 1.2304 -2.5168 -0.0587 2.4060 1.0147 1202
## total_shrub_cover 0.0661 1.4561 -2.8642 0.0619 2.9053 1.1045 215
## avg_veg_height 0.3052 1.1522 -1.9743 0.2856 2.6595 1.0688 415
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 271.7766 5647.1784 0.0609 1.3326 938.5621 1.4866 656
## Avg_Cogongrass_Cover 124.1560 807.0759 0.0803 2.8434 1015.7968 1.7159 125
## total_shrub_cover 301.6863 2872.2726 0.1205 12.7553 1523.5640 1.1578 366
## avg_veg_height 8.7213 81.0108 0.0573 0.7691 55.2534 1.3923 2703
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 25.3412 110.9145 0.0674 1.8836 226.1763 1.5164 45
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2597 1.2142 -3.7871 -2.6150 0.9010 0.9999 1485
## shrub_cover 0.0102 0.8560 -1.6559 -0.0125 1.8380 1.0736 347
## veg_height -0.2882 0.7543 -1.8179 -0.3374 1.3721 1.0082 2157
## week 0.2210 0.7166 -1.2937 0.2473 1.6558 1.0030 2347
## I(week^2) -0.1514 0.5888 -1.3878 -0.1633 1.0796 1.0057 2636
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 87.9062 3082.8100 0.0589 0.8373 59.3560 1.2292 3000
## shrub_cover 6.1960 49.7528 0.0787 0.9936 28.4183 1.0284 3000
## veg_height 4.0855 33.0258 0.0706 0.6589 27.3515 1.2338 3000
## week 4.5832 75.4249 0.0500 0.4911 20.7028 1.2875 3000
## I(week^2) 2.6447 36.0460 0.0378 0.2789 10.0717 1.2873 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 3.4509 6.9987 -1.1074 1.4811
## (Intercept)-Sylvilagus_floridanus 1.7907 2.9245 -1.9197 1.3934
## Avg_Cogongrass_Cover-Canis_latrans 3.2195 7.1839 -1.1437 0.9888
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.0042 2.9922 -11.1633 -1.1958
## total_shrub_cover-Canis_latrans 2.9811 4.4106 -0.3973 1.8598
## total_shrub_cover-Sylvilagus_floridanus -5.2667 8.6897 -34.2880 -3.3296
## avg_veg_height-Canis_latrans 0.3916 1.3148 -2.0666 0.3247
## avg_veg_height-Sylvilagus_floridanus 0.6208 1.8240 -2.1094 0.4009
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 27.0308 3.7901 14
## (Intercept)-Sylvilagus_floridanus 10.4744 1.6242 56
## Avg_Cogongrass_Cover-Canis_latrans 27.5177 4.2984 12
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3148 1.7251 40
## total_shrub_cover-Canis_latrans 13.9012 2.7472 25
## total_shrub_cover-Sylvilagus_floridanus 3.7070 2.2652 10
## avg_veg_height-Canis_latrans 2.9492 1.0780 305
## avg_veg_height-Sylvilagus_floridanus 4.8579 1.3354 118
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7940 0.2304 -3.2465 -2.7932 -2.3554 1.0372
## (Intercept)-Sylvilagus_floridanus -3.3334 0.3029 -3.9357 -3.3317 -2.7626 1.0335
## shrub_cover-Canis_latrans -0.5175 0.2471 -0.9685 -0.5253 0.0114 1.0453
## shrub_cover-Sylvilagus_floridanus 0.5992 0.7047 -0.8405 0.7697 1.6882 1.7073
## veg_height-Canis_latrans -0.7803 0.1966 -1.1654 -0.7783 -0.3985 1.0047
## veg_height-Sylvilagus_floridanus 0.0360 0.3135 -0.5614 0.0265 0.6665 1.2606
## week-Canis_latrans 0.5386 0.2938 -0.0160 0.5282 1.1276 1.0148
## week-Sylvilagus_floridanus -0.0097 0.3911 -0.7550 -0.0084 0.7405 1.0133
## I(week^2)-Canis_latrans -0.2209 0.1225 -0.4655 -0.2208 0.0158 1.0089
## I(week^2)-Sylvilagus_floridanus -0.1414 0.1837 -0.5258 -0.1345 0.1940 1.0134
## ESS
## (Intercept)-Canis_latrans 289
## (Intercept)-Sylvilagus_floridanus 422
## shrub_cover-Canis_latrans 531
## shrub_cover-Sylvilagus_floridanus 32
## veg_height-Canis_latrans 603
## veg_height-Sylvilagus_floridanus 118
## week-Canis_latrans 1006
## week-Sylvilagus_floridanus 666
## I(week^2)-Canis_latrans 1019
## I(week^2)-Sylvilagus_floridanus 591
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0967 1.0010 -1.7124 0.0232 2.3796 1.0151 490
## Tree_Density -1.0719 1.0535 -3.0704 -1.0706 1.1673 1.0058 1172
## Avg_Canopy_Cover 0.5331 1.3563 -2.3317 0.6051 3.0646 1.0069 2708
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.3522 78.5237 0.0576 0.6737 71.0858 1.7366 159
## Tree_Density 6.2884 32.2546 0.0518 0.7330 48.1326 1.0126 2450
## Avg_Canopy_Cover 132.9039 1607.2602 0.5490 10.3813 517.4832 1.2724 1583
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.2053 31.1843 0.0483 0.5058 51.5219 2.5295 61
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1982 1.1035 -3.6110 -2.5288 0.8546 1.0043 1668
## shrub_cover 0.0449 0.7599 -1.5065 0.0265 1.6765 1.0044 2301
## veg_height -0.2350 0.7141 -1.6712 -0.2709 1.3383 1.0000 2660
## week 0.1978 0.7013 -1.4084 0.2256 1.5576 1.0028 2565
## I(week^2) -0.1673 0.5974 -1.3917 -0.1660 1.0596 1.0031 3018
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 113.2413 5504.0820 0.0502 0.6753 62.0582 1.3255 3000
## shrub_cover 3.8185 19.5055 0.0625 0.7054 23.2995 1.1485 3000
## veg_height 7.6786 128.4466 0.0736 0.6784 20.5216 1.3753 3000
## week 4.1823 42.0168 0.0543 0.5271 17.8550 1.0888 3000
## I(week^2) 2.6607 53.4486 0.0385 0.2973 10.5079 1.3253 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.6787 1.8185 -0.9599 0.3483 5.0972
## (Intercept)-Sylvilagus_floridanus -0.2467 1.0381 -2.1461 -0.3015 1.9978
## Tree_Density-Canis_latrans -1.3507 0.8808 -3.3187 -1.2462 0.0779
## Tree_Density-Sylvilagus_floridanus -1.6250 1.2618 -4.4861 -1.4588 0.4545
## Avg_Canopy_Cover-Canis_latrans -0.7542 1.1071 -4.5095 -0.5292 0.4337
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6423 3.3127 0.9917 3.8826 13.1036
## Rhat ESS
## (Intercept)-Canis_latrans 2.1466 30
## (Intercept)-Sylvilagus_floridanus 1.0351 198
## Tree_Density-Canis_latrans 1.0558 466
## Tree_Density-Sylvilagus_floridanus 1.0258 407
## Avg_Canopy_Cover-Canis_latrans 1.9974 20
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1050 88
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6620 0.2176 -3.0989 -2.6513 -2.2493 1.0251
## (Intercept)-Sylvilagus_floridanus -3.1078 0.3072 -3.7472 -3.0977 -2.5475 1.0068
## shrub_cover-Canis_latrans -0.3743 0.2328 -0.8314 -0.3736 0.0865 1.0154
## shrub_cover-Sylvilagus_floridanus 0.4833 0.4226 -0.3191 0.4684 1.3073 1.0146
## veg_height-Canis_latrans -0.7125 0.1932 -1.1130 -0.7062 -0.3557 1.0191
## veg_height-Sylvilagus_floridanus 0.1267 0.2771 -0.4357 0.1343 0.6483 1.0036
## week-Canis_latrans 0.5404 0.2907 -0.0119 0.5368 1.1260 1.0021
## week-Sylvilagus_floridanus -0.0460 0.4031 -0.8378 -0.0299 0.7209 1.0139
## I(week^2)-Canis_latrans -0.2231 0.1177 -0.4543 -0.2231 0.0030 1.0129
## I(week^2)-Sylvilagus_floridanus -0.1257 0.1898 -0.5161 -0.1225 0.2398 1.0138
## ESS
## (Intercept)-Canis_latrans 472
## (Intercept)-Sylvilagus_floridanus 768
## shrub_cover-Canis_latrans 782
## shrub_cover-Sylvilagus_floridanus 429
## veg_height-Canis_latrans 633
## veg_height-Sylvilagus_floridanus 833
## week-Canis_latrans 1085
## week-Sylvilagus_floridanus 980
## I(week^2)-Canis_latrans 1188
## I(week^2)-Sylvilagus_floridanus 514
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2292
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7526 1.2651 -1.9473 0.7519 3.1982 1.0444 359
## Cogon_Patch_Size 0.0236 1.3604 -2.7088 0.0474 2.6652 1.0022 1598
## Avg_Cogongrass_Cover 0.1333 1.0187 -1.8902 0.1122 2.2238 1.0073 674
## total_shrub_cover -0.0934 1.3415 -2.8128 -0.0893 2.5538 1.0360 737
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.5519 240.2826 0.0590 1.3717 105.2517 1.1136 1871
## Cogon_Patch_Size 245.3358 6281.7066 0.1558 10.4340 769.3301 1.3266 2186
## Avg_Cogongrass_Cover 10.1948 83.2643 0.0551 0.9265 56.2471 1.0758 3000
## total_shrub_cover 62.7187 388.5040 0.0983 7.4944 407.7180 1.1033 1059
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.0322 15.009 0.0669 1.0837 35.854 1.3918 66
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2211 1.1966 -3.7052 -2.5849 1.0233 1.0099 1684
## shrub_cover 0.0762 0.8628 -1.5909 0.0550 1.8585 1.0205 1200
## veg_height -0.2526 0.7408 -1.7346 -0.3007 1.4005 1.0095 2322
## week 0.2004 0.7332 -1.4050 0.2352 1.6636 1.0032 2185
## I(week^2) -0.1564 0.5649 -1.2922 -0.1671 1.0706 1.0011 3344
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.4602 83.6494 0.0594 0.8847 62.9167 1.1196 2621
## shrub_cover 6.0509 39.9995 0.0818 1.0455 36.3007 1.0413 3000
## veg_height 3.1066 16.6529 0.0659 0.6209 19.3513 1.0648 3000
## week 3.7764 21.3895 0.0542 0.5451 21.6125 1.0178 3000
## I(week^2) 2.7562 50.5351 0.0386 0.2689 12.1930 1.2572 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.8401 1.8546 -0.6172 1.5027
## (Intercept)-Sylvilagus_floridanus 0.6301 1.9432 -3.2381 0.6651
## Cogon_Patch_Size-Canis_latrans 2.8611 3.0844 -0.3993 2.0729
## Cogon_Patch_Size-Sylvilagus_floridanus -3.7221 5.1923 -20.6808 -2.2509
## Avg_Cogongrass_Cover-Canis_latrans 0.5296 0.8867 -0.7842 0.4033
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2520 1.5230 -3.6405 -0.2027
## total_shrub_cover-Canis_latrans 1.6117 1.6846 -0.5805 1.2434
## total_shrub_cover-Sylvilagus_floridanus -2.9646 3.3817 -11.3334 -2.4817
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.8550 1.0640 127
## (Intercept)-Sylvilagus_floridanus 4.1908 1.0859 105
## Cogon_Patch_Size-Canis_latrans 10.6053 1.0831 118
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2418 1.0087 71
## Avg_Cogongrass_Cover-Canis_latrans 2.6547 1.0199 392
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.7513 1.0366 219
## total_shrub_cover-Canis_latrans 5.5676 1.0937 105
## total_shrub_cover-Sylvilagus_floridanus 2.4248 1.1984 58
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6978 0.2196 -3.1390 -2.6958 -2.2944 1.0046
## (Intercept)-Sylvilagus_floridanus -3.3559 0.3283 -4.0418 -3.3418 -2.7314 1.0773
## shrub_cover-Canis_latrans -0.5047 0.2541 -0.9846 -0.5154 0.0241 1.0255
## shrub_cover-Sylvilagus_floridanus 0.6695 0.5999 -0.5605 0.7406 1.7529 1.3113
## veg_height-Canis_latrans -0.7238 0.2017 -1.1413 -0.7127 -0.3518 1.0196
## veg_height-Sylvilagus_floridanus 0.0326 0.3016 -0.5483 0.0308 0.6350 1.1170
## week-Canis_latrans 0.5588 0.3006 -0.0143 0.5526 1.1580 1.0005
## week-Sylvilagus_floridanus -0.0433 0.3905 -0.8021 -0.0315 0.7230 1.0206
## I(week^2)-Canis_latrans -0.2263 0.1244 -0.4646 -0.2241 0.0100 1.0045
## I(week^2)-Sylvilagus_floridanus -0.1246 0.1912 -0.5237 -0.1190 0.2359 1.0098
## ESS
## (Intercept)-Canis_latrans 597
## (Intercept)-Sylvilagus_floridanus 384
## shrub_cover-Canis_latrans 318
## shrub_cover-Sylvilagus_floridanus 71
## veg_height-Canis_latrans 606
## veg_height-Sylvilagus_floridanus 250
## week-Canis_latrans 1154
## week-Sylvilagus_floridanus 638
## I(week^2)-Canis_latrans 1146
## I(week^2)-Sylvilagus_floridanus 499
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2015
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1499 0.9139 -1.6970 0.1376 2.1380 1.0134 672
## Veg_shannon_index 0.7345 0.8384 -1.1258 0.7675 2.3593 1.0085 1287
## Avg_Cogongrass_Cover 0.2638 1.0147 -1.8140 0.2720 2.3579 1.0015 1589
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.0253 28.8512 0.0591 0.7380 33.0536 1.0182 3000
## Veg_shannon_index 3.5809 17.1138 0.0534 0.5832 23.6011 1.0707 2754
## Avg_Cogongrass_Cover 9.6981 62.3737 0.0832 1.4602 58.3205 1.0575 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2762 2.5893 0.0525 0.5103 7.64 1.0467 459
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1906 1.0979 -3.6260 -2.4862 0.7329 1.0027 1743
## shrub_cover -0.1263 0.6922 -1.5613 -0.1445 1.3613 1.0016 2304
## veg_height -0.2454 0.7607 -1.7515 -0.2737 1.3800 1.0027 2790
## week 0.2434 0.7360 -1.3715 0.2644 1.7666 1.0035 2392
## I(week^2) -0.1386 0.6052 -1.3444 -0.1532 1.1531 1.0050 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.5450 84.9035 0.0554 0.7749 51.4350 1.1086 3000
## shrub_cover 3.0325 21.9246 0.0481 0.4376 16.9449 1.1098 2812
## veg_height 4.4614 34.2698 0.0810 0.7155 24.1619 1.0653 3000
## week 4.1640 35.4169 0.0501 0.5518 19.1318 1.0225 3000
## I(week^2) 2.1283 15.7941 0.0410 0.3115 11.6877 1.0983 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5112 0.7933 -0.9701 0.4679
## (Intercept)-Sylvilagus_floridanus -0.1530 0.8758 -1.8031 -0.1944
## Veg_shannon_index-Canis_latrans 1.1073 0.6356 -0.0199 1.0510
## Veg_shannon_index-Sylvilagus_floridanus 0.7161 0.7453 -0.6028 0.6802
## Avg_Cogongrass_Cover-Canis_latrans 1.1526 0.8180 -0.0641 0.9946
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4003 0.7900 -2.0294 -0.3849
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2316 1.0115 466
## (Intercept)-Sylvilagus_floridanus 1.7238 1.0402 336
## Veg_shannon_index-Canis_latrans 2.4989 1.0055 566
## Veg_shannon_index-Sylvilagus_floridanus 2.3524 1.0471 555
## Avg_Cogongrass_Cover-Canis_latrans 3.1322 1.0003 561
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1232 1.0454 500
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6276 0.2129 -3.0671 -2.6207 -2.2313 1.0045
## (Intercept)-Sylvilagus_floridanus -3.1771 0.3616 -3.9478 -3.1607 -2.5141 1.0131
## shrub_cover-Canis_latrans -0.3483 0.2195 -0.7966 -0.3416 0.0770 1.0110
## shrub_cover-Sylvilagus_floridanus 0.0753 0.4327 -0.6715 0.0346 1.0306 1.0037
## veg_height-Canis_latrans -0.7115 0.1973 -1.1170 -0.7033 -0.3402 1.0139
## veg_height-Sylvilagus_floridanus 0.1321 0.3008 -0.4597 0.1325 0.7089 1.0060
## week-Canis_latrans 0.5697 0.2946 -0.0010 0.5598 1.1629 1.0001
## week-Sylvilagus_floridanus -0.0321 0.3962 -0.8368 -0.0260 0.7364 1.0153
## I(week^2)-Canis_latrans -0.2336 0.1217 -0.4674 -0.2336 0.0023 1.0014
## I(week^2)-Sylvilagus_floridanus -0.1356 0.1941 -0.5235 -0.1320 0.2367 1.0222
## ESS
## (Intercept)-Canis_latrans 702
## (Intercept)-Sylvilagus_floridanus 306
## shrub_cover-Canis_latrans 903
## shrub_cover-Sylvilagus_floridanus 371
## veg_height-Canis_latrans 585
## veg_height-Sylvilagus_floridanus 537
## week-Canis_latrans 1156
## week-Sylvilagus_floridanus 850
## I(week^2)-Canis_latrans 1114
## I(week^2)-Sylvilagus_floridanus 535
#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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.1972
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0988 0.9107 -1.8227 0.0953 1.9716 1.0046 1095
## Avg_Cogongrass_Cover 0.0247 0.9044 -1.8850 0.0274 1.9137 1.0043 2026
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.8806 34.8538 0.0553 0.7287 32.3358 1.1345 3000
## Avg_Cogongrass_Cover 7.5396 77.4040 0.0687 1.0644 36.6737 1.2354 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1612 2.227 0.0508 0.4689 6.2911 1.042 586
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2413 1.0840 -3.6076 -2.5292 0.8149 1.0251 1447
## shrub_cover -0.0722 0.7151 -1.5161 -0.0974 1.5300 1.0026 2010
## veg_height -0.2645 0.7710 -1.8686 -0.2791 1.3597 1.0127 2987
## week 0.2332 0.7284 -1.3918 0.2553 1.6868 1.0031 2216
## I(week^2) -0.1408 0.6114 -1.3371 -0.1623 1.2115 1.0022 2778
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.4134 322.2731 0.0490 0.6738 45.2583 1.1850 3000
## shrub_cover 3.3791 29.8314 0.0486 0.4958 20.2549 1.2819 3000
## veg_height 7.1379 99.9074 0.0865 0.7401 29.1664 1.0986 3000
## week 3.3427 24.7347 0.0560 0.5453 17.5205 1.1659 3000
## I(week^2) 2.3878 17.0374 0.0390 0.3136 13.0492 1.0636 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5038 0.7314 -0.7925 0.4445
## (Intercept)-Sylvilagus_floridanus -0.1757 0.7416 -1.5658 -0.1895
## Avg_Cogongrass_Cover-Canis_latrans 0.6475 0.6465 -0.3482 0.5554
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5712 0.6252 -1.8985 -0.5439
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1260 1.0249 583
## (Intercept)-Sylvilagus_floridanus 1.4099 1.0096 466
## Avg_Cogongrass_Cover-Canis_latrans 2.1791 1.0072 812
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6112 1.0100 803
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6665 0.2203 -3.1070 -2.6651 -2.2536 1.0222
## (Intercept)-Sylvilagus_floridanus -3.1289 0.3359 -3.8651 -3.1067 -2.5355 1.0392
## shrub_cover-Canis_latrans -0.3537 0.2253 -0.7767 -0.3580 0.1040 1.0110
## shrub_cover-Sylvilagus_floridanus 0.1390 0.4294 -0.6161 0.1046 1.0596 1.0022
## veg_height-Canis_latrans -0.7395 0.2018 -1.1565 -0.7334 -0.3545 1.0177
## veg_height-Sylvilagus_floridanus 0.1818 0.2901 -0.4144 0.1868 0.7369 1.0223
## week-Canis_latrans 0.5715 0.3024 -0.0116 0.5670 1.1524 1.0099
## week-Sylvilagus_floridanus -0.0342 0.3992 -0.8342 -0.0423 0.7549 1.0050
## I(week^2)-Canis_latrans -0.2296 0.1217 -0.4764 -0.2280 0.0065 1.0168
## I(week^2)-Sylvilagus_floridanus -0.1295 0.1885 -0.5165 -0.1236 0.2236 1.0341
## ESS
## (Intercept)-Canis_latrans 655
## (Intercept)-Sylvilagus_floridanus 485
## shrub_cover-Canis_latrans 842
## shrub_cover-Sylvilagus_floridanus 387
## veg_height-Canis_latrans 565
## veg_height-Sylvilagus_floridanus 649
## week-Canis_latrans 1001
## week-Sylvilagus_floridanus 779
## I(week^2)-Canis_latrans 1131
## I(week^2)-Sylvilagus_floridanus 651
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2077
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6389 1.0084 -2.5554 -0.6574 1.5324 1.0122 757
## Avg_Cogongrass_Cover -0.4889 1.1985 -2.7832 -0.5336 2.0508 1.0035 1919
## I(Avg_Cogongrass_Cover^2) 1.2052 1.2215 -1.3632 1.2166 3.5940 1.0141 783
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.1249 54.9642 0.0536 0.6761 34.9132 1.0992 1206
## Avg_Cogongrass_Cover 35.5577 239.6796 0.0877 3.0119 238.1040 1.1272 932
## I(Avg_Cogongrass_Cover^2) 83.8168 2707.8708 0.0639 1.4529 148.2811 1.3284 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1803 3.3885 0.0475 0.3961 6.7462 1.1841 374
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2439 1.1085 -3.7023 -2.5426 0.7552 1.0012 1960
## shrub_cover -0.1190 0.6629 -1.3944 -0.1419 1.2985 1.0106 1893
## veg_height -0.2280 0.7689 -1.8575 -0.2370 1.3940 1.0075 2391
## week 0.2291 0.7340 -1.4109 0.2546 1.6346 1.0064 2272
## I(week^2) -0.1711 0.5671 -1.3635 -0.1792 1.0374 1.0004 2560
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.4786 167.7167 0.0605 0.8112 46.5224 1.2188 3000
## shrub_cover 3.4288 26.4824 0.0453 0.4131 17.1896 1.0022 3000
## veg_height 5.1405 45.2787 0.0833 0.7501 27.2607 1.0161 3000
## week 3.6614 51.5982 0.0499 0.4948 17.7314 1.3339 3000
## I(week^2) 9.0358 389.5101 0.0382 0.2863 11.4606 1.3230 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5700 1.0371 -2.5558 -0.6133
## (Intercept)-Sylvilagus_floridanus -1.0960 1.0586 -3.2651 -1.0531
## Avg_Cogongrass_Cover-Canis_latrans 0.4204 1.5118 -1.4587 0.2024
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.5820 2.6790 -8.1814 -2.0428
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6362 2.0203 0.1334 2.2465
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5818 1.4797 -0.1717 1.2676
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5645 1.0640 376
## (Intercept)-Sylvilagus_floridanus 0.8791 1.0076 408
## Avg_Cogongrass_Cover-Canis_latrans 3.2162 1.1724 100
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.3245 1.1484 78
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.8735 1.0460 89
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 5.2956 1.0628 129
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6457 0.2085 -3.0669 -2.6417 -2.2365 1.0018
## (Intercept)-Sylvilagus_floridanus -3.2631 0.3720 -4.0629 -3.2422 -2.6065 1.0242
## shrub_cover-Canis_latrans -0.3247 0.2256 -0.7817 -0.3249 0.1086 1.0010
## shrub_cover-Sylvilagus_floridanus 0.0550 0.3990 -0.6253 0.0217 0.9718 1.0562
## veg_height-Canis_latrans -0.7215 0.2019 -1.1384 -0.7140 -0.3446 1.0039
## veg_height-Sylvilagus_floridanus 0.2106 0.3048 -0.3547 0.2126 0.7984 0.9999
## week-Canis_latrans 0.5570 0.2967 -0.0095 0.5446 1.1346 1.0043
## week-Sylvilagus_floridanus -0.0180 0.3958 -0.8037 -0.0078 0.7551 1.0136
## I(week^2)-Canis_latrans -0.2296 0.1218 -0.4736 -0.2252 0.0008 1.0059
## I(week^2)-Sylvilagus_floridanus -0.1311 0.1863 -0.5073 -0.1282 0.2183 1.0030
## ESS
## (Intercept)-Canis_latrans 802
## (Intercept)-Sylvilagus_floridanus 243
## shrub_cover-Canis_latrans 614
## shrub_cover-Sylvilagus_floridanus 386
## veg_height-Canis_latrans 687
## veg_height-Sylvilagus_floridanus 255
## week-Canis_latrans 1104
## week-Sylvilagus_floridanus 804
## I(week^2)-Canis_latrans 1154
## I(week^2)-Sylvilagus_floridanus 704
# 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 2 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 0.2122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4304 1.5234 -3.2960 -0.5020 2.7610 1.0808 290
## Cogon_Patch_Size 0.3295 1.5093 -2.7834 0.3755 3.1539 1.0133 1007
## Veg_shannon_index 1.2596 1.2882 -1.5546 1.3371 3.7064 1.0103 700
## total_shrub_cover 0.3878 1.4506 -2.4603 0.3514 3.2916 1.0388 315
## Avg_Cogongrass_Cover -0.2325 1.5275 -3.2505 -0.2551 2.7410 1.0093 510
## Tree_Density -1.2579 1.6824 -4.3674 -1.3552 2.2280 1.0043 420
## Avg_Canopy_Cover 0.4839 1.5929 -2.7040 0.5319 3.6102 1.0099 2833
## I(Avg_Cogongrass_Cover^2) 1.1329 1.3784 -1.7509 1.1984 3.6795 1.0157 975
## avg_veg_height -0.2625 1.1707 -2.6074 -0.2736 2.1432 1.0292 551
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 35.2420 435.5274 0.0702 1.5868 175.0476 1.2046
## Cogon_Patch_Size 201.7160 2822.3127 0.1416 16.6538 922.8967 1.1569
## Veg_shannon_index 14.0024 117.8256 0.0552 1.0692 78.9933 1.1171
## total_shrub_cover 98.3376 756.2978 0.0745 3.3185 624.4030 1.1563
## Avg_Cogongrass_Cover 46.1816 568.3340 0.0662 2.1125 235.2363 1.1086
## Tree_Density 448.8251 7670.1057 0.0879 14.7993 1690.7400 1.1057
## Avg_Canopy_Cover 638.9539 5821.3936 0.6120 63.1724 3691.3359 1.1869
## I(Avg_Cogongrass_Cover^2) 62.4348 850.6129 0.0780 3.4482 377.2175 1.2072
## avg_veg_height 17.6401 275.7598 0.0508 0.8002 77.6342 1.2136
## ESS
## (Intercept) 3000
## Cogon_Patch_Size 3000
## Veg_shannon_index 3000
## total_shrub_cover 374
## Avg_Cogongrass_Cover 3000
## Tree_Density 2825
## Avg_Canopy_Cover 952
## I(Avg_Cogongrass_Cover^2) 3000
## avg_veg_height 2264
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.5336 16.413 0.051 0.669 35.1694 1.2691 147
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2347 1.1248 -3.6400 -2.5576 0.7491 1.0026 1831
## shrub_cover -0.0431 0.7907 -1.6927 -0.0630 1.6504 1.0109 1458
## veg_height -0.2155 0.7050 -1.6782 -0.2511 1.3333 1.0056 2418
## week 0.1953 0.6964 -1.4106 0.2415 1.5146 1.0020 2167
## I(week^2) -0.1666 0.5646 -1.3664 -0.1644 1.0490 1.0012 2529
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.0206 135.4111 0.0557 0.7159 56.7549 1.1543 2808
## shrub_cover 9.3367 261.2431 0.0636 0.6722 29.9811 1.3155 3000
## veg_height 5.6489 71.6435 0.0751 0.6547 20.9068 1.1164 3000
## week 5.3151 109.7814 0.0476 0.5173 20.4606 1.3145 3000
## I(week^2) 2.8358 41.2240 0.0377 0.2938 14.7458 1.3097 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.2713 2.3847 -4.7164
## (Intercept)-Sylvilagus_floridanus -1.4052 2.6545 -7.1300
## Cogon_Patch_Size-Canis_latrans 4.7767 4.8398 -0.3503
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4361 4.1815 -12.4350
## Veg_shannon_index-Canis_latrans 1.9812 1.4819 -0.5643
## Veg_shannon_index-Sylvilagus_floridanus 1.8928 1.5944 -0.9324
## total_shrub_cover-Canis_latrans 1.9479 2.1145 -1.0147
## total_shrub_cover-Sylvilagus_floridanus -2.0543 6.1620 -20.2329
## Avg_Cogongrass_Cover-Canis_latrans 0.2877 2.7128 -4.3296
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2836 3.1074 -9.7832
## Tree_Density-Canis_latrans -5.4966 4.2573 -18.0936
## Tree_Density-Sylvilagus_floridanus -5.7803 6.2128 -22.7861
## Avg_Canopy_Cover-Canis_latrans -0.4835 1.0422 -3.0493
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.6646 10.3783 0.9797
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7959 3.2243 0.4095
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6749 2.1434 -1.8281
## avg_veg_height-Canis_latrans -0.3554 1.2147 -2.7005
## avg_veg_height-Sylvilagus_floridanus -0.6159 2.2417 -6.7602
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3384 4.4409 1.0845 157
## (Intercept)-Sylvilagus_floridanus -1.2944 3.5276 1.0586 155
## Cogon_Patch_Size-Canis_latrans 3.5720 16.3073 1.1864 118
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7476 3.9439 1.0118 114
## Veg_shannon_index-Canis_latrans 1.8987 5.0940 1.0303 271
## Veg_shannon_index-Sylvilagus_floridanus 1.7582 5.6937 1.0290 357
## total_shrub_cover-Canis_latrans 1.5547 7.2877 1.0954 54
## total_shrub_cover-Sylvilagus_floridanus -0.3254 4.9880 1.5392 39
## Avg_Cogongrass_Cover-Canis_latrans 0.0314 6.7479 1.0643 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8786 3.3261 1.0645 326
## Tree_Density-Canis_latrans -4.2537 -0.8009 1.0893 78
## Tree_Density-Sylvilagus_floridanus -3.6401 0.7292 1.1427 55
## Avg_Canopy_Cover-Canis_latrans -0.3803 1.2517 1.0187 270
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3687 40.3446 1.1008 32
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9227 13.2290 1.1354 101
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3784 7.0267 1.1206 94
## avg_veg_height-Canis_latrans -0.3578 2.2115 1.0184 494
## avg_veg_height-Sylvilagus_floridanus -0.3509 2.6446 1.0543 134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6453 0.2110 -3.0696 -2.6423 -2.2438 1.0160
## (Intercept)-Sylvilagus_floridanus -3.2189 0.3119 -3.8589 -3.2058 -2.6470 1.0008
## shrub_cover-Canis_latrans -0.4551 0.2368 -0.8875 -0.4644 0.0584 1.0332
## shrub_cover-Sylvilagus_floridanus 0.3769 0.4939 -0.4748 0.3446 1.3502 1.2348
## veg_height-Canis_latrans -0.6844 0.2046 -1.1017 -0.6797 -0.3056 1.0345
## veg_height-Sylvilagus_floridanus 0.1513 0.2890 -0.4152 0.1516 0.7093 1.0415
## week-Canis_latrans 0.5501 0.2997 -0.0094 0.5466 1.1476 1.0097
## week-Sylvilagus_floridanus -0.0226 0.3832 -0.8206 -0.0174 0.7129 1.0090
## I(week^2)-Canis_latrans -0.2256 0.1220 -0.4761 -0.2243 0.0081 1.0035
## I(week^2)-Sylvilagus_floridanus -0.1371 0.1864 -0.5259 -0.1327 0.2160 1.0036
## ESS
## (Intercept)-Canis_latrans 453
## (Intercept)-Sylvilagus_floridanus 419
## shrub_cover-Canis_latrans 501
## shrub_cover-Sylvilagus_floridanus 94
## veg_height-Canis_latrans 513
## veg_height-Sylvilagus_floridanus 591
## week-Canis_latrans 1085
## week-Sylvilagus_floridanus 847
## I(week^2)-Canis_latrans 1061
## I(week^2)-Sylvilagus_floridanus 590
waicOcc(ms_full_full_T25, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -248.81327 21.83652 541.29958
waicOcc(ms_full_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -259.25788 23.14294 564.80164
waicOcc(ms_full_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -257.38496 16.06242 546.89477
waicOcc(ms_full_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -258.94084 22.19762 562.27691
waicOcc(ms_full_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -261.76511 19.69815 562.92652
waicOcc(ms_full_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -263.84058 16.89656 561.47427
waicOcc(ms_full_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -267.34806 12.27936 559.25483
waicOcc(ms_full_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -260.75053 19.22739 559.95584
waicOcc(ms_full_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -245.9494 23.6601 539.2191
waicOcc(ms_null_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -275.957488 5.763679 563.442333
waicOcc(ms_null_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -259.98306 16.51886 553.00384
waicOcc(ms_null_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -271.47741 16.20428 575.36338
waicOcc(ms_null_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -266.64505 11.20505 555.70021
waicOcc(ms_null_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -269.96794 14.17066 568.27720
waicOcc(ms_null_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -270.80437 12.65671 566.92215
waicOcc(ms_null_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -272.76267 11.07805 567.68144
waicOcc(ms_null_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -269.96518 11.59304 563.11644
waicOcc(ms_null_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -255.75501 15.24069 541.99141
waicOcc(ms_week_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -257.27731 19.19182 552.93825
waicOcc(ms_week_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -270.66751 19.17749 579.69000
waicOcc(ms_week_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -275.372903 7.632973 566.011753
waicOcc(ms_week_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -270.06021 14.71526 569.55094
waicOcc(ms_week_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -269.34007 16.46138 571.60290
waicOcc(ms_week_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -266.1259 13.0715 558.3948
waicOcc(ms_week_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -272.11631 12.38614 569.00490
waicOcc(ms_week_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -269.53670 13.44913 565.97166
waicOcc(ms_week_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -255.07985 17.38962 544.93894
waicOcc(ms_cover_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -249.65232 19.77879 538.86222
waicOcc(ms_cover_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -259.97385 22.72944 565.40657
waicOcc(ms_cover_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -267.989896 9.939637 555.859067
waicOcc(ms_cover_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -262.31399 16.98115 558.59029
waicOcc(ms_cover_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -259.09734 18.91824 556.03115
waicOcc(ms_cover_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -257.25625 15.49211 545.49671
waicOcc(ms_cover_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -264.4146 14.8586 558.5464
waicOcc(ms_cover_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -261.60750 16.44072 556.09643
waicOcc(ms_cover_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -247.66078 20.16225 535.64606
waicOcc(ms_weekQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -257.34963 22.30475 559.30875
waicOcc(ms_weekQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -269.04229 21.73351 581.55160
waicOcc(ms_weekQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -273.45981 10.82337 568.56635
waicOcc(ms_weekQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -268.22153 17.33303 571.10912
waicOcc(ms_weekQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -267.41342 19.49829 573.82342
waicOcc(ms_weekQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -264.50743 15.45988 559.93461
waicOcc(ms_weekQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -270.21246 15.85599 572.13688
waicOcc(ms_weekQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -267.55485 16.80356 568.71683
waicOcc(ms_weekQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -253.11105 21.72882 549.67972
waicOcc(ms_fullQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -246.35844 25.57457 543.86604
waicOcc(ms_fullQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -257.05083 26.64933 567.40033
waicOcc(ms_fullQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -265.4198 15.1607 561.1611
waicOcc(ms_fullQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -259.6788 22.4977 564.3530
waicOcc(ms_fullQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -256.35844 24.23876 561.19440
waicOcc(ms_fullQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -254.69755 20.15564 549.70637
waicOcc(ms_fullQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -261.72465 20.18684 563.82298
waicOcc(ms_fullQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -258.96213 22.06675 562.05776
waicOcc(ms_fullQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -245.56373 24.98717 541.10180
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 2
## Currently on species 2 out of 2
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: 0.4092
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.5523
## Sylvilagus_floridanus Bayesian p-value: 0.266
## 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): 0.2122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4304 1.5234 -3.2960 -0.5020 2.7610 1.0808 290
## Cogon_Patch_Size 0.3295 1.5093 -2.7834 0.3755 3.1539 1.0133 1007
## Veg_shannon_index 1.2596 1.2882 -1.5546 1.3371 3.7064 1.0103 700
## total_shrub_cover 0.3878 1.4506 -2.4603 0.3514 3.2916 1.0388 315
## Avg_Cogongrass_Cover -0.2325 1.5275 -3.2505 -0.2551 2.7410 1.0093 510
## Tree_Density -1.2579 1.6824 -4.3674 -1.3552 2.2280 1.0043 420
## Avg_Canopy_Cover 0.4839 1.5929 -2.7040 0.5319 3.6102 1.0099 2833
## I(Avg_Cogongrass_Cover^2) 1.1329 1.3784 -1.7509 1.1984 3.6795 1.0157 975
## avg_veg_height -0.2625 1.1707 -2.6074 -0.2736 2.1432 1.0292 551
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 35.2420 435.5274 0.0702 1.5868 175.0476 1.2046
## Cogon_Patch_Size 201.7160 2822.3127 0.1416 16.6538 922.8967 1.1569
## Veg_shannon_index 14.0024 117.8256 0.0552 1.0692 78.9933 1.1171
## total_shrub_cover 98.3376 756.2978 0.0745 3.3185 624.4030 1.1563
## Avg_Cogongrass_Cover 46.1816 568.3340 0.0662 2.1125 235.2363 1.1086
## Tree_Density 448.8251 7670.1057 0.0879 14.7993 1690.7400 1.1057
## Avg_Canopy_Cover 638.9539 5821.3936 0.6120 63.1724 3691.3359 1.1869
## I(Avg_Cogongrass_Cover^2) 62.4348 850.6129 0.0780 3.4482 377.2175 1.2072
## avg_veg_height 17.6401 275.7598 0.0508 0.8002 77.6342 1.2136
## ESS
## (Intercept) 3000
## Cogon_Patch_Size 3000
## Veg_shannon_index 3000
## total_shrub_cover 374
## Avg_Cogongrass_Cover 3000
## Tree_Density 2825
## Avg_Canopy_Cover 952
## I(Avg_Cogongrass_Cover^2) 3000
## avg_veg_height 2264
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.5336 16.413 0.051 0.669 35.1694 1.2691 147
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2347 1.1248 -3.6400 -2.5576 0.7491 1.0026 1831
## shrub_cover -0.0431 0.7907 -1.6927 -0.0630 1.6504 1.0109 1458
## veg_height -0.2155 0.7050 -1.6782 -0.2511 1.3333 1.0056 2418
## week 0.1953 0.6964 -1.4106 0.2415 1.5146 1.0020 2167
## I(week^2) -0.1666 0.5646 -1.3664 -0.1644 1.0490 1.0012 2529
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.0206 135.4111 0.0557 0.7159 56.7549 1.1543 2808
## shrub_cover 9.3367 261.2431 0.0636 0.6722 29.9811 1.3155 3000
## veg_height 5.6489 71.6435 0.0751 0.6547 20.9068 1.1164 3000
## week 5.3151 109.7814 0.0476 0.5173 20.4606 1.3145 3000
## I(week^2) 2.8358 41.2240 0.0377 0.2938 14.7458 1.3097 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.2713 2.3847 -4.7164
## (Intercept)-Sylvilagus_floridanus -1.4052 2.6545 -7.1300
## Cogon_Patch_Size-Canis_latrans 4.7767 4.8398 -0.3503
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4361 4.1815 -12.4350
## Veg_shannon_index-Canis_latrans 1.9812 1.4819 -0.5643
## Veg_shannon_index-Sylvilagus_floridanus 1.8928 1.5944 -0.9324
## total_shrub_cover-Canis_latrans 1.9479 2.1145 -1.0147
## total_shrub_cover-Sylvilagus_floridanus -2.0543 6.1620 -20.2329
## Avg_Cogongrass_Cover-Canis_latrans 0.2877 2.7128 -4.3296
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2836 3.1074 -9.7832
## Tree_Density-Canis_latrans -5.4966 4.2573 -18.0936
## Tree_Density-Sylvilagus_floridanus -5.7803 6.2128 -22.7861
## Avg_Canopy_Cover-Canis_latrans -0.4835 1.0422 -3.0493
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.6646 10.3783 0.9797
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7959 3.2243 0.4095
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6749 2.1434 -1.8281
## avg_veg_height-Canis_latrans -0.3554 1.2147 -2.7005
## avg_veg_height-Sylvilagus_floridanus -0.6159 2.2417 -6.7602
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3384 4.4409 1.0845 157
## (Intercept)-Sylvilagus_floridanus -1.2944 3.5276 1.0586 155
## Cogon_Patch_Size-Canis_latrans 3.5720 16.3073 1.1864 118
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7476 3.9439 1.0118 114
## Veg_shannon_index-Canis_latrans 1.8987 5.0940 1.0303 271
## Veg_shannon_index-Sylvilagus_floridanus 1.7582 5.6937 1.0290 357
## total_shrub_cover-Canis_latrans 1.5547 7.2877 1.0954 54
## total_shrub_cover-Sylvilagus_floridanus -0.3254 4.9880 1.5392 39
## Avg_Cogongrass_Cover-Canis_latrans 0.0314 6.7479 1.0643 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8786 3.3261 1.0645 326
## Tree_Density-Canis_latrans -4.2537 -0.8009 1.0893 78
## Tree_Density-Sylvilagus_floridanus -3.6401 0.7292 1.1427 55
## Avg_Canopy_Cover-Canis_latrans -0.3803 1.2517 1.0187 270
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3687 40.3446 1.1008 32
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9227 13.2290 1.1354 101
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3784 7.0267 1.1206 94
## avg_veg_height-Canis_latrans -0.3578 2.2115 1.0184 494
## avg_veg_height-Sylvilagus_floridanus -0.3509 2.6446 1.0543 134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6453 0.2110 -3.0696 -2.6423 -2.2438 1.0160
## (Intercept)-Sylvilagus_floridanus -3.2189 0.3119 -3.8589 -3.2058 -2.6470 1.0008
## shrub_cover-Canis_latrans -0.4551 0.2368 -0.8875 -0.4644 0.0584 1.0332
## shrub_cover-Sylvilagus_floridanus 0.3769 0.4939 -0.4748 0.3446 1.3502 1.2348
## veg_height-Canis_latrans -0.6844 0.2046 -1.1017 -0.6797 -0.3056 1.0345
## veg_height-Sylvilagus_floridanus 0.1513 0.2890 -0.4152 0.1516 0.7093 1.0415
## week-Canis_latrans 0.5501 0.2997 -0.0094 0.5466 1.1476 1.0097
## week-Sylvilagus_floridanus -0.0226 0.3832 -0.8206 -0.0174 0.7129 1.0090
## I(week^2)-Canis_latrans -0.2256 0.1220 -0.4761 -0.2243 0.0081 1.0035
## I(week^2)-Sylvilagus_floridanus -0.1371 0.1864 -0.5259 -0.1327 0.2160 1.0036
## ESS
## (Intercept)-Canis_latrans 453
## (Intercept)-Sylvilagus_floridanus 419
## shrub_cover-Canis_latrans 501
## shrub_cover-Sylvilagus_floridanus 94
## veg_height-Canis_latrans 513
## veg_height-Sylvilagus_floridanus 591
## week-Canis_latrans 1085
## week-Sylvilagus_floridanus 847
## I(week^2)-Canis_latrans 1061
## I(week^2)-Sylvilagus_floridanus 590
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$beta.samples)
## 'mcmc' num [1:3000, 1:18] 2.7 3.19 3.3 3.65 2.1 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:18] "(Intercept)-Canis_latrans" "(Intercept)-Sylvilagus_floridanus" "Cogon_Patch_Size-Canis_latrans" "Cogon_Patch_Size-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.946
MCMCplot(ms_fullQ_fullQ_T25$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, 1:2, 1:100] 0.769 0.958 0.945 0.904 0.962 ...
## $ z.0.samples : int [1:3000, 1:2, 1:100] 1 1 1 1 1 1 0 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T25, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:2, 1:100] 0.769 0.958 0.945 0.904 0.962 ...
psi.0.quants <- apply(out.pred$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] 2.35e-05 1.53e-01 1.00 2.31e-05 1.57e-01 ...
## - 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 0.153 0.157 0.166 0.168 0.172 ...
## $ psi.low : num 2.35e-05 2.31e-05 3.07e-05 5.56e-05 7.25e-05 ...
## $ psi.high : num 1 0.999 0.999 0.999 0.999 ...
## $ 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): 0.2122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4304 1.5234 -3.2960 -0.5020 2.7610 1.0808 290
## Cogon_Patch_Size 0.3295 1.5093 -2.7834 0.3755 3.1539 1.0133 1007
## Veg_shannon_index 1.2596 1.2882 -1.5546 1.3371 3.7064 1.0103 700
## total_shrub_cover 0.3878 1.4506 -2.4603 0.3514 3.2916 1.0388 315
## Avg_Cogongrass_Cover -0.2325 1.5275 -3.2505 -0.2551 2.7410 1.0093 510
## Tree_Density -1.2579 1.6824 -4.3674 -1.3552 2.2280 1.0043 420
## Avg_Canopy_Cover 0.4839 1.5929 -2.7040 0.5319 3.6102 1.0099 2833
## I(Avg_Cogongrass_Cover^2) 1.1329 1.3784 -1.7509 1.1984 3.6795 1.0157 975
## avg_veg_height -0.2625 1.1707 -2.6074 -0.2736 2.1432 1.0292 551
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 35.2420 435.5274 0.0702 1.5868 175.0476 1.2046
## Cogon_Patch_Size 201.7160 2822.3127 0.1416 16.6538 922.8967 1.1569
## Veg_shannon_index 14.0024 117.8256 0.0552 1.0692 78.9933 1.1171
## total_shrub_cover 98.3376 756.2978 0.0745 3.3185 624.4030 1.1563
## Avg_Cogongrass_Cover 46.1816 568.3340 0.0662 2.1125 235.2363 1.1086
## Tree_Density 448.8251 7670.1057 0.0879 14.7993 1690.7400 1.1057
## Avg_Canopy_Cover 638.9539 5821.3936 0.6120 63.1724 3691.3359 1.1869
## I(Avg_Cogongrass_Cover^2) 62.4348 850.6129 0.0780 3.4482 377.2175 1.2072
## avg_veg_height 17.6401 275.7598 0.0508 0.8002 77.6342 1.2136
## ESS
## (Intercept) 3000
## Cogon_Patch_Size 3000
## Veg_shannon_index 3000
## total_shrub_cover 374
## Avg_Cogongrass_Cover 3000
## Tree_Density 2825
## Avg_Canopy_Cover 952
## I(Avg_Cogongrass_Cover^2) 3000
## avg_veg_height 2264
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.5336 16.413 0.051 0.669 35.1694 1.2691 147
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2347 1.1248 -3.6400 -2.5576 0.7491 1.0026 1831
## shrub_cover -0.0431 0.7907 -1.6927 -0.0630 1.6504 1.0109 1458
## veg_height -0.2155 0.7050 -1.6782 -0.2511 1.3333 1.0056 2418
## week 0.1953 0.6964 -1.4106 0.2415 1.5146 1.0020 2167
## I(week^2) -0.1666 0.5646 -1.3664 -0.1644 1.0490 1.0012 2529
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.0206 135.4111 0.0557 0.7159 56.7549 1.1543 2808
## shrub_cover 9.3367 261.2431 0.0636 0.6722 29.9811 1.3155 3000
## veg_height 5.6489 71.6435 0.0751 0.6547 20.9068 1.1164 3000
## week 5.3151 109.7814 0.0476 0.5173 20.4606 1.3145 3000
## I(week^2) 2.8358 41.2240 0.0377 0.2938 14.7458 1.3097 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.2713 2.3847 -4.7164
## (Intercept)-Sylvilagus_floridanus -1.4052 2.6545 -7.1300
## Cogon_Patch_Size-Canis_latrans 4.7767 4.8398 -0.3503
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4361 4.1815 -12.4350
## Veg_shannon_index-Canis_latrans 1.9812 1.4819 -0.5643
## Veg_shannon_index-Sylvilagus_floridanus 1.8928 1.5944 -0.9324
## total_shrub_cover-Canis_latrans 1.9479 2.1145 -1.0147
## total_shrub_cover-Sylvilagus_floridanus -2.0543 6.1620 -20.2329
## Avg_Cogongrass_Cover-Canis_latrans 0.2877 2.7128 -4.3296
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2836 3.1074 -9.7832
## Tree_Density-Canis_latrans -5.4966 4.2573 -18.0936
## Tree_Density-Sylvilagus_floridanus -5.7803 6.2128 -22.7861
## Avg_Canopy_Cover-Canis_latrans -0.4835 1.0422 -3.0493
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.6646 10.3783 0.9797
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7959 3.2243 0.4095
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6749 2.1434 -1.8281
## avg_veg_height-Canis_latrans -0.3554 1.2147 -2.7005
## avg_veg_height-Sylvilagus_floridanus -0.6159 2.2417 -6.7602
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3384 4.4409 1.0845 157
## (Intercept)-Sylvilagus_floridanus -1.2944 3.5276 1.0586 155
## Cogon_Patch_Size-Canis_latrans 3.5720 16.3073 1.1864 118
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7476 3.9439 1.0118 114
## Veg_shannon_index-Canis_latrans 1.8987 5.0940 1.0303 271
## Veg_shannon_index-Sylvilagus_floridanus 1.7582 5.6937 1.0290 357
## total_shrub_cover-Canis_latrans 1.5547 7.2877 1.0954 54
## total_shrub_cover-Sylvilagus_floridanus -0.3254 4.9880 1.5392 39
## Avg_Cogongrass_Cover-Canis_latrans 0.0314 6.7479 1.0643 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8786 3.3261 1.0645 326
## Tree_Density-Canis_latrans -4.2537 -0.8009 1.0893 78
## Tree_Density-Sylvilagus_floridanus -3.6401 0.7292 1.1427 55
## Avg_Canopy_Cover-Canis_latrans -0.3803 1.2517 1.0187 270
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3687 40.3446 1.1008 32
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9227 13.2290 1.1354 101
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3784 7.0267 1.1206 94
## avg_veg_height-Canis_latrans -0.3578 2.2115 1.0184 494
## avg_veg_height-Sylvilagus_floridanus -0.3509 2.6446 1.0543 134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6453 0.2110 -3.0696 -2.6423 -2.2438 1.0160
## (Intercept)-Sylvilagus_floridanus -3.2189 0.3119 -3.8589 -3.2058 -2.6470 1.0008
## shrub_cover-Canis_latrans -0.4551 0.2368 -0.8875 -0.4644 0.0584 1.0332
## shrub_cover-Sylvilagus_floridanus 0.3769 0.4939 -0.4748 0.3446 1.3502 1.2348
## veg_height-Canis_latrans -0.6844 0.2046 -1.1017 -0.6797 -0.3056 1.0345
## veg_height-Sylvilagus_floridanus 0.1513 0.2890 -0.4152 0.1516 0.7093 1.0415
## week-Canis_latrans 0.5501 0.2997 -0.0094 0.5466 1.1476 1.0097
## week-Sylvilagus_floridanus -0.0226 0.3832 -0.8206 -0.0174 0.7129 1.0090
## I(week^2)-Canis_latrans -0.2256 0.1220 -0.4761 -0.2243 0.0081 1.0035
## I(week^2)-Sylvilagus_floridanus -0.1371 0.1864 -0.5259 -0.1327 0.2160 1.0036
## ESS
## (Intercept)-Canis_latrans 453
## (Intercept)-Sylvilagus_floridanus 419
## shrub_cover-Canis_latrans 501
## shrub_cover-Sylvilagus_floridanus 94
## veg_height-Canis_latrans 513
## veg_height-Sylvilagus_floridanus 591
## week-Canis_latrans 1085
## week-Sylvilagus_floridanus 847
## I(week^2)-Canis_latrans 1061
## I(week^2)-Sylvilagus_floridanus 590
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$beta.samples)
## 'mcmc' num [1:3000, 1:18] 2.7 3.19 3.3 3.65 2.1 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:18] "(Intercept)-Canis_latrans" "(Intercept)-Sylvilagus_floridanus" "Cogon_Patch_Size-Canis_latrans" "Cogon_Patch_Size-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.946
MCMCplot(ms_fullQ_fullQ_T25$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()
#}