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/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/Kody Melissa/Downloads/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "CameraLocations")
# First day of the study
study_start <- as.Date("2024-05-10")
# Effort Matrix
effort_matrix <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "Daily_EM")
col_names <- names(effort_matrix)
date_cols <- col_names[-1]
converted_dates <- as.character(as.Date(as.numeric(date_cols), origin = "1899-12-30"))
names(effort_matrix) <- c("Plot", converted_dates)
# Pivot, filter, and add custom study week
effort_matrix <- effort_matrix %>%
pivot_longer(
cols = -Plot,
names_to = "date",
values_to = "effort"
) %>%
filter(effort == 1) %>%
mutate(
date = as.Date(date),
study_week = as.integer(floor((date - study_start) / 7)) + 1 # Week 1 starts on study_start
) %>%
dplyr::select(Plot, date, study_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$Avg_Canopy_Cover)
## [1] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia") &
!Name %in% c("Odocoileus_virginianus", "Dasypus_novemcinctus",
"Procyon_lotor", "Sciurus_niger")) %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 2) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format daily observation data
observations_daily <- CameraData %>%
mutate(Date = as.Date(Date)) %>%
group_by(Plot, date = Date, Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge observations with daily effort
observations_daily <- effort_matrix %>%
left_join(observations_daily, by = c("Plot", "date")) %>%
replace_na(list(observations = 0))
observations_species <- observations_daily %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-`NA`) # Remove empty species name if present
# Create detection array (site × date × species)
site_names <- sort(unique(observations_species$Plot))
date_names <- sort(unique(observations_species$date))
species_names <- setdiff(colnames(observations_species), c("Plot", "date", "study_week"))
num_sites <- length(site_names)
num_days <- length(date_names)
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_days, num_species))
dimnames(detection_array) <- list(site_names, as.character(date_names), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(date_names)) {
day <- date_names[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site &
observations_species$date == day &
observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be [num_sites x num_days x num_species]
## [1] 32 258 7
# Backup camera location data
CameraLoc_O2 <- CameraLoc
# Standardize site-level covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, -Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names # site_names should match the detection array
# Scale site covariates
covariates_matrix <- scale(covariates_matrix)
## Detection-level Covariates ----
effort_expanded <- expand.grid(
Plot = site_names,
date = date_names
) %>%
left_join(effort_matrix[, c("Plot", "date", "study_week")], by = c("Plot", "date"))
week_matrix <- matrix(effort_expanded$study_week,
nrow = num_sites,
ncol = num_days,
byrow = FALSE)
# Convert to numeric and scale
week_numeric <- scale(week_matrix)
dim(week_numeric)
## [1] 32 258
# Detection covariates list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_numeric
)
det.covs$week[is.na(det.covs$week)] <- 0
# Check structure
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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258, 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## .. ..$ : 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258] 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## $ 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
# 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%
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## Sampled: 1500 of 5000, 30.00%
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## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
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## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
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## Sampled: 2000 of 5000, 40.00%
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## Sampled: 3000 of 5000, 60.00%
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## Sampled: 3500 of 5000, 70.00%
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## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
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## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.382
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8714 0.4538 -1.7636 -0.8758 0.0618 1.0026 2133
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2297 1.52 0.1814 0.8553 4.478 1.0424 2062
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.703 0.2683 -5.2125 -4.7075 -4.1256 1.0064 968
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4215 0.6263 0.0544 0.2463 1.8741 1.1616 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2579 0.4132 -0.5158 0.2479 1.1039 1.0053
## (Intercept)-Lynx_rufus -0.1332 0.5245 -1.0235 -0.1764 1.0113 1.0028
## (Intercept)-Didelphis_virginiana -1.3239 0.4125 -2.2074 -1.3083 -0.5606 1.0001
## (Intercept)-Sylvilagus_floridanus -0.6280 0.3976 -1.3865 -0.6351 0.1891 1.0043
## (Intercept)-Sciurus_carolinensis -1.3229 0.4219 -2.2249 -1.3015 -0.5330 1.0047
## (Intercept)-Vulpes_vulpes -1.5530 0.6481 -2.8343 -1.5476 -0.2865 1.0570
## (Intercept)-Sus_scrofa -1.7968 0.5674 -3.0334 -1.7558 -0.8042 1.0064
## ESS
## (Intercept)-Canis_latrans 1827
## (Intercept)-Lynx_rufus 389
## (Intercept)-Didelphis_virginiana 2517
## (Intercept)-Sylvilagus_floridanus 1147
## (Intercept)-Sciurus_carolinensis 2304
## (Intercept)-Vulpes_vulpes 349
## (Intercept)-Sus_scrofa 773
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6495 0.1549 -4.9792 -4.6434 -4.3685 1.0701
## (Intercept)-Lynx_rufus -5.2383 0.2905 -5.8281 -5.2097 -4.7601 1.0148
## (Intercept)-Didelphis_virginiana -4.3436 0.2189 -4.7868 -4.3358 -3.9336 1.0259
## (Intercept)-Sylvilagus_floridanus -4.8563 0.2296 -5.3671 -4.8444 -4.4379 1.0363
## (Intercept)-Sciurus_carolinensis -4.3902 0.2248 -4.8647 -4.3821 -3.9689 1.0628
## (Intercept)-Vulpes_vulpes -5.3431 0.5333 -6.6416 -5.2583 -4.5117 1.1782
## (Intercept)-Sus_scrofa -4.7499 0.3691 -5.6010 -4.7237 -4.0820 1.0148
## ESS
## (Intercept)-Canis_latrans 248
## (Intercept)-Lynx_rufus 97
## (Intercept)-Didelphis_virginiana 421
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Sciurus_carolinensis 311
## (Intercept)-Vulpes_vulpes 81
## (Intercept)-Sus_scrofa 176
# 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): 4.4638
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1432 1.1497 -3.2814 -1.1693 1.3877 1.0226 301
## Cogon_Patch_Size -0.5274 1.0505 -2.7294 -0.4777 1.4952 1.0320 354
## Veg_shannon_index 0.6840 0.8264 -1.0864 0.7005 2.3056 1.0262 662
## total_shrub_cover -0.5715 0.8674 -2.4555 -0.5349 1.1368 1.0045 151
## Avg_Cogongrass_Cover 1.9171 0.8958 0.0952 1.9552 3.6097 1.0501 275
## Tree_Density -1.9376 0.9982 -3.9147 -1.9191 0.0105 1.0215 204
## Avg_Canopy_Cover 1.9684 1.0473 -0.2881 1.9895 3.9830 1.0045 787
## avg_veg_height -0.5862 0.6731 -1.8084 -0.6278 0.8567 1.0575 267
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.9757 30.8212 0.6679 8.5586 105.5672 1.1701 60
## Cogon_Patch_Size 9.5560 17.0654 0.1629 3.8875 55.2765 1.0905 161
## Veg_shannon_index 4.4045 8.6173 0.0947 1.8412 24.9669 1.1582 150
## total_shrub_cover 3.9279 6.9241 0.0767 1.5370 23.7576 1.0934 113
## Avg_Cogongrass_Cover 2.4331 5.0780 0.0575 0.8578 15.2074 1.0201 358
## Tree_Density 3.8537 10.9028 0.0570 0.9064 27.9256 1.5516 167
## Avg_Canopy_Cover 13.6265 29.2445 0.3904 5.3171 82.1079 1.1343 122
## avg_veg_height 0.9686 1.8611 0.0509 0.3884 5.6120 1.0412 378
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.1194 7.1284 0.0792 1.5589 26.3239 1.1855 32
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9911 0.3674 -5.6047 -5.0253 -4.1835 1.0109 1353
## shrub_cover 0.5537 0.3949 -0.2116 0.5512 1.3956 1.0284 249
## veg_height 0.0275 0.2335 -0.4247 0.0289 0.4919 1.0070 561
## week -0.0587 1.6529 -3.3210 -0.0545 3.0276 1.0092 634
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.37700e-01 1.103400e+00 0.0674 0.3474 2.890600e+00 1.0405 210
## shrub_cover 7.89100e-01 8.649000e-01 0.1191 0.5379 3.033700e+00 1.0012 394
## veg_height 3.13000e-01 3.277000e-01 0.0617 0.2196 1.110200e+00 1.0155 713
## week 1.41064e+19 1.718674e+20 0.1363 1271.0675 1.644913e+18 1.6532 210
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.2211 1.4413 -1.4249 1.1100
## (Intercept)-Lynx_rufus 2.7227 4.3533 -1.9580 1.5224
## (Intercept)-Didelphis_virginiana -2.9891 1.5609 -6.6094 -2.8457
## (Intercept)-Sylvilagus_floridanus -1.5829 1.7085 -5.3655 -1.5144
## (Intercept)-Sciurus_carolinensis -3.6995 2.4823 -10.2274 -3.2346
## (Intercept)-Vulpes_vulpes -3.1636 2.0005 -7.2804 -3.0937
## (Intercept)-Sus_scrofa -5.2557 3.0587 -13.5383 -4.7033
## Cogon_Patch_Size-Canis_latrans 1.3150 1.7242 -1.0090 0.9705
## Cogon_Patch_Size-Lynx_rufus -0.2952 2.1637 -4.3984 -0.4217
## Cogon_Patch_Size-Didelphis_virginiana 1.4125 1.4016 -0.6795 1.2028
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8027 2.8921 -10.1817 -2.0999
## Cogon_Patch_Size-Sciurus_carolinensis -1.9033 2.1809 -7.4688 -1.5045
## Cogon_Patch_Size-Vulpes_vulpes -1.7562 2.6981 -8.7595 -1.3478
## Cogon_Patch_Size-Sus_scrofa -1.7063 2.3484 -8.3641 -1.2499
## Veg_shannon_index-Canis_latrans 1.6022 1.0116 -0.1022 1.4860
## Veg_shannon_index-Lynx_rufus -0.0420 1.6205 -3.7382 0.1082
## Veg_shannon_index-Didelphis_virginiana 1.4463 1.2701 -0.4338 1.2297
## Veg_shannon_index-Sylvilagus_floridanus 1.3023 1.1617 -0.5259 1.1543
## Veg_shannon_index-Sciurus_carolinensis -0.6373 1.5135 -4.4336 -0.3801
## Veg_shannon_index-Vulpes_vulpes -0.4347 1.5147 -3.9252 -0.1904
## Veg_shannon_index-Sus_scrofa 2.3490 1.9068 -0.0804 1.9488
## total_shrub_cover-Canis_latrans 0.9650 1.2821 -0.8630 0.6872
## total_shrub_cover-Lynx_rufus -1.4024 1.8910 -5.7729 -1.1868
## total_shrub_cover-Didelphis_virginiana -1.2564 1.4602 -4.8198 -0.9715
## total_shrub_cover-Sylvilagus_floridanus -1.0282 1.5127 -4.9496 -0.7767
## total_shrub_cover-Sciurus_carolinensis -0.6327 1.2715 -3.6894 -0.4797
## total_shrub_cover-Vulpes_vulpes -1.5778 2.2554 -8.2594 -1.0348
## total_shrub_cover-Sus_scrofa -0.1842 1.3806 -3.1188 -0.1629
## Avg_Cogongrass_Cover-Canis_latrans 2.5294 1.2172 0.4055 2.4231
## Avg_Cogongrass_Cover-Lynx_rufus 2.6365 1.5013 0.1867 2.4549
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1379 1.1519 0.0093 2.0855
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2438 1.2742 -1.4722 1.3558
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2988 1.1994 0.1341 2.2427
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5782 1.4200 0.1388 2.4101
## Avg_Cogongrass_Cover-Sus_scrofa 1.4686 1.6148 -2.3033 1.6668
## Tree_Density-Canis_latrans -2.8349 1.5178 -6.6223 -2.6228
## Tree_Density-Lynx_rufus -1.0629 1.8558 -3.7984 -1.2384
## Tree_Density-Didelphis_virginiana -2.2433 1.5019 -5.4592 -2.1576
## Tree_Density-Sylvilagus_floridanus -2.6356 1.7714 -7.0320 -2.3910
## Tree_Density-Sciurus_carolinensis -2.5221 1.8023 -6.6165 -2.2711
## Tree_Density-Vulpes_vulpes -2.0670 1.5682 -5.3404 -1.9777
## Tree_Density-Sus_scrofa -2.3333 1.8552 -6.5363 -2.1260
## Avg_Canopy_Cover-Canis_latrans -0.0550 0.8108 -1.7768 -0.0036
## Avg_Canopy_Cover-Lynx_rufus 1.0092 2.5328 -2.9382 0.8603
## Avg_Canopy_Cover-Didelphis_virginiana 4.5380 2.3033 1.6276 3.9948
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.4595 2.9088 1.6315 4.8256
## Avg_Canopy_Cover-Sciurus_carolinensis 4.1470 2.4371 1.2545 3.5186
## Avg_Canopy_Cover-Vulpes_vulpes 3.2010 2.2020 0.5861 2.7026
## Avg_Canopy_Cover-Sus_scrofa 2.6319 1.4551 0.4429 2.4242
## avg_veg_height-Canis_latrans -0.5267 0.7687 -1.9938 -0.5582
## avg_veg_height-Lynx_rufus -0.7608 1.1259 -3.0308 -0.7831
## avg_veg_height-Didelphis_virginiana -0.8214 0.9038 -2.7759 -0.7873
## avg_veg_height-Sylvilagus_floridanus -0.7903 0.8915 -2.6720 -0.7607
## avg_veg_height-Sciurus_carolinensis -0.1740 0.9089 -1.7095 -0.2653
## avg_veg_height-Vulpes_vulpes -0.6703 1.0592 -2.7064 -0.6962
## avg_veg_height-Sus_scrofa -0.6209 0.9158 -2.3790 -0.6454
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.5374 1.0788 280
## (Intercept)-Lynx_rufus 15.7666 1.2922 37
## (Intercept)-Didelphis_virginiana -0.4157 1.0138 181
## (Intercept)-Sylvilagus_floridanus 1.7272 1.0051 230
## (Intercept)-Sciurus_carolinensis -0.3173 1.1095 82
## (Intercept)-Vulpes_vulpes 0.8037 1.0056 158
## (Intercept)-Sus_scrofa -1.0293 1.1556 83
## Cogon_Patch_Size-Canis_latrans 5.5086 1.0009 323
## Cogon_Patch_Size-Lynx_rufus 4.6570 1.0289 179
## Cogon_Patch_Size-Didelphis_virginiana 4.9988 1.0235 187
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9995 1.0318 132
## Cogon_Patch_Size-Sciurus_carolinensis 1.2355 1.1297 177
## Cogon_Patch_Size-Vulpes_vulpes 2.7317 1.1801 107
## Cogon_Patch_Size-Sus_scrofa 1.5773 1.0025 178
## Veg_shannon_index-Canis_latrans 3.8307 1.0119 339
## Veg_shannon_index-Lynx_rufus 2.9443 1.0133 205
## Veg_shannon_index-Didelphis_virginiana 4.6685 1.0405 207
## Veg_shannon_index-Sylvilagus_floridanus 4.1086 1.0526 239
## Veg_shannon_index-Sciurus_carolinensis 1.5213 1.0884 233
## Veg_shannon_index-Vulpes_vulpes 1.8713 1.0915 240
## Veg_shannon_index-Sus_scrofa 7.1158 1.0947 176
## total_shrub_cover-Canis_latrans 4.1670 1.0752 133
## total_shrub_cover-Lynx_rufus 1.8687 1.0159 109
## total_shrub_cover-Didelphis_virginiana 0.9265 1.0537 123
## total_shrub_cover-Sylvilagus_floridanus 1.2973 1.0114 189
## total_shrub_cover-Sciurus_carolinensis 1.4981 1.0031 141
## total_shrub_cover-Vulpes_vulpes 1.0724 1.2102 91
## total_shrub_cover-Sus_scrofa 2.5022 1.0443 295
## Avg_Cogongrass_Cover-Canis_latrans 5.3227 1.0506 327
## Avg_Cogongrass_Cover-Lynx_rufus 6.1982 1.0577 239
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.6209 1.0380 320
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4867 1.0195 340
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.0371 1.0433 320
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.9784 1.0867 228
## Avg_Cogongrass_Cover-Sus_scrofa 4.0736 1.0061 285
## Tree_Density-Canis_latrans -0.5329 1.1647 242
## Tree_Density-Lynx_rufus 3.7516 1.1093 150
## Tree_Density-Didelphis_virginiana 0.6717 1.1143 179
## Tree_Density-Sylvilagus_floridanus 0.0552 1.1704 152
## Tree_Density-Sciurus_carolinensis 0.2906 1.2532 183
## Tree_Density-Vulpes_vulpes 0.7950 1.1172 268
## Tree_Density-Sus_scrofa 0.6207 1.0936 284
## Avg_Canopy_Cover-Canis_latrans 1.4592 1.0583 528
## Avg_Canopy_Cover-Lynx_rufus 5.9293 1.0950 92
## Avg_Canopy_Cover-Didelphis_virginiana 10.7510 1.0320 129
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.3197 1.0755 84
## Avg_Canopy_Cover-Sciurus_carolinensis 10.8447 1.0451 52
## Avg_Canopy_Cover-Vulpes_vulpes 9.5058 1.1808 70
## Avg_Canopy_Cover-Sus_scrofa 6.2241 1.0190 179
## avg_veg_height-Canis_latrans 1.1281 1.0675 485
## avg_veg_height-Lynx_rufus 1.7066 1.0700 303
## avg_veg_height-Didelphis_virginiana 0.8870 1.0206 412
## avg_veg_height-Sylvilagus_floridanus 0.9843 1.0320 445
## avg_veg_height-Sciurus_carolinensis 1.8389 1.0229 472
## avg_veg_height-Vulpes_vulpes 1.5145 1.0484 244
## avg_veg_height-Sus_scrofa 1.4029 1.0419 547
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.779600e+00 1.742000e-01 -5.147500e+00
## (Intercept)-Lynx_rufus -5.661900e+00 3.315000e-01 -6.298200e+00
## (Intercept)-Didelphis_virginiana -4.722500e+00 2.720000e-01 -5.254900e+00
## (Intercept)-Sylvilagus_floridanus -5.003900e+00 2.268000e-01 -5.468000e+00
## (Intercept)-Sciurus_carolinensis -4.777700e+00 2.985000e-01 -5.355000e+00
## (Intercept)-Vulpes_vulpes -5.811900e+00 6.204000e-01 -7.283600e+00
## (Intercept)-Sus_scrofa -5.264500e+00 5.047000e-01 -6.314500e+00
## shrub_cover-Canis_latrans -3.090000e-01 2.158000e-01 -7.106000e-01
## shrub_cover-Lynx_rufus -3.800000e-03 3.519000e-01 -6.260000e-01
## shrub_cover-Didelphis_virginiana 1.166500e+00 3.762000e-01 4.870000e-01
## shrub_cover-Sylvilagus_floridanus 7.369000e-01 3.719000e-01 2.580000e-02
## shrub_cover-Sciurus_carolinensis 1.047800e+00 3.853000e-01 3.087000e-01
## shrub_cover-Vulpes_vulpes 4.838000e-01 6.684000e-01 -9.529000e-01
## shrub_cover-Sus_scrofa 8.955000e-01 7.855000e-01 -5.707000e-01
## veg_height-Canis_latrans -5.459000e-01 1.707000e-01 -8.891000e-01
## veg_height-Lynx_rufus 1.751000e-01 2.194000e-01 -2.799000e-01
## veg_height-Didelphis_virginiana 5.035000e-01 2.276000e-01 6.460000e-02
## veg_height-Sylvilagus_floridanus 2.183000e-01 2.226000e-01 -1.887000e-01
## veg_height-Sciurus_carolinensis 2.411000e-01 2.122000e-01 -1.529000e-01
## veg_height-Vulpes_vulpes -1.766000e-01 3.359000e-01 -8.759000e-01
## veg_height-Sus_scrofa -2.146000e-01 3.247000e-01 -8.922000e-01
## week-Canis_latrans -2.873733e+07 3.466917e+09 -1.665032e+08
## week-Lynx_rufus -3.795359e+07 4.480302e+09 -1.402234e+08
## week-Didelphis_virginiana -1.229739e+08 4.076786e+09 -1.879457e+08
## week-Sylvilagus_floridanus -1.053561e+07 3.728489e+09 -1.821634e+08
## week-Sciurus_carolinensis 4.273639e+06 4.762468e+09 -1.936575e+08
## week-Vulpes_vulpes 4.636675e+07 3.529407e+09 -1.662087e+08
## week-Sus_scrofa -1.657164e+07 3.298313e+09 -1.740353e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7698 -4.464900e+00 1.0510 194
## (Intercept)-Lynx_rufus -5.6396 -5.051800e+00 1.0466 53
## (Intercept)-Didelphis_virginiana -4.7150 -4.212900e+00 1.0189 154
## (Intercept)-Sylvilagus_floridanus -4.9873 -4.608200e+00 1.0641 163
## (Intercept)-Sciurus_carolinensis -4.7675 -4.241800e+00 1.0429 146
## (Intercept)-Vulpes_vulpes -5.7303 -4.818400e+00 1.0598 43
## (Intercept)-Sus_scrofa -5.2384 -4.324900e+00 1.0487 210
## shrub_cover-Canis_latrans -0.3159 1.130000e-01 1.0516 171
## shrub_cover-Lynx_rufus -0.0263 7.128000e-01 1.1704 81
## shrub_cover-Didelphis_virginiana 1.1498 1.948800e+00 1.0313 140
## shrub_cover-Sylvilagus_floridanus 0.7335 1.516200e+00 1.0787 124
## shrub_cover-Sciurus_carolinensis 1.0389 1.801400e+00 1.0069 168
## shrub_cover-Vulpes_vulpes 0.5114 1.834600e+00 1.1923 89
## shrub_cover-Sus_scrofa 0.8506 2.677900e+00 1.0887 207
## veg_height-Canis_latrans -0.5473 -2.112000e-01 1.0022 202
## veg_height-Lynx_rufus 0.1760 5.955000e-01 1.0132 154
## veg_height-Didelphis_virginiana 0.5021 9.548000e-01 1.0653 284
## veg_height-Sylvilagus_floridanus 0.2117 6.673000e-01 1.0135 246
## veg_height-Sciurus_carolinensis 0.2321 6.586000e-01 1.0251 225
## veg_height-Vulpes_vulpes -0.1644 4.403000e-01 1.1093 109
## veg_height-Sus_scrofa -0.2043 3.962000e-01 1.0110 304
## week-Canis_latrans -0.2946 1.254130e+08 1.2526 2908
## week-Lynx_rufus -0.1851 1.798364e+08 1.2957 6867
## week-Didelphis_virginiana -0.3824 1.272315e+08 1.3317 1059
## week-Sylvilagus_floridanus -0.1781 1.564805e+08 1.2796 29738
## week-Sciurus_carolinensis -0.2612 1.310162e+08 1.2515 6996
## week-Vulpes_vulpes -0.0097 1.086873e+08 1.2595 2865
## week-Sus_scrofa -0.1515 1.307300e+08 1.2529 66008
#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): 3.9557
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7419 0.4292 -1.6174 -0.7382 0.1159 1.0138 1491
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1079 1.1917 0.1457 0.7804 4.1124 1.0203 839
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8983 0.3009 -5.4450 -4.9122 -4.2493 1.0099 790
## shrub_cover 0.3381 0.3570 -0.3917 0.3325 1.0692 1.0236 453
## veg_height 0.0235 0.2312 -0.4466 0.0259 0.4741 1.0200 1076
## week 0.1298 1.5885 -2.8868 0.1734 3.3141 1.0113 698
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.636000e-01 8.260000e-01 0.0497 0.2755 1.913100e+00 1.1646 512
## shrub_cover 6.617000e-01 6.714000e-01 0.1116 0.4628 2.534800e+00 1.0016 337
## veg_height 3.025000e-01 3.473000e-01 0.0620 0.2153 1.125700e+00 1.0042 1081
## week 1.049848e+07 1.125729e+08 0.1048 26.8029 3.705923e+07 1.1975 257
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2884 0.3998 -0.4698 0.2758 1.1117 1.0026
## (Intercept)-Lynx_rufus 0.0447 0.5639 -0.9088 -0.0085 1.2605 1.0619
## (Intercept)-Didelphis_virginiana -1.1286 0.4418 -2.0250 -1.1115 -0.3153 1.0037
## (Intercept)-Sylvilagus_floridanus -0.5502 0.4005 -1.3329 -0.5555 0.2291 1.0022
## (Intercept)-Sciurus_carolinensis -1.1398 0.4436 -2.0456 -1.1215 -0.3264 1.0004
## (Intercept)-Vulpes_vulpes -1.4129 0.6468 -2.7104 -1.4124 -0.1504 1.0373
## (Intercept)-Sus_scrofa -1.5665 0.5845 -2.8501 -1.5254 -0.5210 1.0194
## ESS
## (Intercept)-Canis_latrans 1223
## (Intercept)-Lynx_rufus 398
## (Intercept)-Didelphis_virginiana 1320
## (Intercept)-Sylvilagus_floridanus 1963
## (Intercept)-Sciurus_carolinensis 1015
## (Intercept)-Vulpes_vulpes 314
## (Intercept)-Sus_scrofa 825
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7734 0.1769 -5.1222 -4.7761
## (Intercept)-Lynx_rufus -5.4359 0.2949 -5.9970 -5.4352
## (Intercept)-Didelphis_virginiana -4.6533 0.2657 -5.1961 -4.6496
## (Intercept)-Sylvilagus_floridanus -4.9025 0.2094 -5.3007 -4.8991
## (Intercept)-Sciurus_carolinensis -4.6141 0.2713 -5.1703 -4.6043
## (Intercept)-Vulpes_vulpes -5.5584 0.5329 -6.8147 -5.4736
## (Intercept)-Sus_scrofa -5.1408 0.4414 -6.0290 -5.1260
## shrub_cover-Canis_latrans -0.2931 0.2027 -0.6988 -0.2866
## shrub_cover-Lynx_rufus -0.1892 0.3464 -0.8721 -0.2047
## shrub_cover-Didelphis_virginiana 0.9947 0.3285 0.3978 0.9747
## shrub_cover-Sylvilagus_floridanus 0.4867 0.3784 -0.2255 0.4873
## shrub_cover-Sciurus_carolinensis 0.8296 0.3627 0.1036 0.8316
## shrub_cover-Vulpes_vulpes 0.0230 0.6389 -1.3576 0.0556
## shrub_cover-Sus_scrofa 0.6568 0.6798 -0.7943 0.6852
## veg_height-Canis_latrans -0.5633 0.1786 -0.9359 -0.5535
## veg_height-Lynx_rufus 0.1419 0.2288 -0.3254 0.1549
## veg_height-Didelphis_virginiana 0.5113 0.2303 0.0750 0.4983
## veg_height-Sylvilagus_floridanus 0.1675 0.2207 -0.2884 0.1723
## veg_height-Sciurus_carolinensis 0.1788 0.1963 -0.2004 0.1734
## veg_height-Vulpes_vulpes -0.0765 0.2820 -0.6447 -0.0710
## veg_height-Sus_scrofa -0.1895 0.3246 -0.8388 -0.1800
## week-Canis_latrans 41.5186 3208.1568 -1834.7629 0.2276
## week-Lynx_rufus 33.1075 3078.2721 -2077.7428 0.1331
## week-Didelphis_virginiana -8.3937 3114.5638 -1916.2258 0.1722
## week-Sylvilagus_floridanus -98.3492 3265.2607 -2375.9948 0.3785
## week-Sciurus_carolinensis 4.1582 3355.3828 -2460.2800 0.3049
## week-Vulpes_vulpes -40.4276 2653.6115 -1982.5373 0.1570
## week-Sus_scrofa -37.2644 2713.2923 -2008.5735 0.2514
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4159 1.0471 189
## (Intercept)-Lynx_rufus -4.8876 1.3173 95
## (Intercept)-Didelphis_virginiana -4.1524 1.0144 232
## (Intercept)-Sylvilagus_floridanus -4.4938 1.0340 256
## (Intercept)-Sciurus_carolinensis -4.0886 1.0673 263
## (Intercept)-Vulpes_vulpes -4.7162 1.1057 88
## (Intercept)-Sus_scrofa -4.2794 1.0240 301
## shrub_cover-Canis_latrans 0.0844 1.0393 188
## shrub_cover-Lynx_rufus 0.5315 1.1041 110
## shrub_cover-Didelphis_virginiana 1.6657 1.0600 176
## shrub_cover-Sylvilagus_floridanus 1.2658 1.0081 166
## shrub_cover-Sciurus_carolinensis 1.5248 1.0144 243
## shrub_cover-Vulpes_vulpes 1.2520 1.0104 109
## shrub_cover-Sus_scrofa 1.8957 1.0274 282
## veg_height-Canis_latrans -0.2408 1.0301 164
## veg_height-Lynx_rufus 0.5687 1.0940 179
## veg_height-Didelphis_virginiana 0.9775 1.0313 308
## veg_height-Sylvilagus_floridanus 0.6024 1.0891 219
## veg_height-Sciurus_carolinensis 0.5719 1.0412 281
## veg_height-Vulpes_vulpes 0.4470 1.1034 203
## veg_height-Sus_scrofa 0.4115 1.0200 370
## week-Canis_latrans 1900.8652 1.1092 5535
## week-Lynx_rufus 1938.1562 1.1409 6295
## week-Didelphis_virginiana 1960.5543 1.1220 11403
## week-Sylvilagus_floridanus 1643.1577 1.1085 1925
## week-Sciurus_carolinensis 1764.1106 1.1947 13385
## week-Vulpes_vulpes 1554.3025 1.1102 4367
## week-Sus_scrofa 1310.7602 1.1138 7169
#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): 4.7447
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5154 0.6212 -1.7092 -0.5274 0.7342 1.0495 202
## Avg_Cogongrass_Cover 0.1464 0.4849 -0.8279 0.1497 1.1296 1.0014 540
## total_shrub_cover -0.8272 0.6503 -2.2490 -0.7887 0.3319 1.0023 103
## avg_veg_height 0.1853 0.4532 -0.6659 0.1729 1.1150 1.0064 312
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2931 1.9654 0.0598 0.6692 6.2183 1.0478 476
## Avg_Cogongrass_Cover 0.7286 1.3105 0.0459 0.3377 3.5938 1.0907 737
## total_shrub_cover 1.7093 2.7747 0.0713 0.9041 8.5706 1.1066 245
## avg_veg_height 0.4007 0.6186 0.0376 0.2179 1.9444 1.0043 995
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7 2.8212 0.1519 1.8853 10.2193 1.0202 129
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0697 0.2872 -5.6105 -5.0816 -4.4557 1.0073 242
## shrub_cover 0.7435 0.4031 -0.0239 0.7334 1.5690 1.0030 211
## veg_height -0.0230 0.2375 -0.4954 -0.0247 0.4679 1.0471 724
## week 0.0085 1.5968 -3.1019 -0.0021 3.2449 1.0032 1151
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.124000e-01 5.570000e-01 0.0444 0.2500 1.812300e+00 1.0133 184
## shrub_cover 9.249000e-01 9.241000e-01 0.1455 0.6603 3.167000e+00 1.0135 230
## veg_height 3.153000e-01 3.101000e-01 0.0548 0.2265 1.104600e+00 1.0171 603
## week 2.554134e+18 3.595984e+19 0.1352 53385.4707 3.431258e+17 1.4792 349
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2471 0.9236 -1.3490 0.1652
## (Intercept)-Lynx_rufus -0.1388 0.8843 -1.7141 -0.2158
## (Intercept)-Didelphis_virginiana -0.7763 0.7796 -2.3826 -0.7628
## (Intercept)-Sylvilagus_floridanus -0.1663 0.8325 -1.6402 -0.2126
## (Intercept)-Sciurus_carolinensis -0.8330 0.8941 -2.7230 -0.8092
## (Intercept)-Vulpes_vulpes -1.0338 0.9498 -2.9627 -1.0136
## (Intercept)-Sus_scrofa -1.0811 1.0056 -3.1039 -1.0444
## Avg_Cogongrass_Cover-Canis_latrans 0.4724 0.6015 -0.5552 0.4223
## Avg_Cogongrass_Cover-Lynx_rufus 0.5697 0.6950 -0.5227 0.4724
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2914 0.5808 -0.8196 0.2750
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4174 0.6979 -1.9251 -0.3504
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0710 0.5851 -1.1918 0.0940
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2911 0.6661 -0.9585 0.2601
## Avg_Cogongrass_Cover-Sus_scrofa -0.1891 0.8743 -2.1854 -0.0959
## total_shrub_cover-Canis_latrans 0.4716 0.7927 -0.9237 0.3975
## total_shrub_cover-Lynx_rufus -1.4179 0.9904 -3.6420 -1.3234
## total_shrub_cover-Didelphis_virginiana -0.9325 0.7662 -2.7581 -0.8312
## total_shrub_cover-Sylvilagus_floridanus -1.5262 1.0173 -3.9197 -1.3884
## total_shrub_cover-Sciurus_carolinensis -1.0730 1.0069 -3.5565 -0.9092
## total_shrub_cover-Vulpes_vulpes -1.1373 1.2349 -4.2150 -0.9501
## total_shrub_cover-Sus_scrofa -0.7852 1.0697 -3.2170 -0.6632
## avg_veg_height-Canis_latrans 0.2066 0.5400 -0.8415 0.1880
## avg_veg_height-Lynx_rufus 0.0887 0.6783 -1.2502 0.0738
## avg_veg_height-Didelphis_virginiana 0.0514 0.5617 -1.0524 0.0393
## avg_veg_height-Sylvilagus_floridanus 0.0930 0.5686 -0.9835 0.0836
## avg_veg_height-Sciurus_carolinensis 0.5290 0.5913 -0.4815 0.4810
## avg_veg_height-Vulpes_vulpes 0.1233 0.6062 -1.0725 0.1239
## avg_veg_height-Sus_scrofa 0.2468 0.5989 -0.8815 0.2260
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3602 1.0315 266
## (Intercept)-Lynx_rufus 1.7660 1.0506 401
## (Intercept)-Didelphis_virginiana 0.7043 1.0164 293
## (Intercept)-Sylvilagus_floridanus 1.6813 1.0344 308
## (Intercept)-Sciurus_carolinensis 0.8736 1.0586 274
## (Intercept)-Vulpes_vulpes 0.7372 1.0439 219
## (Intercept)-Sus_scrofa 0.7518 1.0275 159
## Avg_Cogongrass_Cover-Canis_latrans 1.8058 1.0106 763
## Avg_Cogongrass_Cover-Lynx_rufus 2.2490 1.0092 807
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5040 1.0087 733
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7759 1.0099 477
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1703 1.0032 624
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6705 1.0042 628
## Avg_Cogongrass_Cover-Sus_scrofa 1.2947 1.0166 515
## total_shrub_cover-Canis_latrans 2.2691 1.0508 186
## total_shrub_cover-Lynx_rufus 0.2927 1.0464 205
## total_shrub_cover-Didelphis_virginiana 0.2829 1.0078 249
## total_shrub_cover-Sylvilagus_floridanus 0.0195 1.0308 175
## total_shrub_cover-Sciurus_carolinensis 0.4020 1.0388 93
## total_shrub_cover-Vulpes_vulpes 0.8039 1.0328 93
## total_shrub_cover-Sus_scrofa 0.9162 1.0371 121
## avg_veg_height-Canis_latrans 1.2936 1.0080 531
## avg_veg_height-Lynx_rufus 1.5242 1.0128 395
## avg_veg_height-Didelphis_virginiana 1.1782 1.0047 532
## avg_veg_height-Sylvilagus_floridanus 1.2535 1.0139 537
## avg_veg_height-Sciurus_carolinensis 1.8395 1.0077 373
## avg_veg_height-Vulpes_vulpes 1.3175 1.0063 489
## avg_veg_height-Sus_scrofa 1.5211 1.0092 551
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.839700e+00 1.989000e-01 -5.260800e+00
## (Intercept)-Lynx_rufus -5.452800e+00 2.868000e-01 -6.032300e+00
## (Intercept)-Didelphis_virginiana -4.814400e+00 2.860000e-01 -5.387500e+00
## (Intercept)-Sylvilagus_floridanus -5.092400e+00 2.200000e-01 -5.528600e+00
## (Intercept)-Sciurus_carolinensis -4.883700e+00 3.337000e-01 -5.575100e+00
## (Intercept)-Vulpes_vulpes -5.666600e+00 5.253000e-01 -6.776800e+00
## (Intercept)-Sus_scrofa -5.501200e+00 4.665000e-01 -6.492000e+00
## shrub_cover-Canis_latrans -2.762000e-01 2.393000e-01 -7.459000e-01
## shrub_cover-Lynx_rufus 2.107000e-01 3.957000e-01 -6.298000e-01
## shrub_cover-Didelphis_virginiana 1.344100e+00 4.139000e-01 5.644000e-01
## shrub_cover-Sylvilagus_floridanus 1.032200e+00 3.493000e-01 3.030000e-01
## shrub_cover-Sciurus_carolinensis 1.270300e+00 4.406000e-01 3.932000e-01
## shrub_cover-Vulpes_vulpes 5.680000e-01 6.770000e-01 -8.600000e-01
## shrub_cover-Sus_scrofa 1.338400e+00 7.763000e-01 -2.032000e-01
## veg_height-Canis_latrans -5.956000e-01 2.022000e-01 -1.008300e+00
## veg_height-Lynx_rufus 1.306000e-01 2.487000e-01 -3.862000e-01
## veg_height-Didelphis_virginiana 4.336000e-01 2.593000e-01 -4.600000e-02
## veg_height-Sylvilagus_floridanus 7.540000e-02 2.229000e-01 -3.192000e-01
## veg_height-Sciurus_carolinensis 2.400000e-01 2.273000e-01 -1.950000e-01
## veg_height-Vulpes_vulpes -1.510000e-01 3.304000e-01 -8.135000e-01
## veg_height-Sus_scrofa -2.769000e-01 3.006000e-01 -8.687000e-01
## week-Canis_latrans 4.017817e+06 1.437704e+09 -3.623834e+07
## week-Lynx_rufus 9.583324e+06 9.242833e+08 -3.997012e+07
## week-Didelphis_virginiana 1.835142e+07 1.942548e+09 -6.078007e+07
## week-Sylvilagus_floridanus -4.338462e+06 1.353906e+09 -2.478112e+07
## week-Sciurus_carolinensis 2.783318e+07 1.325134e+09 -3.629909e+07
## week-Vulpes_vulpes -1.296682e+07 1.515251e+09 -5.352396e+07
## week-Sus_scrofa -2.009308e+07 1.277625e+09 -6.092518e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8323 -4.480000e+00 1.0109 113
## (Intercept)-Lynx_rufus -5.4399 -4.930700e+00 1.0784 104
## (Intercept)-Didelphis_virginiana -4.8087 -4.283200e+00 1.0189 93
## (Intercept)-Sylvilagus_floridanus -5.0949 -4.657300e+00 1.0432 142
## (Intercept)-Sciurus_carolinensis -4.8772 -4.258100e+00 1.0295 69
## (Intercept)-Vulpes_vulpes -5.6074 -4.770900e+00 1.1240 73
## (Intercept)-Sus_scrofa -5.4879 -4.629600e+00 1.0214 103
## shrub_cover-Canis_latrans -0.2663 1.868000e-01 1.0876 133
## shrub_cover-Lynx_rufus 0.2213 9.419000e-01 1.0152 89
## shrub_cover-Didelphis_virginiana 1.3329 2.202600e+00 1.1071 67
## shrub_cover-Sylvilagus_floridanus 1.0486 1.718400e+00 1.1064 109
## shrub_cover-Sciurus_carolinensis 1.2707 2.144600e+00 1.0461 65
## shrub_cover-Vulpes_vulpes 0.5889 1.900300e+00 1.0813 94
## shrub_cover-Sus_scrofa 1.3275 2.853100e+00 1.0111 84
## veg_height-Canis_latrans -0.5847 -2.401000e-01 1.0183 145
## veg_height-Lynx_rufus 0.1379 5.876000e-01 1.0269 136
## veg_height-Didelphis_virginiana 0.4266 9.568000e-01 1.1502 205
## veg_height-Sylvilagus_floridanus 0.0598 5.503000e-01 1.3010 175
## veg_height-Sciurus_carolinensis 0.2442 6.918000e-01 1.0233 205
## veg_height-Vulpes_vulpes -0.1317 4.564000e-01 1.0077 130
## veg_height-Sus_scrofa -0.2767 3.107000e-01 1.1360 243
## week-Canis_latrans -0.2523 5.995525e+07 1.1597 7393
## week-Lynx_rufus -0.2442 5.142505e+07 1.1490 6977
## week-Didelphis_virginiana -0.2944 4.430025e+07 1.2286 5131
## week-Sylvilagus_floridanus -0.2448 1.043607e+08 1.1845 26393
## week-Sciurus_carolinensis 0.1852 7.956045e+07 1.2474 5128
## week-Vulpes_vulpes -0.1527 3.810721e+07 1.2630 6960
## week-Sus_scrofa -0.1740 3.485685e+07 1.1437 1990
#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): 4.197
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9831 0.6046 -2.1013 -1.0030 0.2429 1.0187 477
## Tree_Density -0.7271 0.5259 -1.8461 -0.6946 0.1964 1.0651 600
## Avg_Canopy_Cover 1.1923 0.5797 0.0866 1.1711 2.4130 1.0039 727
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3612 3.3906 0.1532 1.4827 9.0666 1.1057 652
## Tree_Density 1.0868 1.9078 0.0562 0.5183 5.4849 1.0602 663
## Avg_Canopy_Cover 1.8698 2.5547 0.1273 1.1381 7.9508 1.0197 778
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7775 0.9205 0.046 0.4463 3.4315 1.0651 171
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9591 0.3283 -5.4923 -4.9871 -4.2718 1.0408 545
## shrub_cover 0.4468 0.3648 -0.2499 0.4432 1.1886 1.0052 447
## veg_height 0.0400 0.2299 -0.4320 0.0408 0.4947 1.0064 715
## week 0.0483 1.6369 -3.1662 0.0526 3.2626 1.0272 606
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.968000e-01 1.067000e+00 0.0528 0.2662 2.378100e+00 1.0865 149
## shrub_cover 7.893000e-01 8.215000e-01 0.1395 0.5537 2.864400e+00 1.0993 477
## veg_height 3.037000e-01 3.127000e-01 0.0619 0.2232 9.972000e-01 1.0220 937
## week 1.563828e+13 1.927596e+14 0.1248 425.4364 4.529287e+13 1.8338 112
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2309 0.7240 -1.2443 0.2335 1.6409
## (Intercept)-Lynx_rufus 0.2673 1.1697 -1.4926 0.1066 3.2733
## (Intercept)-Didelphis_virginiana -1.5750 0.7042 -3.0108 -1.5472 -0.2463
## (Intercept)-Sylvilagus_floridanus -0.8414 0.6843 -2.1669 -0.8371 0.4859
## (Intercept)-Sciurus_carolinensis -1.6868 0.7651 -3.2972 -1.6444 -0.3559
## (Intercept)-Vulpes_vulpes -1.8611 0.9489 -3.8373 -1.8187 -0.0118
## (Intercept)-Sus_scrofa -2.2282 0.9580 -4.3622 -2.1518 -0.5886
## Tree_Density-Canis_latrans -1.0198 0.6490 -2.4909 -0.9514 0.0439
## Tree_Density-Lynx_rufus 0.1569 0.7809 -1.0732 0.0348 2.0831
## Tree_Density-Didelphis_virginiana -1.0077 0.8297 -3.0720 -0.8814 0.2549
## Tree_Density-Sylvilagus_floridanus -1.0845 0.8359 -3.0125 -0.9669 0.2387
## Tree_Density-Sciurus_carolinensis -0.8818 0.7788 -2.7133 -0.8018 0.4460
## Tree_Density-Vulpes_vulpes -0.6177 0.8141 -2.3294 -0.5923 0.9082
## Tree_Density-Sus_scrofa -0.9729 0.9154 -3.2135 -0.8642 0.4707
## Avg_Canopy_Cover-Canis_latrans -0.1431 0.5056 -1.1117 -0.1498 0.8512
## Avg_Canopy_Cover-Lynx_rufus 0.8285 0.9376 -0.7230 0.7179 3.0471
## Avg_Canopy_Cover-Didelphis_virginiana 1.7605 0.8015 0.5422 1.6479 3.7127
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4368 1.1727 0.7854 2.2221 5.3534
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6592 0.7479 0.4842 1.5613 3.3406
## Avg_Canopy_Cover-Vulpes_vulpes 1.1518 0.7355 -0.0608 1.0677 2.8300
## Avg_Canopy_Cover-Sus_scrofa 1.4406 0.7450 0.2471 1.3424 3.1755
## Rhat ESS
## (Intercept)-Canis_latrans 1.0482 533
## (Intercept)-Lynx_rufus 1.0306 177
## (Intercept)-Didelphis_virginiana 1.0136 739
## (Intercept)-Sylvilagus_floridanus 1.0222 893
## (Intercept)-Sciurus_carolinensis 1.0068 533
## (Intercept)-Vulpes_vulpes 1.0287 257
## (Intercept)-Sus_scrofa 1.0558 621
## Tree_Density-Canis_latrans 1.0167 896
## Tree_Density-Lynx_rufus 1.0052 607
## Tree_Density-Didelphis_virginiana 1.0256 565
## Tree_Density-Sylvilagus_floridanus 1.0232 598
## Tree_Density-Sciurus_carolinensis 1.0588 584
## Tree_Density-Vulpes_vulpes 1.0230 616
## Tree_Density-Sus_scrofa 1.0636 509
## Avg_Canopy_Cover-Canis_latrans 1.0101 1345
## Avg_Canopy_Cover-Lynx_rufus 1.0203 193
## Avg_Canopy_Cover-Didelphis_virginiana 1.0051 622
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0036 396
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0193 471
## Avg_Canopy_Cover-Vulpes_vulpes 1.0070 709
## Avg_Canopy_Cover-Sus_scrofa 1.0032 687
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8187 0.1773 -5.1790 -4.8122
## (Intercept)-Lynx_rufus -5.5507 0.3079 -6.1627 -5.5314
## (Intercept)-Didelphis_virginiana -4.7244 0.2576 -5.2243 -4.7151
## (Intercept)-Sylvilagus_floridanus -4.9587 0.2204 -5.4279 -4.9558
## (Intercept)-Sciurus_carolinensis -4.7303 0.3019 -5.3039 -4.7335
## (Intercept)-Vulpes_vulpes -5.6284 0.5855 -7.3836 -5.5400
## (Intercept)-Sus_scrofa -5.1994 0.4356 -6.1688 -5.1731
## shrub_cover-Canis_latrans -0.2895 0.2055 -0.6902 -0.2909
## shrub_cover-Lynx_rufus -0.2396 0.3292 -0.8956 -0.2417
## shrub_cover-Didelphis_virginiana 1.1131 0.3445 0.4928 1.0894
## shrub_cover-Sylvilagus_floridanus 0.6563 0.3529 -0.0379 0.6589
## shrub_cover-Sciurus_carolinensis 0.9463 0.3676 0.2379 0.9278
## shrub_cover-Vulpes_vulpes 0.1738 0.6594 -1.0997 0.1838
## shrub_cover-Sus_scrofa 0.8218 0.7274 -0.5463 0.7903
## veg_height-Canis_latrans -0.5646 0.1854 -0.9572 -0.5634
## veg_height-Lynx_rufus 0.1959 0.2312 -0.2495 0.2001
## veg_height-Didelphis_virginiana 0.5453 0.2404 0.0900 0.5371
## veg_height-Sylvilagus_floridanus 0.1801 0.2224 -0.2528 0.1822
## veg_height-Sciurus_carolinensis 0.2303 0.2225 -0.1705 0.2182
## veg_height-Vulpes_vulpes -0.1124 0.2944 -0.7375 -0.1004
## veg_height-Sus_scrofa -0.1667 0.3252 -0.8183 -0.1617
## week-Canis_latrans 145416.1097 4275651.5323 -547691.8157 0.0008
## week-Lynx_rufus 69085.4427 2773435.4292 -345144.3568 -0.0644
## week-Didelphis_virginiana 48618.8842 3282425.4328 -556969.6843 -0.2052
## week-Sylvilagus_floridanus -65540.8165 4275235.5467 -562826.6959 0.1656
## week-Sciurus_carolinensis -16300.8518 4684741.4237 -632655.4431 0.0504
## week-Vulpes_vulpes -30844.8209 2447003.5419 -786846.5035 -0.0310
## week-Sus_scrofa -3548.4153 3620667.5850 -359625.6200 0.1817
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4771 1.0032 164
## (Intercept)-Lynx_rufus -5.0063 1.0068 78
## (Intercept)-Didelphis_virginiana -4.2268 1.0691 183
## (Intercept)-Sylvilagus_floridanus -4.5384 1.0914 222
## (Intercept)-Sciurus_carolinensis -4.1305 1.0385 192
## (Intercept)-Vulpes_vulpes -4.7663 1.0090 84
## (Intercept)-Sus_scrofa -4.3986 1.0520 302
## shrub_cover-Canis_latrans 0.0978 1.0019 192
## shrub_cover-Lynx_rufus 0.4121 1.0215 103
## shrub_cover-Didelphis_virginiana 1.8599 1.0255 196
## shrub_cover-Sylvilagus_floridanus 1.3848 1.0450 164
## shrub_cover-Sciurus_carolinensis 1.6637 1.0274 195
## shrub_cover-Vulpes_vulpes 1.4858 1.0401 143
## shrub_cover-Sus_scrofa 2.3521 1.0497 283
## veg_height-Canis_latrans -0.2053 1.0226 176
## veg_height-Lynx_rufus 0.6423 1.0235 158
## veg_height-Didelphis_virginiana 1.0263 1.0584 254
## veg_height-Sylvilagus_floridanus 0.6056 1.0174 265
## veg_height-Sciurus_carolinensis 0.7244 1.0527 231
## veg_height-Vulpes_vulpes 0.4373 1.0353 181
## veg_height-Sus_scrofa 0.4403 1.0062 352
## week-Canis_latrans 441844.6349 1.4003 1097
## week-Lynx_rufus 505249.7957 1.3613 1723
## week-Didelphis_virginiana 605142.4570 1.3139 2269
## week-Sylvilagus_floridanus 432176.3868 1.3119 8772
## week-Sciurus_carolinensis 662857.3335 1.2924 11302
## week-Vulpes_vulpes 676224.1710 1.3071 2727
## week-Sus_scrofa 681262.0747 1.2907 34884
#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): 4.4255
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7457 0.6553 -1.9793 -0.7521 0.6004 1.0311 280
## Cogon_Patch_Size -0.4280 0.7185 -1.9764 -0.3897 0.8933 1.0028 812
## Avg_Cogongrass_Cover 0.4222 0.4355 -0.4271 0.4214 1.2986 1.0473 622
## total_shrub_cover -0.5939 0.6033 -1.9876 -0.5271 0.5114 1.0051 160
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8251 2.8216 0.0753 0.9500 9.1721 1.0112 302
## Cogon_Patch_Size 2.8984 5.8148 0.1039 1.4338 13.7612 1.1060 539
## Avg_Cogongrass_Cover 0.6011 1.1061 0.0454 0.2858 2.9755 1.0397 912
## total_shrub_cover 1.2002 1.8975 0.0671 0.6165 6.1702 1.0196 337
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1054 2.8498 0.2933 2.3628 11.2577 1.1656 204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0053 0.3007 -5.5100 -5.0189 -4.4435 1.0185 455
## shrub_cover 0.6600 0.3882 -0.1094 0.6610 1.4214 1.0154 180
## veg_height 0.0008 0.2278 -0.4456 -0.0024 0.4586 1.0074 803
## week -0.0466 1.6922 -3.2946 -0.0134 3.2612 1.0087 924
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.956000e-01 8.018000e-01 0.0455 0.2238 1.581900e+00 1.1775 282
## shrub_cover 7.668000e-01 8.045000e-01 0.1218 0.5465 2.688400e+00 1.0371 514
## veg_height 3.035000e-01 3.111000e-01 0.0582 0.2196 1.119700e+00 1.0091 1080
## week 5.873934e+12 1.094998e+14 0.1217 331.1418 2.240862e+10 1.5515 317
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2927 1.0456 -1.4489 0.1682
## (Intercept)-Lynx_rufus -0.2782 1.0159 -2.0330 -0.3738
## (Intercept)-Didelphis_virginiana -0.9676 0.9052 -2.7011 -0.9723
## (Intercept)-Sylvilagus_floridanus -0.5663 0.9395 -2.4446 -0.5729
## (Intercept)-Sciurus_carolinensis -1.2011 0.8965 -3.0796 -1.1721
## (Intercept)-Vulpes_vulpes -1.3840 1.0719 -3.6054 -1.3253
## (Intercept)-Sus_scrofa -1.5564 1.1016 -4.1478 -1.4496
## Cogon_Patch_Size-Canis_latrans 0.8930 1.0331 -0.5495 0.6938
## Cogon_Patch_Size-Lynx_rufus -0.5010 1.0434 -2.3803 -0.5584
## Cogon_Patch_Size-Didelphis_virginiana 0.7006 0.6149 -0.3918 0.6636
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5930 1.4029 -5.1379 -1.3029
## Cogon_Patch_Size-Sciurus_carolinensis -1.2655 1.1375 -3.9805 -1.0486
## Cogon_Patch_Size-Vulpes_vulpes -1.0296 1.3140 -4.0929 -0.8031
## Cogon_Patch_Size-Sus_scrofa -0.7974 1.2121 -3.8749 -0.6355
## Avg_Cogongrass_Cover-Canis_latrans 0.5206 0.5164 -0.4017 0.4807
## Avg_Cogongrass_Cover-Lynx_rufus 0.8409 0.7444 -0.2804 0.7250
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2744 0.5372 -0.8862 0.2894
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0459 0.6288 -1.2493 0.0714
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6236 0.5401 -0.3727 0.5978
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5633 0.6200 -0.4969 0.5107
## Avg_Cogongrass_Cover-Sus_scrofa 0.1592 0.7543 -1.5550 0.2132
## total_shrub_cover-Canis_latrans 0.3778 0.6543 -0.7104 0.3059
## total_shrub_cover-Lynx_rufus -1.1499 1.0181 -3.6402 -0.9885
## total_shrub_cover-Didelphis_virginiana -0.8813 0.7717 -2.7027 -0.7650
## total_shrub_cover-Sylvilagus_floridanus -1.1137 0.9932 -3.6586 -0.9402
## total_shrub_cover-Sciurus_carolinensis -0.5915 0.8092 -2.5453 -0.4931
## total_shrub_cover-Vulpes_vulpes -0.8183 0.9777 -3.0881 -0.7035
## total_shrub_cover-Sus_scrofa -0.4029 0.8607 -2.3481 -0.3620
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5727 1.0107 267
## (Intercept)-Lynx_rufus 2.0563 1.1415 299
## (Intercept)-Didelphis_virginiana 0.8403 1.0289 329
## (Intercept)-Sylvilagus_floridanus 1.3439 1.0307 287
## (Intercept)-Sciurus_carolinensis 0.4691 1.0168 252
## (Intercept)-Vulpes_vulpes 0.6214 1.0413 171
## (Intercept)-Sus_scrofa 0.3709 1.0049 230
## Cogon_Patch_Size-Canis_latrans 3.5393 1.0029 691
## Cogon_Patch_Size-Lynx_rufus 1.7026 1.0185 550
## Cogon_Patch_Size-Didelphis_virginiana 1.9912 1.0075 690
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2366 1.0034 341
## Cogon_Patch_Size-Sciurus_carolinensis 0.3297 1.0119 348
## Cogon_Patch_Size-Vulpes_vulpes 0.9009 1.0419 319
## Cogon_Patch_Size-Sus_scrofa 1.1095 1.0169 572
## Avg_Cogongrass_Cover-Canis_latrans 1.6657 1.0436 910
## Avg_Cogongrass_Cover-Lynx_rufus 2.7010 1.0788 668
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3042 1.0111 748
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2471 1.0271 718
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8002 1.0657 668
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.9860 1.0312 900
## Avg_Cogongrass_Cover-Sus_scrofa 1.4931 1.0296 606
## total_shrub_cover-Canis_latrans 1.9002 1.0023 507
## total_shrub_cover-Lynx_rufus 0.4496 1.0119 195
## total_shrub_cover-Didelphis_virginiana 0.3048 1.0146 195
## total_shrub_cover-Sylvilagus_floridanus 0.3575 1.0063 105
## total_shrub_cover-Sciurus_carolinensis 0.7203 1.0049 164
## total_shrub_cover-Vulpes_vulpes 0.7943 1.0188 132
## total_shrub_cover-Sus_scrofa 1.1060 1.0240 401
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7925 0.1696 -5.1266 -4.7912
## (Intercept)-Lynx_rufus -5.3627 0.2629 -5.9558 -5.3445
## (Intercept)-Didelphis_virginiana -4.7940 0.2795 -5.3755 -4.7794
## (Intercept)-Sylvilagus_floridanus -5.0552 0.2361 -5.5358 -5.0487
## (Intercept)-Sciurus_carolinensis -4.8197 0.3138 -5.4439 -4.8244
## (Intercept)-Vulpes_vulpes -5.5759 0.5311 -6.7524 -5.5236
## (Intercept)-Sus_scrofa -5.3503 0.4238 -6.2454 -5.3295
## shrub_cover-Canis_latrans -0.2688 0.2132 -0.6624 -0.2732
## shrub_cover-Lynx_rufus 0.2258 0.3261 -0.4390 0.2226
## shrub_cover-Didelphis_virginiana 1.2679 0.3947 0.5122 1.2560
## shrub_cover-Sylvilagus_floridanus 0.9185 0.3764 0.1072 0.9163
## shrub_cover-Sciurus_carolinensis 1.1221 0.4324 0.3196 1.0962
## shrub_cover-Vulpes_vulpes 0.5178 0.6013 -0.7470 0.5262
## shrub_cover-Sus_scrofa 1.0450 0.7431 -0.5291 1.0473
## veg_height-Canis_latrans -0.5680 0.1778 -0.8918 -0.5641
## veg_height-Lynx_rufus 0.1157 0.2289 -0.3474 0.1209
## veg_height-Didelphis_virginiana 0.4759 0.2527 0.0262 0.4602
## veg_height-Sylvilagus_floridanus 0.0708 0.2266 -0.3562 0.0688
## veg_height-Sciurus_carolinensis 0.2430 0.2222 -0.1731 0.2361
## veg_height-Vulpes_vulpes -0.0904 0.3137 -0.7355 -0.0851
## veg_height-Sus_scrofa -0.2599 0.3288 -0.9251 -0.2437
## week-Canis_latrans -34062.0585 1903555.1381 -11674.3892 -0.2315
## week-Lynx_rufus -38337.9000 2044133.2228 -11542.0220 -0.1602
## week-Didelphis_virginiana 14900.0536 2682085.9766 -14609.5779 -0.2280
## week-Sylvilagus_floridanus -30410.6748 2238751.7000 -14166.9509 -0.1577
## week-Sciurus_carolinensis 4020.0989 2246093.1722 -8785.8608 -0.0129
## week-Vulpes_vulpes -66086.4164 2798390.1836 -12429.6141 0.0131
## week-Sus_scrofa 2842.8400 2265187.9088 -12449.1591 0.0453
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4606 1.0150 182
## (Intercept)-Lynx_rufus -4.8900 1.1646 141
## (Intercept)-Didelphis_virginiana -4.2895 1.0536 145
## (Intercept)-Sylvilagus_floridanus -4.6076 1.0321 137
## (Intercept)-Sciurus_carolinensis -4.2259 1.0207 111
## (Intercept)-Vulpes_vulpes -4.6837 1.1030 62
## (Intercept)-Sus_scrofa -4.5520 1.0396 142
## shrub_cover-Canis_latrans 0.1718 1.0506 158
## shrub_cover-Lynx_rufus 0.8329 1.0314 132
## shrub_cover-Didelphis_virginiana 2.1204 1.0996 109
## shrub_cover-Sylvilagus_floridanus 1.6395 1.0225 83
## shrub_cover-Sciurus_carolinensis 1.9755 1.0056 100
## shrub_cover-Vulpes_vulpes 1.6734 1.0060 140
## shrub_cover-Sus_scrofa 2.5142 1.1244 131
## veg_height-Canis_latrans -0.2240 1.0239 170
## veg_height-Lynx_rufus 0.5556 1.0004 169
## veg_height-Didelphis_virginiana 1.0215 1.0511 202
## veg_height-Sylvilagus_floridanus 0.5331 1.0859 187
## veg_height-Sciurus_carolinensis 0.7044 1.0115 160
## veg_height-Vulpes_vulpes 0.5117 1.0394 163
## veg_height-Sus_scrofa 0.3476 1.0459 296
## week-Canis_latrans 12867.7248 1.3223 2545
## week-Lynx_rufus 11718.7948 1.3250 3159
## week-Didelphis_virginiana 12276.0328 1.2934 19409
## week-Sylvilagus_floridanus 13492.8703 1.3088 7775
## week-Sciurus_carolinensis 17278.9020 1.2906 31998
## week-Vulpes_vulpes 14098.6063 1.3452 1939
## week-Sus_scrofa 14395.9708 1.2905 116498
#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): 3.9612
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9271 0.4895 -1.8895 -0.9203 0.0294 1.0092 733
## Veg_shannon_index 0.2813 0.3921 -0.4859 0.2747 1.0994 1.0003 1393
## Avg_Cogongrass_Cover 0.3460 0.3323 -0.3185 0.3452 1.0089 1.0003 984
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1759 1.5312 0.0771 0.7471 5.0651 1.0251 917
## Veg_shannon_index 0.7286 0.9853 0.0638 0.4138 3.2680 1.0123 886
## Avg_Cogongrass_Cover 0.4385 0.6633 0.0404 0.2503 1.9760 1.0399 1314
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1368 1.187 0.081 0.7956 4.4222 1.0852 228
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8938 0.3189 -5.4555 -4.9034 -4.2842 1.0161 457
## shrub_cover 0.4007 0.3426 -0.2518 0.3880 1.1130 1.0354 452
## veg_height -0.0015 0.2238 -0.4397 -0.0001 0.4318 1.0041 839
## week -0.0573 1.6209 -3.2984 -0.0203 2.9936 1.0083 667
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.428000e-01 7.994000e-01 0.0458 0.2401 2.123500e+00 1.0156 233
## shrub_cover 6.385000e-01 6.583000e-01 0.1058 0.4484 2.329200e+00 1.0646 315
## veg_height 2.910000e-01 3.209000e-01 0.0568 0.1997 1.077300e+00 1.0179 829
## week 1.341414e+13 2.478292e+14 0.1002 1873.7835 6.455954e+12 1.2401 652
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0125 0.6992 -1.3276 0.0016
## (Intercept)-Lynx_rufus -0.4045 0.7367 -1.8000 -0.4341
## (Intercept)-Didelphis_virginiana -1.2495 0.6108 -2.5623 -1.2312
## (Intercept)-Sylvilagus_floridanus -0.7718 0.5930 -1.9117 -0.7813
## (Intercept)-Sciurus_carolinensis -1.2915 0.6114 -2.5836 -1.2628
## (Intercept)-Vulpes_vulpes -1.4777 0.7827 -3.1776 -1.4172
## (Intercept)-Sus_scrofa -1.7171 0.8210 -3.5435 -1.6344
## Veg_shannon_index-Canis_latrans 0.7886 0.4851 -0.0821 0.7490
## Veg_shannon_index-Lynx_rufus -0.2236 0.6437 -1.7022 -0.1567
## Veg_shannon_index-Didelphis_virginiana 0.5285 0.4744 -0.3181 0.4916
## Veg_shannon_index-Sylvilagus_floridanus 0.4173 0.4822 -0.5042 0.4020
## Veg_shannon_index-Sciurus_carolinensis -0.1891 0.4721 -1.1539 -0.1709
## Veg_shannon_index-Vulpes_vulpes -0.1190 0.5386 -1.2496 -0.0969
## Veg_shannon_index-Sus_scrofa 0.8083 0.6843 -0.2977 0.7171
## Avg_Cogongrass_Cover-Canis_latrans 0.6727 0.4612 -0.0923 0.6197
## Avg_Cogongrass_Cover-Lynx_rufus 0.5876 0.4739 -0.2127 0.5516
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4828 0.4251 -0.3140 0.4660
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1411 0.4862 -1.2121 -0.1016
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4244 0.3898 -0.3402 0.4160
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3773 0.4868 -0.5456 0.3627
## Avg_Cogongrass_Cover-Sus_scrofa 0.0355 0.5887 -1.2979 0.0801
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3830 1.0164 476
## (Intercept)-Lynx_rufus 1.2077 1.0213 426
## (Intercept)-Didelphis_virginiana -0.0949 1.0122 769
## (Intercept)-Sylvilagus_floridanus 0.3771 1.0059 805
## (Intercept)-Sciurus_carolinensis -0.1065 1.0210 921
## (Intercept)-Vulpes_vulpes -0.0446 1.0266 428
## (Intercept)-Sus_scrofa -0.3353 1.0254 472
## Veg_shannon_index-Canis_latrans 1.8304 1.0039 1313
## Veg_shannon_index-Lynx_rufus 0.8628 1.0065 855
## Veg_shannon_index-Didelphis_virginiana 1.5673 1.0041 1978
## Veg_shannon_index-Sylvilagus_floridanus 1.4044 1.0028 1440
## Veg_shannon_index-Sciurus_carolinensis 0.6889 1.0038 1394
## Veg_shannon_index-Vulpes_vulpes 0.8513 1.0025 1223
## Veg_shannon_index-Sus_scrofa 2.4480 1.0024 756
## Avg_Cogongrass_Cover-Canis_latrans 1.7402 1.0132 1048
## Avg_Cogongrass_Cover-Lynx_rufus 1.6823 1.0040 1030
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3529 1.0025 1426
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6952 1.0077 1257
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2281 1.0023 2141
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4134 1.0088 1169
## Avg_Cogongrass_Cover-Sus_scrofa 1.0626 1.0043 974
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7611 0.1719 -5.1133 -4.7499
## (Intercept)-Lynx_rufus -5.3952 0.3186 -6.0897 -5.3687
## (Intercept)-Didelphis_virginiana -4.6698 0.2594 -5.1852 -4.6700
## (Intercept)-Sylvilagus_floridanus -4.9236 0.2479 -5.4846 -4.9060
## (Intercept)-Sciurus_carolinensis -4.6163 0.2711 -5.1371 -4.6095
## (Intercept)-Vulpes_vulpes -5.4893 0.5468 -6.8121 -5.3960
## (Intercept)-Sus_scrofa -5.1483 0.4579 -6.2129 -5.1180
## shrub_cover-Canis_latrans -0.2393 0.2034 -0.6347 -0.2426
## shrub_cover-Lynx_rufus -0.1011 0.3250 -0.7766 -0.0873
## shrub_cover-Didelphis_virginiana 1.0568 0.3400 0.4623 1.0320
## shrub_cover-Sylvilagus_floridanus 0.4586 0.3867 -0.2824 0.4587
## shrub_cover-Sciurus_carolinensis 0.8597 0.3693 0.1495 0.8586
## shrub_cover-Vulpes_vulpes 0.0785 0.5965 -1.1685 0.1067
## shrub_cover-Sus_scrofa 0.7336 0.6906 -0.5624 0.7044
## veg_height-Canis_latrans -0.5608 0.1827 -0.9231 -0.5604
## veg_height-Lynx_rufus 0.0994 0.2359 -0.3580 0.0979
## veg_height-Didelphis_virginiana 0.4707 0.2346 0.0455 0.4646
## veg_height-Sylvilagus_floridanus 0.1680 0.2154 -0.2629 0.1640
## veg_height-Sciurus_carolinensis 0.1674 0.2038 -0.2204 0.1694
## veg_height-Vulpes_vulpes -0.1357 0.2939 -0.7324 -0.1235
## veg_height-Sus_scrofa -0.1960 0.3216 -0.8699 -0.1875
## week-Canis_latrans 481.0457 3551784.9154 -582576.6923 -0.0853
## week-Lynx_rufus -99653.8116 3957571.4446 -455090.5164 -0.0083
## week-Didelphis_virginiana -42495.5533 3154455.0966 -488061.8565 0.0883
## week-Sylvilagus_floridanus 37796.3329 3039656.4440 -441729.0546 -0.1737
## week-Sciurus_carolinensis 2000.2520 3809731.4907 -392880.2993 -0.0757
## week-Vulpes_vulpes -45947.9416 3789850.2224 -462440.4440 0.0282
## week-Sus_scrofa 15474.2543 2248181.0296 -408352.2742 -0.0550
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4491 1.0138 206
## (Intercept)-Lynx_rufus -4.8455 1.0020 82
## (Intercept)-Didelphis_virginiana -4.1450 1.0357 212
## (Intercept)-Sylvilagus_floridanus -4.4875 1.0500 168
## (Intercept)-Sciurus_carolinensis -4.0973 1.0252 253
## (Intercept)-Vulpes_vulpes -4.6595 1.0552 65
## (Intercept)-Sus_scrofa -4.3342 1.0579 183
## shrub_cover-Canis_latrans 0.1597 1.0094 192
## shrub_cover-Lynx_rufus 0.4901 1.1095 93
## shrub_cover-Didelphis_virginiana 1.8213 1.0852 200
## shrub_cover-Sylvilagus_floridanus 1.2335 1.0553 146
## shrub_cover-Sciurus_carolinensis 1.5900 1.0440 233
## shrub_cover-Vulpes_vulpes 1.2098 1.0729 182
## shrub_cover-Sus_scrofa 2.1823 1.0466 240
## veg_height-Canis_latrans -0.2190 1.0225 181
## veg_height-Lynx_rufus 0.5540 1.0147 173
## veg_height-Didelphis_virginiana 0.9544 1.0062 268
## veg_height-Sylvilagus_floridanus 0.6000 1.0616 224
## veg_height-Sciurus_carolinensis 0.5674 1.0008 307
## veg_height-Vulpes_vulpes 0.4077 1.0696 187
## veg_height-Sus_scrofa 0.4113 1.0069 363
## week-Canis_latrans 408290.3060 1.1398 20829
## week-Lynx_rufus 554297.0854 1.1968 2102
## week-Didelphis_virginiana 503730.5228 1.1583 4759
## week-Sylvilagus_floridanus 631064.2481 1.1669 12938
## week-Sciurus_carolinensis 601829.0061 1.1314 28018
## week-Vulpes_vulpes 480590.9794 1.2453 6467
## week-Sus_scrofa 519019.2037 1.1163 2569
#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): 3.9608
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6526 0.5453 -2.7261 -1.6472 -0.5506 1.0264 558
## Avg_Cogongrass_Cover -0.7186 0.4925 -1.7518 -0.7045 0.2006 1.0217 389
## I(Avg_Cogongrass_Cover^2) 0.8968 0.4052 0.1326 0.8822 1.7183 1.0248 513
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1598 1.4522 0.0741 0.7026 5.1071 1.0221 777
## Avg_Cogongrass_Cover 0.6792 1.6772 0.0439 0.3408 3.2459 1.1483 1829
## I(Avg_Cogongrass_Cover^2) 0.5343 0.8070 0.0408 0.2679 2.7373 1.0305 577
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0157 1.1275 0.0664 0.664 4.0231 1.0405 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9110 0.2910 -5.4238 -4.9273 -4.3495 1.0049 649
## shrub_cover 0.4027 0.3499 -0.3050 0.4009 1.1162 1.0051 451
## veg_height 0.0033 0.2275 -0.4532 0.0034 0.4685 1.0213 542
## week -0.0586 1.6870 -3.5822 -0.0035 3.1603 1.0030 408
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.777000e-01 7.301000e-01 0.0434 0.2165 1.504300e+00 1.0398 457
## shrub_cover 6.655000e-01 6.999000e-01 0.1068 0.4704 2.435400e+00 1.0329 641
## veg_height 2.903000e-01 2.803000e-01 0.0565 0.2114 1.052200e+00 1.0565 674
## week 3.412333e+10 3.968789e+11 0.0812 24.8806 1.481696e+10 1.8915 214
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7571 0.7624 -2.2079 -0.7904
## (Intercept)-Lynx_rufus -1.2947 0.7401 -2.7017 -1.2877
## (Intercept)-Didelphis_virginiana -1.8808 0.6856 -3.3654 -1.8456
## (Intercept)-Sylvilagus_floridanus -1.5027 0.6856 -2.8971 -1.5116
## (Intercept)-Sciurus_carolinensis -2.1798 0.7694 -3.8509 -2.1271
## (Intercept)-Vulpes_vulpes -2.3562 0.8730 -4.3036 -2.2847
## (Intercept)-Sus_scrofa -2.2902 0.8382 -4.1870 -2.2208
## Avg_Cogongrass_Cover-Canis_latrans -0.3400 0.6212 -1.4798 -0.3756
## Avg_Cogongrass_Cover-Lynx_rufus -0.5539 0.6847 -1.8915 -0.5811
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4551 0.6290 -1.6266 -0.4753
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3005 0.7028 -2.8737 -1.2320
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8123 0.6375 -2.1740 -0.7982
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7552 0.6892 -2.1524 -0.7293
## Avg_Cogongrass_Cover-Sus_scrofa -1.0108 0.7555 -2.6999 -0.9648
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3962 0.8057 0.2299 1.2414
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2086 0.5605 0.2920 1.1398
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6498 0.4786 -0.2270 0.6166
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8196 0.4538 -0.0188 0.8047
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0152 0.4559 0.2184 0.9776
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9081 0.4991 0.0633 0.8595
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4638 0.7001 -1.2180 0.5212
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7913 1.0076 593
## (Intercept)-Lynx_rufus 0.2156 1.0124 473
## (Intercept)-Didelphis_virginiana -0.5904 1.0075 686
## (Intercept)-Sylvilagus_floridanus -0.1552 1.0429 603
## (Intercept)-Sciurus_carolinensis -0.8451 1.0113 641
## (Intercept)-Vulpes_vulpes -0.7795 1.0241 441
## (Intercept)-Sus_scrofa -0.8380 1.0455 524
## Avg_Cogongrass_Cover-Canis_latrans 0.9792 1.0095 782
## Avg_Cogongrass_Cover-Lynx_rufus 0.8466 1.0058 602
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8919 1.0084 828
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1209 1.0137 639
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3781 1.0079 697
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5609 1.0261 589
## Avg_Cogongrass_Cover-Sus_scrofa 0.3557 1.0106 497
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4623 1.0264 393
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5469 1.0003 553
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6745 1.0422 507
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7603 1.0389 635
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0544 1.0228 641
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0644 1.0037 529
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.6746 1.0419 368
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7832 0.1657 -5.1167 -4.7760
## (Intercept)-Lynx_rufus -5.3716 0.2843 -5.9745 -5.3675
## (Intercept)-Didelphis_virginiana -4.7160 0.2627 -5.2408 -4.7173
## (Intercept)-Sylvilagus_floridanus -4.9231 0.2207 -5.3662 -4.9166
## (Intercept)-Sciurus_carolinensis -4.6576 0.2721 -5.2015 -4.6564
## (Intercept)-Vulpes_vulpes -5.4420 0.4953 -6.5697 -5.3605
## (Intercept)-Sus_scrofa -5.1447 0.4186 -6.0201 -5.1261
## shrub_cover-Canis_latrans -0.2426 0.1870 -0.6010 -0.2429
## shrub_cover-Lynx_rufus -0.0905 0.3378 -0.8145 -0.0683
## shrub_cover-Didelphis_virginiana 1.0824 0.3617 0.4396 1.0686
## shrub_cover-Sylvilagus_floridanus 0.5087 0.3839 -0.2223 0.4953
## shrub_cover-Sciurus_carolinensis 0.8786 0.3689 0.1667 0.8645
## shrub_cover-Vulpes_vulpes 0.1130 0.5846 -1.0535 0.1061
## shrub_cover-Sus_scrofa 0.7077 0.6912 -0.6692 0.6936
## veg_height-Canis_latrans -0.5668 0.1670 -0.9050 -0.5625
## veg_height-Lynx_rufus 0.1394 0.2366 -0.3133 0.1449
## veg_height-Didelphis_virginiana 0.4817 0.2484 0.0088 0.4791
## veg_height-Sylvilagus_floridanus 0.1699 0.2188 -0.2798 0.1736
## veg_height-Sciurus_carolinensis 0.1819 0.2053 -0.2084 0.1781
## veg_height-Vulpes_vulpes -0.0986 0.2945 -0.6592 -0.0975
## veg_height-Sus_scrofa -0.2211 0.3222 -0.8603 -0.2221
## week-Canis_latrans -710.2582 199164.2065 -23446.1883 -0.0572
## week-Lynx_rufus -5043.0498 222653.5388 -26451.8263 -0.1127
## week-Didelphis_virginiana -3170.4252 176308.5545 -26263.9637 -0.0947
## week-Sylvilagus_floridanus -1.8947 162735.1943 -21112.9178 -0.0457
## week-Sciurus_carolinensis -3484.6511 139505.4969 -30556.5512 -0.1431
## week-Vulpes_vulpes -3701.8164 174797.6828 -30600.8662 -0.1610
## week-Sus_scrofa -2442.1395 160734.6700 -20284.8351 -0.0955
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4825 1.0143 218
## (Intercept)-Lynx_rufus -4.8392 1.1987 89
## (Intercept)-Didelphis_virginiana -4.2278 1.1114 222
## (Intercept)-Sylvilagus_floridanus -4.5049 1.0066 206
## (Intercept)-Sciurus_carolinensis -4.1286 1.0772 223
## (Intercept)-Vulpes_vulpes -4.6545 1.1332 108
## (Intercept)-Sus_scrofa -4.3490 1.0133 238
## shrub_cover-Canis_latrans 0.1392 1.0234 233
## shrub_cover-Lynx_rufus 0.5258 1.1113 110
## shrub_cover-Didelphis_virginiana 1.8770 1.0842 168
## shrub_cover-Sylvilagus_floridanus 1.2922 1.0054 155
## shrub_cover-Sciurus_carolinensis 1.5853 1.0114 249
## shrub_cover-Vulpes_vulpes 1.2306 1.0072 196
## shrub_cover-Sus_scrofa 2.1222 1.0565 280
## veg_height-Canis_latrans -0.2603 1.0208 221
## veg_height-Lynx_rufus 0.5943 1.0377 143
## veg_height-Didelphis_virginiana 0.9698 1.1350 200
## veg_height-Sylvilagus_floridanus 0.5997 1.0513 224
## veg_height-Sciurus_carolinensis 0.5879 1.0593 272
## veg_height-Vulpes_vulpes 0.5057 1.0322 181
## veg_height-Sus_scrofa 0.4044 1.0135 334
## week-Canis_latrans 24783.8753 1.2916 35084
## week-Lynx_rufus 25544.4128 1.3406 2890
## week-Didelphis_virginiana 18651.2403 1.3222 2064
## week-Sylvilagus_floridanus 29209.8521 1.2903 7430
## week-Sciurus_carolinensis 15985.1251 1.3512 3066
## week-Vulpes_vulpes 28170.5053 1.3343 2928
## week-Sus_scrofa 33036.1499 1.3132 7076
## 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): 4.4815
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0607 1.2327 -4.2871 -2.1725 0.5118 1.0098 714
## Cogon_Patch_Size -0.0453 1.0880 -2.3254 -0.0316 2.0087 1.0368 472
## Veg_shannon_index 0.8087 0.7969 -0.8751 0.8330 2.2999 1.0266 365
## total_shrub_cover -0.5032 0.8051 -2.2384 -0.4620 0.9965 1.1194 178
## Avg_Cogongrass_Cover -0.2440 1.1842 -2.6188 -0.2446 2.0387 1.0209 152
## Tree_Density -2.1306 0.9217 -4.0004 -2.1090 -0.3111 1.0101 238
## Avg_Canopy_Cover 2.0025 1.0803 -0.2692 2.0290 4.0716 1.0085 950
## I(Avg_Cogongrass_Cover^2) 1.6764 0.7372 0.2831 1.6417 3.2244 1.0569 235
## avg_veg_height -0.4189 0.7210 -1.8763 -0.4332 0.9642 1.0346 208
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.4512 27.1503 0.5381 9.4155 82.5896 1.0539 96
## Cogon_Patch_Size 11.2163 18.7198 0.3284 5.6933 58.6968 1.0193 205
## Veg_shannon_index 3.8473 7.0149 0.0828 1.6771 21.0258 1.0392 168
## total_shrub_cover 2.7872 5.1110 0.0662 1.1938 16.2703 1.0205 169
## Avg_Cogongrass_Cover 2.0711 4.0401 0.0595 0.7501 12.3965 1.1495 341
## Tree_Density 3.6379 14.8346 0.0573 0.7389 24.1546 1.5292 77
## Avg_Canopy_Cover 11.8578 19.3403 0.5767 6.2560 54.7148 1.0541 232
## I(Avg_Cogongrass_Cover^2) 1.9135 4.9655 0.0521 0.5186 12.7634 1.0761 139
## avg_veg_height 1.0218 2.4069 0.0491 0.4136 5.9046 1.0996 243
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3532 1.9241 0.0472 0.6196 6.7782 1.2845 71
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9871 0.3483 -5.5983 -5.0050 -4.2415 1.0102 972
## shrub_cover 0.5131 0.3698 -0.2353 0.5157 1.2456 1.0100 620
## veg_height 0.0364 0.2365 -0.4416 0.0379 0.4961 1.0089 902
## week -0.0590 1.6184 -3.2699 -0.0477 2.9544 1.0086 817
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.106000e-01 1.237700e+00 0.0723 0.3696 2.438100e+00 1.0910 426
## shrub_cover 7.676000e-01 7.558000e-01 0.1365 0.5448 2.717000e+00 1.0125 431
## veg_height 3.291000e-01 3.733000e-01 0.0655 0.2367 1.092700e+00 1.0197 1011
## week 2.946512e+10 3.622598e+11 0.1184 78.5942 8.546798e+10 1.8372 222
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7112 1.3623 -3.4256 -0.6757
## (Intercept)-Lynx_rufus 0.7428 2.6937 -3.2298 0.3130
## (Intercept)-Didelphis_virginiana -4.3704 1.7173 -8.2030 -4.1986
## (Intercept)-Sylvilagus_floridanus -3.0719 1.7004 -6.7684 -2.9783
## (Intercept)-Sciurus_carolinensis -5.0593 2.0585 -9.7042 -4.7680
## (Intercept)-Vulpes_vulpes -5.1628 2.4207 -10.5315 -4.8954
## (Intercept)-Sus_scrofa -6.1672 2.7021 -12.4367 -5.7879
## Cogon_Patch_Size-Canis_latrans 2.7409 2.0501 -0.1679 2.3657
## Cogon_Patch_Size-Lynx_rufus -0.0527 2.4231 -4.7653 -0.0504
## Cogon_Patch_Size-Didelphis_virginiana 2.2335 1.3445 0.0270 2.1143
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2843 2.6103 -8.5848 -1.7618
## Cogon_Patch_Size-Sciurus_carolinensis -1.3696 2.0499 -6.3925 -1.0777
## Cogon_Patch_Size-Vulpes_vulpes -0.9062 2.5385 -6.4153 -0.7173
## Cogon_Patch_Size-Sus_scrofa -1.4359 2.5165 -7.8309 -0.9354
## Veg_shannon_index-Canis_latrans 1.7336 0.9774 0.1610 1.6362
## Veg_shannon_index-Lynx_rufus -0.1974 1.6425 -4.2129 0.0861
## Veg_shannon_index-Didelphis_virginiana 1.3886 1.1573 -0.5085 1.2658
## Veg_shannon_index-Sylvilagus_floridanus 1.2338 0.9979 -0.6184 1.1839
## Veg_shannon_index-Sciurus_carolinensis -0.3418 1.3986 -3.6443 -0.1700
## Veg_shannon_index-Vulpes_vulpes 0.0872 1.4199 -3.0517 0.2193
## Veg_shannon_index-Sus_scrofa 2.3577 1.6457 0.0770 2.0590
## total_shrub_cover-Canis_latrans 0.6849 0.9393 -0.7728 0.5343
## total_shrub_cover-Lynx_rufus -1.2540 1.7766 -5.1667 -1.0663
## total_shrub_cover-Didelphis_virginiana -1.1640 1.3226 -4.3445 -0.9182
## total_shrub_cover-Sylvilagus_floridanus -0.7664 1.3672 -4.0325 -0.5809
## total_shrub_cover-Sciurus_carolinensis -0.5871 1.3746 -3.8188 -0.4046
## total_shrub_cover-Vulpes_vulpes -0.8935 1.4181 -4.4588 -0.6982
## total_shrub_cover-Sus_scrofa -0.2290 1.3367 -3.1197 -0.1909
## Avg_Cogongrass_Cover-Canis_latrans -0.0794 1.4252 -2.8407 -0.1268
## Avg_Cogongrass_Cover-Lynx_rufus 0.2707 1.7718 -2.9354 0.1512
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3193 1.5259 -3.3147 -0.3142
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9999 1.6548 -4.6489 -0.9037
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2885 1.5270 -3.4241 -0.3028
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0116 1.6781 -3.2036 -0.0431
## Avg_Cogongrass_Cover-Sus_scrofa -0.4261 1.7427 -3.8117 -0.4489
## Tree_Density-Canis_latrans -2.9007 1.5390 -6.6932 -2.6325
## Tree_Density-Lynx_rufus -1.5057 1.7096 -4.2624 -1.6589
## Tree_Density-Didelphis_virginiana -2.3519 1.2787 -5.1696 -2.2250
## Tree_Density-Sylvilagus_floridanus -2.7709 1.6401 -6.8392 -2.5410
## Tree_Density-Sciurus_carolinensis -2.6693 1.8980 -6.6916 -2.4239
## Tree_Density-Vulpes_vulpes -2.3116 1.6448 -5.6669 -2.1694
## Tree_Density-Sus_scrofa -2.5320 1.7905 -7.1570 -2.3336
## Avg_Canopy_Cover-Canis_latrans -0.0745 0.7392 -1.5173 -0.0936
## Avg_Canopy_Cover-Lynx_rufus 1.3479 2.4015 -2.6997 0.9689
## Avg_Canopy_Cover-Didelphis_virginiana 4.6051 2.0997 1.6818 4.2117
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.3425 2.3962 1.7036 4.9913
## Avg_Canopy_Cover-Sciurus_carolinensis 4.0409 2.0691 1.2365 3.6290
## Avg_Canopy_Cover-Vulpes_vulpes 3.1349 1.9739 0.4871 2.7293
## Avg_Canopy_Cover-Sus_scrofa 2.6808 1.5733 0.4220 2.4073
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2766 1.2283 0.6080 2.0524
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4637 1.4099 0.6496 2.1471
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3962 0.8565 -0.2619 1.3609
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4636 0.9511 -0.2893 1.4284
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9808 1.0833 0.3284 1.8267
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0409 1.0498 0.3722 1.9216
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1236 1.5227 -2.9396 1.3111
## avg_veg_height-Canis_latrans -0.3315 0.7423 -1.7710 -0.3376
## avg_veg_height-Lynx_rufus -0.6489 1.2245 -3.3571 -0.5739
## avg_veg_height-Didelphis_virginiana -0.6742 1.0432 -3.0873 -0.5950
## avg_veg_height-Sylvilagus_floridanus -0.4712 0.8983 -2.2783 -0.4793
## avg_veg_height-Sciurus_carolinensis 0.0386 0.9405 -1.6150 -0.0308
## avg_veg_height-Vulpes_vulpes -0.6060 1.1227 -2.9541 -0.5688
## avg_veg_height-Sus_scrofa -0.3876 0.9871 -2.2579 -0.3948
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9762 1.0012 458
## (Intercept)-Lynx_rufus 6.8485 1.0631 95
## (Intercept)-Didelphis_virginiana -1.5373 1.0407 259
## (Intercept)-Sylvilagus_floridanus 0.0014 1.0082 184
## (Intercept)-Sciurus_carolinensis -1.8000 1.0590 121
## (Intercept)-Vulpes_vulpes -1.0313 1.1080 96
## (Intercept)-Sus_scrofa -2.0466 1.0560 66
## Cogon_Patch_Size-Canis_latrans 7.8095 1.0062 188
## Cogon_Patch_Size-Lynx_rufus 4.9079 1.1738 173
## Cogon_Patch_Size-Didelphis_virginiana 5.2340 1.0268 398
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1427 1.0738 205
## Cogon_Patch_Size-Sciurus_carolinensis 1.8038 1.0474 307
## Cogon_Patch_Size-Vulpes_vulpes 4.1771 1.0737 149
## Cogon_Patch_Size-Sus_scrofa 1.9852 1.0162 248
## Veg_shannon_index-Canis_latrans 3.9403 1.0239 580
## Veg_shannon_index-Lynx_rufus 2.2716 1.1398 103
## Veg_shannon_index-Didelphis_virginiana 3.9492 1.0479 396
## Veg_shannon_index-Sylvilagus_floridanus 3.4314 1.0245 751
## Veg_shannon_index-Sciurus_carolinensis 1.8315 1.0151 210
## Veg_shannon_index-Vulpes_vulpes 2.4607 1.0164 298
## Veg_shannon_index-Sus_scrofa 6.4774 1.0425 181
## total_shrub_cover-Canis_latrans 2.9193 1.0004 215
## total_shrub_cover-Lynx_rufus 1.9364 1.3364 96
## total_shrub_cover-Didelphis_virginiana 0.7973 1.0844 226
## total_shrub_cover-Sylvilagus_floridanus 1.2262 1.0293 233
## total_shrub_cover-Sciurus_carolinensis 1.6550 1.0420 217
## total_shrub_cover-Vulpes_vulpes 1.3646 1.0869 216
## total_shrub_cover-Sus_scrofa 2.3853 1.1374 364
## Avg_Cogongrass_Cover-Canis_latrans 2.7659 1.0150 239
## Avg_Cogongrass_Cover-Lynx_rufus 4.1604 1.0167 219
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6983 1.0167 215
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9193 1.0926 246
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6161 1.0248 196
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3207 1.0242 209
## Avg_Cogongrass_Cover-Sus_scrofa 2.9276 1.0237 246
## Tree_Density-Canis_latrans -0.7597 1.0666 134
## Tree_Density-Lynx_rufus 2.5101 1.0991 135
## Tree_Density-Didelphis_virginiana 0.0083 1.0285 311
## Tree_Density-Sylvilagus_floridanus -0.2941 1.0760 147
## Tree_Density-Sciurus_carolinensis -0.0088 1.2405 76
## Tree_Density-Vulpes_vulpes 0.3439 1.1561 198
## Tree_Density-Sus_scrofa 0.3415 1.1573 142
## Avg_Canopy_Cover-Canis_latrans 1.4155 1.0138 835
## Avg_Canopy_Cover-Lynx_rufus 7.1233 1.1598 110
## Avg_Canopy_Cover-Didelphis_virginiana 9.5303 1.0031 230
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.9324 1.0020 184
## Avg_Canopy_Cover-Sciurus_carolinensis 9.1387 1.0132 183
## Avg_Canopy_Cover-Vulpes_vulpes 8.1143 1.0771 183
## Avg_Canopy_Cover-Sus_scrofa 6.5257 1.0980 227
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5201 1.0304 154
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.3075 1.0376 130
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1105 1.0214 308
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4320 1.0522 206
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.6943 1.0171 196
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.5514 1.0342 180
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.5723 1.0406 171
## avg_veg_height-Canis_latrans 1.1492 1.0140 517
## avg_veg_height-Lynx_rufus 1.4772 1.0440 204
## avg_veg_height-Didelphis_virginiana 1.1065 1.0589 298
## avg_veg_height-Sylvilagus_floridanus 1.2486 1.0393 326
## avg_veg_height-Sciurus_carolinensis 2.1611 1.0075 358
## avg_veg_height-Vulpes_vulpes 1.4025 1.0251 159
## avg_veg_height-Sus_scrofa 1.5325 1.0154 349
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7299 0.1629 -5.0884 -4.7224
## (Intercept)-Lynx_rufus -5.6877 0.3002 -6.2451 -5.7010
## (Intercept)-Didelphis_virginiana -4.7213 0.2634 -5.2314 -4.7268
## (Intercept)-Sylvilagus_floridanus -4.9654 0.2170 -5.4124 -4.9624
## (Intercept)-Sciurus_carolinensis -4.7666 0.2889 -5.3591 -4.7642
## (Intercept)-Vulpes_vulpes -5.7945 0.5659 -6.9222 -5.7421
## (Intercept)-Sus_scrofa -5.2992 0.4691 -6.2097 -5.2828
## shrub_cover-Canis_latrans -0.3181 0.2165 -0.7126 -0.3207
## shrub_cover-Lynx_rufus -0.0183 0.3719 -0.7308 -0.0128
## shrub_cover-Didelphis_virginiana 1.1091 0.3629 0.4164 1.0835
## shrub_cover-Sylvilagus_floridanus 0.6784 0.3542 -0.0145 0.6789
## shrub_cover-Sciurus_carolinensis 1.0351 0.3698 0.3386 1.0378
## shrub_cover-Vulpes_vulpes 0.3159 0.5882 -0.9205 0.3221
## shrub_cover-Sus_scrofa 0.9892 0.7629 -0.4851 0.9728
## veg_height-Canis_latrans -0.5204 0.1621 -0.8486 -0.5135
## veg_height-Lynx_rufus 0.2179 0.2250 -0.2362 0.2265
## veg_height-Didelphis_virginiana 0.5475 0.2391 0.0937 0.5413
## veg_height-Sylvilagus_floridanus 0.1847 0.2395 -0.2702 0.1885
## veg_height-Sciurus_carolinensis 0.2370 0.2151 -0.1744 0.2292
## veg_height-Vulpes_vulpes -0.1644 0.3123 -0.8002 -0.1533
## veg_height-Sus_scrofa -0.2426 0.3353 -0.9421 -0.2338
## week-Canis_latrans -1010.2050 168970.8546 -6098.3116 -0.1441
## week-Lynx_rufus -2505.9002 170958.2890 -8056.8731 -0.0689
## week-Didelphis_virginiana 889.8161 171383.8873 -7221.2240 -0.0763
## week-Sylvilagus_floridanus -3531.7446 153208.5201 -5328.6115 0.0632
## week-Sciurus_carolinensis -2354.2702 160155.1903 -6555.8440 -0.0011
## week-Vulpes_vulpes -3927.2532 175708.1641 -10989.8645 -0.0160
## week-Sus_scrofa -1278.1000 139962.1030 -6662.9945 -0.1038
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4213 1.0273 207
## (Intercept)-Lynx_rufus -5.0906 1.0211 81
## (Intercept)-Didelphis_virginiana -4.1997 1.0019 198
## (Intercept)-Sylvilagus_floridanus -4.5482 1.0204 188
## (Intercept)-Sciurus_carolinensis -4.2149 1.0572 174
## (Intercept)-Vulpes_vulpes -4.7735 1.0436 65
## (Intercept)-Sus_scrofa -4.3871 1.1022 146
## shrub_cover-Canis_latrans 0.1347 1.0514 171
## shrub_cover-Lynx_rufus 0.6757 1.2432 77
## shrub_cover-Didelphis_virginiana 1.8639 1.0314 129
## shrub_cover-Sylvilagus_floridanus 1.3697 1.0125 133
## shrub_cover-Sciurus_carolinensis 1.7321 1.0219 156
## shrub_cover-Vulpes_vulpes 1.5361 1.0670 151
## shrub_cover-Sus_scrofa 2.4753 1.0115 160
## veg_height-Canis_latrans -0.2166 1.0288 238
## veg_height-Lynx_rufus 0.6342 1.0991 132
## veg_height-Didelphis_virginiana 1.0429 1.0065 191
## veg_height-Sylvilagus_floridanus 0.6695 1.0264 214
## veg_height-Sciurus_carolinensis 0.6549 1.0435 241
## veg_height-Vulpes_vulpes 0.4528 1.0301 131
## veg_height-Sus_scrofa 0.3946 1.0017 274
## week-Canis_latrans 7915.4160 1.2944 1688
## week-Lynx_rufus 5310.2387 1.3120 11454
## week-Didelphis_virginiana 5833.2138 1.2930 23139
## week-Sylvilagus_floridanus 8233.9569 1.3422 2049
## week-Sciurus_carolinensis 6311.4263 1.3118 6410
## week-Vulpes_vulpes 6792.4635 1.3395 5342
## week-Sus_scrofa 7371.1575 1.2990 7865
# 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): 2.7042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4179 1.1143 -3.4082 -1.5146 1.0442 1.0330 559
## Cogon_Patch_Size -0.6782 0.9175 -2.5870 -0.6478 1.1345 1.0167 725
## Veg_shannon_index 0.6091 0.7224 -0.8655 0.6140 2.0727 1.0057 824
## total_shrub_cover -0.0169 0.5210 -1.1025 0.0132 0.9382 1.0059 433
## Avg_Cogongrass_Cover 1.9452 0.7591 0.4105 1.9495 3.4001 1.0021 331
## Tree_Density -1.9208 0.8545 -3.6227 -1.8836 -0.3382 1.0498 325
## Avg_Canopy_Cover 1.8097 0.8104 0.1590 1.8023 3.4476 1.0797 510
## avg_veg_height -0.6470 0.5905 -1.8163 -0.6627 0.5876 1.0080 275
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.5112 18.4723 1.0744 8.1067 56.7608 1.1143 100
## Cogon_Patch_Size 7.8884 13.2926 0.2411 3.8907 43.1536 1.1307 195
## Veg_shannon_index 3.4520 5.9539 0.0993 1.6744 16.7114 1.0380 254
## total_shrub_cover 0.9495 1.8285 0.0499 0.4158 5.0620 1.0240 487
## Avg_Cogongrass_Cover 1.4306 3.2125 0.0519 0.5337 7.8478 1.0199 466
## Tree_Density 2.9197 8.3283 0.0589 0.8303 19.5257 1.1272 209
## Avg_Canopy_Cover 4.1921 6.7342 0.2153 2.2820 20.0294 1.0997 281
## avg_veg_height 0.5572 0.8945 0.0421 0.2799 2.8846 1.0725 864
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5537 2.125 0.0717 0.8256 7.1737 1.4663 114
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7216 0.3659 -5.3213 -4.7493 -3.9082 1.016 1494
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6977 1.0522 0.0865 0.4198 3.029 1.0789 333
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.9317 1.1120 -1.0397 0.8424
## (Intercept)-Lynx_rufus 1.7683 2.8788 -1.7203 1.0326
## (Intercept)-Didelphis_virginiana -3.2064 1.2134 -5.9079 -3.1060
## (Intercept)-Sylvilagus_floridanus -1.9949 1.2914 -4.8953 -1.8973
## (Intercept)-Sciurus_carolinensis -3.6852 1.4331 -7.2547 -3.4803
## (Intercept)-Vulpes_vulpes -3.3723 1.8992 -7.0056 -3.3728
## (Intercept)-Sus_scrofa -5.3102 2.0779 -9.9697 -5.0326
## Cogon_Patch_Size-Canis_latrans 1.2129 1.5676 -0.8669 0.8654
## Cogon_Patch_Size-Lynx_rufus -0.6983 1.8261 -3.8278 -0.8436
## Cogon_Patch_Size-Didelphis_virginiana 1.3009 1.0528 -0.3595 1.1730
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6885 2.1734 -8.1471 -2.2338
## Cogon_Patch_Size-Sciurus_carolinensis -2.0870 1.6503 -6.1438 -1.7490
## Cogon_Patch_Size-Vulpes_vulpes -1.9057 2.2679 -7.6304 -1.5374
## Cogon_Patch_Size-Sus_scrofa -1.8083 2.0374 -6.9838 -1.3850
## Veg_shannon_index-Canis_latrans 1.5261 0.8979 0.1205 1.3996
## Veg_shannon_index-Lynx_rufus -0.1181 1.4835 -3.4503 -0.0018
## Veg_shannon_index-Didelphis_virginiana 1.0241 0.9093 -0.5512 0.9464
## Veg_shannon_index-Sylvilagus_floridanus 1.1203 0.9125 -0.4563 1.0283
## Veg_shannon_index-Sciurus_carolinensis -0.3981 0.9706 -2.6141 -0.3121
## Veg_shannon_index-Vulpes_vulpes -0.4958 1.3009 -3.5859 -0.2990
## Veg_shannon_index-Sus_scrofa 2.2341 1.6545 -0.0328 1.9060
## total_shrub_cover-Canis_latrans 0.4506 0.6843 -0.7548 0.3875
## total_shrub_cover-Lynx_rufus -0.5881 1.1010 -3.3726 -0.3999
## total_shrub_cover-Didelphis_virginiana -0.2881 0.7470 -2.0921 -0.2120
## total_shrub_cover-Sylvilagus_floridanus 0.0307 0.7242 -1.4235 0.0157
## total_shrub_cover-Sciurus_carolinensis 0.1564 0.6455 -1.1410 0.1554
## total_shrub_cover-Vulpes_vulpes -0.1768 0.8604 -2.0645 -0.1030
## total_shrub_cover-Sus_scrofa 0.2668 0.8050 -1.2437 0.2379
## Avg_Cogongrass_Cover-Canis_latrans 2.3950 1.0147 0.7206 2.2848
## Avg_Cogongrass_Cover-Lynx_rufus 2.5014 1.1819 0.6089 2.3578
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1501 0.8638 0.5884 2.1072
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5370 0.9869 -0.5469 1.5678
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2930 0.9653 0.6161 2.1896
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3466 1.1685 0.3624 2.2337
## Avg_Cogongrass_Cover-Sus_scrofa 1.4170 1.2280 -1.5980 1.5584
## Tree_Density-Canis_latrans -2.6158 1.3236 -6.0160 -2.3926
## Tree_Density-Lynx_rufus -0.9706 1.4209 -3.2778 -1.1061
## Tree_Density-Didelphis_virginiana -2.2552 1.0992 -4.8170 -2.1395
## Tree_Density-Sylvilagus_floridanus -2.5832 1.4590 -6.3383 -2.2912
## Tree_Density-Sciurus_carolinensis -2.6268 1.5657 -6.5711 -2.2877
## Tree_Density-Vulpes_vulpes -1.9547 1.4921 -5.0275 -1.9005
## Tree_Density-Sus_scrofa -2.2024 1.4803 -5.6365 -2.0212
## Avg_Canopy_Cover-Canis_latrans 0.1760 0.7457 -1.2930 0.1838
## Avg_Canopy_Cover-Lynx_rufus 1.0551 1.5522 -1.8070 0.9338
## Avg_Canopy_Cover-Didelphis_virginiana 3.0518 1.1640 1.2462 2.8915
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7152 1.8000 1.2315 3.3993
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5939 1.0438 0.9806 2.4480
## Avg_Canopy_Cover-Vulpes_vulpes 2.4058 1.3321 0.4334 2.1831
## Avg_Canopy_Cover-Sus_scrofa 2.2418 1.0335 0.5953 2.0967
## avg_veg_height-Canis_latrans -0.7033 0.6671 -1.9506 -0.7184
## avg_veg_height-Lynx_rufus -0.6391 0.8838 -2.3021 -0.6593
## avg_veg_height-Didelphis_virginiana -0.7451 0.7288 -2.1687 -0.7392
## avg_veg_height-Sylvilagus_floridanus -0.8081 0.7265 -2.2659 -0.7971
## avg_veg_height-Sciurus_carolinensis -0.3256 0.7263 -1.6797 -0.3563
## avg_veg_height-Vulpes_vulpes -0.7692 0.8131 -2.3859 -0.7607
## avg_veg_height-Sus_scrofa -0.7161 0.7789 -2.2959 -0.7150
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.4207 1.0339 316
## (Intercept)-Lynx_rufus 10.3406 1.2287 68
## (Intercept)-Didelphis_virginiana -1.0808 1.0209 405
## (Intercept)-Sylvilagus_floridanus 0.3486 1.0886 299
## (Intercept)-Sciurus_carolinensis -1.4203 1.1676 265
## (Intercept)-Vulpes_vulpes 0.6824 1.0402 136
## (Intercept)-Sus_scrofa -2.0713 1.1320 143
## Cogon_Patch_Size-Canis_latrans 5.3449 1.0371 305
## Cogon_Patch_Size-Lynx_rufus 3.5013 1.0838 185
## Cogon_Patch_Size-Didelphis_virginiana 3.8838 1.1051 372
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1186 1.0710 154
## Cogon_Patch_Size-Sciurus_carolinensis 0.1865 1.0622 382
## Cogon_Patch_Size-Vulpes_vulpes 1.6935 1.0406 218
## Cogon_Patch_Size-Sus_scrofa 1.0307 1.1242 293
## Veg_shannon_index-Canis_latrans 3.6505 1.0158 405
## Veg_shannon_index-Lynx_rufus 2.7463 1.0180 249
## Veg_shannon_index-Didelphis_virginiana 3.0637 1.0143 878
## Veg_shannon_index-Sylvilagus_floridanus 3.1939 1.0120 597
## Veg_shannon_index-Sciurus_carolinensis 1.2649 1.0257 375
## Veg_shannon_index-Vulpes_vulpes 1.4343 1.0443 267
## Veg_shannon_index-Sus_scrofa 6.3149 1.0172 277
## total_shrub_cover-Canis_latrans 2.0087 1.0263 599
## total_shrub_cover-Lynx_rufus 1.1138 1.0429 228
## total_shrub_cover-Didelphis_virginiana 0.9868 1.0228 682
## total_shrub_cover-Sylvilagus_floridanus 1.5748 1.0075 725
## total_shrub_cover-Sciurus_carolinensis 1.4377 1.0052 947
## total_shrub_cover-Vulpes_vulpes 1.3712 1.0109 331
## total_shrub_cover-Sus_scrofa 1.9436 1.0322 808
## Avg_Cogongrass_Cover-Canis_latrans 4.7820 1.0118 288
## Avg_Cogongrass_Cover-Lynx_rufus 5.1410 1.0029 329
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9952 1.0084 463
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4756 1.0053 420
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4296 1.0051 360
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.1139 1.0292 335
## Avg_Cogongrass_Cover-Sus_scrofa 3.4256 1.0025 463
## Tree_Density-Canis_latrans -0.7578 1.0671 256
## Tree_Density-Lynx_rufus 2.4463 1.0480 178
## Tree_Density-Didelphis_virginiana -0.5135 1.0816 516
## Tree_Density-Sylvilagus_floridanus -0.5227 1.0755 219
## Tree_Density-Sciurus_carolinensis -0.6376 1.1010 195
## Tree_Density-Vulpes_vulpes 0.9131 1.0257 267
## Tree_Density-Sus_scrofa 0.1800 1.1091 339
## Avg_Canopy_Cover-Canis_latrans 1.6374 1.0021 749
## Avg_Canopy_Cover-Lynx_rufus 4.4440 1.0996 190
## Avg_Canopy_Cover-Didelphis_virginiana 5.8446 1.0632 200
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1340 1.0896 240
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1074 1.0947 344
## Avg_Canopy_Cover-Vulpes_vulpes 5.6957 1.1839 191
## Avg_Canopy_Cover-Sus_scrofa 4.7216 1.0252 355
## avg_veg_height-Canis_latrans 0.6252 1.0071 405
## avg_veg_height-Lynx_rufus 1.1855 1.0162 211
## avg_veg_height-Didelphis_virginiana 0.6442 1.0034 415
## avg_veg_height-Sylvilagus_floridanus 0.5792 1.0060 353
## avg_veg_height-Sciurus_carolinensis 1.2375 1.0070 405
## avg_veg_height-Vulpes_vulpes 0.8736 1.0096 319
## avg_veg_height-Sus_scrofa 0.8186 1.0123 464
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6718 0.1630 -5.0044 -4.6748 -4.3584 1.0297
## (Intercept)-Lynx_rufus -5.5528 0.3202 -6.1570 -5.5521 -4.9271 1.3704
## (Intercept)-Didelphis_virginiana -4.3117 0.2306 -4.7922 -4.2979 -3.8984 1.0064
## (Intercept)-Sylvilagus_floridanus -4.9560 0.2360 -5.4432 -4.9552 -4.5097 1.0581
## (Intercept)-Sciurus_carolinensis -4.3586 0.2156 -4.7981 -4.3558 -3.9518 1.0358
## (Intercept)-Vulpes_vulpes -5.6259 0.5626 -6.8600 -5.5892 -4.6571 1.1298
## (Intercept)-Sus_scrofa -4.7479 0.3492 -5.4986 -4.7270 -4.1188 1.0034
## ESS
## (Intercept)-Canis_latrans 207
## (Intercept)-Lynx_rufus 64
## (Intercept)-Didelphis_virginiana 330
## (Intercept)-Sylvilagus_floridanus 164
## (Intercept)-Sciurus_carolinensis 392
## (Intercept)-Vulpes_vulpes 60
## (Intercept)-Sus_scrofa 265
# 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): 2.4893
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0436 0.5343 -2.1451 -1.0429 0.0676 1.0036 687
## Avg_Cogongrass_Cover 0.3263 0.3938 -0.4186 0.3228 1.0779 1.0068 765
## total_shrub_cover -0.0563 0.3395 -0.7334 -0.0568 0.6008 1.0047 1024
## avg_veg_height -0.0534 0.3772 -0.7973 -0.0566 0.6957 1.0101 549
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3945 1.8665 0.0782 0.8103 6.4099 1.0252 789
## Avg_Cogongrass_Cover 0.4679 0.8111 0.0425 0.2562 2.1964 1.0076 1130
## total_shrub_cover 0.4198 0.6184 0.0394 0.2362 2.1002 1.0120 923
## avg_veg_height 0.2957 0.4668 0.0372 0.1718 1.2745 1.0918 1416
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5852 1.3805 0.1525 1.2292 5.2312 1.0211 267
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7009 0.3281 -5.2394 -4.7233 -4.0365 1.005 1342
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5408 1.0553 0.0565 0.3019 2.4465 1.0638 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0264 0.7868 -1.4924 -0.0439
## (Intercept)-Lynx_rufus -0.5327 0.8173 -2.0190 -0.5689
## (Intercept)-Didelphis_virginiana -1.4203 0.6430 -2.7743 -1.3798
## (Intercept)-Sylvilagus_floridanus -0.8608 0.6680 -2.1938 -0.8601
## (Intercept)-Sciurus_carolinensis -1.4976 0.6878 -2.9767 -1.4577
## (Intercept)-Vulpes_vulpes -1.5876 0.9116 -3.5139 -1.5507
## (Intercept)-Sus_scrofa -1.8693 0.8194 -3.7630 -1.7939
## Avg_Cogongrass_Cover-Canis_latrans 0.5477 0.5008 -0.3413 0.5115
## Avg_Cogongrass_Cover-Lynx_rufus 0.6393 0.5504 -0.2875 0.5806
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4791 0.4707 -0.4036 0.4774
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1402 0.5410 -1.3156 -0.1031
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3826 0.4581 -0.5128 0.3726
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4106 0.5312 -0.5728 0.3979
## Avg_Cogongrass_Cover-Sus_scrofa -0.0282 0.6754 -1.5356 0.0495
## total_shrub_cover-Canis_latrans 0.3596 0.4642 -0.4414 0.3224
## total_shrub_cover-Lynx_rufus -0.4875 0.5772 -1.8206 -0.4156
## total_shrub_cover-Didelphis_virginiana -0.1045 0.4018 -0.9367 -0.0986
## total_shrub_cover-Sylvilagus_floridanus -0.1650 0.4265 -1.0136 -0.1505
## total_shrub_cover-Sciurus_carolinensis 0.0023 0.4077 -0.8081 0.0012
## total_shrub_cover-Vulpes_vulpes -0.1437 0.5229 -1.2737 -0.1262
## total_shrub_cover-Sus_scrofa 0.1911 0.4913 -0.7051 0.1687
## avg_veg_height-Canis_latrans -0.0728 0.4433 -0.9403 -0.0620
## avg_veg_height-Lynx_rufus -0.0455 0.5478 -1.1326 -0.0586
## avg_veg_height-Didelphis_virginiana -0.0845 0.4612 -1.0187 -0.0835
## avg_veg_height-Sylvilagus_floridanus -0.2072 0.4654 -1.1607 -0.2027
## avg_veg_height-Sciurus_carolinensis 0.2371 0.4778 -0.6333 0.2217
## avg_veg_height-Vulpes_vulpes -0.1521 0.5132 -1.2005 -0.1360
## avg_veg_height-Sus_scrofa -0.0772 0.4999 -1.0712 -0.0690
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5663 1.0091 457
## (Intercept)-Lynx_rufus 1.2018 1.0283 327
## (Intercept)-Didelphis_virginiana -0.2483 1.0010 960
## (Intercept)-Sylvilagus_floridanus 0.5260 1.0251 631
## (Intercept)-Sciurus_carolinensis -0.2574 1.0127 536
## (Intercept)-Vulpes_vulpes 0.1842 1.0886 216
## (Intercept)-Sus_scrofa -0.4689 1.0051 515
## Avg_Cogongrass_Cover-Canis_latrans 1.6225 1.0236 927
## Avg_Cogongrass_Cover-Lynx_rufus 1.8377 1.0038 1010
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4237 1.0218 1128
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7674 1.0065 988
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2739 1.0139 936
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4874 1.0043 773
## Avg_Cogongrass_Cover-Sus_scrofa 1.0948 1.0074 610
## total_shrub_cover-Canis_latrans 1.4054 1.0110 1087
## total_shrub_cover-Lynx_rufus 0.4840 1.0047 698
## total_shrub_cover-Didelphis_virginiana 0.6757 0.9996 1925
## total_shrub_cover-Sylvilagus_floridanus 0.6259 1.0093 1308
## total_shrub_cover-Sciurus_carolinensis 0.8088 0.9998 1738
## total_shrub_cover-Vulpes_vulpes 0.8477 1.0097 1050
## total_shrub_cover-Sus_scrofa 1.2364 1.0121 1287
## avg_veg_height-Canis_latrans 0.8011 1.0135 989
## avg_veg_height-Lynx_rufus 1.0684 1.0140 744
## avg_veg_height-Didelphis_virginiana 0.8015 1.0087 982
## avg_veg_height-Sylvilagus_floridanus 0.6914 1.0072 861
## avg_veg_height-Sciurus_carolinensis 1.2381 1.0069 885
## avg_veg_height-Vulpes_vulpes 0.8233 1.0035 802
## avg_veg_height-Sus_scrofa 0.9070 1.0039 927
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6626 0.1519 -4.9517 -4.6608 -4.3632 1.0159
## (Intercept)-Lynx_rufus -5.2946 0.2581 -5.8148 -5.2873 -4.8243 1.0610
## (Intercept)-Didelphis_virginiana -4.3597 0.2375 -4.8542 -4.3505 -3.9199 1.0488
## (Intercept)-Sylvilagus_floridanus -4.8656 0.2217 -5.3129 -4.8572 -4.4345 1.0600
## (Intercept)-Sciurus_carolinensis -4.3860 0.2223 -4.8824 -4.3749 -3.9767 1.0402
## (Intercept)-Vulpes_vulpes -5.4882 0.6318 -7.0784 -5.3732 -4.5557 1.3144
## (Intercept)-Sus_scrofa -4.7129 0.3220 -5.3673 -4.7078 -4.1149 1.0079
## ESS
## (Intercept)-Canis_latrans 269
## (Intercept)-Lynx_rufus 94
## (Intercept)-Didelphis_virginiana 291
## (Intercept)-Sylvilagus_floridanus 178
## (Intercept)-Sciurus_carolinensis 334
## (Intercept)-Vulpes_vulpes 50
## (Intercept)-Sus_scrofa 264
# 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): 2.5328
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1576 0.6598 -2.4033 -1.1759 0.2436 1.0254 887
## Tree_Density -0.8372 0.5660 -2.0962 -0.7926 0.1805 1.0146 600
## Avg_Canopy_Cover 1.0227 0.4513 0.1344 1.0116 1.9584 1.0224 1054
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0531 4.2386 0.2682 1.8822 12.9964 1.1325 306
## Tree_Density 1.3799 2.9130 0.0579 0.5433 7.6856 1.0463 457
## Avg_Canopy_Cover 1.0000 1.9728 0.0765 0.5842 4.1537 1.0468 1560
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5378 0.6596 0.045 0.3206 2.2109 1.0207 242
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7209 0.3252 -5.2857 -4.7442 -3.988 1.0076 1257
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5586 0.783 0.0606 0.3339 2.3318 1.0845 132
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3417 0.8246 -1.0292 0.2791 2.1470
## (Intercept)-Lynx_rufus 0.2090 1.3366 -1.5361 -0.0153 3.0879
## (Intercept)-Didelphis_virginiana -1.9772 0.6896 -3.4844 -1.9390 -0.7520
## (Intercept)-Sylvilagus_floridanus -1.0564 0.6889 -2.4598 -1.0339 0.3203
## (Intercept)-Sciurus_carolinensis -2.0291 0.7120 -3.5276 -1.9809 -0.7531
## (Intercept)-Vulpes_vulpes -2.0088 0.9918 -3.9373 -2.0270 0.0935
## (Intercept)-Sus_scrofa -2.6648 0.9476 -4.7054 -2.5648 -1.0471
## Tree_Density-Canis_latrans -1.0765 0.6618 -2.6226 -0.9943 -0.0279
## Tree_Density-Lynx_rufus 0.1382 0.8188 -1.1511 0.0233 2.2380
## Tree_Density-Didelphis_virginiana -1.2288 0.9531 -3.7153 -1.0385 0.0931
## Tree_Density-Sylvilagus_floridanus -1.2612 0.9328 -3.4635 -1.1214 0.0489
## Tree_Density-Sciurus_carolinensis -1.0685 0.8883 -3.4937 -0.9351 0.2629
## Tree_Density-Vulpes_vulpes -0.6405 0.9947 -2.4860 -0.6212 1.0543
## Tree_Density-Sus_scrofa -1.2070 1.1941 -4.2088 -0.9906 0.3807
## Avg_Canopy_Cover-Canis_latrans 0.0619 0.5272 -0.9811 0.0542 1.1024
## Avg_Canopy_Cover-Lynx_rufus 0.7164 0.7357 -0.6143 0.6703 2.2975
## Avg_Canopy_Cover-Didelphis_virginiana 1.3134 0.5217 0.4193 1.2749 2.4566
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7805 0.8351 0.6096 1.6179 3.8291
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2656 0.5148 0.3752 1.2201 2.3936
## Avg_Canopy_Cover-Vulpes_vulpes 1.0322 0.6155 -0.0739 0.9826 2.3677
## Avg_Canopy_Cover-Sus_scrofa 1.2502 0.5568 0.2776 1.2055 2.4768
## Rhat ESS
## (Intercept)-Canis_latrans 1.1589 204
## (Intercept)-Lynx_rufus 1.2266 80
## (Intercept)-Didelphis_virginiana 1.0327 717
## (Intercept)-Sylvilagus_floridanus 1.0045 892
## (Intercept)-Sciurus_carolinensis 1.0347 842
## (Intercept)-Vulpes_vulpes 1.0431 156
## (Intercept)-Sus_scrofa 1.0419 484
## Tree_Density-Canis_latrans 1.0006 912
## Tree_Density-Lynx_rufus 1.0123 467
## Tree_Density-Didelphis_virginiana 1.0156 530
## Tree_Density-Sylvilagus_floridanus 1.0110 533
## Tree_Density-Sciurus_carolinensis 1.0249 513
## Tree_Density-Vulpes_vulpes 1.0205 378
## Tree_Density-Sus_scrofa 1.1108 322
## Avg_Canopy_Cover-Canis_latrans 1.0036 1114
## Avg_Canopy_Cover-Lynx_rufus 1.0192 565
## Avg_Canopy_Cover-Didelphis_virginiana 1.0225 1351
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0129 630
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0160 1123
## Avg_Canopy_Cover-Vulpes_vulpes 1.0292 791
## Avg_Canopy_Cover-Sus_scrofa 1.0109 1177
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7308 0.1732 -5.0997 -4.7215 -4.4119 1.0611
## (Intercept)-Lynx_rufus -5.4141 0.3052 -6.0652 -5.4088 -4.8598 1.0963
## (Intercept)-Didelphis_virginiana -4.3326 0.2218 -4.7776 -4.3173 -3.9300 1.0278
## (Intercept)-Sylvilagus_floridanus -4.8615 0.2236 -5.3076 -4.8572 -4.4281 1.0488
## (Intercept)-Sciurus_carolinensis -4.3805 0.2321 -4.8640 -4.3690 -3.9429 1.0524
## (Intercept)-Vulpes_vulpes -5.4998 0.6383 -6.8918 -5.3636 -4.5493 1.2444
## (Intercept)-Sus_scrofa -4.7403 0.3354 -5.4259 -4.7327 -4.1067 1.0374
## ESS
## (Intercept)-Canis_latrans 178
## (Intercept)-Lynx_rufus 66
## (Intercept)-Didelphis_virginiana 333
## (Intercept)-Sylvilagus_floridanus 228
## (Intercept)-Sciurus_carolinensis 299
## (Intercept)-Vulpes_vulpes 56
## (Intercept)-Sus_scrofa 300
# 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): 2.4902
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1511 0.6049 -2.3282 -1.1680 0.1402 1.0041 683
## Cogon_Patch_Size -0.4499 0.6562 -1.8344 -0.4173 0.7866 1.0146 966
## Avg_Cogongrass_Cover 0.4194 0.3606 -0.2773 0.4127 1.1749 1.0103 639
## total_shrub_cover 0.0457 0.3661 -0.6507 0.0460 0.7660 1.0162 908
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9329 3.1255 0.0959 1.0963 8.8807 1.0389 353
## Cogon_Patch_Size 2.5648 3.8301 0.1272 1.3608 12.9304 1.0471 300
## Avg_Cogongrass_Cover 0.4297 0.5725 0.0408 0.2495 1.9670 1.0334 1239
## total_shrub_cover 0.4327 0.6939 0.0404 0.2233 2.1910 1.0633 470
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0834 2.151 0.1629 1.4796 7.7402 1.0581 150
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6906 0.3631 -5.2823 -4.7178 -3.9299 1.0112 876
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6082 1.3184 0.0594 0.3084 2.91 1.058 263
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1278 0.9506 -1.5792 0.0782
## (Intercept)-Lynx_rufus -0.6175 0.9332 -2.1840 -0.6909
## (Intercept)-Didelphis_virginiana -1.5036 0.7512 -3.0903 -1.4875
## (Intercept)-Sylvilagus_floridanus -1.0711 0.7790 -2.6342 -1.0695
## (Intercept)-Sciurus_carolinensis -1.7520 0.7579 -3.3561 -1.6997
## (Intercept)-Vulpes_vulpes -1.8171 1.0029 -3.9142 -1.7775
## (Intercept)-Sus_scrofa -2.1368 0.9439 -4.2332 -2.0295
## Cogon_Patch_Size-Canis_latrans 0.9117 1.0588 -0.4061 0.7046
## Cogon_Patch_Size-Lynx_rufus -0.6516 0.8615 -2.2150 -0.6691
## Cogon_Patch_Size-Didelphis_virginiana 0.7790 0.7554 -0.2910 0.7004
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5332 1.2876 -4.8663 -1.2468
## Cogon_Patch_Size-Sciurus_carolinensis -1.1937 0.9761 -3.6209 -1.0167
## Cogon_Patch_Size-Vulpes_vulpes -1.0975 1.2132 -4.0387 -0.8952
## Cogon_Patch_Size-Sus_scrofa -0.8290 1.1461 -3.6514 -0.6176
## Avg_Cogongrass_Cover-Canis_latrans 0.4281 0.4366 -0.3749 0.4120
## Avg_Cogongrass_Cover-Lynx_rufus 0.8050 0.5636 -0.1005 0.7239
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3500 0.4416 -0.5326 0.3446
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0895 0.5065 -0.9417 0.1041
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6857 0.4512 -0.1193 0.6573
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5262 0.5022 -0.4263 0.5106
## Avg_Cogongrass_Cover-Sus_scrofa 0.1223 0.5897 -1.1628 0.1793
## total_shrub_cover-Canis_latrans 0.3975 0.4901 -0.4480 0.3548
## total_shrub_cover-Lynx_rufus -0.3160 0.6079 -1.6844 -0.2570
## total_shrub_cover-Didelphis_virginiana -0.1239 0.4594 -1.0728 -0.1088
## total_shrub_cover-Sylvilagus_floridanus -0.0238 0.4778 -1.0064 -0.0190
## total_shrub_cover-Sciurus_carolinensis 0.1262 0.4382 -0.7272 0.1259
## total_shrub_cover-Vulpes_vulpes -0.0206 0.5715 -1.2286 -0.0102
## total_shrub_cover-Sus_scrofa 0.2787 0.5303 -0.6321 0.2402
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1527 1.0415 310
## (Intercept)-Lynx_rufus 1.3006 1.0238 203
## (Intercept)-Didelphis_virginiana -0.0536 1.0040 717
## (Intercept)-Sylvilagus_floridanus 0.4833 1.0065 619
## (Intercept)-Sciurus_carolinensis -0.3113 1.0317 515
## (Intercept)-Vulpes_vulpes 0.1427 1.0458 262
## (Intercept)-Sus_scrofa -0.5304 1.0453 363
## Cogon_Patch_Size-Canis_latrans 3.6211 1.0024 447
## Cogon_Patch_Size-Lynx_rufus 1.1970 1.0129 619
## Cogon_Patch_Size-Didelphis_virginiana 2.3063 1.0894 242
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1699 1.0514 367
## Cogon_Patch_Size-Sciurus_carolinensis 0.1626 1.0185 437
## Cogon_Patch_Size-Vulpes_vulpes 0.7259 1.0231 373
## Cogon_Patch_Size-Sus_scrofa 0.8037 1.0157 505
## Avg_Cogongrass_Cover-Canis_latrans 1.3308 1.0116 1180
## Avg_Cogongrass_Cover-Lynx_rufus 2.1351 1.0015 897
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1916 1.0078 999
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0338 1.0688 618
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6641 1.0104 934
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5787 1.0014 1100
## Avg_Cogongrass_Cover-Sus_scrofa 1.1605 1.0286 627
## total_shrub_cover-Canis_latrans 1.4894 1.0032 869
## total_shrub_cover-Lynx_rufus 0.6732 1.0239 577
## total_shrub_cover-Didelphis_virginiana 0.7229 1.0383 1211
## total_shrub_cover-Sylvilagus_floridanus 0.9285 1.0137 933
## total_shrub_cover-Sciurus_carolinensis 0.9988 1.0048 1093
## total_shrub_cover-Vulpes_vulpes 1.0781 1.0090 864
## total_shrub_cover-Sus_scrofa 1.4252 1.0090 998
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6417 0.1555 -4.9479 -4.6364 -4.3447 1.0173
## (Intercept)-Lynx_rufus -5.3218 0.3032 -5.9716 -5.3012 -4.7938 1.1415
## (Intercept)-Didelphis_virginiana -4.3573 0.2296 -4.8421 -4.3527 -3.9258 1.0328
## (Intercept)-Sylvilagus_floridanus -4.9113 0.2509 -5.4327 -4.8929 -4.4617 1.1353
## (Intercept)-Sciurus_carolinensis -4.3808 0.2257 -4.8250 -4.3766 -3.9581 1.0102
## (Intercept)-Vulpes_vulpes -5.4919 0.7076 -7.1059 -5.3410 -4.4325 1.0249
## (Intercept)-Sus_scrofa -4.7809 0.3625 -5.5801 -4.7508 -4.1480 1.0338
## ESS
## (Intercept)-Canis_latrans 249
## (Intercept)-Lynx_rufus 85
## (Intercept)-Didelphis_virginiana 286
## (Intercept)-Sylvilagus_floridanus 141
## (Intercept)-Sciurus_carolinensis 316
## (Intercept)-Vulpes_vulpes 37
## (Intercept)-Sus_scrofa 251
# 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): 2.4
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0908 0.5254 -2.1411 -1.0965 -0.0301 1.0037 1399
## Veg_shannon_index 0.2939 0.3959 -0.5045 0.2906 1.0961 1.0016 1427
## Avg_Cogongrass_Cover 0.3158 0.3297 -0.3562 0.3115 0.9681 1.0024 1401
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5426 2.0848 0.0859 0.9624 6.9273 1.0178 1138
## Veg_shannon_index 0.7779 1.7796 0.0609 0.4437 3.3701 1.1836 1025
## Avg_Cogongrass_Cover 0.4572 0.7389 0.0424 0.2498 2.0079 1.0382 1286
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9633 1.1339 0.0566 0.619 3.6212 1.075 190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.687 0.2803 -5.1874 -4.7038 -4.1159 1.0065 872
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3883 0.521 0.052 0.2507 1.5365 1.0264 574
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0208 0.7082 -1.3427 0.0295
## (Intercept)-Lynx_rufus -0.4673 0.7168 -1.7996 -0.4971
## (Intercept)-Didelphis_virginiana -1.4764 0.6075 -2.6907 -1.4520
## (Intercept)-Sylvilagus_floridanus -0.8349 0.5935 -1.9882 -0.8351
## (Intercept)-Sciurus_carolinensis -1.5107 0.6023 -2.8487 -1.4810
## (Intercept)-Vulpes_vulpes -1.7480 0.7398 -3.3469 -1.7153
## (Intercept)-Sus_scrofa -2.1120 0.8315 -4.0144 -2.0255
## Veg_shannon_index-Canis_latrans 0.8160 0.4678 0.0008 0.7773
## Veg_shannon_index-Lynx_rufus -0.3149 0.6729 -2.0106 -0.2294
## Veg_shannon_index-Didelphis_virginiana 0.5554 0.4656 -0.2996 0.5183
## Veg_shannon_index-Sylvilagus_floridanus 0.4570 0.4769 -0.4104 0.4299
## Veg_shannon_index-Sciurus_carolinensis -0.1274 0.4464 -1.0633 -0.1053
## Veg_shannon_index-Vulpes_vulpes -0.1080 0.5462 -1.2410 -0.0948
## Veg_shannon_index-Sus_scrofa 0.8671 0.7030 -0.2843 0.7816
## Avg_Cogongrass_Cover-Canis_latrans 0.6045 0.4331 -0.1722 0.5637
## Avg_Cogongrass_Cover-Lynx_rufus 0.6080 0.4515 -0.1694 0.5619
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4979 0.4021 -0.2844 0.4804
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1386 0.4713 -1.2041 -0.0887
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4006 0.3862 -0.3808 0.4065
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3381 0.4832 -0.6235 0.3398
## Avg_Cogongrass_Cover-Sus_scrofa -0.0596 0.6058 -1.4662 0.0295
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4555 1.0139 503
## (Intercept)-Lynx_rufus 1.0226 1.0158 450
## (Intercept)-Didelphis_virginiana -0.3443 1.0124 1128
## (Intercept)-Sylvilagus_floridanus 0.3157 1.0082 647
## (Intercept)-Sciurus_carolinensis -0.3991 1.0171 1216
## (Intercept)-Vulpes_vulpes -0.4105 1.0139 492
## (Intercept)-Sus_scrofa -0.7257 1.0147 598
## Veg_shannon_index-Canis_latrans 1.8397 1.0000 1212
## Veg_shannon_index-Lynx_rufus 0.7573 1.0010 674
## Veg_shannon_index-Didelphis_virginiana 1.5856 1.0013 1518
## Veg_shannon_index-Sylvilagus_floridanus 1.4495 1.0043 1416
## Veg_shannon_index-Sciurus_carolinensis 0.7014 1.0084 1324
## Veg_shannon_index-Vulpes_vulpes 0.8764 0.9998 1140
## Veg_shannon_index-Sus_scrofa 2.5130 1.0061 764
## Avg_Cogongrass_Cover-Canis_latrans 1.5593 1.0026 1426
## Avg_Cogongrass_Cover-Lynx_rufus 1.6210 1.0015 1498
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3008 1.0003 1339
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6654 1.0005 885
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1580 1.0043 1565
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3283 1.0015 1278
## Avg_Cogongrass_Cover-Sus_scrofa 0.9255 1.0059 672
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6390 0.1535 -4.9671 -4.6346 -4.3538 1.0324
## (Intercept)-Lynx_rufus -5.2496 0.2470 -5.7447 -5.2451 -4.7967 1.0672
## (Intercept)-Didelphis_virginiana -4.3249 0.2236 -4.7684 -4.3188 -3.8886 1.0465
## (Intercept)-Sylvilagus_floridanus -4.8915 0.2271 -5.3607 -4.8796 -4.4687 1.0054
## (Intercept)-Sciurus_carolinensis -4.3671 0.2400 -4.8646 -4.3561 -3.9304 1.0070
## (Intercept)-Vulpes_vulpes -5.2749 0.4634 -6.3015 -5.2022 -4.5390 1.0181
## (Intercept)-Sus_scrofa -4.7452 0.3394 -5.4366 -4.7403 -4.1266 1.0171
## ESS
## (Intercept)-Canis_latrans 236
## (Intercept)-Lynx_rufus 126
## (Intercept)-Didelphis_virginiana 352
## (Intercept)-Sylvilagus_floridanus 199
## (Intercept)-Sciurus_carolinensis 270
## (Intercept)-Vulpes_vulpes 102
## (Intercept)-Sus_scrofa 304
# 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): 2.4102
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7531 0.5739 -2.9519 -1.7438 -0.6417 1.0089 493
## Avg_Cogongrass_Cover -0.7603 0.4762 -1.7584 -0.7436 0.1385 1.0023 581
## I(Avg_Cogongrass_Cover^2) 0.8582 0.4066 0.1105 0.8407 1.7025 1.0068 558
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2219 1.8638 0.0637 0.7254 5.3662 1.0295 1101
## Avg_Cogongrass_Cover 0.5760 0.8176 0.0448 0.3116 2.6278 1.0107 1040
## I(Avg_Cogongrass_Cover^2) 0.6912 2.4649 0.0399 0.2489 3.4146 1.4814 283
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.833 0.8912 0.0714 0.5469 3.2359 1.1467 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7142 0.3118 -5.2606 -4.7283 -3.9964 1.0171 1264
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5409 0.9435 0.0562 0.2959 2.6758 1.0736 149
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7992 0.7631 -2.3021 -0.8053
## (Intercept)-Lynx_rufus -1.3922 0.7834 -2.8671 -1.4245
## (Intercept)-Didelphis_virginiana -1.9946 0.6509 -3.3842 -1.9595
## (Intercept)-Sylvilagus_floridanus -1.5883 0.6906 -2.9808 -1.5770
## (Intercept)-Sciurus_carolinensis -2.3587 0.7346 -3.9316 -2.3043
## (Intercept)-Vulpes_vulpes -2.3925 0.8860 -4.2368 -2.3542
## (Intercept)-Sus_scrofa -2.4111 0.7703 -4.0606 -2.3530
## Avg_Cogongrass_Cover-Canis_latrans -0.4836 0.5818 -1.5410 -0.5135
## Avg_Cogongrass_Cover-Lynx_rufus -0.5387 0.6065 -1.7454 -0.5525
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4490 0.6021 -1.6057 -0.4681
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2706 0.6623 -2.7705 -1.2081
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8359 0.6019 -2.1300 -0.8157
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8178 0.6550 -2.1933 -0.7887
## Avg_Cogongrass_Cover-Sus_scrofa -1.0922 0.7198 -2.7771 -1.0278
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3673 0.9124 0.2570 1.1556
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1833 0.5548 0.3399 1.1191
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6016 0.4359 -0.2066 0.5860
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8174 0.4838 -0.0041 0.7805
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9913 0.4216 0.2505 0.9660
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9306 0.5197 0.1112 0.8678
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3204 0.7703 -1.3399 0.4210
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7390 1.0071 577
## (Intercept)-Lynx_rufus 0.2731 1.0199 339
## (Intercept)-Didelphis_virginiana -0.7980 0.9999 966
## (Intercept)-Sylvilagus_floridanus -0.2355 1.0257 587
## (Intercept)-Sciurus_carolinensis -1.0771 1.0030 586
## (Intercept)-Vulpes_vulpes -0.7614 1.0040 334
## (Intercept)-Sus_scrofa -1.0425 1.0022 659
## Avg_Cogongrass_Cover-Canis_latrans 0.7762 1.0012 931
## Avg_Cogongrass_Cover-Lynx_rufus 0.6976 1.0012 879
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8570 1.0042 875
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1163 1.0035 613
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2881 1.0116 756
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3747 1.0015 701
## Avg_Cogongrass_Cover-Sus_scrofa 0.1424 1.0062 637
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.6825 1.0977 261
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4165 1.0078 551
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4661 1.0128 616
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8418 1.0098 512
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8979 1.0020 695
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1909 1.0149 439
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3550 1.0950 283
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6707 0.1476 -4.9703 -4.6711 -4.3807 1.0741
## (Intercept)-Lynx_rufus -5.2765 0.3057 -5.9397 -5.2329 -4.7597 1.0192
## (Intercept)-Didelphis_virginiana -4.3523 0.2225 -4.8230 -4.3415 -3.9417 1.0721
## (Intercept)-Sylvilagus_floridanus -4.9107 0.2651 -5.4665 -4.8998 -4.4103 1.0212
## (Intercept)-Sciurus_carolinensis -4.3871 0.2365 -4.8785 -4.3736 -3.9592 1.0047
## (Intercept)-Vulpes_vulpes -5.4940 0.6279 -7.0770 -5.3744 -4.6116 1.1125
## (Intercept)-Sus_scrofa -4.8019 0.3792 -5.6972 -4.7697 -4.1526 1.0260
## ESS
## (Intercept)-Canis_latrans 232
## (Intercept)-Lynx_rufus 87
## (Intercept)-Didelphis_virginiana 320
## (Intercept)-Sylvilagus_floridanus 126
## (Intercept)-Sciurus_carolinensis 310
## (Intercept)-Vulpes_vulpes 44
## (Intercept)-Sus_scrofa 204
# 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): 2.8187
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0430 1.2938 -4.2614 -2.1624 0.7745 1.0005 355
## Cogon_Patch_Size -0.2635 1.0664 -2.4714 -0.2103 1.7977 1.0099 861
## Veg_shannon_index 0.6730 0.7544 -0.9927 0.7125 2.1420 1.0225 832
## total_shrub_cover 0.0169 0.5874 -1.1476 0.0270 1.2000 1.1060 386
## Avg_Cogongrass_Cover -0.1298 1.1786 -2.4353 -0.1302 2.2252 1.0559 146
## Tree_Density -2.2111 0.9191 -3.9448 -2.2115 -0.4201 1.0054 281
## Avg_Canopy_Cover 1.8845 0.9061 0.0574 1.8707 3.6844 1.0203 270
## I(Avg_Cogongrass_Cover^2) 1.5548 0.7940 0.0493 1.5363 3.2500 1.0283 128
## avg_veg_height -0.4899 0.6150 -1.7277 -0.4853 0.7185 1.0124 302
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 25.6306 49.1118 0.6451 11.9009 140.6566 1.3663 60
## Cogon_Patch_Size 15.0066 37.7958 0.3909 6.1263 78.9693 1.2683 112
## Veg_shannon_index 4.1510 7.1986 0.0870 1.8291 22.2353 1.0290 151
## total_shrub_cover 1.0767 3.1388 0.0472 0.4382 5.6199 1.2442 816
## Avg_Cogongrass_Cover 1.3834 3.0190 0.0529 0.5465 7.8853 1.1618 622
## Tree_Density 3.2862 10.4534 0.0575 0.7186 22.5755 1.4443 147
## Avg_Canopy_Cover 7.4015 22.1685 0.1542 2.6439 44.8169 1.4226 73
## I(Avg_Cogongrass_Cover^2) 3.6025 19.6123 0.0539 0.5667 19.2879 1.9638 104
## avg_veg_height 0.6505 1.0382 0.0419 0.3072 3.2858 1.0327 620
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6794 8.795 0.0528 0.6079 22.1388 2.6864 21
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7375 0.346 -5.3387 -4.7633 -3.9475 1.0034 1511
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6893 0.7984 0.1127 0.4624 2.6885 1.0422 464
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5299 1.6638 -3.4427 -0.6421
## (Intercept)-Lynx_rufus 1.1314 2.9465 -3.2314 0.5928
## (Intercept)-Didelphis_virginiana -4.9874 2.0645 -9.7326 -4.7224
## (Intercept)-Sylvilagus_floridanus -3.5231 2.0453 -8.6959 -3.2218
## (Intercept)-Sciurus_carolinensis -5.9698 3.4592 -12.7775 -5.3303
## (Intercept)-Vulpes_vulpes -5.5332 2.6710 -11.8720 -5.1824
## (Intercept)-Sus_scrofa -6.9263 3.1522 -13.9801 -6.3670
## Cogon_Patch_Size-Canis_latrans 2.9281 2.5707 -0.0919 2.3319
## Cogon_Patch_Size-Lynx_rufus -0.5968 2.6013 -6.3205 -0.6107
## Cogon_Patch_Size-Didelphis_virginiana 2.3935 1.4520 0.1888 2.2221
## Cogon_Patch_Size-Sylvilagus_floridanus -2.9010 3.0438 -11.3032 -2.1011
## Cogon_Patch_Size-Sciurus_carolinensis -1.8445 2.2498 -8.0022 -1.2913
## Cogon_Patch_Size-Vulpes_vulpes -1.7678 3.4589 -9.7351 -1.1396
## Cogon_Patch_Size-Sus_scrofa -1.5545 2.3768 -7.5809 -1.0746
## Veg_shannon_index-Canis_latrans 1.9220 1.3589 0.1729 1.6848
## Veg_shannon_index-Lynx_rufus -0.3234 1.7216 -4.5357 -0.0513
## Veg_shannon_index-Didelphis_virginiana 1.0142 0.9544 -0.8128 0.9680
## Veg_shannon_index-Sylvilagus_floridanus 1.0882 0.9873 -0.5983 1.0069
## Veg_shannon_index-Sciurus_carolinensis -0.4226 1.1601 -3.0725 -0.2748
## Veg_shannon_index-Vulpes_vulpes -0.1117 1.3136 -3.1533 0.0646
## Veg_shannon_index-Sus_scrofa 2.3299 1.6778 0.0718 2.0108
## total_shrub_cover-Canis_latrans 0.4458 0.7462 -0.8180 0.3796
## total_shrub_cover-Lynx_rufus -0.5021 1.2465 -3.4407 -0.3460
## total_shrub_cover-Didelphis_virginiana -0.2833 0.7990 -2.0273 -0.2139
## total_shrub_cover-Sylvilagus_floridanus 0.0250 0.8008 -1.6150 0.0422
## total_shrub_cover-Sciurus_carolinensis 0.2711 0.7105 -1.0767 0.2390
## total_shrub_cover-Vulpes_vulpes -0.1592 0.9124 -2.2573 -0.0930
## total_shrub_cover-Sus_scrofa 0.3187 0.8338 -1.2306 0.2590
## Avg_Cogongrass_Cover-Canis_latrans -0.0361 1.3996 -2.7007 -0.0544
## Avg_Cogongrass_Cover-Lynx_rufus 0.2516 1.5260 -2.6528 0.2144
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0238 1.4118 -2.8298 -0.0393
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5149 1.4653 -3.5837 -0.4371
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0923 1.3873 -2.7519 -0.0705
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0032 1.5115 -2.8699 -0.0180
## Avg_Cogongrass_Cover-Sus_scrofa -0.5182 1.5066 -3.7831 -0.4530
## Tree_Density-Canis_latrans -2.9178 1.3801 -6.2593 -2.7516
## Tree_Density-Lynx_rufus -1.4557 1.7573 -4.0241 -1.7204
## Tree_Density-Didelphis_virginiana -2.6039 1.4233 -5.9101 -2.4256
## Tree_Density-Sylvilagus_floridanus -2.7495 1.4312 -6.0321 -2.5465
## Tree_Density-Sciurus_carolinensis -2.9223 1.7524 -7.2293 -2.6419
## Tree_Density-Vulpes_vulpes -2.2246 1.5795 -5.3148 -2.2388
## Tree_Density-Sus_scrofa -2.4612 1.4403 -5.5729 -2.4106
## Avg_Canopy_Cover-Canis_latrans 0.1234 1.0586 -2.0352 0.1392
## Avg_Canopy_Cover-Lynx_rufus 1.4873 2.0613 -2.6810 1.3946
## Avg_Canopy_Cover-Didelphis_virginiana 3.3725 1.6911 1.3250 3.0586
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2287 2.4762 1.2376 3.7163
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7322 1.3240 0.9463 2.5121
## Avg_Canopy_Cover-Vulpes_vulpes 2.7724 2.1625 0.3835 2.3129
## Avg_Canopy_Cover-Sus_scrofa 2.2209 1.0663 0.5058 2.0835
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4832 2.0052 0.6297 2.0805
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5217 2.0460 0.5347 2.1171
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3250 0.8758 -0.3312 1.2957
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4073 0.9587 -0.2479 1.3255
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8254 0.9091 0.3500 1.7274
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9804 1.1108 0.2885 1.8305
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.6298 1.8218 -3.5677 0.9514
## avg_veg_height-Canis_latrans -0.5539 0.7286 -1.9548 -0.5457
## avg_veg_height-Lynx_rufus -0.5211 0.9363 -2.5244 -0.5122
## avg_veg_height-Didelphis_virginiana -0.6217 0.8020 -2.2645 -0.5890
## avg_veg_height-Sylvilagus_floridanus -0.5407 0.7714 -2.0897 -0.5335
## avg_veg_height-Sciurus_carolinensis -0.1792 0.7838 -1.6195 -0.2178
## avg_veg_height-Vulpes_vulpes -0.6642 0.9145 -2.6386 -0.6250
## avg_veg_height-Sus_scrofa -0.4805 0.8157 -2.1160 -0.4933
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.1315 1.0190 209
## (Intercept)-Lynx_rufus 8.3149 1.0175 81
## (Intercept)-Didelphis_virginiana -2.0034 1.1445 96
## (Intercept)-Sylvilagus_floridanus -0.6365 1.3210 55
## (Intercept)-Sciurus_carolinensis -2.5945 1.6394 40
## (Intercept)-Vulpes_vulpes -1.3410 1.1131 77
## (Intercept)-Sus_scrofa -2.6189 1.1830 59
## Cogon_Patch_Size-Canis_latrans 9.5405 1.0740 145
## Cogon_Patch_Size-Lynx_rufus 5.0772 1.0391 141
## Cogon_Patch_Size-Didelphis_virginiana 5.7177 1.1345 98
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5100 1.0947 82
## Cogon_Patch_Size-Sciurus_carolinensis 0.9568 1.0727 261
## Cogon_Patch_Size-Vulpes_vulpes 2.4351 1.2757 111
## Cogon_Patch_Size-Sus_scrofa 1.5535 1.0442 294
## Veg_shannon_index-Canis_latrans 5.5376 1.1764 103
## Veg_shannon_index-Lynx_rufus 2.4391 1.0233 195
## Veg_shannon_index-Didelphis_virginiana 3.0926 1.0007 1060
## Veg_shannon_index-Sylvilagus_floridanus 3.2903 1.0599 512
## Veg_shannon_index-Sciurus_carolinensis 1.5284 1.0424 307
## Veg_shannon_index-Vulpes_vulpes 2.1530 1.0319 323
## Veg_shannon_index-Sus_scrofa 6.4837 1.0007 229
## total_shrub_cover-Canis_latrans 2.1378 1.0328 380
## total_shrub_cover-Lynx_rufus 1.5748 1.0788 192
## total_shrub_cover-Didelphis_virginiana 1.1338 1.0215 621
## total_shrub_cover-Sylvilagus_floridanus 1.6971 1.0544 521
## total_shrub_cover-Sciurus_carolinensis 1.7450 1.0365 555
## total_shrub_cover-Vulpes_vulpes 1.5111 1.0876 514
## total_shrub_cover-Sus_scrofa 2.1584 1.0422 636
## Avg_Cogongrass_Cover-Canis_latrans 2.7303 1.0417 241
## Avg_Cogongrass_Cover-Lynx_rufus 3.4242 1.0561 234
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8751 1.0217 206
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.2252 1.0367 228
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6407 1.0287 205
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.1013 1.0529 244
## Avg_Cogongrass_Cover-Sus_scrofa 2.3037 1.0185 234
## Tree_Density-Canis_latrans -0.8087 1.0000 197
## Tree_Density-Lynx_rufus 3.0972 1.2002 147
## Tree_Density-Didelphis_virginiana -0.5302 1.1819 90
## Tree_Density-Sylvilagus_floridanus -0.5534 1.0563 153
## Tree_Density-Sciurus_carolinensis -0.6861 1.1681 96
## Tree_Density-Vulpes_vulpes 0.6913 1.0113 245
## Tree_Density-Sus_scrofa 0.2119 1.0329 326
## Avg_Canopy_Cover-Canis_latrans 2.3765 1.1613 166
## Avg_Canopy_Cover-Lynx_rufus 5.8625 1.0328 162
## Avg_Canopy_Cover-Didelphis_virginiana 7.5416 1.2806 93
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.6101 1.2804 66
## Avg_Canopy_Cover-Sciurus_carolinensis 6.0229 1.1337 150
## Avg_Canopy_Cover-Vulpes_vulpes 8.9799 1.1320 97
## Avg_Canopy_Cover-Sus_scrofa 4.8014 1.0069 241
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.0677 1.7104 53
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.2439 1.4583 62
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.2460 1.0402 141
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.6066 1.0153 173
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.9260 1.0571 216
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.5082 1.1182 165
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.9265 1.1615 123
## avg_veg_height-Canis_latrans 0.9004 1.0197 310
## avg_veg_height-Lynx_rufus 1.2988 1.0029 393
## avg_veg_height-Didelphis_virginiana 0.8922 1.0062 381
## avg_veg_height-Sylvilagus_floridanus 1.0170 1.0007 469
## avg_veg_height-Sciurus_carolinensis 1.4776 1.0201 458
## avg_veg_height-Vulpes_vulpes 1.1201 1.0021 432
## avg_veg_height-Sus_scrofa 1.2160 1.0132 519
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6545 0.1569 -4.9936 -4.6431 -4.3724 1.0344
## (Intercept)-Lynx_rufus -5.6644 0.3088 -6.2552 -5.6668 -5.0273 1.0886
## (Intercept)-Didelphis_virginiana -4.3173 0.2185 -4.7796 -4.3024 -3.9214 1.0204
## (Intercept)-Sylvilagus_floridanus -4.9468 0.2382 -5.4359 -4.9349 -4.4957 1.0059
## (Intercept)-Sciurus_carolinensis -4.3518 0.2277 -4.8223 -4.3451 -3.9317 1.0211
## (Intercept)-Vulpes_vulpes -5.6683 0.5119 -6.7141 -5.6694 -4.7098 1.0449
## (Intercept)-Sus_scrofa -4.7091 0.3670 -5.5605 -4.6853 -4.0607 1.0462
## ESS
## (Intercept)-Canis_latrans 236
## (Intercept)-Lynx_rufus 56
## (Intercept)-Didelphis_virginiana 368
## (Intercept)-Sylvilagus_floridanus 173
## (Intercept)-Sciurus_carolinensis 329
## (Intercept)-Vulpes_vulpes 75
## (Intercept)-Sus_scrofa 257
# 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): 3.964
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9077 0.5136 -1.9185 -0.8972 0.1498 1.0167 588
## Avg_Cogongrass_Cover 0.3130 0.3307 -0.3404 0.3083 0.9753 1.0033 942
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.072 1.7403 0.0601 0.5765 5.4906 1.0523 492
## Avg_Cogongrass_Cover 0.381 0.5304 0.0409 0.2201 1.7646 1.0107 1251
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6078 1.7961 0.1344 1.0951 6.1478 1.0577 129
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9179 0.2914 -5.4563 -4.9296 -4.2876 1.0079 482
## shrub_cover 0.4357 0.3511 -0.2224 0.4186 1.1831 1.0060 536
## veg_height 0.0074 0.2227 -0.4423 0.0097 0.4523 1.0127 732
## week 0.0560 1.6104 -3.0324 0.0284 3.1753 1.0131 409
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.017000e-01 5.750000e-01 0.0432 0.2399 1.690600e+00 1.0184 272
## shrub_cover 6.550000e-01 7.165000e-01 0.0966 0.4518 2.335200e+00 1.0239 470
## veg_height 3.030000e-01 3.176000e-01 0.0570 0.2138 1.181000e+00 1.0225 935
## week 1.089257e+08 1.390139e+09 0.0977 140.3441 4.217275e+08 1.6965 121
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0538 0.7705 -1.5200 -0.0665
## (Intercept)-Lynx_rufus -0.5161 0.7569 -1.9104 -0.5571
## (Intercept)-Didelphis_virginiana -1.1550 0.6125 -2.4445 -1.1361
## (Intercept)-Sylvilagus_floridanus -0.7735 0.6506 -2.0763 -0.7564
## (Intercept)-Sciurus_carolinensis -1.2646 0.6581 -2.6434 -1.2266
## (Intercept)-Vulpes_vulpes -1.4126 0.7729 -3.1115 -1.3708
## (Intercept)-Sus_scrofa -1.4903 0.8154 -3.3202 -1.4283
## Avg_Cogongrass_Cover-Canis_latrans 0.5096 0.4154 -0.2004 0.4690
## Avg_Cogongrass_Cover-Lynx_rufus 0.5657 0.4630 -0.1918 0.5244
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3861 0.3930 -0.3513 0.3739
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1523 0.4526 -1.1115 -0.1214
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4259 0.3771 -0.2719 0.4154
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3910 0.4462 -0.4342 0.3639
## Avg_Cogongrass_Cover-Sus_scrofa 0.0616 0.5505 -1.2083 0.1018
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5388 1.0235 377
## (Intercept)-Lynx_rufus 1.1256 1.0265 334
## (Intercept)-Didelphis_virginiana 0.0219 1.0035 760
## (Intercept)-Sylvilagus_floridanus 0.4713 1.0044 530
## (Intercept)-Sciurus_carolinensis -0.0725 1.0135 608
## (Intercept)-Vulpes_vulpes 0.0192 1.0373 279
## (Intercept)-Sus_scrofa -0.1173 1.0457 412
## Avg_Cogongrass_Cover-Canis_latrans 1.4508 1.0036 1069
## Avg_Cogongrass_Cover-Lynx_rufus 1.6181 1.0009 941
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1857 1.0007 1704
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6589 1.0001 1064
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2165 1.0132 1699
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3360 1.0042 1134
## Avg_Cogongrass_Cover-Sus_scrofa 1.0302 1.0057 904
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7697 0.1697 -5.1255 -4.7638
## (Intercept)-Lynx_rufus -5.4078 0.2978 -6.0775 -5.3944
## (Intercept)-Didelphis_virginiana -4.6712 0.2705 -5.2260 -4.6689
## (Intercept)-Sylvilagus_floridanus -4.9263 0.2198 -5.3819 -4.9241
## (Intercept)-Sciurus_carolinensis -4.6735 0.2641 -5.2278 -4.6713
## (Intercept)-Vulpes_vulpes -5.5286 0.5470 -6.8221 -5.4584
## (Intercept)-Sus_scrofa -5.1595 0.4173 -6.0046 -5.1490
## shrub_cover-Canis_latrans -0.2409 0.1962 -0.6261 -0.2370
## shrub_cover-Lynx_rufus -0.0708 0.3169 -0.6590 -0.0838
## shrub_cover-Didelphis_virginiana 1.0506 0.3451 0.4520 1.0297
## shrub_cover-Sylvilagus_floridanus 0.5094 0.3904 -0.2662 0.5192
## shrub_cover-Sciurus_carolinensis 0.8882 0.3889 0.1614 0.8749
## shrub_cover-Vulpes_vulpes 0.1886 0.5552 -0.8942 0.1847
## shrub_cover-Sus_scrofa 0.7935 0.6463 -0.4318 0.7681
## veg_height-Canis_latrans -0.5569 0.1822 -0.9253 -0.5495
## veg_height-Lynx_rufus 0.1088 0.2385 -0.3475 0.1021
## veg_height-Didelphis_virginiana 0.4685 0.2236 0.0523 0.4635
## veg_height-Sylvilagus_floridanus 0.1924 0.2248 -0.2542 0.1930
## veg_height-Sciurus_carolinensis 0.1919 0.2058 -0.2039 0.1889
## veg_height-Vulpes_vulpes -0.1167 0.2857 -0.7063 -0.1178
## veg_height-Sus_scrofa -0.2266 0.3123 -0.8588 -0.2239
## week-Canis_latrans -180.5823 10619.2264 -4782.0808 -0.1489
## week-Lynx_rufus -131.8157 13689.0188 -5005.1619 -0.1208
## week-Didelphis_virginiana -366.6455 11083.1714 -5842.5542 -0.2831
## week-Sylvilagus_floridanus -353.6124 8481.0059 -5266.1718 -0.0754
## week-Sciurus_carolinensis 67.4573 11263.3986 -5229.5677 0.0363
## week-Vulpes_vulpes 55.2173 9172.4413 -4203.1496 0.1089
## week-Sus_scrofa -25.9313 10815.7332 -5189.1315 0.0263
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4542 1.0053 201
## (Intercept)-Lynx_rufus -4.8606 1.0289 99
## (Intercept)-Didelphis_virginiana -4.1603 1.0307 216
## (Intercept)-Sylvilagus_floridanus -4.5046 1.0420 214
## (Intercept)-Sciurus_carolinensis -4.1689 1.0628 211
## (Intercept)-Vulpes_vulpes -4.6695 1.0096 94
## (Intercept)-Sus_scrofa -4.3512 1.0223 259
## shrub_cover-Canis_latrans 0.1247 1.0215 208
## shrub_cover-Lynx_rufus 0.5445 1.0550 148
## shrub_cover-Didelphis_virginiana 1.7687 1.0634 174
## shrub_cover-Sylvilagus_floridanus 1.2586 1.0286 138
## shrub_cover-Sciurus_carolinensis 1.6463 1.0606 190
## shrub_cover-Vulpes_vulpes 1.3314 1.0262 210
## shrub_cover-Sus_scrofa 2.1646 1.0137 354
## veg_height-Canis_latrans -0.2295 1.0494 158
## veg_height-Lynx_rufus 0.5732 1.0413 173
## veg_height-Didelphis_virginiana 0.9107 1.0112 323
## veg_height-Sylvilagus_floridanus 0.6272 1.0050 217
## veg_height-Sciurus_carolinensis 0.6012 1.0551 250
## veg_height-Vulpes_vulpes 0.4499 1.0759 173
## veg_height-Sus_scrofa 0.3849 1.0259 386
## week-Canis_latrans 4907.3044 1.2455 1704
## week-Lynx_rufus 3564.9065 1.2787 13162
## week-Didelphis_virginiana 4043.8652 1.3317 863
## week-Sylvilagus_floridanus 3766.7198 1.3212 420
## week-Sciurus_carolinensis 4348.7549 1.2448 5203
## week-Vulpes_vulpes 4986.1232 1.2411 8394
## week-Sus_scrofa 3520.7878 1.2774 6305
# 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): 2.4065
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9815 0.5140 -1.9795 -0.9864 0.0260 1.002 616
## Avg_Cogongrass_Cover 0.2455 0.3195 -0.3932 0.2475 0.8683 1.005 1006
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3012 2.0801 0.0766 0.7506 5.5602 1.0678 264
## Avg_Cogongrass_Cover 0.3880 0.4796 0.0409 0.2218 1.7382 1.0091 1287
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1376 1.0635 0.0742 0.8161 3.9458 1.0248 224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7027 0.3218 -5.2834 -4.7154 -4.0556 1.0051 1002
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4996 0.7298 0.0623 0.305 2.1614 1.0281 183
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0234 0.7182 -1.4027 0.0183
## (Intercept)-Lynx_rufus -0.4727 0.9833 -1.8870 -0.5526
## (Intercept)-Didelphis_virginiana -1.3690 0.6013 -2.6364 -1.3291
## (Intercept)-Sylvilagus_floridanus -0.8402 0.6047 -2.0276 -0.8474
## (Intercept)-Sciurus_carolinensis -1.4162 0.6140 -2.7144 -1.3897
## (Intercept)-Vulpes_vulpes -1.5000 0.8322 -3.1817 -1.4924
## (Intercept)-Sus_scrofa -1.7894 0.7544 -3.4505 -1.7141
## Avg_Cogongrass_Cover-Canis_latrans 0.4024 0.3755 -0.2835 0.3858
## Avg_Cogongrass_Cover-Lynx_rufus 0.5562 0.4577 -0.2208 0.5216
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3798 0.3801 -0.3245 0.3785
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2064 0.4471 -1.1888 -0.1681
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3911 0.3667 -0.3266 0.3849
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3242 0.4347 -0.5534 0.3170
## Avg_Cogongrass_Cover-Sus_scrofa -0.0902 0.5392 -1.3120 -0.0363
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4883 1.0086 392
## (Intercept)-Lynx_rufus 1.0930 1.1264 125
## (Intercept)-Didelphis_virginiana -0.2493 1.0067 1093
## (Intercept)-Sylvilagus_floridanus 0.3954 1.0241 852
## (Intercept)-Sciurus_carolinensis -0.2999 1.0086 976
## (Intercept)-Vulpes_vulpes 0.1563 1.0110 234
## (Intercept)-Sus_scrofa -0.5021 1.0288 693
## Avg_Cogongrass_Cover-Canis_latrans 1.1993 1.0059 1442
## Avg_Cogongrass_Cover-Lynx_rufus 1.5824 1.0059 1267
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1360 1.0018 1539
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5807 1.0166 1328
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1434 1.0019 1687
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1984 1.0025 1182
## Avg_Cogongrass_Cover-Sus_scrofa 0.8199 1.0207 938
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6350 0.1550 -4.9584 -4.6252 -4.3547 1.0317
## (Intercept)-Lynx_rufus -5.2825 0.2651 -5.8355 -5.2683 -4.7829 1.0325
## (Intercept)-Didelphis_virginiana -4.3419 0.2300 -4.8484 -4.3270 -3.9289 1.0326
## (Intercept)-Sylvilagus_floridanus -4.8762 0.2410 -5.3961 -4.8641 -4.4688 1.1092
## (Intercept)-Sciurus_carolinensis -4.3706 0.2167 -4.8001 -4.3769 -3.9372 1.0027
## (Intercept)-Vulpes_vulpes -5.4886 0.5853 -6.8383 -5.3881 -4.5934 1.0237
## (Intercept)-Sus_scrofa -4.7691 0.3467 -5.5078 -4.7535 -4.1368 1.0113
## ESS
## (Intercept)-Canis_latrans 242
## (Intercept)-Lynx_rufus 111
## (Intercept)-Didelphis_virginiana 315
## (Intercept)-Sylvilagus_floridanus 167
## (Intercept)-Sciurus_carolinensis 336
## (Intercept)-Vulpes_vulpes 80
## (Intercept)-Sus_scrofa 218
# 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): 3.4025
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0126 0.5116 -2.0268 -1.0173 0.0527 1.0140 589
## Avg_Cogongrass_Cover 0.2627 0.3136 -0.3835 0.2652 0.8701 1.0129 1232
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2616 1.6962 0.0746 0.7530 5.3286 1.0206 638
## Avg_Cogongrass_Cover 0.3978 0.5633 0.0401 0.2311 1.7972 1.0361 1093
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2995 1.2367 0.1116 0.96 4.6374 1.1317 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7079 0.2931 -5.252 -4.7197 -4.0786 1.0023 1157
## week -0.1510 1.6809 -3.621 -0.0998 3.0753 1.0041 792
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.837000e-01 6.627000e-01 0.0651 0.2959 2.08620e+00 1.0343 172
## week 8.387403e+13 9.966746e+14 0.1424 222.5641 1.93177e+13 1.8700 182
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0222 0.7436 -1.4830 -0.0292
## (Intercept)-Lynx_rufus -0.5528 0.7320 -1.8770 -0.5886
## (Intercept)-Didelphis_virginiana -1.3692 0.6388 -2.6993 -1.3442
## (Intercept)-Sylvilagus_floridanus -0.8599 0.6147 -2.1123 -0.8593
## (Intercept)-Sciurus_carolinensis -1.4714 0.6562 -2.9000 -1.4287
## (Intercept)-Vulpes_vulpes -1.5621 0.8116 -3.3073 -1.5321
## (Intercept)-Sus_scrofa -1.7918 0.7901 -3.5476 -1.7071
## Avg_Cogongrass_Cover-Canis_latrans 0.4212 0.3945 -0.2819 0.3976
## Avg_Cogongrass_Cover-Lynx_rufus 0.5734 0.4614 -0.1824 0.5258
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4058 0.3766 -0.3137 0.3942
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1753 0.4394 -1.1199 -0.1429
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4139 0.3654 -0.2857 0.4062
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3220 0.4426 -0.5715 0.3226
## Avg_Cogongrass_Cover-Sus_scrofa -0.0832 0.5591 -1.3706 -0.0076
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5015 1.0018 435
## (Intercept)-Lynx_rufus 1.0426 1.0091 497
## (Intercept)-Didelphis_virginiana -0.2225 1.0091 733
## (Intercept)-Sylvilagus_floridanus 0.4012 1.0048 682
## (Intercept)-Sciurus_carolinensis -0.3053 1.0109 622
## (Intercept)-Vulpes_vulpes 0.0246 1.0096 323
## (Intercept)-Sus_scrofa -0.4182 1.0033 562
## Avg_Cogongrass_Cover-Canis_latrans 1.3011 1.0098 1627
## Avg_Cogongrass_Cover-Lynx_rufus 1.5853 1.0164 1230
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1580 1.0148 1775
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6012 1.0037 1170
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1524 1.0085 1858
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2289 1.0140 1289
## Avg_Cogongrass_Cover-Sus_scrofa 0.8006 1.0029 925
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6659 1.492000e-01 -4.9722 -4.6631
## (Intercept)-Lynx_rufus -5.3231 2.812000e-01 -5.8998 -5.3085
## (Intercept)-Didelphis_virginiana -4.3175 2.159000e-01 -4.7563 -4.3048
## (Intercept)-Sylvilagus_floridanus -4.8686 2.229000e-01 -5.3403 -4.8544
## (Intercept)-Sciurus_carolinensis -4.3716 2.310000e-01 -4.8498 -4.3598
## (Intercept)-Vulpes_vulpes -5.4410 6.025000e-01 -6.9250 -5.3245
## (Intercept)-Sus_scrofa -4.7695 3.340000e-01 -5.4428 -4.7586
## week-Canis_latrans 68042.7807 8.516796e+06 -219955.8386 -0.2125
## week-Lynx_rufus -24247.1481 9.185407e+06 -250893.3884 -0.1814
## week-Didelphis_virginiana 245418.1497 1.132883e+07 -362813.4771 -0.3126
## week-Sylvilagus_floridanus 257559.9805 8.343332e+06 -148191.3356 -0.5279
## week-Sciurus_carolinensis -91545.9594 9.259015e+06 -316432.3481 -0.2830
## week-Vulpes_vulpes 93147.3247 9.526293e+06 -228705.4773 -0.2208
## week-Sus_scrofa 162528.4917 6.638102e+06 -220633.1100 -0.3763
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3815 1.0260 232
## (Intercept)-Lynx_rufus -4.8220 1.0074 99
## (Intercept)-Didelphis_virginiana -3.9242 1.0170 371
## (Intercept)-Sylvilagus_floridanus -4.4792 1.0167 201
## (Intercept)-Sciurus_carolinensis -3.9450 1.0129 286
## (Intercept)-Vulpes_vulpes -4.5614 1.0766 58
## (Intercept)-Sus_scrofa -4.1571 1.0171 282
## week-Canis_latrans 234678.0157 1.2967 12057
## week-Lynx_rufus 178262.3444 1.2910 29316
## week-Didelphis_virginiana 145276.3829 1.3364 1947
## week-Sylvilagus_floridanus 300833.2046 1.3822 890
## week-Sciurus_carolinensis 137816.8264 1.3001 23136
## week-Vulpes_vulpes 274406.3635 1.2999 5015
## week-Sus_scrofa 344419.3301 1.3489 1319
# 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): 3.693
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3934 1.0743 -3.3910 -1.4634 0.9416 1.0089 281
## Cogon_Patch_Size -0.7171 0.9069 -2.5760 -0.6915 1.0197 1.0170 742
## Veg_shannon_index 0.5860 0.7098 -0.9499 0.5975 1.9637 1.0133 546
## total_shrub_cover 0.0170 0.5146 -0.9978 0.0214 0.9996 1.0236 554
## Avg_Cogongrass_Cover 1.9465 0.7032 0.5719 1.9130 3.3340 1.0078 351
## Tree_Density -1.8896 0.7795 -3.4395 -1.8722 -0.4024 1.0312 293
## Avg_Canopy_Cover 1.7681 0.7578 0.1655 1.7755 3.2721 1.0017 706
## avg_veg_height -0.6595 0.5695 -1.8050 -0.6615 0.4995 1.0035 295
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.0921 23.1816 0.6610 7.4712 67.8812 1.2031 68
## Cogon_Patch_Size 7.8649 14.1792 0.2732 3.7152 43.2935 1.0585 146
## Veg_shannon_index 3.1302 5.0276 0.0939 1.4185 17.7361 1.0331 175
## total_shrub_cover 0.9558 3.6905 0.0459 0.3796 5.1800 1.3783 343
## Avg_Cogongrass_Cover 1.2030 2.0627 0.0543 0.5254 6.4544 1.0205 614
## Tree_Density 2.1285 4.9904 0.0574 0.7477 12.3441 1.0178 530
## Avg_Canopy_Cover 3.8785 7.5003 0.1438 1.9331 18.4598 1.1216 245
## avg_veg_height 0.5388 0.7794 0.0439 0.2856 2.5711 1.0067 1018
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4937 2.3161 0.05 0.707 7.8611 1.5977 89
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7085 0.3864 -5.3186 -4.7474 -3.7529 1.0018 1551
## week -0.0353 1.6942 -3.6472 0.0057 3.0547 1.0330 452
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.096000e-01 1.079700e+00 0.0852 0.4164 3.055300e+00 1.0024 290
## week 1.601663e+16 4.101174e+17 0.0961 530.5059 2.145398e+16 1.4143 964
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8456 1.2381 -1.4907 0.7824
## (Intercept)-Lynx_rufus 1.8827 3.7740 -1.9911 0.8288
## (Intercept)-Didelphis_virginiana -3.1984 1.1645 -5.7850 -3.0834
## (Intercept)-Sylvilagus_floridanus -1.9261 1.2086 -4.5669 -1.8387
## (Intercept)-Sciurus_carolinensis -3.5717 1.4009 -6.9406 -3.4033
## (Intercept)-Vulpes_vulpes -3.3895 1.7574 -7.2320 -3.2866
## (Intercept)-Sus_scrofa -5.0728 2.0547 -9.9209 -4.7792
## Cogon_Patch_Size-Canis_latrans 1.2422 1.6958 -0.8901 0.8709
## Cogon_Patch_Size-Lynx_rufus -0.6777 1.7599 -3.8657 -0.8481
## Cogon_Patch_Size-Didelphis_virginiana 1.2355 0.9863 -0.4050 1.1312
## Cogon_Patch_Size-Sylvilagus_floridanus -2.7367 2.1790 -8.9570 -2.1876
## Cogon_Patch_Size-Sciurus_carolinensis -2.2237 1.8406 -6.4756 -1.8287
## Cogon_Patch_Size-Vulpes_vulpes -2.0091 2.2264 -7.8542 -1.6101
## Cogon_Patch_Size-Sus_scrofa -1.7950 1.9683 -6.8999 -1.4107
## Veg_shannon_index-Canis_latrans 1.4172 0.8031 0.0685 1.3465
## Veg_shannon_index-Lynx_rufus -0.1362 1.6155 -4.4610 0.0970
## Veg_shannon_index-Didelphis_virginiana 0.9867 0.9144 -0.5679 0.9025
## Veg_shannon_index-Sylvilagus_floridanus 1.0418 0.8388 -0.3797 0.9761
## Veg_shannon_index-Sciurus_carolinensis -0.3701 0.9130 -2.4615 -0.2929
## Veg_shannon_index-Vulpes_vulpes -0.4351 1.1649 -3.0009 -0.3001
## Veg_shannon_index-Sus_scrofa 2.1631 1.5431 -0.0248 1.8884
## total_shrub_cover-Canis_latrans 0.4766 0.6822 -0.6219 0.4023
## total_shrub_cover-Lynx_rufus -0.4972 1.1392 -3.0441 -0.3381
## total_shrub_cover-Didelphis_virginiana -0.2464 0.7081 -1.7561 -0.2023
## total_shrub_cover-Sylvilagus_floridanus 0.0467 0.6725 -1.3102 0.0555
## total_shrub_cover-Sciurus_carolinensis 0.1763 0.6166 -0.9908 0.1510
## total_shrub_cover-Vulpes_vulpes -0.1425 0.8898 -2.1829 -0.0685
## total_shrub_cover-Sus_scrofa 0.2610 0.8057 -1.1941 0.2247
## Avg_Cogongrass_Cover-Canis_latrans 2.3335 0.9550 0.6909 2.2522
## Avg_Cogongrass_Cover-Lynx_rufus 2.4653 1.1183 0.6005 2.3474
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1184 0.8834 0.4540 2.0861
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5314 0.9152 -0.4342 1.5452
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2530 0.9090 0.6827 2.1983
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3044 1.0418 0.4714 2.2128
## Avg_Cogongrass_Cover-Sus_scrofa 1.5174 1.0883 -0.8514 1.5985
## Tree_Density-Canis_latrans -2.5392 1.2489 -5.4175 -2.3366
## Tree_Density-Lynx_rufus -1.0371 1.2275 -3.1046 -1.0723
## Tree_Density-Didelphis_virginiana -2.2231 1.0736 -4.6715 -2.0910
## Tree_Density-Sylvilagus_floridanus -2.4036 1.1975 -5.1864 -2.2570
## Tree_Density-Sciurus_carolinensis -2.4201 1.2710 -5.4457 -2.2473
## Tree_Density-Vulpes_vulpes -1.9114 1.2382 -4.4097 -1.8943
## Tree_Density-Sus_scrofa -2.1528 1.2906 -5.3010 -1.9924
## Avg_Canopy_Cover-Canis_latrans 0.2798 0.7526 -1.1741 0.2888
## Avg_Canopy_Cover-Lynx_rufus 1.0212 1.4892 -1.6924 0.9104
## Avg_Canopy_Cover-Didelphis_virginiana 2.9722 1.1479 1.3117 2.7719
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4894 1.6948 1.1830 3.1318
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4478 0.9544 0.9787 2.2981
## Avg_Canopy_Cover-Vulpes_vulpes 2.2387 1.1636 0.4321 2.0444
## Avg_Canopy_Cover-Sus_scrofa 2.1422 0.9876 0.5228 2.0357
## avg_veg_height-Canis_latrans -0.7140 0.6324 -1.9171 -0.7190
## avg_veg_height-Lynx_rufus -0.6673 0.8175 -2.2756 -0.6831
## avg_veg_height-Didelphis_virginiana -0.7357 0.7189 -2.1544 -0.7333
## avg_veg_height-Sylvilagus_floridanus -0.8173 0.7197 -2.3226 -0.8045
## avg_veg_height-Sciurus_carolinensis -0.3249 0.7229 -1.6764 -0.3385
## avg_veg_height-Vulpes_vulpes -0.7959 0.7951 -2.4149 -0.7788
## avg_veg_height-Sus_scrofa -0.7076 0.7499 -2.2371 -0.6979
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.5885 1.0341 168
## (Intercept)-Lynx_rufus 12.7647 1.3332 33
## (Intercept)-Didelphis_virginiana -1.2084 1.0077 498
## (Intercept)-Sylvilagus_floridanus 0.2350 1.0269 319
## (Intercept)-Sciurus_carolinensis -1.2875 1.0064 231
## (Intercept)-Vulpes_vulpes -0.1187 1.0315 168
## (Intercept)-Sus_scrofa -1.8836 1.0158 156
## Cogon_Patch_Size-Canis_latrans 5.8052 1.0339 243
## Cogon_Patch_Size-Lynx_rufus 3.5822 1.0294 290
## Cogon_Patch_Size-Didelphis_virginiana 3.4321 1.0400 449
## Cogon_Patch_Size-Sylvilagus_floridanus -0.0703 1.0800 236
## Cogon_Patch_Size-Sciurus_carolinensis 0.0523 1.0679 219
## Cogon_Patch_Size-Vulpes_vulpes 1.2401 1.0176 231
## Cogon_Patch_Size-Sus_scrofa 0.8461 1.0169 252
## Veg_shannon_index-Canis_latrans 3.2226 1.0147 592
## Veg_shannon_index-Lynx_rufus 2.3080 1.0680 180
## Veg_shannon_index-Didelphis_virginiana 2.9452 1.0072 736
## Veg_shannon_index-Sylvilagus_floridanus 2.9242 1.0175 567
## Veg_shannon_index-Sciurus_carolinensis 1.1741 1.0159 432
## Veg_shannon_index-Vulpes_vulpes 1.4658 1.0087 255
## Veg_shannon_index-Sus_scrofa 5.9926 1.0047 249
## total_shrub_cover-Canis_latrans 2.0843 1.0190 507
## total_shrub_cover-Lynx_rufus 1.1406 1.0813 209
## total_shrub_cover-Didelphis_virginiana 0.9952 1.0337 780
## total_shrub_cover-Sylvilagus_floridanus 1.3232 1.0035 850
## total_shrub_cover-Sciurus_carolinensis 1.4895 1.0020 1086
## total_shrub_cover-Vulpes_vulpes 1.3767 1.0355 335
## total_shrub_cover-Sus_scrofa 1.9191 1.0199 904
## Avg_Cogongrass_Cover-Canis_latrans 4.4602 1.0021 392
## Avg_Cogongrass_Cover-Lynx_rufus 5.0839 1.0373 343
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9060 1.0016 474
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2384 1.0197 522
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3222 1.0194 418
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7122 1.0024 386
## Avg_Cogongrass_Cover-Sus_scrofa 3.4492 1.0101 571
## Tree_Density-Canis_latrans -0.7199 1.0187 317
## Tree_Density-Lynx_rufus 1.6920 1.0756 274
## Tree_Density-Didelphis_virginiana -0.4937 1.0056 426
## Tree_Density-Sylvilagus_floridanus -0.4269 1.0072 425
## Tree_Density-Sciurus_carolinensis -0.5844 1.0103 326
## Tree_Density-Vulpes_vulpes 0.5523 1.0279 278
## Tree_Density-Sus_scrofa -0.0934 1.0079 361
## Avg_Canopy_Cover-Canis_latrans 1.7667 1.0273 486
## Avg_Canopy_Cover-Lynx_rufus 4.3301 1.0338 224
## Avg_Canopy_Cover-Didelphis_virginiana 5.6181 1.0787 284
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.7759 1.0265 246
## Avg_Canopy_Cover-Sciurus_carolinensis 4.7921 1.0522 394
## Avg_Canopy_Cover-Vulpes_vulpes 5.0732 1.0339 388
## Avg_Canopy_Cover-Sus_scrofa 4.3842 1.0250 431
## avg_veg_height-Canis_latrans 0.5275 1.0102 527
## avg_veg_height-Lynx_rufus 1.0226 1.0097 363
## avg_veg_height-Didelphis_virginiana 0.6739 1.0020 567
## avg_veg_height-Sylvilagus_floridanus 0.5619 1.0188 454
## avg_veg_height-Sciurus_carolinensis 1.2051 1.0012 454
## avg_veg_height-Vulpes_vulpes 0.7410 1.0028 390
## avg_veg_height-Sus_scrofa 0.8081 1.0132 511
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6436 1.560000e-01 -4.946000e+00
## (Intercept)-Lynx_rufus -5.5574 3.658000e-01 -6.351800e+00
## (Intercept)-Didelphis_virginiana -4.3170 2.311000e-01 -4.788400e+00
## (Intercept)-Sylvilagus_floridanus -4.9316 2.273000e-01 -5.415900e+00
## (Intercept)-Sciurus_carolinensis -4.3503 2.174000e-01 -4.796300e+00
## (Intercept)-Vulpes_vulpes -5.6395 5.777000e-01 -6.861000e+00
## (Intercept)-Sus_scrofa -4.7577 3.618000e-01 -5.520200e+00
## week-Canis_latrans -1409378.9435 7.020761e+07 -3.121655e+07
## week-Lynx_rufus 3914869.8335 2.371869e+08 -2.837815e+07
## week-Didelphis_virginiana -4973718.8593 2.013427e+08 -3.573464e+07
## week-Sylvilagus_floridanus -565117.2753 1.073581e+08 -3.327868e+07
## week-Sciurus_carolinensis 1759996.8196 8.499486e+07 -2.444820e+07
## week-Vulpes_vulpes -1911922.7163 1.276670e+08 -2.962150e+07
## week-Sus_scrofa 968251.5601 1.463572e+08 -3.217290e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6420 -4.3407 1.0504 266
## (Intercept)-Lynx_rufus -5.5262 -4.9080 1.0284 44
## (Intercept)-Didelphis_virginiana -4.3099 -3.8817 1.0309 321
## (Intercept)-Sylvilagus_floridanus -4.9235 -4.4937 1.0022 191
## (Intercept)-Sciurus_carolinensis -4.3427 -3.9522 1.0058 365
## (Intercept)-Vulpes_vulpes -5.6068 -4.6291 1.0236 67
## (Intercept)-Sus_scrofa -4.7451 -4.0810 1.0292 224
## week-Canis_latrans 0.0595 26210728.6311 1.2670 2829
## week-Lynx_rufus 0.2255 28819190.7322 1.3067 3373
## week-Didelphis_virginiana 0.2186 23822138.7824 1.3553 1819
## week-Sylvilagus_floridanus 0.1277 26860269.6168 1.2783 27172
## week-Sciurus_carolinensis 0.2757 35184329.9637 1.2637 2757
## week-Vulpes_vulpes 0.3482 33365591.0787 1.3062 2373
## week-Sus_scrofa 0.0183 24280108.2090 1.2850 4313
# 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): 3.4608
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0878 0.5773 -2.3230 -1.0672 0.1012 1.0160 396
## Avg_Cogongrass_Cover 0.3253 0.3961 -0.4471 0.3287 1.1273 1.0510 691
## total_shrub_cover -0.0545 0.3518 -0.7829 -0.0486 0.6277 1.0042 968
## avg_veg_height -0.0625 0.3574 -0.7765 -0.0554 0.6280 1.0223 602
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4765 2.1300 0.0803 0.8811 6.6938 1.0418 632
## Avg_Cogongrass_Cover 0.5010 1.0549 0.0435 0.2560 2.3256 1.0228 1401
## total_shrub_cover 0.4656 0.7902 0.0390 0.2474 2.1670 1.0227 1115
## avg_veg_height 0.2870 0.3577 0.0349 0.1763 1.2505 1.0093 1439
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7344 2.1699 0.1236 1.1435 7.2577 1.1003 87
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7018 0.2856 -5.1951 -4.7214 -4.0658 1.0173 1100
## week -0.0258 1.7041 -3.3989 0.0027 3.1588 1.0185 406
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.389000e-01 5.597000e-01 0.0603 0.2869 1.685600e+00 1.0553 520
## week 2.903154e+12 3.592156e+13 0.0913 114.5326 3.230489e+12 1.8314 278
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0342 0.8905 -1.7537 -0.0427
## (Intercept)-Lynx_rufus -0.5641 0.8708 -2.2875 -0.5952
## (Intercept)-Didelphis_virginiana -1.4462 0.6672 -2.8770 -1.4142
## (Intercept)-Sylvilagus_floridanus -0.8731 0.7202 -2.2929 -0.8872
## (Intercept)-Sciurus_carolinensis -1.5438 0.7267 -3.1671 -1.4724
## (Intercept)-Vulpes_vulpes -1.6742 0.8132 -3.3559 -1.6243
## (Intercept)-Sus_scrofa -1.9498 0.8611 -3.9611 -1.8704
## Avg_Cogongrass_Cover-Canis_latrans 0.5538 0.5127 -0.3434 0.5288
## Avg_Cogongrass_Cover-Lynx_rufus 0.6325 0.5538 -0.3149 0.5967
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4905 0.4665 -0.3634 0.4803
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1449 0.5432 -1.3824 -0.1028
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3624 0.4501 -0.5387 0.3681
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4405 0.5582 -0.6187 0.4336
## Avg_Cogongrass_Cover-Sus_scrofa -0.0354 0.6709 -1.5509 0.0370
## total_shrub_cover-Canis_latrans 0.3874 0.4720 -0.4182 0.3328
## total_shrub_cover-Lynx_rufus -0.5178 0.6112 -1.9704 -0.4367
## total_shrub_cover-Didelphis_virginiana -0.1227 0.4199 -0.9851 -0.1125
## total_shrub_cover-Sylvilagus_floridanus -0.1804 0.4304 -1.0920 -0.1584
## total_shrub_cover-Sciurus_carolinensis 0.0026 0.4175 -0.8275 0.0016
## total_shrub_cover-Vulpes_vulpes -0.1644 0.5210 -1.2695 -0.1386
## total_shrub_cover-Sus_scrofa 0.2011 0.5292 -0.7453 0.1703
## avg_veg_height-Canis_latrans -0.0770 0.4372 -0.9579 -0.0796
## avg_veg_height-Lynx_rufus -0.0641 0.5085 -1.0767 -0.0696
## avg_veg_height-Didelphis_virginiana -0.0896 0.4337 -0.9761 -0.0860
## avg_veg_height-Sylvilagus_floridanus -0.2078 0.4577 -1.1034 -0.1987
## avg_veg_height-Sciurus_carolinensis 0.2288 0.4646 -0.6245 0.2110
## avg_veg_height-Vulpes_vulpes -0.1527 0.4943 -1.1864 -0.1405
## avg_veg_height-Sus_scrofa -0.0688 0.4779 -1.0168 -0.0696
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7723 1.0312 233
## (Intercept)-Lynx_rufus 1.3194 1.0351 227
## (Intercept)-Didelphis_virginiana -0.1645 1.0057 830
## (Intercept)-Sylvilagus_floridanus 0.5961 1.0077 294
## (Intercept)-Sciurus_carolinensis -0.2173 1.0070 588
## (Intercept)-Vulpes_vulpes -0.1567 1.0374 359
## (Intercept)-Sus_scrofa -0.6041 1.0262 454
## Avg_Cogongrass_Cover-Canis_latrans 1.6274 1.0285 959
## Avg_Cogongrass_Cover-Lynx_rufus 1.8715 1.0252 970
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4785 1.0293 1134
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8057 1.0339 902
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2508 1.0172 1028
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5715 1.0092 911
## Avg_Cogongrass_Cover-Sus_scrofa 1.0646 1.0333 659
## total_shrub_cover-Canis_latrans 1.4473 1.0042 1333
## total_shrub_cover-Lynx_rufus 0.4544 1.0088 769
## total_shrub_cover-Didelphis_virginiana 0.6801 1.0071 1669
## total_shrub_cover-Sylvilagus_floridanus 0.6206 1.0007 1267
## total_shrub_cover-Sciurus_carolinensis 0.8162 1.0009 1799
## total_shrub_cover-Vulpes_vulpes 0.7629 1.0041 1053
## total_shrub_cover-Sus_scrofa 1.3621 1.0046 1180
## avg_veg_height-Canis_latrans 0.8118 1.0071 836
## avg_veg_height-Lynx_rufus 0.9266 1.0107 843
## avg_veg_height-Didelphis_virginiana 0.7423 1.0144 968
## avg_veg_height-Sylvilagus_floridanus 0.6703 1.0138 1092
## avg_veg_height-Sciurus_carolinensis 1.1958 1.0143 1039
## avg_veg_height-Vulpes_vulpes 0.7753 1.0109 967
## avg_veg_height-Sus_scrofa 0.8810 1.0169 966
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6734 0.1539 -4.9923 -4.6677
## (Intercept)-Lynx_rufus -5.3163 0.2597 -5.8220 -5.3147
## (Intercept)-Didelphis_virginiana -4.3370 0.2254 -4.7871 -4.3341
## (Intercept)-Sylvilagus_floridanus -4.8761 0.2186 -5.3394 -4.8721
## (Intercept)-Sciurus_carolinensis -4.3679 0.2184 -4.8366 -4.3562
## (Intercept)-Vulpes_vulpes -5.3969 0.4835 -6.4557 -5.3522
## (Intercept)-Sus_scrofa -4.7223 0.3150 -5.3653 -4.7047
## week-Canis_latrans 26088.0239 1593226.3718 -175278.5327 0.1651
## week-Lynx_rufus -45192.4326 1620320.3614 -344066.2462 -0.0694
## week-Didelphis_virginiana -15941.0748 1417184.7328 -261949.9637 -0.0637
## week-Sylvilagus_floridanus -716.2530 2122190.8500 -161589.3715 0.0743
## week-Sciurus_carolinensis 25300.2609 1533410.7268 -314310.3470 -0.0505
## week-Vulpes_vulpes 3937.2904 1364388.8587 -189395.9550 0.0503
## week-Sus_scrofa 49406.6730 1758386.5561 -224566.2942 -0.1439
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3914 1.1007 258
## (Intercept)-Lynx_rufus -4.8120 1.0696 109
## (Intercept)-Didelphis_virginiana -3.9085 1.0488 327
## (Intercept)-Sylvilagus_floridanus -4.4614 1.1347 213
## (Intercept)-Sciurus_carolinensis -3.9756 1.0173 433
## (Intercept)-Vulpes_vulpes -4.6070 1.0924 84
## (Intercept)-Sus_scrofa -4.1374 1.0804 270
## week-Canis_latrans 279455.8016 1.3168 5062
## week-Lynx_rufus 114429.5660 1.3658 2787
## week-Didelphis_virginiana 160170.4413 1.3029 2517
## week-Sylvilagus_floridanus 276503.7938 1.2904 10013
## week-Sciurus_carolinensis 186923.3092 1.3172 3522
## week-Vulpes_vulpes 198890.2735 1.2912 31917
## week-Sus_scrofa 204963.3836 1.3669 999
# 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): 3.3712
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8731 0.4547 -1.7437 -0.8914 0.0365 1.0053 2170
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2647 1.571 0.1921 0.8651 4.4976 1.01 1665
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6974 0.2793 -5.1941 -4.7088 -4.1358 1.0219 1209
## week 0.0471 1.6098 -3.0853 0.0987 3.1892 1.0094 558
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.475000e-01 2.078100e+00 0.0514 0.2623 1.605900e+00 1.3234 2032
## week 7.824468e+13 1.407165e+15 0.1245 71.5925 4.872868e+11 1.5693 320
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2656 0.4188 -0.4890 0.2574 1.1218 1.0012
## (Intercept)-Lynx_rufus -0.0941 0.5266 -0.9745 -0.1401 1.0596 1.0240
## (Intercept)-Didelphis_virginiana -1.3453 0.4146 -2.1934 -1.3359 -0.5771 1.0019
## (Intercept)-Sylvilagus_floridanus -0.6434 0.4172 -1.4595 -0.6539 0.1678 1.0203
## (Intercept)-Sciurus_carolinensis -1.3333 0.4213 -2.2010 -1.3165 -0.5586 1.0102
## (Intercept)-Vulpes_vulpes -1.6004 0.5860 -2.8015 -1.5727 -0.4819 1.0198
## (Intercept)-Sus_scrofa -1.8235 0.5706 -3.0443 -1.7798 -0.8184 1.0228
## ESS
## (Intercept)-Canis_latrans 1556
## (Intercept)-Lynx_rufus 346
## (Intercept)-Didelphis_virginiana 2306
## (Intercept)-Sylvilagus_floridanus 1661
## (Intercept)-Sciurus_carolinensis 2220
## (Intercept)-Vulpes_vulpes 500
## (Intercept)-Sus_scrofa 1262
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6501 1.465000e-01 -4.9450 -4.6500
## (Intercept)-Lynx_rufus -5.2698 2.731000e-01 -5.8532 -5.2501
## (Intercept)-Didelphis_virginiana -4.3573 2.282000e-01 -4.8374 -4.3519
## (Intercept)-Sylvilagus_floridanus -4.8472 2.328000e-01 -5.3551 -4.8370
## (Intercept)-Sciurus_carolinensis -4.3805 2.255000e-01 -4.8680 -4.3712
## (Intercept)-Vulpes_vulpes -5.3464 4.672000e-01 -6.4113 -5.2851
## (Intercept)-Sus_scrofa -4.7391 3.337000e-01 -5.4510 -4.7270
## week-Canis_latrans -31291.1923 7.457910e+06 -20655.9720 0.1298
## week-Lynx_rufus 174950.6626 7.188231e+06 -18340.9470 0.2488
## week-Didelphis_virginiana -54652.5495 9.902016e+06 -19320.5306 0.2166
## week-Sylvilagus_floridanus -16681.6856 5.440356e+06 -15324.0050 0.1302
## week-Sciurus_carolinensis -6327.7242 9.925984e+06 -15727.1341 0.2094
## week-Vulpes_vulpes -46724.3497 1.103213e+07 -11281.2584 0.1776
## week-Sus_scrofa 53719.6476 7.650389e+06 -23035.6453 0.1147
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3733 1.0157 257
## (Intercept)-Lynx_rufus -4.7635 1.0513 103
## (Intercept)-Didelphis_virginiana -3.9272 1.0074 331
## (Intercept)-Sylvilagus_floridanus -4.4358 1.0802 181
## (Intercept)-Sciurus_carolinensis -3.9693 1.0530 316
## (Intercept)-Vulpes_vulpes -4.5800 1.0081 116
## (Intercept)-Sus_scrofa -4.1362 1.0698 294
## week-Canis_latrans 17001.3407 1.2921 68447
## week-Lynx_rufus 22693.3148 1.3485 2331
## week-Didelphis_virginiana 16017.1229 1.2935 24549
## week-Sylvilagus_floridanus 30199.2347 1.2914 21728
## week-Sciurus_carolinensis 26911.8084 1.2904 239923
## week-Vulpes_vulpes 35428.8514 1.2922 31916
## week-Sus_scrofa 19702.8200 1.2953 28929
#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): 3.4135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0960 0.5261 -2.1947 -1.1114 0.0268 1.0116 897
## Veg_shannon_index 0.2965 0.3865 -0.4879 0.2912 1.0666 1.0051 1383
## Avg_Cogongrass_Cover 0.3178 0.3329 -0.3728 0.3208 0.9679 1.0013 1237
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5142 1.8408 0.1073 0.9815 6.3692 1.0266 1032
## Veg_shannon_index 0.7526 1.0216 0.0572 0.4380 3.3602 1.0142 1028
## Avg_Cogongrass_Cover 0.4453 0.6034 0.0416 0.2505 1.9765 1.0187 1132
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9843 1.1296 0.0665 0.6553 3.8526 1.0695 235
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7017 0.2786 -5.1907 -4.7173 -4.1130 1.0078 1396
## week -0.0756 1.7674 -3.7071 -0.0702 3.3659 1.0085 435
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.379000e-01 9.537000e-01 0.0524 0.2551 1.688200e+00 1.0660 804
## week 1.860554e+10 2.369242e+11 0.1466 435.7673 7.478139e+10 1.1591 405
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0249 0.7114 -1.3598 0.0259
## (Intercept)-Lynx_rufus -0.4416 0.7638 -1.8434 -0.4815
## (Intercept)-Didelphis_virginiana -1.5173 0.6039 -2.7696 -1.5097
## (Intercept)-Sylvilagus_floridanus -0.8883 0.6202 -2.1280 -0.8765
## (Intercept)-Sciurus_carolinensis -1.5284 0.6366 -2.8589 -1.4909
## (Intercept)-Vulpes_vulpes -1.7146 0.8171 -3.3096 -1.7009
## (Intercept)-Sus_scrofa -2.1090 0.8205 -3.8574 -2.0400
## Veg_shannon_index-Canis_latrans 0.8432 0.4786 0.0014 0.8069
## Veg_shannon_index-Lynx_rufus -0.2528 0.6369 -1.7162 -0.2004
## Veg_shannon_index-Didelphis_virginiana 0.5702 0.4588 -0.2355 0.5427
## Veg_shannon_index-Sylvilagus_floridanus 0.4623 0.4686 -0.4205 0.4419
## Veg_shannon_index-Sciurus_carolinensis -0.1167 0.4559 -1.0667 -0.0908
## Veg_shannon_index-Vulpes_vulpes -0.1143 0.5385 -1.1899 -0.0990
## Veg_shannon_index-Sus_scrofa 0.8602 0.6617 -0.2190 0.7863
## Avg_Cogongrass_Cover-Canis_latrans 0.6028 0.4310 -0.1380 0.5667
## Avg_Cogongrass_Cover-Lynx_rufus 0.6134 0.4603 -0.2060 0.5803
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4887 0.3973 -0.2820 0.4767
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1579 0.4955 -1.2269 -0.1192
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4237 0.3874 -0.3374 0.4178
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3298 0.4827 -0.6732 0.3448
## Avg_Cogongrass_Cover-Sus_scrofa -0.0213 0.5888 -1.4103 0.0466
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4625 1.0083 579
## (Intercept)-Lynx_rufus 1.1539 1.0025 433
## (Intercept)-Didelphis_virginiana -0.3936 1.0106 1221
## (Intercept)-Sylvilagus_floridanus 0.3098 1.0061 717
## (Intercept)-Sciurus_carolinensis -0.3610 1.0104 972
## (Intercept)-Vulpes_vulpes -0.0424 1.0831 238
## (Intercept)-Sus_scrofa -0.6545 1.0135 566
## Veg_shannon_index-Canis_latrans 1.9013 1.0033 1398
## Veg_shannon_index-Lynx_rufus 0.8218 1.0062 825
## Veg_shannon_index-Didelphis_virginiana 1.5235 1.0013 1859
## Veg_shannon_index-Sylvilagus_floridanus 1.4583 1.0011 1765
## Veg_shannon_index-Sciurus_carolinensis 0.6984 1.0166 1503
## Veg_shannon_index-Vulpes_vulpes 0.9042 1.0197 968
## Veg_shannon_index-Sus_scrofa 2.3351 1.0090 755
## Avg_Cogongrass_Cover-Canis_latrans 1.5859 1.0048 1677
## Avg_Cogongrass_Cover-Lynx_rufus 1.6428 1.0008 1317
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2933 1.0023 1658
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7137 1.0032 1199
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1790 1.0033 2017
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2764 1.0089 1363
## Avg_Cogongrass_Cover-Sus_scrofa 0.9309 1.0007 1005
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6511 0.1501 -4.9474 -4.6525
## (Intercept)-Lynx_rufus -5.2837 0.2841 -5.8755 -5.2632
## (Intercept)-Didelphis_virginiana -4.3556 0.2373 -4.8825 -4.3455
## (Intercept)-Sylvilagus_floridanus -4.8503 0.2210 -5.3422 -4.8366
## (Intercept)-Sciurus_carolinensis -4.3844 0.2304 -4.8550 -4.3825
## (Intercept)-Vulpes_vulpes -5.3378 0.5237 -6.5882 -5.2543
## (Intercept)-Sus_scrofa -4.7319 0.3186 -5.3681 -4.7283
## week-Canis_latrans -1857.0498 120722.1781 -108462.6530 -0.1471
## week-Lynx_rufus 1964.4213 108190.9084 -86674.8325 -0.0811
## week-Didelphis_virginiana -2990.3111 126531.2293 -100732.6663 -0.2572
## week-Sylvilagus_floridanus 990.0669 110228.0761 -79678.5003 -0.1912
## week-Sciurus_carolinensis 4839.9754 132371.1474 -75977.5007 -0.0662
## week-Vulpes_vulpes 1412.6889 127231.4227 -92638.7375 -0.0223
## week-Sus_scrofa -1002.4071 108611.8191 -101720.8687 -0.1626
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3549 1.0087 254
## (Intercept)-Lynx_rufus -4.7769 1.0798 100
## (Intercept)-Didelphis_virginiana -3.9285 1.0229 292
## (Intercept)-Sylvilagus_floridanus -4.4530 1.0078 207
## (Intercept)-Sciurus_carolinensis -3.9443 1.0091 328
## (Intercept)-Vulpes_vulpes -4.5300 1.0807 80
## (Intercept)-Sus_scrofa -4.1257 1.0162 301
## week-Canis_latrans 71394.0811 1.1194 11675
## week-Lynx_rufus 122777.9620 1.0805 5663
## week-Didelphis_virginiana 82692.4224 1.0919 3213
## week-Sylvilagus_floridanus 101160.8427 1.1146 5626
## week-Sciurus_carolinensis 127884.6153 1.0875 2021
## week-Vulpes_vulpes 87088.1425 1.0886 15186
## week-Sus_scrofa 86378.7934 1.0849 10180
# 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): 3.4667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2169 0.5802 -2.3654 -1.2128 -0.0027 1.0066 609
## Cogon_Patch_Size -0.5006 0.6617 -1.9419 -0.4644 0.7695 1.0020 1031
## Avg_Cogongrass_Cover 0.4560 0.3686 -0.2947 0.4629 1.1856 1.0060 658
## total_shrub_cover 0.0674 0.3481 -0.6095 0.0677 0.7671 1.0201 821
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8601 2.5730 0.1018 1.0787 8.3818 1.0353 697
## Cogon_Patch_Size 2.8523 4.3439 0.1484 1.5544 12.7000 1.0224 801
## Avg_Cogongrass_Cover 0.4782 0.9936 0.0416 0.2518 2.1937 1.1285 1396
## total_shrub_cover 0.4033 0.6145 0.0386 0.2175 1.8698 1.0473 915
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.092 2.0602 0.1772 1.5283 7.4082 1.0179 205
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7066 0.3070 -5.2437 -4.7220 -4.0766 1.0092 715
## week -0.0874 1.6394 -3.3207 -0.0981 3.1432 1.0486 737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.860000e-01 9.478000e-01 0.0617 0.2919 2.050700e+00 1.1135 682
## week 1.125472e+18 1.771921e+19 0.1288 391.9674 4.335817e+16 1.6439 257
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0452 0.8947 -1.7255 0.0317
## (Intercept)-Lynx_rufus -0.6490 0.9033 -2.2862 -0.6997
## (Intercept)-Didelphis_virginiana -1.5403 0.7154 -3.0303 -1.5212
## (Intercept)-Sylvilagus_floridanus -1.1652 0.7617 -2.7131 -1.1428
## (Intercept)-Sciurus_carolinensis -1.8056 0.8157 -3.6788 -1.7278
## (Intercept)-Vulpes_vulpes -1.9150 0.9305 -3.8880 -1.8603
## (Intercept)-Sus_scrofa -2.1622 0.9106 -4.2742 -2.0561
## Cogon_Patch_Size-Canis_latrans 0.8878 0.9443 -0.3910 0.7189
## Cogon_Patch_Size-Lynx_rufus -0.7424 1.0022 -2.6111 -0.7607
## Cogon_Patch_Size-Didelphis_virginiana 0.7872 0.6351 -0.2526 0.7308
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6623 1.2898 -4.9940 -1.3906
## Cogon_Patch_Size-Sciurus_carolinensis -1.2902 1.0208 -3.8177 -1.1201
## Cogon_Patch_Size-Vulpes_vulpes -1.1684 1.3138 -4.2197 -0.9459
## Cogon_Patch_Size-Sus_scrofa -0.8075 1.1770 -3.6522 -0.6474
## Avg_Cogongrass_Cover-Canis_latrans 0.4572 0.4315 -0.3635 0.4440
## Avg_Cogongrass_Cover-Lynx_rufus 0.8853 0.6030 -0.0786 0.8062
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3854 0.4615 -0.5607 0.3843
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1061 0.5167 -0.9949 0.1321
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.7277 0.4578 -0.0930 0.6973
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5454 0.5086 -0.4260 0.5383
## Avg_Cogongrass_Cover-Sus_scrofa 0.1340 0.6546 -1.3626 0.2091
## total_shrub_cover-Canis_latrans 0.3970 0.4613 -0.3609 0.3584
## total_shrub_cover-Lynx_rufus -0.2841 0.5717 -1.5759 -0.2363
## total_shrub_cover-Didelphis_virginiana -0.1051 0.4379 -1.0144 -0.0933
## total_shrub_cover-Sylvilagus_floridanus 0.0370 0.4644 -0.8974 0.0321
## total_shrub_cover-Sciurus_carolinensis 0.1614 0.4219 -0.6463 0.1496
## total_shrub_cover-Vulpes_vulpes -0.0188 0.5399 -1.1755 0.0064
## total_shrub_cover-Sus_scrofa 0.2803 0.5120 -0.6512 0.2460
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8400 1.0079 452
## (Intercept)-Lynx_rufus 1.3136 1.0291 303
## (Intercept)-Didelphis_virginiana -0.1819 1.0230 831
## (Intercept)-Sylvilagus_floridanus 0.3062 1.0141 723
## (Intercept)-Sciurus_carolinensis -0.4042 1.0040 550
## (Intercept)-Vulpes_vulpes -0.2223 1.0272 386
## (Intercept)-Sus_scrofa -0.6672 1.0396 391
## Cogon_Patch_Size-Canis_latrans 3.3628 1.0124 487
## Cogon_Patch_Size-Lynx_rufus 1.4183 1.0028 403
## Cogon_Patch_Size-Didelphis_virginiana 2.1883 1.0015 938
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0771 1.0115 486
## Cogon_Patch_Size-Sciurus_carolinensis 0.1849 1.0022 650
## Cogon_Patch_Size-Vulpes_vulpes 0.8231 1.0272 410
## Cogon_Patch_Size-Sus_scrofa 1.1174 1.0018 687
## Avg_Cogongrass_Cover-Canis_latrans 1.3461 1.0006 1412
## Avg_Cogongrass_Cover-Lynx_rufus 2.3352 1.0020 764
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3098 1.0010 1000
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0602 1.0067 699
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7138 1.0034 1203
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5516 1.0035 1120
## Avg_Cogongrass_Cover-Sus_scrofa 1.1804 1.0038 769
## total_shrub_cover-Canis_latrans 1.4267 1.0361 837
## total_shrub_cover-Lynx_rufus 0.6936 1.0032 706
## total_shrub_cover-Didelphis_virginiana 0.7267 1.0015 1225
## total_shrub_cover-Sylvilagus_floridanus 0.9586 1.0131 996
## total_shrub_cover-Sciurus_carolinensis 1.0104 1.0237 1444
## total_shrub_cover-Vulpes_vulpes 0.9972 1.0012 983
## total_shrub_cover-Sus_scrofa 1.3829 1.0205 1154
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.667600e+00 1.651000e-01 -5.004900e+00
## (Intercept)-Lynx_rufus -5.291700e+00 2.911000e-01 -5.920800e+00
## (Intercept)-Didelphis_virginiana -4.332000e+00 2.201000e-01 -4.778400e+00
## (Intercept)-Sylvilagus_floridanus -4.892700e+00 2.405000e-01 -5.373500e+00
## (Intercept)-Sciurus_carolinensis -4.381700e+00 2.231000e-01 -4.836600e+00
## (Intercept)-Vulpes_vulpes -5.411000e+00 5.129000e-01 -6.558000e+00
## (Intercept)-Sus_scrofa -4.757400e+00 3.448000e-01 -5.491900e+00
## week-Canis_latrans -3.037683e+07 7.055662e+08 -1.253537e+07
## week-Lynx_rufus -2.977025e+07 1.031584e+09 -1.818361e+07
## week-Didelphis_virginiana -1.827025e+07 1.134428e+09 -2.701932e+07
## week-Sylvilagus_floridanus 1.432808e+07 1.061421e+09 -2.120147e+07
## week-Sciurus_carolinensis 1.664244e+07 8.820809e+08 -1.558599e+07
## week-Vulpes_vulpes -3.708319e+05 6.665058e+08 -1.201260e+07
## week-Sus_scrofa 2.793814e+07 8.556877e+08 -2.354744e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6611 -4.3577 1.0222 209
## (Intercept)-Lynx_rufus -5.2814 -4.7648 1.1107 92
## (Intercept)-Didelphis_virginiana -4.3279 -3.9329 1.0134 337
## (Intercept)-Sylvilagus_floridanus -4.8976 -4.4213 1.0778 166
## (Intercept)-Sciurus_carolinensis -4.3728 -3.9717 1.0033 335
## (Intercept)-Vulpes_vulpes -5.3483 -4.5920 1.0560 98
## (Intercept)-Sus_scrofa -4.7412 -4.1217 1.0316 208
## week-Canis_latrans -0.1594 23434279.9153 1.4748 530
## week-Lynx_rufus -0.1346 13570336.2538 1.3698 1255
## week-Didelphis_virginiana -0.2657 9887951.5777 1.3139 3696
## week-Sylvilagus_floridanus -0.3834 14459892.7063 1.3097 5032
## week-Sciurus_carolinensis -0.1860 21746421.4286 1.3266 2233
## week-Vulpes_vulpes -0.2763 20349491.1099 1.2883 20691
## week-Sus_scrofa -0.2278 10962002.0387 1.3898 981
#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): 3.5292
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1624 0.6738 -2.4321 -1.1822 0.3808 1.0380 743
## Tree_Density -0.8144 0.5712 -2.0713 -0.7681 0.1989 1.0190 511
## Avg_Canopy_Cover 1.0036 0.4490 0.1495 0.9913 1.9308 1.0498 745
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3371 6.8263 0.2145 1.9267 15.4207 1.6230 187
## Tree_Density 1.2473 2.3502 0.0554 0.5200 7.1622 1.0378 503
## Avg_Canopy_Cover 0.9014 1.2426 0.0734 0.5453 3.6542 1.0061 1167
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.505 0.6556 0.0426 0.2882 2.3194 1.0391 244
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7141 0.2954 -5.2438 -4.7301 -4.0805 1.0333 1234
## week -0.0412 1.6776 -3.2203 -0.0404 3.2530 1.0032 868
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.143000e-01 8.203000e-01 0.0630 0.3183 2.161600e+00 1.0951 441
## week 6.425872e+13 9.520725e+14 0.1447 5595.3622 7.349244e+13 1.2580 474
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2753 0.7851 -1.1352 0.2183 1.9916
## (Intercept)-Lynx_rufus 0.3488 1.5516 -1.5747 0.0208 4.4830
## (Intercept)-Didelphis_virginiana -1.9485 0.6830 -3.4328 -1.9023 -0.7242
## (Intercept)-Sylvilagus_floridanus -1.0589 0.6742 -2.4212 -1.0399 0.2573
## (Intercept)-Sciurus_carolinensis -1.9911 0.7076 -3.6022 -1.9286 -0.7860
## (Intercept)-Vulpes_vulpes -2.1373 0.9052 -4.1196 -2.1043 -0.4151
## (Intercept)-Sus_scrofa -2.6727 0.9282 -4.7968 -2.5577 -1.1405
## Tree_Density-Canis_latrans -1.0450 0.6655 -2.6228 -0.9534 0.0076
## Tree_Density-Lynx_rufus 0.1318 0.8317 -1.1837 0.0211 2.2518
## Tree_Density-Didelphis_virginiana -1.1602 0.8992 -3.3850 -0.9753 0.1074
## Tree_Density-Sylvilagus_floridanus -1.2285 0.9068 -3.4389 -1.0785 0.0913
## Tree_Density-Sciurus_carolinensis -1.0523 0.8967 -3.3971 -0.8951 0.1952
## Tree_Density-Vulpes_vulpes -0.6595 0.8624 -2.4582 -0.6110 0.8582
## Tree_Density-Sus_scrofa -1.1225 1.0772 -3.8224 -0.9271 0.3389
## Avg_Canopy_Cover-Canis_latrans 0.0716 0.5136 -0.9305 0.0624 1.1111
## Avg_Canopy_Cover-Lynx_rufus 0.7562 0.7595 -0.5171 0.6786 2.5280
## Avg_Canopy_Cover-Didelphis_virginiana 1.2969 0.5252 0.3739 1.2581 2.4571
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7438 0.7756 0.6171 1.5919 3.6743
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2578 0.5170 0.3575 1.2149 2.4230
## Avg_Canopy_Cover-Vulpes_vulpes 0.9948 0.5823 -0.0596 0.9685 2.2732
## Avg_Canopy_Cover-Sus_scrofa 1.2477 0.5608 0.2500 1.1958 2.4402
## Rhat ESS
## (Intercept)-Canis_latrans 1.1057 338
## (Intercept)-Lynx_rufus 1.7219 55
## (Intercept)-Didelphis_virginiana 1.0123 864
## (Intercept)-Sylvilagus_floridanus 1.0072 1011
## (Intercept)-Sciurus_carolinensis 1.0590 621
## (Intercept)-Vulpes_vulpes 1.0088 288
## (Intercept)-Sus_scrofa 1.0724 499
## Tree_Density-Canis_latrans 1.0096 662
## Tree_Density-Lynx_rufus 1.0521 391
## Tree_Density-Didelphis_virginiana 1.0108 471
## Tree_Density-Sylvilagus_floridanus 1.0395 477
## Tree_Density-Sciurus_carolinensis 1.0465 615
## Tree_Density-Vulpes_vulpes 1.0204 605
## Tree_Density-Sus_scrofa 1.0394 333
## Avg_Canopy_Cover-Canis_latrans 1.0132 1033
## Avg_Canopy_Cover-Lynx_rufus 1.1764 330
## Avg_Canopy_Cover-Didelphis_virginiana 1.0173 1240
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0150 588
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0018 1324
## Avg_Canopy_Cover-Vulpes_vulpes 1.0241 775
## Avg_Canopy_Cover-Sus_scrofa 1.0082 1057
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6877 1.651000e-01 -5.0559
## (Intercept)-Lynx_rufus -5.4341 3.306000e-01 -6.1520
## (Intercept)-Didelphis_virginiana -4.3193 2.155000e-01 -4.7558
## (Intercept)-Sylvilagus_floridanus -4.8517 2.359000e-01 -5.3651
## (Intercept)-Sciurus_carolinensis -4.3996 2.431000e-01 -4.9311
## (Intercept)-Vulpes_vulpes -5.3913 4.892000e-01 -6.4199
## (Intercept)-Sus_scrofa -4.7269 3.446000e-01 -5.4508
## week-Canis_latrans -53949.1006 7.116068e+06 -1521658.6611
## week-Lynx_rufus -13690.3423 6.949719e+06 -1335675.2238
## week-Didelphis_virginiana -131649.6286 6.860217e+06 -1493899.7196
## week-Sylvilagus_floridanus 214046.3606 7.470555e+06 -1324672.6441
## week-Sciurus_carolinensis -57016.0279 7.819847e+06 -1887265.6845
## week-Vulpes_vulpes 217198.4380 1.058489e+07 -1213042.7514
## week-Sus_scrofa 377637.4197 7.134230e+06 -1165233.2939
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6792 -4.3853 1.0539 208
## (Intercept)-Lynx_rufus -5.3947 -4.8660 1.7406 55
## (Intercept)-Didelphis_virginiana -4.3143 -3.9067 1.0067 362
## (Intercept)-Sylvilagus_floridanus -4.8349 -4.4310 1.0475 168
## (Intercept)-Sciurus_carolinensis -4.3857 -3.9570 1.0029 271
## (Intercept)-Vulpes_vulpes -5.3562 -4.5510 1.1208 105
## (Intercept)-Sus_scrofa -4.7068 -4.1050 1.0212 299
## week-Canis_latrans -0.0606 1582106.7652 1.1076 4507
## week-Lynx_rufus -0.1888 1427136.1009 1.1223 31050
## week-Didelphis_virginiana -0.1296 1560970.2928 1.1159 2129
## week-Sylvilagus_floridanus -0.4998 1487276.7494 1.1886 1382
## week-Sciurus_carolinensis -0.1490 1288280.8652 1.1234 1989
## week-Vulpes_vulpes -0.4546 1374670.3825 1.1393 1858
## week-Sus_scrofa -0.0177 1995883.3602 1.1205 641
# 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): 3.4283
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7836 0.5737 -2.9170 -1.7928 -0.5618 1.0124 671
## Avg_Cogongrass_Cover -0.7548 0.4812 -1.7052 -0.7502 0.1850 1.0015 477
## I(Avg_Cogongrass_Cover^2) 0.8563 0.4323 0.0446 0.8454 1.7541 1.0035 678
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3922 2.0692 0.0782 0.8155 5.9528 1.0266 856
## Avg_Cogongrass_Cover 0.6469 0.9995 0.0476 0.3427 3.1827 1.0397 762
## I(Avg_Cogongrass_Cover^2) 0.7036 1.4236 0.0429 0.2895 3.7827 1.0157 576
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7902 0.9133 0.055 0.4851 3.3551 1.1038 193
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7044 0.2852 -5.1991 -4.7135 -4.0731 1.0133 1042
## week -0.0733 1.6449 -3.2362 -0.0742 3.0784 1.0008 1010
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.459000e-01 8.426000e-01 0.0605 0.2740 1.926100e+00 1.2177 441
## week 6.065091e+20 7.532427e+21 0.1393 244.2335 1.213233e+21 1.8280 134
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7668 0.7605 -2.1752 -0.7897
## (Intercept)-Lynx_rufus -1.4263 0.7826 -2.8780 -1.4561
## (Intercept)-Didelphis_virginiana -2.0532 0.6719 -3.4764 -2.0224
## (Intercept)-Sylvilagus_floridanus -1.5832 0.7033 -3.0085 -1.5768
## (Intercept)-Sciurus_carolinensis -2.4316 0.7553 -4.0455 -2.3628
## (Intercept)-Vulpes_vulpes -2.5353 0.9114 -4.4932 -2.4472
## (Intercept)-Sus_scrofa -2.5021 0.8126 -4.3011 -2.4200
## Avg_Cogongrass_Cover-Canis_latrans -0.4526 0.5817 -1.4885 -0.4755
## Avg_Cogongrass_Cover-Lynx_rufus -0.5017 0.6680 -1.6818 -0.5307
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4219 0.6115 -1.5260 -0.4535
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2969 0.6777 -2.7702 -1.2415
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8356 0.6039 -2.0628 -0.8228
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8087 0.6701 -2.2070 -0.7997
## Avg_Cogongrass_Cover-Sus_scrofa -1.1247 0.7593 -2.8600 -1.0387
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.4380 0.9029 0.2390 1.2199
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2124 0.5767 0.3193 1.1417
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5851 0.4407 -0.2406 0.5740
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7996 0.4585 -0.0384 0.7874
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0053 0.4256 0.2148 0.9824
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9230 0.5217 0.0858 0.8741
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2733 0.7064 -1.4771 0.3804
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7689 1.0235 572
## (Intercept)-Lynx_rufus 0.1562 1.0354 469
## (Intercept)-Didelphis_virginiana -0.7603 1.0179 964
## (Intercept)-Sylvilagus_floridanus -0.1397 1.0159 544
## (Intercept)-Sciurus_carolinensis -1.1175 1.0185 436
## (Intercept)-Vulpes_vulpes -0.9754 1.0088 379
## (Intercept)-Sus_scrofa -1.1180 1.0232 418
## Avg_Cogongrass_Cover-Canis_latrans 0.7970 1.0024 927
## Avg_Cogongrass_Cover-Lynx_rufus 0.8910 1.0055 858
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8674 1.0050 942
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1471 1.0025 683
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2993 1.0018 735
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5173 1.0076 692
## Avg_Cogongrass_Cover-Sus_scrofa 0.1753 1.0052 657
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7055 1.0262 256
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5859 1.0063 543
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4905 1.0204 673
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7718 1.0062 659
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9229 1.0136 567
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0790 1.0574 431
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3852 1.0214 483
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.676100e+00 1.492000e-01 -4.979100e+00
## (Intercept)-Lynx_rufus -5.249300e+00 2.548000e-01 -5.801000e+00
## (Intercept)-Didelphis_virginiana -4.366200e+00 2.384000e-01 -4.842400e+00
## (Intercept)-Sylvilagus_floridanus -4.881100e+00 2.359000e-01 -5.377400e+00
## (Intercept)-Sciurus_carolinensis -4.362300e+00 2.154000e-01 -4.816800e+00
## (Intercept)-Vulpes_vulpes -5.388100e+00 5.358000e-01 -6.651200e+00
## (Intercept)-Sus_scrofa -4.805300e+00 3.506000e-01 -5.525400e+00
## week-Canis_latrans -4.076937e+08 2.332489e+10 -3.452502e+08
## week-Lynx_rufus 1.520216e+08 2.483603e+10 -5.277928e+07
## week-Didelphis_virginiana -1.727919e+08 2.233582e+10 -5.000537e+08
## week-Sylvilagus_floridanus -3.339937e+08 2.346080e+10 -8.356614e+07
## week-Sciurus_carolinensis -5.825033e+07 2.450070e+10 -1.843040e+08
## week-Vulpes_vulpes 1.160275e+08 2.473982e+10 -1.137408e+08
## week-Sus_scrofa -5.683110e+08 3.008675e+10 -4.769434e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6757 -4.384600e+00 1.0365 300
## (Intercept)-Lynx_rufus -5.2352 -4.802300e+00 1.0602 128
## (Intercept)-Didelphis_virginiana -4.3544 -3.934500e+00 1.0244 246
## (Intercept)-Sylvilagus_floridanus -4.8612 -4.449800e+00 1.0163 178
## (Intercept)-Sciurus_carolinensis -4.3527 -3.959800e+00 1.0066 379
## (Intercept)-Vulpes_vulpes -5.3195 -4.529500e+00 1.1128 83
## (Intercept)-Sus_scrofa -4.7941 -4.157000e+00 1.0154 262
## week-Canis_latrans -0.1874 1.719268e+08 1.3205 6149
## week-Lynx_rufus -0.2513 6.411059e+08 1.2941 13794
## week-Didelphis_virginiana 0.0221 8.012792e+07 1.2963 11463
## week-Sylvilagus_floridanus -0.0905 3.229383e+08 1.3104 3548
## week-Sciurus_carolinensis -0.4608 1.787459e+08 1.2909 10981
## week-Vulpes_vulpes -0.3233 1.883081e+08 1.2925 34983
## week-Sus_scrofa -0.6616 1.496916e+08 1.3255 2345
# 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): 3.8645
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9329 1.3564 -4.3789 -2.0375 1.0298 1.0099 266
## Cogon_Patch_Size -0.1733 1.1181 -2.4737 -0.1383 2.0083 1.0019 688
## Veg_shannon_index 0.6518 0.7896 -1.0223 0.6639 2.2103 1.0092 561
## total_shrub_cover -0.0086 0.6306 -1.3702 0.0080 1.2234 1.0149 365
## Avg_Cogongrass_Cover -0.0539 1.1289 -2.3804 0.0005 1.9730 1.0687 158
## Tree_Density -2.3164 0.8914 -4.1731 -2.2822 -0.6813 1.0545 197
## Avg_Canopy_Cover 1.8769 0.9382 -0.1170 1.8908 3.7479 1.0104 962
## I(Avg_Cogongrass_Cover^2) 1.5591 0.7384 0.2506 1.5280 3.1227 1.0944 172
## avg_veg_height -0.4577 0.6612 -1.7939 -0.4724 0.8758 1.0072 216
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.1380 45.9229 1.4643 13.7238 140.2433 1.1476 97
## Cogon_Patch_Size 21.9068 76.0589 0.4231 7.1509 140.7252 1.5344 169
## Veg_shannon_index 4.2688 7.6152 0.0822 1.8044 25.0550 1.1627 114
## total_shrub_cover 1.4805 3.0473 0.0507 0.5453 9.2307 1.0701 160
## Avg_Cogongrass_Cover 1.8469 5.3821 0.0478 0.5792 10.3931 1.1348 244
## Tree_Density 2.0950 6.1507 0.0537 0.5680 15.9725 1.5631 205
## Avg_Canopy_Cover 8.1288 15.7517 0.1782 3.3288 46.4797 1.2512 92
## I(Avg_Cogongrass_Cover^2) 1.7422 5.0085 0.0505 0.5157 12.1911 1.3863 85
## avg_veg_height 0.7940 1.7282 0.0438 0.3421 4.2767 1.1267 632
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4795 4.9291 0.0619 0.8724 16.9903 1.2125 66
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7284 0.3856 -5.3707 -4.7604 -3.9138 1.0116 1359
## week -0.0997 1.6330 -3.2716 -0.0761 3.0540 1.0039 625
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.974000e-01 1.419300e+00 0.1239 0.4997 2.988100e+00 1.0988 445
## week 4.901534e+17 6.655014e+18 0.1144 105.1150 1.120594e+18 1.7512 266
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0667 1.7011 -2.8058 -0.2955
## (Intercept)-Lynx_rufus 1.9589 3.7565 -3.0434 0.9871
## (Intercept)-Didelphis_virginiana -4.7719 1.8654 -9.3032 -4.5843
## (Intercept)-Sylvilagus_floridanus -3.7794 2.3273 -9.8158 -3.3371
## (Intercept)-Sciurus_carolinensis -5.7878 2.2741 -11.2529 -5.4189
## (Intercept)-Vulpes_vulpes -5.7088 2.5571 -11.5838 -5.3690
## (Intercept)-Sus_scrofa -7.1359 3.1112 -15.0246 -6.4955
## Cogon_Patch_Size-Canis_latrans 3.1948 3.2221 -0.1279 2.3640
## Cogon_Patch_Size-Lynx_rufus 0.3316 3.5792 -4.9039 -0.2167
## Cogon_Patch_Size-Didelphis_virginiana 2.4394 1.4623 0.2341 2.2249
## Cogon_Patch_Size-Sylvilagus_floridanus -3.5645 4.1738 -15.6379 -2.3414
## Cogon_Patch_Size-Sciurus_carolinensis -1.9588 2.3212 -8.0974 -1.3846
## Cogon_Patch_Size-Vulpes_vulpes -1.8159 3.3273 -10.9572 -1.1507
## Cogon_Patch_Size-Sus_scrofa -1.7001 2.8954 -9.5703 -1.0285
## Veg_shannon_index-Canis_latrans 1.8126 1.1672 0.1450 1.6096
## Veg_shannon_index-Lynx_rufus -0.3995 1.9758 -5.4935 -0.0512
## Veg_shannon_index-Didelphis_virginiana 1.0470 0.9786 -0.6837 0.9769
## Veg_shannon_index-Sylvilagus_floridanus 1.1655 1.0032 -0.5388 1.0665
## Veg_shannon_index-Sciurus_carolinensis -0.4147 1.2559 -3.2470 -0.2813
## Veg_shannon_index-Vulpes_vulpes -0.2073 1.5393 -3.8880 0.0504
## Veg_shannon_index-Sus_scrofa 2.3891 1.8034 0.0520 1.9769
## total_shrub_cover-Canis_latrans 0.4998 0.8776 -0.9056 0.4030
## total_shrub_cover-Lynx_rufus -0.6579 1.4627 -4.4980 -0.4087
## total_shrub_cover-Didelphis_virginiana -0.3805 0.8848 -2.4049 -0.2785
## total_shrub_cover-Sylvilagus_floridanus -0.0222 0.8467 -1.9159 0.0176
## total_shrub_cover-Sciurus_carolinensis 0.2697 0.7469 -1.1166 0.2483
## total_shrub_cover-Vulpes_vulpes -0.2576 1.0985 -2.7949 -0.1381
## total_shrub_cover-Sus_scrofa 0.4302 1.0260 -1.2986 0.3049
## Avg_Cogongrass_Cover-Canis_latrans -0.0141 1.4017 -2.9207 0.0632
## Avg_Cogongrass_Cover-Lynx_rufus 0.4312 1.7416 -2.6682 0.3620
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0626 1.3750 -2.8072 0.0892
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5512 1.5594 -4.0632 -0.4037
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0321 1.4192 -3.0285 -0.0124
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1382 1.5010 -2.9792 0.1751
## Avg_Cogongrass_Cover-Sus_scrofa -0.4936 1.6609 -4.2013 -0.2791
## Tree_Density-Canis_latrans -2.8600 1.3524 -6.1358 -2.6790
## Tree_Density-Lynx_rufus -1.8993 1.4516 -4.4685 -1.9679
## Tree_Density-Didelphis_virginiana -2.5394 1.1067 -4.9025 -2.4398
## Tree_Density-Sylvilagus_floridanus -2.7835 1.3590 -6.1840 -2.6078
## Tree_Density-Sciurus_carolinensis -2.7782 1.4180 -6.0323 -2.5442
## Tree_Density-Vulpes_vulpes -2.3578 1.3815 -5.2946 -2.2963
## Tree_Density-Sus_scrofa -2.5359 1.3707 -5.7523 -2.3693
## Avg_Canopy_Cover-Canis_latrans 0.0056 1.0180 -2.2341 0.0669
## Avg_Canopy_Cover-Lynx_rufus 1.4531 2.3921 -2.9415 1.3595
## Avg_Canopy_Cover-Didelphis_virginiana 3.4426 1.6361 1.3527 3.1038
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7148 2.6575 1.3681 4.1158
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7387 1.2880 0.9581 2.5111
## Avg_Canopy_Cover-Vulpes_vulpes 2.9645 2.0099 0.5902 2.4876
## Avg_Canopy_Cover-Sus_scrofa 2.2989 1.1535 0.4766 2.1654
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1471 1.0628 0.5620 1.9673
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3221 1.4424 0.5086 2.0462
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2861 0.8166 -0.1945 1.2371
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3545 0.8708 -0.2213 1.3085
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8001 0.9125 0.3228 1.7028
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9649 1.1654 0.3189 1.7911
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9185 1.2840 -1.9954 1.0311
## avg_veg_height-Canis_latrans -0.5431 0.7327 -2.0319 -0.5345
## avg_veg_height-Lynx_rufus -0.4879 1.1149 -3.1499 -0.4374
## avg_veg_height-Didelphis_virginiana -0.5707 0.8478 -2.3290 -0.5702
## avg_veg_height-Sylvilagus_floridanus -0.4949 0.8212 -2.1989 -0.4909
## avg_veg_height-Sciurus_carolinensis -0.1099 0.7687 -1.5645 -0.1450
## avg_veg_height-Vulpes_vulpes -0.6950 0.9749 -2.7851 -0.6438
## avg_veg_height-Sus_scrofa -0.4900 0.9094 -2.4132 -0.4819
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.0224 1.0790 228
## (Intercept)-Lynx_rufus 11.6495 1.0111 51
## (Intercept)-Didelphis_virginiana -1.6859 1.1285 156
## (Intercept)-Sylvilagus_floridanus -0.5861 1.1808 118
## (Intercept)-Sciurus_carolinensis -2.5842 1.2511 87
## (Intercept)-Vulpes_vulpes -1.8246 1.1185 126
## (Intercept)-Sus_scrofa -2.9024 1.1684 85
## Cogon_Patch_Size-Canis_latrans 12.5537 1.2553 108
## Cogon_Patch_Size-Lynx_rufus 10.0648 1.0476 64
## Cogon_Patch_Size-Didelphis_virginiana 5.9435 1.1147 111
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7053 1.2185 93
## Cogon_Patch_Size-Sciurus_carolinensis 0.8858 1.0970 270
## Cogon_Patch_Size-Vulpes_vulpes 2.9687 1.0205 146
## Cogon_Patch_Size-Sus_scrofa 1.9769 1.0943 179
## Veg_shannon_index-Canis_latrans 4.6136 1.0939 170
## Veg_shannon_index-Lynx_rufus 2.7356 1.0506 149
## Veg_shannon_index-Didelphis_virginiana 3.2184 1.0253 497
## Veg_shannon_index-Sylvilagus_floridanus 3.4242 1.0181 561
## Veg_shannon_index-Sciurus_carolinensis 1.6027 1.1178 319
## Veg_shannon_index-Vulpes_vulpes 2.1554 1.0899 298
## Veg_shannon_index-Sus_scrofa 6.9656 1.0897 149
## total_shrub_cover-Canis_latrans 2.5970 1.0352 467
## total_shrub_cover-Lynx_rufus 1.6433 1.0395 176
## total_shrub_cover-Didelphis_virginiana 1.1236 1.0180 297
## total_shrub_cover-Sylvilagus_floridanus 1.5517 1.0297 630
## total_shrub_cover-Sciurus_carolinensis 1.8675 1.0266 748
## total_shrub_cover-Vulpes_vulpes 1.5972 1.0102 392
## total_shrub_cover-Sus_scrofa 2.8676 1.0226 550
## Avg_Cogongrass_Cover-Canis_latrans 2.5112 1.0927 185
## Avg_Cogongrass_Cover-Lynx_rufus 3.9859 1.0348 258
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6167 1.0470 216
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9945 1.0754 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6177 1.0751 224
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.0216 1.0641 191
## Avg_Cogongrass_Cover-Sus_scrofa 2.1780 1.0758 264
## Tree_Density-Canis_latrans -0.9338 1.1845 159
## Tree_Density-Lynx_rufus 1.5727 1.0856 178
## Tree_Density-Didelphis_virginiana -0.6814 1.1211 243
## Tree_Density-Sylvilagus_floridanus -0.7955 1.1437 165
## Tree_Density-Sciurus_carolinensis -0.8582 1.1969 182
## Tree_Density-Vulpes_vulpes 0.2403 1.0728 350
## Tree_Density-Sus_scrofa -0.1582 1.0480 404
## Avg_Canopy_Cover-Canis_latrans 1.7951 1.0795 118
## Avg_Canopy_Cover-Lynx_rufus 6.9649 1.0385 104
## Avg_Canopy_Cover-Didelphis_virginiana 7.4845 1.1232 122
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.9762 1.1123 128
## Avg_Canopy_Cover-Sciurus_carolinensis 5.8086 1.1243 118
## Avg_Canopy_Cover-Vulpes_vulpes 8.7798 1.1289 126
## Avg_Canopy_Cover-Sus_scrofa 4.9769 1.0253 436
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6403 1.1752 260
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6422 1.2915 213
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.0691 1.0574 145
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2426 1.1010 226
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.8835 1.1083 143
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.7793 1.1866 128
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0417 1.0423 111
## avg_veg_height-Canis_latrans 0.8989 1.0080 400
## avg_veg_height-Lynx_rufus 1.6043 1.0066 201
## avg_veg_height-Didelphis_virginiana 1.0808 1.0029 323
## avg_veg_height-Sylvilagus_floridanus 1.1394 1.0066 416
## avg_veg_height-Sciurus_carolinensis 1.4784 1.0033 549
## avg_veg_height-Vulpes_vulpes 1.1086 1.0048 311
## avg_veg_height-Sus_scrofa 1.3188 1.0118 356
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.679900e+00 1.736000e-01 -5.034600e+00
## (Intercept)-Lynx_rufus -5.681800e+00 2.798000e-01 -6.208200e+00
## (Intercept)-Didelphis_virginiana -4.294200e+00 2.297000e-01 -4.770300e+00
## (Intercept)-Sylvilagus_floridanus -4.950000e+00 2.392000e-01 -5.464300e+00
## (Intercept)-Sciurus_carolinensis -4.331100e+00 2.300000e-01 -4.803800e+00
## (Intercept)-Vulpes_vulpes -5.753400e+00 5.586000e-01 -6.967200e+00
## (Intercept)-Sus_scrofa -4.732900e+00 3.655000e-01 -5.474500e+00
## week-Canis_latrans -2.268609e+07 6.104072e+08 -2.075959e+08
## week-Lynx_rufus 1.263065e+07 5.390966e+08 -1.132603e+08
## week-Didelphis_virginiana 1.387797e+07 5.664788e+08 -9.880231e+07
## week-Sylvilagus_floridanus 2.294943e+07 6.720803e+08 -1.604283e+08
## week-Sciurus_carolinensis 5.613575e+06 6.483663e+08 -1.629708e+08
## week-Vulpes_vulpes 7.291816e+06 6.129516e+08 -1.386768e+08
## week-Sus_scrofa -7.068967e+06 6.113971e+08 -1.028344e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6775 -4.356600e+00 1.0573 193
## (Intercept)-Lynx_rufus -5.6905 -5.130000e+00 1.1134 69
## (Intercept)-Didelphis_virginiana -4.2845 -3.872600e+00 1.0546 306
## (Intercept)-Sylvilagus_floridanus -4.9336 -4.516700e+00 1.0298 164
## (Intercept)-Sciurus_carolinensis -4.3141 -3.917100e+00 1.0158 292
## (Intercept)-Vulpes_vulpes -5.6906 -4.820200e+00 1.1413 51
## (Intercept)-Sus_scrofa -4.7186 -4.049900e+00 1.0209 248
## week-Canis_latrans -0.3196 1.152919e+08 1.4216 1981
## week-Lynx_rufus -0.1946 1.954361e+08 1.3440 2033
## week-Didelphis_virginiana -0.1951 1.592287e+08 1.3489 3475
## week-Sylvilagus_floridanus -0.0880 1.201965e+08 1.4019 2089
## week-Sciurus_carolinensis -0.0953 1.680046e+08 1.2978 2749
## week-Vulpes_vulpes -0.0426 1.413727e+08 1.3044 13451
## week-Sus_scrofa -0.3424 1.534208e+08 1.3036 3044
# 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): 2.737
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8867 0.5133 -1.9362 -0.8758 0.1389 1.0115 486
## Avg_Cogongrass_Cover 0.2894 0.3325 -0.3838 0.2940 0.9492 1.0076 1033
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2169 1.9958 0.0628 0.6729 5.5227 1.0214 664
## Avg_Cogongrass_Cover 0.4198 0.6829 0.0405 0.2399 1.7805 1.0276 941
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4769 1.4065 0.1004 1.0817 5.3382 1.0341 177
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9341 0.2876 -5.4535 -4.9446 -4.3127 1.0103 628
## shrub_cover 0.4045 0.3536 -0.2924 0.3948 1.1233 1.0166 612
## veg_height -0.0160 0.2294 -0.4811 -0.0113 0.4282 1.0003 638
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4132 0.6456 0.0480 0.2458 1.7764 1.0026 447
## shrub_cover 0.6955 0.7182 0.1018 0.4643 2.6659 1.0238 421
## veg_height 0.3210 0.3879 0.0605 0.2265 1.1429 1.1017 946
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0259 0.7437 -1.3815 0.0120
## (Intercept)-Lynx_rufus -0.4620 0.7545 -1.8003 -0.5213
## (Intercept)-Didelphis_virginiana -1.1815 0.6546 -2.5244 -1.1605
## (Intercept)-Sylvilagus_floridanus -0.7490 0.6064 -1.9510 -0.7472
## (Intercept)-Sciurus_carolinensis -1.2608 0.6867 -2.7666 -1.2102
## (Intercept)-Vulpes_vulpes -1.4512 0.7912 -3.1977 -1.3999
## (Intercept)-Sus_scrofa -1.5493 0.7924 -3.2815 -1.4699
## Avg_Cogongrass_Cover-Canis_latrans 0.5021 0.4177 -0.2132 0.4694
## Avg_Cogongrass_Cover-Lynx_rufus 0.5526 0.4790 -0.2451 0.5091
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4036 0.3965 -0.3489 0.3896
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1743 0.4552 -1.1789 -0.1389
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3895 0.3952 -0.3532 0.3856
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3923 0.4655 -0.4617 0.3783
## Avg_Cogongrass_Cover-Sus_scrofa 0.0119 0.6066 -1.3246 0.0717
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5182 1.0024 410
## (Intercept)-Lynx_rufus 1.1752 1.0247 332
## (Intercept)-Didelphis_virginiana 0.0640 1.0059 715
## (Intercept)-Sylvilagus_floridanus 0.4128 1.0144 803
## (Intercept)-Sciurus_carolinensis -0.0479 1.0096 525
## (Intercept)-Vulpes_vulpes -0.0561 1.0138 392
## (Intercept)-Sus_scrofa -0.1870 1.0194 640
## Avg_Cogongrass_Cover-Canis_latrans 1.3918 1.0051 1330
## Avg_Cogongrass_Cover-Lynx_rufus 1.6335 1.0021 792
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2682 1.0137 997
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6028 1.0191 953
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1987 1.0120 1527
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4012 1.0037 1135
## Avg_Cogongrass_Cover-Sus_scrofa 1.0711 1.0178 649
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8052 0.1707 -5.1599 -4.8062 -4.4710 1.0335
## (Intercept)-Lynx_rufus -5.4394 0.2961 -6.0930 -5.4148 -4.9284 1.1615
## (Intercept)-Didelphis_virginiana -4.6838 0.2754 -5.2192 -4.6770 -4.1371 1.1116
## (Intercept)-Sylvilagus_floridanus -4.9259 0.2175 -5.3387 -4.9256 -4.5054 1.0398
## (Intercept)-Sciurus_carolinensis -4.6915 0.2918 -5.2836 -4.6901 -4.1383 1.0105
## (Intercept)-Vulpes_vulpes -5.5334 0.5068 -6.7130 -5.4931 -4.6889 1.0249
## (Intercept)-Sus_scrofa -5.1804 0.4286 -6.1216 -5.1754 -4.3616 1.0237
## shrub_cover-Canis_latrans -0.2570 0.1932 -0.6332 -0.2628 0.1149 1.0180
## shrub_cover-Lynx_rufus -0.1316 0.3573 -0.8941 -0.1215 0.5069 1.2913
## shrub_cover-Didelphis_virginiana 1.0518 0.3720 0.4179 1.0253 1.8975 1.0506
## shrub_cover-Sylvilagus_floridanus 0.4948 0.3589 -0.2276 0.4943 1.1936 1.1349
## shrub_cover-Sciurus_carolinensis 0.9190 0.3603 0.2257 0.9085 1.6591 1.0161
## shrub_cover-Vulpes_vulpes 0.0978 0.6138 -1.1016 0.0922 1.3860 1.0438
## shrub_cover-Sus_scrofa 0.7500 0.6957 -0.6223 0.7374 2.1725 1.0238
## veg_height-Canis_latrans -0.5977 0.1730 -0.9385 -0.5978 -0.2524 1.0104
## veg_height-Lynx_rufus 0.0819 0.2397 -0.4232 0.0935 0.5317 1.0035
## veg_height-Didelphis_virginiana 0.4875 0.2410 0.0068 0.4820 0.9667 1.0493
## veg_height-Sylvilagus_floridanus 0.1569 0.2132 -0.2592 0.1584 0.5562 1.0644
## veg_height-Sciurus_carolinensis 0.1977 0.2088 -0.2043 0.1944 0.6209 1.0177
## veg_height-Vulpes_vulpes -0.1601 0.3273 -0.9220 -0.1241 0.3935 1.0521
## veg_height-Sus_scrofa -0.2246 0.3456 -0.9639 -0.2126 0.4246 1.1247
## ESS
## (Intercept)-Canis_latrans 193
## (Intercept)-Lynx_rufus 98
## (Intercept)-Didelphis_virginiana 174
## (Intercept)-Sylvilagus_floridanus 214
## (Intercept)-Sciurus_carolinensis 210
## (Intercept)-Vulpes_vulpes 118
## (Intercept)-Sus_scrofa 284
## shrub_cover-Canis_latrans 200
## shrub_cover-Lynx_rufus 102
## shrub_cover-Didelphis_virginiana 107
## shrub_cover-Sylvilagus_floridanus 174
## shrub_cover-Sciurus_carolinensis 213
## shrub_cover-Vulpes_vulpes 173
## shrub_cover-Sus_scrofa 342
## veg_height-Canis_latrans 194
## veg_height-Lynx_rufus 161
## veg_height-Didelphis_virginiana 280
## veg_height-Sylvilagus_floridanus 257
## veg_height-Sciurus_carolinensis 205
## veg_height-Vulpes_vulpes 150
## veg_height-Sus_scrofa 232
# 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): 3.3102
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2235 1.1558 -3.4515 -1.2545 1.1746 1.0704 308
## Cogon_Patch_Size -0.4019 1.0323 -2.4564 -0.3857 1.6536 1.0673 199
## Veg_shannon_index 0.5787 0.8574 -1.3358 0.5922 2.2587 1.0214 802
## total_shrub_cover -0.6352 0.9382 -2.8760 -0.5297 0.9438 1.1804 168
## Avg_Cogongrass_Cover 1.7776 0.9653 -0.2161 1.7925 3.5688 1.0211 185
## Tree_Density -1.7241 0.9662 -3.6512 -1.7338 0.3132 1.0195 326
## Avg_Canopy_Cover 1.9089 1.0794 -0.4517 1.9503 3.9751 1.0440 403
## avg_veg_height -0.4696 0.7258 -1.8001 -0.4872 1.0351 1.0529 181
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.9822 20.7515 0.2381 6.9824 75.8852 1.1046 88
## Cogon_Patch_Size 7.6696 20.7810 0.1236 3.2916 38.3441 1.2004 523
## Veg_shannon_index 6.6278 15.2765 0.0928 2.1825 40.4934 1.7118 37
## total_shrub_cover 9.3684 28.1324 0.0834 1.5615 77.9133 1.3338 42
## Avg_Cogongrass_Cover 3.5763 13.1939 0.0560 0.7235 25.9328 1.6657 83
## Tree_Density 6.9047 25.8772 0.0660 1.1716 56.0179 2.3766 85
## Avg_Canopy_Cover 25.3589 70.3807 0.5261 5.6083 217.5425 2.8749 27
## avg_veg_height 0.9417 2.5310 0.0458 0.3634 5.0746 1.1647 416
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.249 20.8325 0.0635 1.7262 52.0737 2.512 49
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0062 0.3385 -5.5737 -5.0261 -4.2950 1.0172 873
## shrub_cover 0.5510 0.3640 -0.1369 0.5401 1.2733 1.0417 284
## veg_height 0.0119 0.2340 -0.4602 0.0099 0.4834 1.0033 557
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5401 0.8074 0.0676 0.3385 2.0663 1.0140 346
## shrub_cover 0.7044 0.6600 0.1268 0.5253 2.4268 1.0237 456
## veg_height 0.3141 0.3078 0.0636 0.2274 1.1030 1.0048 807
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.9269 1.6800 -2.5478 0.8584
## (Intercept)-Lynx_rufus 1.7522 2.8088 -2.7860 1.3121
## (Intercept)-Didelphis_virginiana -2.8700 1.5079 -6.4299 -2.6910
## (Intercept)-Sylvilagus_floridanus -1.4590 1.6107 -4.8922 -1.4033
## (Intercept)-Sciurus_carolinensis -3.1850 1.8358 -7.4124 -3.0179
## (Intercept)-Vulpes_vulpes -3.1038 1.9704 -7.6755 -2.9331
## (Intercept)-Sus_scrofa -4.7700 2.9330 -12.3886 -4.1918
## Cogon_Patch_Size-Canis_latrans 1.2194 1.6640 -0.9589 0.8908
## Cogon_Patch_Size-Lynx_rufus -0.2821 1.9492 -3.9301 -0.3566
## Cogon_Patch_Size-Didelphis_virginiana 1.5007 1.4198 -0.4966 1.2300
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2016 2.3125 -7.8836 -1.7542
## Cogon_Patch_Size-Sciurus_carolinensis -1.7525 2.1282 -6.9881 -1.3841
## Cogon_Patch_Size-Vulpes_vulpes -1.3022 2.4906 -7.3638 -0.9015
## Cogon_Patch_Size-Sus_scrofa -1.3901 1.9491 -6.3749 -1.0944
## Veg_shannon_index-Canis_latrans 1.7738 1.5527 -0.1323 1.4940
## Veg_shannon_index-Lynx_rufus -0.3513 1.7301 -4.5368 -0.0440
## Veg_shannon_index-Didelphis_virginiana 1.5063 1.3486 -0.4370 1.2406
## Veg_shannon_index-Sylvilagus_floridanus 1.4093 1.4203 -0.5956 1.1682
## Veg_shannon_index-Sciurus_carolinensis -0.9226 2.0579 -6.5775 -0.4737
## Veg_shannon_index-Vulpes_vulpes -0.7816 1.8684 -5.8784 -0.4200
## Veg_shannon_index-Sus_scrofa 2.5209 1.9446 -0.0199 2.0815
## total_shrub_cover-Canis_latrans 1.0563 1.6463 -0.9763 0.6656
## total_shrub_cover-Lynx_rufus -2.1641 2.8413 -10.1475 -1.3652
## total_shrub_cover-Didelphis_virginiana -1.4372 1.9134 -6.8592 -0.9714
## total_shrub_cover-Sylvilagus_floridanus -1.5794 2.5791 -9.2373 -0.8590
## total_shrub_cover-Sciurus_carolinensis -0.8681 1.7956 -6.0548 -0.5494
## total_shrub_cover-Vulpes_vulpes -2.3026 4.0006 -15.7550 -1.0830
## total_shrub_cover-Sus_scrofa -0.3234 1.4730 -3.5989 -0.2422
## Avg_Cogongrass_Cover-Canis_latrans 2.5382 1.6599 0.4117 2.2653
## Avg_Cogongrass_Cover-Lynx_rufus 2.5605 1.7519 -0.0453 2.2902
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1157 1.4105 -0.1165 1.9660
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9856 1.6570 -3.0737 1.2356
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1532 1.4890 -0.2890 2.0200
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4627 1.6387 -0.1650 2.2448
## Avg_Cogongrass_Cover-Sus_scrofa 1.3120 1.7377 -2.8245 1.5268
## Tree_Density-Canis_latrans -2.9730 2.3321 -9.4946 -2.4368
## Tree_Density-Lynx_rufus -0.9257 1.5192 -3.6891 -1.0212
## Tree_Density-Didelphis_virginiana -2.0074 1.5613 -5.1587 -1.9627
## Tree_Density-Sylvilagus_floridanus -2.6898 2.5321 -7.6883 -2.2711
## Tree_Density-Sciurus_carolinensis -2.3225 1.9518 -7.4306 -2.1067
## Tree_Density-Vulpes_vulpes -1.5066 2.1303 -5.3666 -1.6683
## Tree_Density-Sus_scrofa -2.4624 3.1098 -8.6413 -1.9857
## Avg_Canopy_Cover-Canis_latrans -0.1718 0.9086 -2.3075 -0.0957
## Avg_Canopy_Cover-Lynx_rufus 0.8122 2.3470 -3.8747 0.7274
## Avg_Canopy_Cover-Didelphis_virginiana 4.5420 2.6618 1.5839 3.7906
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.3289 5.0977 1.6859 4.7873
## Avg_Canopy_Cover-Sciurus_carolinensis 4.9837 4.7726 1.3582 3.5149
## Avg_Canopy_Cover-Vulpes_vulpes 4.0245 3.6062 0.7461 2.9587
## Avg_Canopy_Cover-Sus_scrofa 2.7080 1.6343 0.3858 2.4245
## avg_veg_height-Canis_latrans -0.4216 0.8291 -1.9650 -0.4537
## avg_veg_height-Lynx_rufus -0.5907 1.1721 -3.0320 -0.5642
## avg_veg_height-Didelphis_virginiana -0.6663 0.9274 -2.5300 -0.6599
## avg_veg_height-Sylvilagus_floridanus -0.6349 0.9855 -2.6485 -0.6334
## avg_veg_height-Sciurus_carolinensis -0.0954 1.0284 -1.8757 -0.1672
## avg_veg_height-Vulpes_vulpes -0.4921 1.0506 -2.4136 -0.5360
## avg_veg_height-Sus_scrofa -0.5260 0.9861 -2.5357 -0.5119
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.6523 1.1151 87
## (Intercept)-Lynx_rufus 8.5838 1.0728 78
## (Intercept)-Didelphis_virginiana -0.3170 1.0446 136
## (Intercept)-Sylvilagus_floridanus 1.6267 1.1159 177
## (Intercept)-Sciurus_carolinensis -0.1016 1.0661 232
## (Intercept)-Vulpes_vulpes 0.3121 1.0631 156
## (Intercept)-Sus_scrofa -0.8019 1.1952 68
## Cogon_Patch_Size-Canis_latrans 5.1875 1.0175 376
## Cogon_Patch_Size-Lynx_rufus 4.0523 1.0076 166
## Cogon_Patch_Size-Didelphis_virginiana 5.0489 1.2855 63
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3203 1.0484 165
## Cogon_Patch_Size-Sciurus_carolinensis 1.5384 1.0386 133
## Cogon_Patch_Size-Vulpes_vulpes 2.6880 1.0409 190
## Cogon_Patch_Size-Sus_scrofa 1.7489 1.0461 323
## Veg_shannon_index-Canis_latrans 5.4699 1.5514 45
## Veg_shannon_index-Lynx_rufus 2.4141 1.1696 232
## Veg_shannon_index-Didelphis_virginiana 4.8419 1.1986 100
## Veg_shannon_index-Sylvilagus_floridanus 5.0473 1.3673 90
## Veg_shannon_index-Sciurus_carolinensis 1.4462 1.6617 81
## Veg_shannon_index-Vulpes_vulpes 1.6883 1.2168 171
## Veg_shannon_index-Sus_scrofa 7.4220 1.1915 137
## total_shrub_cover-Canis_latrans 5.4215 1.3747 68
## total_shrub_cover-Lynx_rufus 1.3325 1.5908 45
## total_shrub_cover-Didelphis_virginiana 0.7877 1.3878 46
## total_shrub_cover-Sylvilagus_floridanus 1.1847 1.4180 66
## total_shrub_cover-Sciurus_carolinensis 1.5574 1.2743 132
## total_shrub_cover-Vulpes_vulpes 1.0702 1.4389 41
## total_shrub_cover-Sus_scrofa 2.4623 1.0885 255
## Avg_Cogongrass_Cover-Canis_latrans 6.9060 1.2907 64
## Avg_Cogongrass_Cover-Lynx_rufus 7.1031 1.1146 201
## Avg_Cogongrass_Cover-Didelphis_virginiana 5.3884 1.1291 118
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4913 1.1592 121
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.4676 1.1782 241
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.7201 1.0790 183
## Avg_Cogongrass_Cover-Sus_scrofa 4.1183 1.1124 156
## Tree_Density-Canis_latrans -0.5074 1.5796 79
## Tree_Density-Lynx_rufus 2.5521 1.0236 253
## Tree_Density-Didelphis_virginiana 1.0839 1.0425 329
## Tree_Density-Sylvilagus_floridanus 0.2136 1.3832 92
## Tree_Density-Sciurus_carolinensis 1.0313 1.0643 228
## Tree_Density-Vulpes_vulpes 4.0189 1.1357 154
## Tree_Density-Sus_scrofa 0.7924 1.4490 90
## Avg_Canopy_Cover-Canis_latrans 1.4035 1.3119 135
## Avg_Canopy_Cover-Lynx_rufus 6.0358 1.1507 108
## Avg_Canopy_Cover-Didelphis_virginiana 11.7840 1.4666 49
## Avg_Canopy_Cover-Sylvilagus_floridanus 22.3832 2.3698 21
## Avg_Canopy_Cover-Sciurus_carolinensis 20.5747 2.7957 31
## Avg_Canopy_Cover-Vulpes_vulpes 14.9335 2.4655 18
## Avg_Canopy_Cover-Sus_scrofa 6.7919 1.2225 202
## avg_veg_height-Canis_latrans 1.3483 1.0432 222
## avg_veg_height-Lynx_rufus 1.7078 1.0602 178
## avg_veg_height-Didelphis_virginiana 1.1925 1.0401 178
## avg_veg_height-Sylvilagus_floridanus 1.3460 1.0475 255
## avg_veg_height-Sciurus_carolinensis 2.1794 1.0501 237
## avg_veg_height-Vulpes_vulpes 1.6851 1.0414 228
## avg_veg_height-Sus_scrofa 1.3590 1.0203 368
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7887 0.1847 -5.1719 -4.7882 -4.4455 1.0288
## (Intercept)-Lynx_rufus -5.6630 0.2984 -6.2423 -5.6674 -5.1176 1.0779
## (Intercept)-Didelphis_virginiana -4.6888 0.2878 -5.2358 -4.6857 -4.1117 1.0144
## (Intercept)-Sylvilagus_floridanus -5.0369 0.2243 -5.4762 -5.0339 -4.5964 1.1696
## (Intercept)-Sciurus_carolinensis -4.8015 0.3255 -5.4354 -4.8164 -4.1549 1.1064
## (Intercept)-Vulpes_vulpes -5.7828 0.5121 -6.8543 -5.7339 -4.8934 1.0866
## (Intercept)-Sus_scrofa -5.2437 0.4858 -6.3096 -5.2191 -4.3193 1.0274
## shrub_cover-Canis_latrans -0.3029 0.2157 -0.7419 -0.3037 0.1177 1.0095
## shrub_cover-Lynx_rufus 0.0068 0.3673 -0.7137 -0.0124 0.7769 1.3544
## shrub_cover-Didelphis_virginiana 1.1214 0.3832 0.4589 1.0976 1.9511 1.0594
## shrub_cover-Sylvilagus_floridanus 0.7908 0.3905 -0.0855 0.8084 1.5097 1.1669
## shrub_cover-Sciurus_carolinensis 1.0560 0.3944 0.2696 1.0720 1.7789 1.0854
## shrub_cover-Vulpes_vulpes 0.4527 0.5625 -0.6410 0.4564 1.5585 1.1142
## shrub_cover-Sus_scrofa 0.8662 0.7136 -0.5173 0.8683 2.2921 1.0179
## veg_height-Canis_latrans -0.5343 0.1649 -0.8834 -0.5263 -0.2401 1.0467
## veg_height-Lynx_rufus 0.1631 0.2358 -0.3115 0.1710 0.5953 1.0056
## veg_height-Didelphis_virginiana 0.4958 0.2384 0.0554 0.4928 0.9796 1.0142
## veg_height-Sylvilagus_floridanus 0.1728 0.2349 -0.2948 0.1765 0.6086 1.0397
## veg_height-Sciurus_carolinensis 0.2631 0.2144 -0.1343 0.2534 0.7213 1.1248
## veg_height-Vulpes_vulpes -0.2149 0.3157 -0.8427 -0.2108 0.3994 1.0124
## veg_height-Sus_scrofa -0.2509 0.3571 -0.9661 -0.2381 0.4274 1.0134
## ESS
## (Intercept)-Canis_latrans 174
## (Intercept)-Lynx_rufus 83
## (Intercept)-Didelphis_virginiana 176
## (Intercept)-Sylvilagus_floridanus 205
## (Intercept)-Sciurus_carolinensis 97
## (Intercept)-Vulpes_vulpes 99
## (Intercept)-Sus_scrofa 128
## shrub_cover-Canis_latrans 167
## shrub_cover-Lynx_rufus 68
## shrub_cover-Didelphis_virginiana 137
## shrub_cover-Sylvilagus_floridanus 77
## shrub_cover-Sciurus_carolinensis 111
## shrub_cover-Vulpes_vulpes 96
## shrub_cover-Sus_scrofa 221
## veg_height-Canis_latrans 195
## veg_height-Lynx_rufus 134
## veg_height-Didelphis_virginiana 259
## veg_height-Sylvilagus_floridanus 210
## veg_height-Sciurus_carolinensis 165
## veg_height-Vulpes_vulpes 140
## veg_height-Sus_scrofa 304
# 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): 3.4692
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4937 0.6672 -1.7389 -0.5435 0.9120 1.0884 188
## Avg_Cogongrass_Cover 0.1976 0.5080 -0.8427 0.2050 1.1626 1.0110 376
## total_shrub_cover -0.8196 0.7064 -2.3761 -0.7461 0.4162 1.1313 140
## avg_veg_height 0.1447 0.5069 -0.8060 0.1251 1.1961 1.0230 290
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3117 2.0901 0.0593 0.6976 6.3583 1.0433 458
## Avg_Cogongrass_Cover 0.8524 2.1282 0.0513 0.3395 4.6054 1.1379 423
## total_shrub_cover 1.9400 3.2444 0.0864 0.9942 9.9729 1.1553 237
## avg_veg_height 0.4884 0.7958 0.0395 0.2474 2.4541 1.0289 576
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2414 3.6592 0.1817 2.1862 11.9993 1.0885 132
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0578 0.3619 -5.6726 -5.0772 -4.3393 1.0781 398
## shrub_cover 0.6915 0.4187 -0.1436 0.6933 1.5398 1.0646 217
## veg_height -0.0120 0.2364 -0.4800 -0.0147 0.4740 1.0285 399
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5228 1.2908 0.0434 0.2662 2.2929 1.5090 280
## shrub_cover 0.8652 0.9021 0.1488 0.5946 3.3027 1.0425 392
## veg_height 0.3013 0.2792 0.0621 0.2193 1.0567 1.0274 722
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2653 0.9181 -1.2930 0.1964
## (Intercept)-Lynx_rufus -0.1452 0.9474 -1.8193 -0.2054
## (Intercept)-Didelphis_virginiana -0.7048 0.8672 -2.3874 -0.7329
## (Intercept)-Sylvilagus_floridanus -0.1439 0.9310 -1.6964 -0.2403
## (Intercept)-Sciurus_carolinensis -0.8705 0.8988 -2.6352 -0.8767
## (Intercept)-Vulpes_vulpes -0.9353 1.0873 -3.1382 -0.9397
## (Intercept)-Sus_scrofa -1.1191 0.9947 -3.2672 -1.0650
## Avg_Cogongrass_Cover-Canis_latrans 0.5392 0.6524 -0.5926 0.4872
## Avg_Cogongrass_Cover-Lynx_rufus 0.6461 0.7741 -0.5786 0.5587
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3249 0.6212 -0.8563 0.3245
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4191 0.8020 -2.2139 -0.3189
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1162 0.6093 -1.1698 0.1410
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3810 0.7048 -0.9531 0.3711
## Avg_Cogongrass_Cover-Sus_scrofa -0.1612 0.8580 -2.2982 -0.0675
## total_shrub_cover-Canis_latrans 0.5532 0.8282 -0.7896 0.4468
## total_shrub_cover-Lynx_rufus -1.5655 1.0978 -4.2088 -1.4100
## total_shrub_cover-Didelphis_virginiana -1.0249 0.8478 -3.0295 -0.8922
## total_shrub_cover-Sylvilagus_floridanus -1.5884 1.2324 -4.6594 -1.3263
## total_shrub_cover-Sciurus_carolinensis -0.9810 0.9838 -3.2893 -0.8152
## total_shrub_cover-Vulpes_vulpes -1.1660 1.2239 -4.1340 -0.9487
## total_shrub_cover-Sus_scrofa -0.6843 1.1037 -3.2298 -0.5770
## avg_veg_height-Canis_latrans 0.1706 0.5791 -0.9240 0.1469
## avg_veg_height-Lynx_rufus 0.0373 0.7573 -1.3939 0.0190
## avg_veg_height-Didelphis_virginiana -0.0439 0.6181 -1.2841 -0.0373
## avg_veg_height-Sylvilagus_floridanus 0.0623 0.6386 -1.1293 0.0326
## avg_veg_height-Sciurus_carolinensis 0.5288 0.6654 -0.6067 0.4595
## avg_veg_height-Vulpes_vulpes 0.0544 0.6851 -1.2795 0.0420
## avg_veg_height-Sus_scrofa 0.1913 0.6538 -1.0579 0.1541
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2112 1.0348 250
## (Intercept)-Lynx_rufus 1.8666 1.0485 284
## (Intercept)-Didelphis_virginiana 1.0720 1.0731 175
## (Intercept)-Sylvilagus_floridanus 1.8612 1.0685 307
## (Intercept)-Sciurus_carolinensis 0.9317 1.0401 267
## (Intercept)-Vulpes_vulpes 1.2979 1.1024 140
## (Intercept)-Sus_scrofa 0.8057 1.0238 222
## Avg_Cogongrass_Cover-Canis_latrans 1.9426 1.0082 624
## Avg_Cogongrass_Cover-Lynx_rufus 2.5827 1.0193 445
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5785 1.0051 680
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8303 1.1003 302
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2249 1.0290 337
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8109 1.0035 490
## Avg_Cogongrass_Cover-Sus_scrofa 1.2228 1.0235 470
## total_shrub_cover-Canis_latrans 2.5836 1.0416 194
## total_shrub_cover-Lynx_rufus 0.1572 1.1960 184
## total_shrub_cover-Didelphis_virginiana 0.2699 1.1541 99
## total_shrub_cover-Sylvilagus_floridanus -0.0071 1.1022 111
## total_shrub_cover-Sciurus_carolinensis 0.4476 1.1366 161
## total_shrub_cover-Vulpes_vulpes 0.7395 1.1026 118
## total_shrub_cover-Sus_scrofa 1.2707 1.0636 219
## avg_veg_height-Canis_latrans 1.4363 1.0236 274
## avg_veg_height-Lynx_rufus 1.6020 1.0209 326
## avg_veg_height-Didelphis_virginiana 1.1868 1.0021 467
## avg_veg_height-Sylvilagus_floridanus 1.5030 1.0077 281
## avg_veg_height-Sciurus_carolinensis 1.9919 1.0401 337
## avg_veg_height-Vulpes_vulpes 1.3988 1.0175 289
## avg_veg_height-Sus_scrofa 1.6059 1.0188 443
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8409 0.1895 -5.2126 -4.8440 -4.4803 1.0015
## (Intercept)-Lynx_rufus -5.4220 0.3011 -6.0732 -5.3949 -4.8856 1.1157
## (Intercept)-Didelphis_virginiana -4.8579 0.3024 -5.4636 -4.8488 -4.3108 1.1163
## (Intercept)-Sylvilagus_floridanus -5.0867 0.2247 -5.5137 -5.0932 -4.6416 1.2497
## (Intercept)-Sciurus_carolinensis -4.8298 0.2923 -5.3982 -4.8303 -4.2343 1.0613
## (Intercept)-Vulpes_vulpes -5.7915 0.6966 -7.5034 -5.6804 -4.7558 1.4998
## (Intercept)-Sus_scrofa -5.4318 0.4757 -6.4959 -5.4168 -4.5325 1.0401
## shrub_cover-Canis_latrans -0.2948 0.2314 -0.7361 -0.2967 0.1550 1.0361
## shrub_cover-Lynx_rufus 0.2187 0.3615 -0.5328 0.2410 0.8640 1.0311
## shrub_cover-Didelphis_virginiana 1.3688 0.4176 0.5604 1.3635 2.1677 1.1683
## shrub_cover-Sylvilagus_floridanus 0.9574 0.3733 0.2026 0.9736 1.6835 1.1384
## shrub_cover-Sciurus_carolinensis 1.1738 0.4301 0.3372 1.1845 2.0369 1.0249
## shrub_cover-Vulpes_vulpes 0.4850 0.6261 -0.7925 0.4784 1.6586 1.1483
## shrub_cover-Sus_scrofa 1.1445 0.7710 -0.5950 1.1819 2.6038 1.0645
## veg_height-Canis_latrans -0.5798 0.1782 -0.9426 -0.5707 -0.2489 1.0862
## veg_height-Lynx_rufus 0.1224 0.2488 -0.4037 0.1369 0.5756 1.0705
## veg_height-Didelphis_virginiana 0.4719 0.2523 0.0030 0.4635 1.0067 1.0275
## veg_height-Sylvilagus_floridanus 0.0864 0.2304 -0.3779 0.0926 0.5189 1.0073
## veg_height-Sciurus_carolinensis 0.2122 0.2107 -0.1600 0.2046 0.6617 1.0085
## veg_height-Vulpes_vulpes -0.1568 0.3236 -0.8478 -0.1366 0.4423 1.0969
## veg_height-Sus_scrofa -0.2537 0.3586 -1.0224 -0.2394 0.4116 1.0733
## ESS
## (Intercept)-Canis_latrans 154
## (Intercept)-Lynx_rufus 114
## (Intercept)-Didelphis_virginiana 112
## (Intercept)-Sylvilagus_floridanus 159
## (Intercept)-Sciurus_carolinensis 101
## (Intercept)-Vulpes_vulpes 43
## (Intercept)-Sus_scrofa 114
## shrub_cover-Canis_latrans 139
## shrub_cover-Lynx_rufus 112
## shrub_cover-Didelphis_virginiana 74
## shrub_cover-Sylvilagus_floridanus 86
## shrub_cover-Sciurus_carolinensis 73
## shrub_cover-Vulpes_vulpes 112
## shrub_cover-Sus_scrofa 127
## veg_height-Canis_latrans 128
## veg_height-Lynx_rufus 133
## veg_height-Didelphis_virginiana 205
## veg_height-Sylvilagus_floridanus 173
## veg_height-Sciurus_carolinensis 233
## veg_height-Vulpes_vulpes 146
## veg_height-Sus_scrofa 163
# 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): 2.7357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7497 0.4309 -1.5894 -0.7556 0.1321 1.0013 1026
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1316 1.3057 0.158 0.7576 4.218 1.0019 1262
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9142 0.2915 -5.4499 -4.9257 -4.2813 1.0010 708
## shrub_cover 0.3764 0.3645 -0.3342 0.3734 1.1143 1.0015 473
## veg_height 0.0104 0.2257 -0.4349 0.0107 0.4657 1.0114 623
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3890 0.5511 0.0465 0.2273 1.6473 1.0156 264
## shrub_cover 0.6987 0.6946 0.1146 0.4882 2.4747 1.0097 612
## veg_height 0.2959 0.2864 0.0591 0.2182 0.9857 1.0225 889
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3046 0.4169 -0.4344 0.2721 1.1929 0.9998
## (Intercept)-Lynx_rufus -0.0136 0.5561 -0.9172 -0.0646 1.1483 1.0160
## (Intercept)-Didelphis_virginiana -1.1209 0.4459 -2.0345 -1.1117 -0.2967 1.0019
## (Intercept)-Sylvilagus_floridanus -0.5502 0.4104 -1.3550 -0.5605 0.2867 0.9996
## (Intercept)-Sciurus_carolinensis -1.1415 0.4258 -2.0272 -1.1310 -0.3502 1.0038
## (Intercept)-Vulpes_vulpes -1.4141 0.6222 -2.6473 -1.4065 -0.1700 1.0131
## (Intercept)-Sus_scrofa -1.5658 0.5708 -2.8013 -1.5333 -0.5232 1.0021
## ESS
## (Intercept)-Canis_latrans 1102
## (Intercept)-Lynx_rufus 290
## (Intercept)-Didelphis_virginiana 1305
## (Intercept)-Sylvilagus_floridanus 1439
## (Intercept)-Sciurus_carolinensis 1754
## (Intercept)-Vulpes_vulpes 242
## (Intercept)-Sus_scrofa 937
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7847 0.1722 -5.1352 -4.7798 -4.4702 1.0150
## (Intercept)-Lynx_rufus -5.3678 0.2785 -6.0036 -5.3437 -4.8547 1.0202
## (Intercept)-Didelphis_virginiana -4.6820 0.2684 -5.2286 -4.6768 -4.1418 1.0324
## (Intercept)-Sylvilagus_floridanus -4.9256 0.2289 -5.3686 -4.9251 -4.4662 1.0122
## (Intercept)-Sciurus_carolinensis -4.6513 0.2552 -5.1437 -4.6481 -4.1571 1.0338
## (Intercept)-Vulpes_vulpes -5.4987 0.5287 -6.8713 -5.4095 -4.6979 1.0334
## (Intercept)-Sus_scrofa -5.1394 0.4215 -6.0095 -5.1316 -4.3506 1.0130
## shrub_cover-Canis_latrans -0.2738 0.1868 -0.6603 -0.2705 0.0803 1.0532
## shrub_cover-Lynx_rufus -0.1448 0.3583 -0.8277 -0.1486 0.5401 1.0461
## shrub_cover-Didelphis_virginiana 1.0516 0.3470 0.3873 1.0424 1.7075 1.0060
## shrub_cover-Sylvilagus_floridanus 0.4702 0.3845 -0.2472 0.4624 1.2767 1.0117
## shrub_cover-Sciurus_carolinensis 0.8913 0.3596 0.1975 0.8908 1.5858 1.0341
## shrub_cover-Vulpes_vulpes 0.0650 0.6332 -1.2823 0.0932 1.2346 1.0581
## shrub_cover-Sus_scrofa 0.7162 0.7006 -0.6553 0.7008 2.0889 1.0106
## veg_height-Canis_latrans -0.5718 0.1775 -0.9434 -0.5712 -0.2333 1.0331
## veg_height-Lynx_rufus 0.1369 0.2317 -0.3098 0.1348 0.5969 1.0391
## veg_height-Didelphis_virginiana 0.5111 0.2328 0.0542 0.5056 0.9790 1.0217
## veg_height-Sylvilagus_floridanus 0.1612 0.2147 -0.2603 0.1626 0.5732 1.0137
## veg_height-Sciurus_carolinensis 0.1813 0.1978 -0.2214 0.1866 0.5595 1.0367
## veg_height-Vulpes_vulpes -0.1245 0.2971 -0.7377 -0.1147 0.4410 1.0205
## veg_height-Sus_scrofa -0.2420 0.3439 -0.9408 -0.2208 0.4026 1.0370
## ESS
## (Intercept)-Canis_latrans 190
## (Intercept)-Lynx_rufus 100
## (Intercept)-Didelphis_virginiana 191
## (Intercept)-Sylvilagus_floridanus 203
## (Intercept)-Sciurus_carolinensis 264
## (Intercept)-Vulpes_vulpes 70
## (Intercept)-Sus_scrofa 358
## shrub_cover-Canis_latrans 221
## shrub_cover-Lynx_rufus 118
## shrub_cover-Didelphis_virginiana 165
## shrub_cover-Sylvilagus_floridanus 139
## shrub_cover-Sciurus_carolinensis 274
## shrub_cover-Vulpes_vulpes 172
## shrub_cover-Sus_scrofa 294
## veg_height-Canis_latrans 188
## veg_height-Lynx_rufus 183
## veg_height-Didelphis_virginiana 230
## veg_height-Sylvilagus_floridanus 255
## veg_height-Sciurus_carolinensis 278
## veg_height-Vulpes_vulpes 183
## veg_height-Sus_scrofa 276
#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): 2.7448
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9139 0.5484 -1.9617 -0.9264 0.1941 1.0286 409
## Veg_shannon_index 0.2760 0.4156 -0.5359 0.2683 1.0865 1.0202 1083
## Avg_Cogongrass_Cover 0.3386 0.3535 -0.3782 0.3364 1.0510 1.0040 1042
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7052 5.3055 0.0725 0.7799 8.0075 1.6991 267
## Veg_shannon_index 0.7753 1.2954 0.0511 0.4197 3.4556 1.0480 858
## Avg_Cogongrass_Cover 0.4692 0.8834 0.0430 0.2588 2.2297 1.1418 884
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2888 1.2664 0.0733 0.9023 4.6066 1.0708 245
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9067 0.3216 -5.4547 -4.9257 -4.2595 1.0243 564
## shrub_cover 0.4107 0.3629 -0.3162 0.4054 1.1350 1.0247 410
## veg_height 0.0027 0.2285 -0.4483 0.0092 0.4526 1.0101 904
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4706 0.8904 0.0428 0.2559 2.1321 1.0280 302
## shrub_cover 0.6917 0.9460 0.0992 0.4692 2.5456 1.1011 678
## veg_height 0.3026 0.2856 0.0592 0.2189 1.0527 1.0100 747
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0344 0.7618 -1.3843 0.0236
## (Intercept)-Lynx_rufus -0.1972 1.4792 -1.8405 -0.4442
## (Intercept)-Didelphis_virginiana -1.2561 0.6321 -2.5604 -1.2140
## (Intercept)-Sylvilagus_floridanus -0.7834 0.6317 -2.0786 -0.7702
## (Intercept)-Sciurus_carolinensis -1.2782 0.6401 -2.6795 -1.2391
## (Intercept)-Vulpes_vulpes -1.4339 0.8873 -3.1172 -1.4338
## (Intercept)-Sus_scrofa -1.7906 0.8730 -3.7577 -1.6929
## Veg_shannon_index-Canis_latrans 0.7847 0.4936 -0.0676 0.7424
## Veg_shannon_index-Lynx_rufus -0.2330 0.6947 -1.8959 -0.1727
## Veg_shannon_index-Didelphis_virginiana 0.5351 0.4737 -0.3158 0.5097
## Veg_shannon_index-Sylvilagus_floridanus 0.3726 0.4714 -0.5488 0.3549
## Veg_shannon_index-Sciurus_carolinensis -0.2074 0.4976 -1.2872 -0.1816
## Veg_shannon_index-Vulpes_vulpes -0.1105 0.5764 -1.2775 -0.0782
## Veg_shannon_index-Sus_scrofa 0.8357 0.7434 -0.3250 0.7350
## Avg_Cogongrass_Cover-Canis_latrans 0.6777 0.4787 -0.1198 0.6162
## Avg_Cogongrass_Cover-Lynx_rufus 0.5958 0.5264 -0.2423 0.5422
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4734 0.4221 -0.3122 0.4743
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1521 0.4798 -1.1675 -0.1172
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3970 0.4026 -0.4125 0.3968
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4199 0.5331 -0.5696 0.3952
## Avg_Cogongrass_Cover-Sus_scrofa 0.0265 0.6180 -1.3826 0.0795
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6550 1.0213 283
## (Intercept)-Lynx_rufus 2.7502 1.6465 86
## (Intercept)-Didelphis_virginiana -0.0933 1.0036 902
## (Intercept)-Sylvilagus_floridanus 0.4523 1.0360 681
## (Intercept)-Sciurus_carolinensis -0.0748 1.0114 881
## (Intercept)-Vulpes_vulpes 0.3176 1.0272 180
## (Intercept)-Sus_scrofa -0.3476 1.0233 492
## Veg_shannon_index-Canis_latrans 1.8386 1.0025 1217
## Veg_shannon_index-Lynx_rufus 0.9375 1.0235 578
## Veg_shannon_index-Didelphis_virginiana 1.5627 1.0008 1694
## Veg_shannon_index-Sylvilagus_floridanus 1.3615 1.0021 1617
## Veg_shannon_index-Sciurus_carolinensis 0.6577 1.0120 1110
## Veg_shannon_index-Vulpes_vulpes 0.9367 1.0204 781
## Veg_shannon_index-Sus_scrofa 2.5812 1.0137 669
## Avg_Cogongrass_Cover-Canis_latrans 1.8281 1.0117 712
## Avg_Cogongrass_Cover-Lynx_rufus 1.7245 1.0274 869
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3672 1.0015 1239
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7010 1.0017 1195
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1852 1.0017 1333
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5368 1.0201 800
## Avg_Cogongrass_Cover-Sus_scrofa 1.0565 1.0132 821
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7624 0.1730 -5.1301 -4.7566 -4.4435 1.0195
## (Intercept)-Lynx_rufus -5.4441 0.3594 -6.2262 -5.3906 -4.8666 1.2562
## (Intercept)-Didelphis_virginiana -4.6985 0.2615 -5.2105 -4.7060 -4.1970 1.0437
## (Intercept)-Sylvilagus_floridanus -4.9031 0.2210 -5.3248 -4.9044 -4.4838 1.1037
## (Intercept)-Sciurus_carolinensis -4.6732 0.2844 -5.2260 -4.6744 -4.1363 1.0354
## (Intercept)-Vulpes_vulpes -5.5717 0.5779 -6.9961 -5.4742 -4.7029 1.0900
## (Intercept)-Sus_scrofa -5.1781 0.4057 -6.0059 -5.1595 -4.4187 1.0441
## shrub_cover-Canis_latrans -0.2395 0.1978 -0.6192 -0.2388 0.1482 1.0253
## shrub_cover-Lynx_rufus -0.0903 0.3517 -0.9092 -0.0652 0.5291 1.0673
## shrub_cover-Didelphis_virginiana 1.0573 0.3752 0.3933 1.0365 1.8385 1.1285
## shrub_cover-Sylvilagus_floridanus 0.5255 0.4001 -0.3265 0.5574 1.2722 1.0080
## shrub_cover-Sciurus_carolinensis 0.9235 0.3742 0.2060 0.9006 1.6897 1.0375
## shrub_cover-Vulpes_vulpes 0.1220 0.6507 -1.2776 0.1680 1.2850 1.0154
## shrub_cover-Sus_scrofa 0.7519 0.6736 -0.5298 0.7156 2.1269 1.0469
## veg_height-Canis_latrans -0.5737 0.1771 -0.9258 -0.5720 -0.2291 1.0276
## veg_height-Lynx_rufus 0.1180 0.2406 -0.3547 0.1104 0.5822 1.0147
## veg_height-Didelphis_virginiana 0.5044 0.2477 0.0442 0.5001 1.0178 1.0351
## veg_height-Sylvilagus_floridanus 0.1762 0.2253 -0.2680 0.1784 0.6026 1.0387
## veg_height-Sciurus_carolinensis 0.1768 0.2050 -0.2195 0.1750 0.5847 1.0042
## veg_height-Vulpes_vulpes -0.1753 0.3041 -0.7820 -0.1676 0.3958 1.0990
## veg_height-Sus_scrofa -0.2390 0.3141 -0.8760 -0.2316 0.3486 1.0153
## ESS
## (Intercept)-Canis_latrans 213
## (Intercept)-Lynx_rufus 62
## (Intercept)-Didelphis_virginiana 183
## (Intercept)-Sylvilagus_floridanus 219
## (Intercept)-Sciurus_carolinensis 178
## (Intercept)-Vulpes_vulpes 83
## (Intercept)-Sus_scrofa 313
## shrub_cover-Canis_latrans 201
## shrub_cover-Lynx_rufus 103
## shrub_cover-Didelphis_virginiana 164
## shrub_cover-Sylvilagus_floridanus 144
## shrub_cover-Sciurus_carolinensis 207
## shrub_cover-Vulpes_vulpes 150
## shrub_cover-Sus_scrofa 240
## veg_height-Canis_latrans 170
## veg_height-Lynx_rufus 168
## veg_height-Didelphis_virginiana 242
## veg_height-Sylvilagus_floridanus 206
## veg_height-Sciurus_carolinensis 268
## veg_height-Vulpes_vulpes 178
## veg_height-Sus_scrofa 353
# 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): 3.2175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8680 0.6449 -2.1179 -0.8777 0.4267 1.0349 399
## Cogon_Patch_Size -0.4644 0.6977 -1.9637 -0.4241 0.8276 1.0033 783
## Avg_Cogongrass_Cover 0.4421 0.4349 -0.4567 0.4395 1.3025 1.0027 374
## total_shrub_cover -0.4446 0.5795 -1.6737 -0.4052 0.5809 1.0945 192
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7793 3.0257 0.0618 0.8476 9.7723 1.0090 338
## Cogon_Patch_Size 3.0844 5.4951 0.1488 1.5930 14.4040 1.0528 520
## Avg_Cogongrass_Cover 0.6231 1.3904 0.0452 0.3078 2.9841 1.0419 1015
## total_shrub_cover 1.1356 2.0948 0.0526 0.4962 6.1484 1.0864 157
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1468 2.867 0.2225 2.3141 10.8958 1.1323 140
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9795 0.2935 -5.5103 -4.9953 -4.3850 1.0683 470
## shrub_cover 0.5636 0.4004 -0.2171 0.5682 1.3575 1.0031 160
## veg_height -0.0004 0.2351 -0.4712 0.0027 0.4700 1.0154 585
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3960 0.5578 0.0460 0.2347 1.7601 1.0166 334
## shrub_cover 0.7725 0.9121 0.1253 0.5273 2.8518 1.0457 292
## veg_height 0.2983 0.3392 0.0583 0.2087 1.0539 1.0338 925
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1658 1.0115 -1.6499 0.0930
## (Intercept)-Lynx_rufus -0.4466 1.0101 -2.1477 -0.5253
## (Intercept)-Didelphis_virginiana -1.1118 0.8322 -2.8261 -1.0734
## (Intercept)-Sylvilagus_floridanus -0.7208 0.9744 -2.6509 -0.7437
## (Intercept)-Sciurus_carolinensis -1.3115 0.8404 -3.2013 -1.2711
## (Intercept)-Vulpes_vulpes -1.5336 1.1082 -3.9446 -1.4476
## (Intercept)-Sus_scrofa -1.6612 1.0735 -4.1201 -1.5317
## Cogon_Patch_Size-Canis_latrans 0.8977 1.0396 -0.4874 0.6877
## Cogon_Patch_Size-Lynx_rufus -0.5880 1.0431 -2.5234 -0.6267
## Cogon_Patch_Size-Didelphis_virginiana 0.7719 0.6253 -0.3019 0.7099
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6763 1.4451 -5.4808 -1.3674
## Cogon_Patch_Size-Sciurus_carolinensis -1.3266 1.0793 -3.9379 -1.1364
## Cogon_Patch_Size-Vulpes_vulpes -1.2010 1.4727 -5.0677 -0.9464
## Cogon_Patch_Size-Sus_scrofa -0.9027 1.2242 -4.0228 -0.6918
## Avg_Cogongrass_Cover-Canis_latrans 0.5315 0.5122 -0.3924 0.5072
## Avg_Cogongrass_Cover-Lynx_rufus 0.8643 0.6851 -0.2500 0.7588
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3026 0.5141 -0.7498 0.3160
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0757 0.6306 -1.2675 0.1026
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6565 0.5396 -0.2784 0.6238
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.6280 0.6278 -0.4466 0.5742
## Avg_Cogongrass_Cover-Sus_scrofa 0.1617 0.7917 -1.7319 0.2335
## total_shrub_cover-Canis_latrans 0.4120 0.7353 -0.7465 0.3186
## total_shrub_cover-Lynx_rufus -0.8676 0.9505 -2.9963 -0.7288
## total_shrub_cover-Didelphis_virginiana -0.7103 0.6695 -2.2575 -0.6406
## total_shrub_cover-Sylvilagus_floridanus -0.8310 0.9560 -3.1185 -0.6887
## total_shrub_cover-Sciurus_carolinensis -0.4234 0.6929 -1.9379 -0.3552
## total_shrub_cover-Vulpes_vulpes -0.6476 1.0782 -2.9212 -0.5146
## total_shrub_cover-Sus_scrofa -0.2499 0.8511 -2.0568 -0.1997
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3981 1.0370 308
## (Intercept)-Lynx_rufus 1.5753 1.0406 254
## (Intercept)-Didelphis_virginiana 0.4220 1.0378 510
## (Intercept)-Sylvilagus_floridanus 1.3314 1.0151 366
## (Intercept)-Sciurus_carolinensis 0.2181 1.0239 368
## (Intercept)-Vulpes_vulpes 0.3806 1.0646 226
## (Intercept)-Sus_scrofa 0.0823 1.0259 314
## Cogon_Patch_Size-Canis_latrans 3.4421 1.0139 475
## Cogon_Patch_Size-Lynx_rufus 1.8670 1.0104 479
## Cogon_Patch_Size-Didelphis_virginiana 2.1470 1.0151 744
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2652 1.0188 413
## Cogon_Patch_Size-Sciurus_carolinensis 0.1946 1.0105 478
## Cogon_Patch_Size-Vulpes_vulpes 0.7756 1.0039 295
## Cogon_Patch_Size-Sus_scrofa 0.9064 1.0192 445
## Avg_Cogongrass_Cover-Canis_latrans 1.6689 1.0002 598
## Avg_Cogongrass_Cover-Lynx_rufus 2.4877 1.0123 672
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2740 1.0016 1026
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2798 1.0030 635
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8255 1.0082 723
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.1298 1.0009 662
## Avg_Cogongrass_Cover-Sus_scrofa 1.5464 1.0051 467
## total_shrub_cover-Canis_latrans 2.1246 1.0408 207
## total_shrub_cover-Lynx_rufus 0.7090 1.0619 169
## total_shrub_cover-Didelphis_virginiana 0.3518 1.0694 154
## total_shrub_cover-Sylvilagus_floridanus 0.6200 1.1631 92
## total_shrub_cover-Sciurus_carolinensis 0.7627 1.0638 272
## total_shrub_cover-Vulpes_vulpes 1.0143 1.1290 130
## total_shrub_cover-Sus_scrofa 1.4212 1.0243 283
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7776 0.1744 -5.1159 -4.7725 -4.4615 1.2095
## (Intercept)-Lynx_rufus -5.3888 0.2764 -6.0265 -5.3703 -4.9190 1.0598
## (Intercept)-Didelphis_virginiana -4.7158 0.2652 -5.2266 -4.7201 -4.2035 1.0709
## (Intercept)-Sylvilagus_floridanus -5.0387 0.2455 -5.5186 -5.0477 -4.5422 1.1162
## (Intercept)-Sciurus_carolinensis -4.8010 0.3070 -5.3956 -4.7866 -4.2164 1.0504
## (Intercept)-Vulpes_vulpes -5.5976 0.5341 -6.8048 -5.5145 -4.7540 1.0684
## (Intercept)-Sus_scrofa -5.2755 0.4368 -6.2015 -5.2655 -4.4539 1.0439
## shrub_cover-Canis_latrans -0.2829 0.2314 -0.7534 -0.2824 0.1538 1.0159
## shrub_cover-Lynx_rufus 0.1336 0.3663 -0.6199 0.1456 0.7851 1.0735
## shrub_cover-Didelphis_virginiana 1.1538 0.3528 0.5045 1.1389 1.8462 1.1042
## shrub_cover-Sylvilagus_floridanus 0.7623 0.4134 -0.1193 0.7784 1.4857 1.1736
## shrub_cover-Sciurus_carolinensis 1.0726 0.3778 0.3383 1.0839 1.8063 1.0185
## shrub_cover-Vulpes_vulpes 0.3344 0.7387 -1.2747 0.3734 1.6512 1.0908
## shrub_cover-Sus_scrofa 0.9739 0.7252 -0.5334 0.9998 2.3121 1.0268
## veg_height-Canis_latrans -0.5463 0.1785 -0.8966 -0.5455 -0.2080 1.1559
## veg_height-Lynx_rufus 0.1066 0.2274 -0.3554 0.1095 0.5389 1.0759
## veg_height-Didelphis_virginiana 0.4680 0.2288 0.0470 0.4592 0.9601 1.0051
## veg_height-Sylvilagus_floridanus 0.1028 0.2401 -0.3439 0.0982 0.5864 1.0143
## veg_height-Sciurus_carolinensis 0.2591 0.2210 -0.1534 0.2573 0.6964 1.0056
## veg_height-Vulpes_vulpes -0.1166 0.3068 -0.7947 -0.0922 0.4056 1.0303
## veg_height-Sus_scrofa -0.2416 0.3413 -1.0129 -0.2207 0.3655 1.0226
## ESS
## (Intercept)-Canis_latrans 124
## (Intercept)-Lynx_rufus 105
## (Intercept)-Didelphis_virginiana 215
## (Intercept)-Sylvilagus_floridanus 153
## (Intercept)-Sciurus_carolinensis 153
## (Intercept)-Vulpes_vulpes 86
## (Intercept)-Sus_scrofa 178
## shrub_cover-Canis_latrans 112
## shrub_cover-Lynx_rufus 80
## shrub_cover-Didelphis_virginiana 135
## shrub_cover-Sylvilagus_floridanus 64
## shrub_cover-Sciurus_carolinensis 120
## shrub_cover-Vulpes_vulpes 88
## shrub_cover-Sus_scrofa 246
## veg_height-Canis_latrans 170
## veg_height-Lynx_rufus 171
## veg_height-Didelphis_virginiana 296
## veg_height-Sylvilagus_floridanus 166
## veg_height-Sciurus_carolinensis 216
## veg_height-Vulpes_vulpes 165
## veg_height-Sus_scrofa 201
#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): 2.9657
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9696 0.6707 -2.2455 -1.0010 0.4836 1.0024 542
## Tree_Density -0.7478 0.5462 -1.9113 -0.7124 0.2471 1.0013 558
## Avg_Canopy_Cover 1.1760 0.5557 0.1277 1.1685 2.3271 1.0047 771
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8536 3.9256 0.2449 1.7575 11.7147 1.0786 265
## Tree_Density 1.1615 2.3780 0.0526 0.4914 6.6119 1.0236 392
## Avg_Canopy_Cover 1.8514 3.4735 0.1371 1.0410 7.8144 1.0721 922
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6037 0.7458 0.0514 0.3673 2.7457 1.0669 189
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9437 0.3271 -5.5194 -4.9645 -4.2219 1.0196 427
## shrub_cover 0.3910 0.3505 -0.2807 0.3782 1.1219 1.0127 638
## veg_height 0.0281 0.2331 -0.4438 0.0297 0.4941 1.0042 638
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5283 0.8807 0.0621 0.3077 2.2324 1.0253 342
## shrub_cover 0.7014 0.7341 0.1166 0.5029 2.4855 1.0572 635
## veg_height 0.3183 0.3037 0.0598 0.2349 1.1410 1.0070 768
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2992 0.6722 -0.9571 0.2679 1.7592
## (Intercept)-Lynx_rufus 0.5020 1.4571 -1.4166 0.2349 4.0714
## (Intercept)-Didelphis_virginiana -1.6321 0.7182 -3.1231 -1.6076 -0.3194
## (Intercept)-Sylvilagus_floridanus -0.7913 0.6864 -2.1793 -0.7901 0.5606
## (Intercept)-Sciurus_carolinensis -1.7123 0.7632 -3.3647 -1.6709 -0.3122
## (Intercept)-Vulpes_vulpes -1.8033 0.9904 -3.7402 -1.8090 0.2256
## (Intercept)-Sus_scrofa -2.4104 0.9745 -4.6264 -2.3339 -0.7005
## Tree_Density-Canis_latrans -1.0249 0.6734 -2.5873 -0.9343 0.0298
## Tree_Density-Lynx_rufus 0.1250 0.8068 -1.1535 0.0267 2.0220
## Tree_Density-Didelphis_virginiana -1.0374 0.8560 -3.0535 -0.9146 0.2803
## Tree_Density-Sylvilagus_floridanus -1.1451 0.8826 -3.3146 -1.0196 0.2398
## Tree_Density-Sciurus_carolinensis -0.9268 0.8183 -2.8520 -0.8145 0.3787
## Tree_Density-Vulpes_vulpes -0.6351 0.9224 -2.4586 -0.6108 0.9368
## Tree_Density-Sus_scrofa -0.9878 1.0091 -3.3407 -0.8417 0.5600
## Avg_Canopy_Cover-Canis_latrans -0.1236 0.4990 -1.1230 -0.1248 0.8558
## Avg_Canopy_Cover-Lynx_rufus 0.8582 0.9720 -0.7565 0.7483 3.0527
## Avg_Canopy_Cover-Didelphis_virginiana 1.6998 0.7727 0.4984 1.5825 3.5257
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4427 1.1997 0.7450 2.2365 5.2737
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6226 0.7461 0.4525 1.5081 3.4320
## Avg_Canopy_Cover-Vulpes_vulpes 1.1608 0.7248 -0.1027 1.1086 2.8278
## Avg_Canopy_Cover-Sus_scrofa 1.4016 0.6595 0.2570 1.3486 2.8882
## Rhat ESS
## (Intercept)-Canis_latrans 1.0069 687
## (Intercept)-Lynx_rufus 1.0976 106
## (Intercept)-Didelphis_virginiana 1.0063 798
## (Intercept)-Sylvilagus_floridanus 1.0102 908
## (Intercept)-Sciurus_carolinensis 1.0070 561
## (Intercept)-Vulpes_vulpes 1.0103 203
## (Intercept)-Sus_scrofa 1.0230 575
## Tree_Density-Canis_latrans 1.0014 851
## Tree_Density-Lynx_rufus 1.0057 455
## Tree_Density-Didelphis_virginiana 1.0014 586
## Tree_Density-Sylvilagus_floridanus 1.0155 533
## Tree_Density-Sciurus_carolinensis 1.0157 685
## Tree_Density-Vulpes_vulpes 1.0199 353
## Tree_Density-Sus_scrofa 1.0273 514
## Avg_Canopy_Cover-Canis_latrans 1.0040 905
## Avg_Canopy_Cover-Lynx_rufus 1.0297 351
## Avg_Canopy_Cover-Didelphis_virginiana 1.0130 557
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0004 381
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0261 749
## Avg_Canopy_Cover-Vulpes_vulpes 1.0018 692
## Avg_Canopy_Cover-Sus_scrofa 1.0075 1072
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7866 0.1847 -5.1583 -4.7761 -4.4612 1.1621
## (Intercept)-Lynx_rufus -5.6072 0.3247 -6.3197 -5.5796 -5.0503 1.0518
## (Intercept)-Didelphis_virginiana -4.6941 0.2836 -5.3138 -4.6743 -4.1869 1.0726
## (Intercept)-Sylvilagus_floridanus -4.9377 0.2251 -5.3670 -4.9392 -4.4893 1.0258
## (Intercept)-Sciurus_carolinensis -4.6968 0.2920 -5.2864 -4.6863 -4.1468 1.0262
## (Intercept)-Vulpes_vulpes -5.6662 0.5783 -6.9881 -5.5930 -4.7214 1.0262
## (Intercept)-Sus_scrofa -5.1359 0.4456 -6.0315 -5.1362 -4.2791 1.0118
## shrub_cover-Canis_latrans -0.2702 0.2036 -0.6776 -0.2689 0.1437 1.1467
## shrub_cover-Lynx_rufus -0.2118 0.3264 -0.8770 -0.2106 0.4362 1.1745
## shrub_cover-Didelphis_virginiana 1.0473 0.3514 0.4418 1.0332 1.8238 1.1678
## shrub_cover-Sylvilagus_floridanus 0.6221 0.3387 -0.0352 0.6270 1.2960 1.0527
## shrub_cover-Sciurus_carolinensis 0.8763 0.3645 0.1793 0.8661 1.6371 1.0283
## shrub_cover-Vulpes_vulpes 0.0970 0.6095 -1.0375 0.0932 1.3268 1.1429
## shrub_cover-Sus_scrofa 0.6968 0.7065 -0.6891 0.6878 2.1550 1.0121
## veg_height-Canis_latrans -0.5838 0.1854 -0.9611 -0.5808 -0.2543 1.1103
## veg_height-Lynx_rufus 0.1446 0.2299 -0.2961 0.1386 0.5871 1.0025
## veg_height-Didelphis_virginiana 0.5452 0.2500 0.0625 0.5411 1.0407 1.0173
## veg_height-Sylvilagus_floridanus 0.1710 0.2251 -0.2952 0.1759 0.5840 1.0352
## veg_height-Sciurus_carolinensis 0.2331 0.2134 -0.1594 0.2297 0.6561 1.0114
## veg_height-Vulpes_vulpes -0.1045 0.2905 -0.6671 -0.0928 0.4329 1.0352
## veg_height-Sus_scrofa -0.2100 0.3269 -0.8959 -0.1932 0.4158 1.0305
## ESS
## (Intercept)-Canis_latrans 107
## (Intercept)-Lynx_rufus 67
## (Intercept)-Didelphis_virginiana 147
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Sciurus_carolinensis 173
## (Intercept)-Vulpes_vulpes 63
## (Intercept)-Sus_scrofa 370
## shrub_cover-Canis_latrans 187
## shrub_cover-Lynx_rufus 104
## shrub_cover-Didelphis_virginiana 159
## shrub_cover-Sylvilagus_floridanus 192
## shrub_cover-Sciurus_carolinensis 182
## shrub_cover-Vulpes_vulpes 155
## shrub_cover-Sus_scrofa 513
## veg_height-Canis_latrans 118
## veg_height-Lynx_rufus 142
## veg_height-Didelphis_virginiana 244
## veg_height-Sylvilagus_floridanus 237
## veg_height-Sciurus_carolinensis 245
## veg_height-Vulpes_vulpes 189
## veg_height-Sus_scrofa 384
# 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): 2.7465
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6656 0.5889 -2.8329 -1.6658 -0.5143 1.0139 421
## Avg_Cogongrass_Cover -0.7558 0.4988 -1.7607 -0.7542 0.1813 1.0035 517
## I(Avg_Cogongrass_Cover^2) 0.9156 0.4184 0.1401 0.8897 1.8255 1.0055 458
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2759 1.9486 0.0682 0.7109 5.8144 1.0328 875
## Avg_Cogongrass_Cover 0.6372 0.9132 0.0497 0.3451 3.1561 1.0500 695
## I(Avg_Cogongrass_Cover^2) 0.5410 0.9774 0.0378 0.2521 2.8450 1.0653 474
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1023 1.1449 0.0839 0.723 4.0866 1.0677 193
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9053 0.3250 -5.4374 -4.9296 -4.2180 1.0791 390
## shrub_cover 0.4260 0.3493 -0.2517 0.4188 1.1237 1.0899 394
## veg_height 0.0233 0.2181 -0.4197 0.0190 0.4644 1.0021 879
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5264 1.5414 0.0462 0.2361 2.7748 1.5317 92
## shrub_cover 0.6552 0.6325 0.0932 0.4631 2.3600 1.0068 568
## veg_height 0.2919 0.3779 0.0566 0.2019 1.0070 1.1382 1068
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7922 0.7628 -2.2843 -0.8086
## (Intercept)-Lynx_rufus -1.3050 0.7994 -2.7927 -1.3196
## (Intercept)-Didelphis_virginiana -1.8826 0.6966 -3.3625 -1.8480
## (Intercept)-Sylvilagus_floridanus -1.5159 0.7021 -2.9320 -1.5077
## (Intercept)-Sciurus_carolinensis -2.1999 0.7850 -3.9115 -2.1367
## (Intercept)-Vulpes_vulpes -2.4128 0.9826 -4.5905 -2.3182
## (Intercept)-Sus_scrofa -2.2989 0.8572 -4.2461 -2.2307
## Avg_Cogongrass_Cover-Canis_latrans -0.3571 0.6123 -1.5291 -0.3902
## Avg_Cogongrass_Cover-Lynx_rufus -0.5994 0.6552 -1.8875 -0.5955
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5099 0.6301 -1.6711 -0.5175
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3147 0.7177 -2.8998 -1.2485
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8482 0.6342 -2.2358 -0.8086
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7925 0.7217 -2.3137 -0.7718
## Avg_Cogongrass_Cover-Sus_scrofa -1.0287 0.7764 -2.7351 -0.9609
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3589 0.7599 0.2807 1.2064
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2159 0.5367 0.3562 1.1488
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6445 0.4718 -0.2056 0.6144
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8391 0.4892 -0.0084 0.8036
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0443 0.4474 0.2492 1.0111
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9605 0.5023 0.0922 0.9136
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4984 0.7460 -1.1754 0.5578
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7477 1.0132 560
## (Intercept)-Lynx_rufus 0.3932 1.0204 393
## (Intercept)-Didelphis_virginiana -0.5740 1.0059 578
## (Intercept)-Sylvilagus_floridanus -0.1298 1.0098 609
## (Intercept)-Sciurus_carolinensis -0.8169 1.0182 441
## (Intercept)-Vulpes_vulpes -0.6516 1.0336 374
## (Intercept)-Sus_scrofa -0.7854 1.0002 523
## Avg_Cogongrass_Cover-Canis_latrans 0.9390 1.0165 855
## Avg_Cogongrass_Cover-Lynx_rufus 0.6906 1.0153 651
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8221 1.0083 800
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0980 1.0054 612
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3083 1.0012 670
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5671 1.0068 642
## Avg_Cogongrass_Cover-Sus_scrofa 0.2955 1.0142 489
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3018 1.0270 333
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5027 1.0070 585
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6708 1.0042 413
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9225 1.0034 424
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0486 1.0076 602
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1348 1.0252 565
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.8084 1.0313 331
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7617 0.1756 -5.1077 -4.7591 -4.4263 1.0981
## (Intercept)-Lynx_rufus -5.3861 0.3017 -6.0386 -5.3666 -4.8438 1.0303
## (Intercept)-Didelphis_virginiana -4.6658 0.2556 -5.1668 -4.6673 -4.1615 1.0147
## (Intercept)-Sylvilagus_floridanus -4.9360 0.2379 -5.3984 -4.9347 -4.4920 1.0200
## (Intercept)-Sciurus_carolinensis -4.6927 0.2890 -5.2645 -4.6924 -4.1150 1.0795
## (Intercept)-Vulpes_vulpes -5.5528 0.6808 -7.7303 -5.4192 -4.6548 1.3094
## (Intercept)-Sus_scrofa -5.2010 0.4294 -6.1572 -5.1859 -4.3882 1.0099
## shrub_cover-Canis_latrans -0.2302 0.1968 -0.6039 -0.2363 0.1625 1.0230
## shrub_cover-Lynx_rufus -0.0623 0.3317 -0.7792 -0.0344 0.5270 1.1071
## shrub_cover-Didelphis_virginiana 1.0154 0.3350 0.3455 1.0148 1.6865 1.0863
## shrub_cover-Sylvilagus_floridanus 0.5093 0.3976 -0.2215 0.4861 1.3003 1.0327
## shrub_cover-Sciurus_carolinensis 0.8880 0.3900 0.0776 0.8944 1.6479 1.0532
## shrub_cover-Vulpes_vulpes 0.0976 0.6297 -1.0944 0.0935 1.3827 1.0958
## shrub_cover-Sus_scrofa 0.8098 0.6968 -0.5421 0.8092 2.2066 1.0493
## veg_height-Canis_latrans -0.5282 0.1690 -0.8772 -0.5186 -0.2224 1.1918
## veg_height-Lynx_rufus 0.1674 0.2258 -0.2873 0.1753 0.6002 1.0189
## veg_height-Didelphis_virginiana 0.4778 0.2465 0.0410 0.4571 0.9799 1.0273
## veg_height-Sylvilagus_floridanus 0.1639 0.2217 -0.2828 0.1751 0.5881 1.0234
## veg_height-Sciurus_carolinensis 0.2270 0.2238 -0.1752 0.2109 0.6864 1.0020
## veg_height-Vulpes_vulpes -0.1129 0.2768 -0.6805 -0.1069 0.4052 1.0203
## veg_height-Sus_scrofa -0.2240 0.3300 -0.8942 -0.2151 0.3926 1.0293
## ESS
## (Intercept)-Canis_latrans 192
## (Intercept)-Lynx_rufus 98
## (Intercept)-Didelphis_virginiana 204
## (Intercept)-Sylvilagus_floridanus 178
## (Intercept)-Sciurus_carolinensis 213
## (Intercept)-Vulpes_vulpes 44
## (Intercept)-Sus_scrofa 303
## shrub_cover-Canis_latrans 201
## shrub_cover-Lynx_rufus 129
## shrub_cover-Didelphis_virginiana 208
## shrub_cover-Sylvilagus_floridanus 147
## shrub_cover-Sciurus_carolinensis 218
## shrub_cover-Vulpes_vulpes 133
## shrub_cover-Sus_scrofa 267
## veg_height-Canis_latrans 178
## veg_height-Lynx_rufus 197
## veg_height-Didelphis_virginiana 225
## veg_height-Sylvilagus_floridanus 231
## veg_height-Sciurus_carolinensis 221
## veg_height-Vulpes_vulpes 213
## veg_height-Sus_scrofa 313
# 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): 3.3448
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9997 1.3298 -4.3686 -2.1030 0.9338 1.0218 424
## Cogon_Patch_Size 0.0066 1.1497 -2.4835 0.0548 2.1325 1.0314 362
## Veg_shannon_index 0.7130 0.9026 -1.1412 0.7476 2.4140 1.0892 360
## total_shrub_cover -0.7441 0.9594 -2.8296 -0.6475 0.9624 1.1811 148
## Avg_Cogongrass_Cover -0.2709 1.2645 -2.6691 -0.2722 2.2338 1.0166 133
## Tree_Density -2.1457 1.0585 -4.2081 -2.1714 0.1376 1.0301 177
## Avg_Canopy_Cover 2.0599 1.1288 -0.3168 2.0854 4.1857 1.0153 1350
## I(Avg_Cogongrass_Cover^2) 1.8272 0.8638 0.0847 1.8138 3.5940 1.0063 152
## avg_veg_height -0.3718 0.7479 -1.8005 -0.3812 1.2045 1.0152 258
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.3497 38.1424 0.4366 12.5061 123.8873 1.2398 86
## Cogon_Patch_Size 14.6647 29.9814 0.1348 5.6691 86.3150 1.3705 96
## Veg_shannon_index 5.8247 11.3911 0.0895 2.3697 31.6025 1.1467 138
## total_shrub_cover 5.1906 8.9967 0.0868 2.1051 30.4087 1.2927 62
## Avg_Cogongrass_Cover 2.6028 5.7570 0.0565 0.7761 17.3323 1.0646 481
## Tree_Density 3.9863 8.5459 0.0658 1.0274 25.8378 1.3391 266
## Avg_Canopy_Cover 15.9574 30.6583 0.7173 8.5286 74.4634 1.2439 216
## I(Avg_Cogongrass_Cover^2) 1.7195 4.6229 0.0506 0.5377 10.4894 1.1327 276
## avg_veg_height 1.1291 2.4213 0.0506 0.4170 6.2629 1.0625 552
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9258 3.0762 0.052 0.7471 11.0178 1.1516 101
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0178 0.3439 -5.5718 -5.0412 -4.2721 1.0448 468
## shrub_cover 0.5892 0.3796 -0.1322 0.5753 1.3632 1.1975 195
## veg_height 0.0447 0.2547 -0.4639 0.0464 0.5637 1.0032 838
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5207 0.9333 0.0609 0.2980 2.3944 1.2567 561
## shrub_cover 0.7893 1.2433 0.1361 0.5325 2.8752 1.1952 1160
## veg_height 0.3535 0.4404 0.0674 0.2488 1.1864 1.0470 1274
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4590 1.6463 -3.4188 -0.5412
## (Intercept)-Lynx_rufus 1.0224 3.0357 -3.3860 0.4887
## (Intercept)-Didelphis_virginiana -4.6955 2.0261 -9.3483 -4.4458
## (Intercept)-Sylvilagus_floridanus -2.9969 1.9308 -6.9944 -2.9575
## (Intercept)-Sciurus_carolinensis -5.3041 2.1703 -10.2101 -4.9949
## (Intercept)-Vulpes_vulpes -6.0304 2.8912 -12.8413 -5.6957
## (Intercept)-Sus_scrofa -6.9367 3.2321 -14.8533 -6.4496
## Cogon_Patch_Size-Canis_latrans 3.0669 2.9826 -0.2331 2.2331
## Cogon_Patch_Size-Lynx_rufus 0.1625 2.3661 -4.4132 0.1368
## Cogon_Patch_Size-Didelphis_virginiana 2.3039 1.4756 0.0191 2.1096
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2764 2.9759 -10.0746 -1.5810
## Cogon_Patch_Size-Sciurus_carolinensis -1.5182 2.5712 -8.2503 -1.0350
## Cogon_Patch_Size-Vulpes_vulpes -1.3856 3.4533 -9.8122 -0.7870
## Cogon_Patch_Size-Sus_scrofa -1.3904 2.7003 -8.4386 -0.8817
## Veg_shannon_index-Canis_latrans 1.8165 1.2118 -0.0465 1.6479
## Veg_shannon_index-Lynx_rufus -0.5913 1.9557 -5.4495 -0.3219
## Veg_shannon_index-Didelphis_virginiana 1.6827 1.4620 -0.5327 1.4180
## Veg_shannon_index-Sylvilagus_floridanus 1.2528 1.1344 -0.8143 1.1837
## Veg_shannon_index-Sciurus_carolinensis -0.6604 1.7734 -5.0926 -0.3687
## Veg_shannon_index-Vulpes_vulpes -0.2090 1.7176 -4.3459 0.0718
## Veg_shannon_index-Sus_scrofa 2.6982 2.2099 -0.0065 2.1063
## total_shrub_cover-Canis_latrans 0.9127 1.2461 -0.8930 0.6793
## total_shrub_cover-Lynx_rufus -2.1377 2.3808 -7.9680 -1.7829
## total_shrub_cover-Didelphis_virginiana -1.7081 1.7487 -6.0974 -1.3614
## total_shrub_cover-Sylvilagus_floridanus -1.1877 1.6710 -5.1727 -0.9165
## total_shrub_cover-Sciurus_carolinensis -0.8332 1.5600 -4.8033 -0.5885
## total_shrub_cover-Vulpes_vulpes -1.6391 2.2573 -7.9083 -1.0983
## total_shrub_cover-Sus_scrofa -0.2950 1.5426 -3.7939 -0.2188
## Avg_Cogongrass_Cover-Canis_latrans 0.0207 1.6418 -2.9173 -0.0572
## Avg_Cogongrass_Cover-Lynx_rufus 0.2498 1.8896 -2.8885 0.0956
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3487 1.6025 -3.5088 -0.3488
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0212 1.7630 -4.7988 -0.8846
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3329 1.6430 -3.4681 -0.3493
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0171 1.8612 -3.2860 -0.0901
## Avg_Cogongrass_Cover-Sus_scrofa -0.6176 1.8482 -4.7992 -0.5198
## Tree_Density-Canis_latrans -3.1127 1.6522 -7.3065 -2.8494
## Tree_Density-Lynx_rufus -1.4036 1.8532 -4.2170 -1.6260
## Tree_Density-Didelphis_virginiana -2.4604 1.4317 -5.3879 -2.4611
## Tree_Density-Sylvilagus_floridanus -2.8122 1.7116 -6.8588 -2.6369
## Tree_Density-Sciurus_carolinensis -2.7818 1.7194 -6.9586 -2.6465
## Tree_Density-Vulpes_vulpes -2.1838 1.8382 -5.7473 -2.2313
## Tree_Density-Sus_scrofa -2.6105 1.7469 -6.6361 -2.4662
## Avg_Canopy_Cover-Canis_latrans -0.1983 0.8266 -1.9948 -0.1659
## Avg_Canopy_Cover-Lynx_rufus 1.3881 2.7775 -3.7634 1.0258
## Avg_Canopy_Cover-Didelphis_virginiana 5.1099 2.4062 1.8242 4.6615
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.0171 2.6175 1.9442 5.6084
## Avg_Canopy_Cover-Sciurus_carolinensis 4.5635 2.9198 1.3155 3.9724
## Avg_Canopy_Cover-Vulpes_vulpes 3.8144 2.2815 0.7298 3.3637
## Avg_Canopy_Cover-Sus_scrofa 2.8141 1.5397 0.3806 2.5816
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2924 1.0715 0.5221 2.1781
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5843 1.5165 0.5048 2.3178
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5812 0.9937 -0.2632 1.5494
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6875 1.1007 -0.3906 1.6213
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0529 1.0166 0.2511 1.9716
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1252 1.0360 0.1933 2.0566
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.4297 1.5698 -2.0981 1.5805
## avg_veg_height-Canis_latrans -0.2907 0.8231 -1.8789 -0.3231
## avg_veg_height-Lynx_rufus -0.6801 1.2657 -3.5102 -0.5932
## avg_veg_height-Didelphis_virginiana -0.6343 1.0457 -2.9450 -0.5843
## avg_veg_height-Sylvilagus_floridanus -0.3893 0.9759 -2.3524 -0.3680
## avg_veg_height-Sciurus_carolinensis 0.0938 1.0293 -1.6348 0.0172
## avg_veg_height-Vulpes_vulpes -0.6128 1.1756 -3.1043 -0.5654
## avg_veg_height-Sus_scrofa -0.2785 0.9715 -2.0950 -0.3338
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.2959 1.0917 306
## (Intercept)-Lynx_rufus 8.8679 1.0737 64
## (Intercept)-Didelphis_virginiana -1.3731 1.0597 196
## (Intercept)-Sylvilagus_floridanus 0.9779 1.1053 214
## (Intercept)-Sciurus_carolinensis -1.8812 1.0727 143
## (Intercept)-Vulpes_vulpes -1.5949 1.1144 129
## (Intercept)-Sus_scrofa -2.1961 1.1357 115
## Cogon_Patch_Size-Canis_latrans 11.1892 1.1278 200
## Cogon_Patch_Size-Lynx_rufus 5.1998 1.0214 172
## Cogon_Patch_Size-Didelphis_virginiana 5.8080 1.2098 175
## Cogon_Patch_Size-Sylvilagus_floridanus 1.6008 1.1166 118
## Cogon_Patch_Size-Sciurus_carolinensis 2.1842 1.3486 166
## Cogon_Patch_Size-Vulpes_vulpes 2.7876 1.3211 85
## Cogon_Patch_Size-Sus_scrofa 2.1158 1.2691 235
## Veg_shannon_index-Canis_latrans 4.6417 1.0181 242
## Veg_shannon_index-Lynx_rufus 2.2640 1.1950 134
## Veg_shannon_index-Didelphis_virginiana 5.3737 1.0184 210
## Veg_shannon_index-Sylvilagus_floridanus 3.7594 1.0137 753
## Veg_shannon_index-Sciurus_carolinensis 1.8802 1.2607 130
## Veg_shannon_index-Vulpes_vulpes 2.5587 1.1580 226
## Veg_shannon_index-Sus_scrofa 8.4449 1.0427 108
## total_shrub_cover-Canis_latrans 3.9535 1.1228 190
## total_shrub_cover-Lynx_rufus 1.5362 1.4048 57
## total_shrub_cover-Didelphis_virginiana 0.7024 1.1462 155
## total_shrub_cover-Sylvilagus_floridanus 1.3630 1.1363 102
## total_shrub_cover-Sciurus_carolinensis 1.6515 1.1401 113
## total_shrub_cover-Vulpes_vulpes 1.1388 1.3831 108
## total_shrub_cover-Sus_scrofa 2.5816 1.1295 258
## Avg_Cogongrass_Cover-Canis_latrans 3.5284 1.0329 214
## Avg_Cogongrass_Cover-Lynx_rufus 4.5923 1.0171 213
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8019 1.0216 206
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0523 1.0001 242
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.0159 1.0233 244
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.9866 1.0283 199
## Avg_Cogongrass_Cover-Sus_scrofa 2.5482 1.0102 243
## Tree_Density-Canis_latrans -0.6019 1.0731 213
## Tree_Density-Lynx_rufus 3.0842 1.1385 139
## Tree_Density-Didelphis_virginiana 0.5084 1.0275 275
## Tree_Density-Sylvilagus_floridanus 0.1684 1.0159 215
## Tree_Density-Sciurus_carolinensis 0.3002 1.0799 247
## Tree_Density-Vulpes_vulpes 1.9260 1.0306 224
## Tree_Density-Sus_scrofa 0.5368 1.0064 267
## Avg_Canopy_Cover-Canis_latrans 1.3223 1.0329 437
## Avg_Canopy_Cover-Lynx_rufus 7.3702 1.3125 78
## Avg_Canopy_Cover-Didelphis_virginiana 11.2401 1.0345 179
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.9326 1.0955 152
## Avg_Canopy_Cover-Sciurus_carolinensis 12.3365 1.3468 126
## Avg_Canopy_Cover-Vulpes_vulpes 9.3720 1.1106 172
## Avg_Canopy_Cover-Sus_scrofa 6.4474 1.0551 406
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6827 1.0070 238
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1481 1.0201 217
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.5980 1.0068 176
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.0169 1.0013 223
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.2679 1.0310 219
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.3614 1.0162 260
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 4.1062 1.0204 143
## avg_veg_height-Canis_latrans 1.3377 1.0233 317
## avg_veg_height-Lynx_rufus 1.6452 1.0135 245
## avg_veg_height-Didelphis_virginiana 1.3134 1.0051 468
## avg_veg_height-Sylvilagus_floridanus 1.5558 1.0174 464
## avg_veg_height-Sciurus_carolinensis 2.3979 1.0119 385
## avg_veg_height-Vulpes_vulpes 1.5671 1.0193 329
## avg_veg_height-Sus_scrofa 1.7589 1.0099 441
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7500 0.1557 -5.0641 -4.7452 -4.4561 1.0988
## (Intercept)-Lynx_rufus -5.6162 0.2666 -6.1041 -5.6158 -5.1021 1.0858
## (Intercept)-Didelphis_virginiana -4.8005 0.2961 -5.3970 -4.7820 -4.2377 1.0474
## (Intercept)-Sylvilagus_floridanus -5.0516 0.2212 -5.4927 -5.0555 -4.6221 1.1359
## (Intercept)-Sciurus_carolinensis -4.8292 0.3118 -5.4574 -4.8218 -4.2064 1.0131
## (Intercept)-Vulpes_vulpes -5.7188 0.4649 -6.7354 -5.6913 -4.8868 1.0313
## (Intercept)-Sus_scrofa -5.2909 0.4641 -6.2990 -5.2641 -4.4325 1.1697
## shrub_cover-Canis_latrans -0.3020 0.2009 -0.6853 -0.3049 0.0939 1.0360
## shrub_cover-Lynx_rufus 0.0979 0.3730 -0.6057 0.0802 0.8467 1.3696
## shrub_cover-Didelphis_virginiana 1.1827 0.3905 0.5116 1.1461 2.0711 1.1379
## shrub_cover-Sylvilagus_floridanus 0.7444 0.3846 -0.0250 0.7537 1.4628 1.0405
## shrub_cover-Sciurus_carolinensis 1.0986 0.3904 0.2544 1.1182 1.8448 1.0181
## shrub_cover-Vulpes_vulpes 0.4672 0.5837 -0.6027 0.4686 1.6676 1.3198
## shrub_cover-Sus_scrofa 1.0149 0.7673 -0.4172 0.9690 2.7298 1.2228
## veg_height-Canis_latrans -0.5397 0.1679 -0.8692 -0.5349 -0.2281 1.0310
## veg_height-Lynx_rufus 0.2467 0.2311 -0.2159 0.2525 0.6730 1.0206
## veg_height-Didelphis_virginiana 0.5816 0.2554 0.1110 0.5694 1.0984 1.0353
## veg_height-Sylvilagus_floridanus 0.1733 0.2470 -0.2948 0.1681 0.6728 1.0062
## veg_height-Sciurus_carolinensis 0.2847 0.2390 -0.1442 0.2671 0.8014 1.0538
## veg_height-Vulpes_vulpes -0.1283 0.3123 -0.7417 -0.1268 0.4734 1.0065
## veg_height-Sus_scrofa -0.2896 0.3223 -0.9339 -0.2762 0.3098 1.0056
## ESS
## (Intercept)-Canis_latrans 242
## (Intercept)-Lynx_rufus 101
## (Intercept)-Didelphis_virginiana 100
## (Intercept)-Sylvilagus_floridanus 210
## (Intercept)-Sciurus_carolinensis 124
## (Intercept)-Vulpes_vulpes 132
## (Intercept)-Sus_scrofa 147
## shrub_cover-Canis_latrans 193
## shrub_cover-Lynx_rufus 77
## shrub_cover-Didelphis_virginiana 120
## shrub_cover-Sylvilagus_floridanus 136
## shrub_cover-Sciurus_carolinensis 139
## shrub_cover-Vulpes_vulpes 120
## shrub_cover-Sus_scrofa 109
## veg_height-Canis_latrans 167
## veg_height-Lynx_rufus 123
## veg_height-Didelphis_virginiana 177
## veg_height-Sylvilagus_floridanus 155
## veg_height-Sciurus_carolinensis 185
## veg_height-Vulpes_vulpes 146
## veg_height-Sus_scrofa 320
#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): 3.4698
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.862 0.4269 -1.6594 -0.8716 0.0132 1.0025 1589
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2125 1.4172 0.1836 0.8285 4.551 1.0444 851
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7123 0.2776 -5.2172 -4.7182 -4.1093 1.0234 914
## week 0.0018 1.6766 -3.2289 -0.0360 3.2612 1.0048 603
## I(week^2) -0.0475 1.6536 -3.2477 -0.0067 3.0435 1.0022 598
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 4.117000e-01 5.606000e-01 0.0572 2.607000e-01 1.608500e+00 1.0690
## week 7.790079e+30 1.157032e+32 0.1207 1.982133e+16 2.610431e+29 1.6839
## I(week^2) 2.519237e+08 3.901538e+09 0.1134 1.258179e+02 1.062050e+08 1.6512
## ESS
## (Intercept) 452
## week 154
## I(week^2) 261
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2586 0.4268 -0.4967 0.2418 1.1149 1.0070
## (Intercept)-Lynx_rufus -0.1080 0.6243 -1.0200 -0.1669 1.0685 1.0619
## (Intercept)-Didelphis_virginiana -1.3228 0.4166 -2.1972 -1.3072 -0.5445 1.0024
## (Intercept)-Sylvilagus_floridanus -0.6235 0.3987 -1.4180 -0.6236 0.1687 1.0036
## (Intercept)-Sciurus_carolinensis -1.3265 0.4217 -2.1996 -1.3099 -0.5360 1.0042
## (Intercept)-Vulpes_vulpes -1.5415 0.6188 -2.8177 -1.5274 -0.3628 1.0432
## (Intercept)-Sus_scrofa -1.7940 0.5330 -2.9200 -1.7661 -0.8287 1.0116
## ESS
## (Intercept)-Canis_latrans 1451
## (Intercept)-Lynx_rufus 207
## (Intercept)-Didelphis_virginiana 2147
## (Intercept)-Sylvilagus_floridanus 1409
## (Intercept)-Sciurus_carolinensis 2345
## (Intercept)-Vulpes_vulpes 487
## (Intercept)-Sus_scrofa 1301
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.653300e+00 1.610000e-01 -4.989800e+00
## (Intercept)-Lynx_rufus -5.257700e+00 2.763000e-01 -5.822700e+00
## (Intercept)-Didelphis_virginiana -4.341900e+00 2.259000e-01 -4.775300e+00
## (Intercept)-Sylvilagus_floridanus -4.850500e+00 2.279000e-01 -5.320900e+00
## (Intercept)-Sciurus_carolinensis -4.389300e+00 2.196000e-01 -4.832500e+00
## (Intercept)-Vulpes_vulpes -5.367800e+00 4.795000e-01 -6.344500e+00
## (Intercept)-Sus_scrofa -4.771800e+00 3.392000e-01 -5.493600e+00
## week-Canis_latrans 2.957910e+13 3.207586e+15 -1.623669e+13
## week-Lynx_rufus -7.894082e+13 2.950905e+15 -1.376236e+13
## week-Didelphis_virginiana -4.031420e+13 2.085601e+15 -1.576582e+13
## week-Sylvilagus_floridanus -2.796046e+12 2.037858e+15 -1.071074e+13
## week-Sciurus_carolinensis -8.561142e+13 2.911586e+15 -2.227995e+13
## week-Vulpes_vulpes 1.038783e+13 2.423557e+15 -1.401485e+13
## week-Sus_scrofa -1.392377e+13 1.995967e+15 -1.125654e+13
## I(week^2)-Canis_latrans -4.322460e+01 1.366298e+04 -2.354893e+03
## I(week^2)-Lynx_rufus -3.536934e+02 2.010382e+04 -2.215063e+03
## I(week^2)-Didelphis_virginiana 2.738102e+02 1.300200e+04 -1.671305e+03
## I(week^2)-Sylvilagus_floridanus -2.909961e+02 1.788437e+04 -1.977614e+03
## I(week^2)-Sciurus_carolinensis 3.742467e+02 1.406730e+04 -2.178183e+03
## I(week^2)-Vulpes_vulpes -1.772969e+02 1.014179e+04 -2.739945e+03
## I(week^2)-Sus_scrofa -1.888010e+02 1.512172e+04 -1.994257e+03
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6443 -4.363300e+00 1.0342 202
## (Intercept)-Lynx_rufus -5.2426 -4.774200e+00 1.1187 102
## (Intercept)-Didelphis_virginiana -4.3437 -3.907600e+00 1.0060 313
## (Intercept)-Sylvilagus_floridanus -4.8395 -4.426800e+00 1.0192 196
## (Intercept)-Sciurus_carolinensis -4.3834 -3.977200e+00 1.1028 361
## (Intercept)-Vulpes_vulpes -5.3311 -4.548500e+00 1.0552 114
## (Intercept)-Sus_scrofa -4.7549 -4.120000e+00 1.0974 253
## week-Canis_latrans 0.1211 1.089588e+13 1.2988 1864
## week-Lynx_rufus -0.3453 8.608608e+12 1.3599 976
## week-Didelphis_virginiana -0.2826 9.455985e+12 1.3271 1516
## week-Sylvilagus_floridanus -0.5160 1.185749e+13 1.2905 6920
## week-Sciurus_carolinensis -0.1330 5.840550e+12 1.3739 994
## week-Vulpes_vulpes -0.0072 8.936366e+12 1.2922 33130
## week-Sus_scrofa -0.0823 1.517819e+13 1.2952 8405
## I(week^2)-Canis_latrans 0.0707 2.478233e+03 1.2875 115388
## I(week^2)-Lynx_rufus -0.0655 1.740364e+03 1.3198 3017
## I(week^2)-Didelphis_virginiana 0.0916 2.492550e+03 1.3196 3247
## I(week^2)-Sylvilagus_floridanus 0.0857 2.371086e+03 1.3170 3219
## I(week^2)-Sciurus_carolinensis -0.0965 2.669147e+03 1.3451 2060
## I(week^2)-Vulpes_vulpes -0.0448 1.777377e+03 1.3079 5086
## I(week^2)-Sus_scrofa 0.1167 2.058781e+03 1.2980 4799
#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): 3.8045
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3809 1.0675 -3.2736 -1.4761 0.8589 1.0204 677
## Cogon_Patch_Size -0.6318 0.9088 -2.5320 -0.6214 1.1628 1.0076 859
## Veg_shannon_index 0.6010 0.7270 -0.9819 0.6262 1.9529 1.0048 897
## total_shrub_cover -0.0072 0.5567 -1.1232 -0.0092 1.1114 1.0080 476
## Avg_Cogongrass_Cover 1.9266 0.7536 0.5241 1.8964 3.4515 1.0368 296
## Tree_Density -1.9177 0.7892 -3.5324 -1.9071 -0.3987 1.0635 260
## Avg_Canopy_Cover 1.7697 0.7788 0.1419 1.7725 3.2723 1.0063 635
## avg_veg_height -0.6199 0.5383 -1.6389 -0.6290 0.5080 1.0402 383
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.8692 15.7262 1.1253 8.1683 54.4738 1.1329 194
## Cogon_Patch_Size 8.0453 16.8316 0.2569 3.6929 43.8243 1.1343 285
## Veg_shannon_index 3.9887 10.6648 0.0922 1.5218 21.5176 1.7340 113
## total_shrub_cover 1.1317 2.4203 0.0518 0.4552 6.7492 1.1239 290
## Avg_Cogongrass_Cover 1.6869 4.6137 0.0553 0.5462 10.0101 1.0723 422
## Tree_Density 1.9134 4.0424 0.0511 0.6665 11.6082 1.1867 337
## Avg_Canopy_Cover 4.2974 9.3826 0.1625 2.0818 22.9768 1.3079 113
## avg_veg_height 0.5320 0.8638 0.0402 0.2493 2.9634 1.0122 875
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8114 3.8366 0.0662 0.6649 10.8481 1.1204 84
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7245 0.3474 -5.3270 -4.7520 -3.9457 1.0082 1854
## week 0.0798 1.6313 -3.0808 0.0332 3.1451 1.0031 754
## I(week^2) -0.0257 1.6467 -3.3650 0.0252 3.2224 1.0009 1179
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 6.743000e-01 1.147400e+00 0.1007 0.4265 2.778200e+00 1.0973
## week 8.886389e+15 1.629731e+17 0.1146 58301.3622 8.078217e+15 1.5594
## I(week^2) 2.051820e+24 6.088719e+25 0.1770 74109.1834 2.096584e+23 1.3991
## ESS
## (Intercept) 429
## week 368
## I(week^2) 1118
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.0772 1.2341 -1.1409 1.0148
## (Intercept)-Lynx_rufus 1.7618 2.3324 -1.5902 1.2969
## (Intercept)-Didelphis_virginiana -3.2700 1.2375 -5.9552 -3.1358
## (Intercept)-Sylvilagus_floridanus -1.9160 1.3124 -4.8386 -1.8323
## (Intercept)-Sciurus_carolinensis -3.5874 1.3590 -6.7708 -3.4197
## (Intercept)-Vulpes_vulpes -3.2364 1.7555 -6.8153 -3.2415
## (Intercept)-Sus_scrofa -5.2363 2.1197 -10.4497 -4.8957
## Cogon_Patch_Size-Canis_latrans 1.2644 1.6377 -0.9416 0.9456
## Cogon_Patch_Size-Lynx_rufus -0.6630 1.7011 -3.5315 -0.8193
## Cogon_Patch_Size-Didelphis_virginiana 1.2737 1.0782 -0.4484 1.1525
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8671 2.4835 -9.4962 -2.2599
## Cogon_Patch_Size-Sciurus_carolinensis -2.0674 1.6383 -6.2561 -1.7184
## Cogon_Patch_Size-Vulpes_vulpes -1.6650 2.1159 -7.0210 -1.3953
## Cogon_Patch_Size-Sus_scrofa -1.6258 1.9589 -6.3750 -1.2679
## Veg_shannon_index-Canis_latrans 1.4846 0.9101 0.0377 1.3781
## Veg_shannon_index-Lynx_rufus -0.2935 1.7745 -5.1326 0.0520
## Veg_shannon_index-Didelphis_virginiana 1.0585 0.9142 -0.5536 0.9795
## Veg_shannon_index-Sylvilagus_floridanus 1.1083 0.9081 -0.3785 1.0053
## Veg_shannon_index-Sciurus_carolinensis -0.3199 0.9489 -2.4563 -0.2335
## Veg_shannon_index-Vulpes_vulpes -0.5806 1.7276 -4.2497 -0.3197
## Veg_shannon_index-Sus_scrofa 2.3093 1.8077 0.0886 1.8904
## total_shrub_cover-Canis_latrans 0.5291 0.7427 -0.6415 0.4128
## total_shrub_cover-Lynx_rufus -0.6064 1.1450 -3.3801 -0.4333
## total_shrub_cover-Didelphis_virginiana -0.2972 0.7440 -1.9768 -0.2313
## total_shrub_cover-Sylvilagus_floridanus 0.0497 0.7184 -1.4084 0.0501
## total_shrub_cover-Sciurus_carolinensis 0.1920 0.6744 -1.1128 0.1624
## total_shrub_cover-Vulpes_vulpes -0.2612 0.9584 -2.5573 -0.1459
## total_shrub_cover-Sus_scrofa 0.3004 0.8397 -1.1604 0.2399
## Avg_Cogongrass_Cover-Canis_latrans 2.3608 0.9953 0.7523 2.2774
## Avg_Cogongrass_Cover-Lynx_rufus 2.5454 1.2268 0.6724 2.3948
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1037 0.8795 0.5445 2.0663
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4867 0.9765 -0.4625 1.4971
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2916 0.9440 0.6645 2.2163
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3555 1.2157 0.4003 2.2292
## Avg_Cogongrass_Cover-Sus_scrofa 1.3467 1.3523 -2.1065 1.5117
## Tree_Density-Canis_latrans -2.4938 1.2433 -5.5433 -2.2781
## Tree_Density-Lynx_rufus -1.1441 1.2295 -3.1967 -1.2339
## Tree_Density-Didelphis_virginiana -2.2299 1.0302 -4.5773 -2.1351
## Tree_Density-Sylvilagus_floridanus -2.4204 1.2588 -5.5733 -2.2431
## Tree_Density-Sciurus_carolinensis -2.3949 1.2195 -5.4197 -2.2345
## Tree_Density-Vulpes_vulpes -1.8862 1.2352 -4.5753 -1.8737
## Tree_Density-Sus_scrofa -2.0729 1.2162 -4.7124 -1.9994
## Avg_Canopy_Cover-Canis_latrans 0.2295 0.7898 -1.3200 0.2367
## Avg_Canopy_Cover-Lynx_rufus 0.9444 1.4413 -1.8720 0.8700
## Avg_Canopy_Cover-Didelphis_virginiana 2.9981 1.1671 1.2883 2.8267
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5476 1.7890 1.1992 3.2155
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4916 1.0013 0.9540 2.3453
## Avg_Canopy_Cover-Vulpes_vulpes 2.5053 1.7440 0.5605 2.1666
## Avg_Canopy_Cover-Sus_scrofa 2.1876 0.9776 0.5715 2.0683
## avg_veg_height-Canis_latrans -0.6826 0.6134 -1.9100 -0.6864
## avg_veg_height-Lynx_rufus -0.6114 0.8223 -2.1607 -0.6371
## avg_veg_height-Didelphis_virginiana -0.6887 0.6917 -2.0587 -0.6881
## avg_veg_height-Sylvilagus_floridanus -0.7449 0.6743 -2.1066 -0.7185
## avg_veg_height-Sciurus_carolinensis -0.2988 0.6783 -1.5375 -0.3494
## avg_veg_height-Vulpes_vulpes -0.7063 0.7747 -2.2807 -0.7103
## avg_veg_height-Sus_scrofa -0.6899 0.7340 -2.1219 -0.6798
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8374 1.1107 284
## (Intercept)-Lynx_rufus 7.4529 1.0933 96
## (Intercept)-Didelphis_virginiana -1.2554 1.0747 474
## (Intercept)-Sylvilagus_floridanus 0.3942 1.0490 340
## (Intercept)-Sciurus_carolinensis -1.4514 1.0636 224
## (Intercept)-Vulpes_vulpes 0.4121 1.1422 153
## (Intercept)-Sus_scrofa -2.1666 1.3009 113
## Cogon_Patch_Size-Canis_latrans 5.6847 1.0219 482
## Cogon_Patch_Size-Lynx_rufus 3.2594 1.0055 284
## Cogon_Patch_Size-Didelphis_virginiana 3.8032 1.0276 232
## Cogon_Patch_Size-Sylvilagus_floridanus -0.0850 1.0673 213
## Cogon_Patch_Size-Sciurus_carolinensis 0.1533 1.0662 396
## Cogon_Patch_Size-Vulpes_vulpes 1.8485 1.1164 166
## Cogon_Patch_Size-Sus_scrofa 1.1343 1.0805 381
## Veg_shannon_index-Canis_latrans 3.5234 1.0686 271
## Veg_shannon_index-Lynx_rufus 2.1427 1.2337 119
## Veg_shannon_index-Didelphis_virginiana 3.0571 1.0311 573
## Veg_shannon_index-Sylvilagus_floridanus 3.3689 1.0092 451
## Veg_shannon_index-Sciurus_carolinensis 1.2687 1.0366 458
## Veg_shannon_index-Vulpes_vulpes 1.5264 1.2546 97
## Veg_shannon_index-Sus_scrofa 7.0346 1.2437 174
## total_shrub_cover-Canis_latrans 2.2954 1.0432 376
## total_shrub_cover-Lynx_rufus 1.1356 1.0855 238
## total_shrub_cover-Didelphis_virginiana 1.0047 1.0174 608
## total_shrub_cover-Sylvilagus_floridanus 1.5327 1.0051 713
## total_shrub_cover-Sciurus_carolinensis 1.6219 1.0042 636
## total_shrub_cover-Vulpes_vulpes 1.3226 1.0363 285
## total_shrub_cover-Sus_scrofa 2.0945 1.0078 690
## Avg_Cogongrass_Cover-Canis_latrans 4.5118 1.1011 356
## Avg_Cogongrass_Cover-Lynx_rufus 5.4164 1.0468 283
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9566 1.0568 326
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3282 1.0187 420
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4007 1.0656 295
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0802 1.0539 292
## Avg_Cogongrass_Cover-Sus_scrofa 3.5195 1.0122 332
## Tree_Density-Canis_latrans -0.6624 1.0300 408
## Tree_Density-Lynx_rufus 1.6219 1.0707 253
## Tree_Density-Didelphis_virginiana -0.4992 1.0213 353
## Tree_Density-Sylvilagus_floridanus -0.4973 1.0465 263
## Tree_Density-Sciurus_carolinensis -0.5798 1.0469 423
## Tree_Density-Vulpes_vulpes 0.5053 1.0235 321
## Tree_Density-Sus_scrofa 0.1644 1.0639 416
## Avg_Canopy_Cover-Canis_latrans 1.7652 1.0094 515
## Avg_Canopy_Cover-Lynx_rufus 3.9547 1.0014 171
## Avg_Canopy_Cover-Didelphis_virginiana 5.8330 1.0595 178
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6776 1.0230 205
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6930 1.0368 201
## Avg_Canopy_Cover-Vulpes_vulpes 6.2065 1.3180 65
## Avg_Canopy_Cover-Sus_scrofa 4.4456 1.0728 365
## avg_veg_height-Canis_latrans 0.5071 1.0544 534
## avg_veg_height-Lynx_rufus 1.1180 1.0285 408
## avg_veg_height-Didelphis_virginiana 0.7186 1.0329 502
## avg_veg_height-Sylvilagus_floridanus 0.5832 1.0141 571
## avg_veg_height-Sciurus_carolinensis 1.2083 1.0817 555
## avg_veg_height-Vulpes_vulpes 0.8905 1.0191 444
## avg_veg_height-Sus_scrofa 0.7462 1.0246 553
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.690200e+00 1.641000e-01 -5.024000e+00
## (Intercept)-Lynx_rufus -5.592500e+00 2.837000e-01 -6.144500e+00
## (Intercept)-Didelphis_virginiana -4.292300e+00 2.212000e-01 -4.744500e+00
## (Intercept)-Sylvilagus_floridanus -4.924100e+00 2.446000e-01 -5.485600e+00
## (Intercept)-Sciurus_carolinensis -4.373700e+00 2.360000e-01 -4.855500e+00
## (Intercept)-Vulpes_vulpes -5.637300e+00 5.629000e-01 -6.942700e+00
## (Intercept)-Sus_scrofa -4.723700e+00 3.557000e-01 -5.481700e+00
## week-Canis_latrans -1.537801e+06 1.095609e+08 -1.945949e+07
## week-Lynx_rufus -1.473292e+06 8.491187e+07 -2.294390e+07
## week-Didelphis_virginiana 1.974626e+05 8.761979e+07 -2.121810e+07
## week-Sylvilagus_floridanus 1.686148e+06 8.885356e+07 -1.917857e+07
## week-Sciurus_carolinensis 3.272279e+05 9.208887e+07 -1.499755e+07
## week-Vulpes_vulpes 1.046306e+04 6.662138e+07 -2.407283e+07
## week-Sus_scrofa 8.706611e+05 9.598727e+07 -2.908758e+07
## I(week^2)-Canis_latrans 2.981334e+10 1.163780e+12 -2.817386e+09
## I(week^2)-Lynx_rufus 7.219256e+09 1.185057e+12 -4.222987e+09
## I(week^2)-Didelphis_virginiana 5.146047e+09 9.136693e+11 -7.211361e+09
## I(week^2)-Sylvilagus_floridanus -2.867847e+10 1.798414e+12 -7.528559e+09
## I(week^2)-Sciurus_carolinensis -4.276830e+09 1.723930e+12 -4.501327e+09
## I(week^2)-Vulpes_vulpes -2.775590e+10 9.286039e+11 -1.372746e+10
## I(week^2)-Sus_scrofa 2.524817e+10 8.823004e+11 -4.098341e+09
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6832 -4.377000e+00 1.0115 207
## (Intercept)-Lynx_rufus -5.5924 -5.025400e+00 1.0694 78
## (Intercept)-Didelphis_virginiana -4.2863 -3.891800e+00 1.0140 356
## (Intercept)-Sylvilagus_floridanus -4.8943 -4.508300e+00 1.0590 139
## (Intercept)-Sciurus_carolinensis -4.3595 -3.928400e+00 1.0155 305
## (Intercept)-Vulpes_vulpes -5.5741 -4.705300e+00 1.1425 55
## (Intercept)-Sus_scrofa -4.7086 -4.074300e+00 1.0151 272
## week-Canis_latrans 0.2322 2.512668e+07 1.3104 5029
## week-Lynx_rufus 0.0476 2.497516e+07 1.3199 2224
## week-Didelphis_virginiana 0.0238 1.866142e+07 1.2909 17077
## week-Sylvilagus_floridanus 0.1144 2.272022e+07 1.3251 2252
## week-Sciurus_carolinensis 0.2779 1.877082e+07 1.2917 17839
## week-Vulpes_vulpes 0.4666 1.799706e+07 1.2903 25518
## week-Sus_scrofa 0.0403 1.804198e+07 1.2986 2559
## I(week^2)-Canis_latrans -0.0713 1.119408e+10 1.3543 1791
## I(week^2)-Lynx_rufus 0.0616 4.903405e+09 1.2940 22308
## I(week^2)-Didelphis_virginiana -0.0489 3.996758e+09 1.2935 23461
## I(week^2)-Sylvilagus_floridanus -0.1771 4.931164e+09 1.3155 4896
## I(week^2)-Sciurus_carolinensis -0.0468 3.516808e+09 1.2910 39444
## I(week^2)-Vulpes_vulpes -0.0196 2.359337e+09 1.3766 766
## I(week^2)-Sus_scrofa -0.1447 6.638744e+09 1.3696 713
#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): 3.6145
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0368 0.5279 -2.0710 -1.0466 0.0384 1.0069 724
## Avg_Cogongrass_Cover 0.3001 0.3903 -0.4711 0.2997 1.0799 1.0030 558
## total_shrub_cover -0.0326 0.3505 -0.7519 -0.0259 0.6324 1.0077 947
## avg_veg_height -0.0406 0.3676 -0.7710 -0.0465 0.6803 1.0119 477
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3180 1.5882 0.0803 0.8226 5.4595 1.0229 710
## Avg_Cogongrass_Cover 0.4515 0.7293 0.0391 0.2403 2.2460 1.0215 1049
## total_shrub_cover 0.4453 0.7463 0.0394 0.2341 2.1700 1.0180 556
## avg_veg_height 0.3102 0.4752 0.0366 0.1829 1.3387 1.0170 1282
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5844 1.3733 0.1345 1.2087 5.2092 1.0025 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7187 0.3139 -5.3112 -4.7268 -4.0675 1.0174 725
## week 0.0393 1.6431 -3.2844 0.0586 3.1846 1.0036 695
## I(week^2) 0.0831 1.6066 -3.0519 0.0982 3.1833 1.0159 681
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.154000e-01 6.925000e-01 0.0617 0.3234 2.147000e+00 1.0352 160
## week 2.261120e+10 3.614138e+11 0.1068 154.6213 2.797646e+09 1.6348 194
## I(week^2) 1.511688e+18 3.807077e+19 0.1068 34.9615 1.568665e+15 1.4392 655
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0052 0.7996 -1.4247 -0.0223
## (Intercept)-Lynx_rufus -0.5425 0.8277 -2.1231 -0.5889
## (Intercept)-Didelphis_virginiana -1.4377 0.6781 -2.8228 -1.3958
## (Intercept)-Sylvilagus_floridanus -0.8430 0.6855 -2.2392 -0.8422
## (Intercept)-Sciurus_carolinensis -1.4942 0.6793 -2.9345 -1.4614
## (Intercept)-Vulpes_vulpes -1.5312 0.8619 -3.2636 -1.5291
## (Intercept)-Sus_scrofa -1.8522 0.8097 -3.6359 -1.7866
## Avg_Cogongrass_Cover-Canis_latrans 0.5089 0.4928 -0.3625 0.4886
## Avg_Cogongrass_Cover-Lynx_rufus 0.6291 0.5386 -0.3401 0.5777
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4455 0.4527 -0.4161 0.4313
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1315 0.5402 -1.2947 -0.0846
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3630 0.4480 -0.5136 0.3534
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3796 0.5392 -0.6580 0.3672
## Avg_Cogongrass_Cover-Sus_scrofa -0.0125 0.6494 -1.5331 0.0682
## total_shrub_cover-Canis_latrans 0.3697 0.4692 -0.4520 0.3231
## total_shrub_cover-Lynx_rufus -0.5024 0.6100 -1.9726 -0.4193
## total_shrub_cover-Didelphis_virginiana -0.0969 0.4138 -0.9507 -0.0869
## total_shrub_cover-Sylvilagus_floridanus -0.1532 0.4405 -1.0618 -0.1516
## total_shrub_cover-Sciurus_carolinensis 0.0270 0.4069 -0.7947 0.0373
## total_shrub_cover-Vulpes_vulpes -0.1283 0.5415 -1.2317 -0.1265
## total_shrub_cover-Sus_scrofa 0.1977 0.5262 -0.7226 0.1613
## avg_veg_height-Canis_latrans -0.0574 0.4312 -0.9177 -0.0481
## avg_veg_height-Lynx_rufus -0.0149 0.5381 -1.0278 -0.0261
## avg_veg_height-Didelphis_virginiana -0.0681 0.4423 -0.9606 -0.0647
## avg_veg_height-Sylvilagus_floridanus -0.1905 0.4650 -1.1670 -0.1864
## avg_veg_height-Sciurus_carolinensis 0.2537 0.4793 -0.6482 0.2308
## avg_veg_height-Vulpes_vulpes -0.1385 0.5037 -1.1671 -0.1189
## avg_veg_height-Sus_scrofa -0.0721 0.4903 -1.0670 -0.0612
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6976 1.0379 456
## (Intercept)-Lynx_rufus 1.1845 1.0288 355
## (Intercept)-Didelphis_virginiana -0.2072 0.9999 714
## (Intercept)-Sylvilagus_floridanus 0.5059 1.0038 674
## (Intercept)-Sciurus_carolinensis -0.2525 1.0014 705
## (Intercept)-Vulpes_vulpes 0.1746 1.0140 223
## (Intercept)-Sus_scrofa -0.4773 1.0313 512
## Avg_Cogongrass_Cover-Canis_latrans 1.5557 1.0018 902
## Avg_Cogongrass_Cover-Lynx_rufus 1.8197 1.0011 905
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3888 1.0013 884
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8049 1.0049 810
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2580 1.0071 895
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4877 1.0086 660
## Avg_Cogongrass_Cover-Sus_scrofa 1.0444 1.0125 782
## total_shrub_cover-Canis_latrans 1.4122 1.0005 809
## total_shrub_cover-Lynx_rufus 0.4722 1.0074 546
## total_shrub_cover-Didelphis_virginiana 0.6964 1.0096 1454
## total_shrub_cover-Sylvilagus_floridanus 0.6683 1.0094 1062
## total_shrub_cover-Sciurus_carolinensis 0.8338 1.0016 1461
## total_shrub_cover-Vulpes_vulpes 0.8271 1.0188 805
## total_shrub_cover-Sus_scrofa 1.3459 1.0082 1053
## avg_veg_height-Canis_latrans 0.7885 1.0093 863
## avg_veg_height-Lynx_rufus 1.0827 1.0101 657
## avg_veg_height-Didelphis_virginiana 0.7666 1.0024 767
## avg_veg_height-Sylvilagus_floridanus 0.6735 1.0042 831
## avg_veg_height-Sciurus_carolinensis 1.2462 1.0067 774
## avg_veg_height-Vulpes_vulpes 0.8177 1.0083 773
## avg_veg_height-Sus_scrofa 0.8455 1.0036 958
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.684400e+00 1.682000e-01 -5.0295
## (Intercept)-Lynx_rufus -5.331100e+00 2.922000e-01 -5.9205
## (Intercept)-Didelphis_virginiana -4.314700e+00 2.225000e-01 -4.7700
## (Intercept)-Sylvilagus_floridanus -4.912600e+00 2.375000e-01 -5.3785
## (Intercept)-Sciurus_carolinensis -4.375800e+00 2.481000e-01 -4.8870
## (Intercept)-Vulpes_vulpes -5.492500e+00 6.470000e-01 -6.9778
## (Intercept)-Sus_scrofa -4.793700e+00 3.669000e-01 -5.5757
## week-Canis_latrans 8.711250e+02 1.175120e+05 -10955.7608
## week-Lynx_rufus -2.302373e+03 1.542646e+05 -11774.7839
## week-Didelphis_virginiana -2.467978e+03 1.286875e+05 -12287.9870
## week-Sylvilagus_floridanus 2.403034e+03 1.059133e+05 -10839.5692
## week-Sciurus_carolinensis -4.072476e+02 1.290996e+05 -9728.6077
## week-Vulpes_vulpes 1.494211e+03 1.388200e+05 -12535.4692
## week-Sus_scrofa 1.446708e+03 1.444384e+05 -12327.7199
## I(week^2)-Canis_latrans -1.150785e+07 6.344200e+08 -441709.6785
## I(week^2)-Lynx_rufus 1.487141e+07 1.611390e+09 -801955.4383
## I(week^2)-Didelphis_virginiana -2.957899e+07 1.159090e+09 -1088722.7279
## I(week^2)-Sylvilagus_floridanus -4.057556e+06 7.175800e+08 -1375555.1522
## I(week^2)-Sciurus_carolinensis 2.022814e+07 1.442989e+09 -840958.9506
## I(week^2)-Vulpes_vulpes -9.731182e+06 8.714758e+08 -750457.8516
## I(week^2)-Sus_scrofa -6.507433e+06 5.746846e+08 -652853.8332
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6790 -4.3869 1.0204 168
## (Intercept)-Lynx_rufus -5.3278 -4.8000 1.0589 107
## (Intercept)-Didelphis_virginiana -4.3033 -3.8977 1.0167 306
## (Intercept)-Sylvilagus_floridanus -4.9150 -4.4525 1.0782 163
## (Intercept)-Sciurus_carolinensis -4.3613 -3.9325 1.0881 285
## (Intercept)-Vulpes_vulpes -5.3796 -4.5170 1.0350 41
## (Intercept)-Sus_scrofa -4.7741 -4.1429 1.0447 229
## week-Canis_latrans -0.0159 14634.6268 1.2949 6777
## week-Lynx_rufus 0.2179 10941.8529 1.3107 4130
## week-Didelphis_virginiana 0.1673 13066.3295 1.3231 2136
## week-Sylvilagus_floridanus 0.0013 10968.8958 1.3356 1378
## week-Sciurus_carolinensis 0.1182 12664.1142 1.2909 11920
## week-Vulpes_vulpes -0.1251 12451.0228 1.3018 11600
## week-Sus_scrofa 0.0353 12788.8368 1.2991 12888
## I(week^2)-Canis_latrans 0.2254 2096620.5522 1.3228 2927
## I(week^2)-Lynx_rufus 0.3809 1363854.5364 1.2988 15030
## I(week^2)-Didelphis_virginiana 0.2393 762593.5433 1.3538 1394
## I(week^2)-Sylvilagus_floridanus 0.1021 626536.5865 1.2935 31848
## I(week^2)-Sciurus_carolinensis 0.2144 1003295.3989 1.3098 5462
## I(week^2)-Vulpes_vulpes 0.1016 828717.1930 1.3027 8358
## I(week^2)-Sus_scrofa 0.1964 1093065.3711 1.3031 7295
#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): 3.5963
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1869 0.6396 -2.4351 -1.2000 0.2204 0.9998 979
## Tree_Density -0.7630 0.5214 -1.9179 -0.7228 0.1578 1.0053 593
## Avg_Canopy_Cover 0.9991 0.4309 0.1925 0.9828 1.8892 1.0012 960
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5644 2.9845 0.1991 1.7449 10.1729 1.0131 600
## Tree_Density 1.0595 2.0405 0.0497 0.5012 5.1867 1.0722 595
## Avg_Canopy_Cover 0.9513 1.1705 0.0699 0.5756 4.1387 1.0047 711
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5401 0.6126 0.0485 0.3329 2.2312 1.0416 260
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7151 0.2779 -5.2280 -4.7318 -4.1302 1.0408 1118
## week -0.0087 1.6838 -3.3570 0.0139 3.2944 1.0315 513
## I(week^2) -0.0017 1.6248 -3.1992 0.0226 3.1595 1.0006 674
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.679000e-01 5.505000e-01 0.0696 0.3146 1.826000e+00 1.0836 503
## week 2.062524e+16 3.031990e+17 0.1247 280.9643 2.610644e+15 1.6912 240
## I(week^2) 8.517020e+08 1.613878e+10 0.0823 161.9553 1.333223e+09 1.4208 515
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2727 0.7650 -1.0877 0.2340 1.8570
## (Intercept)-Lynx_rufus 0.0492 1.0227 -1.5733 -0.0728 2.5784
## (Intercept)-Didelphis_virginiana -1.9141 0.6705 -3.3429 -1.8855 -0.6699
## (Intercept)-Sylvilagus_floridanus -1.0572 0.6588 -2.4212 -1.0371 0.1825
## (Intercept)-Sciurus_carolinensis -2.0097 0.7005 -3.5413 -1.9465 -0.7900
## (Intercept)-Vulpes_vulpes -2.0533 0.8877 -3.8145 -2.0429 -0.3190
## (Intercept)-Sus_scrofa -2.5298 0.8430 -4.3600 -2.4779 -1.0487
## Tree_Density-Canis_latrans -0.9732 0.5984 -2.3276 -0.9008 -0.0057
## Tree_Density-Lynx_rufus 0.1658 0.7679 -0.9876 0.0427 2.0735
## Tree_Density-Didelphis_virginiana -1.0588 0.8146 -3.0177 -0.9272 0.1392
## Tree_Density-Sylvilagus_floridanus -1.1661 0.8261 -3.1592 -1.0320 0.0566
## Tree_Density-Sciurus_carolinensis -1.0198 0.8274 -2.9839 -0.8680 0.1892
## Tree_Density-Vulpes_vulpes -0.6580 0.8157 -2.4256 -0.6138 0.8450
## Tree_Density-Sus_scrofa -0.9434 0.8587 -2.9940 -0.8009 0.3751
## Avg_Canopy_Cover-Canis_latrans 0.0487 0.5217 -0.9602 0.0416 1.1021
## Avg_Canopy_Cover-Lynx_rufus 0.6746 0.6969 -0.6346 0.6319 2.2272
## Avg_Canopy_Cover-Didelphis_virginiana 1.3102 0.5285 0.4060 1.2661 2.5042
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8189 0.8517 0.5879 1.6608 3.9274
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2676 0.5274 0.3441 1.2205 2.4044
## Avg_Canopy_Cover-Vulpes_vulpes 1.0098 0.5813 -0.0317 0.9610 2.3457
## Avg_Canopy_Cover-Sus_scrofa 1.2317 0.5617 0.2229 1.1793 2.4624
## Rhat ESS
## (Intercept)-Canis_latrans 1.0499 360
## (Intercept)-Lynx_rufus 1.0015 217
## (Intercept)-Didelphis_virginiana 1.0033 993
## (Intercept)-Sylvilagus_floridanus 1.0084 904
## (Intercept)-Sciurus_carolinensis 1.0012 809
## (Intercept)-Vulpes_vulpes 1.0249 362
## (Intercept)-Sus_scrofa 1.0018 619
## Tree_Density-Canis_latrans 1.0046 979
## Tree_Density-Lynx_rufus 1.0033 524
## Tree_Density-Didelphis_virginiana 1.0084 656
## Tree_Density-Sylvilagus_floridanus 1.0023 619
## Tree_Density-Sciurus_carolinensis 1.0051 597
## Tree_Density-Vulpes_vulpes 1.0163 645
## Tree_Density-Sus_scrofa 1.0007 747
## Avg_Canopy_Cover-Canis_latrans 1.0019 774
## Avg_Canopy_Cover-Lynx_rufus 1.0037 434
## Avg_Canopy_Cover-Didelphis_virginiana 1.0017 1076
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0057 457
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0007 1293
## Avg_Canopy_Cover-Vulpes_vulpes 1.0117 759
## Avg_Canopy_Cover-Sus_scrofa 1.0098 1173
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6758 1.652000e-01 -5.0110
## (Intercept)-Lynx_rufus -5.3824 2.777000e-01 -5.9565
## (Intercept)-Didelphis_virginiana -4.3334 2.297000e-01 -4.7853
## (Intercept)-Sylvilagus_floridanus -4.8523 2.249000e-01 -5.3178
## (Intercept)-Sciurus_carolinensis -4.3689 2.193000e-01 -4.8192
## (Intercept)-Vulpes_vulpes -5.3990 4.968000e-01 -6.4891
## (Intercept)-Sus_scrofa -4.7659 3.735000e-01 -5.5666
## week-Canis_latrans 4923043.6682 1.738736e+08 -1513546.1793
## week-Lynx_rufus 463009.0251 1.262217e+08 -1463671.8148
## week-Didelphis_virginiana 5702793.9334 1.146397e+08 -989280.9347
## week-Sylvilagus_floridanus -1387847.9377 9.468970e+07 -1212797.3537
## week-Sciurus_carolinensis -5658910.3162 1.049888e+08 -3049084.8819
## week-Vulpes_vulpes -2265586.2972 1.252103e+08 -3968378.8029
## week-Sus_scrofa -174800.1548 1.106044e+08 -1338082.8011
## I(week^2)-Canis_latrans 284.8868 3.301440e+04 -5691.3749
## I(week^2)-Lynx_rufus 440.0226 2.197896e+04 -7406.9894
## I(week^2)-Didelphis_virginiana 468.8839 3.141677e+04 -7625.9081
## I(week^2)-Sylvilagus_floridanus 757.8461 2.570832e+04 -7939.0958
## I(week^2)-Sciurus_carolinensis -221.1425 1.956277e+04 -9980.3126
## I(week^2)-Vulpes_vulpes -72.1290 2.127603e+04 -8598.7526
## I(week^2)-Sus_scrofa -928.3465 2.644750e+04 -9267.6957
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6733 -4.3636 1.0768 208
## (Intercept)-Lynx_rufus -5.3751 -4.8809 1.0660 93
## (Intercept)-Didelphis_virginiana -4.3224 -3.9120 1.0827 335
## (Intercept)-Sylvilagus_floridanus -4.8436 -4.4240 1.0319 213
## (Intercept)-Sciurus_carolinensis -4.3617 -3.9510 1.0044 499
## (Intercept)-Vulpes_vulpes -5.3503 -4.5708 1.0681 112
## (Intercept)-Sus_scrofa -4.7396 -4.1327 1.1527 240
## week-Canis_latrans 0.1892 2361173.7540 1.3680 1295
## week-Lynx_rufus -0.1846 1702409.9442 1.2917 24479
## week-Didelphis_virginiana -0.2515 2059018.3514 1.5176 438
## week-Sylvilagus_floridanus -0.0577 2030694.6451 1.3116 5165
## week-Sciurus_carolinensis 0.0659 1563295.4214 1.5538 496
## week-Vulpes_vulpes -0.1210 878278.2373 1.3226 2488
## week-Sus_scrofa -0.1294 2066314.6575 1.2906 12274
## I(week^2)-Canis_latrans -0.0686 10459.5322 1.2051 27264
## I(week^2)-Lynx_rufus -0.1619 10845.4352 1.2322 1608
## I(week^2)-Didelphis_virginiana -0.1290 10322.0087 1.1912 1595
## I(week^2)-Sylvilagus_floridanus -0.0807 7794.3439 1.2166 1512
## I(week^2)-Sciurus_carolinensis 0.0754 6645.2210 1.1633 6142
## I(week^2)-Vulpes_vulpes 0.1296 7838.8069 1.1739 3100
## I(week^2)-Sus_scrofa -0.0139 9082.4308 1.2389 1075
#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): 3.5477
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1962 0.6504 -2.5439 -1.2020 0.0831 1.0366 432
## Cogon_Patch_Size -0.5057 0.7014 -2.0634 -0.4657 0.8437 1.0115 828
## Avg_Cogongrass_Cover 0.4325 0.3891 -0.3463 0.4203 1.2103 1.0161 554
## total_shrub_cover 0.0489 0.3639 -0.6454 0.0450 0.7945 1.0118 807
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1042 2.8570 0.0951 1.2418 9.4440 1.0200 261
## Cogon_Patch_Size 3.0413 5.1971 0.1302 1.6083 14.1927 1.0510 250
## Avg_Cogongrass_Cover 0.4590 0.6453 0.0405 0.2527 2.1126 1.0202 866
## total_shrub_cover 0.4401 0.7644 0.0403 0.2355 2.0733 1.1693 780
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3551 2.5491 0.1714 1.6564 8.9414 1.2002 190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7226 0.2965 -5.2322 -4.7410 -4.1141 1.0120 1105
## week -0.0464 1.6492 -3.2546 -0.0038 3.2366 1.0095 925
## I(week^2) -0.0852 1.6130 -3.2004 -0.0893 3.1862 1.0063 688
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 4.651000e-01 6.886000e-01 0.0626 0.2964 1.907900e+00 1.0595
## week 6.189693e+29 1.468541e+31 0.1351 134764.8807 1.680020e+27 1.4570
## I(week^2) 6.739529e+10 1.341460e+12 0.1081 112.3665 4.154696e+08 1.5217
## ESS
## (Intercept) 394
## week 515
## I(week^2) 389
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1162 0.9667 -1.7372 0.1052
## (Intercept)-Lynx_rufus -0.5525 1.0860 -2.5176 -0.6287
## (Intercept)-Didelphis_virginiana -1.4942 0.7867 -3.1413 -1.4693
## (Intercept)-Sylvilagus_floridanus -1.1495 0.8311 -2.8506 -1.1049
## (Intercept)-Sciurus_carolinensis -1.8199 0.8727 -3.7192 -1.7571
## (Intercept)-Vulpes_vulpes -1.9499 1.0066 -4.1506 -1.8866
## (Intercept)-Sus_scrofa -2.2384 1.1161 -4.7072 -2.1134
## Cogon_Patch_Size-Canis_latrans 0.8971 0.9827 -0.4528 0.7141
## Cogon_Patch_Size-Lynx_rufus -0.5867 1.2922 -2.4682 -0.7110
## Cogon_Patch_Size-Didelphis_virginiana 0.7358 0.6400 -0.2961 0.6974
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6919 1.3618 -5.0151 -1.4003
## Cogon_Patch_Size-Sciurus_carolinensis -1.3179 1.0425 -3.9020 -1.1173
## Cogon_Patch_Size-Vulpes_vulpes -1.1857 1.2777 -4.3212 -0.9917
## Cogon_Patch_Size-Sus_scrofa -0.9758 1.2961 -4.2235 -0.7348
## Avg_Cogongrass_Cover-Canis_latrans 0.4447 0.4409 -0.4082 0.4321
## Avg_Cogongrass_Cover-Lynx_rufus 0.8365 0.5933 -0.0916 0.7694
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3893 0.4506 -0.5276 0.3980
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0873 0.5122 -0.9862 0.1186
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.7180 0.4717 -0.1374 0.6856
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5331 0.5303 -0.5054 0.5169
## Avg_Cogongrass_Cover-Sus_scrofa 0.1078 0.6728 -1.4275 0.1810
## total_shrub_cover-Canis_latrans 0.4071 0.5051 -0.4475 0.3477
## total_shrub_cover-Lynx_rufus -0.3060 0.6367 -1.7262 -0.2390
## total_shrub_cover-Didelphis_virginiana -0.1249 0.4450 -1.0452 -0.1059
## total_shrub_cover-Sylvilagus_floridanus -0.0236 0.4884 -1.0435 -0.0153
## total_shrub_cover-Sciurus_carolinensis 0.1437 0.4327 -0.6591 0.1250
## total_shrub_cover-Vulpes_vulpes -0.0367 0.5396 -1.1676 -0.0201
## total_shrub_cover-Sus_scrofa 0.2964 0.5600 -0.7137 0.2417
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1587 1.0162 324
## (Intercept)-Lynx_rufus 2.0867 1.0974 167
## (Intercept)-Didelphis_virginiana 0.0298 1.0132 441
## (Intercept)-Sylvilagus_floridanus 0.4652 1.0183 426
## (Intercept)-Sciurus_carolinensis -0.2308 1.0181 375
## (Intercept)-Vulpes_vulpes -0.1844 1.0413 269
## (Intercept)-Sus_scrofa -0.4406 1.0084 207
## Cogon_Patch_Size-Canis_latrans 3.3059 1.0032 573
## Cogon_Patch_Size-Lynx_rufus 2.2777 1.1312 199
## Cogon_Patch_Size-Didelphis_virginiana 2.0078 1.0175 698
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0996 1.0230 332
## Cogon_Patch_Size-Sciurus_carolinensis 0.1890 1.0363 494
## Cogon_Patch_Size-Vulpes_vulpes 0.6707 1.0108 356
## Cogon_Patch_Size-Sus_scrofa 0.9314 1.0178 355
## Avg_Cogongrass_Cover-Canis_latrans 1.3641 1.0071 809
## Avg_Cogongrass_Cover-Lynx_rufus 2.2111 1.0265 659
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2821 1.0106 829
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0313 1.0183 701
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7604 1.0206 671
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6593 1.0182 1032
## Avg_Cogongrass_Cover-Sus_scrofa 1.2202 1.0171 722
## total_shrub_cover-Canis_latrans 1.5949 1.0054 889
## total_shrub_cover-Lynx_rufus 0.7803 1.0071 614
## total_shrub_cover-Didelphis_virginiana 0.6933 1.0025 1266
## total_shrub_cover-Sylvilagus_floridanus 0.9231 1.0027 958
## total_shrub_cover-Sciurus_carolinensis 1.0388 1.0156 1337
## total_shrub_cover-Vulpes_vulpes 0.9776 1.0112 1062
## total_shrub_cover-Sus_scrofa 1.5366 1.0166 987
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.657300e+00 1.604000e-01 -4.994500e+00
## (Intercept)-Lynx_rufus -5.334700e+00 2.753000e-01 -5.922800e+00
## (Intercept)-Didelphis_virginiana -4.342500e+00 2.260000e-01 -4.772100e+00
## (Intercept)-Sylvilagus_floridanus -4.910600e+00 2.289000e-01 -5.398300e+00
## (Intercept)-Sciurus_carolinensis -4.387700e+00 2.336000e-01 -4.880100e+00
## (Intercept)-Vulpes_vulpes -5.394800e+00 5.156000e-01 -6.566300e+00
## (Intercept)-Sus_scrofa -4.788300e+00 3.560000e-01 -5.579000e+00
## week-Canis_latrans 4.180613e+12 6.542434e+14 -2.664396e+12
## week-Lynx_rufus 1.360659e+13 8.299775e+14 -2.271510e+12
## week-Didelphis_virginiana -3.727133e+12 5.402765e+14 -2.194358e+12
## week-Sylvilagus_floridanus 1.324793e+13 5.280424e+14 -1.993150e+12
## week-Sciurus_carolinensis 7.292281e+11 6.275461e+14 -3.903257e+12
## week-Vulpes_vulpes 5.922363e+12 4.261230e+14 -3.154315e+12
## week-Sus_scrofa 6.862577e+12 6.433065e+14 -3.346753e+12
## I(week^2)-Canis_latrans 3.653414e+03 2.886149e+05 -5.132337e+03
## I(week^2)-Lynx_rufus 1.877864e+03 2.810356e+05 -6.643476e+03
## I(week^2)-Didelphis_virginiana -3.704472e+03 2.500086e+05 -4.582738e+03
## I(week^2)-Sylvilagus_floridanus -1.097766e+04 2.923895e+05 -4.776511e+03
## I(week^2)-Sciurus_carolinensis -2.444109e+03 1.973096e+05 -6.123904e+03
## I(week^2)-Vulpes_vulpes 4.257013e+03 2.094379e+05 -4.358066e+03
## I(week^2)-Sus_scrofa 5.016648e+03 2.616895e+05 -3.973164e+03
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6563 -4.354900e+00 1.0056 262
## (Intercept)-Lynx_rufus -5.3197 -4.848100e+00 1.0176 101
## (Intercept)-Didelphis_virginiana -4.3403 -3.918400e+00 1.0032 318
## (Intercept)-Sylvilagus_floridanus -4.8958 -4.512700e+00 1.0404 143
## (Intercept)-Sciurus_carolinensis -4.3725 -3.966900e+00 1.0045 301
## (Intercept)-Vulpes_vulpes -5.3414 -4.542900e+00 1.1305 84
## (Intercept)-Sus_scrofa -4.7607 -4.164000e+00 1.0969 234
## week-Canis_latrans 0.1892 3.630727e+12 1.2944 25399
## week-Lynx_rufus -0.0599 4.306486e+12 1.3169 3847
## week-Didelphis_virginiana -0.3048 2.878419e+12 1.2951 6294
## week-Sylvilagus_floridanus -0.0606 2.824799e+12 1.3517 1612
## week-Sciurus_carolinensis 0.0782 2.302056e+12 1.2905 21581
## week-Vulpes_vulpes -0.4657 1.581320e+12 1.3095 4393
## week-Sus_scrofa -0.1096 2.083351e+12 1.3017 8474
## I(week^2)-Canis_latrans -0.2479 3.902455e+03 1.3068 5801
## I(week^2)-Lynx_rufus -0.2675 4.902448e+03 1.2951 19597
## I(week^2)-Didelphis_virginiana -0.2172 5.111061e+03 1.3127 4602
## I(week^2)-Sylvilagus_floridanus -0.3474 4.411747e+03 1.4252 695
## I(week^2)-Sciurus_carolinensis -0.1240 5.043993e+03 1.3055 6349
## I(week^2)-Vulpes_vulpes -0.3373 4.067230e+03 1.3321 2121
## I(week^2)-Sus_scrofa -0.0950 6.968111e+03 1.3260 2668
#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): 3.512
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0540 0.5417 -2.1057 -1.0699 0.1089 1.0194 564
## Veg_shannon_index 0.3033 0.4060 -0.5371 0.3093 1.0902 1.0061 1541
## Avg_Cogongrass_Cover 0.2961 0.3394 -0.3499 0.3042 0.9489 1.0067 1229
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4960 1.7891 0.0923 0.9687 6.3328 1.0289 968
## Veg_shannon_index 0.8059 1.2259 0.0560 0.4589 3.6496 1.0235 883
## Avg_Cogongrass_Cover 0.4356 0.6953 0.0415 0.2470 1.9807 1.0186 1285
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9452 0.959 0.0712 0.6411 3.4918 1.058 237
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7085 0.3308 -5.2683 -4.7344 -3.9874 1.0213 916
## week 0.0794 1.6269 -3.2146 0.1112 3.2973 1.0062 484
## I(week^2) 0.0629 1.6594 -3.2220 0.0336 3.3426 1.0190 472
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.392000e-01 9.351000e-01 0.0558 0.3045 2.386700e+00 1.1715 183
## week 1.978431e+09 4.657544e+10 0.0983 16.7963 1.332068e+08 1.4595 611
## I(week^2) 1.995680e+08 2.558988e+09 0.0887 60.3917 8.655724e+08 1.7986 665
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0258 0.7253 -1.3907 0.0370
## (Intercept)-Lynx_rufus -0.4090 0.7844 -1.8297 -0.4619
## (Intercept)-Didelphis_virginiana -1.5030 0.6004 -2.8075 -1.4758
## (Intercept)-Sylvilagus_floridanus -0.8350 0.6250 -2.0511 -0.8561
## (Intercept)-Sciurus_carolinensis -1.4947 0.6023 -2.7800 -1.4622
## (Intercept)-Vulpes_vulpes -1.6407 0.8278 -3.2385 -1.6363
## (Intercept)-Sus_scrofa -2.1104 0.8292 -3.8576 -2.0311
## Veg_shannon_index-Canis_latrans 0.8451 0.4739 -0.0044 0.8143
## Veg_shannon_index-Lynx_rufus -0.3198 0.7116 -2.0009 -0.2183
## Veg_shannon_index-Didelphis_virginiana 0.5579 0.4775 -0.3149 0.5344
## Veg_shannon_index-Sylvilagus_floridanus 0.4520 0.4829 -0.4732 0.4267
## Veg_shannon_index-Sciurus_carolinensis -0.1414 0.4597 -1.1218 -0.1114
## Veg_shannon_index-Vulpes_vulpes -0.1072 0.5639 -1.3194 -0.0856
## Veg_shannon_index-Sus_scrofa 0.9080 0.6770 -0.2101 0.8231
## Avg_Cogongrass_Cover-Canis_latrans 0.5893 0.4303 -0.1874 0.5585
## Avg_Cogongrass_Cover-Lynx_rufus 0.5769 0.4670 -0.2104 0.5483
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4880 0.4013 -0.2721 0.4764
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1508 0.4803 -1.2295 -0.1078
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3860 0.3959 -0.3598 0.3831
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3332 0.4838 -0.5984 0.3376
## Avg_Cogongrass_Cover-Sus_scrofa -0.0642 0.5809 -1.4386 0.0060
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4719 1.0044 423
## (Intercept)-Lynx_rufus 1.3137 1.0077 283
## (Intercept)-Didelphis_virginiana -0.4087 1.0057 1057
## (Intercept)-Sylvilagus_floridanus 0.3990 1.0068 756
## (Intercept)-Sciurus_carolinensis -0.4066 1.0038 1280
## (Intercept)-Vulpes_vulpes -0.0031 1.0282 303
## (Intercept)-Sus_scrofa -0.7234 1.0224 479
## Veg_shannon_index-Canis_latrans 1.8798 1.0036 1429
## Veg_shannon_index-Lynx_rufus 0.7890 1.0058 572
## Veg_shannon_index-Didelphis_virginiana 1.5578 1.0006 1880
## Veg_shannon_index-Sylvilagus_floridanus 1.4127 1.0219 1460
## Veg_shannon_index-Sciurus_carolinensis 0.6739 1.0006 1541
## Veg_shannon_index-Vulpes_vulpes 0.9227 1.0007 1213
## Veg_shannon_index-Sus_scrofa 2.4303 1.0120 970
## Avg_Cogongrass_Cover-Canis_latrans 1.5333 1.0015 1442
## Avg_Cogongrass_Cover-Lynx_rufus 1.6242 1.0054 1301
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3160 1.0107 1447
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6577 1.0220 1193
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1901 1.0024 1977
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3261 1.0101 1239
## Avg_Cogongrass_Cover-Sus_scrofa 0.9158 1.0059 825
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6745 0.1491 -4.9713 -4.6706
## (Intercept)-Lynx_rufus -5.3256 0.2713 -5.9024 -5.3110
## (Intercept)-Didelphis_virginiana -4.3183 0.2236 -4.8049 -4.3109
## (Intercept)-Sylvilagus_floridanus -4.8981 0.2352 -5.4073 -4.8865
## (Intercept)-Sciurus_carolinensis -4.3922 0.2318 -4.8703 -4.3854
## (Intercept)-Vulpes_vulpes -5.4544 0.6245 -7.2191 -5.3826
## (Intercept)-Sus_scrofa -4.7708 0.3656 -5.5786 -4.7423
## week-Canis_latrans 1696.8129 46924.3340 -1138.8514 0.1024
## week-Lynx_rufus 1277.3317 47359.9807 -903.7968 0.0906
## week-Didelphis_virginiana 1393.7121 71477.9853 -796.9984 0.1481
## week-Sylvilagus_floridanus -878.4153 37089.2765 -1726.7860 0.1740
## week-Sciurus_carolinensis -313.0797 26883.5167 -1715.9374 0.1264
## week-Vulpes_vulpes -866.6065 46014.1811 -1228.4237 0.0779
## week-Sus_scrofa 458.2065 36663.1488 -1516.6953 0.0633
## I(week^2)-Canis_latrans -135.3916 11934.4202 -8285.1466 0.2045
## I(week^2)-Lynx_rufus -213.0932 13647.0945 -8100.8066 0.0473
## I(week^2)-Didelphis_virginiana 83.9172 11327.2335 -8559.3167 0.1215
## I(week^2)-Sylvilagus_floridanus -170.9211 10479.8581 -6386.7032 0.1718
## I(week^2)-Sciurus_carolinensis 35.0999 12059.7649 -7060.5509 0.2693
## I(week^2)-Vulpes_vulpes -151.8860 11722.8564 -7393.0025 0.1981
## I(week^2)-Sus_scrofa -570.3998 14342.8831 -10624.5477 0.1750
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3974 1.0587 277
## (Intercept)-Lynx_rufus -4.8477 1.0090 97
## (Intercept)-Didelphis_virginiana -3.9205 1.0426 252
## (Intercept)-Sylvilagus_floridanus -4.4534 1.0531 154
## (Intercept)-Sciurus_carolinensis -3.9684 1.0254 327
## (Intercept)-Vulpes_vulpes -4.4767 1.0501 64
## (Intercept)-Sus_scrofa -4.1384 1.0536 204
## week-Canis_latrans 1313.9402 1.4163 699
## week-Lynx_rufus 1587.2872 1.3592 1337
## week-Didelphis_virginiana 1564.0972 1.3278 2499
## week-Sylvilagus_floridanus 887.2618 1.3426 1340
## week-Sciurus_carolinensis 1060.4256 1.3032 8586
## week-Vulpes_vulpes 938.7255 1.3260 3351
## week-Sus_scrofa 977.4042 1.3059 4671
## I(week^2)-Canis_latrans 7481.4661 1.3003 7066
## I(week^2)-Lynx_rufus 7452.6254 1.3128 6163
## I(week^2)-Didelphis_virginiana 6184.7141 1.2949 4701
## I(week^2)-Sylvilagus_floridanus 7295.7254 1.3161 5735
## I(week^2)-Sciurus_carolinensis 6740.9623 1.2906 7272
## I(week^2)-Vulpes_vulpes 6500.6142 1.3071 2680
## I(week^2)-Sus_scrofa 4524.5685 1.4361 2511
#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): 3.501
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0288 0.5293 -2.1100 -1.0269 0.0143 1.0075 625
## Avg_Cogongrass_Cover 0.2662 0.3125 -0.3972 0.2720 0.8659 1.0026 1371
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2855 1.4939 0.0840 0.8265 5.2418 1.0055 903
## Avg_Cogongrass_Cover 0.3830 0.5651 0.0413 0.2103 1.7035 1.0110 1350
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2515 1.3069 0.0857 0.8995 4.5305 1.0423 157
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6949 0.2998 -5.2052 -4.7148 -4.0562 1.0282 780
## week 0.0246 1.6873 -3.3083 0.0767 3.2500 1.0058 457
## I(week^2) 0.1008 1.6223 -3.0702 0.1120 3.2017 1.0116 538
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.366000e-01 9.685000e-01 0.0581 0.2546 1.753000e+00 1.0347 276
## week 4.702329e+22 5.877720e+23 0.1039 829.4437 1.418208e+23 1.8221 117
## I(week^2) 3.482252e+10 7.482114e+11 0.0790 25.9271 8.930517e+08 1.4885 472
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0125 0.7372 -1.4094 0.0201
## (Intercept)-Lynx_rufus -0.5254 0.7360 -1.9068 -0.5630
## (Intercept)-Didelphis_virginiana -1.3990 0.6094 -2.6413 -1.3749
## (Intercept)-Sylvilagus_floridanus -0.8262 0.6394 -2.1113 -0.8240
## (Intercept)-Sciurus_carolinensis -1.4630 0.6446 -2.8882 -1.4119
## (Intercept)-Vulpes_vulpes -1.6556 0.7812 -3.2563 -1.6106
## (Intercept)-Sus_scrofa -1.8250 0.7632 -3.5333 -1.7747
## Avg_Cogongrass_Cover-Canis_latrans 0.4065 0.3737 -0.2762 0.3836
## Avg_Cogongrass_Cover-Lynx_rufus 0.5536 0.4318 -0.1874 0.5155
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4088 0.3770 -0.3089 0.3910
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1629 0.4305 -1.0882 -0.1179
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4009 0.3555 -0.2905 0.3975
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3335 0.4363 -0.4968 0.3310
## Avg_Cogongrass_Cover-Sus_scrofa -0.0615 0.5478 -1.3475 0.0195
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4753 1.0065 532
## (Intercept)-Lynx_rufus 1.0428 1.0004 374
## (Intercept)-Didelphis_virginiana -0.2182 1.0040 982
## (Intercept)-Sylvilagus_floridanus 0.4820 1.0065 438
## (Intercept)-Sciurus_carolinensis -0.3070 1.0242 478
## (Intercept)-Vulpes_vulpes -0.2297 1.0099 503
## (Intercept)-Sus_scrofa -0.4788 1.0142 659
## Avg_Cogongrass_Cover-Canis_latrans 1.1986 1.0018 1640
## Avg_Cogongrass_Cover-Lynx_rufus 1.4836 1.0006 1502
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1820 1.0006 2075
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5860 1.0054 1324
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1244 1.0041 1519
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2397 1.0074 1497
## Avg_Cogongrass_Cover-Sus_scrofa 0.8005 1.0015 985
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.673900e+00 1.553000e-01 -4.973100e+00
## (Intercept)-Lynx_rufus -5.281400e+00 2.698000e-01 -5.873800e+00
## (Intercept)-Didelphis_virginiana -4.339500e+00 2.217000e-01 -4.785900e+00
## (Intercept)-Sylvilagus_floridanus -4.866900e+00 2.230000e-01 -5.339000e+00
## (Intercept)-Sciurus_carolinensis -4.395000e+00 2.182000e-01 -4.825200e+00
## (Intercept)-Vulpes_vulpes -5.315400e+00 5.098000e-01 -6.608500e+00
## (Intercept)-Sus_scrofa -4.759500e+00 3.370000e-01 -5.474000e+00
## week-Canis_latrans 1.507941e+09 1.983484e+11 -2.864946e+10
## week-Lynx_rufus -1.887171e+09 2.425690e+11 -4.313622e+10
## week-Didelphis_virginiana 4.396510e+09 2.098318e+11 -3.664220e+10
## week-Sylvilagus_floridanus -1.195517e+09 1.445381e+11 -2.121901e+10
## week-Sciurus_carolinensis -6.350400e+08 1.924068e+11 -4.762244e+10
## week-Vulpes_vulpes -4.034811e+09 2.313850e+11 -2.969341e+10
## week-Sus_scrofa -3.891197e+09 1.595110e+11 -2.238592e+10
## I(week^2)-Canis_latrans -1.676761e+03 1.367161e+05 -3.313227e+03
## I(week^2)-Lynx_rufus 2.787971e+03 1.232545e+05 -3.989191e+03
## I(week^2)-Didelphis_virginiana -3.072030e+03 1.444225e+05 -5.050492e+03
## I(week^2)-Sylvilagus_floridanus -8.456394e+02 2.050924e+05 -4.312836e+03
## I(week^2)-Sciurus_carolinensis 2.273817e+02 1.849198e+05 -5.728456e+03
## I(week^2)-Vulpes_vulpes 4.088353e+03 1.483729e+05 -2.478089e+03
## I(week^2)-Sus_scrofa -1.566414e+03 1.476664e+05 -3.376423e+03
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6718 -4.358300e+00 1.0018 238
## (Intercept)-Lynx_rufus -5.2676 -4.813100e+00 1.0151 109
## (Intercept)-Didelphis_virginiana -4.3335 -3.932200e+00 1.0852 346
## (Intercept)-Sylvilagus_floridanus -4.8575 -4.448300e+00 1.0186 185
## (Intercept)-Sciurus_carolinensis -4.3899 -3.955500e+00 1.0127 333
## (Intercept)-Vulpes_vulpes -5.2503 -4.550000e+00 1.0217 79
## (Intercept)-Sus_scrofa -4.7389 -4.132900e+00 1.0247 289
## week-Canis_latrans 0.2805 3.094916e+10 1.2961 3106
## week-Lynx_rufus 0.3508 2.419092e+10 1.2964 15539
## week-Didelphis_virginiana 0.2591 2.208805e+10 1.3334 3862
## week-Sylvilagus_floridanus 0.4023 4.733329e+10 1.2972 3246
## week-Sciurus_carolinensis 0.2613 3.747114e+10 1.2914 22387
## week-Vulpes_vulpes 0.5300 3.614230e+10 1.3203 4589
## week-Sus_scrofa 0.4423 2.655977e+10 1.3484 2591
## I(week^2)-Canis_latrans 0.2368 3.984347e+03 1.2905 8799
## I(week^2)-Lynx_rufus 0.2072 4.953364e+03 1.3445 1719
## I(week^2)-Didelphis_virginiana 0.2448 3.979843e+03 1.3326 2070
## I(week^2)-Sylvilagus_floridanus 0.1841 3.818624e+03 1.2898 57123
## I(week^2)-Sciurus_carolinensis 0.2762 3.106915e+03 1.2888 252912
## I(week^2)-Vulpes_vulpes 0.3504 6.080716e+03 1.3519 1504
## I(week^2)-Sus_scrofa 0.1508 3.577120e+03 1.3025 7950
# 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): 3.5008
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7738 0.5755 -2.9680 -1.7665 -0.6803 1.0024 513
## Avg_Cogongrass_Cover -0.7373 0.4734 -1.6881 -0.7272 0.1540 1.0030 495
## I(Avg_Cogongrass_Cover^2) 0.8358 0.4089 0.0952 0.8100 1.7166 1.0091 511
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2756 1.7157 0.0700 0.7318 5.8213 1.0016 715
## Avg_Cogongrass_Cover 0.5909 0.9349 0.0484 0.3052 2.8074 1.0270 936
## I(Avg_Cogongrass_Cover^2) 0.6560 1.4776 0.0398 0.2604 3.7826 1.0859 371
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9696 1.0878 0.076 0.6122 3.9829 1.0202 190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7141 0.2644 -5.1827 -4.7199 -4.1321 1.0122 985
## week -0.0521 1.6492 -3.1663 -0.0565 3.3038 1.0202 503
## I(week^2) -0.0374 1.6667 -3.2796 -0.0245 3.2447 1.0045 969
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.744000e-01 4.405000e-01 0.0556 0.2425 1.568500e+00 1.0511 277
## week 7.564531e+12 1.177443e+14 0.0968 24.2682 5.353977e+12 1.4078 597
## I(week^2) 7.709920e+13 1.522862e+15 0.1885 4945.3017 2.012294e+10 1.5251 314
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8244 0.8016 -2.4128 -0.8485
## (Intercept)-Lynx_rufus -1.4585 0.7248 -2.8698 -1.4713
## (Intercept)-Didelphis_virginiana -2.0322 0.7008 -3.4864 -2.0128
## (Intercept)-Sylvilagus_floridanus -1.5857 0.6862 -3.0519 -1.5660
## (Intercept)-Sciurus_carolinensis -2.3532 0.7503 -3.9938 -2.2929
## (Intercept)-Vulpes_vulpes -2.5060 0.8824 -4.3666 -2.4408
## (Intercept)-Sus_scrofa -2.4731 0.8624 -4.4030 -2.3892
## Avg_Cogongrass_Cover-Canis_latrans -0.4654 0.5827 -1.5674 -0.4902
## Avg_Cogongrass_Cover-Lynx_rufus -0.5174 0.6159 -1.6884 -0.5470
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4278 0.6131 -1.5598 -0.4515
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2524 0.6500 -2.6868 -1.2017
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7920 0.5947 -2.0722 -0.7699
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8201 0.6679 -2.2134 -0.7777
## Avg_Cogongrass_Cover-Sus_scrofa -1.0714 0.7231 -2.6799 -0.9990
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3657 0.9144 0.2143 1.1470
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1587 0.5568 0.2952 1.0975
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5940 0.4351 -0.2147 0.5801
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8059 0.4943 -0.0245 0.7585
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9762 0.4147 0.2261 0.9533
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9024 0.5232 0.0691 0.8433
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2994 0.6777 -1.4287 0.4015
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7972 1.0016 549
## (Intercept)-Lynx_rufus -0.0343 1.0068 563
## (Intercept)-Didelphis_virginiana -0.7277 1.0055 508
## (Intercept)-Sylvilagus_floridanus -0.3067 1.0065 507
## (Intercept)-Sciurus_carolinensis -1.0385 1.0029 543
## (Intercept)-Vulpes_vulpes -0.9674 1.0099 376
## (Intercept)-Sus_scrofa -1.0486 1.0103 433
## Avg_Cogongrass_Cover-Canis_latrans 0.7659 1.0208 783
## Avg_Cogongrass_Cover-Lynx_rufus 0.7734 1.0004 768
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8690 1.0026 723
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1362 1.0090 385
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3495 1.0048 519
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3977 1.0061 646
## Avg_Cogongrass_Cover-Sus_scrofa 0.2087 1.0019 426
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.9396 1.0145 283
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4439 1.0138 395
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5173 1.0047 604
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8433 1.0203 390
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8719 1.0021 628
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0450 1.0647 337
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3323 1.0020 475
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6865 0.1534 -5.0111
## (Intercept)-Lynx_rufus -5.2177 0.2421 -5.6860
## (Intercept)-Didelphis_virginiana -4.3594 0.2303 -4.8334
## (Intercept)-Sylvilagus_floridanus -4.8875 0.2422 -5.4190
## (Intercept)-Sciurus_carolinensis -4.3853 0.2293 -4.8424
## (Intercept)-Vulpes_vulpes -5.3104 0.4772 -6.4159
## (Intercept)-Sus_scrofa -4.7555 0.3686 -5.5247
## week-Canis_latrans 35425.6118 2970814.4645 -172073.9321
## week-Lynx_rufus -49590.3305 2088095.4188 -305353.3634
## week-Didelphis_virginiana -41020.5243 2115038.6833 -163946.0535
## week-Sylvilagus_floridanus -21879.2242 2577648.7947 -305308.0649
## week-Sciurus_carolinensis -70295.4332 2229253.1858 -307519.7956
## week-Vulpes_vulpes 57871.8946 2174009.2020 -190253.9620
## week-Sus_scrofa -24043.0629 2159924.3138 -328067.3285
## I(week^2)-Canis_latrans -10394.0309 7485251.7637 -28575.2141
## I(week^2)-Lynx_rufus 106824.3868 7930597.0952 -17957.9316
## I(week^2)-Didelphis_virginiana -99310.7182 6912829.0192 -16806.6293
## I(week^2)-Sylvilagus_floridanus -197683.1961 8059472.8398 -24049.7628
## I(week^2)-Sciurus_carolinensis 37611.2050 6958278.8847 -22179.6032
## I(week^2)-Vulpes_vulpes 30523.0840 8218975.3555 -27619.1397
## I(week^2)-Sus_scrofa -48769.8480 6073902.6594 -46979.1146
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6799 -4.3962 1.0446 233
## (Intercept)-Lynx_rufus -5.2084 -4.7547 1.0861 136
## (Intercept)-Didelphis_virginiana -4.3535 -3.9398 1.0118 228
## (Intercept)-Sylvilagus_floridanus -4.8745 -4.4558 1.0438 171
## (Intercept)-Sciurus_carolinensis -4.3823 -3.9437 1.0083 285
## (Intercept)-Vulpes_vulpes -5.2470 -4.5406 1.1880 99
## (Intercept)-Sus_scrofa -4.7358 -4.0919 1.0337 229
## week-Canis_latrans 0.0246 324464.0476 1.1720 1314
## week-Lynx_rufus -0.0848 146847.3696 1.1610 4345
## week-Didelphis_virginiana -0.0649 369385.1618 1.2487 903
## week-Sylvilagus_floridanus -0.3181 234232.9402 1.1536 31744
## week-Sciurus_carolinensis -0.1942 224196.0356 1.2246 2401
## week-Vulpes_vulpes -0.1503 488154.5707 1.1723 4338
## week-Sus_scrofa 0.0071 206995.7439 1.2257 10798
## I(week^2)-Canis_latrans 0.0691 27467.3073 1.2905 28293
## I(week^2)-Lynx_rufus 0.6366 45909.3222 1.3082 5212
## I(week^2)-Didelphis_virginiana -0.2605 52322.9922 1.3116 4219
## I(week^2)-Sylvilagus_floridanus -0.0656 37787.9403 1.3495 1557
## I(week^2)-Sciurus_carolinensis -0.0281 29019.4702 1.2932 48961
## I(week^2)-Vulpes_vulpes -0.0930 19611.6554 1.2918 13495
## I(week^2)-Sus_scrofa -0.0286 18029.2142 1.2966 9824
# 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): 3.8783
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9833 1.3370 -4.2875 -2.1014 0.9056 1.0344 478
## Cogon_Patch_Size -0.2079 1.0725 -2.3654 -0.1773 1.8841 1.0096 647
## Veg_shannon_index 0.6190 0.8021 -1.1351 0.6282 2.1422 1.0011 548
## total_shrub_cover 0.0548 0.6135 -1.2475 0.0762 1.1972 1.0095 460
## Avg_Cogongrass_Cover -0.0604 1.0330 -2.1917 -0.0417 1.9115 1.0268 201
## Tree_Density -2.1591 0.9092 -3.8857 -2.1516 -0.4117 1.0307 205
## Avg_Canopy_Cover 1.9349 0.8920 0.0796 1.9326 3.6891 1.0414 553
## I(Avg_Cogongrass_Cover^2) 1.5088 0.7334 0.1134 1.4777 3.0537 1.0284 280
## avg_veg_height -0.5172 0.6034 -1.7140 -0.5061 0.7049 1.0320 310
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 25.9184 47.9260 1.2013 13.0937 124.6912 1.1252 274
## Cogon_Patch_Size 13.0045 22.2486 0.4963 6.2666 68.2883 1.1491 185
## Veg_shannon_index 5.7976 11.1562 0.1051 2.2361 34.7275 1.0936 97
## total_shrub_cover 1.4032 3.3395 0.0501 0.4937 8.7752 1.1419 108
## Avg_Cogongrass_Cover 1.6475 3.4811 0.0527 0.5043 11.4325 1.1082 476
## Tree_Density 3.5910 9.2812 0.0545 0.8302 25.6187 1.0474 199
## Avg_Canopy_Cover 6.9080 13.5677 0.1867 3.0364 36.7157 1.0936 143
## I(Avg_Cogongrass_Cover^2) 2.0081 4.4476 0.0499 0.5855 13.2733 1.1827 247
## avg_veg_height 0.6784 1.4360 0.0448 0.3315 3.3416 1.1997 878
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3871 1.7822 0.0509 0.7195 6.4043 1.2318 120
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7161 0.4002 -5.3610 -4.7563 -3.8567 1.0229 1180
## week -0.0353 1.6550 -3.2776 -0.0754 3.0995 1.0038 563
## I(week^2) -0.0020 1.6987 -3.5385 0.0540 3.2903 1.0346 371
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.579000e-01 1.556800e+00 0.0969 0.5118 3.465700e+00 1.1250 635
## week 3.604446e+19 3.777387e+20 0.1801 31412.0721 2.128885e+20 2.0059 391
## I(week^2) 1.506784e+17 2.427122e+18 0.0862 22.4439 6.705269e+14 1.6309 310
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2946 1.5258 -3.0478 -0.4036
## (Intercept)-Lynx_rufus 1.3308 2.9412 -2.8404 0.7218
## (Intercept)-Didelphis_virginiana -4.8632 1.7026 -8.8532 -4.6340
## (Intercept)-Sylvilagus_floridanus -3.3718 1.6271 -6.9595 -3.2113
## (Intercept)-Sciurus_carolinensis -5.6620 2.0681 -10.5002 -5.3626
## (Intercept)-Vulpes_vulpes -5.4705 2.6969 -11.5868 -5.2999
## (Intercept)-Sus_scrofa -7.2746 3.0792 -14.7537 -6.7874
## Cogon_Patch_Size-Canis_latrans 3.1369 2.8612 -0.0619 2.4029
## Cogon_Patch_Size-Lynx_rufus -0.4307 2.4884 -5.0698 -0.6670
## Cogon_Patch_Size-Didelphis_virginiana 2.3572 1.2479 0.2642 2.2466
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6116 2.5659 -9.3269 -1.9911
## Cogon_Patch_Size-Sciurus_carolinensis -1.8509 2.1633 -7.5397 -1.4288
## Cogon_Patch_Size-Vulpes_vulpes -1.5051 2.7159 -8.3165 -1.0774
## Cogon_Patch_Size-Sus_scrofa -1.5222 2.2212 -7.2248 -1.0717
## Veg_shannon_index-Canis_latrans 2.0024 1.3578 0.1704 1.7724
## Veg_shannon_index-Lynx_rufus -0.6548 2.2445 -7.3948 -0.2140
## Veg_shannon_index-Didelphis_virginiana 1.0430 1.0398 -0.7590 0.9355
## Veg_shannon_index-Sylvilagus_floridanus 1.1512 1.0832 -0.6700 1.0224
## Veg_shannon_index-Sciurus_carolinensis -0.5921 1.2092 -3.3669 -0.4377
## Veg_shannon_index-Vulpes_vulpes -0.3365 1.6981 -4.3356 -0.0999
## Veg_shannon_index-Sus_scrofa 2.7709 2.1799 0.1123 2.2250
## total_shrub_cover-Canis_latrans 0.5197 0.8494 -0.9570 0.4190
## total_shrub_cover-Lynx_rufus -0.6218 1.5259 -4.5759 -0.3348
## total_shrub_cover-Didelphis_virginiana -0.2682 0.8340 -2.2018 -0.1912
## total_shrub_cover-Sylvilagus_floridanus 0.1175 0.7989 -1.4183 0.1006
## total_shrub_cover-Sciurus_carolinensis 0.3264 0.8066 -1.1292 0.2830
## total_shrub_cover-Vulpes_vulpes -0.1614 1.0451 -2.6773 -0.0371
## total_shrub_cover-Sus_scrofa 0.4426 0.9725 -1.3313 0.3503
## Avg_Cogongrass_Cover-Canis_latrans 0.0152 1.2371 -2.4271 0.0342
## Avg_Cogongrass_Cover-Lynx_rufus 0.3765 1.5220 -2.2716 0.3070
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0633 1.2694 -2.4101 0.0585
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4793 1.3950 -3.6785 -0.3644
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0206 1.3032 -2.6109 0.0338
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0922 1.4909 -2.6465 0.0655
## Avg_Cogongrass_Cover-Sus_scrofa -0.5135 1.4993 -3.9959 -0.4062
## Tree_Density-Canis_latrans -3.0015 1.6091 -6.8892 -2.6901
## Tree_Density-Lynx_rufus -1.3799 1.7143 -3.9572 -1.5537
## Tree_Density-Didelphis_virginiana -2.4460 1.1370 -5.0557 -2.3459
## Tree_Density-Sylvilagus_floridanus -2.8218 1.4346 -6.3204 -2.6249
## Tree_Density-Sciurus_carolinensis -2.9707 1.8683 -7.6754 -2.6299
## Tree_Density-Vulpes_vulpes -2.2731 1.5861 -5.9292 -2.1934
## Tree_Density-Sus_scrofa -2.3828 1.4845 -5.9665 -2.2636
## Avg_Canopy_Cover-Canis_latrans 0.0801 1.0372 -2.0760 0.1159
## Avg_Canopy_Cover-Lynx_rufus 1.4994 2.0017 -2.1280 1.3641
## Avg_Canopy_Cover-Didelphis_virginiana 3.3250 1.2915 1.3602 3.1309
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.5168 2.4679 1.3026 3.9794
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8160 1.4620 1.0173 2.5713
## Avg_Canopy_Cover-Vulpes_vulpes 2.8864 1.9218 0.3764 2.4822
## Avg_Canopy_Cover-Sus_scrofa 2.3630 1.2070 0.4838 2.1803
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2538 1.1886 0.6157 2.0064
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3579 1.4198 0.4947 2.0359
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2576 0.7791 -0.2127 1.2188
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3214 0.8823 -0.2390 1.2580
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7840 0.9296 0.3209 1.6606
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9757 1.1366 0.3311 1.7933
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.6410 1.4273 -3.2322 0.8712
## avg_veg_height-Canis_latrans -0.5691 0.7281 -2.0436 -0.5570
## avg_veg_height-Lynx_rufus -0.5200 0.9130 -2.5915 -0.4811
## avg_veg_height-Didelphis_virginiana -0.6255 0.8153 -2.3889 -0.5848
## avg_veg_height-Sylvilagus_floridanus -0.5635 0.7702 -2.2454 -0.5460
## avg_veg_height-Sciurus_carolinensis -0.1794 0.7736 -1.6025 -0.2213
## avg_veg_height-Vulpes_vulpes -0.7657 0.9186 -2.7594 -0.7025
## avg_veg_height-Sus_scrofa -0.5673 0.8305 -2.3183 -0.5424
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.2065 1.0926 198
## (Intercept)-Lynx_rufus 8.7777 1.1830 65
## (Intercept)-Didelphis_virginiana -2.1524 1.0147 286
## (Intercept)-Sylvilagus_floridanus -0.6792 1.0656 152
## (Intercept)-Sciurus_carolinensis -2.5202 1.0746 156
## (Intercept)-Vulpes_vulpes -0.4569 1.1822 99
## (Intercept)-Sus_scrofa -2.8288 1.0722 87
## Cogon_Patch_Size-Canis_latrans 11.0943 1.1470 140
## Cogon_Patch_Size-Lynx_rufus 5.2254 1.0042 156
## Cogon_Patch_Size-Didelphis_virginiana 5.0997 1.0307 193
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7022 1.0411 162
## Cogon_Patch_Size-Sciurus_carolinensis 0.9175 1.0236 331
## Cogon_Patch_Size-Vulpes_vulpes 2.7488 1.0254 198
## Cogon_Patch_Size-Sus_scrofa 1.6139 1.0175 280
## Veg_shannon_index-Canis_latrans 5.8102 1.0679 125
## Veg_shannon_index-Lynx_rufus 2.5458 1.0129 71
## Veg_shannon_index-Didelphis_virginiana 3.4360 1.0274 317
## Veg_shannon_index-Sylvilagus_floridanus 3.6936 1.0135 341
## Veg_shannon_index-Sciurus_carolinensis 1.3870 1.0173 295
## Veg_shannon_index-Vulpes_vulpes 2.1983 1.0596 179
## Veg_shannon_index-Sus_scrofa 8.5306 1.0609 118
## total_shrub_cover-Canis_latrans 2.4654 1.0349 475
## total_shrub_cover-Lynx_rufus 1.5976 1.0509 107
## total_shrub_cover-Didelphis_virginiana 1.1433 1.0176 499
## total_shrub_cover-Sylvilagus_floridanus 1.6779 0.9996 750
## total_shrub_cover-Sciurus_carolinensis 1.9843 1.0154 480
## total_shrub_cover-Vulpes_vulpes 1.5627 1.0237 307
## total_shrub_cover-Sus_scrofa 2.7502 1.0187 632
## Avg_Cogongrass_Cover-Canis_latrans 2.3725 1.0494 283
## Avg_Cogongrass_Cover-Lynx_rufus 3.6650 1.0528 244
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.5342 1.0530 264
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9531 1.0658 247
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4348 1.0121 304
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.1036 1.0250 283
## Avg_Cogongrass_Cover-Sus_scrofa 2.0722 1.0523 261
## Tree_Density-Canis_latrans -0.7900 1.0451 231
## Tree_Density-Lynx_rufus 3.1409 1.0127 181
## Tree_Density-Didelphis_virginiana -0.5401 1.0059 466
## Tree_Density-Sylvilagus_floridanus -0.7012 1.0227 177
## Tree_Density-Sciurus_carolinensis -0.8086 1.1616 132
## Tree_Density-Vulpes_vulpes 0.6105 1.0609 227
## Tree_Density-Sus_scrofa 0.2309 1.0825 269
## Avg_Canopy_Cover-Canis_latrans 1.9916 1.0549 184
## Avg_Canopy_Cover-Lynx_rufus 5.8413 1.1603 112
## Avg_Canopy_Cover-Didelphis_virginiana 6.2468 1.0139 301
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.0615 1.0168 100
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1228 1.0541 146
## Avg_Canopy_Cover-Vulpes_vulpes 8.0303 1.0835 159
## Avg_Canopy_Cover-Sus_scrofa 5.1628 1.0337 209
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.2953 1.0349 167
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.2473 1.0834 207
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.8528 1.0457 256
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2670 1.0387 302
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.9242 1.0013 333
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.9521 1.0385 247
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.7911 1.1122 210
## avg_veg_height-Canis_latrans 0.8113 1.0289 472
## avg_veg_height-Lynx_rufus 1.1624 1.0162 535
## avg_veg_height-Didelphis_virginiana 0.9072 1.0353 360
## avg_veg_height-Sylvilagus_floridanus 0.9013 1.0218 456
## avg_veg_height-Sciurus_carolinensis 1.4686 1.0105 474
## avg_veg_height-Vulpes_vulpes 0.8865 1.0592 289
## avg_veg_height-Sus_scrofa 1.0052 1.0204 558
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.674900e+00 1.567000e-01 -4.986100e+00
## (Intercept)-Lynx_rufus -5.675400e+00 3.229000e-01 -6.249000e+00
## (Intercept)-Didelphis_virginiana -4.295100e+00 2.174000e-01 -4.718600e+00
## (Intercept)-Sylvilagus_floridanus -4.922700e+00 2.284000e-01 -5.472200e+00
## (Intercept)-Sciurus_carolinensis -4.365600e+00 2.346000e-01 -4.855700e+00
## (Intercept)-Vulpes_vulpes -5.768900e+00 6.793000e-01 -7.200300e+00
## (Intercept)-Sus_scrofa -4.685700e+00 3.684000e-01 -5.466000e+00
## week-Canis_latrans 4.226803e+07 5.478881e+09 -3.639425e+09
## week-Lynx_rufus 2.230179e+07 5.696691e+09 -3.996159e+09
## week-Didelphis_virginiana 5.421624e+07 5.237823e+09 -3.510148e+09
## week-Sylvilagus_floridanus -2.833767e+08 8.418353e+09 -4.181513e+09
## week-Sciurus_carolinensis 1.450859e+08 6.689812e+09 -4.009566e+09
## week-Vulpes_vulpes 6.282628e+07 5.420207e+09 -4.092632e+09
## week-Sus_scrofa -1.654152e+07 5.374703e+09 -4.691372e+09
## I(week^2)-Canis_latrans -1.050160e+07 4.444047e+08 -2.129291e+06
## I(week^2)-Lynx_rufus -4.409612e+06 4.093442e+08 -7.897668e+05
## I(week^2)-Didelphis_virginiana 1.582497e+06 2.122926e+08 -1.595408e+06
## I(week^2)-Sylvilagus_floridanus -7.506001e+06 3.508807e+08 -5.630652e+05
## I(week^2)-Sciurus_carolinensis -2.698765e+06 4.319682e+08 -1.679448e+06
## I(week^2)-Vulpes_vulpes -1.492168e+07 4.727694e+08 -1.033406e+06
## I(week^2)-Sus_scrofa 7.761647e+06 2.684925e+08 -2.203662e+06
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6725 -4.364300e+00 1.2522 230
## (Intercept)-Lynx_rufus -5.7026 -4.993300e+00 1.4164 57
## (Intercept)-Didelphis_virginiana -4.2980 -3.885500e+00 1.0999 322
## (Intercept)-Sylvilagus_floridanus -4.8993 -4.497700e+00 1.0025 183
## (Intercept)-Sciurus_carolinensis -4.3530 -3.933300e+00 1.0677 277
## (Intercept)-Vulpes_vulpes -5.6766 -4.687600e+00 1.1992 51
## (Intercept)-Sus_scrofa -4.6700 -4.012100e+00 1.0235 213
## week-Canis_latrans -0.1813 5.122577e+09 1.2960 4660
## week-Lynx_rufus -0.1548 3.946202e+09 1.2920 5055
## week-Didelphis_virginiana -0.1065 3.837095e+09 1.3010 2497
## week-Sylvilagus_floridanus -0.3230 2.610844e+09 1.3976 1980
## week-Sciurus_carolinensis -0.1701 4.192565e+09 1.3368 1288
## week-Vulpes_vulpes -0.2691 3.762056e+09 1.3042 2639
## week-Sus_scrofa -0.2452 2.638983e+09 1.2915 10368
## I(week^2)-Canis_latrans 0.2146 1.199576e+06 1.3449 1734
## I(week^2)-Lynx_rufus 0.1668 3.125063e+06 1.3019 8236
## I(week^2)-Didelphis_virginiana 0.2172 1.579342e+06 1.2959 15945
## I(week^2)-Sylvilagus_floridanus 0.1009 4.149680e+06 1.3352 1868
## I(week^2)-Sciurus_carolinensis 0.0530 1.265537e+06 1.2942 24481
## I(week^2)-Vulpes_vulpes 0.1497 2.034177e+06 1.3862 1054
## I(week^2)-Sus_scrofa 0.0275 1.344175e+06 1.3712 1408
#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): 4.1242
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7152 0.4373 -1.5673 -0.7227 0.1958 1.0075 1820
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1979 1.5627 0.1536 0.804 4.5109 1.0445 1479
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9220 0.2902 -5.4607 -4.9331 -4.3565 1.0025 930
## shrub_cover 0.4095 0.3727 -0.3080 0.4048 1.1757 1.0033 668
## veg_height 0.0083 0.2338 -0.4852 0.0109 0.4609 1.0044 618
## week -0.0442 1.6442 -3.3165 -0.0142 3.2280 1.0031 581
## I(week^2) -0.0494 1.6368 -3.3253 -0.0517 3.1158 1.0030 882
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.487000e-01 7.784000e-01 0.0510 0.2583 1.957800e+00 1.0435 223
## shrub_cover 7.587000e-01 9.142000e-01 0.1238 0.5211 2.809700e+00 1.0236 763
## veg_height 3.069000e-01 3.660000e-01 0.0599 0.2171 1.107600e+00 1.0368 972
## week 6.195322e+09 9.963039e+10 0.1044 95.0619 1.291606e+10 1.6266 726
## I(week^2) 8.304974e+15 1.286403e+17 0.1425 1164.2948 1.054802e+15 1.6556 287
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3066 0.4169 -0.4571 0.2945 1.2155 1.0049
## (Intercept)-Lynx_rufus 0.0922 0.6187 -0.8703 0.0217 1.3718 1.0217
## (Intercept)-Didelphis_virginiana -1.1213 0.4369 -1.9981 -1.1007 -0.3013 1.0016
## (Intercept)-Sylvilagus_floridanus -0.5255 0.4059 -1.2853 -0.5314 0.2890 1.0025
## (Intercept)-Sciurus_carolinensis -1.1500 0.4565 -2.1039 -1.1345 -0.3070 1.0036
## (Intercept)-Vulpes_vulpes -1.4586 0.6164 -2.6511 -1.4456 -0.2489 1.0082
## (Intercept)-Sus_scrofa -1.5419 0.5829 -2.7756 -1.5141 -0.4766 1.0047
## ESS
## (Intercept)-Canis_latrans 1650
## (Intercept)-Lynx_rufus 276
## (Intercept)-Didelphis_virginiana 1930
## (Intercept)-Sylvilagus_floridanus 1179
## (Intercept)-Sciurus_carolinensis 963
## (Intercept)-Vulpes_vulpes 388
## (Intercept)-Sus_scrofa 597
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7578 1.695000e-01 -5.1044
## (Intercept)-Lynx_rufus -5.4697 2.995000e-01 -6.0764
## (Intercept)-Didelphis_virginiana -4.6562 2.584000e-01 -5.1404
## (Intercept)-Sylvilagus_floridanus -4.9420 2.233000e-01 -5.4169
## (Intercept)-Sciurus_carolinensis -4.6567 2.768000e-01 -5.2171
## (Intercept)-Vulpes_vulpes -5.5223 5.170000e-01 -6.6559
## (Intercept)-Sus_scrofa -5.1952 4.848000e-01 -6.4008
## shrub_cover-Canis_latrans -0.2747 1.990000e-01 -0.6592
## shrub_cover-Lynx_rufus -0.1867 3.481000e-01 -0.9438
## shrub_cover-Didelphis_virginiana 1.0466 3.442000e-01 0.4044
## shrub_cover-Sylvilagus_floridanus 0.5489 3.638000e-01 -0.1463
## shrub_cover-Sciurus_carolinensis 0.9105 3.746000e-01 0.2050
## shrub_cover-Vulpes_vulpes 0.1111 6.270000e-01 -1.1576
## shrub_cover-Sus_scrofa 0.8390 7.568000e-01 -0.6083
## veg_height-Canis_latrans -0.5590 1.742000e-01 -0.9168
## veg_height-Lynx_rufus 0.1483 2.364000e-01 -0.3242
## veg_height-Didelphis_virginiana 0.5105 2.329000e-01 0.0665
## veg_height-Sylvilagus_floridanus 0.1456 2.230000e-01 -0.2891
## veg_height-Sciurus_carolinensis 0.1960 1.971000e-01 -0.1746
## veg_height-Vulpes_vulpes -0.1052 3.374000e-01 -0.8376
## veg_height-Sus_scrofa -0.2634 3.574000e-01 -1.0421
## week-Canis_latrans 519.1574 7.298936e+04 -15486.2668
## week-Lynx_rufus -1439.8337 6.113090e+04 -13138.7581
## week-Didelphis_virginiana 2721.4447 7.740886e+04 -13511.3858
## week-Sylvilagus_floridanus -435.5991 7.484708e+04 -15271.0854
## week-Sciurus_carolinensis 679.7363 6.626942e+04 -17517.2778
## week-Vulpes_vulpes 691.5062 8.751222e+04 -19635.4735
## week-Sus_scrofa 366.1121 6.698523e+04 -15184.2900
## I(week^2)-Canis_latrans -610819.6181 7.695136e+07 -1117257.6887
## I(week^2)-Lynx_rufus 1069550.3107 7.654340e+07 -1034879.5860
## I(week^2)-Didelphis_virginiana 572837.6221 8.365972e+07 -986651.7902
## I(week^2)-Sylvilagus_floridanus 357932.1074 8.110722e+07 -1361359.3716
## I(week^2)-Sciurus_carolinensis 631333.6394 9.523533e+07 -740509.6443
## I(week^2)-Vulpes_vulpes 434511.7063 8.626006e+07 -945300.5361
## I(week^2)-Sus_scrofa -150778.6888 9.001105e+07 -1374745.3667
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7496 -4.4417 1.1203 207
## (Intercept)-Lynx_rufus -5.4540 -4.9248 1.1633 89
## (Intercept)-Didelphis_virginiana -4.6582 -4.1509 1.0178 223
## (Intercept)-Sylvilagus_floridanus -4.9284 -4.5203 1.0302 215
## (Intercept)-Sciurus_carolinensis -4.6471 -4.1346 1.0288 211
## (Intercept)-Vulpes_vulpes -5.4511 -4.6864 1.0587 87
## (Intercept)-Sus_scrofa -5.1475 -4.3666 1.0101 126
## shrub_cover-Canis_latrans -0.2794 0.1233 1.0353 188
## shrub_cover-Lynx_rufus -0.1792 0.4722 1.0864 107
## shrub_cover-Didelphis_virginiana 1.0378 1.7607 1.0327 217
## shrub_cover-Sylvilagus_floridanus 0.5492 1.2690 1.0080 169
## shrub_cover-Sciurus_carolinensis 0.8919 1.7393 1.0410 182
## shrub_cover-Vulpes_vulpes 0.1209 1.3156 1.0318 182
## shrub_cover-Sus_scrofa 0.8123 2.5216 1.0752 194
## veg_height-Canis_latrans -0.5530 -0.2292 1.1644 173
## veg_height-Lynx_rufus 0.1570 0.5982 1.0202 142
## veg_height-Didelphis_virginiana 0.5029 0.9887 1.0012 255
## veg_height-Sylvilagus_floridanus 0.1440 0.5827 1.0067 223
## veg_height-Sciurus_carolinensis 0.1924 0.5999 1.0242 302
## veg_height-Vulpes_vulpes -0.0838 0.4782 1.0173 144
## veg_height-Sus_scrofa -0.2417 0.3335 1.0881 237
## week-Canis_latrans 0.1146 17341.5299 1.2916 6030
## week-Lynx_rufus -0.0170 19785.3401 1.3579 3664
## week-Didelphis_virginiana 0.1109 21153.7526 1.4032 1212
## week-Sylvilagus_floridanus 0.0024 15066.5392 1.2909 13629
## week-Sciurus_carolinensis 0.0289 15020.1939 1.3017 4114
## week-Vulpes_vulpes -0.0133 13657.3825 1.3008 4171
## week-Sus_scrofa -0.1499 15952.6820 1.2916 4003
## I(week^2)-Canis_latrans -0.2147 1263191.6511 1.2966 7261
## I(week^2)-Lynx_rufus -0.0060 1546969.3787 1.3097 3907
## I(week^2)-Didelphis_virginiana -0.1578 1400714.0857 1.2950 11669
## I(week^2)-Sylvilagus_floridanus -0.1491 906208.3752 1.2923 8732
## I(week^2)-Sciurus_carolinensis -0.1813 1057079.8130 1.2947 7131
## I(week^2)-Vulpes_vulpes -0.2595 860116.9492 1.2928 15049
## I(week^2)-Sus_scrofa -0.1887 750063.9685 1.2906 43922
#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): 4.6182
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0897 1.1948 -3.3648 -1.1300 1.3518 1.0254 311
## Cogon_Patch_Size -0.3732 1.0691 -2.5274 -0.3648 1.6298 1.0576 293
## Veg_shannon_index 0.5888 0.9319 -1.4525 0.6292 2.4295 1.0143 480
## total_shrub_cover -0.7558 1.0588 -3.0014 -0.6340 1.1019 1.3457 75
## Avg_Cogongrass_Cover 1.9137 0.9856 -0.1276 1.9439 3.7693 1.0260 239
## Tree_Density -1.9413 0.9796 -3.7751 -1.9619 0.0748 1.0692 196
## Avg_Canopy_Cover 1.9031 1.2089 -0.8654 1.9650 4.1785 1.0640 721
## avg_veg_height -0.5312 0.7508 -1.9239 -0.5683 0.9392 1.0965 208
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.5126 30.7201 0.8192 10.1661 90.0794 1.0264 115
## Cogon_Patch_Size 12.2595 34.3051 0.1430 3.9925 74.9544 1.2837 110
## Veg_shannon_index 7.7569 15.1326 0.1023 3.1044 44.2753 1.2342 72
## total_shrub_cover 7.6886 16.1465 0.0748 2.2678 48.3675 1.2325 113
## Avg_Cogongrass_Cover 2.4445 5.3354 0.0609 0.8285 15.5317 1.0607 397
## Tree_Density 3.3574 6.9134 0.0647 0.9391 21.2721 1.0416 223
## Avg_Canopy_Cover 23.4213 45.9089 0.4725 9.2395 136.1596 1.3797 60
## avg_veg_height 1.0678 2.8042 0.0478 0.4155 6.0515 1.0865 884
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.8921 8.5309 0.1007 2.3136 31.3582 1.1101 43
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0213 0.3592 -5.6238 -5.0438 -4.2895 1.0901 565
## shrub_cover 0.5594 0.4077 -0.2439 0.5492 1.3856 1.2270 165
## veg_height 0.0213 0.2426 -0.4851 0.0256 0.5015 1.0050 658
## week 0.0585 1.6634 -3.2179 0.0310 3.2851 1.0023 378
## I(week^2) 0.1090 1.5931 -3.0642 0.1353 3.2784 1.0109 1487
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 5.475000e-01 2.282300e+00 0.0548 0.2958 2.117100e+00 1.2985
## shrub_cover 8.076000e-01 8.133000e-01 0.1487 0.5786 2.837100e+00 1.1187
## veg_height 3.335000e-01 3.893000e-01 0.0668 0.2394 1.142300e+00 1.0471
## week 2.313589e+05 4.304835e+06 0.0766 6.1494 7.619990e+05 1.5095
## I(week^2) 4.350854e+20 1.027265e+22 0.2309 16661.3658 4.347387e+19 1.4585
## ESS
## (Intercept) 1283
## shrub_cover 627
## veg_height 1344
## week 711
## I(week^2) 636
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.4421 1.5684 -1.3787 1.2886
## (Intercept)-Lynx_rufus 2.2378 3.4206 -1.9906 1.5230
## (Intercept)-Didelphis_virginiana -3.1023 1.8042 -7.0924 -2.9676
## (Intercept)-Sylvilagus_floridanus -1.3905 1.9542 -5.5002 -1.4165
## (Intercept)-Sciurus_carolinensis -3.4493 2.2005 -8.5697 -3.2381
## (Intercept)-Vulpes_vulpes -3.6978 2.9091 -10.6632 -3.3928
## (Intercept)-Sus_scrofa -5.3279 3.3285 -12.9109 -4.9400
## Cogon_Patch_Size-Canis_latrans 1.5383 2.0719 -1.1156 1.0800
## Cogon_Patch_Size-Lynx_rufus -0.0530 2.2978 -4.2847 -0.1939
## Cogon_Patch_Size-Didelphis_virginiana 1.6373 1.5175 -0.6424 1.3974
## Cogon_Patch_Size-Sylvilagus_floridanus -2.5846 2.8411 -10.4956 -1.8910
## Cogon_Patch_Size-Sciurus_carolinensis -1.8109 2.3506 -7.7748 -1.3548
## Cogon_Patch_Size-Vulpes_vulpes -1.7830 3.1949 -8.9733 -1.1663
## Cogon_Patch_Size-Sus_scrofa -1.8137 2.9013 -10.4559 -1.1798
## Veg_shannon_index-Canis_latrans 1.6811 1.1229 -0.1940 1.5691
## Veg_shannon_index-Lynx_rufus -0.6060 2.0728 -5.8948 -0.2363
## Veg_shannon_index-Didelphis_virginiana 1.7076 1.6201 -0.4927 1.3669
## Veg_shannon_index-Sylvilagus_floridanus 1.5040 1.3886 -0.6021 1.2826
## Veg_shannon_index-Sciurus_carolinensis -1.0260 1.8306 -5.5058 -0.6647
## Veg_shannon_index-Vulpes_vulpes -1.0350 2.0760 -6.0560 -0.6092
## Veg_shannon_index-Sus_scrofa 2.8658 2.2982 -0.0037 2.3431
## total_shrub_cover-Canis_latrans 1.3056 1.6085 -0.8718 0.9243
## total_shrub_cover-Lynx_rufus -2.3570 2.7401 -9.3484 -1.7845
## total_shrub_cover-Didelphis_virginiana -1.7556 2.0679 -7.2307 -1.2443
## total_shrub_cover-Sylvilagus_floridanus -1.5474 2.1098 -6.8834 -1.0182
## total_shrub_cover-Sciurus_carolinensis -1.5595 2.4684 -8.1399 -0.9029
## total_shrub_cover-Vulpes_vulpes -1.5300 2.3982 -7.9281 -0.9386
## total_shrub_cover-Sus_scrofa -0.4474 1.7715 -4.6604 -0.2937
## Avg_Cogongrass_Cover-Canis_latrans 2.5230 1.2023 0.3949 2.4584
## Avg_Cogongrass_Cover-Lynx_rufus 2.6308 1.5042 0.1374 2.4846
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1085 1.2360 -0.2844 2.0781
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1960 1.5089 -2.3825 1.3779
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2149 1.2513 -0.0915 2.1935
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4944 1.4470 -0.1074 2.3960
## Avg_Cogongrass_Cover-Sus_scrofa 1.5930 1.5903 -1.8964 1.7178
## Tree_Density-Canis_latrans -2.8384 1.5141 -6.7618 -2.5670
## Tree_Density-Lynx_rufus -1.1263 1.5932 -3.5659 -1.3045
## Tree_Density-Didelphis_virginiana -2.0864 1.5041 -4.9325 -2.1025
## Tree_Density-Sylvilagus_floridanus -2.6442 1.6884 -6.8520 -2.4425
## Tree_Density-Sciurus_carolinensis -2.3117 1.6630 -5.9629 -2.2122
## Tree_Density-Vulpes_vulpes -1.9096 1.7782 -5.2039 -1.9447
## Tree_Density-Sus_scrofa -2.3900 1.7255 -6.5003 -2.1977
## Avg_Canopy_Cover-Canis_latrans -0.2574 0.9138 -2.2650 -0.1956
## Avg_Canopy_Cover-Lynx_rufus 0.5715 2.2840 -3.9395 0.4627
## Avg_Canopy_Cover-Didelphis_virginiana 5.2414 2.6712 1.6950 4.6628
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.5076 4.2224 1.6445 5.4987
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2845 3.4081 1.4722 4.4209
## Avg_Canopy_Cover-Vulpes_vulpes 4.1344 3.1710 0.6249 3.2963
## Avg_Canopy_Cover-Sus_scrofa 2.8512 1.7389 0.2494 2.5880
## avg_veg_height-Canis_latrans -0.4362 0.8547 -2.0086 -0.4847
## avg_veg_height-Lynx_rufus -0.7043 1.1319 -3.0100 -0.6911
## avg_veg_height-Didelphis_virginiana -0.7487 0.9935 -2.7172 -0.7193
## avg_veg_height-Sylvilagus_floridanus -0.6693 0.9792 -2.5898 -0.6611
## avg_veg_height-Sciurus_carolinensis -0.0590 1.0895 -1.7892 -0.1710
## avg_veg_height-Vulpes_vulpes -0.6560 1.0833 -2.9364 -0.6629
## avg_veg_height-Sus_scrofa -0.5789 1.0118 -2.6219 -0.5986
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.0418 1.0157 182
## (Intercept)-Lynx_rufus 11.1559 1.2050 68
## (Intercept)-Didelphis_virginiana 0.3948 1.0648 315
## (Intercept)-Sylvilagus_floridanus 2.5642 1.1431 132
## (Intercept)-Sciurus_carolinensis 0.5193 1.0799 109
## (Intercept)-Vulpes_vulpes 1.3220 1.1239 70
## (Intercept)-Sus_scrofa -0.3233 1.0596 98
## Cogon_Patch_Size-Canis_latrans 6.8967 1.0046 277
## Cogon_Patch_Size-Lynx_rufus 5.0564 1.0642 189
## Cogon_Patch_Size-Didelphis_virginiana 5.1855 1.0530 207
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9492 1.1193 95
## Cogon_Patch_Size-Sciurus_carolinensis 1.5665 1.0575 227
## Cogon_Patch_Size-Vulpes_vulpes 2.3687 1.1620 81
## Cogon_Patch_Size-Sus_scrofa 1.8212 1.1315 88
## Veg_shannon_index-Canis_latrans 4.0949 1.0327 321
## Veg_shannon_index-Lynx_rufus 2.6554 1.2026 147
## Veg_shannon_index-Didelphis_virginiana 5.6606 1.1655 135
## Veg_shannon_index-Sylvilagus_floridanus 5.0711 1.1475 154
## Veg_shannon_index-Sciurus_carolinensis 1.4303 1.1592 116
## Veg_shannon_index-Vulpes_vulpes 1.8693 1.0840 123
## Veg_shannon_index-Sus_scrofa 8.7567 1.1590 142
## total_shrub_cover-Canis_latrans 5.1467 1.1140 139
## total_shrub_cover-Lynx_rufus 1.4977 1.3455 46
## total_shrub_cover-Didelphis_virginiana 0.8655 1.5300 72
## total_shrub_cover-Sylvilagus_floridanus 1.2274 1.2751 74
## total_shrub_cover-Sciurus_carolinensis 1.3325 1.3389 87
## total_shrub_cover-Vulpes_vulpes 1.5648 1.2941 87
## total_shrub_cover-Sus_scrofa 2.9737 1.3081 119
## Avg_Cogongrass_Cover-Canis_latrans 5.1346 1.0330 393
## Avg_Cogongrass_Cover-Lynx_rufus 6.2817 1.0390 312
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.6232 1.0422 402
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.6568 1.0217 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.7820 1.0480 252
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.6468 1.0142 269
## Avg_Cogongrass_Cover-Sus_scrofa 4.3574 1.0139 317
## Tree_Density-Canis_latrans -0.6286 1.0077 304
## Tree_Density-Lynx_rufus 2.7284 1.0634 178
## Tree_Density-Didelphis_virginiana 1.1385 1.1307 218
## Tree_Density-Sylvilagus_floridanus 0.0818 1.0232 272
## Tree_Density-Sciurus_carolinensis 0.9172 1.0248 274
## Tree_Density-Vulpes_vulpes 2.1522 1.0940 227
## Tree_Density-Sus_scrofa 0.5204 1.0112 331
## Avg_Canopy_Cover-Canis_latrans 1.3547 1.1410 304
## Avg_Canopy_Cover-Lynx_rufus 5.5216 1.2946 82
## Avg_Canopy_Cover-Didelphis_virginiana 11.8380 1.1635 133
## Avg_Canopy_Cover-Sylvilagus_floridanus 18.2974 1.2006 60
## Avg_Canopy_Cover-Sciurus_carolinensis 13.6592 1.2747 70
## Avg_Canopy_Cover-Vulpes_vulpes 12.9248 1.0974 71
## Avg_Canopy_Cover-Sus_scrofa 6.9361 1.1675 171
## avg_veg_height-Canis_latrans 1.2960 1.0612 251
## avg_veg_height-Lynx_rufus 1.5072 1.0968 301
## avg_veg_height-Didelphis_virginiana 1.1332 1.0670 343
## avg_veg_height-Sylvilagus_floridanus 1.3574 1.0351 290
## avg_veg_height-Sciurus_carolinensis 2.4107 1.0454 219
## avg_veg_height-Vulpes_vulpes 1.4497 1.0437 219
## avg_veg_height-Sus_scrofa 1.4376 1.0573 292
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.817500e+00 1.754000e-01 -5.176400e+00
## (Intercept)-Lynx_rufus -5.593500e+00 3.106000e-01 -6.222300e+00
## (Intercept)-Didelphis_virginiana -4.750500e+00 2.803000e-01 -5.308000e+00
## (Intercept)-Sylvilagus_floridanus -4.995000e+00 2.215000e-01 -5.441800e+00
## (Intercept)-Sciurus_carolinensis -4.899700e+00 3.202000e-01 -5.561300e+00
## (Intercept)-Vulpes_vulpes -5.777900e+00 5.532000e-01 -6.948800e+00
## (Intercept)-Sus_scrofa -5.271700e+00 4.860000e-01 -6.315500e+00
## shrub_cover-Canis_latrans -3.283000e-01 2.255000e-01 -7.508000e-01
## shrub_cover-Lynx_rufus 4.200000e-02 3.749000e-01 -6.645000e-01
## shrub_cover-Didelphis_virginiana 1.182700e+00 3.843000e-01 4.781000e-01
## shrub_cover-Sylvilagus_floridanus 7.426000e-01 4.170000e-01 -1.650000e-01
## shrub_cover-Sciurus_carolinensis 1.194800e+00 4.547000e-01 3.323000e-01
## shrub_cover-Vulpes_vulpes 4.106000e-01 6.177000e-01 -8.457000e-01
## shrub_cover-Sus_scrofa 9.159000e-01 7.896000e-01 -5.524000e-01
## veg_height-Canis_latrans -5.612000e-01 1.732000e-01 -9.312000e-01
## veg_height-Lynx_rufus 1.647000e-01 2.239000e-01 -2.687000e-01
## veg_height-Didelphis_virginiana 5.210000e-01 2.333000e-01 8.690000e-02
## veg_height-Sylvilagus_floridanus 2.056000e-01 2.154000e-01 -2.358000e-01
## veg_height-Sciurus_carolinensis 2.878000e-01 2.209000e-01 -1.381000e-01
## veg_height-Vulpes_vulpes -1.840000e-01 3.447000e-01 -8.708000e-01
## veg_height-Sus_scrofa -2.483000e-01 3.380000e-01 -9.593000e-01
## week-Canis_latrans -3.742600e+00 3.614210e+02 -2.615374e+02
## week-Lynx_rufus -5.306000e+00 5.913736e+02 -2.140019e+02
## week-Didelphis_virginiana -6.982200e+00 3.757632e+02 -2.478039e+02
## week-Sylvilagus_floridanus 1.007930e+01 4.769818e+02 -2.816909e+02
## week-Sciurus_carolinensis 6.728700e+00 5.649431e+02 -2.474895e+02
## week-Vulpes_vulpes -8.623800e+00 4.094049e+02 -2.936952e+02
## week-Sus_scrofa -1.705900e+00 7.037069e+02 -2.596177e+02
## I(week^2)-Canis_latrans 2.585664e+08 1.862952e+10 -4.558635e+08
## I(week^2)-Lynx_rufus -1.716468e+08 1.507739e+10 -3.888461e+08
## I(week^2)-Didelphis_virginiana -2.452927e+08 1.470860e+10 -3.359498e+08
## I(week^2)-Sylvilagus_floridanus 7.985587e+08 2.328438e+10 -2.319152e+08
## I(week^2)-Sciurus_carolinensis -1.013827e+08 2.073869e+10 -3.850906e+08
## I(week^2)-Vulpes_vulpes -1.833877e+08 1.986567e+10 -5.194016e+08
## I(week^2)-Sus_scrofa -1.002118e+08 1.324177e+10 -4.847323e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8083 -4.493900e+00 1.1364 142
## (Intercept)-Lynx_rufus -5.5725 -5.016800e+00 1.1605 67
## (Intercept)-Didelphis_virginiana -4.7433 -4.211400e+00 1.1266 115
## (Intercept)-Sylvilagus_floridanus -4.9917 -4.584300e+00 1.1338 205
## (Intercept)-Sciurus_carolinensis -4.9002 -4.264300e+00 1.2737 109
## (Intercept)-Vulpes_vulpes -5.7337 -4.852800e+00 1.2412 66
## (Intercept)-Sus_scrofa -5.2429 -4.404700e+00 1.1730 138
## shrub_cover-Canis_latrans -0.3404 1.375000e-01 1.0346 134
## shrub_cover-Lynx_rufus 0.0430 7.301000e-01 1.1566 80
## shrub_cover-Didelphis_virginiana 1.1593 1.969700e+00 1.2082 124
## shrub_cover-Sylvilagus_floridanus 0.7685 1.520500e+00 1.3825 92
## shrub_cover-Sciurus_carolinensis 1.1878 2.096400e+00 1.3731 88
## shrub_cover-Vulpes_vulpes 0.4145 1.617500e+00 1.1733 79
## shrub_cover-Sus_scrofa 0.8803 2.549100e+00 1.2235 130
## veg_height-Canis_latrans -0.5572 -2.403000e-01 1.0775 192
## veg_height-Lynx_rufus 0.1582 6.109000e-01 1.0133 143
## veg_height-Didelphis_virginiana 0.5067 1.005400e+00 1.0175 287
## veg_height-Sylvilagus_floridanus 0.2123 6.188000e-01 1.0267 226
## veg_height-Sciurus_carolinensis 0.2882 7.345000e-01 1.0769 221
## veg_height-Vulpes_vulpes -0.1786 4.841000e-01 1.0865 118
## veg_height-Sus_scrofa -0.2308 3.824000e-01 1.1194 280
## week-Canis_latrans 0.0082 2.034234e+02 1.2665 5275
## week-Lynx_rufus 0.1786 2.479361e+02 1.2767 5707
## week-Didelphis_virginiana 0.1489 2.239850e+02 1.2535 5245
## week-Sylvilagus_floridanus 0.0488 2.854377e+02 1.3085 1087
## week-Sciurus_carolinensis 0.1394 2.254436e+02 1.2933 2497
## week-Vulpes_vulpes 0.0234 2.040629e+02 1.2768 2825
## week-Sus_scrofa 0.1149 2.505255e+02 1.2834 46969
## I(week^2)-Canis_latrans 0.4064 4.823805e+08 1.3094 6707
## I(week^2)-Lynx_rufus 0.5178 6.681091e+08 1.3032 5727
## I(week^2)-Didelphis_virginiana 0.5190 5.869704e+08 1.3178 3366
## I(week^2)-Sylvilagus_floridanus 0.4426 7.467540e+08 1.4029 797
## I(week^2)-Sciurus_carolinensis 0.6630 6.061301e+08 1.2927 29296
## I(week^2)-Vulpes_vulpes 0.7036 2.591428e+08 1.2988 10402
## I(week^2)-Sus_scrofa 0.7232 1.609772e+08 1.2961 14330
#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): 4.842
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4841 0.6714 -1.7243 -0.5290 1.0251 1.0375 172
## Avg_Cogongrass_Cover 0.2223 0.4827 -0.7564 0.2304 1.1756 1.0394 466
## total_shrub_cover -0.7809 0.6672 -2.2912 -0.7177 0.3741 1.0474 171
## avg_veg_height 0.1324 0.4511 -0.7208 0.1270 1.0455 1.0001 318
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1349 1.7065 0.0537 0.5778 5.5707 1.0341 351
## Avg_Cogongrass_Cover 0.6956 1.1498 0.0449 0.3245 3.7190 1.0371 596
## total_shrub_cover 2.0981 5.4483 0.0755 0.9486 10.8165 1.1469 394
## avg_veg_height 0.4269 0.6462 0.0405 0.2314 2.0956 1.0308 534
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8634 2.7999 0.2549 2.1246 9.7617 1.1945 122
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0468 0.3231 -5.6025 -5.0663 -4.3803 1.0360 314
## shrub_cover 0.6969 0.4062 -0.1594 0.7087 1.4776 1.0320 210
## veg_height -0.0093 0.2341 -0.4804 -0.0062 0.4492 1.0029 518
## week -0.0826 1.7020 -3.4522 -0.0235 3.2107 1.0118 692
## I(week^2) -0.0536 1.6516 -3.2436 -0.0326 3.1061 1.0021 621
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 4.324000e-01 9.423000e-01 0.0492 0.2402 1.844200e+00 1.0927
## shrub_cover 9.080000e-01 1.175200e+00 0.1360 0.6109 3.449000e+00 1.0489
## veg_height 2.960000e-01 2.831000e-01 0.0595 0.2136 1.027500e+00 1.0071
## week 5.595917e+23 1.826362e+25 0.1207 123554.3016 2.011491e+22 1.3809
## I(week^2) 1.766274e+12 2.008873e+13 0.1477 2666.7291 2.219086e+12 1.8198
## ESS
## (Intercept) 757
## shrub_cover 274
## veg_height 672
## week 1086
## I(week^2) 113
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1977 0.8823 -1.3324 0.1160
## (Intercept)-Lynx_rufus -0.1297 0.9534 -1.7726 -0.2221
## (Intercept)-Didelphis_virginiana -0.7029 0.8506 -2.3374 -0.7241
## (Intercept)-Sylvilagus_floridanus -0.1799 0.9509 -1.8315 -0.2541
## (Intercept)-Sciurus_carolinensis -0.7841 0.8655 -2.4803 -0.7770
## (Intercept)-Vulpes_vulpes -0.9213 1.0093 -2.9927 -0.8869
## (Intercept)-Sus_scrofa -1.0366 0.9863 -3.1461 -0.9948
## Avg_Cogongrass_Cover-Canis_latrans 0.5457 0.6173 -0.5385 0.4931
## Avg_Cogongrass_Cover-Lynx_rufus 0.6110 0.7042 -0.5613 0.5388
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3062 0.5949 -0.8223 0.2878
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3748 0.7338 -2.0755 -0.2936
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1423 0.5892 -1.1326 0.1611
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3435 0.6671 -0.8896 0.3192
## Avg_Cogongrass_Cover-Sus_scrofa -0.0228 0.8210 -1.7827 0.0278
## total_shrub_cover-Canis_latrans 0.5219 0.8058 -0.7608 0.4197
## total_shrub_cover-Lynx_rufus -1.4472 1.0612 -3.8071 -1.3349
## total_shrub_cover-Didelphis_virginiana -0.9717 0.8700 -3.1315 -0.8445
## total_shrub_cover-Sylvilagus_floridanus -1.5516 1.1361 -4.3328 -1.3241
## total_shrub_cover-Sciurus_carolinensis -1.0498 1.0028 -3.6180 -0.8640
## total_shrub_cover-Vulpes_vulpes -0.9210 1.2972 -3.9197 -0.7808
## total_shrub_cover-Sus_scrofa -0.6797 1.1214 -3.4596 -0.5613
## avg_veg_height-Canis_latrans 0.1608 0.5501 -0.8648 0.1530
## avg_veg_height-Lynx_rufus 0.0549 0.6693 -1.3135 0.0564
## avg_veg_height-Didelphis_virginiana -0.0364 0.5717 -1.1844 -0.0283
## avg_veg_height-Sylvilagus_floridanus 0.0593 0.5851 -1.0602 0.0334
## avg_veg_height-Sciurus_carolinensis 0.4959 0.6035 -0.5708 0.4499
## avg_veg_height-Vulpes_vulpes 0.0248 0.6068 -1.1910 0.0252
## avg_veg_height-Sus_scrofa 0.1950 0.6121 -0.9541 0.1727
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1955 1.0126 301
## (Intercept)-Lynx_rufus 1.9822 1.0383 215
## (Intercept)-Didelphis_virginiana 1.0286 1.0218 216
## (Intercept)-Sylvilagus_floridanus 1.9595 1.0411 229
## (Intercept)-Sciurus_carolinensis 0.9712 1.0312 208
## (Intercept)-Vulpes_vulpes 1.0688 1.0191 126
## (Intercept)-Sus_scrofa 0.8631 1.0419 166
## Avg_Cogongrass_Cover-Canis_latrans 1.9090 1.0040 675
## Avg_Cogongrass_Cover-Lynx_rufus 2.3294 1.0006 550
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5470 1.0016 662
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8923 1.0493 489
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2241 1.0316 610
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6913 1.0204 351
## Avg_Cogongrass_Cover-Sus_scrofa 1.4886 1.0409 476
## total_shrub_cover-Canis_latrans 2.3711 1.0696 210
## total_shrub_cover-Lynx_rufus 0.3014 1.0653 156
## total_shrub_cover-Didelphis_virginiana 0.3020 1.0364 138
## total_shrub_cover-Sylvilagus_floridanus 0.0622 1.1151 119
## total_shrub_cover-Sciurus_carolinensis 0.3525 1.0292 158
## total_shrub_cover-Vulpes_vulpes 1.5898 1.2561 64
## total_shrub_cover-Sus_scrofa 1.3290 1.0182 191
## avg_veg_height-Canis_latrans 1.2375 1.0007 579
## avg_veg_height-Lynx_rufus 1.2998 1.0028 492
## avg_veg_height-Didelphis_virginiana 1.0584 1.0124 578
## avg_veg_height-Sylvilagus_floridanus 1.2811 1.0038 343
## avg_veg_height-Sciurus_carolinensis 1.7849 1.0096 354
## avg_veg_height-Vulpes_vulpes 1.2454 1.0115 536
## avg_veg_height-Sus_scrofa 1.4477 1.0058 386
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.816800e+00 1.918000e-01 -5.223700e+00
## (Intercept)-Lynx_rufus -5.435900e+00 2.754000e-01 -5.955700e+00
## (Intercept)-Didelphis_virginiana -4.847000e+00 2.959000e-01 -5.447100e+00
## (Intercept)-Sylvilagus_floridanus -5.101300e+00 2.358000e-01 -5.582500e+00
## (Intercept)-Sciurus_carolinensis -4.853300e+00 3.049000e-01 -5.455100e+00
## (Intercept)-Vulpes_vulpes -5.688100e+00 5.379000e-01 -6.816000e+00
## (Intercept)-Sus_scrofa -5.362100e+00 4.357000e-01 -6.250200e+00
## shrub_cover-Canis_latrans -2.762000e-01 2.374000e-01 -7.198000e-01
## shrub_cover-Lynx_rufus 2.005000e-01 3.577000e-01 -5.127000e-01
## shrub_cover-Didelphis_virginiana 1.328100e+00 3.696000e-01 6.481000e-01
## shrub_cover-Sylvilagus_floridanus 1.006900e+00 4.051000e-01 2.355000e-01
## shrub_cover-Sciurus_carolinensis 1.234000e+00 3.859000e-01 4.627000e-01
## shrub_cover-Vulpes_vulpes 4.286000e-01 7.732000e-01 -1.372400e+00
## shrub_cover-Sus_scrofa 1.155900e+00 7.183000e-01 -3.569000e-01
## veg_height-Canis_latrans -5.765000e-01 1.756000e-01 -9.445000e-01
## veg_height-Lynx_rufus 1.268000e-01 2.351000e-01 -3.328000e-01
## veg_height-Didelphis_virginiana 4.788000e-01 2.434000e-01 2.130000e-02
## veg_height-Sylvilagus_floridanus 7.650000e-02 2.444000e-01 -3.972000e-01
## veg_height-Sciurus_carolinensis 2.323000e-01 2.147000e-01 -1.934000e-01
## veg_height-Vulpes_vulpes -1.134000e-01 3.245000e-01 -7.894000e-01
## veg_height-Sus_scrofa -2.853000e-01 3.401000e-01 -1.026900e+00
## week-Canis_latrans 3.272293e+09 3.462559e+11 -9.584620e+09
## week-Lynx_rufus -9.078955e+08 6.349160e+11 -5.359020e+09
## week-Didelphis_virginiana -4.822387e+09 4.268916e+11 -6.632450e+09
## week-Sylvilagus_floridanus -1.272134e+09 2.619618e+11 -5.551563e+09
## week-Sciurus_carolinensis -4.745324e+09 4.770893e+11 -5.474383e+09
## week-Vulpes_vulpes -1.748215e+10 8.712172e+11 -6.468521e+09
## week-Sus_scrofa -6.911338e+08 4.686969e+11 -6.083231e+09
## I(week^2)-Canis_latrans 1.970094e+04 1.268025e+06 -1.131378e+05
## I(week^2)-Lynx_rufus 2.550175e+04 1.048469e+06 -9.958686e+04
## I(week^2)-Didelphis_virginiana -6.881250e+03 1.490232e+06 -8.875164e+04
## I(week^2)-Sylvilagus_floridanus -2.587768e+04 9.765541e+05 -9.740196e+04
## I(week^2)-Sciurus_carolinensis -1.763056e+04 1.169773e+06 -1.523648e+05
## I(week^2)-Vulpes_vulpes 3.873950e+04 1.244724e+06 -1.076784e+05
## I(week^2)-Sus_scrofa -1.199913e+04 1.130507e+06 -1.633226e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8120 -4.477000e+00 1.0437 154
## (Intercept)-Lynx_rufus -5.4287 -4.932700e+00 1.0941 124
## (Intercept)-Didelphis_virginiana -4.8385 -4.288400e+00 1.1753 143
## (Intercept)-Sylvilagus_floridanus -5.0981 -4.633300e+00 1.1825 141
## (Intercept)-Sciurus_carolinensis -4.8403 -4.285800e+00 1.0474 117
## (Intercept)-Vulpes_vulpes -5.6233 -4.798300e+00 1.0174 79
## (Intercept)-Sus_scrofa -5.3648 -4.518300e+00 1.0393 130
## shrub_cover-Canis_latrans -0.2837 2.134000e-01 1.1262 111
## shrub_cover-Lynx_rufus 0.2111 8.800000e-01 1.0273 109
## shrub_cover-Didelphis_virginiana 1.3078 2.133700e+00 1.0807 98
## shrub_cover-Sylvilagus_floridanus 0.9931 1.812700e+00 1.0938 87
## shrub_cover-Sciurus_carolinensis 1.2265 2.020300e+00 1.0828 92
## shrub_cover-Vulpes_vulpes 0.5008 1.805900e+00 1.0742 66
## shrub_cover-Sus_scrofa 1.1664 2.536300e+00 1.0360 150
## veg_height-Canis_latrans -0.5688 -2.559000e-01 1.0551 188
## veg_height-Lynx_rufus 0.1279 6.015000e-01 1.0122 144
## veg_height-Didelphis_virginiana 0.4729 9.920000e-01 1.0321 186
## veg_height-Sylvilagus_floridanus 0.0733 5.651000e-01 1.0912 146
## veg_height-Sciurus_carolinensis 0.2343 6.476000e-01 1.0301 199
## veg_height-Vulpes_vulpes -0.0958 4.672000e-01 1.0138 128
## veg_height-Sus_scrofa -0.2516 3.207000e-01 1.0041 244
## week-Canis_latrans -0.1935 6.501702e+09 1.2992 17497
## week-Lynx_rufus -0.0547 6.316046e+09 1.2906 21679
## week-Didelphis_virginiana -0.2241 7.102900e+09 1.3030 7336
## week-Sylvilagus_floridanus -0.2233 7.053162e+09 1.2927 19980
## week-Sciurus_carolinensis -0.0174 7.229182e+09 1.3002 8510
## week-Vulpes_vulpes 0.1529 6.081741e+09 1.3299 2213
## week-Sus_scrofa -0.1987 6.697848e+09 1.2906 52936
## I(week^2)-Canis_latrans -0.1608 1.632316e+05 1.2403 1807
## I(week^2)-Lynx_rufus -0.1841 1.620925e+05 1.2927 2034
## I(week^2)-Didelphis_virginiana -0.1951 1.478029e+05 1.2740 12636
## I(week^2)-Sylvilagus_floridanus -0.0786 1.735322e+05 1.3626 585
## I(week^2)-Sciurus_carolinensis -0.3395 1.258862e+05 1.2929 7380
## I(week^2)-Vulpes_vulpes -0.2654 1.035165e+05 1.3472 824
## I(week^2)-Sus_scrofa -0.1484 9.438471e+04 1.2587 3320
#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): 4.3198
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0062 0.6281 -2.2233 -1.0290 0.3401 1.0216 385
## Tree_Density -0.7417 0.5386 -1.9036 -0.7061 0.2518 1.0106 674
## Avg_Canopy_Cover 1.1878 0.5843 0.0632 1.1521 2.4873 1.0286 686
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5320 3.4537 0.1477 1.5610 10.6543 1.0381 347
## Tree_Density 1.1341 2.0156 0.0572 0.5297 6.0291 1.0188 675
## Avg_Canopy_Cover 1.9246 2.9620 0.1427 1.1616 7.7688 1.1171 715
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7943 1.3457 0.0452 0.3699 4.653 1.3224 146
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9583 0.3200 -5.5225 -4.9720 -4.2939 1.0758 451
## shrub_cover 0.4524 0.3559 -0.2327 0.4459 1.1971 1.0198 655
## veg_height 0.0303 0.2374 -0.4797 0.0324 0.4941 1.0028 652
## week -0.0749 1.6622 -3.3848 -0.1028 3.2969 1.0188 504
## I(week^2) 0.1472 1.7081 -3.1856 0.1274 3.4879 1.0115 813
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4697 9.604000e-01 0.0520 0.2677 2.034300e+00 1.2826 612
## shrub_cover 0.7246 8.078000e-01 0.1115 0.5019 2.765800e+00 1.0210 926
## veg_height 0.3284 3.315000e-01 0.0642 0.2399 1.115800e+00 1.0266 747
## week 2741738.2673 5.368743e+07 0.0678 13.8445 4.250832e+05 1.5242 394
## I(week^2) 1546550.9358 1.283136e+07 0.1165 227.7408 1.125408e+07 1.2748 245
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2372 0.7475 -1.1795 0.2260 1.7203
## (Intercept)-Lynx_rufus 0.3046 1.1867 -1.6070 0.1370 3.1207
## (Intercept)-Didelphis_virginiana -1.6044 0.7387 -3.1617 -1.5562 -0.2496
## (Intercept)-Sylvilagus_floridanus -0.8628 0.7027 -2.2234 -0.8741 0.5862
## (Intercept)-Sciurus_carolinensis -1.6733 0.7533 -3.3054 -1.6386 -0.3326
## (Intercept)-Vulpes_vulpes -1.8657 0.8871 -3.6165 -1.8451 -0.1139
## (Intercept)-Sus_scrofa -2.2353 0.9396 -4.2522 -2.1584 -0.6162
## Tree_Density-Canis_latrans -1.0168 0.6651 -2.5921 -0.9410 0.1033
## Tree_Density-Lynx_rufus 0.1566 0.8157 -1.1655 0.0527 2.1165
## Tree_Density-Didelphis_virginiana -1.0644 0.8674 -3.2293 -0.9056 0.1877
## Tree_Density-Sylvilagus_floridanus -1.1515 0.9124 -3.4076 -0.9978 0.2338
## Tree_Density-Sciurus_carolinensis -0.9109 0.8023 -2.7971 -0.8153 0.4278
## Tree_Density-Vulpes_vulpes -0.6242 0.8229 -2.3643 -0.5819 0.9338
## Tree_Density-Sus_scrofa -0.9526 0.9511 -3.2776 -0.8093 0.4908
## Avg_Canopy_Cover-Canis_latrans -0.1443 0.5200 -1.1889 -0.1437 0.8933
## Avg_Canopy_Cover-Lynx_rufus 0.8291 0.9229 -0.7184 0.7200 3.0064
## Avg_Canopy_Cover-Didelphis_virginiana 1.7949 0.8118 0.5659 1.6807 3.7676
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4683 1.0962 0.8363 2.2910 5.0624
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6889 0.7808 0.4934 1.5695 3.5779
## Avg_Canopy_Cover-Vulpes_vulpes 1.1708 0.7701 -0.0213 1.0979 3.0079
## Avg_Canopy_Cover-Sus_scrofa 1.4216 0.7532 0.2092 1.3132 3.1920
## Rhat ESS
## (Intercept)-Canis_latrans 1.0261 367
## (Intercept)-Lynx_rufus 1.0432 123
## (Intercept)-Didelphis_virginiana 1.0063 694
## (Intercept)-Sylvilagus_floridanus 1.0050 912
## (Intercept)-Sciurus_carolinensis 1.0075 593
## (Intercept)-Vulpes_vulpes 1.0334 296
## (Intercept)-Sus_scrofa 1.0113 478
## Tree_Density-Canis_latrans 1.0032 827
## Tree_Density-Lynx_rufus 1.0018 519
## Tree_Density-Didelphis_virginiana 1.0106 573
## Tree_Density-Sylvilagus_floridanus 1.0114 306
## Tree_Density-Sciurus_carolinensis 1.0052 783
## Tree_Density-Vulpes_vulpes 1.0069 674
## Tree_Density-Sus_scrofa 1.0109 560
## Avg_Canopy_Cover-Canis_latrans 1.0027 872
## Avg_Canopy_Cover-Lynx_rufus 1.0193 356
## Avg_Canopy_Cover-Didelphis_virginiana 1.0163 432
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0452 426
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0223 327
## Avg_Canopy_Cover-Vulpes_vulpes 1.0854 427
## Avg_Canopy_Cover-Sus_scrofa 1.0108 860
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8024 0.1680 -5.1599 -4.7930
## (Intercept)-Lynx_rufus -5.5647 0.2983 -6.1595 -5.5589
## (Intercept)-Didelphis_virginiana -4.7394 0.2960 -5.3815 -4.7278
## (Intercept)-Sylvilagus_floridanus -4.9293 0.2072 -5.3521 -4.9207
## (Intercept)-Sciurus_carolinensis -4.7494 0.2871 -5.3435 -4.7399
## (Intercept)-Vulpes_vulpes -5.5766 0.5488 -6.8012 -5.5228
## (Intercept)-Sus_scrofa -5.2103 0.4528 -6.2248 -5.1867
## shrub_cover-Canis_latrans -0.2630 0.2116 -0.6745 -0.2602
## shrub_cover-Lynx_rufus -0.1793 0.3255 -0.8440 -0.1688
## shrub_cover-Didelphis_virginiana 1.0897 0.3340 0.4577 1.0895
## shrub_cover-Sylvilagus_floridanus 0.6127 0.3462 -0.0602 0.6190
## shrub_cover-Sciurus_carolinensis 0.9604 0.3274 0.3154 0.9603
## shrub_cover-Vulpes_vulpes 0.1694 0.5781 -0.9551 0.1625
## shrub_cover-Sus_scrofa 0.8072 0.6988 -0.5875 0.7770
## veg_height-Canis_latrans -0.5705 0.1821 -0.9305 -0.5644
## veg_height-Lynx_rufus 0.1657 0.2139 -0.2531 0.1699
## veg_height-Didelphis_virginiana 0.5661 0.2575 0.1013 0.5566
## veg_height-Sylvilagus_floridanus 0.1782 0.2237 -0.2731 0.1850
## veg_height-Sciurus_carolinensis 0.2469 0.2046 -0.1302 0.2352
## veg_height-Vulpes_vulpes -0.1858 0.3294 -0.9121 -0.1586
## veg_height-Sus_scrofa -0.1982 0.3492 -0.9486 -0.1803
## week-Canis_latrans 3.5381 1638.0229 -244.9607 -0.1021
## week-Lynx_rufus 13.8070 2302.4800 -218.4793 -0.0818
## week-Didelphis_virginiana -24.4840 1556.4153 -191.0068 -0.1466
## week-Sylvilagus_floridanus -36.5124 2154.0155 -192.4915 -0.1456
## week-Sciurus_carolinensis 20.7474 1391.5616 -202.4147 -0.1279
## week-Vulpes_vulpes 5.1559 1764.0066 -202.7441 -0.0768
## week-Sus_scrofa -37.3987 1496.8067 -209.8347 -0.2077
## I(week^2)-Canis_latrans 3.8301 1079.1055 -1326.6830 0.2447
## I(week^2)-Lynx_rufus -8.4755 1210.1134 -1274.6273 0.3350
## I(week^2)-Didelphis_virginiana 43.3808 1139.3488 -1019.1201 0.2945
## I(week^2)-Sylvilagus_floridanus 6.4042 1176.9176 -1334.9570 0.3387
## I(week^2)-Sciurus_carolinensis 8.4637 1211.4389 -1337.6836 0.1008
## I(week^2)-Vulpes_vulpes 10.5882 1178.8637 -1122.5734 0.3256
## I(week^2)-Sus_scrofa 18.7635 1119.7587 -1350.8942 0.1755
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4967 1.0077 193
## (Intercept)-Lynx_rufus -4.9821 1.1805 81
## (Intercept)-Didelphis_virginiana -4.1828 1.0898 137
## (Intercept)-Sylvilagus_floridanus -4.5411 1.0319 254
## (Intercept)-Sciurus_carolinensis -4.2102 1.0570 195
## (Intercept)-Vulpes_vulpes -4.6305 1.2023 84
## (Intercept)-Sus_scrofa -4.3743 1.0561 260
## shrub_cover-Canis_latrans 0.1407 1.0704 182
## shrub_cover-Lynx_rufus 0.4637 1.0610 91
## shrub_cover-Didelphis_virginiana 1.7599 1.0231 131
## shrub_cover-Sylvilagus_floridanus 1.2773 1.1428 175
## shrub_cover-Sciurus_carolinensis 1.5792 1.0049 263
## shrub_cover-Vulpes_vulpes 1.3250 1.0514 177
## shrub_cover-Sus_scrofa 2.2073 1.0193 351
## veg_height-Canis_latrans -0.2188 1.0001 172
## veg_height-Lynx_rufus 0.5895 1.0566 180
## veg_height-Didelphis_virginiana 1.0690 1.0244 245
## veg_height-Sylvilagus_floridanus 0.5898 1.0042 250
## veg_height-Sciurus_carolinensis 0.6749 1.0296 270
## veg_height-Vulpes_vulpes 0.3764 1.1040 143
## veg_height-Sus_scrofa 0.4635 1.0116 300
## week-Canis_latrans 216.2040 1.2864 34850
## week-Lynx_rufus 221.4986 1.2938 18808
## week-Didelphis_virginiana 223.1218 1.3135 3121
## week-Sylvilagus_floridanus 232.9550 1.3196 3027
## week-Sciurus_carolinensis 226.5564 1.3048 3941
## week-Vulpes_vulpes 181.5444 1.2887 94403
## week-Sus_scrofa 225.6092 1.3426 1667
## I(week^2)-Canis_latrans 1337.8978 1.0260 5042
## I(week^2)-Lynx_rufus 1149.5313 1.0388 1548
## I(week^2)-Didelphis_virginiana 1507.8091 1.0351 1275
## I(week^2)-Sylvilagus_floridanus 1281.8797 1.0211 4925
## I(week^2)-Sciurus_carolinensis 1237.2215 1.0283 8048
## I(week^2)-Vulpes_vulpes 1335.4994 1.0553 10052
## I(week^2)-Sus_scrofa 1318.9347 1.0361 2577
#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): 4.5607
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7366 0.6958 -2.0872 -0.7657 0.7407 1.1038 219
## Cogon_Patch_Size -0.4400 0.7292 -2.0407 -0.3972 0.9215 1.0202 659
## Avg_Cogongrass_Cover 0.3989 0.4381 -0.4910 0.3990 1.2962 1.0178 529
## total_shrub_cover -0.6826 0.6251 -2.0941 -0.6227 0.3536 1.1090 127
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7119 2.8028 0.0616 0.8522 8.6321 1.0282 392
## Cogon_Patch_Size 3.5258 6.4653 0.1278 1.5452 21.7925 1.1957 135
## Avg_Cogongrass_Cover 0.5971 1.1093 0.0448 0.2977 2.9667 1.0451 891
## total_shrub_cover 1.3765 3.0558 0.0580 0.5877 7.3049 1.0293 249
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.9281 5.1553 0.2786 2.4823 17.6595 1.2852 49
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0310 0.2992 -5.5718 -5.0426 -4.4691 1.0406 282
## shrub_cover 0.6767 0.3780 -0.0833 0.6694 1.3898 1.1013 272
## veg_height -0.0033 0.2396 -0.4749 0.0029 0.4831 1.0462 411
## week -0.1018 1.6569 -3.3659 -0.1343 3.1256 1.0157 1027
## I(week^2) -0.0208 1.6766 -3.4328 -0.0010 3.3406 1.0227 539
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.041000e-01 6.71200e-01 0.0451 0.2305 1.776500e+00 1.2428 177
## shrub_cover 7.579000e-01 7.38000e-01 0.1233 0.5439 2.723700e+00 1.0327 493
## veg_height 3.300000e-01 3.51800e-01 0.0592 0.2359 1.209900e+00 1.0150 624
## week 5.547388e+10 5.74512e+11 0.1759 2131.5377 3.360360e+11 1.6008 354
## I(week^2) 1.362672e+09 1.56567e+10 0.0941 74.8640 4.248789e+09 1.3519 319
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2543 1.0136 -1.4844 0.1739
## (Intercept)-Lynx_rufus -0.3264 0.9996 -2.1628 -0.4149
## (Intercept)-Didelphis_virginiana -1.0014 0.9093 -2.8743 -0.9904
## (Intercept)-Sylvilagus_floridanus -0.5809 1.0078 -2.5108 -0.6093
## (Intercept)-Sciurus_carolinensis -1.1673 0.9655 -3.2653 -1.1289
## (Intercept)-Vulpes_vulpes -1.4066 1.1646 -3.9323 -1.3019
## (Intercept)-Sus_scrofa -1.4181 1.1161 -4.0236 -1.2842
## Cogon_Patch_Size-Canis_latrans 0.9809 1.0914 -0.4749 0.7683
## Cogon_Patch_Size-Lynx_rufus -0.4974 1.0513 -2.4113 -0.5457
## Cogon_Patch_Size-Didelphis_virginiana 0.7829 0.6575 -0.3211 0.7208
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7056 1.7027 -6.3189 -1.2882
## Cogon_Patch_Size-Sciurus_carolinensis -1.3532 1.3209 -4.7640 -1.0614
## Cogon_Patch_Size-Vulpes_vulpes -1.2146 1.3859 -4.6008 -0.9638
## Cogon_Patch_Size-Sus_scrofa -0.8813 1.4239 -4.7232 -0.6271
## Avg_Cogongrass_Cover-Canis_latrans 0.4982 0.5120 -0.3770 0.4527
## Avg_Cogongrass_Cover-Lynx_rufus 0.8329 0.7441 -0.3229 0.7298
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2622 0.5344 -0.8254 0.2757
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0341 0.6432 -1.3252 0.0478
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5840 0.5846 -0.4129 0.5399
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5584 0.6217 -0.5006 0.5076
## Avg_Cogongrass_Cover-Sus_scrofa 0.1568 0.7357 -1.4897 0.1988
## total_shrub_cover-Canis_latrans 0.3248 0.7794 -0.8933 0.2448
## total_shrub_cover-Lynx_rufus -1.1954 1.0605 -3.6749 -1.0417
## total_shrub_cover-Didelphis_virginiana -0.9503 0.8349 -2.9713 -0.7997
## total_shrub_cover-Sylvilagus_floridanus -1.1930 1.0951 -3.8857 -0.9938
## total_shrub_cover-Sciurus_carolinensis -0.7650 0.8617 -2.8842 -0.6304
## total_shrub_cover-Vulpes_vulpes -0.8706 1.0167 -3.3351 -0.7438
## total_shrub_cover-Sus_scrofa -0.5570 0.9368 -2.6676 -0.4897
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4292 1.1190 246
## (Intercept)-Lynx_rufus 1.8884 1.0985 301
## (Intercept)-Didelphis_virginiana 0.9195 1.0252 267
## (Intercept)-Sylvilagus_floridanus 1.6584 1.1092 254
## (Intercept)-Sciurus_carolinensis 0.6594 1.0336 271
## (Intercept)-Vulpes_vulpes 0.7373 1.0356 229
## (Intercept)-Sus_scrofa 0.5816 1.0085 227
## Cogon_Patch_Size-Canis_latrans 3.7948 1.0407 364
## Cogon_Patch_Size-Lynx_rufus 1.8697 1.0878 388
## Cogon_Patch_Size-Didelphis_virginiana 2.3128 1.0153 548
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3253 1.0491 214
## Cogon_Patch_Size-Sciurus_carolinensis 0.3283 1.0233 209
## Cogon_Patch_Size-Vulpes_vulpes 0.7864 1.0643 298
## Cogon_Patch_Size-Sus_scrofa 1.0549 1.1401 259
## Avg_Cogongrass_Cover-Canis_latrans 1.6406 1.0012 771
## Avg_Cogongrass_Cover-Lynx_rufus 2.6341 1.0028 644
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2671 1.0090 933
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2642 1.0254 500
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8631 1.0089 753
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.9240 1.0057 740
## Avg_Cogongrass_Cover-Sus_scrofa 1.4838 1.0160 455
## total_shrub_cover-Canis_latrans 2.1606 1.0238 184
## total_shrub_cover-Lynx_rufus 0.4170 1.0311 118
## total_shrub_cover-Didelphis_virginiana 0.2474 1.1156 116
## total_shrub_cover-Sylvilagus_floridanus 0.2926 1.1045 80
## total_shrub_cover-Sciurus_carolinensis 0.5760 1.0186 110
## total_shrub_cover-Vulpes_vulpes 0.7947 1.1328 144
## total_shrub_cover-Sus_scrofa 1.2052 1.0125 175
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8253 0.1860 -5.2104 -4.8172
## (Intercept)-Lynx_rufus -5.4068 0.2640 -5.9371 -5.4022
## (Intercept)-Didelphis_virginiana -4.7669 0.2926 -5.3674 -4.7528
## (Intercept)-Sylvilagus_floridanus -5.0413 0.2165 -5.4516 -5.0483
## (Intercept)-Sciurus_carolinensis -4.8903 0.3733 -5.6663 -4.8717
## (Intercept)-Vulpes_vulpes -5.6145 0.5462 -6.8192 -5.5267
## (Intercept)-Sus_scrofa -5.3684 0.4121 -6.2145 -5.3486
## shrub_cover-Canis_latrans -0.2550 0.2236 -0.6933 -0.2610
## shrub_cover-Lynx_rufus 0.2466 0.3555 -0.5204 0.2592
## shrub_cover-Didelphis_virginiana 1.2379 0.3880 0.5322 1.2259
## shrub_cover-Sylvilagus_floridanus 0.8816 0.3814 0.1112 0.8863
## shrub_cover-Sciurus_carolinensis 1.2086 0.4329 0.3898 1.1979
## shrub_cover-Vulpes_vulpes 0.5475 0.6190 -0.8027 0.5687
## shrub_cover-Sus_scrofa 1.1330 0.6678 -0.2623 1.1562
## veg_height-Canis_latrans -0.5781 0.1844 -0.9662 -0.5738
## veg_height-Lynx_rufus 0.1288 0.2479 -0.3684 0.1374
## veg_height-Didelphis_virginiana 0.4755 0.2419 0.0424 0.4625
## veg_height-Sylvilagus_floridanus 0.0619 0.2228 -0.3868 0.0688
## veg_height-Sciurus_carolinensis 0.2693 0.2468 -0.2016 0.2609
## veg_height-Vulpes_vulpes -0.1150 0.3216 -0.8026 -0.1012
## veg_height-Sus_scrofa -0.3084 0.3399 -1.0014 -0.3100
## week-Canis_latrans -4981.3247 203267.5022 -147809.0034 -0.1383
## week-Lynx_rufus 1905.8112 198438.0024 -164520.7316 -0.5040
## week-Didelphis_virginiana -556.3770 205330.1673 -153500.4963 -0.5221
## week-Sylvilagus_floridanus 6262.5655 243746.8399 -138859.6055 -0.3831
## week-Sciurus_carolinensis 3900.1961 178488.0097 -133298.0890 -0.4459
## week-Vulpes_vulpes -4084.6917 224294.3129 -152832.9581 -0.1872
## week-Sus_scrofa -5140.2921 203390.0148 -162564.6624 -0.1613
## I(week^2)-Canis_latrans 537.7165 43727.3090 -10035.2016 -0.0292
## I(week^2)-Lynx_rufus -845.6798 41522.3061 -11866.6884 0.1657
## I(week^2)-Didelphis_virginiana -497.6322 37204.1576 -11366.0641 0.0069
## I(week^2)-Sylvilagus_floridanus -1229.7476 39941.6119 -11153.1266 -0.0502
## I(week^2)-Sciurus_carolinensis 525.1406 34993.2456 -11506.6222 0.1224
## I(week^2)-Vulpes_vulpes 161.8016 32466.0177 -10685.6342 0.0034
## I(week^2)-Sus_scrofa -318.8728 35654.0461 -11360.6461 0.0174
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4695 1.0533 134
## (Intercept)-Lynx_rufus -4.8921 1.0911 106
## (Intercept)-Didelphis_virginiana -4.1942 1.0554 120
## (Intercept)-Sylvilagus_floridanus -4.6022 1.1858 184
## (Intercept)-Sciurus_carolinensis -4.2118 1.0693 62
## (Intercept)-Vulpes_vulpes -4.7843 1.1931 56
## (Intercept)-Sus_scrofa -4.5788 1.0089 187
## shrub_cover-Canis_latrans 0.1816 1.0911 138
## shrub_cover-Lynx_rufus 0.8573 1.0284 114
## shrub_cover-Didelphis_virginiana 2.0200 1.1898 123
## shrub_cover-Sylvilagus_floridanus 1.6222 1.2977 116
## shrub_cover-Sciurus_carolinensis 2.1059 1.0144 92
## shrub_cover-Vulpes_vulpes 1.6727 1.1522 126
## shrub_cover-Sus_scrofa 2.4212 1.0460 153
## veg_height-Canis_latrans -0.2387 1.0215 181
## veg_height-Lynx_rufus 0.5945 1.0713 132
## veg_height-Didelphis_virginiana 1.0023 1.1476 219
## veg_height-Sylvilagus_floridanus 0.5075 1.0115 184
## veg_height-Sciurus_carolinensis 0.7745 1.0094 115
## veg_height-Vulpes_vulpes 0.4773 1.2553 128
## veg_height-Sus_scrofa 0.3397 1.0396 204
## week-Canis_latrans 146952.0129 1.1319 2480
## week-Lynx_rufus 207362.2726 1.1556 3991
## week-Didelphis_virginiana 120922.9343 1.1915 10731
## week-Sylvilagus_floridanus 184740.8547 1.2056 3832
## week-Sciurus_carolinensis 200182.6018 1.2010 6790
## week-Vulpes_vulpes 157624.7242 1.2349 1025
## week-Sus_scrofa 110966.7286 1.1988 2209
## I(week^2)-Canis_latrans 7361.2278 1.1682 3572
## I(week^2)-Lynx_rufus 10185.2850 1.2181 2013
## I(week^2)-Didelphis_virginiana 9094.5375 1.1131 4430
## I(week^2)-Sylvilagus_floridanus 10396.7352 1.2511 652
## I(week^2)-Sciurus_carolinensis 10314.3887 1.1365 1125
## I(week^2)-Vulpes_vulpes 9402.9359 1.1704 9912
## I(week^2)-Sus_scrofa 9115.2040 1.1335 5012
#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): 4.1268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9508 0.5347 -1.9848 -0.9606 0.1326 1.0029 577
## Veg_shannon_index 0.2834 0.3938 -0.5260 0.2859 1.0350 1.0152 1428
## Avg_Cogongrass_Cover 0.3277 0.3391 -0.3837 0.3308 0.9646 1.0057 1006
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3118 1.5812 0.0752 0.7969 5.4427 1.0584 666
## Veg_shannon_index 0.7317 1.1320 0.0578 0.4013 3.4136 1.0538 901
## Avg_Cogongrass_Cover 0.4656 0.6892 0.0409 0.2566 2.1682 1.0227 1061
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2187 1.3671 0.0835 0.7955 4.7727 1.0238 168
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8994 0.3006 -5.3823 -4.9121 -4.3127 1.0175 847
## shrub_cover 0.4117 0.3524 -0.2819 0.4076 1.1134 1.0078 632
## veg_height 0.0197 0.2326 -0.4296 0.0177 0.5028 1.0003 680
## week -0.0413 1.6727 -3.3785 -0.0779 3.3086 1.0273 651
## I(week^2) 0.0217 1.6270 -3.1659 -0.0170 3.2315 1.0379 521
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.053000e-01 1.037000e+00 0.0454 0.2291 1.614400e+00 1.1255 371
## shrub_cover 6.783000e-01 8.282000e-01 0.0964 0.4561 2.571900e+00 1.1935 152
## veg_height 3.075000e-01 3.078000e-01 0.0562 0.2226 1.119400e+00 1.0052 1263
## week 2.755579e+16 4.364553e+17 0.1119 587.7169 2.485824e+15 1.6413 256
## I(week^2) 7.690111e+09 2.125540e+11 0.0930 29.7473 2.206740e+08 1.4143 752
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0083 0.7498 -1.4912 -0.0141
## (Intercept)-Lynx_rufus -0.3802 0.8059 -1.8921 -0.4152
## (Intercept)-Didelphis_virginiana -1.2889 0.6283 -2.5741 -1.2770
## (Intercept)-Sylvilagus_floridanus -0.8132 0.6261 -2.1323 -0.7926
## (Intercept)-Sciurus_carolinensis -1.2992 0.6273 -2.5779 -1.2832
## (Intercept)-Vulpes_vulpes -1.5810 0.7745 -3.1845 -1.5536
## (Intercept)-Sus_scrofa -1.8226 0.8216 -3.5759 -1.7511
## Veg_shannon_index-Canis_latrans 0.7792 0.4772 -0.0507 0.7409
## Veg_shannon_index-Lynx_rufus -0.2141 0.6807 -1.7890 -0.1435
## Veg_shannon_index-Didelphis_virginiana 0.5396 0.4795 -0.3244 0.5029
## Veg_shannon_index-Sylvilagus_floridanus 0.3921 0.4474 -0.4766 0.3767
## Veg_shannon_index-Sciurus_carolinensis -0.1864 0.4953 -1.2476 -0.1384
## Veg_shannon_index-Vulpes_vulpes -0.0927 0.5450 -1.2544 -0.0568
## Veg_shannon_index-Sus_scrofa 0.8251 0.7114 -0.3205 0.7441
## Avg_Cogongrass_Cover-Canis_latrans 0.6802 0.4662 -0.0996 0.6299
## Avg_Cogongrass_Cover-Lynx_rufus 0.5921 0.4733 -0.2248 0.5501
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4692 0.4212 -0.3125 0.4565
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1592 0.4775 -1.2086 -0.1114
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4083 0.4024 -0.3957 0.3903
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3748 0.4962 -0.6109 0.3702
## Avg_Cogongrass_Cover-Sus_scrofa 0.0133 0.6321 -1.4456 0.0917
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4582 1.0215 226
## (Intercept)-Lynx_rufus 1.3317 1.0315 340
## (Intercept)-Didelphis_virginiana -0.0691 1.0082 756
## (Intercept)-Sylvilagus_floridanus 0.3613 1.0124 781
## (Intercept)-Sciurus_carolinensis -0.0921 1.0132 764
## (Intercept)-Vulpes_vulpes -0.1117 1.0224 393
## (Intercept)-Sus_scrofa -0.3523 1.0024 449
## Veg_shannon_index-Canis_latrans 1.8221 1.0156 1465
## Veg_shannon_index-Lynx_rufus 0.9212 1.0095 653
## Veg_shannon_index-Didelphis_virginiana 1.5730 1.0192 1529
## Veg_shannon_index-Sylvilagus_floridanus 1.3077 1.0198 1726
## Veg_shannon_index-Sciurus_carolinensis 0.6476 1.0054 1333
## Veg_shannon_index-Vulpes_vulpes 0.9134 1.0062 1139
## Veg_shannon_index-Sus_scrofa 2.4380 1.0277 783
## Avg_Cogongrass_Cover-Canis_latrans 1.7265 1.0133 1120
## Avg_Cogongrass_Cover-Lynx_rufus 1.6289 1.0188 1043
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3660 1.0009 1162
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6432 1.0077 783
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2271 1.0085 1994
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4022 1.0060 1029
## Avg_Cogongrass_Cover-Sus_scrofa 1.0696 1.0206 750
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7496 1.695000e-01 -5.0889
## (Intercept)-Lynx_rufus -5.3902 2.898000e-01 -5.9821
## (Intercept)-Didelphis_virginiana -4.6830 2.463000e-01 -5.1684
## (Intercept)-Sylvilagus_floridanus -4.9128 2.263000e-01 -5.3874
## (Intercept)-Sciurus_carolinensis -4.6884 2.834000e-01 -5.2761
## (Intercept)-Vulpes_vulpes -5.4547 5.200000e-01 -6.6379
## (Intercept)-Sus_scrofa -5.1269 4.835000e-01 -6.0003
## shrub_cover-Canis_latrans -0.2310 1.996000e-01 -0.6166
## shrub_cover-Lynx_rufus -0.0685 3.484000e-01 -0.7882
## shrub_cover-Didelphis_virginiana 1.0339 3.661000e-01 0.3719
## shrub_cover-Sylvilagus_floridanus 0.5124 3.960000e-01 -0.2337
## shrub_cover-Sciurus_carolinensis 0.9187 3.835000e-01 0.1759
## shrub_cover-Vulpes_vulpes 0.1432 5.675000e-01 -1.0276
## shrub_cover-Sus_scrofa 0.6998 7.859000e-01 -0.6713
## veg_height-Canis_latrans -0.5541 1.765000e-01 -0.9089
## veg_height-Lynx_rufus 0.1145 2.381000e-01 -0.3863
## veg_height-Didelphis_virginiana 0.5038 2.473000e-01 0.0331
## veg_height-Sylvilagus_floridanus 0.1778 2.351000e-01 -0.2799
## veg_height-Sciurus_carolinensis 0.1827 2.133000e-01 -0.2134
## veg_height-Vulpes_vulpes -0.1122 3.042000e-01 -0.8023
## veg_height-Sus_scrofa -0.2324 3.296000e-01 -0.9320
## week-Canis_latrans 926008.0017 1.614790e+08 -2006084.9934
## week-Lynx_rufus -4609254.2185 2.057582e+08 -2728082.7355
## week-Didelphis_virginiana 669808.5660 1.317309e+08 -4029968.1009
## week-Sylvilagus_floridanus -3146261.0880 1.137219e+08 -4991820.8013
## week-Sciurus_carolinensis -3594507.3223 1.542117e+08 -4769697.9187
## week-Vulpes_vulpes 2335338.2421 1.470937e+08 -3161422.5980
## week-Sus_scrofa -354994.6621 1.244450e+08 -2585023.5753
## I(week^2)-Canis_latrans -259.1698 9.582673e+04 -2507.7043
## I(week^2)-Lynx_rufus -711.0257 6.146873e+04 -2168.8961
## I(week^2)-Didelphis_virginiana 661.6303 1.041392e+05 -3168.9304
## I(week^2)-Sylvilagus_floridanus 869.1975 6.466450e+04 -1818.4371
## I(week^2)-Sciurus_carolinensis 1941.2512 6.809918e+04 -1951.9467
## I(week^2)-Vulpes_vulpes 1863.7327 7.288039e+04 -2137.7574
## I(week^2)-Sus_scrofa 1286.1058 7.299117e+04 -2428.0341
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7476 -4.4375 1.0254 196
## (Intercept)-Lynx_rufus -5.3807 -4.8636 1.2430 106
## (Intercept)-Didelphis_virginiana -4.6809 -4.2188 1.1326 231
## (Intercept)-Sylvilagus_floridanus -4.9049 -4.4831 1.0167 176
## (Intercept)-Sciurus_carolinensis -4.6748 -4.1783 1.0704 194
## (Intercept)-Vulpes_vulpes -5.3647 -4.6260 1.0861 87
## (Intercept)-Sus_scrofa -5.1039 -4.3138 1.1350 177
## shrub_cover-Canis_latrans -0.2323 0.1687 1.0491 211
## shrub_cover-Lynx_rufus -0.0746 0.6162 1.1378 109
## shrub_cover-Didelphis_virginiana 1.0085 1.8229 1.0841 164
## shrub_cover-Sylvilagus_floridanus 0.5083 1.2874 1.0472 135
## shrub_cover-Sciurus_carolinensis 0.9095 1.6822 1.0243 200
## shrub_cover-Vulpes_vulpes 0.1564 1.1590 1.0427 223
## shrub_cover-Sus_scrofa 0.6572 2.2083 1.1560 130
## veg_height-Canis_latrans -0.5505 -0.2282 1.0154 189
## veg_height-Lynx_rufus 0.1250 0.5646 1.1512 177
## veg_height-Didelphis_virginiana 0.4995 0.9898 1.0349 229
## veg_height-Sylvilagus_floridanus 0.1774 0.6494 1.1172 207
## veg_height-Sciurus_carolinensis 0.1707 0.6188 1.1486 235
## veg_height-Vulpes_vulpes -0.0937 0.4439 1.0342 161
## veg_height-Sus_scrofa -0.2120 0.3752 1.0184 360
## week-Canis_latrans -0.2796 3478429.8525 1.2936 19892
## week-Lynx_rufus -0.1644 4292791.3444 1.3397 1902
## week-Didelphis_virginiana -0.0395 2665993.8491 1.2930 20912
## week-Sylvilagus_floridanus -0.2496 1943540.6951 1.3642 1369
## week-Sciurus_carolinensis -0.1683 2488238.8623 1.3438 1854
## week-Vulpes_vulpes 0.0688 2380617.5013 1.3153 3209
## week-Sus_scrofa -0.1158 3738855.8617 1.2912 16306
## I(week^2)-Canis_latrans -0.0117 1700.9091 1.2904 109353
## I(week^2)-Lynx_rufus 0.0631 2635.2596 1.3002 7228
## I(week^2)-Didelphis_virginiana -0.0780 2159.7415 1.2937 27084
## I(week^2)-Sylvilagus_floridanus -0.1100 2622.5810 1.3004 8177
## I(week^2)-Sciurus_carolinensis 0.0790 2529.2985 1.3655 1199
## I(week^2)-Vulpes_vulpes 0.0994 2447.4789 1.3555 1681
## I(week^2)-Sus_scrofa -0.0194 2451.9217 1.3265 2934
#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): 4.1135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8873 0.5214 -1.9107 -0.8832 0.1279 1.0492 495
## Avg_Cogongrass_Cover 0.2687 0.3069 -0.3507 0.2721 0.8654 1.0231 1105
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1076 1.7690 0.0677 0.6305 5.2424 1.0347 974
## Avg_Cogongrass_Cover 0.3864 0.6437 0.0383 0.2126 1.7326 1.0135 1324
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3583 1.3264 0.0893 0.9427 4.7373 1.2403 187
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9017 0.2925 -5.3963 -4.9153 -4.3093 1.0130 839
## shrub_cover 0.3739 0.3566 -0.3380 0.3637 1.0946 1.0201 486
## veg_height 0.0132 0.2253 -0.4357 0.0126 0.4445 1.0302 850
## week -0.0597 1.6092 -3.1449 -0.0930 3.0499 1.0031 666
## I(week^2) 0.0716 1.6467 -2.9921 0.0652 3.4699 1.0386 483
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.096000e-01 6.811000e-01 0.0457 0.2329 1.839000e+00 1.0041 347
## shrub_cover 6.455000e-01 7.455000e-01 0.0940 0.4416 2.423200e+00 1.0040 330
## veg_height 3.088000e-01 3.509000e-01 0.0632 0.2190 1.049600e+00 1.0384 783
## week 7.940995e+18 8.880780e+19 0.1119 202.5796 2.342566e+19 1.9325 192
## I(week^2) 4.429479e+12 4.450348e+13 0.0921 1937.7071 2.330667e+13 1.1440 326
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0249 0.7250 -1.4280 0.0165
## (Intercept)-Lynx_rufus -0.4634 0.7581 -1.8975 -0.4872
## (Intercept)-Didelphis_virginiana -1.1549 0.6047 -2.3707 -1.1425
## (Intercept)-Sylvilagus_floridanus -0.7387 0.6105 -1.9806 -0.7235
## (Intercept)-Sciurus_carolinensis -1.2524 0.6465 -2.6393 -1.2177
## (Intercept)-Vulpes_vulpes -1.4632 0.7565 -3.0443 -1.4143
## (Intercept)-Sus_scrofa -1.5211 0.7588 -3.2057 -1.4515
## Avg_Cogongrass_Cover-Canis_latrans 0.4585 0.3920 -0.2418 0.4292
## Avg_Cogongrass_Cover-Lynx_rufus 0.5285 0.4303 -0.1909 0.4928
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3628 0.3822 -0.3624 0.3521
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1795 0.4508 -1.1570 -0.1366
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3933 0.3863 -0.3339 0.3766
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3576 0.4193 -0.4425 0.3451
## Avg_Cogongrass_Cover-Sus_scrofa -0.0055 0.5776 -1.3784 0.0478
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5378 1.1111 290
## (Intercept)-Lynx_rufus 1.1663 1.0792 347
## (Intercept)-Didelphis_virginiana 0.0064 1.0031 866
## (Intercept)-Sylvilagus_floridanus 0.4124 1.0178 751
## (Intercept)-Sciurus_carolinensis -0.0536 1.0146 608
## (Intercept)-Vulpes_vulpes -0.0664 1.0075 497
## (Intercept)-Sus_scrofa -0.1939 1.0210 574
## Avg_Cogongrass_Cover-Canis_latrans 1.3292 1.0041 1596
## Avg_Cogongrass_Cover-Lynx_rufus 1.4815 1.0246 1274
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1687 1.0111 1412
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5958 1.0036 1024
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1931 1.0211 1680
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2486 1.0020 1322
## Avg_Cogongrass_Cover-Sus_scrofa 0.9482 1.0222 828
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.783500e+00 1.672000e-01 -5.110900e+00
## (Intercept)-Lynx_rufus -5.402200e+00 2.885000e-01 -6.014800e+00
## (Intercept)-Didelphis_virginiana -4.664500e+00 2.779000e-01 -5.211500e+00
## (Intercept)-Sylvilagus_floridanus -4.900700e+00 2.390000e-01 -5.385000e+00
## (Intercept)-Sciurus_carolinensis -4.676900e+00 2.757000e-01 -5.208700e+00
## (Intercept)-Vulpes_vulpes -5.483800e+00 5.496000e-01 -6.856500e+00
## (Intercept)-Sus_scrofa -5.144400e+00 4.156000e-01 -6.034800e+00
## shrub_cover-Canis_latrans -2.579000e-01 2.047000e-01 -6.553000e-01
## shrub_cover-Lynx_rufus -1.176000e-01 3.392000e-01 -8.055000e-01
## shrub_cover-Didelphis_virginiana 1.007900e+00 3.439000e-01 4.267000e-01
## shrub_cover-Sylvilagus_floridanus 5.028000e-01 3.601000e-01 -2.366000e-01
## shrub_cover-Sciurus_carolinensis 8.763000e-01 3.651000e-01 1.766000e-01
## shrub_cover-Vulpes_vulpes 1.201000e-01 6.318000e-01 -1.176400e+00
## shrub_cover-Sus_scrofa 6.665000e-01 6.906000e-01 -6.249000e-01
## veg_height-Canis_latrans -5.676000e-01 1.680000e-01 -8.974000e-01
## veg_height-Lynx_rufus 1.062000e-01 2.408000e-01 -3.575000e-01
## veg_height-Didelphis_virginiana 5.190000e-01 2.376000e-01 8.210000e-02
## veg_height-Sylvilagus_floridanus 1.789000e-01 2.164000e-01 -2.354000e-01
## veg_height-Sciurus_carolinensis 2.014000e-01 2.103000e-01 -1.811000e-01
## veg_height-Vulpes_vulpes -8.350000e-02 2.703000e-01 -6.107000e-01
## veg_height-Sus_scrofa -2.396000e-01 3.295000e-01 -9.005000e-01
## week-Canis_latrans 1.396801e+07 2.298099e+09 -5.800400e+08
## week-Lynx_rufus -4.988535e+05 2.739919e+09 -2.916257e+08
## week-Didelphis_virginiana -4.603238e+07 2.557111e+09 -4.188142e+08
## week-Sylvilagus_floridanus 2.454293e+07 2.417069e+09 -4.528012e+08
## week-Sciurus_carolinensis 7.034062e+07 2.941700e+09 -1.346737e+08
## week-Vulpes_vulpes -6.480828e+07 2.115231e+09 -6.192841e+08
## week-Sus_scrofa -1.222630e+07 2.335920e+09 -2.970330e+08
## I(week^2)-Canis_latrans -1.559177e+04 1.831883e+06 -1.294104e+06
## I(week^2)-Lynx_rufus -8.936862e+03 1.696422e+06 -1.663741e+06
## I(week^2)-Didelphis_virginiana 4.741359e+04 1.896878e+06 -1.307770e+06
## I(week^2)-Sylvilagus_floridanus -6.543766e+03 1.692938e+06 -1.422004e+06
## I(week^2)-Sciurus_carolinensis -1.071111e+04 2.018034e+06 -1.608516e+06
## I(week^2)-Vulpes_vulpes -1.557535e+04 1.675552e+06 -1.726874e+06
## I(week^2)-Sus_scrofa 2.896729e+04 1.700596e+06 -1.451297e+06
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7770 -4.471600e+00 1.0586 218
## (Intercept)-Lynx_rufus -5.3857 -4.883200e+00 1.0266 77
## (Intercept)-Didelphis_virginiana -4.6641 -4.142700e+00 1.0182 134
## (Intercept)-Sylvilagus_floridanus -4.8955 -4.440800e+00 1.0310 183
## (Intercept)-Sciurus_carolinensis -4.6730 -4.132500e+00 1.0449 241
## (Intercept)-Vulpes_vulpes -5.4101 -4.632300e+00 1.0037 73
## (Intercept)-Sus_scrofa -5.1408 -4.330900e+00 1.0775 254
## shrub_cover-Canis_latrans -0.2578 1.426000e-01 1.0131 191
## shrub_cover-Lynx_rufus -0.1317 5.509000e-01 1.0677 117
## shrub_cover-Didelphis_virginiana 0.9834 1.730100e+00 1.0290 124
## shrub_cover-Sylvilagus_floridanus 0.5051 1.164600e+00 1.0403 173
## shrub_cover-Sciurus_carolinensis 0.8764 1.592900e+00 1.0303 219
## shrub_cover-Vulpes_vulpes 0.1253 1.353100e+00 1.0364 147
## shrub_cover-Sus_scrofa 0.6457 2.133100e+00 1.0408 241
## veg_height-Canis_latrans -0.5703 -2.356000e-01 1.0202 201
## veg_height-Lynx_rufus 0.0979 6.082000e-01 1.0294 166
## veg_height-Didelphis_virginiana 0.5133 9.954000e-01 1.0222 300
## veg_height-Sylvilagus_floridanus 0.1719 6.169000e-01 1.0335 240
## veg_height-Sciurus_carolinensis 0.1927 6.427000e-01 1.0260 235
## veg_height-Vulpes_vulpes -0.0771 4.426000e-01 1.0283 193
## veg_height-Sus_scrofa -0.2250 3.872000e-01 1.1099 358
## week-Canis_latrans -0.4008 1.695251e+08 1.2940 32375
## week-Lynx_rufus -0.1954 4.273080e+08 1.2904 3638
## week-Didelphis_virginiana -0.1810 1.527128e+08 1.3223 3428
## week-Sylvilagus_floridanus -0.1546 2.397979e+08 1.3006 1670
## week-Sciurus_carolinensis -0.1207 3.470584e+08 1.3462 3597
## week-Vulpes_vulpes -0.3773 8.089083e+07 1.3809 1103
## week-Sus_scrofa -0.1770 2.154725e+08 1.2931 9296
## I(week^2)-Canis_latrans 0.1101 1.591695e+06 1.1228 2822
## I(week^2)-Lynx_rufus 0.1356 1.505263e+06 1.1080 4159
## I(week^2)-Didelphis_virginiana 0.0538 2.002971e+06 1.1077 3336
## I(week^2)-Sylvilagus_floridanus 0.1012 1.275226e+06 1.1084 5291
## I(week^2)-Sciurus_carolinensis -0.0675 1.509861e+06 1.1312 19620
## I(week^2)-Vulpes_vulpes 0.2277 1.392222e+06 1.1118 3143
## I(week^2)-Sus_scrofa 0.1744 1.790720e+06 1.1088 1642
# 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): 4.1482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6422 0.5860 -2.8013 -1.6441 -0.4702 1.0476 559
## Avg_Cogongrass_Cover -0.7504 0.5351 -1.8176 -0.7412 0.2877 1.0365 405
## I(Avg_Cogongrass_Cover^2) 0.9196 0.4441 0.0836 0.8996 1.8526 1.0431 425
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3659 2.0812 0.0723 0.7465 6.2955 1.0059 574
## Avg_Cogongrass_Cover 0.7730 1.5823 0.0457 0.3481 3.9888 1.0136 603
## I(Avg_Cogongrass_Cover^2) 0.9710 8.8105 0.0414 0.2744 4.0198 1.5176 533
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.018 1.3314 0.0641 0.6287 4.3905 1.1328 98
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9257 0.2753 -5.4312 -4.9426 -4.2977 1.0087 547
## shrub_cover 0.3983 0.3603 -0.2828 0.3977 1.1268 1.0157 463
## veg_height 0.0173 0.2328 -0.4382 0.0182 0.4739 1.0058 646
## week 0.0594 1.6648 -3.1914 0.0315 3.3245 1.0207 517
## I(week^2) 0.0155 1.6190 -3.1086 0.0081 3.3124 1.0232 685
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.682000e-01 4.811000e-01 0.0471 0.2328 1.522000e+00 1.0516 376
## shrub_cover 7.272000e-01 1.129100e+00 0.1018 0.4922 2.621000e+00 1.0915 904
## veg_height 3.047000e-01 3.065000e-01 0.0584 0.2238 1.081700e+00 1.0376 1114
## week 6.612582e+08 7.021969e+09 0.1125 65.7724 2.473136e+09 1.3779 204
## I(week^2) 4.173797e+11 4.283160e+12 0.0953 1087.8980 3.689423e+11 2.0299 98
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7664 0.7971 -2.3245 -0.7626
## (Intercept)-Lynx_rufus -1.2061 0.8282 -2.7278 -1.2670
## (Intercept)-Didelphis_virginiana -1.8354 0.6814 -3.2717 -1.7927
## (Intercept)-Sylvilagus_floridanus -1.4747 0.7139 -2.8506 -1.4624
## (Intercept)-Sciurus_carolinensis -2.2468 0.7609 -3.8531 -2.1870
## (Intercept)-Vulpes_vulpes -2.4374 0.9095 -4.4901 -2.3557
## (Intercept)-Sus_scrofa -2.3105 0.8728 -4.1755 -2.2503
## Avg_Cogongrass_Cover-Canis_latrans -0.3406 0.6466 -1.4959 -0.3739
## Avg_Cogongrass_Cover-Lynx_rufus -0.6557 0.7495 -2.1156 -0.6477
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4646 0.6471 -1.6714 -0.4969
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3452 0.7623 -3.0866 -1.2650
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8586 0.6526 -2.2026 -0.8399
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8211 0.7176 -2.3073 -0.7983
## Avg_Cogongrass_Cover-Sus_scrofa -1.0694 0.8202 -2.8824 -0.9801
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.4552 0.8829 0.2466 1.2729
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.3450 1.1592 0.3243 1.1825
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6282 0.4835 -0.2725 0.6226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8515 0.5007 -0.0313 0.8076
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0340 0.4571 0.2086 1.0047
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9254 0.5079 0.0453 0.8868
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4745 0.7674 -1.3049 0.5478
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7705 1.0736 351
## (Intercept)-Lynx_rufus 0.5809 1.0584 292
## (Intercept)-Didelphis_virginiana -0.5665 1.0099 794
## (Intercept)-Sylvilagus_floridanus -0.0744 1.0228 610
## (Intercept)-Sciurus_carolinensis -0.9467 1.0107 625
## (Intercept)-Vulpes_vulpes -0.8644 1.0087 408
## (Intercept)-Sus_scrofa -0.8103 1.0090 476
## Avg_Cogongrass_Cover-Canis_latrans 1.1165 1.0364 714
## Avg_Cogongrass_Cover-Lynx_rufus 0.8642 1.0144 595
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9067 1.0441 493
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0863 1.0082 473
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3253 1.0264 530
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5759 1.0130 528
## Avg_Cogongrass_Cover-Sus_scrofa 0.3424 1.0068 575
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7231 1.0847 300
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0461 1.5132 102
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6568 1.0648 469
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9912 1.0094 560
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0493 1.0154 566
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0796 1.0184 494
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.7123 1.1120 256
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7958 0.1671 -5.0984 -4.7989
## (Intercept)-Lynx_rufus -5.4522 0.3204 -6.1096 -5.4425
## (Intercept)-Didelphis_virginiana -4.6948 0.2623 -5.2233 -4.6926
## (Intercept)-Sylvilagus_floridanus -4.9513 0.2290 -5.4627 -4.9315
## (Intercept)-Sciurus_carolinensis -4.6662 0.2813 -5.2477 -4.6523
## (Intercept)-Vulpes_vulpes -5.4285 0.4651 -6.4202 -5.3950
## (Intercept)-Sus_scrofa -5.1561 0.4122 -5.9806 -5.1527
## shrub_cover-Canis_latrans -0.2411 0.2065 -0.6453 -0.2409
## shrub_cover-Lynx_rufus -0.1968 0.3628 -1.0083 -0.1681
## shrub_cover-Didelphis_virginiana 1.0554 0.3857 0.3332 1.0342
## shrub_cover-Sylvilagus_floridanus 0.4945 0.3869 -0.2595 0.4985
## shrub_cover-Sciurus_carolinensis 0.8460 0.3641 0.1555 0.8405
## shrub_cover-Vulpes_vulpes 0.0830 0.5920 -1.1825 0.1305
## shrub_cover-Sus_scrofa 0.7842 0.7113 -0.6369 0.7651
## veg_height-Canis_latrans -0.5719 0.1738 -0.9282 -0.5605
## veg_height-Lynx_rufus 0.1941 0.2275 -0.2537 0.1940
## veg_height-Didelphis_virginiana 0.4914 0.2544 0.0409 0.4853
## veg_height-Sylvilagus_floridanus 0.1717 0.2448 -0.2937 0.1708
## veg_height-Sciurus_carolinensis 0.2063 0.1980 -0.1758 0.2020
## veg_height-Vulpes_vulpes -0.0994 0.2953 -0.7232 -0.0870
## veg_height-Sus_scrofa -0.2400 0.3233 -0.9211 -0.2272
## week-Canis_latrans 434.0744 26045.9492 -7411.7291 0.1030
## week-Lynx_rufus 546.9299 21982.4082 -9848.2855 0.2452
## week-Didelphis_virginiana -181.9570 25712.9964 -11276.0880 0.0079
## week-Sylvilagus_floridanus 227.2166 27540.5862 -11925.2588 -0.0307
## week-Sciurus_carolinensis 576.9374 26787.4648 -8313.0371 0.0997
## week-Vulpes_vulpes -22.9451 17231.5740 -9299.5607 0.2096
## week-Sus_scrofa -74.1504 21528.5726 -9800.7620 0.0857
## I(week^2)-Canis_latrans 22787.8638 696937.2853 -71082.1708 0.0152
## I(week^2)-Lynx_rufus 9056.7615 684027.0588 -64927.3981 0.0792
## I(week^2)-Didelphis_virginiana 6924.2143 547578.5638 -94497.6360 0.2223
## I(week^2)-Sylvilagus_floridanus 5174.8712 593876.4912 -93956.1109 0.0330
## I(week^2)-Sciurus_carolinensis -9060.2303 663191.8952 -72458.4818 0.0917
## I(week^2)-Vulpes_vulpes -5399.2725 595241.5895 -122517.1607 -0.0845
## I(week^2)-Sus_scrofa -5509.5283 549953.6270 -104109.3765 0.0276
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4712 1.0166 184
## (Intercept)-Lynx_rufus -4.8683 1.0715 79
## (Intercept)-Didelphis_virginiana -4.2124 1.0349 172
## (Intercept)-Sylvilagus_floridanus -4.5343 1.0700 163
## (Intercept)-Sciurus_carolinensis -4.1350 1.0352 240
## (Intercept)-Vulpes_vulpes -4.6182 1.0468 116
## (Intercept)-Sus_scrofa -4.3891 1.0189 369
## shrub_cover-Canis_latrans 0.1576 1.0677 177
## shrub_cover-Lynx_rufus 0.4873 1.0008 90
## shrub_cover-Didelphis_virginiana 1.8401 1.0944 152
## shrub_cover-Sylvilagus_floridanus 1.2404 1.0097 149
## shrub_cover-Sciurus_carolinensis 1.5592 1.0173 265
## shrub_cover-Vulpes_vulpes 1.1598 1.0503 193
## shrub_cover-Sus_scrofa 2.1660 1.0311 282
## veg_height-Canis_latrans -0.2548 1.0935 169
## veg_height-Lynx_rufus 0.6290 1.0075 165
## veg_height-Didelphis_virginiana 1.0270 1.0962 208
## veg_height-Sylvilagus_floridanus 0.6662 1.0446 185
## veg_height-Sciurus_carolinensis 0.6040 1.0284 282
## veg_height-Vulpes_vulpes 0.4358 1.0132 166
## veg_height-Sus_scrofa 0.3516 1.0146 301
## week-Canis_latrans 12757.2587 1.2577 8174
## week-Lynx_rufus 11299.8876 1.2875 581
## week-Didelphis_virginiana 9212.6374 1.1709 5952
## week-Sylvilagus_floridanus 8878.1094 1.1654 4428
## week-Sciurus_carolinensis 11350.0472 1.2802 3889
## week-Vulpes_vulpes 10626.8273 1.1555 19722
## week-Sus_scrofa 8855.7208 1.2034 4883
## I(week^2)-Canis_latrans 77748.8582 1.3934 1067
## I(week^2)-Lynx_rufus 104453.2004 1.3098 14535
## I(week^2)-Didelphis_virginiana 72447.8602 1.3054 15136
## I(week^2)-Sylvilagus_floridanus 84854.4685 1.2991 11806
## I(week^2)-Sciurus_carolinensis 73124.9371 1.3125 3618
## I(week^2)-Vulpes_vulpes 58864.5185 1.2966 33344
## I(week^2)-Sus_scrofa 80144.8494 1.3010 6929
# 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): 4.8277
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7611 1.3715 -4.1602 -1.8755 1.2430 1.0240 258
## Cogon_Patch_Size 0.0632 1.1657 -2.3790 0.0831 2.3392 1.0442 273
## Veg_shannon_index 0.8464 0.9872 -1.2228 0.8587 2.8158 1.0507 248
## total_shrub_cover -0.6918 1.0668 -3.0130 -0.5810 1.1435 1.0156 99
## Avg_Cogongrass_Cover -0.3677 1.2830 -2.8903 -0.3706 2.1479 1.0526 181
## Tree_Density -2.0728 1.1242 -4.3309 -2.0688 0.1262 1.0548 166
## Avg_Canopy_Cover 1.9448 1.3184 -0.9501 2.0418 4.3395 1.0503 328
## I(Avg_Cogongrass_Cover^2) 1.9060 0.9117 0.1020 1.8655 3.8443 1.0129 166
## avg_veg_height -0.3937 0.8219 -2.0234 -0.3950 1.2655 1.1332 164
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.3723 43.5782 0.2528 14.1368 151.2573 1.1327 94
## Cogon_Patch_Size 13.4467 22.1899 0.1930 6.2588 70.0411 1.1999 158
## Veg_shannon_index 13.7656 68.7601 0.0875 2.5068 87.5600 2.1094 62
## total_shrub_cover 11.1610 42.4720 0.0702 1.5160 93.8073 2.3594 48
## Avg_Cogongrass_Cover 3.6641 10.8067 0.0600 0.9542 23.8504 1.2149 139
## Tree_Density 8.1127 28.1069 0.0567 0.9318 65.9402 1.0702 135
## Avg_Canopy_Cover 64.7163 241.9896 0.6949 10.9576 574.8394 2.4832 19
## I(Avg_Cogongrass_Cover^2) 3.3502 17.9480 0.0571 0.6366 23.5847 1.3951 358
## avg_veg_height 1.7343 5.2203 0.0475 0.4837 10.6973 1.2476 315
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.8225 8.3341 0.0523 1.0359 31.49 1.0044 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0468 0.3893 -5.7023 -5.0801 -4.1345 1.0459 355
## shrub_cover 0.5703 0.3910 -0.1874 0.5562 1.3379 1.0215 214
## veg_height 0.0309 0.2430 -0.4852 0.0362 0.4790 1.0341 443
## week -0.0549 1.6705 -3.4176 -0.0347 3.0976 1.0016 505
## I(week^2) 0.1399 1.7256 -3.0851 0.1051 3.6967 1.0106 350
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.924000e-01 1.389800e+00 0.0650 0.3546 3.414000e+00 1.0851 131
## shrub_cover 8.217000e-01 9.117000e-01 0.1505 0.5681 3.012800e+00 1.0848 133
## veg_height 3.687000e-01 5.196000e-01 0.0715 0.2585 1.300700e+00 1.0513 858
## week 7.736803e+08 9.968227e+09 0.1006 53.7844 1.840345e+09 1.2115 469
## I(week^2) 9.336132e+18 1.214704e+20 0.0857 77.4360 1.103260e+19 1.7866 140
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4487 1.7969 -3.4240 -0.6204
## (Intercept)-Lynx_rufus 2.0790 3.7990 -3.1139 1.3985
## (Intercept)-Didelphis_virginiana -4.7310 2.2315 -9.7595 -4.5481
## (Intercept)-Sylvilagus_floridanus -3.1671 2.2133 -8.2022 -2.9835
## (Intercept)-Sciurus_carolinensis -5.1221 2.5960 -10.9373 -4.9573
## (Intercept)-Vulpes_vulpes -5.3746 3.0478 -11.5183 -5.2606
## (Intercept)-Sus_scrofa -6.7148 3.7832 -16.0893 -6.1553
## Cogon_Patch_Size-Canis_latrans 2.9757 2.5201 -0.2815 2.3865
## Cogon_Patch_Size-Lynx_rufus 0.0114 2.7762 -6.0833 0.0023
## Cogon_Patch_Size-Didelphis_virginiana 2.6935 1.9507 0.0592 2.3675
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2536 2.6663 -8.8819 -1.7280
## Cogon_Patch_Size-Sciurus_carolinensis -1.3373 2.5581 -8.1577 -0.9219
## Cogon_Patch_Size-Vulpes_vulpes -1.2364 2.7788 -7.9673 -0.7835
## Cogon_Patch_Size-Sus_scrofa -1.1526 2.5470 -7.5862 -0.7422
## Veg_shannon_index-Canis_latrans 2.0477 1.4450 -0.0573 1.8097
## Veg_shannon_index-Lynx_rufus -0.1928 2.2197 -5.3203 0.1216
## Veg_shannon_index-Didelphis_virginiana 1.8806 1.7375 -0.5471 1.5307
## Veg_shannon_index-Sylvilagus_floridanus 1.6834 1.6330 -0.5380 1.3981
## Veg_shannon_index-Sciurus_carolinensis -0.6691 2.2061 -4.9835 -0.3187
## Veg_shannon_index-Vulpes_vulpes -0.7623 4.1086 -12.9414 0.0796
## Veg_shannon_index-Sus_scrofa 3.3934 4.0383 0.0618 2.4616
## total_shrub_cover-Canis_latrans 0.8285 1.5953 -1.7098 0.5631
## total_shrub_cover-Lynx_rufus -2.1105 3.8962 -11.9559 -1.2752
## total_shrub_cover-Didelphis_virginiana -2.0775 3.0864 -12.0359 -1.2786
## total_shrub_cover-Sylvilagus_floridanus -1.5096 2.5089 -8.9836 -0.9071
## total_shrub_cover-Sciurus_carolinensis -1.1973 2.6926 -9.7836 -0.6078
## total_shrub_cover-Vulpes_vulpes -1.4805 2.5295 -7.8030 -1.0132
## total_shrub_cover-Sus_scrofa -1.0185 3.4184 -12.4492 -0.3248
## Avg_Cogongrass_Cover-Canis_latrans 0.0001 1.8920 -3.2932 -0.1623
## Avg_Cogongrass_Cover-Lynx_rufus 0.0836 1.9449 -3.3112 -0.0632
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4290 1.7934 -3.8726 -0.4870
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2616 1.9957 -5.5071 -1.0787
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2907 1.9009 -3.9376 -0.3411
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0354 2.0563 -3.5294 -0.1678
## Avg_Cogongrass_Cover-Sus_scrofa -0.9028 2.2179 -5.6671 -0.7763
## Tree_Density-Canis_latrans -3.4022 2.2488 -9.3793 -2.8430
## Tree_Density-Lynx_rufus -1.3587 2.5009 -4.9207 -1.6685
## Tree_Density-Didelphis_virginiana -2.5136 1.8380 -6.6297 -2.3547
## Tree_Density-Sylvilagus_floridanus -3.1074 2.5444 -9.8316 -2.6084
## Tree_Density-Sciurus_carolinensis -2.7554 2.2436 -8.4347 -2.4930
## Tree_Density-Vulpes_vulpes -2.2120 2.2203 -7.4499 -2.1415
## Tree_Density-Sus_scrofa -2.7669 2.7674 -9.8046 -2.3397
## Avg_Canopy_Cover-Canis_latrans -0.3008 1.0018 -2.4336 -0.2370
## Avg_Canopy_Cover-Lynx_rufus 1.7704 4.0049 -5.6874 1.5104
## Avg_Canopy_Cover-Didelphis_virginiana 6.2572 4.2740 1.7785 5.1016
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.5489 5.3481 1.9797 6.0043
## Avg_Canopy_Cover-Sciurus_carolinensis 6.9860 8.0399 1.3749 4.4403
## Avg_Canopy_Cover-Vulpes_vulpes 5.6448 6.9560 0.7627 3.5393
## Avg_Canopy_Cover-Sus_scrofa 3.9905 5.3341 0.3387 2.7229
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6216 1.4322 0.8073 2.3528
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8624 1.6682 0.7129 2.5044
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6594 1.0775 -0.3675 1.5820
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7229 1.2431 -0.6163 1.6530
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.3472 1.3159 0.2758 2.1357
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4011 1.4504 0.3423 2.1799
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2189 2.0329 -4.5265 1.5233
## avg_veg_height-Canis_latrans -0.3236 0.8968 -2.1085 -0.3290
## avg_veg_height-Lynx_rufus -0.7999 1.5831 -4.5919 -0.6487
## avg_veg_height-Didelphis_virginiana -0.7500 1.2122 -3.5153 -0.6619
## avg_veg_height-Sylvilagus_floridanus -0.3887 1.1248 -2.6256 -0.3824
## avg_veg_height-Sciurus_carolinensis 0.1815 1.2660 -1.8619 0.0872
## avg_veg_height-Vulpes_vulpes -0.5020 1.3559 -3.1753 -0.4992
## avg_veg_height-Sus_scrofa -0.3575 1.1756 -2.7569 -0.3582
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8545 1.0058 222
## (Intercept)-Lynx_rufus 10.9110 1.1160 62
## (Intercept)-Didelphis_virginiana -0.9648 1.1064 79
## (Intercept)-Sylvilagus_floridanus 0.8216 1.0679 112
## (Intercept)-Sciurus_carolinensis -0.5952 1.0900 108
## (Intercept)-Vulpes_vulpes 0.3265 1.0363 69
## (Intercept)-Sus_scrofa -0.9992 1.3267 47
## Cogon_Patch_Size-Canis_latrans 9.5024 1.0594 190
## Cogon_Patch_Size-Lynx_rufus 6.0692 1.1252 182
## Cogon_Patch_Size-Didelphis_virginiana 7.0488 1.1269 104
## Cogon_Patch_Size-Sylvilagus_floridanus 1.7758 1.0178 138
## Cogon_Patch_Size-Sciurus_carolinensis 2.5312 1.0480 231
## Cogon_Patch_Size-Vulpes_vulpes 3.4892 1.0377 212
## Cogon_Patch_Size-Sus_scrofa 2.5627 1.0219 195
## Veg_shannon_index-Canis_latrans 5.6088 1.1136 119
## Veg_shannon_index-Lynx_rufus 3.5183 1.0763 137
## Veg_shannon_index-Didelphis_virginiana 6.4787 1.1682 114
## Veg_shannon_index-Sylvilagus_floridanus 5.8887 1.2024 72
## Veg_shannon_index-Sciurus_carolinensis 2.3342 1.1511 142
## Veg_shannon_index-Vulpes_vulpes 2.7483 2.2759 24
## Veg_shannon_index-Sus_scrofa 12.3154 1.4876 40
## total_shrub_cover-Canis_latrans 4.8644 1.0585 75
## total_shrub_cover-Lynx_rufus 2.2886 1.3648 32
## total_shrub_cover-Didelphis_virginiana 0.9153 1.7799 18
## total_shrub_cover-Sylvilagus_floridanus 1.2675 1.5175 35
## total_shrub_cover-Sciurus_carolinensis 1.7712 1.7288 37
## total_shrub_cover-Vulpes_vulpes 2.0233 1.0703 120
## total_shrub_cover-Sus_scrofa 2.7174 2.1493 14
## Avg_Cogongrass_Cover-Canis_latrans 4.4469 1.0582 176
## Avg_Cogongrass_Cover-Lynx_rufus 4.6378 1.0393 260
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0541 1.0516 244
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0794 1.0147 198
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.5993 1.0544 224
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7980 1.0941 130
## Avg_Cogongrass_Cover-Sus_scrofa 2.7421 1.0448 159
## Tree_Density-Canis_latrans -0.5753 1.0981 91
## Tree_Density-Lynx_rufus 5.2898 1.1317 171
## Tree_Density-Didelphis_virginiana 0.6098 1.0934 187
## Tree_Density-Sylvilagus_floridanus 0.1933 1.1608 97
## Tree_Density-Sciurus_carolinensis 1.0999 1.3632 112
## Tree_Density-Vulpes_vulpes 2.6383 1.0545 121
## Tree_Density-Sus_scrofa 0.8312 1.1406 95
## Avg_Canopy_Cover-Canis_latrans 1.5876 1.0154 330
## Avg_Canopy_Cover-Lynx_rufus 11.0655 1.2294 56
## Avg_Canopy_Cover-Didelphis_virginiana 19.3484 1.3293 56
## Avg_Canopy_Cover-Sylvilagus_floridanus 21.9914 1.2160 47
## Avg_Canopy_Cover-Sciurus_carolinensis 35.9070 2.7448 9
## Avg_Canopy_Cover-Vulpes_vulpes 29.0082 1.8664 20
## Avg_Canopy_Cover-Sus_scrofa 24.8972 2.5241 23
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.6127 1.0627 77
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.4576 1.1428 114
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 4.1383 1.0399 142
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.5190 1.0505 166
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 5.7005 1.0398 153
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9939 1.0207 163
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 4.1595 1.0721 88
## avg_veg_height-Canis_latrans 1.4669 1.1102 354
## avg_veg_height-Lynx_rufus 1.7683 1.0179 195
## avg_veg_height-Didelphis_virginiana 1.3974 1.0802 245
## avg_veg_height-Sylvilagus_floridanus 2.0001 1.0711 275
## avg_veg_height-Sciurus_carolinensis 3.2462 1.0908 295
## avg_veg_height-Vulpes_vulpes 2.3814 1.1325 160
## avg_veg_height-Sus_scrofa 2.0108 1.0810 288
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.786700e+00 1.940000e-01 -5.232100e+00
## (Intercept)-Lynx_rufus -5.740900e+00 3.070000e-01 -6.339600e+00
## (Intercept)-Didelphis_virginiana -4.822300e+00 2.909000e-01 -5.439900e+00
## (Intercept)-Sylvilagus_floridanus -5.010100e+00 2.160000e-01 -5.470000e+00
## (Intercept)-Sciurus_carolinensis -4.852900e+00 2.952000e-01 -5.395300e+00
## (Intercept)-Vulpes_vulpes -5.931700e+00 6.761000e-01 -7.816200e+00
## (Intercept)-Sus_scrofa -5.364600e+00 5.144000e-01 -6.592100e+00
## shrub_cover-Canis_latrans -2.844000e-01 2.235000e-01 -7.121000e-01
## shrub_cover-Lynx_rufus -4.420000e-02 3.569000e-01 -6.950000e-01
## shrub_cover-Didelphis_virginiana 1.240600e+00 4.068000e-01 5.264000e-01
## shrub_cover-Sylvilagus_floridanus 7.430000e-01 3.976000e-01 -4.190000e-02
## shrub_cover-Sciurus_carolinensis 1.122200e+00 3.768000e-01 3.602000e-01
## shrub_cover-Vulpes_vulpes 3.860000e-01 5.775000e-01 -7.616000e-01
## shrub_cover-Sus_scrofa 1.021500e+00 8.355000e-01 -5.962000e-01
## veg_height-Canis_latrans -5.592000e-01 1.719000e-01 -8.932000e-01
## veg_height-Lynx_rufus 2.383000e-01 2.154000e-01 -1.735000e-01
## veg_height-Didelphis_virginiana 5.729000e-01 2.357000e-01 1.252000e-01
## veg_height-Sylvilagus_floridanus 1.736000e-01 2.314000e-01 -2.759000e-01
## veg_height-Sciurus_carolinensis 2.746000e-01 2.260000e-01 -1.657000e-01
## veg_height-Vulpes_vulpes -2.062000e-01 3.696000e-01 -1.027000e+00
## veg_height-Sus_scrofa -2.562000e-01 3.564000e-01 -1.063700e+00
## week-Canis_latrans 4.049994e+02 2.016399e+04 -4.812217e+03
## week-Lynx_rufus -3.737473e+02 2.948101e+04 -5.951515e+03
## week-Didelphis_virginiana -6.306688e+02 2.297188e+04 -6.698956e+03
## week-Sylvilagus_floridanus 7.568246e+02 2.666691e+04 -3.915197e+03
## week-Sciurus_carolinensis -1.389517e+02 2.796398e+04 -4.352418e+03
## week-Vulpes_vulpes 8.965176e+02 2.361436e+04 -5.147815e+03
## week-Sus_scrofa 4.620409e+02 2.091741e+04 -6.637502e+03
## I(week^2)-Canis_latrans 7.231978e+06 2.436501e+09 -2.674919e+08
## I(week^2)-Lynx_rufus 6.904567e+07 2.791704e+09 -1.260500e+08
## I(week^2)-Didelphis_virginiana -6.736932e+07 3.030640e+09 -1.385641e+08
## I(week^2)-Sylvilagus_floridanus 5.423948e+07 2.430779e+09 -8.106263e+07
## I(week^2)-Sciurus_carolinensis -4.656114e+07 4.233077e+09 -1.362741e+08
## I(week^2)-Vulpes_vulpes -1.395325e+08 3.846359e+09 -2.221477e+08
## I(week^2)-Sus_scrofa 5.363212e+07 2.839453e+09 -9.447737e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7734 -4.436000e+00 1.0216 127
## (Intercept)-Lynx_rufus -5.7411 -5.153600e+00 1.1550 74
## (Intercept)-Didelphis_virginiana -4.8186 -4.284100e+00 1.1586 126
## (Intercept)-Sylvilagus_floridanus -5.0083 -4.601400e+00 1.0957 192
## (Intercept)-Sciurus_carolinensis -4.8602 -4.281000e+00 1.1344 73
## (Intercept)-Vulpes_vulpes -5.8126 -4.908100e+00 1.1312 31
## (Intercept)-Sus_scrofa -5.3231 -4.444900e+00 1.2172 88
## shrub_cover-Canis_latrans -0.2958 1.787000e-01 1.0328 155
## shrub_cover-Lynx_rufus -0.0755 6.631000e-01 1.2355 62
## shrub_cover-Didelphis_virginiana 1.2089 2.129500e+00 1.1652 82
## shrub_cover-Sylvilagus_floridanus 0.7519 1.472300e+00 1.0661 120
## shrub_cover-Sciurus_carolinensis 1.1289 1.855300e+00 1.1116 153
## shrub_cover-Vulpes_vulpes 0.3926 1.498100e+00 1.0671 117
## shrub_cover-Sus_scrofa 0.9885 2.880700e+00 1.2093 82
## veg_height-Canis_latrans -0.5615 -2.153000e-01 1.0832 202
## veg_height-Lynx_rufus 0.2389 6.628000e-01 1.0339 150
## veg_height-Didelphis_virginiana 0.5724 1.037300e+00 1.0305 206
## veg_height-Sylvilagus_floridanus 0.1750 5.982000e-01 1.0455 191
## veg_height-Sciurus_carolinensis 0.2781 7.082000e-01 1.0131 189
## veg_height-Vulpes_vulpes -0.1939 4.530000e-01 1.1885 99
## veg_height-Sus_scrofa -0.2285 3.548000e-01 1.0201 269
## week-Canis_latrans 0.0448 6.644910e+03 1.1282 2474
## week-Lynx_rufus -0.0283 5.531241e+03 1.1448 8961
## week-Didelphis_virginiana 0.0939 4.849614e+03 1.0981 3000
## week-Sylvilagus_floridanus 0.0860 8.407879e+03 1.1519 2360
## week-Sciurus_carolinensis -0.0983 8.796262e+03 1.1168 5894
## week-Vulpes_vulpes 0.1892 8.037294e+03 1.1018 1958
## week-Sus_scrofa -0.0640 4.394471e+03 1.1026 3486
## I(week^2)-Canis_latrans 0.3473 5.026581e+07 1.2912 4887
## I(week^2)-Lynx_rufus 0.2652 1.099031e+08 1.3500 1723
## I(week^2)-Didelphis_virginiana 0.1345 9.086811e+07 1.3388 1857
## I(week^2)-Sylvilagus_floridanus 0.3560 8.868094e+07 1.3391 1688
## I(week^2)-Sciurus_carolinensis 0.2846 8.427241e+07 1.3024 5422
## I(week^2)-Vulpes_vulpes 0.1288 3.329977e+07 1.4157 913
## I(week^2)-Sus_scrofa 0.2535 1.479890e+08 1.3255 2830
waicOcc(ms_full_full, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -855.53414 43.52142 1798.11112
waicOcc(ms_full_cover, by.sp = FALSE)
## elpd pD WAIC
## -886.46273 42.23688 1857.39922
waicOcc(ms_full_canopy, by.sp = FALSE)
## elpd pD WAIC
## -880.87171 31.55044 1824.84430
waicOcc(ms_full_move, by.sp = FALSE)
## elpd pD WAIC
## -881.93558 40.26328 1844.39772
waicOcc(ms_full_forage, by.sp = FALSE)
## elpd pD WAIC
## -890.5866 34.8568 1850.8867
waicOcc(ms_full_cogon, by.sp = FALSE)
## elpd pD WAIC
## -893.30683 31.83023 1850.27411
waicOcc(ms_full_null, by.sp = FALSE)
## elpd pD WAIC
## -906.55109 18.93902 1850.98022
waicOcc(ms_full_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -889.41238 33.21176 1845.24828
waicOcc(ms_full_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -852.61636 40.62317 1786.47907
waicOcc(ms_null_null, by.sp = FALSE)
## elpd pD WAIC
## -925.07069 15.87434 1881.89005
waicOcc(ms_null_full, by.sp = FALSE)
## elpd pD WAIC
## -874.79529 41.73936 1833.06930
waicOcc(ms_null_cover, by.sp = FALSE)
## elpd pD WAIC
## -908.07850 36.13042 1888.41784
waicOcc(ms_null_canopy, by.sp = FALSE)
## elpd pD WAIC
## -901.35259 28.18607 1859.07733
waicOcc(ms_null_move, by.sp = FALSE)
## elpd pD WAIC
## -902.35781 35.39284 1875.50130
waicOcc(ms_null_forage, by.sp = FALSE)
## elpd pD WAIC
## -908.18229 31.12468 1878.61395
waicOcc(ms_null_cogon, by.sp = FALSE)
## elpd pD WAIC
## -911.92443 27.91177 1879.67242
waicOcc(ms_null_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -907.42272 30.04274 1874.93092
waicOcc(ms_null_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -871.15119 40.00031 1822.30300
waicOcc(ms_week_full, by.sp = FALSE)
## elpd pD WAIC
## -875.1343 41.7958 1833.8603
waicOcc(ms_week_cover, by.sp = FALSE)
## elpd pD WAIC
## -908.18584 35.53911 1887.44990
waicOcc(ms_week_null, by.sp = FALSE)
## elpd pD WAIC
## -924.78132 15.91202 1881.38668
waicOcc(ms_week_forage, by.sp = FALSE)
## elpd pD WAIC
## -908.09065 31.86506 1879.91141
waicOcc(ms_week_move, by.sp = FALSE)
## elpd pD WAIC
## -901.94783 35.35778 1874.61122
waicOcc(ms_week_canopy, by.sp = FALSE)
## elpd pD WAIC
## -901.53795 27.92633 1858.92855
waicOcc(ms_week_cogon, by.sp = FALSE)
## elpd pD WAIC
## -911.39990 27.64417 1878.08813
waicOcc(ms_week_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -907.33739 29.88281 1874.44039
waicOcc(ms_week_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -870.33886 39.94773 1820.57319
waicOcc(ms_cover_full, by.sp = FALSE)
## elpd pD WAIC
## -855.22038 44.65005 1799.74086
waicOcc(ms_cover_cover, by.sp = FALSE)
## elpd pD WAIC
## -885.64709 42.91547 1857.12513
waicOcc(ms_cover_null, by.sp = FALSE)
## elpd pD WAIC
## -906.71308 18.80259 1851.03134
waicOcc(ms_cover_forage, by.sp = FALSE)
## elpd pD WAIC
## -890.0313 35.7546 1851.5718
waicOcc(ms_cover_move, by.sp = FALSE)
## elpd pD WAIC
## -881.72437 40.54062 1844.52998
waicOcc(ms_cover_canopy, by.sp = FALSE)
## elpd pD WAIC
## -881.16479 30.97894 1824.28746
waicOcc(ms_cover_cogon, by.sp = FALSE)
## elpd pD WAIC
## -893.17083 32.27741 1850.89647
waicOcc(ms_cover_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -889.00809 34.89431 1847.80480
waicOcc(ms_cover_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -851.84605 43.19492 1790.08193
waicOcc(ms_weekQ_full, by.sp = FALSE)
## elpd pD WAIC
## -875.36391 40.57352 1831.87487
waicOcc(ms_weekQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -908.37553 36.25934 1889.26974
waicOcc(ms_weekQ_null, by.sp = FALSE)
## elpd pD WAIC
## -924.82341 15.89537 1881.43756
waicOcc(ms_weekQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -907.97190 31.50266 1878.94912
waicOcc(ms_weekQ_move, by.sp = FALSE)
## elpd pD WAIC
## -901.54250 36.07757 1875.24014
waicOcc(ms_weekQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -901.29122 27.82613 1858.23471
waicOcc(ms_weekQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -911.8826 27.3678 1878.5009
waicOcc(ms_weekQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -906.98359 30.49825 1874.96370
waicOcc(ms_weekQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -870.39013 40.33827 1821.45681
waicOcc(ms_fullQ_full, by.sp = FALSE)
## elpd pD WAIC
## -854.01280 45.76178 1799.54917
waicOcc(ms_fullQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -885.79810 42.48052 1856.55724
waicOcc(ms_fullQ_null, by.sp = FALSE)
## elpd pD WAIC
## -906.28372 19.79109 1852.14963
waicOcc(ms_fullQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -890.4594 35.7480 1852.4147
waicOcc(ms_fullQ_move, by.sp = FALSE)
## elpd pD WAIC
## -881.32399 41.99165 1846.63128
waicOcc(ms_fullQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -880.82717 31.68474 1825.02381
waicOcc(ms_fullQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -893.82992 31.37325 1850.40634
waicOcc(ms_fullQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -889.15817 34.20223 1846.72080
waicOcc(ms_fullQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -851.44958 43.58526 1790.06968
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: 0.404
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.6493
## Lynx_rufus Bayesian p-value: 0.3083
## Didelphis_virginiana Bayesian p-value: 0.3827
## Sylvilagus_floridanus Bayesian p-value: 0.364
## Sciurus_carolinensis Bayesian p-value: 0.319
## Vulpes_vulpes Bayesian p-value: 0.278
## Sus_scrofa Bayesian p-value: 0.5267
## 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): 4.8277
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7611 1.3715 -4.1602 -1.8755 1.2430 1.0240 258
## Cogon_Patch_Size 0.0632 1.1657 -2.3790 0.0831 2.3392 1.0442 273
## Veg_shannon_index 0.8464 0.9872 -1.2228 0.8587 2.8158 1.0507 248
## total_shrub_cover -0.6918 1.0668 -3.0130 -0.5810 1.1435 1.0156 99
## Avg_Cogongrass_Cover -0.3677 1.2830 -2.8903 -0.3706 2.1479 1.0526 181
## Tree_Density -2.0728 1.1242 -4.3309 -2.0688 0.1262 1.0548 166
## Avg_Canopy_Cover 1.9448 1.3184 -0.9501 2.0418 4.3395 1.0503 328
## I(Avg_Cogongrass_Cover^2) 1.9060 0.9117 0.1020 1.8655 3.8443 1.0129 166
## avg_veg_height -0.3937 0.8219 -2.0234 -0.3950 1.2655 1.1332 164
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.3723 43.5782 0.2528 14.1368 151.2573 1.1327 94
## Cogon_Patch_Size 13.4467 22.1899 0.1930 6.2588 70.0411 1.1999 158
## Veg_shannon_index 13.7656 68.7601 0.0875 2.5068 87.5600 2.1094 62
## total_shrub_cover 11.1610 42.4720 0.0702 1.5160 93.8073 2.3594 48
## Avg_Cogongrass_Cover 3.6641 10.8067 0.0600 0.9542 23.8504 1.2149 139
## Tree_Density 8.1127 28.1069 0.0567 0.9318 65.9402 1.0702 135
## Avg_Canopy_Cover 64.7163 241.9896 0.6949 10.9576 574.8394 2.4832 19
## I(Avg_Cogongrass_Cover^2) 3.3502 17.9480 0.0571 0.6366 23.5847 1.3951 358
## avg_veg_height 1.7343 5.2203 0.0475 0.4837 10.6973 1.2476 315
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.8225 8.3341 0.0523 1.0359 31.49 1.0044 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0468 0.3893 -5.7023 -5.0801 -4.1345 1.0459 355
## shrub_cover 0.5703 0.3910 -0.1874 0.5562 1.3379 1.0215 214
## veg_height 0.0309 0.2430 -0.4852 0.0362 0.4790 1.0341 443
## week -0.0549 1.6705 -3.4176 -0.0347 3.0976 1.0016 505
## I(week^2) 0.1399 1.7256 -3.0851 0.1051 3.6967 1.0106 350
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.924000e-01 1.389800e+00 0.0650 0.3546 3.414000e+00 1.0851 131
## shrub_cover 8.217000e-01 9.117000e-01 0.1505 0.5681 3.012800e+00 1.0848 133
## veg_height 3.687000e-01 5.196000e-01 0.0715 0.2585 1.300700e+00 1.0513 858
## week 7.736803e+08 9.968227e+09 0.1006 53.7844 1.840345e+09 1.2115 469
## I(week^2) 9.336132e+18 1.214704e+20 0.0857 77.4360 1.103260e+19 1.7866 140
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4487 1.7969 -3.4240 -0.6204
## (Intercept)-Lynx_rufus 2.0790 3.7990 -3.1139 1.3985
## (Intercept)-Didelphis_virginiana -4.7310 2.2315 -9.7595 -4.5481
## (Intercept)-Sylvilagus_floridanus -3.1671 2.2133 -8.2022 -2.9835
## (Intercept)-Sciurus_carolinensis -5.1221 2.5960 -10.9373 -4.9573
## (Intercept)-Vulpes_vulpes -5.3746 3.0478 -11.5183 -5.2606
## (Intercept)-Sus_scrofa -6.7148 3.7832 -16.0893 -6.1553
## Cogon_Patch_Size-Canis_latrans 2.9757 2.5201 -0.2815 2.3865
## Cogon_Patch_Size-Lynx_rufus 0.0114 2.7762 -6.0833 0.0023
## Cogon_Patch_Size-Didelphis_virginiana 2.6935 1.9507 0.0592 2.3675
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2536 2.6663 -8.8819 -1.7280
## Cogon_Patch_Size-Sciurus_carolinensis -1.3373 2.5581 -8.1577 -0.9219
## Cogon_Patch_Size-Vulpes_vulpes -1.2364 2.7788 -7.9673 -0.7835
## Cogon_Patch_Size-Sus_scrofa -1.1526 2.5470 -7.5862 -0.7422
## Veg_shannon_index-Canis_latrans 2.0477 1.4450 -0.0573 1.8097
## Veg_shannon_index-Lynx_rufus -0.1928 2.2197 -5.3203 0.1216
## Veg_shannon_index-Didelphis_virginiana 1.8806 1.7375 -0.5471 1.5307
## Veg_shannon_index-Sylvilagus_floridanus 1.6834 1.6330 -0.5380 1.3981
## Veg_shannon_index-Sciurus_carolinensis -0.6691 2.2061 -4.9835 -0.3187
## Veg_shannon_index-Vulpes_vulpes -0.7623 4.1086 -12.9414 0.0796
## Veg_shannon_index-Sus_scrofa 3.3934 4.0383 0.0618 2.4616
## total_shrub_cover-Canis_latrans 0.8285 1.5953 -1.7098 0.5631
## total_shrub_cover-Lynx_rufus -2.1105 3.8962 -11.9559 -1.2752
## total_shrub_cover-Didelphis_virginiana -2.0775 3.0864 -12.0359 -1.2786
## total_shrub_cover-Sylvilagus_floridanus -1.5096 2.5089 -8.9836 -0.9071
## total_shrub_cover-Sciurus_carolinensis -1.1973 2.6926 -9.7836 -0.6078
## total_shrub_cover-Vulpes_vulpes -1.4805 2.5295 -7.8030 -1.0132
## total_shrub_cover-Sus_scrofa -1.0185 3.4184 -12.4492 -0.3248
## Avg_Cogongrass_Cover-Canis_latrans 0.0001 1.8920 -3.2932 -0.1623
## Avg_Cogongrass_Cover-Lynx_rufus 0.0836 1.9449 -3.3112 -0.0632
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4290 1.7934 -3.8726 -0.4870
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2616 1.9957 -5.5071 -1.0787
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2907 1.9009 -3.9376 -0.3411
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0354 2.0563 -3.5294 -0.1678
## Avg_Cogongrass_Cover-Sus_scrofa -0.9028 2.2179 -5.6671 -0.7763
## Tree_Density-Canis_latrans -3.4022 2.2488 -9.3793 -2.8430
## Tree_Density-Lynx_rufus -1.3587 2.5009 -4.9207 -1.6685
## Tree_Density-Didelphis_virginiana -2.5136 1.8380 -6.6297 -2.3547
## Tree_Density-Sylvilagus_floridanus -3.1074 2.5444 -9.8316 -2.6084
## Tree_Density-Sciurus_carolinensis -2.7554 2.2436 -8.4347 -2.4930
## Tree_Density-Vulpes_vulpes -2.2120 2.2203 -7.4499 -2.1415
## Tree_Density-Sus_scrofa -2.7669 2.7674 -9.8046 -2.3397
## Avg_Canopy_Cover-Canis_latrans -0.3008 1.0018 -2.4336 -0.2370
## Avg_Canopy_Cover-Lynx_rufus 1.7704 4.0049 -5.6874 1.5104
## Avg_Canopy_Cover-Didelphis_virginiana 6.2572 4.2740 1.7785 5.1016
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.5489 5.3481 1.9797 6.0043
## Avg_Canopy_Cover-Sciurus_carolinensis 6.9860 8.0399 1.3749 4.4403
## Avg_Canopy_Cover-Vulpes_vulpes 5.6448 6.9560 0.7627 3.5393
## Avg_Canopy_Cover-Sus_scrofa 3.9905 5.3341 0.3387 2.7229
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6216 1.4322 0.8073 2.3528
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8624 1.6682 0.7129 2.5044
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6594 1.0775 -0.3675 1.5820
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7229 1.2431 -0.6163 1.6530
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.3472 1.3159 0.2758 2.1357
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4011 1.4504 0.3423 2.1799
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2189 2.0329 -4.5265 1.5233
## avg_veg_height-Canis_latrans -0.3236 0.8968 -2.1085 -0.3290
## avg_veg_height-Lynx_rufus -0.7999 1.5831 -4.5919 -0.6487
## avg_veg_height-Didelphis_virginiana -0.7500 1.2122 -3.5153 -0.6619
## avg_veg_height-Sylvilagus_floridanus -0.3887 1.1248 -2.6256 -0.3824
## avg_veg_height-Sciurus_carolinensis 0.1815 1.2660 -1.8619 0.0872
## avg_veg_height-Vulpes_vulpes -0.5020 1.3559 -3.1753 -0.4992
## avg_veg_height-Sus_scrofa -0.3575 1.1756 -2.7569 -0.3582
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8545 1.0058 222
## (Intercept)-Lynx_rufus 10.9110 1.1160 62
## (Intercept)-Didelphis_virginiana -0.9648 1.1064 79
## (Intercept)-Sylvilagus_floridanus 0.8216 1.0679 112
## (Intercept)-Sciurus_carolinensis -0.5952 1.0900 108
## (Intercept)-Vulpes_vulpes 0.3265 1.0363 69
## (Intercept)-Sus_scrofa -0.9992 1.3267 47
## Cogon_Patch_Size-Canis_latrans 9.5024 1.0594 190
## Cogon_Patch_Size-Lynx_rufus 6.0692 1.1252 182
## Cogon_Patch_Size-Didelphis_virginiana 7.0488 1.1269 104
## Cogon_Patch_Size-Sylvilagus_floridanus 1.7758 1.0178 138
## Cogon_Patch_Size-Sciurus_carolinensis 2.5312 1.0480 231
## Cogon_Patch_Size-Vulpes_vulpes 3.4892 1.0377 212
## Cogon_Patch_Size-Sus_scrofa 2.5627 1.0219 195
## Veg_shannon_index-Canis_latrans 5.6088 1.1136 119
## Veg_shannon_index-Lynx_rufus 3.5183 1.0763 137
## Veg_shannon_index-Didelphis_virginiana 6.4787 1.1682 114
## Veg_shannon_index-Sylvilagus_floridanus 5.8887 1.2024 72
## Veg_shannon_index-Sciurus_carolinensis 2.3342 1.1511 142
## Veg_shannon_index-Vulpes_vulpes 2.7483 2.2759 24
## Veg_shannon_index-Sus_scrofa 12.3154 1.4876 40
## total_shrub_cover-Canis_latrans 4.8644 1.0585 75
## total_shrub_cover-Lynx_rufus 2.2886 1.3648 32
## total_shrub_cover-Didelphis_virginiana 0.9153 1.7799 18
## total_shrub_cover-Sylvilagus_floridanus 1.2675 1.5175 35
## total_shrub_cover-Sciurus_carolinensis 1.7712 1.7288 37
## total_shrub_cover-Vulpes_vulpes 2.0233 1.0703 120
## total_shrub_cover-Sus_scrofa 2.7174 2.1493 14
## Avg_Cogongrass_Cover-Canis_latrans 4.4469 1.0582 176
## Avg_Cogongrass_Cover-Lynx_rufus 4.6378 1.0393 260
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0541 1.0516 244
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0794 1.0147 198
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.5993 1.0544 224
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7980 1.0941 130
## Avg_Cogongrass_Cover-Sus_scrofa 2.7421 1.0448 159
## Tree_Density-Canis_latrans -0.5753 1.0981 91
## Tree_Density-Lynx_rufus 5.2898 1.1317 171
## Tree_Density-Didelphis_virginiana 0.6098 1.0934 187
## Tree_Density-Sylvilagus_floridanus 0.1933 1.1608 97
## Tree_Density-Sciurus_carolinensis 1.0999 1.3632 112
## Tree_Density-Vulpes_vulpes 2.6383 1.0545 121
## Tree_Density-Sus_scrofa 0.8312 1.1406 95
## Avg_Canopy_Cover-Canis_latrans 1.5876 1.0154 330
## Avg_Canopy_Cover-Lynx_rufus 11.0655 1.2294 56
## Avg_Canopy_Cover-Didelphis_virginiana 19.3484 1.3293 56
## Avg_Canopy_Cover-Sylvilagus_floridanus 21.9914 1.2160 47
## Avg_Canopy_Cover-Sciurus_carolinensis 35.9070 2.7448 9
## Avg_Canopy_Cover-Vulpes_vulpes 29.0082 1.8664 20
## Avg_Canopy_Cover-Sus_scrofa 24.8972 2.5241 23
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.6127 1.0627 77
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.4576 1.1428 114
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 4.1383 1.0399 142
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.5190 1.0505 166
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 5.7005 1.0398 153
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9939 1.0207 163
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 4.1595 1.0721 88
## avg_veg_height-Canis_latrans 1.4669 1.1102 354
## avg_veg_height-Lynx_rufus 1.7683 1.0179 195
## avg_veg_height-Didelphis_virginiana 1.3974 1.0802 245
## avg_veg_height-Sylvilagus_floridanus 2.0001 1.0711 275
## avg_veg_height-Sciurus_carolinensis 3.2462 1.0908 295
## avg_veg_height-Vulpes_vulpes 2.3814 1.1325 160
## avg_veg_height-Sus_scrofa 2.0108 1.0810 288
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.786700e+00 1.940000e-01 -5.232100e+00
## (Intercept)-Lynx_rufus -5.740900e+00 3.070000e-01 -6.339600e+00
## (Intercept)-Didelphis_virginiana -4.822300e+00 2.909000e-01 -5.439900e+00
## (Intercept)-Sylvilagus_floridanus -5.010100e+00 2.160000e-01 -5.470000e+00
## (Intercept)-Sciurus_carolinensis -4.852900e+00 2.952000e-01 -5.395300e+00
## (Intercept)-Vulpes_vulpes -5.931700e+00 6.761000e-01 -7.816200e+00
## (Intercept)-Sus_scrofa -5.364600e+00 5.144000e-01 -6.592100e+00
## shrub_cover-Canis_latrans -2.844000e-01 2.235000e-01 -7.121000e-01
## shrub_cover-Lynx_rufus -4.420000e-02 3.569000e-01 -6.950000e-01
## shrub_cover-Didelphis_virginiana 1.240600e+00 4.068000e-01 5.264000e-01
## shrub_cover-Sylvilagus_floridanus 7.430000e-01 3.976000e-01 -4.190000e-02
## shrub_cover-Sciurus_carolinensis 1.122200e+00 3.768000e-01 3.602000e-01
## shrub_cover-Vulpes_vulpes 3.860000e-01 5.775000e-01 -7.616000e-01
## shrub_cover-Sus_scrofa 1.021500e+00 8.355000e-01 -5.962000e-01
## veg_height-Canis_latrans -5.592000e-01 1.719000e-01 -8.932000e-01
## veg_height-Lynx_rufus 2.383000e-01 2.154000e-01 -1.735000e-01
## veg_height-Didelphis_virginiana 5.729000e-01 2.357000e-01 1.252000e-01
## veg_height-Sylvilagus_floridanus 1.736000e-01 2.314000e-01 -2.759000e-01
## veg_height-Sciurus_carolinensis 2.746000e-01 2.260000e-01 -1.657000e-01
## veg_height-Vulpes_vulpes -2.062000e-01 3.696000e-01 -1.027000e+00
## veg_height-Sus_scrofa -2.562000e-01 3.564000e-01 -1.063700e+00
## week-Canis_latrans 4.049994e+02 2.016399e+04 -4.812217e+03
## week-Lynx_rufus -3.737473e+02 2.948101e+04 -5.951515e+03
## week-Didelphis_virginiana -6.306688e+02 2.297188e+04 -6.698956e+03
## week-Sylvilagus_floridanus 7.568246e+02 2.666691e+04 -3.915197e+03
## week-Sciurus_carolinensis -1.389517e+02 2.796398e+04 -4.352418e+03
## week-Vulpes_vulpes 8.965176e+02 2.361436e+04 -5.147815e+03
## week-Sus_scrofa 4.620409e+02 2.091741e+04 -6.637502e+03
## I(week^2)-Canis_latrans 7.231978e+06 2.436501e+09 -2.674919e+08
## I(week^2)-Lynx_rufus 6.904567e+07 2.791704e+09 -1.260500e+08
## I(week^2)-Didelphis_virginiana -6.736932e+07 3.030640e+09 -1.385641e+08
## I(week^2)-Sylvilagus_floridanus 5.423948e+07 2.430779e+09 -8.106263e+07
## I(week^2)-Sciurus_carolinensis -4.656114e+07 4.233077e+09 -1.362741e+08
## I(week^2)-Vulpes_vulpes -1.395325e+08 3.846359e+09 -2.221477e+08
## I(week^2)-Sus_scrofa 5.363212e+07 2.839453e+09 -9.447737e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7734 -4.436000e+00 1.0216 127
## (Intercept)-Lynx_rufus -5.7411 -5.153600e+00 1.1550 74
## (Intercept)-Didelphis_virginiana -4.8186 -4.284100e+00 1.1586 126
## (Intercept)-Sylvilagus_floridanus -5.0083 -4.601400e+00 1.0957 192
## (Intercept)-Sciurus_carolinensis -4.8602 -4.281000e+00 1.1344 73
## (Intercept)-Vulpes_vulpes -5.8126 -4.908100e+00 1.1312 31
## (Intercept)-Sus_scrofa -5.3231 -4.444900e+00 1.2172 88
## shrub_cover-Canis_latrans -0.2958 1.787000e-01 1.0328 155
## shrub_cover-Lynx_rufus -0.0755 6.631000e-01 1.2355 62
## shrub_cover-Didelphis_virginiana 1.2089 2.129500e+00 1.1652 82
## shrub_cover-Sylvilagus_floridanus 0.7519 1.472300e+00 1.0661 120
## shrub_cover-Sciurus_carolinensis 1.1289 1.855300e+00 1.1116 153
## shrub_cover-Vulpes_vulpes 0.3926 1.498100e+00 1.0671 117
## shrub_cover-Sus_scrofa 0.9885 2.880700e+00 1.2093 82
## veg_height-Canis_latrans -0.5615 -2.153000e-01 1.0832 202
## veg_height-Lynx_rufus 0.2389 6.628000e-01 1.0339 150
## veg_height-Didelphis_virginiana 0.5724 1.037300e+00 1.0305 206
## veg_height-Sylvilagus_floridanus 0.1750 5.982000e-01 1.0455 191
## veg_height-Sciurus_carolinensis 0.2781 7.082000e-01 1.0131 189
## veg_height-Vulpes_vulpes -0.1939 4.530000e-01 1.1885 99
## veg_height-Sus_scrofa -0.2285 3.548000e-01 1.0201 269
## week-Canis_latrans 0.0448 6.644910e+03 1.1282 2474
## week-Lynx_rufus -0.0283 5.531241e+03 1.1448 8961
## week-Didelphis_virginiana 0.0939 4.849614e+03 1.0981 3000
## week-Sylvilagus_floridanus 0.0860 8.407879e+03 1.1519 2360
## week-Sciurus_carolinensis -0.0983 8.796262e+03 1.1168 5894
## week-Vulpes_vulpes 0.1892 8.037294e+03 1.1018 1958
## week-Sus_scrofa -0.0640 4.394471e+03 1.1026 3486
## I(week^2)-Canis_latrans 0.3473 5.026581e+07 1.2912 4887
## I(week^2)-Lynx_rufus 0.2652 1.099031e+08 1.3500 1723
## I(week^2)-Didelphis_virginiana 0.1345 9.086811e+07 1.3388 1857
## I(week^2)-Sylvilagus_floridanus 0.3560 8.868094e+07 1.3391 1688
## I(week^2)-Sciurus_carolinensis 0.2846 8.427241e+07 1.3024 5422
## I(week^2)-Vulpes_vulpes 0.1288 3.329977e+07 1.4157 913
## I(week^2)-Sus_scrofa 0.2535 1.479890e+08 1.3255 2830
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$beta.samples)
## 'mcmc' num [1:3000, 1:63] -0.676 -0.462 -1.527 0.12 -1.405 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:63] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.015
MCMCplot(ms_fullQ_fullQ$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] 0.0299 0.1463 0.0646 0.5277 0.4721 ...
## $ z.0.samples : int [1:3000, 1:7, 1:100] 0 0 0 0 0 0 0 0 1 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:7, 1:100] 0.0299 0.1463 0.0646 0.5277 0.4721 ...
psi.0.quants <- apply(out.pred$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] 4.03e-06 3.17e-02 9.99e-01 4.16e-06 3.32e-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 0.0317 0.0332 0.0317 0.0314 0.0316 ...
## $ psi.low : num 4.03e-06 4.16e-06 5.20e-06 5.07e-06 4.84e-06 ...
## $ psi.high : num 0.999 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) # 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): 4.8277
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7611 1.3715 -4.1602 -1.8755 1.2430 1.0240 258
## Cogon_Patch_Size 0.0632 1.1657 -2.3790 0.0831 2.3392 1.0442 273
## Veg_shannon_index 0.8464 0.9872 -1.2228 0.8587 2.8158 1.0507 248
## total_shrub_cover -0.6918 1.0668 -3.0130 -0.5810 1.1435 1.0156 99
## Avg_Cogongrass_Cover -0.3677 1.2830 -2.8903 -0.3706 2.1479 1.0526 181
## Tree_Density -2.0728 1.1242 -4.3309 -2.0688 0.1262 1.0548 166
## Avg_Canopy_Cover 1.9448 1.3184 -0.9501 2.0418 4.3395 1.0503 328
## I(Avg_Cogongrass_Cover^2) 1.9060 0.9117 0.1020 1.8655 3.8443 1.0129 166
## avg_veg_height -0.3937 0.8219 -2.0234 -0.3950 1.2655 1.1332 164
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.3723 43.5782 0.2528 14.1368 151.2573 1.1327 94
## Cogon_Patch_Size 13.4467 22.1899 0.1930 6.2588 70.0411 1.1999 158
## Veg_shannon_index 13.7656 68.7601 0.0875 2.5068 87.5600 2.1094 62
## total_shrub_cover 11.1610 42.4720 0.0702 1.5160 93.8073 2.3594 48
## Avg_Cogongrass_Cover 3.6641 10.8067 0.0600 0.9542 23.8504 1.2149 139
## Tree_Density 8.1127 28.1069 0.0567 0.9318 65.9402 1.0702 135
## Avg_Canopy_Cover 64.7163 241.9896 0.6949 10.9576 574.8394 2.4832 19
## I(Avg_Cogongrass_Cover^2) 3.3502 17.9480 0.0571 0.6366 23.5847 1.3951 358
## avg_veg_height 1.7343 5.2203 0.0475 0.4837 10.6973 1.2476 315
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.8225 8.3341 0.0523 1.0359 31.49 1.0044 68
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0468 0.3893 -5.7023 -5.0801 -4.1345 1.0459 355
## shrub_cover 0.5703 0.3910 -0.1874 0.5562 1.3379 1.0215 214
## veg_height 0.0309 0.2430 -0.4852 0.0362 0.4790 1.0341 443
## week -0.0549 1.6705 -3.4176 -0.0347 3.0976 1.0016 505
## I(week^2) 0.1399 1.7256 -3.0851 0.1051 3.6967 1.0106 350
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.924000e-01 1.389800e+00 0.0650 0.3546 3.414000e+00 1.0851 131
## shrub_cover 8.217000e-01 9.117000e-01 0.1505 0.5681 3.012800e+00 1.0848 133
## veg_height 3.687000e-01 5.196000e-01 0.0715 0.2585 1.300700e+00 1.0513 858
## week 7.736803e+08 9.968227e+09 0.1006 53.7844 1.840345e+09 1.2115 469
## I(week^2) 9.336132e+18 1.214704e+20 0.0857 77.4360 1.103260e+19 1.7866 140
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4487 1.7969 -3.4240 -0.6204
## (Intercept)-Lynx_rufus 2.0790 3.7990 -3.1139 1.3985
## (Intercept)-Didelphis_virginiana -4.7310 2.2315 -9.7595 -4.5481
## (Intercept)-Sylvilagus_floridanus -3.1671 2.2133 -8.2022 -2.9835
## (Intercept)-Sciurus_carolinensis -5.1221 2.5960 -10.9373 -4.9573
## (Intercept)-Vulpes_vulpes -5.3746 3.0478 -11.5183 -5.2606
## (Intercept)-Sus_scrofa -6.7148 3.7832 -16.0893 -6.1553
## Cogon_Patch_Size-Canis_latrans 2.9757 2.5201 -0.2815 2.3865
## Cogon_Patch_Size-Lynx_rufus 0.0114 2.7762 -6.0833 0.0023
## Cogon_Patch_Size-Didelphis_virginiana 2.6935 1.9507 0.0592 2.3675
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2536 2.6663 -8.8819 -1.7280
## Cogon_Patch_Size-Sciurus_carolinensis -1.3373 2.5581 -8.1577 -0.9219
## Cogon_Patch_Size-Vulpes_vulpes -1.2364 2.7788 -7.9673 -0.7835
## Cogon_Patch_Size-Sus_scrofa -1.1526 2.5470 -7.5862 -0.7422
## Veg_shannon_index-Canis_latrans 2.0477 1.4450 -0.0573 1.8097
## Veg_shannon_index-Lynx_rufus -0.1928 2.2197 -5.3203 0.1216
## Veg_shannon_index-Didelphis_virginiana 1.8806 1.7375 -0.5471 1.5307
## Veg_shannon_index-Sylvilagus_floridanus 1.6834 1.6330 -0.5380 1.3981
## Veg_shannon_index-Sciurus_carolinensis -0.6691 2.2061 -4.9835 -0.3187
## Veg_shannon_index-Vulpes_vulpes -0.7623 4.1086 -12.9414 0.0796
## Veg_shannon_index-Sus_scrofa 3.3934 4.0383 0.0618 2.4616
## total_shrub_cover-Canis_latrans 0.8285 1.5953 -1.7098 0.5631
## total_shrub_cover-Lynx_rufus -2.1105 3.8962 -11.9559 -1.2752
## total_shrub_cover-Didelphis_virginiana -2.0775 3.0864 -12.0359 -1.2786
## total_shrub_cover-Sylvilagus_floridanus -1.5096 2.5089 -8.9836 -0.9071
## total_shrub_cover-Sciurus_carolinensis -1.1973 2.6926 -9.7836 -0.6078
## total_shrub_cover-Vulpes_vulpes -1.4805 2.5295 -7.8030 -1.0132
## total_shrub_cover-Sus_scrofa -1.0185 3.4184 -12.4492 -0.3248
## Avg_Cogongrass_Cover-Canis_latrans 0.0001 1.8920 -3.2932 -0.1623
## Avg_Cogongrass_Cover-Lynx_rufus 0.0836 1.9449 -3.3112 -0.0632
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4290 1.7934 -3.8726 -0.4870
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2616 1.9957 -5.5071 -1.0787
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2907 1.9009 -3.9376 -0.3411
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0354 2.0563 -3.5294 -0.1678
## Avg_Cogongrass_Cover-Sus_scrofa -0.9028 2.2179 -5.6671 -0.7763
## Tree_Density-Canis_latrans -3.4022 2.2488 -9.3793 -2.8430
## Tree_Density-Lynx_rufus -1.3587 2.5009 -4.9207 -1.6685
## Tree_Density-Didelphis_virginiana -2.5136 1.8380 -6.6297 -2.3547
## Tree_Density-Sylvilagus_floridanus -3.1074 2.5444 -9.8316 -2.6084
## Tree_Density-Sciurus_carolinensis -2.7554 2.2436 -8.4347 -2.4930
## Tree_Density-Vulpes_vulpes -2.2120 2.2203 -7.4499 -2.1415
## Tree_Density-Sus_scrofa -2.7669 2.7674 -9.8046 -2.3397
## Avg_Canopy_Cover-Canis_latrans -0.3008 1.0018 -2.4336 -0.2370
## Avg_Canopy_Cover-Lynx_rufus 1.7704 4.0049 -5.6874 1.5104
## Avg_Canopy_Cover-Didelphis_virginiana 6.2572 4.2740 1.7785 5.1016
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.5489 5.3481 1.9797 6.0043
## Avg_Canopy_Cover-Sciurus_carolinensis 6.9860 8.0399 1.3749 4.4403
## Avg_Canopy_Cover-Vulpes_vulpes 5.6448 6.9560 0.7627 3.5393
## Avg_Canopy_Cover-Sus_scrofa 3.9905 5.3341 0.3387 2.7229
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6216 1.4322 0.8073 2.3528
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.8624 1.6682 0.7129 2.5044
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6594 1.0775 -0.3675 1.5820
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7229 1.2431 -0.6163 1.6530
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.3472 1.3159 0.2758 2.1357
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4011 1.4504 0.3423 2.1799
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2189 2.0329 -4.5265 1.5233
## avg_veg_height-Canis_latrans -0.3236 0.8968 -2.1085 -0.3290
## avg_veg_height-Lynx_rufus -0.7999 1.5831 -4.5919 -0.6487
## avg_veg_height-Didelphis_virginiana -0.7500 1.2122 -3.5153 -0.6619
## avg_veg_height-Sylvilagus_floridanus -0.3887 1.1248 -2.6256 -0.3824
## avg_veg_height-Sciurus_carolinensis 0.1815 1.2660 -1.8619 0.0872
## avg_veg_height-Vulpes_vulpes -0.5020 1.3559 -3.1753 -0.4992
## avg_veg_height-Sus_scrofa -0.3575 1.1756 -2.7569 -0.3582
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8545 1.0058 222
## (Intercept)-Lynx_rufus 10.9110 1.1160 62
## (Intercept)-Didelphis_virginiana -0.9648 1.1064 79
## (Intercept)-Sylvilagus_floridanus 0.8216 1.0679 112
## (Intercept)-Sciurus_carolinensis -0.5952 1.0900 108
## (Intercept)-Vulpes_vulpes 0.3265 1.0363 69
## (Intercept)-Sus_scrofa -0.9992 1.3267 47
## Cogon_Patch_Size-Canis_latrans 9.5024 1.0594 190
## Cogon_Patch_Size-Lynx_rufus 6.0692 1.1252 182
## Cogon_Patch_Size-Didelphis_virginiana 7.0488 1.1269 104
## Cogon_Patch_Size-Sylvilagus_floridanus 1.7758 1.0178 138
## Cogon_Patch_Size-Sciurus_carolinensis 2.5312 1.0480 231
## Cogon_Patch_Size-Vulpes_vulpes 3.4892 1.0377 212
## Cogon_Patch_Size-Sus_scrofa 2.5627 1.0219 195
## Veg_shannon_index-Canis_latrans 5.6088 1.1136 119
## Veg_shannon_index-Lynx_rufus 3.5183 1.0763 137
## Veg_shannon_index-Didelphis_virginiana 6.4787 1.1682 114
## Veg_shannon_index-Sylvilagus_floridanus 5.8887 1.2024 72
## Veg_shannon_index-Sciurus_carolinensis 2.3342 1.1511 142
## Veg_shannon_index-Vulpes_vulpes 2.7483 2.2759 24
## Veg_shannon_index-Sus_scrofa 12.3154 1.4876 40
## total_shrub_cover-Canis_latrans 4.8644 1.0585 75
## total_shrub_cover-Lynx_rufus 2.2886 1.3648 32
## total_shrub_cover-Didelphis_virginiana 0.9153 1.7799 18
## total_shrub_cover-Sylvilagus_floridanus 1.2675 1.5175 35
## total_shrub_cover-Sciurus_carolinensis 1.7712 1.7288 37
## total_shrub_cover-Vulpes_vulpes 2.0233 1.0703 120
## total_shrub_cover-Sus_scrofa 2.7174 2.1493 14
## Avg_Cogongrass_Cover-Canis_latrans 4.4469 1.0582 176
## Avg_Cogongrass_Cover-Lynx_rufus 4.6378 1.0393 260
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0541 1.0516 244
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0794 1.0147 198
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.5993 1.0544 224
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7980 1.0941 130
## Avg_Cogongrass_Cover-Sus_scrofa 2.7421 1.0448 159
## Tree_Density-Canis_latrans -0.5753 1.0981 91
## Tree_Density-Lynx_rufus 5.2898 1.1317 171
## Tree_Density-Didelphis_virginiana 0.6098 1.0934 187
## Tree_Density-Sylvilagus_floridanus 0.1933 1.1608 97
## Tree_Density-Sciurus_carolinensis 1.0999 1.3632 112
## Tree_Density-Vulpes_vulpes 2.6383 1.0545 121
## Tree_Density-Sus_scrofa 0.8312 1.1406 95
## Avg_Canopy_Cover-Canis_latrans 1.5876 1.0154 330
## Avg_Canopy_Cover-Lynx_rufus 11.0655 1.2294 56
## Avg_Canopy_Cover-Didelphis_virginiana 19.3484 1.3293 56
## Avg_Canopy_Cover-Sylvilagus_floridanus 21.9914 1.2160 47
## Avg_Canopy_Cover-Sciurus_carolinensis 35.9070 2.7448 9
## Avg_Canopy_Cover-Vulpes_vulpes 29.0082 1.8664 20
## Avg_Canopy_Cover-Sus_scrofa 24.8972 2.5241 23
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.6127 1.0627 77
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.4576 1.1428 114
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 4.1383 1.0399 142
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.5190 1.0505 166
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 5.7005 1.0398 153
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9939 1.0207 163
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 4.1595 1.0721 88
## avg_veg_height-Canis_latrans 1.4669 1.1102 354
## avg_veg_height-Lynx_rufus 1.7683 1.0179 195
## avg_veg_height-Didelphis_virginiana 1.3974 1.0802 245
## avg_veg_height-Sylvilagus_floridanus 2.0001 1.0711 275
## avg_veg_height-Sciurus_carolinensis 3.2462 1.0908 295
## avg_veg_height-Vulpes_vulpes 2.3814 1.1325 160
## avg_veg_height-Sus_scrofa 2.0108 1.0810 288
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.786700e+00 1.940000e-01 -5.232100e+00
## (Intercept)-Lynx_rufus -5.740900e+00 3.070000e-01 -6.339600e+00
## (Intercept)-Didelphis_virginiana -4.822300e+00 2.909000e-01 -5.439900e+00
## (Intercept)-Sylvilagus_floridanus -5.010100e+00 2.160000e-01 -5.470000e+00
## (Intercept)-Sciurus_carolinensis -4.852900e+00 2.952000e-01 -5.395300e+00
## (Intercept)-Vulpes_vulpes -5.931700e+00 6.761000e-01 -7.816200e+00
## (Intercept)-Sus_scrofa -5.364600e+00 5.144000e-01 -6.592100e+00
## shrub_cover-Canis_latrans -2.844000e-01 2.235000e-01 -7.121000e-01
## shrub_cover-Lynx_rufus -4.420000e-02 3.569000e-01 -6.950000e-01
## shrub_cover-Didelphis_virginiana 1.240600e+00 4.068000e-01 5.264000e-01
## shrub_cover-Sylvilagus_floridanus 7.430000e-01 3.976000e-01 -4.190000e-02
## shrub_cover-Sciurus_carolinensis 1.122200e+00 3.768000e-01 3.602000e-01
## shrub_cover-Vulpes_vulpes 3.860000e-01 5.775000e-01 -7.616000e-01
## shrub_cover-Sus_scrofa 1.021500e+00 8.355000e-01 -5.962000e-01
## veg_height-Canis_latrans -5.592000e-01 1.719000e-01 -8.932000e-01
## veg_height-Lynx_rufus 2.383000e-01 2.154000e-01 -1.735000e-01
## veg_height-Didelphis_virginiana 5.729000e-01 2.357000e-01 1.252000e-01
## veg_height-Sylvilagus_floridanus 1.736000e-01 2.314000e-01 -2.759000e-01
## veg_height-Sciurus_carolinensis 2.746000e-01 2.260000e-01 -1.657000e-01
## veg_height-Vulpes_vulpes -2.062000e-01 3.696000e-01 -1.027000e+00
## veg_height-Sus_scrofa -2.562000e-01 3.564000e-01 -1.063700e+00
## week-Canis_latrans 4.049994e+02 2.016399e+04 -4.812217e+03
## week-Lynx_rufus -3.737473e+02 2.948101e+04 -5.951515e+03
## week-Didelphis_virginiana -6.306688e+02 2.297188e+04 -6.698956e+03
## week-Sylvilagus_floridanus 7.568246e+02 2.666691e+04 -3.915197e+03
## week-Sciurus_carolinensis -1.389517e+02 2.796398e+04 -4.352418e+03
## week-Vulpes_vulpes 8.965176e+02 2.361436e+04 -5.147815e+03
## week-Sus_scrofa 4.620409e+02 2.091741e+04 -6.637502e+03
## I(week^2)-Canis_latrans 7.231978e+06 2.436501e+09 -2.674919e+08
## I(week^2)-Lynx_rufus 6.904567e+07 2.791704e+09 -1.260500e+08
## I(week^2)-Didelphis_virginiana -6.736932e+07 3.030640e+09 -1.385641e+08
## I(week^2)-Sylvilagus_floridanus 5.423948e+07 2.430779e+09 -8.106263e+07
## I(week^2)-Sciurus_carolinensis -4.656114e+07 4.233077e+09 -1.362741e+08
## I(week^2)-Vulpes_vulpes -1.395325e+08 3.846359e+09 -2.221477e+08
## I(week^2)-Sus_scrofa 5.363212e+07 2.839453e+09 -9.447737e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7734 -4.436000e+00 1.0216 127
## (Intercept)-Lynx_rufus -5.7411 -5.153600e+00 1.1550 74
## (Intercept)-Didelphis_virginiana -4.8186 -4.284100e+00 1.1586 126
## (Intercept)-Sylvilagus_floridanus -5.0083 -4.601400e+00 1.0957 192
## (Intercept)-Sciurus_carolinensis -4.8602 -4.281000e+00 1.1344 73
## (Intercept)-Vulpes_vulpes -5.8126 -4.908100e+00 1.1312 31
## (Intercept)-Sus_scrofa -5.3231 -4.444900e+00 1.2172 88
## shrub_cover-Canis_latrans -0.2958 1.787000e-01 1.0328 155
## shrub_cover-Lynx_rufus -0.0755 6.631000e-01 1.2355 62
## shrub_cover-Didelphis_virginiana 1.2089 2.129500e+00 1.1652 82
## shrub_cover-Sylvilagus_floridanus 0.7519 1.472300e+00 1.0661 120
## shrub_cover-Sciurus_carolinensis 1.1289 1.855300e+00 1.1116 153
## shrub_cover-Vulpes_vulpes 0.3926 1.498100e+00 1.0671 117
## shrub_cover-Sus_scrofa 0.9885 2.880700e+00 1.2093 82
## veg_height-Canis_latrans -0.5615 -2.153000e-01 1.0832 202
## veg_height-Lynx_rufus 0.2389 6.628000e-01 1.0339 150
## veg_height-Didelphis_virginiana 0.5724 1.037300e+00 1.0305 206
## veg_height-Sylvilagus_floridanus 0.1750 5.982000e-01 1.0455 191
## veg_height-Sciurus_carolinensis 0.2781 7.082000e-01 1.0131 189
## veg_height-Vulpes_vulpes -0.1939 4.530000e-01 1.1885 99
## veg_height-Sus_scrofa -0.2285 3.548000e-01 1.0201 269
## week-Canis_latrans 0.0448 6.644910e+03 1.1282 2474
## week-Lynx_rufus -0.0283 5.531241e+03 1.1448 8961
## week-Didelphis_virginiana 0.0939 4.849614e+03 1.0981 3000
## week-Sylvilagus_floridanus 0.0860 8.407879e+03 1.1519 2360
## week-Sciurus_carolinensis -0.0983 8.796262e+03 1.1168 5894
## week-Vulpes_vulpes 0.1892 8.037294e+03 1.1018 1958
## week-Sus_scrofa -0.0640 4.394471e+03 1.1026 3486
## I(week^2)-Canis_latrans 0.3473 5.026581e+07 1.2912 4887
## I(week^2)-Lynx_rufus 0.2652 1.099031e+08 1.3500 1723
## I(week^2)-Didelphis_virginiana 0.1345 9.086811e+07 1.3388 1857
## I(week^2)-Sylvilagus_floridanus 0.3560 8.868094e+07 1.3391 1688
## I(week^2)-Sciurus_carolinensis 0.2846 8.427241e+07 1.3024 5422
## I(week^2)-Vulpes_vulpes 0.1288 3.329977e+07 1.4157 913
## I(week^2)-Sus_scrofa 0.2535 1.479890e+08 1.3255 2830
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$beta.samples)
## 'mcmc' num [1:3000, 1:63] -0.676 -0.462 -1.527 0.12 -1.405 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:63] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.015
MCMCplot(ms_fullQ_fullQ$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
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/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/Kody Melissa/Downloads/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "CameraLocations")
# First day of the study
study_start <- as.Date("2024-05-10")
# Effort Matrix
effort_matrix <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "Daily_EM")
col_names <- names(effort_matrix)
date_cols <- col_names[-1]
converted_dates <- as.character(as.Date(as.numeric(date_cols), origin = "1899-12-30"))
names(effort_matrix) <- c("Plot", converted_dates)
# Pivot, filter, and add custom study week
effort_matrix <- effort_matrix %>%
pivot_longer(
cols = -Plot,
names_to = "date",
values_to = "effort"
) %>%
filter(effort == 1) %>%
mutate(
date = as.Date(date),
study_week = as.integer(floor((date - study_start) / 7)) + 1 # Week 1 starts on study_start
) %>%
dplyr::select(Plot, date, study_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)
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 daily observation data
observations_daily <- CameraData %>%
mutate(Date = as.Date(Date)) %>%
group_by(Plot, date = Date, Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge observations with daily effort
observations_daily <- effort_matrix %>%
left_join(observations_daily, by = c("Plot", "date")) %>%
replace_na(list(observations = 0))
observations_species <- observations_daily %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-`NA`) # Remove empty species name if present
# Create detection array (site × date × species)
site_names <- sort(unique(observations_species$Plot))
date_names <- sort(unique(observations_species$date))
species_names <- setdiff(colnames(observations_species), c("Plot", "date", "study_week"))
num_sites <- length(site_names)
num_days <- length(date_names)
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_days, num_species))
dimnames(detection_array) <- list(site_names, as.character(date_names), 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(date_names)) {
day <- date_names[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site &
observations_species$date == day &
observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be [num_sites x num_days x num_species]
## [1] 32 258 8
# Backup camera location data
CameraLoc_O2 <- CameraLoc
# Standardize site-level covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, -Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names # site_names should match the detection array
# Scale site covariates
covariates_matrix <- scale(covariates_matrix)
## Detection-level Covariates ----
effort_expanded <- expand.grid(
Plot = site_names,
date = date_names
) %>%
left_join(effort_matrix[, c("Plot", "date", "study_week")], by = c("Plot", "date"))
week_matrix <- matrix(effort_expanded$study_week,
nrow = num_sites,
ncol = num_days,
byrow = FALSE)
# Convert to numeric and scale
week_numeric <- scale(week_matrix)
dim(week_numeric)
## [1] 32 258
# Detection covariates list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_numeric
)
det.covs$week[is.na(det.covs$week)] <- 0
# Check structure
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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258, 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## .. ..$ : 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258] 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## $ 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
# 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%
## -------------------------------------------------
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
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## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
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## Sampled: 1500 of 5000, 30.00%
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## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
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## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
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): 2.8167
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8478 0.3749 -1.6054 -0.8481 -0.1096 1.0186 1348
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9893 0.8934 0.1503 0.7402 3.3191 1.005 1204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7695 0.2341 -5.1943 -4.7732 -4.3153 1.0573 893
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3216 0.3654 0.0533 0.2237 1.1674 1.0394 331
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2543 0.4237 -0.5254 0.2546 1.0742 1.0015
## (Intercept)-Lynx_rufus -0.1050 0.5252 -0.9614 -0.1551 1.0534 1.1105
## (Intercept)-Didelphis_virginiana -1.3010 0.4167 -2.1587 -1.2833 -0.5458 1.0021
## (Intercept)-Sylvilagus_floridanus -0.6225 0.4071 -1.3771 -0.6476 0.2190 1.0053
## (Intercept)-Meleagris_gallopavo -0.6760 0.4291 -1.5291 -0.6791 0.1779 1.0063
## (Intercept)-Sciurus_carolinensis -1.3080 0.4213 -2.2046 -1.2863 -0.5195 1.0002
## (Intercept)-Vulpes_vulpes -1.5521 0.6045 -2.7050 -1.5469 -0.3733 1.0102
## (Intercept)-Sus_scrofa -1.7400 0.5317 -2.8643 -1.7136 -0.7669 1.0038
## ESS
## (Intercept)-Canis_latrans 1605
## (Intercept)-Lynx_rufus 394
## (Intercept)-Didelphis_virginiana 2366
## (Intercept)-Sylvilagus_floridanus 1393
## (Intercept)-Meleagris_gallopavo 1166
## (Intercept)-Sciurus_carolinensis 2235
## (Intercept)-Vulpes_vulpes 375
## (Intercept)-Sus_scrofa 1361
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6670 0.1597 -4.9809 -4.6675 -4.3594 1.0317
## (Intercept)-Lynx_rufus -5.2947 0.2822 -5.8786 -5.2792 -4.7969 1.2870
## (Intercept)-Didelphis_virginiana -4.3847 0.2225 -4.8339 -4.3836 -3.9583 1.0449
## (Intercept)-Sylvilagus_floridanus -4.8731 0.2197 -5.3353 -4.8642 -4.4561 1.0138
## (Intercept)-Meleagris_gallopavo -5.0177 0.2368 -5.5113 -5.0070 -4.5687 1.0279
## (Intercept)-Sciurus_carolinensis -4.4175 0.2233 -4.8726 -4.4031 -4.0064 1.0073
## (Intercept)-Vulpes_vulpes -5.3286 0.4937 -6.5178 -5.2470 -4.5528 1.1511
## (Intercept)-Sus_scrofa -4.7566 0.3343 -5.4691 -4.7421 -4.1496 1.0522
## ESS
## (Intercept)-Canis_latrans 211
## (Intercept)-Lynx_rufus 99
## (Intercept)-Didelphis_virginiana 383
## (Intercept)-Sylvilagus_floridanus 223
## (Intercept)-Meleagris_gallopavo 198
## (Intercept)-Sciurus_carolinensis 375
## (Intercept)-Vulpes_vulpes 105
## (Intercept)-Sus_scrofa 243
# 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): 5.3915
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2047 1.0431 -3.2420 -1.1985 0.9207 1.0038 500
## Cogon_Patch_Size -0.1388 0.9772 -2.1739 -0.1318 1.8775 1.0462 245
## Veg_shannon_index 0.8411 0.8264 -0.8592 0.8573 2.4608 1.0156 446
## total_shrub_cover -1.2885 1.0528 -3.5531 -1.2412 0.7466 1.0095 248
## Avg_Cogongrass_Cover 1.5705 0.9746 -0.2707 1.5365 3.5834 1.0669 211
## Tree_Density -1.9622 0.9197 -3.9339 -1.9435 -0.1743 1.0437 179
## Avg_Canopy_Cover 2.1156 1.0695 -0.2457 2.1522 4.1353 1.0566 1143
## avg_veg_height -0.4035 0.7267 -1.8099 -0.4339 1.1175 1.1802 182
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9789 21.2085 0.2515 6.3537 52.2767 1.0639 362
## Cogon_Patch_Size 6.9781 12.6184 0.1255 2.9130 39.5463 1.0242 128
## Veg_shannon_index 5.1101 8.7874 0.1017 2.2288 26.4460 1.3430 71
## total_shrub_cover 10.5885 18.4722 0.1783 4.7412 58.2789 1.1655 77
## Avg_Cogongrass_Cover 3.1988 7.1572 0.0600 1.0386 19.2337 1.0715 189
## Tree_Density 2.7994 6.9987 0.0606 0.8808 18.0159 1.2226 175
## Avg_Canopy_Cover 15.0707 25.3996 0.4842 7.1619 81.8397 1.3496 59
## avg_veg_height 0.9810 1.7456 0.0460 0.4024 5.6410 1.0471 517
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.1736 9.6568 0.1111 3.0611 29.3392 1.335 59
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1312 0.2730 -5.6468 -5.1382 -4.5557 1.0459 421
## shrub_cover 0.4838 0.3765 -0.2463 0.4819 1.2859 1.0118 292
## veg_height -0.0333 0.2261 -0.4964 -0.0282 0.4078 1.0532 426
## week 0.1846 1.5868 -2.9144 0.1734 3.2691 1.0171 854
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.532000e-01 7.026000e-01 0.0638 0.3011 1.708600e+00 1.2070 539
## shrub_cover 9.974000e-01 9.308000e-01 0.1934 0.7219 3.198900e+00 1.0102 242
## veg_height 3.170000e-01 3.326000e-01 0.0624 0.2348 1.029900e+00 1.0537 541
## week 1.700842e+07 2.205137e+08 0.1374 127.5056 3.393946e+07 1.1264 384
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.0413 1.7441 -2.1069 0.9353
## (Intercept)-Lynx_rufus 1.5254 2.5863 -2.3449 1.2214
## (Intercept)-Didelphis_virginiana -2.7961 1.7481 -7.0533 -2.5863
## (Intercept)-Sylvilagus_floridanus -1.2804 1.6918 -4.7213 -1.3222
## (Intercept)-Meleagris_gallopavo -2.0371 2.0932 -6.6507 -1.8517
## (Intercept)-Sciurus_carolinensis -3.1367 2.0190 -7.6485 -2.8955
## (Intercept)-Vulpes_vulpes -2.7694 2.1465 -7.4050 -2.6835
## (Intercept)-Sus_scrofa -3.9158 2.5371 -9.8125 -3.6333
## Cogon_Patch_Size-Canis_latrans 1.3372 1.7024 -1.0196 0.9965
## Cogon_Patch_Size-Lynx_rufus 0.1368 2.1627 -4.5712 0.1343
## Cogon_Patch_Size-Didelphis_virginiana 1.5677 1.5107 -0.5739 1.2922
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9701 2.3067 -8.0974 -1.5286
## Cogon_Patch_Size-Meleagris_gallopavo 0.5220 1.6932 -2.1320 0.2702
## Cogon_Patch_Size-Sciurus_carolinensis -1.4186 2.0766 -6.4036 -1.0563
## Cogon_Patch_Size-Vulpes_vulpes -0.8479 2.3095 -6.5523 -0.5931
## Cogon_Patch_Size-Sus_scrofa -1.0811 1.9582 -5.9844 -0.7709
## Veg_shannon_index-Canis_latrans 1.6157 1.0040 -0.1485 1.5193
## Veg_shannon_index-Lynx_rufus -0.4609 1.8736 -4.9442 -0.1854
## Veg_shannon_index-Didelphis_virginiana 1.7759 1.4396 -0.3449 1.5587
## Veg_shannon_index-Sylvilagus_floridanus 1.4697 1.2576 -0.6186 1.3284
## Veg_shannon_index-Meleagris_gallopavo 2.1793 1.7181 -0.2512 1.8474
## Veg_shannon_index-Sciurus_carolinensis -0.6278 1.6086 -4.5680 -0.3834
## Veg_shannon_index-Vulpes_vulpes -0.4683 1.7688 -4.6286 -0.1797
## Veg_shannon_index-Sus_scrofa 2.4332 1.7650 -0.1880 2.0706
## total_shrub_cover-Canis_latrans 1.5239 1.7695 -0.7562 1.1071
## total_shrub_cover-Lynx_rufus -2.9801 2.4484 -8.6067 -2.6395
## total_shrub_cover-Didelphis_virginiana -2.3038 2.1007 -7.7486 -1.8543
## total_shrub_cover-Sylvilagus_floridanus -1.8222 2.1150 -6.9354 -1.4937
## total_shrub_cover-Meleagris_gallopavo -3.7846 2.4201 -9.5983 -3.3824
## total_shrub_cover-Sciurus_carolinensis -1.6600 2.3992 -7.3753 -1.2070
## total_shrub_cover-Vulpes_vulpes -2.6736 3.2520 -10.8692 -1.9983
## total_shrub_cover-Sus_scrofa -1.1923 2.2875 -6.9632 -0.8594
## Avg_Cogongrass_Cover-Canis_latrans 2.2871 1.3613 0.0751 2.1488
## Avg_Cogongrass_Cover-Lynx_rufus 2.6024 1.7995 -0.0756 2.3001
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.8176 1.3317 -0.4884 1.7083
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7798 1.4093 -2.2945 0.8494
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9260 1.7973 -2.7954 1.0443
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0084 1.4190 -0.4327 1.8645
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3417 1.6236 -0.2967 2.1028
## Avg_Cogongrass_Cover-Sus_scrofa 1.1301 1.7273 -2.7160 1.2403
## Tree_Density-Canis_latrans -2.6858 1.4356 -6.1703 -2.4507
## Tree_Density-Lynx_rufus -1.2491 1.3940 -3.8750 -1.3145
## Tree_Density-Didelphis_virginiana -2.1570 1.5106 -5.3619 -2.0951
## Tree_Density-Sylvilagus_floridanus -2.6036 1.6485 -6.4503 -2.3605
## Tree_Density-Meleagris_gallopavo -2.3350 1.5680 -6.2148 -2.1277
## Tree_Density-Sciurus_carolinensis -2.2052 1.4879 -5.4349 -2.1142
## Tree_Density-Vulpes_vulpes -2.0072 1.6611 -5.2979 -1.9671
## Tree_Density-Sus_scrofa -2.1543 1.5849 -5.7631 -2.0160
## Avg_Canopy_Cover-Canis_latrans -0.2381 0.9066 -2.1889 -0.2075
## Avg_Canopy_Cover-Lynx_rufus 0.5185 1.9778 -3.3764 0.4762
## Avg_Canopy_Cover-Didelphis_virginiana 4.8829 2.4801 1.7834 4.3137
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.1835 3.3652 1.9290 5.4478
## Avg_Canopy_Cover-Meleagris_gallopavo 3.4299 2.1013 0.6761 2.9794
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6729 2.4896 1.5059 4.1258
## Avg_Canopy_Cover-Vulpes_vulpes 3.7568 2.2992 0.7883 3.2001
## Avg_Canopy_Cover-Sus_scrofa 2.8044 1.7088 0.2609 2.4887
## avg_veg_height-Canis_latrans -0.3171 0.8103 -1.8136 -0.3317
## avg_veg_height-Lynx_rufus -0.6053 1.1576 -3.0970 -0.5707
## avg_veg_height-Didelphis_virginiana -0.6733 1.0067 -2.8801 -0.6439
## avg_veg_height-Sylvilagus_floridanus -0.5356 0.9346 -2.5080 -0.5325
## avg_veg_height-Meleagris_gallopavo -0.4238 1.1786 -2.8676 -0.3885
## avg_veg_height-Sciurus_carolinensis 0.0793 0.9892 -1.5302 -0.0194
## avg_veg_height-Vulpes_vulpes -0.4416 1.0970 -2.6177 -0.4569
## avg_veg_height-Sus_scrofa -0.4038 1.0053 -2.3658 -0.4106
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.0593 1.0081 186
## (Intercept)-Lynx_rufus 6.9105 1.0892 98
## (Intercept)-Didelphis_virginiana 0.1187 1.0229 244
## (Intercept)-Sylvilagus_floridanus 2.2807 1.0093 157
## (Intercept)-Meleagris_gallopavo 1.9393 1.0551 150
## (Intercept)-Sciurus_carolinensis 0.1331 1.0248 151
## (Intercept)-Vulpes_vulpes 1.6135 1.0680 94
## (Intercept)-Sus_scrofa 0.3010 1.0417 131
## Cogon_Patch_Size-Canis_latrans 5.7037 1.0589 211
## Cogon_Patch_Size-Lynx_rufus 4.4640 1.0120 191
## Cogon_Patch_Size-Didelphis_virginiana 5.3556 1.0234 110
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4206 1.0454 132
## Cogon_Patch_Size-Meleagris_gallopavo 4.8510 1.1188 150
## Cogon_Patch_Size-Sciurus_carolinensis 1.6809 1.0333 155
## Cogon_Patch_Size-Vulpes_vulpes 3.4172 1.0825 107
## Cogon_Patch_Size-Sus_scrofa 2.2120 1.0083 255
## Veg_shannon_index-Canis_latrans 3.8299 1.0130 432
## Veg_shannon_index-Lynx_rufus 2.4360 1.1039 171
## Veg_shannon_index-Didelphis_virginiana 5.3706 1.1054 222
## Veg_shannon_index-Sylvilagus_floridanus 4.3440 1.0520 212
## Veg_shannon_index-Meleagris_gallopavo 6.6674 1.1137 117
## Veg_shannon_index-Sciurus_carolinensis 1.7788 1.0825 166
## Veg_shannon_index-Vulpes_vulpes 2.0363 1.2095 163
## Veg_shannon_index-Sus_scrofa 6.7808 1.0800 214
## total_shrub_cover-Canis_latrans 5.7871 1.1416 83
## total_shrub_cover-Lynx_rufus 1.1024 1.0018 55
## total_shrub_cover-Didelphis_virginiana 0.6015 1.0110 161
## total_shrub_cover-Sylvilagus_floridanus 1.4203 1.1121 118
## total_shrub_cover-Meleagris_gallopavo -0.2634 1.0573 93
## total_shrub_cover-Sciurus_carolinensis 1.6180 1.1652 92
## total_shrub_cover-Vulpes_vulpes 1.9681 1.0396 68
## total_shrub_cover-Sus_scrofa 2.3396 1.0657 88
## Avg_Cogongrass_Cover-Canis_latrans 5.4707 1.0481 255
## Avg_Cogongrass_Cover-Lynx_rufus 7.1145 1.0169 160
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.8046 1.0064 207
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4082 1.0928 246
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.0877 1.0973 218
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.3205 1.0223 248
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.3219 1.0273 189
## Avg_Cogongrass_Cover-Sus_scrofa 4.1352 1.0617 245
## Tree_Density-Canis_latrans -0.6179 1.0393 278
## Tree_Density-Lynx_rufus 1.6459 1.0663 219
## Tree_Density-Didelphis_virginiana 0.6692 1.0607 282
## Tree_Density-Sylvilagus_floridanus -0.0493 1.0527 147
## Tree_Density-Meleagris_gallopavo 0.1624 1.0698 188
## Tree_Density-Sciurus_carolinensis 0.5828 1.0462 245
## Tree_Density-Vulpes_vulpes 1.1780 1.0711 194
## Tree_Density-Sus_scrofa 0.7844 1.0442 270
## Avg_Canopy_Cover-Canis_latrans 1.4916 1.0990 172
## Avg_Canopy_Cover-Lynx_rufus 4.7075 1.1051 185
## Avg_Canopy_Cover-Didelphis_virginiana 11.1027 1.0362 97
## Avg_Canopy_Cover-Sylvilagus_floridanus 14.8310 1.2203 97
## Avg_Canopy_Cover-Meleagris_gallopavo 8.5350 1.0114 164
## Avg_Canopy_Cover-Sciurus_carolinensis 11.6597 1.0792 105
## Avg_Canopy_Cover-Vulpes_vulpes 9.9297 1.2251 76
## Avg_Canopy_Cover-Sus_scrofa 6.8708 1.0576 159
## avg_veg_height-Canis_latrans 1.3515 1.1306 283
## avg_veg_height-Lynx_rufus 1.7131 1.1407 259
## avg_veg_height-Didelphis_virginiana 1.1977 1.0791 251
## avg_veg_height-Sylvilagus_floridanus 1.3162 1.0755 347
## avg_veg_height-Meleagris_gallopavo 1.8147 1.1126 234
## avg_veg_height-Sciurus_carolinensis 2.3680 1.0633 272
## avg_veg_height-Vulpes_vulpes 1.8163 1.1394 226
## avg_veg_height-Sus_scrofa 1.5990 1.1077 338
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7976 0.1637 -5.1130 -4.7980
## (Intercept)-Lynx_rufus -5.5866 0.2889 -6.1767 -5.5722
## (Intercept)-Didelphis_virginiana -4.7950 0.3020 -5.3718 -4.7913
## (Intercept)-Sylvilagus_floridanus -5.0391 0.2205 -5.4741 -5.0395
## (Intercept)-Meleagris_gallopavo -5.5486 0.3829 -6.3440 -5.5200
## (Intercept)-Sciurus_carolinensis -4.8583 0.2931 -5.4319 -4.8601
## (Intercept)-Vulpes_vulpes -5.8506 0.4900 -6.9319 -5.8247
## (Intercept)-Sus_scrofa -5.3978 0.4796 -6.3985 -5.3732
## shrub_cover-Canis_latrans -0.3357 0.2126 -0.7352 -0.3419
## shrub_cover-Lynx_rufus 0.1650 0.3576 -0.5687 0.1694
## shrub_cover-Didelphis_virginiana 1.2630 0.4132 0.5540 1.2161
## shrub_cover-Sylvilagus_floridanus 0.7726 0.3570 0.0506 0.7711
## shrub_cover-Meleagris_gallopavo -0.5517 0.3825 -1.3492 -0.5357
## shrub_cover-Sciurus_carolinensis 1.1754 0.3936 0.3993 1.1810
## shrub_cover-Vulpes_vulpes 0.4137 0.6234 -0.8970 0.3962
## shrub_cover-Sus_scrofa 1.1468 0.8261 -0.4699 1.1462
## veg_height-Canis_latrans -0.5682 0.1756 -0.9292 -0.5591
## veg_height-Lynx_rufus 0.1260 0.2417 -0.3224 0.1208
## veg_height-Didelphis_virginiana 0.5233 0.2532 0.0603 0.5208
## veg_height-Sylvilagus_floridanus 0.2005 0.2295 -0.2514 0.2016
## veg_height-Meleagris_gallopavo -0.2887 0.3693 -1.0556 -0.2713
## veg_height-Sciurus_carolinensis 0.2628 0.2161 -0.1438 0.2544
## veg_height-Vulpes_vulpes -0.2635 0.3610 -1.0875 -0.2276
## veg_height-Sus_scrofa -0.2953 0.3251 -0.9696 -0.2866
## week-Canis_latrans 35.7118 4059.5347 -2191.9043 0.3948
## week-Lynx_rufus 38.4169 4368.1808 -2086.5903 0.2933
## week-Didelphis_virginiana -42.7133 4060.5975 -1954.8206 0.4666
## week-Sylvilagus_floridanus -83.6629 3933.2908 -1813.0532 0.3548
## week-Meleagris_gallopavo -62.1789 3427.2721 -2473.8561 0.3246
## week-Sciurus_carolinensis 35.8128 4698.3552 -1843.0615 0.3847
## week-Vulpes_vulpes -20.8465 4727.0946 -2116.6641 0.4124
## week-Sus_scrofa 48.2967 3477.6103 -1883.2997 0.5071
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4737 1.0355 225
## (Intercept)-Lynx_rufus -5.0551 1.0751 95
## (Intercept)-Didelphis_virginiana -4.2278 1.1241 128
## (Intercept)-Sylvilagus_floridanus -4.6206 1.0060 213
## (Intercept)-Meleagris_gallopavo -4.8568 1.0357 110
## (Intercept)-Sciurus_carolinensis -4.2501 1.0648 138
## (Intercept)-Vulpes_vulpes -4.9797 1.2244 92
## (Intercept)-Sus_scrofa -4.4905 1.1178 112
## shrub_cover-Canis_latrans 0.0786 1.0554 160
## shrub_cover-Lynx_rufus 0.8643 1.0382 80
## shrub_cover-Didelphis_virginiana 2.1950 1.1007 73
## shrub_cover-Sylvilagus_floridanus 1.4903 1.0848 107
## shrub_cover-Meleagris_gallopavo 0.1773 1.0550 146
## shrub_cover-Sciurus_carolinensis 1.9692 1.0110 92
## shrub_cover-Vulpes_vulpes 1.6410 1.1639 68
## shrub_cover-Sus_scrofa 2.7931 1.1540 119
## veg_height-Canis_latrans -0.2354 1.0182 191
## veg_height-Lynx_rufus 0.6046 1.1558 131
## veg_height-Didelphis_virginiana 1.0244 1.0386 226
## veg_height-Sylvilagus_floridanus 0.6412 1.0317 230
## veg_height-Meleagris_gallopavo 0.4132 1.1212 125
## veg_height-Sciurus_carolinensis 0.6973 1.0940 240
## veg_height-Vulpes_vulpes 0.3881 1.1416 85
## veg_height-Sus_scrofa 0.3308 1.0743 232
## week-Canis_latrans 1875.5439 1.1854 9973
## week-Lynx_rufus 1823.2360 1.1745 4677
## week-Didelphis_virginiana 1716.3989 1.1393 3858
## week-Sylvilagus_floridanus 1712.7465 1.1598 2860
## week-Meleagris_gallopavo 1630.8919 1.1347 8329
## week-Sciurus_carolinensis 1823.7076 1.2025 15682
## week-Vulpes_vulpes 2154.1102 1.2300 83833
## week-Sus_scrofa 2149.2507 1.1534 10995
#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): 4.8158
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5983 0.3931 -1.4014 -0.5999 0.2022 1.0468 875
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9912 0.9801 0.1283 0.7155 3.6306 1.0296 692
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0226 0.3165 -5.6098 -5.0341 -4.3550 1.0155 1008
## shrub_cover 0.1711 0.3845 -0.6221 0.1768 0.9212 1.0430 622
## veg_height -0.0534 0.2210 -0.4843 -0.0542 0.3975 1.0002 604
## week 0.1118 1.7677 -3.2666 0.1043 3.8688 1.0298 257
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.670000e-01 6.559000e-01 0.0721 0.3833 2.220200e+00 1.1633 121
## shrub_cover 9.662000e-01 9.327000e-01 0.1817 0.7060 3.321000e+00 1.1122 342
## veg_height 2.758000e-01 2.686000e-01 0.0561 0.2031 9.368000e-01 1.0192 519
## week 5.729232e+13 1.134978e+15 0.1122 179.8526 5.979602e+12 1.5238 456
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3131 0.4014 -0.4458 0.2967 1.1258 1.0195
## (Intercept)-Lynx_rufus 0.1206 0.5737 -0.8411 0.0552 1.4514 1.0447
## (Intercept)-Didelphis_virginiana -1.0894 0.4344 -1.9935 -1.0786 -0.2832 1.0157
## (Intercept)-Sylvilagus_floridanus -0.5313 0.3976 -1.3348 -0.5353 0.2737 1.0039
## (Intercept)-Meleagris_gallopavo -0.0358 0.6371 -1.1231 -0.0924 1.3730 1.1688
## (Intercept)-Sciurus_carolinensis -1.1045 0.4378 -2.0108 -1.0952 -0.2928 1.0093
## (Intercept)-Vulpes_vulpes -1.2740 0.6203 -2.5246 -1.2625 -0.0406 1.0531
## (Intercept)-Sus_scrofa -1.4322 0.6052 -2.7320 -1.3993 -0.3754 1.0078
## ESS
## (Intercept)-Canis_latrans 1452
## (Intercept)-Lynx_rufus 223
## (Intercept)-Didelphis_virginiana 1056
## (Intercept)-Sylvilagus_floridanus 1458
## (Intercept)-Meleagris_gallopavo 134
## (Intercept)-Sciurus_carolinensis 1368
## (Intercept)-Vulpes_vulpes 320
## (Intercept)-Sus_scrofa 690
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7649 1.649000e-01 -5.0930 -4.7613
## (Intercept)-Lynx_rufus -5.5064 3.000000e-01 -6.1276 -5.4903
## (Intercept)-Didelphis_virginiana -4.6105 2.590000e-01 -5.1459 -4.6000
## (Intercept)-Sylvilagus_floridanus -4.8955 2.308000e-01 -5.3585 -4.8939
## (Intercept)-Meleagris_gallopavo -5.8219 4.745000e-01 -6.7446 -5.8073
## (Intercept)-Sciurus_carolinensis -4.6444 2.833000e-01 -5.2114 -4.6463
## (Intercept)-Vulpes_vulpes -5.6969 5.331000e-01 -6.8407 -5.6469
## (Intercept)-Sus_scrofa -5.2774 4.794000e-01 -6.2533 -5.2655
## shrub_cover-Canis_latrans -0.2843 1.953000e-01 -0.6885 -0.2815
## shrub_cover-Lynx_rufus -0.3058 3.246000e-01 -0.9464 -0.2990
## shrub_cover-Didelphis_virginiana 1.0143 3.448000e-01 0.3871 1.0046
## shrub_cover-Sylvilagus_floridanus 0.4272 4.381000e-01 -0.3877 0.4087
## shrub_cover-Meleagris_gallopavo -0.8558 4.126000e-01 -1.6744 -0.8214
## shrub_cover-Sciurus_carolinensis 0.8606 3.609000e-01 0.1665 0.8565
## shrub_cover-Vulpes_vulpes -0.2590 6.341000e-01 -1.5675 -0.2188
## shrub_cover-Sus_scrofa 0.8208 8.143000e-01 -0.7802 0.8341
## veg_height-Canis_latrans -0.5602 1.497000e-01 -0.8634 -0.5538
## veg_height-Lynx_rufus 0.1024 2.311000e-01 -0.3656 0.1081
## veg_height-Didelphis_virginiana 0.4516 2.381000e-01 0.0039 0.4501
## veg_height-Sylvilagus_floridanus 0.1407 2.285000e-01 -0.3193 0.1445
## veg_height-Meleagris_gallopavo -0.3641 3.934000e-01 -1.1884 -0.3410
## veg_height-Sciurus_carolinensis 0.1780 2.169000e-01 -0.2480 0.1764
## veg_height-Vulpes_vulpes -0.1178 2.868000e-01 -0.7590 -0.0983
## veg_height-Sus_scrofa -0.2788 3.284000e-01 -0.9425 -0.2743
## week-Canis_latrans -209641.2728 7.370968e+06 -91318.6403 0.3798
## week-Lynx_rufus 33662.0779 4.366892e+06 -75296.5432 0.4599
## week-Didelphis_virginiana -263705.9017 7.555421e+06 -83873.2820 0.3276
## week-Sylvilagus_floridanus 3076.7791 5.494371e+06 -58658.0457 0.3626
## week-Meleagris_gallopavo 70713.4075 5.958403e+06 -51234.8861 0.5212
## week-Sciurus_carolinensis -42100.2070 1.214864e+07 -87620.3387 0.4297
## week-Vulpes_vulpes 43819.2348 4.740696e+06 -72116.0616 0.3526
## week-Sus_scrofa -30129.3628 5.336383e+06 -57636.1836 0.2229
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4588 1.0175 198
## (Intercept)-Lynx_rufus -4.9420 1.1542 91
## (Intercept)-Didelphis_virginiana -4.1208 1.1167 209
## (Intercept)-Sylvilagus_floridanus -4.4517 1.0111 209
## (Intercept)-Meleagris_gallopavo -4.9596 1.6113 36
## (Intercept)-Sciurus_carolinensis -4.1067 1.0353 198
## (Intercept)-Vulpes_vulpes -4.7774 1.2631 101
## (Intercept)-Sus_scrofa -4.3306 1.0572 227
## shrub_cover-Canis_latrans 0.0875 1.0818 211
## shrub_cover-Lynx_rufus 0.3233 1.0670 123
## shrub_cover-Didelphis_virginiana 1.7120 1.0123 170
## shrub_cover-Sylvilagus_floridanus 1.3820 1.1204 125
## shrub_cover-Meleagris_gallopavo -0.1198 1.5886 50
## shrub_cover-Sciurus_carolinensis 1.5753 1.0242 268
## shrub_cover-Vulpes_vulpes 0.9271 1.1922 148
## shrub_cover-Sus_scrofa 2.5021 1.0446 235
## veg_height-Canis_latrans -0.2852 1.0062 254
## veg_height-Lynx_rufus 0.5591 1.0398 180
## veg_height-Didelphis_virginiana 0.9493 1.0696 224
## veg_height-Sylvilagus_floridanus 0.5868 1.0009 216
## veg_height-Meleagris_gallopavo 0.3975 1.0135 116
## veg_height-Sciurus_carolinensis 0.6055 1.0193 249
## veg_height-Vulpes_vulpes 0.3891 1.0615 194
## veg_height-Sus_scrofa 0.3562 1.0022 313
## week-Canis_latrans 50091.5663 1.3688 1784
## week-Lynx_rufus 58901.2590 1.2962 15128
## week-Didelphis_virginiana 48925.1915 1.4068 1061
## week-Sylvilagus_floridanus 86350.9846 1.2904 8841
## week-Meleagris_gallopavo 90306.1261 1.3043 12944
## week-Sciurus_carolinensis 63137.8672 1.2916 56167
## week-Vulpes_vulpes 83589.0586 1.2989 9327
## week-Sus_scrofa 93609.5439 1.2936 5594
#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): 5.5932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4851 0.6402 -1.7782 -0.4963 0.8700 1.1768 102
## Avg_Cogongrass_Cover 0.0349 0.5004 -0.9556 0.0416 1.0009 1.0114 440
## total_shrub_cover -1.1167 0.6693 -2.6037 -1.0693 0.0988 1.1057 204
## avg_veg_height 0.1260 0.4986 -0.8879 0.1176 1.1360 1.0382 252
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8883 1.4404 0.0576 0.4599 4.1375 1.0562 556
## Avg_Cogongrass_Cover 0.9314 1.6027 0.0519 0.4582 4.4577 1.0255 628
## total_shrub_cover 2.1136 2.8288 0.1037 1.2903 9.4530 1.0555 138
## avg_veg_height 0.5063 0.8976 0.0432 0.2507 2.3418 1.1455 204
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.0862 4.0296 0.3332 2.9296 14.7114 1.198 87
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1773 0.2587 -5.6718 -5.1797 -4.6459 1.1220 161
## shrub_cover 0.5966 0.4016 -0.1969 0.5976 1.3896 1.0514 381
## veg_height -0.0428 0.2340 -0.5045 -0.0442 0.4282 1.0632 213
## week 0.0810 1.6318 -3.1740 0.1171 3.1258 1.0056 800
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.413000e-01 3.757000e-01 0.0497 0.2318 1.329100e+00 1.2374 165
## shrub_cover 1.119500e+00 1.081900e+00 0.2190 0.8405 3.503800e+00 1.0226 351
## veg_height 3.150000e-01 2.751000e-01 0.0682 0.2391 1.040000e+00 1.0077 590
## week 2.646727e+14 4.700849e+15 0.1132 257.8878 2.420914e+14 1.5757 393
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0907 0.8609 -1.5436 0.0505
## (Intercept)-Lynx_rufus -0.2223 0.8559 -1.9235 -0.2402
## (Intercept)-Didelphis_virginiana -0.6456 0.8628 -2.3854 -0.6414
## (Intercept)-Sylvilagus_floridanus -0.2564 0.8531 -1.8445 -0.3087
## (Intercept)-Meleagris_gallopavo -0.5973 0.8628 -2.3613 -0.5978
## (Intercept)-Sciurus_carolinensis -0.7023 0.8646 -2.4827 -0.6813
## (Intercept)-Vulpes_vulpes -0.7970 0.9547 -2.6493 -0.7943
## (Intercept)-Sus_scrofa -0.9342 0.9622 -3.0433 -0.8789
## Avg_Cogongrass_Cover-Canis_latrans 0.4956 0.6791 -0.6725 0.4363
## Avg_Cogongrass_Cover-Lynx_rufus 0.5913 0.8013 -0.7036 0.4845
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2704 0.6623 -0.9414 0.2414
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5560 0.7255 -2.2073 -0.4784
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5169 0.8607 -2.4818 -0.4403
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0010 0.6260 -1.2947 0.0249
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3088 0.7766 -1.0877 0.2551
## Avg_Cogongrass_Cover-Sus_scrofa -0.2663 0.8406 -2.1346 -0.1997
## total_shrub_cover-Canis_latrans 0.5454 0.8458 -0.8611 0.4480
## total_shrub_cover-Lynx_rufus -1.7530 1.1323 -4.3073 -1.6246
## total_shrub_cover-Didelphis_virginiana -1.2684 0.8793 -3.3899 -1.1470
## total_shrub_cover-Sylvilagus_floridanus -1.8767 1.1870 -4.6985 -1.6786
## total_shrub_cover-Meleagris_gallopavo -1.9626 1.0957 -4.5246 -1.8164
## total_shrub_cover-Sciurus_carolinensis -1.2300 0.9802 -3.5825 -1.0734
## total_shrub_cover-Vulpes_vulpes -1.4322 1.2809 -4.3842 -1.2400
## total_shrub_cover-Sus_scrofa -0.9059 1.0966 -3.3379 -0.8202
## avg_veg_height-Canis_latrans 0.2054 0.5672 -0.8969 0.1837
## avg_veg_height-Lynx_rufus 0.0418 0.7676 -1.5583 0.0516
## avg_veg_height-Didelphis_virginiana 0.0044 0.6310 -1.2898 0.0060
## avg_veg_height-Sylvilagus_floridanus 0.0954 0.6232 -1.1370 0.0869
## avg_veg_height-Meleagris_gallopavo -0.1552 0.9308 -2.2945 -0.0761
## avg_veg_height-Sciurus_carolinensis 0.5283 0.6285 -0.6052 0.4907
## avg_veg_height-Vulpes_vulpes 0.0754 0.6875 -1.3414 0.0927
## avg_veg_height-Sus_scrofa 0.2076 0.6676 -1.1679 0.2049
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9804 1.0504 275
## (Intercept)-Lynx_rufus 1.5036 1.1029 160
## (Intercept)-Didelphis_virginiana 1.1855 1.1093 162
## (Intercept)-Sylvilagus_floridanus 1.6323 1.1042 197
## (Intercept)-Meleagris_gallopavo 1.1259 1.0888 215
## (Intercept)-Sciurus_carolinensis 0.9701 1.1266 212
## (Intercept)-Vulpes_vulpes 1.0904 1.0917 136
## (Intercept)-Sus_scrofa 0.7933 1.0967 216
## Avg_Cogongrass_Cover-Canis_latrans 2.0555 1.0018 491
## Avg_Cogongrass_Cover-Lynx_rufus 2.4236 1.0156 463
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7658 1.0078 334
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6792 1.0307 483
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9528 1.0414 411
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1925 1.0139 718
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.9719 1.0073 490
## Avg_Cogongrass_Cover-Sus_scrofa 1.2477 1.0054 463
## total_shrub_cover-Canis_latrans 2.4535 1.0242 128
## total_shrub_cover-Lynx_rufus 0.1022 1.1132 156
## total_shrub_cover-Didelphis_virginiana 0.1454 1.0477 137
## total_shrub_cover-Sylvilagus_floridanus -0.1364 1.1290 123
## total_shrub_cover-Meleagris_gallopavo -0.3061 1.0308 179
## total_shrub_cover-Sciurus_carolinensis 0.2806 1.1629 174
## total_shrub_cover-Vulpes_vulpes 0.6742 1.1281 129
## total_shrub_cover-Sus_scrofa 1.0182 1.0740 271
## avg_veg_height-Canis_latrans 1.3520 1.0153 468
## avg_veg_height-Lynx_rufus 1.5537 1.0370 412
## avg_veg_height-Didelphis_virginiana 1.2835 1.0297 320
## avg_veg_height-Sylvilagus_floridanus 1.3599 1.0483 370
## avg_veg_height-Meleagris_gallopavo 1.3864 1.1005 154
## avg_veg_height-Sciurus_carolinensis 1.9190 1.0171 497
## avg_veg_height-Vulpes_vulpes 1.3815 1.0101 430
## avg_veg_height-Sus_scrofa 1.5474 1.0204 455
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8786 1.811000e-01 -5.2488
## (Intercept)-Lynx_rufus -5.5062 2.860000e-01 -6.1415
## (Intercept)-Didelphis_virginiana -4.9486 2.901000e-01 -5.4970
## (Intercept)-Sylvilagus_floridanus -5.1272 2.348000e-01 -5.5956
## (Intercept)-Meleagris_gallopavo -5.4725 4.103000e-01 -6.2454
## (Intercept)-Sciurus_carolinensis -4.9234 2.966000e-01 -5.4841
## (Intercept)-Vulpes_vulpes -5.7726 5.250000e-01 -6.9447
## (Intercept)-Sus_scrofa -5.4562 3.963000e-01 -6.2472
## shrub_cover-Canis_latrans -0.3230 2.360000e-01 -0.7899
## shrub_cover-Lynx_rufus 0.1777 3.557000e-01 -0.5740
## shrub_cover-Didelphis_virginiana 1.5115 4.709000e-01 0.6462
## shrub_cover-Sylvilagus_floridanus 0.9931 3.630000e-01 0.2729
## shrub_cover-Meleagris_gallopavo -0.4691 4.215000e-01 -1.2640
## shrub_cover-Sciurus_carolinensis 1.2850 3.714000e-01 0.5409
## shrub_cover-Vulpes_vulpes 0.6160 7.509000e-01 -0.8006
## shrub_cover-Sus_scrofa 1.2977 7.032000e-01 -0.1325
## veg_height-Canis_latrans -0.6391 1.888000e-01 -1.0232
## veg_height-Lynx_rufus 0.1188 2.586000e-01 -0.4171
## veg_height-Didelphis_virginiana 0.4652 2.624000e-01 -0.0365
## veg_height-Sylvilagus_floridanus 0.0865 2.496000e-01 -0.4487
## veg_height-Meleagris_gallopavo -0.2325 4.625000e-01 -1.1667
## veg_height-Sciurus_carolinensis 0.2753 2.270000e-01 -0.1468
## veg_height-Vulpes_vulpes -0.1797 3.568000e-01 -0.9019
## veg_height-Sus_scrofa -0.2267 3.131000e-01 -0.8381
## week-Canis_latrans 169227.8456 1.564383e+07 -1694367.3982
## week-Lynx_rufus 144801.4254 1.520185e+07 -963071.7379
## week-Didelphis_virginiana -38057.6221 1.201049e+07 -757406.3451
## week-Sylvilagus_floridanus -619950.1618 1.438838e+07 -2109711.4824
## week-Meleagris_gallopavo 144946.1491 1.195584e+07 -949696.5075
## week-Sciurus_carolinensis -576680.1672 1.920925e+07 -1281201.9018
## week-Vulpes_vulpes 226675.3751 2.094092e+07 -551932.8000
## week-Sus_scrofa 232081.8567 1.831561e+07 -611605.6574
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8724 -4.5412 1.1021 152
## (Intercept)-Lynx_rufus -5.4781 -4.9897 1.5972 110
## (Intercept)-Didelphis_virginiana -4.9550 -4.3679 1.0458 90
## (Intercept)-Sylvilagus_floridanus -5.1277 -4.6883 1.0093 170
## (Intercept)-Meleagris_gallopavo -5.4800 -4.6719 1.2279 134
## (Intercept)-Sciurus_carolinensis -4.9265 -4.3233 1.1341 100
## (Intercept)-Vulpes_vulpes -5.7069 -4.9215 1.4806 81
## (Intercept)-Sus_scrofa -5.4600 -4.6711 1.0923 153
## shrub_cover-Canis_latrans -0.3204 0.1282 1.0088 128
## shrub_cover-Lynx_rufus 0.1820 0.8380 1.2084 110
## shrub_cover-Didelphis_virginiana 1.4941 2.5612 1.0286 47
## shrub_cover-Sylvilagus_floridanus 0.9922 1.6661 1.1632 76
## shrub_cover-Meleagris_gallopavo -0.4734 0.3784 1.1144 172
## shrub_cover-Sciurus_carolinensis 1.2948 2.0316 1.0914 115
## shrub_cover-Vulpes_vulpes 0.6196 2.2072 1.3787 75
## shrub_cover-Sus_scrofa 1.2896 2.7584 1.0854 109
## veg_height-Canis_latrans -0.6360 -0.2895 1.0553 116
## veg_height-Lynx_rufus 0.1241 0.5944 1.0219 132
## veg_height-Didelphis_virginiana 0.4605 0.9877 1.0369 149
## veg_height-Sylvilagus_floridanus 0.1011 0.5547 1.0909 147
## veg_height-Meleagris_gallopavo -0.2351 0.6644 1.0727 93
## veg_height-Sciurus_carolinensis 0.2716 0.7578 1.0272 170
## veg_height-Vulpes_vulpes -0.1697 0.5074 1.2088 101
## veg_height-Sus_scrofa -0.2264 0.4000 1.0707 283
## week-Canis_latrans 0.0803 1079646.5947 1.3021 6952
## week-Lynx_rufus 0.4887 1646744.6682 1.2993 8210
## week-Didelphis_virginiana -0.1328 1525392.5950 1.2914 11788
## week-Sylvilagus_floridanus 0.0909 924054.0475 1.4641 583
## week-Meleagris_gallopavo 0.3263 2490947.0739 1.3051 14225
## week-Sciurus_carolinensis -0.0719 1110877.5580 1.3774 1284
## week-Vulpes_vulpes 0.4409 2317567.1784 1.3021 7242
## week-Sus_scrofa 0.3238 1895996.3095 1.3062 11057
#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): 5.0092
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8607 0.5974 -1.9731 -0.8665 0.4190 1.0183 612
## Tree_Density -0.8406 0.5591 -2.0401 -0.8084 0.1409 1.0044 436
## Avg_Canopy_Cover 1.3020 0.5545 0.3036 1.2707 2.4800 1.0237 454
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3059 3.0207 0.0969 1.4715 9.7693 1.0124 421
## Tree_Density 1.2099 3.6734 0.0501 0.4783 6.1412 1.4139 231
## Avg_Canopy_Cover 1.7601 2.3238 0.1311 1.1048 7.4980 1.0425 364
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0469 1.3266 0.0626 0.5758 4.9535 1.2616 74
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0971 0.3018 -5.6448 -5.1121 -4.4643 1.0226 412
## shrub_cover 0.2758 0.3797 -0.4574 0.2647 1.0465 1.0174 776
## veg_height -0.0130 0.2224 -0.4524 -0.0145 0.4228 1.0008 592
## week -0.1231 1.6332 -3.4190 -0.1172 3.0121 1.0441 429
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.276000e-01 6.93100e-01 0.0810 0.3590 1.918400e+00 1.0486 328
## shrub_cover 9.987000e-01 1.02440e+00 0.1849 0.7432 3.315300e+00 1.0854 844
## veg_height 3.063000e-01 2.60800e-01 0.0656 0.2354 9.560000e-01 1.0038 842
## week 5.916945e+13 1.45593e+15 0.0830 121.2428 1.159199e+11 1.4459 632
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1719 0.7216 -1.2567 0.1777 1.6166
## (Intercept)-Lynx_rufus 0.4427 1.3356 -1.5723 0.2234 3.7924
## (Intercept)-Didelphis_virginiana -1.5524 0.7665 -3.1896 -1.4923 -0.1805
## (Intercept)-Sylvilagus_floridanus -0.8113 0.7156 -2.1485 -0.8051 0.6773
## (Intercept)-Meleagris_gallopavo -0.3590 0.8976 -2.0181 -0.3978 1.4987
## (Intercept)-Sciurus_carolinensis -1.6832 0.7815 -3.4246 -1.6270 -0.3086
## (Intercept)-Vulpes_vulpes -1.6391 0.9678 -3.5252 -1.6326 0.3494
## (Intercept)-Sus_scrofa -2.1004 0.9252 -4.1059 -2.0347 -0.5011
## Tree_Density-Canis_latrans -1.0447 0.6544 -2.5441 -0.9693 0.0216
## Tree_Density-Lynx_rufus 0.0698 0.9039 -1.2920 -0.0464 2.1175
## Tree_Density-Didelphis_virginiana -1.1238 0.9427 -3.3501 -0.9567 0.2092
## Tree_Density-Sylvilagus_floridanus -1.1996 0.8986 -3.3760 -1.0558 0.1820
## Tree_Density-Meleagris_gallopavo -1.0596 0.9479 -3.4016 -0.9064 0.4631
## Tree_Density-Sciurus_carolinensis -0.9907 0.8571 -3.0541 -0.8782 0.3680
## Tree_Density-Vulpes_vulpes -0.6493 1.1054 -2.4673 -0.6555 0.9596
## Tree_Density-Sus_scrofa -1.0072 0.9080 -3.1459 -0.8874 0.4545
## Avg_Canopy_Cover-Canis_latrans -0.1402 0.5206 -1.2095 -0.1358 0.8613
## Avg_Canopy_Cover-Lynx_rufus 0.9917 0.9220 -0.5944 0.9153 3.0159
## Avg_Canopy_Cover-Didelphis_virginiana 1.8493 0.8494 0.5695 1.7152 3.8503
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.5561 1.2083 0.8673 2.3516 5.5542
## Avg_Canopy_Cover-Meleagris_gallopavo 1.8091 0.9301 0.3361 1.6864 4.0108
## Avg_Canopy_Cover-Sciurus_carolinensis 1.6953 0.7699 0.4714 1.5827 3.5054
## Avg_Canopy_Cover-Vulpes_vulpes 1.2086 0.7858 -0.1161 1.1167 2.9984
## Avg_Canopy_Cover-Sus_scrofa 1.4618 0.7649 0.2554 1.3718 3.2237
## Rhat ESS
## (Intercept)-Canis_latrans 1.0041 451
## (Intercept)-Lynx_rufus 1.0060 153
## (Intercept)-Didelphis_virginiana 1.0322 551
## (Intercept)-Sylvilagus_floridanus 1.0628 508
## (Intercept)-Meleagris_gallopavo 1.0190 308
## (Intercept)-Sciurus_carolinensis 1.0032 564
## (Intercept)-Vulpes_vulpes 1.0035 311
## (Intercept)-Sus_scrofa 1.0039 532
## Tree_Density-Canis_latrans 1.0010 706
## Tree_Density-Lynx_rufus 1.0317 264
## Tree_Density-Didelphis_virginiana 1.0187 446
## Tree_Density-Sylvilagus_floridanus 1.0057 572
## Tree_Density-Meleagris_gallopavo 1.0149 467
## Tree_Density-Sciurus_carolinensis 1.0014 499
## Tree_Density-Vulpes_vulpes 1.1449 176
## Tree_Density-Sus_scrofa 1.0264 393
## Avg_Canopy_Cover-Canis_latrans 1.0024 715
## Avg_Canopy_Cover-Lynx_rufus 1.0173 378
## Avg_Canopy_Cover-Didelphis_virginiana 1.0375 402
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0189 259
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0100 340
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0065 415
## Avg_Canopy_Cover-Vulpes_vulpes 1.0437 433
## Avg_Canopy_Cover-Sus_scrofa 1.0227 613
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8105 1.700000e-01 -5.1396 -4.8033
## (Intercept)-Lynx_rufus -5.6882 2.966000e-01 -6.2152 -5.7001
## (Intercept)-Didelphis_virginiana -4.7387 2.737000e-01 -5.2960 -4.7345
## (Intercept)-Sylvilagus_floridanus -4.9867 2.397000e-01 -5.4582 -4.9836
## (Intercept)-Meleagris_gallopavo -5.7326 4.255000e-01 -6.6657 -5.6961
## (Intercept)-Sciurus_carolinensis -4.7341 2.937000e-01 -5.3281 -4.7307
## (Intercept)-Vulpes_vulpes -5.7744 5.595000e-01 -7.1254 -5.7537
## (Intercept)-Sus_scrofa -5.2518 4.683000e-01 -6.2119 -5.2552
## shrub_cover-Canis_latrans -0.3024 2.149000e-01 -0.7107 -0.3091
## shrub_cover-Lynx_rufus -0.3194 3.303000e-01 -1.0102 -0.3106
## shrub_cover-Didelphis_virginiana 1.1177 3.430000e-01 0.4802 1.1103
## shrub_cover-Sylvilagus_floridanus 0.6455 3.774000e-01 -0.0845 0.6573
## shrub_cover-Meleagris_gallopavo -0.7783 4.065000e-01 -1.6551 -0.7610
## shrub_cover-Sciurus_carolinensis 0.9927 3.916000e-01 0.2322 0.9880
## shrub_cover-Vulpes_vulpes 0.0210 6.430000e-01 -1.3253 0.0435
## shrub_cover-Sus_scrofa 0.8312 7.960000e-01 -0.7419 0.8376
## veg_height-Canis_latrans -0.5961 1.784000e-01 -0.9519 -0.5932
## veg_height-Lynx_rufus 0.1913 2.202000e-01 -0.2557 0.1973
## veg_height-Didelphis_virginiana 0.5497 2.473000e-01 0.0864 0.5422
## veg_height-Sylvilagus_floridanus 0.1783 2.368000e-01 -0.2916 0.1789
## veg_height-Meleagris_gallopavo -0.2825 3.554000e-01 -1.0098 -0.2762
## veg_height-Sciurus_carolinensis 0.2237 2.204000e-01 -0.1870 0.2145
## veg_height-Vulpes_vulpes -0.1553 3.073000e-01 -0.8369 -0.1366
## veg_height-Sus_scrofa -0.2320 3.360000e-01 -0.9383 -0.2331
## week-Canis_latrans -260969.4265 9.101067e+06 -20957.2908 -0.0996
## week-Lynx_rufus -5246.0822 5.253257e+06 -17985.1198 -0.1633
## week-Didelphis_virginiana -11092.1594 8.427290e+06 -15902.9027 -0.2153
## week-Sylvilagus_floridanus -72478.7138 1.032159e+07 -24004.9912 -0.3783
## week-Meleagris_gallopavo -327984.1817 6.870758e+06 -27905.9867 -0.1503
## week-Sciurus_carolinensis 93702.1039 5.467374e+06 -17905.3742 -0.1878
## week-Vulpes_vulpes 45696.6688 8.171083e+06 -19970.7340 -0.3052
## week-Sus_scrofa 199120.5469 7.558377e+06 -19763.7438 -0.3854
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4904 1.0404 208
## (Intercept)-Lynx_rufus -5.0727 1.0174 63
## (Intercept)-Didelphis_virginiana -4.2063 1.1834 128
## (Intercept)-Sylvilagus_floridanus -4.5375 1.1009 199
## (Intercept)-Meleagris_gallopavo -4.9836 1.0966 68
## (Intercept)-Sciurus_carolinensis -4.1646 1.0113 125
## (Intercept)-Vulpes_vulpes -4.8306 1.0674 62
## (Intercept)-Sus_scrofa -4.3032 1.0468 234
## shrub_cover-Canis_latrans 0.1168 1.0415 176
## shrub_cover-Lynx_rufus 0.3126 1.0233 91
## shrub_cover-Didelphis_virginiana 1.8217 1.1342 155
## shrub_cover-Sylvilagus_floridanus 1.3671 1.1637 102
## shrub_cover-Meleagris_gallopavo -0.0036 1.1311 76
## shrub_cover-Sciurus_carolinensis 1.7624 1.0530 134
## shrub_cover-Vulpes_vulpes 1.1786 1.0119 129
## shrub_cover-Sus_scrofa 2.3614 1.0361 261
## veg_height-Canis_latrans -0.2551 1.0144 182
## veg_height-Lynx_rufus 0.5871 1.0046 168
## veg_height-Didelphis_virginiana 1.0477 1.0609 250
## veg_height-Sylvilagus_floridanus 0.6485 1.0696 222
## veg_height-Meleagris_gallopavo 0.3816 1.0504 127
## veg_height-Sciurus_carolinensis 0.6708 1.0411 176
## veg_height-Vulpes_vulpes 0.3904 1.0456 154
## veg_height-Sus_scrofa 0.4077 1.0122 366
## week-Canis_latrans 15348.5217 1.3694 1195
## week-Lynx_rufus 21751.1836 1.2905 45297
## week-Didelphis_virginiana 26648.8215 1.2905 42290
## week-Sylvilagus_floridanus 16985.9566 1.2951 20272
## week-Meleagris_gallopavo 18354.3570 1.5013 536
## week-Sciurus_carolinensis 22801.9269 1.3197 3356
## week-Vulpes_vulpes 23553.4983 1.2935 37695
## week-Sus_scrofa 19357.6241 1.3581 1707
#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): 5.3862
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6387 0.6115 -1.8901 -0.6382 0.5755 1.0138 243
## Cogon_Patch_Size -0.2185 0.6222 -1.5234 -0.1890 0.9275 1.0207 778
## Avg_Cogongrass_Cover 0.1978 0.4683 -0.7449 0.1969 1.1113 1.0224 394
## total_shrub_cover -0.9825 0.6664 -2.4660 -0.9440 0.2213 1.0383 104
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0365 1.5011 0.0538 0.5298 5.3039 1.0472 493
## Cogon_Patch_Size 2.3920 5.0915 0.0853 1.0762 12.6231 1.0563 314
## Avg_Cogongrass_Cover 0.7929 1.2321 0.0449 0.3836 4.1803 1.1140 383
## total_shrub_cover 1.9576 2.9419 0.0992 1.0234 9.6716 1.0863 277
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.5889 4.8431 0.5009 3.2352 18.727 1.3454 54
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1810 0.2859 -5.7475 -5.1750 -4.6333 1.0094 160
## shrub_cover 0.5451 0.4179 -0.2728 0.5375 1.4054 1.0098 309
## veg_height -0.0318 0.2269 -0.4732 -0.0304 0.4077 1.0421 616
## week 0.0254 1.6366 -3.1610 -0.0474 3.3452 1.0007 777
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.122000e-01 5.378000e-01 0.0530 0.2749 1.601200e+00 1.1663 280
## shrub_cover 1.202300e+00 1.160900e+00 0.2247 0.8785 4.116800e+00 1.0054 279
## veg_height 3.202000e-01 2.961000e-01 0.0606 0.2358 1.158300e+00 1.0294 381
## week 9.038094e+09 1.379777e+11 0.1557 723.4656 1.809363e+09 1.6646 228
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0343 0.9508 -1.6521 -0.0515
## (Intercept)-Lynx_rufus -0.3178 0.9426 -2.0161 -0.3676
## (Intercept)-Didelphis_virginiana -0.7818 0.8329 -2.4236 -0.7846
## (Intercept)-Sylvilagus_floridanus -0.4878 0.8812 -2.2080 -0.5208
## (Intercept)-Meleagris_gallopavo -0.6999 0.8847 -2.4367 -0.7116
## (Intercept)-Sciurus_carolinensis -0.9089 0.8980 -2.7875 -0.8872
## (Intercept)-Vulpes_vulpes -1.0811 0.9635 -3.1032 -1.0123
## (Intercept)-Sus_scrofa -1.0648 0.9632 -3.1613 -1.0251
## Cogon_Patch_Size-Canis_latrans 0.8856 0.9888 -0.4467 0.6944
## Cogon_Patch_Size-Lynx_rufus -0.2461 1.1196 -2.3163 -0.2809
## Cogon_Patch_Size-Didelphis_virginiana 0.8002 0.6963 -0.3404 0.7251
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2279 1.3443 -4.7835 -0.9199
## Cogon_Patch_Size-Meleagris_gallopavo 0.1021 0.9166 -1.5722 0.0513
## Cogon_Patch_Size-Sciurus_carolinensis -1.0699 1.2420 -4.0314 -0.8117
## Cogon_Patch_Size-Vulpes_vulpes -0.7938 1.1979 -3.8355 -0.5803
## Cogon_Patch_Size-Sus_scrofa -0.4382 1.1331 -3.2518 -0.2967
## Avg_Cogongrass_Cover-Canis_latrans 0.4436 0.5238 -0.5082 0.4166
## Avg_Cogongrass_Cover-Lynx_rufus 0.7356 0.8332 -0.5088 0.6082
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1608 0.5784 -1.0119 0.1701
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2091 0.7063 -1.7576 -0.1608
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3943 0.9386 -2.5800 -0.2933
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4595 0.6067 -0.6136 0.4217
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4425 0.6678 -0.7028 0.3954
## Avg_Cogongrass_Cover-Sus_scrofa -0.0166 0.8230 -1.8641 0.0441
## total_shrub_cover-Canis_latrans 0.4117 0.8414 -1.0105 0.3353
## total_shrub_cover-Lynx_rufus -1.3553 1.2122 -4.0070 -1.2203
## total_shrub_cover-Didelphis_virginiana -1.2660 0.9890 -3.5407 -1.1120
## total_shrub_cover-Sylvilagus_floridanus -1.5893 1.2609 -4.9382 -1.3475
## total_shrub_cover-Meleagris_gallopavo -1.7979 1.0693 -4.2357 -1.6624
## total_shrub_cover-Sciurus_carolinensis -1.0318 0.9841 -3.2824 -0.9026
## total_shrub_cover-Vulpes_vulpes -1.1910 1.2505 -4.0614 -1.0737
## total_shrub_cover-Sus_scrofa -0.8594 1.1073 -3.3329 -0.8027
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1021 1.0345 305
## (Intercept)-Lynx_rufus 1.8604 1.0219 303
## (Intercept)-Didelphis_virginiana 0.8306 1.0303 259
## (Intercept)-Sylvilagus_floridanus 1.2972 1.0089 331
## (Intercept)-Meleagris_gallopavo 1.0570 1.0199 329
## (Intercept)-Sciurus_carolinensis 0.7776 1.0182 344
## (Intercept)-Vulpes_vulpes 0.6285 1.0801 315
## (Intercept)-Sus_scrofa 0.6997 1.0291 401
## Cogon_Patch_Size-Canis_latrans 3.4963 1.0139 553
## Cogon_Patch_Size-Lynx_rufus 2.3145 1.0159 429
## Cogon_Patch_Size-Didelphis_virginiana 2.3218 1.0278 413
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4634 1.0106 414
## Cogon_Patch_Size-Meleagris_gallopavo 2.0800 1.0088 564
## Cogon_Patch_Size-Sciurus_carolinensis 0.4507 1.0305 226
## Cogon_Patch_Size-Vulpes_vulpes 1.0197 1.0244 416
## Cogon_Patch_Size-Sus_scrofa 1.4468 1.0127 537
## Avg_Cogongrass_Cover-Canis_latrans 1.5990 1.0526 602
## Avg_Cogongrass_Cover-Lynx_rufus 2.7710 1.1368 329
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3153 1.0302 667
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0919 1.0228 403
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1404 1.0236 249
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6942 1.0179 683
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.8320 1.0293 593
## Avg_Cogongrass_Cover-Sus_scrofa 1.5517 1.0074 555
## total_shrub_cover-Canis_latrans 2.3982 1.0199 259
## total_shrub_cover-Lynx_rufus 0.7335 1.1532 168
## total_shrub_cover-Didelphis_virginiana 0.1481 1.0983 180
## total_shrub_cover-Sylvilagus_floridanus 0.1225 1.1528 84
## total_shrub_cover-Meleagris_gallopavo -0.0431 1.0455 331
## total_shrub_cover-Sciurus_carolinensis 0.5278 1.0147 140
## total_shrub_cover-Vulpes_vulpes 1.0382 1.0561 150
## total_shrub_cover-Sus_scrofa 1.0727 1.0239 265
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8215 0.1864 -5.1884 -4.8155
## (Intercept)-Lynx_rufus -5.5128 0.3185 -6.1615 -5.4929
## (Intercept)-Didelphis_virginiana -4.8931 0.3157 -5.5633 -4.8878
## (Intercept)-Sylvilagus_floridanus -5.0997 0.2416 -5.6386 -5.0901
## (Intercept)-Meleagris_gallopavo -5.5589 0.4463 -6.4952 -5.5142
## (Intercept)-Sciurus_carolinensis -4.9705 0.3251 -5.6122 -4.9695
## (Intercept)-Vulpes_vulpes -5.8484 0.5802 -7.1391 -5.8071
## (Intercept)-Sus_scrofa -5.5317 0.4089 -6.3288 -5.5494
## shrub_cover-Canis_latrans -0.3002 0.2082 -0.6996 -0.2996
## shrub_cover-Lynx_rufus 0.0244 0.3804 -0.6923 0.0259
## shrub_cover-Didelphis_virginiana 1.3658 0.4569 0.5194 1.3468
## shrub_cover-Sylvilagus_floridanus 0.9512 0.4174 0.1519 0.9406
## shrub_cover-Meleagris_gallopavo -0.6036 0.4352 -1.4967 -0.5908
## shrub_cover-Sciurus_carolinensis 1.2585 0.4065 0.4363 1.2777
## shrub_cover-Vulpes_vulpes 0.5104 0.7433 -0.9438 0.5362
## shrub_cover-Sus_scrofa 1.3452 0.7392 -0.1791 1.3675
## veg_height-Canis_latrans -0.5790 0.1798 -0.9247 -0.5812
## veg_height-Lynx_rufus 0.0902 0.2321 -0.3824 0.0794
## veg_height-Didelphis_virginiana 0.5121 0.2575 0.0278 0.5056
## veg_height-Sylvilagus_floridanus 0.0929 0.2234 -0.3282 0.0844
## veg_height-Meleagris_gallopavo -0.3170 0.4098 -1.0894 -0.3256
## veg_height-Sciurus_carolinensis 0.3023 0.2406 -0.1466 0.2974
## veg_height-Vulpes_vulpes -0.1130 0.3398 -0.7927 -0.0986
## veg_height-Sus_scrofa -0.2923 0.3520 -1.0378 -0.2727
## week-Canis_latrans -1186.3203 90807.3022 -8090.0252 -0.0627
## week-Lynx_rufus -0.0925 76137.5638 -5893.1221 -0.0998
## week-Didelphis_virginiana -1208.6045 75794.7058 -10418.1640 -0.0072
## week-Sylvilagus_floridanus 2478.1573 89894.4415 -7006.5851 -0.0895
## week-Meleagris_gallopavo 456.2468 121221.6747 -6987.2635 -0.0653
## week-Sciurus_carolinensis 1339.3098 91429.6647 -8511.8812 -0.2064
## week-Vulpes_vulpes 205.8815 92572.9438 -8210.1658 -0.1157
## week-Sus_scrofa -1876.4998 103085.5519 -15432.2418 -0.2681
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4856 1.1696 172
## (Intercept)-Lynx_rufus -4.9519 1.0418 81
## (Intercept)-Didelphis_virginiana -4.2878 1.1680 66
## (Intercept)-Sylvilagus_floridanus -4.6302 1.1021 81
## (Intercept)-Meleagris_gallopavo -4.7619 1.0632 104
## (Intercept)-Sciurus_carolinensis -4.3633 1.0299 95
## (Intercept)-Vulpes_vulpes -4.8780 1.1337 73
## (Intercept)-Sus_scrofa -4.7056 1.0244 181
## shrub_cover-Canis_latrans 0.1154 1.0097 174
## shrub_cover-Lynx_rufus 0.7368 1.0682 96
## shrub_cover-Didelphis_virginiana 2.3474 1.0626 58
## shrub_cover-Sylvilagus_floridanus 1.7992 1.0493 91
## shrub_cover-Meleagris_gallopavo 0.2776 1.1147 140
## shrub_cover-Sciurus_carolinensis 2.0456 1.0203 62
## shrub_cover-Vulpes_vulpes 1.8856 1.0869 80
## shrub_cover-Sus_scrofa 2.7896 1.0398 150
## veg_height-Canis_latrans -0.2259 1.0987 181
## veg_height-Lynx_rufus 0.5507 1.1517 163
## veg_height-Didelphis_virginiana 1.0073 1.1379 189
## veg_height-Sylvilagus_floridanus 0.5440 1.0514 138
## veg_height-Meleagris_gallopavo 0.4705 1.0315 129
## veg_height-Sciurus_carolinensis 0.7870 1.0029 164
## veg_height-Vulpes_vulpes 0.5212 1.2323 127
## veg_height-Sus_scrofa 0.3307 1.0088 200
## week-Canis_latrans 8852.9767 1.3081 7169
## week-Lynx_rufus 9105.3722 1.2901 108182
## week-Didelphis_virginiana 7051.2196 1.3153 4481
## week-Sylvilagus_floridanus 10977.0182 1.3645 1907
## week-Meleagris_gallopavo 10291.0818 1.2908 23202
## week-Sciurus_carolinensis 7431.6572 1.3121 5023
## week-Vulpes_vulpes 8764.7802 1.2903 64771
## week-Sus_scrofa 7445.5981 1.3218 2859
#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): 4.839
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8747 0.4885 -1.8903 -0.8609 0.0652 1.0438 569
## Veg_shannon_index 0.3304 0.3823 -0.4770 0.3334 1.0612 1.0041 844
## Avg_Cogongrass_Cover 0.2420 0.3589 -0.5080 0.2494 0.9383 1.0272 830
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1049 1.5065 0.0664 0.6740 4.6474 1.0540 601
## Veg_shannon_index 0.6282 0.9153 0.0523 0.3852 2.4757 1.0886 1019
## Avg_Cogongrass_Cover 0.5781 0.8059 0.0471 0.3264 2.5467 1.0261 710
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5764 1.5695 0.0876 1.111 5.5799 1.1673 196
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0298 0.2935 -5.5866 -5.0341 -4.3956 1.0130 561
## shrub_cover 0.2120 0.3556 -0.4794 0.2093 0.9293 1.0140 803
## veg_height -0.0236 0.2086 -0.4397 -0.0249 0.3906 1.0078 548
## week 0.0235 1.7048 -3.2971 -0.0240 3.6162 1.0143 400
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.014000e-01 5.204000e-01 0.0672 0.3435 1.917900e+00 1.1341 289
## shrub_cover 8.678000e-01 8.091000e-01 0.1582 0.6515 2.796400e+00 1.0406 248
## veg_height 2.749000e-01 2.701000e-01 0.0610 0.2007 9.169000e-01 1.0069 712
## week 1.639098e+09 2.700961e+10 0.0718 24.4981 5.167049e+08 1.6097 280
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0403 0.7454 -1.4279 -0.0581
## (Intercept)-Lynx_rufus -0.4088 0.7795 -1.7952 -0.4528
## (Intercept)-Didelphis_virginiana -1.2467 0.6493 -2.5762 -1.2304
## (Intercept)-Sylvilagus_floridanus -0.7761 0.6210 -2.0424 -0.7696
## (Intercept)-Meleagris_gallopavo -0.4857 0.8760 -1.9196 -0.5655
## (Intercept)-Sciurus_carolinensis -1.2487 0.6594 -2.6354 -1.2170
## (Intercept)-Vulpes_vulpes -1.3586 0.8056 -3.1331 -1.3204
## (Intercept)-Sus_scrofa -1.6699 0.8329 -3.5443 -1.5837
## Veg_shannon_index-Canis_latrans 0.8160 0.4898 -0.0682 0.7966
## Veg_shannon_index-Lynx_rufus -0.1454 0.6207 -1.5332 -0.0850
## Veg_shannon_index-Didelphis_virginiana 0.5703 0.4806 -0.3020 0.5457
## Veg_shannon_index-Sylvilagus_floridanus 0.4155 0.4851 -0.5373 0.3950
## Veg_shannon_index-Meleagris_gallopavo 0.5693 0.6024 -0.4915 0.5289
## Veg_shannon_index-Sciurus_carolinensis -0.1706 0.4861 -1.1967 -0.1418
## Veg_shannon_index-Vulpes_vulpes -0.0600 0.5668 -1.2450 -0.0309
## Veg_shannon_index-Sus_scrofa 0.7835 0.6712 -0.3645 0.7155
## Avg_Cogongrass_Cover-Canis_latrans 0.6824 0.4826 -0.1337 0.6451
## Avg_Cogongrass_Cover-Lynx_rufus 0.6109 0.5606 -0.2892 0.5526
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4659 0.4461 -0.3863 0.4586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2151 0.4933 -1.2864 -0.1743
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2199 0.7439 -1.8874 -0.1640
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3994 0.4127 -0.3730 0.3896
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3929 0.5389 -0.5986 0.3611
## Avg_Cogongrass_Cover-Sus_scrofa -0.0499 0.6610 -1.5699 0.0245
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4978 1.0104 423
## (Intercept)-Lynx_rufus 1.3337 1.0023 404
## (Intercept)-Didelphis_virginiana -0.0276 1.0231 605
## (Intercept)-Sylvilagus_floridanus 0.4574 1.0105 619
## (Intercept)-Meleagris_gallopavo 1.4018 1.0623 271
## (Intercept)-Sciurus_carolinensis -0.0358 1.0180 804
## (Intercept)-Vulpes_vulpes 0.1530 1.0871 415
## (Intercept)-Sus_scrofa -0.2570 1.0511 578
## Veg_shannon_index-Canis_latrans 1.8636 1.0108 1008
## Veg_shannon_index-Lynx_rufus 0.9579 1.0060 760
## Veg_shannon_index-Didelphis_virginiana 1.6080 1.0009 1094
## Veg_shannon_index-Sylvilagus_floridanus 1.4473 1.0035 1301
## Veg_shannon_index-Meleagris_gallopavo 1.8840 1.0132 923
## Veg_shannon_index-Sciurus_carolinensis 0.7204 1.0033 926
## Veg_shannon_index-Vulpes_vulpes 0.9750 1.0015 995
## Veg_shannon_index-Sus_scrofa 2.3077 1.0085 675
## Avg_Cogongrass_Cover-Canis_latrans 1.7629 1.0409 1032
## Avg_Cogongrass_Cover-Lynx_rufus 1.8250 1.0264 835
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3969 1.0209 1441
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6577 1.0017 1195
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0740 1.0182 415
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2274 1.0044 1574
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4835 1.0128 999
## Avg_Cogongrass_Cover-Sus_scrofa 1.0889 1.0083 794
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7513 0.1751 -5.1197 -4.7468
## (Intercept)-Lynx_rufus -5.4868 0.3063 -6.1517 -5.4672
## (Intercept)-Didelphis_virginiana -4.6669 0.2610 -5.1914 -4.6638
## (Intercept)-Sylvilagus_floridanus -4.9388 0.2357 -5.4297 -4.9307
## (Intercept)-Meleagris_gallopavo -5.7132 0.4218 -6.5082 -5.7095
## (Intercept)-Sciurus_carolinensis -4.6774 0.2898 -5.2622 -4.6560
## (Intercept)-Vulpes_vulpes -5.7279 0.5375 -6.7922 -5.7047
## (Intercept)-Sus_scrofa -5.2002 0.4656 -6.1890 -5.1818
## shrub_cover-Canis_latrans -0.2726 0.2058 -0.6634 -0.2698
## shrub_cover-Lynx_rufus -0.2220 0.3391 -0.9014 -0.2141
## shrub_cover-Didelphis_virginiana 1.0646 0.3744 0.3732 1.0483
## shrub_cover-Sylvilagus_floridanus 0.4051 0.4064 -0.3392 0.3991
## shrub_cover-Meleagris_gallopavo -0.7904 0.4024 -1.5739 -0.7900
## shrub_cover-Sciurus_carolinensis 0.9170 0.3881 0.1751 0.8934
## shrub_cover-Vulpes_vulpes -0.0647 0.5901 -1.2300 -0.0594
## shrub_cover-Sus_scrofa 0.7345 0.7358 -0.7476 0.7404
## veg_height-Canis_latrans -0.5600 0.1731 -0.9272 -0.5537
## veg_height-Lynx_rufus 0.0449 0.2618 -0.5048 0.0501
## veg_height-Didelphis_virginiana 0.4697 0.2412 0.0327 0.4577
## veg_height-Sylvilagus_floridanus 0.1949 0.2188 -0.2389 0.1941
## veg_height-Meleagris_gallopavo -0.2221 0.4154 -0.9527 -0.2492
## veg_height-Sciurus_carolinensis 0.1831 0.2093 -0.2177 0.1745
## veg_height-Vulpes_vulpes -0.1326 0.2961 -0.7157 -0.1368
## veg_height-Sus_scrofa -0.2163 0.3139 -0.8726 -0.2098
## week-Canis_latrans -146.6069 36898.6860 -2709.1461 0.2228
## week-Lynx_rufus 512.8615 43083.0479 -4559.0781 0.0609
## week-Didelphis_virginiana 431.8911 26190.1214 -2074.9942 0.0791
## week-Sylvilagus_floridanus -211.4279 30485.5172 -3175.9303 -0.0596
## week-Meleagris_gallopavo 528.5297 43665.2482 -1654.0005 0.0455
## week-Sciurus_carolinensis 1428.9909 41020.1083 -1682.0907 0.1083
## week-Vulpes_vulpes -295.1464 33933.9492 -1704.0221 0.0776
## week-Sus_scrofa -526.6628 42002.0401 -2710.0579 -0.0013
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4228 1.0490 190
## (Intercept)-Lynx_rufus -4.9381 1.1700 69
## (Intercept)-Didelphis_virginiana -4.1711 1.0763 212
## (Intercept)-Sylvilagus_floridanus -4.4945 1.0333 197
## (Intercept)-Meleagris_gallopavo -4.8828 1.1347 73
## (Intercept)-Sciurus_carolinensis -4.1612 1.0092 234
## (Intercept)-Vulpes_vulpes -4.7865 1.1054 85
## (Intercept)-Sus_scrofa -4.3561 1.2123 293
## shrub_cover-Canis_latrans 0.1171 1.0052 194
## shrub_cover-Lynx_rufus 0.4343 1.0132 100
## shrub_cover-Didelphis_virginiana 1.8330 1.0296 168
## shrub_cover-Sylvilagus_floridanus 1.2445 1.1314 132
## shrub_cover-Meleagris_gallopavo -0.0066 1.0985 78
## shrub_cover-Sciurus_carolinensis 1.7090 1.0134 214
## shrub_cover-Vulpes_vulpes 1.0938 1.0341 173
## shrub_cover-Sus_scrofa 2.1998 1.0722 249
## veg_height-Canis_latrans -0.2433 1.0545 179
## veg_height-Lynx_rufus 0.5370 1.0185 154
## veg_height-Didelphis_virginiana 0.9615 1.0727 220
## veg_height-Sylvilagus_floridanus 0.6204 1.0118 219
## veg_height-Meleagris_gallopavo 0.6249 1.1076 93
## veg_height-Sciurus_carolinensis 0.6016 1.0026 266
## veg_height-Vulpes_vulpes 0.4628 1.0516 187
## veg_height-Sus_scrofa 0.3853 1.0329 385
## week-Canis_latrans 1740.3695 1.2840 67550
## week-Lynx_rufus 1602.4835 1.3045 5354
## week-Didelphis_virginiana 3007.3871 1.3042 4200
## week-Sylvilagus_floridanus 1744.5092 1.2861 5275
## week-Meleagris_gallopavo 2959.9084 1.3021 6325
## week-Sciurus_carolinensis 3003.3478 1.4007 884
## week-Vulpes_vulpes 2239.0458 1.2880 7627
## week-Sus_scrofa 2167.5344 1.3029 12238
#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): 4.8173
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4627 0.5713 -2.6497 -1.4458 -0.3452 1.0728 228
## Avg_Cogongrass_Cover -0.7444 0.4967 -1.7821 -0.7323 0.2112 1.0870 364
## I(Avg_Cogongrass_Cover^2) 0.7904 0.3977 0.0482 0.7767 1.6188 1.0349 367
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2123 1.7886 0.0655 0.7156 5.1483 1.0472 589
## Avg_Cogongrass_Cover 0.5888 0.8779 0.0501 0.3270 2.6859 1.0357 723
## I(Avg_Cogongrass_Cover^2) 0.4620 0.6912 0.0413 0.2559 2.1378 1.0255 673
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4206 1.665 0.0742 0.9631 5.7174 1.1159 151
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0566 0.3117 -5.6250 -5.0646 -4.4347 1.0396 669
## shrub_cover 0.2212 0.3655 -0.5204 0.2153 0.9694 1.0144 533
## veg_height -0.0295 0.2242 -0.4722 -0.0287 0.4041 1.0492 382
## week 0.0334 1.6839 -3.1621 0.0092 3.6441 1.0051 527
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.411000e-01 7.11200e-01 0.0648 0.3565 2.17120e+00 1.0463 224
## shrub_cover 9.067000e-01 8.43800e-01 0.1616 0.6746 3.18940e+00 1.0731 311
## veg_height 2.912000e-01 2.53900e-01 0.0615 0.2176 1.00140e+00 1.0332 675
## week 6.258244e+22 1.00131e+24 0.0976 345.7026 2.41019e+21 1.6350 211
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7007 0.8164 -2.3435 -0.7238
## (Intercept)-Lynx_rufus -1.1920 0.8089 -2.7496 -1.2052
## (Intercept)-Didelphis_virginiana -1.7441 0.7032 -3.2703 -1.7120
## (Intercept)-Sylvilagus_floridanus -1.4010 0.7198 -2.8973 -1.3732
## (Intercept)-Meleagris_gallopavo -0.9131 0.8872 -2.6281 -0.9509
## (Intercept)-Sciurus_carolinensis -2.0763 0.7984 -3.7772 -2.0254
## (Intercept)-Vulpes_vulpes -2.1153 0.9048 -3.9917 -2.0670
## (Intercept)-Sus_scrofa -2.1545 0.8852 -4.0914 -2.0674
## Avg_Cogongrass_Cover-Canis_latrans -0.3328 0.6039 -1.4220 -0.3650
## Avg_Cogongrass_Cover-Lynx_rufus -0.5720 0.6731 -1.8974 -0.5765
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4456 0.6145 -1.6325 -0.4505
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2333 0.7151 -2.8223 -1.1696
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0419 0.7580 -2.7252 -0.9904
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7953 0.6488 -2.1760 -0.7840
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7205 0.6965 -2.1583 -0.7067
## Avg_Cogongrass_Cover-Sus_scrofa -0.9514 0.7491 -2.6210 -0.8843
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2292 0.7132 0.1684 1.1180
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1175 0.5671 0.1803 1.0717
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5876 0.4443 -0.2302 0.5630
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7673 0.4868 -0.0467 0.7349
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4029 0.6414 -0.9217 0.4055
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9695 0.4599 0.1464 0.9274
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9054 0.5303 0.0294 0.8553
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4687 0.6000 -0.8301 0.5073
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9393 1.1017 248
## (Intercept)-Lynx_rufus 0.4663 1.0831 257
## (Intercept)-Didelphis_virginiana -0.4503 1.0387 479
## (Intercept)-Sylvilagus_floridanus -0.0457 1.0465 567
## (Intercept)-Meleagris_gallopavo 0.9072 1.1484 205
## (Intercept)-Sciurus_carolinensis -0.6978 1.0121 397
## (Intercept)-Vulpes_vulpes -0.4054 1.0125 368
## (Intercept)-Sus_scrofa -0.7149 1.0354 380
## Avg_Cogongrass_Cover-Canis_latrans 0.9751 1.0362 652
## Avg_Cogongrass_Cover-Lynx_rufus 0.8155 1.0399 438
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7894 1.0584 573
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0119 1.0479 378
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.3091 1.0639 416
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4063 1.0546 473
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5978 1.0494 482
## Avg_Cogongrass_Cover-Sus_scrofa 0.4084 1.0738 449
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0747 1.0190 390
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3982 1.0090 501
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5669 1.0588 634
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8076 1.0230 498
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.7040 1.0172 336
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9762 1.0321 473
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1408 1.0268 345
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5859 1.1011 447
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.816700e+00 1.871000e-01 -5.204400e+00
## (Intercept)-Lynx_rufus -5.478700e+00 2.957000e-01 -6.093100e+00
## (Intercept)-Didelphis_virginiana -4.682500e+00 2.691000e-01 -5.278400e+00
## (Intercept)-Sylvilagus_floridanus -4.963200e+00 2.458000e-01 -5.465100e+00
## (Intercept)-Meleagris_gallopavo -5.813700e+00 5.016000e-01 -6.754700e+00
## (Intercept)-Sciurus_carolinensis -4.709800e+00 3.067000e-01 -5.355600e+00
## (Intercept)-Vulpes_vulpes -5.742300e+00 5.595000e-01 -7.061900e+00
## (Intercept)-Sus_scrofa -5.260200e+00 4.683000e-01 -6.178700e+00
## shrub_cover-Canis_latrans -2.885000e-01 2.109000e-01 -7.021000e-01
## shrub_cover-Lynx_rufus -1.648000e-01 3.397000e-01 -8.094000e-01
## shrub_cover-Didelphis_virginiana 1.057000e+00 3.692000e-01 3.838000e-01
## shrub_cover-Sylvilagus_floridanus 4.774000e-01 3.907000e-01 -2.684000e-01
## shrub_cover-Meleagris_gallopavo -8.034000e-01 4.318000e-01 -1.682400e+00
## shrub_cover-Sciurus_carolinensis 8.857000e-01 4.029000e-01 1.127000e-01
## shrub_cover-Vulpes_vulpes -4.460000e-02 6.638000e-01 -1.359200e+00
## shrub_cover-Sus_scrofa 8.036000e-01 7.703000e-01 -7.503000e-01
## veg_height-Canis_latrans -6.013000e-01 1.809000e-01 -9.919000e-01
## veg_height-Lynx_rufus 1.542000e-01 2.361000e-01 -3.134000e-01
## veg_height-Didelphis_virginiana 4.763000e-01 2.403000e-01 2.900000e-02
## veg_height-Sylvilagus_floridanus 1.590000e-01 2.500000e-01 -3.030000e-01
## veg_height-Meleagris_gallopavo -2.386000e-01 3.955000e-01 -1.012400e+00
## veg_height-Sciurus_carolinensis 2.147000e-01 2.147000e-01 -1.863000e-01
## veg_height-Vulpes_vulpes -1.492000e-01 3.277000e-01 -8.141000e-01
## veg_height-Sus_scrofa -2.471000e-01 3.469000e-01 -9.763000e-01
## week-Canis_latrans -1.034308e+09 2.367262e+11 -2.499151e+08
## week-Lynx_rufus -5.640251e+09 2.172101e+11 -3.784181e+08
## week-Didelphis_virginiana -3.694786e+08 2.441977e+11 -2.716922e+08
## week-Sylvilagus_floridanus 7.967209e+09 2.921005e+11 -2.565722e+08
## week-Meleagris_gallopavo -9.043053e+07 2.273069e+11 -1.440260e+08
## week-Sciurus_carolinensis 3.605564e+07 1.713997e+11 -1.355036e+08
## week-Vulpes_vulpes -5.142147e+09 1.818932e+11 -6.550921e+08
## week-Sus_scrofa 2.917813e+08 2.239002e+11 -1.628630e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8090 -4.465100e+00 1.1560 113
## (Intercept)-Lynx_rufus -5.4724 -4.936900e+00 1.1344 103
## (Intercept)-Didelphis_virginiana -4.6716 -4.205500e+00 1.0822 136
## (Intercept)-Sylvilagus_floridanus -4.9529 -4.499500e+00 1.0548 162
## (Intercept)-Meleagris_gallopavo -5.7746 -4.884400e+00 1.4442 32
## (Intercept)-Sciurus_carolinensis -4.6831 -4.167500e+00 1.0108 167
## (Intercept)-Vulpes_vulpes -5.6628 -4.832800e+00 1.0064 86
## (Intercept)-Sus_scrofa -5.2688 -4.287100e+00 1.0239 260
## shrub_cover-Canis_latrans -0.2865 1.310000e-01 1.0918 180
## shrub_cover-Lynx_rufus -0.1586 4.874000e-01 1.0173 118
## shrub_cover-Didelphis_virginiana 1.0317 1.846500e+00 1.0124 157
## shrub_cover-Sylvilagus_floridanus 0.4702 1.229400e+00 1.1000 118
## shrub_cover-Meleagris_gallopavo -0.7763 -2.210000e-02 1.5158 54
## shrub_cover-Sciurus_carolinensis 0.8833 1.711700e+00 1.0079 225
## shrub_cover-Vulpes_vulpes -0.0466 1.241400e+00 1.0238 150
## shrub_cover-Sus_scrofa 0.7914 2.356400e+00 1.0025 248
## veg_height-Canis_latrans -0.5934 -2.587000e-01 1.1060 154
## veg_height-Lynx_rufus 0.1527 5.964000e-01 1.0220 171
## veg_height-Didelphis_virginiana 0.4637 9.683000e-01 1.0230 198
## veg_height-Sylvilagus_floridanus 0.1463 6.539000e-01 1.0856 169
## veg_height-Meleagris_gallopavo -0.2321 5.816000e-01 1.1733 114
## veg_height-Sciurus_carolinensis 0.2092 6.703000e-01 1.0034 200
## veg_height-Vulpes_vulpes -0.1248 4.242000e-01 1.0709 147
## veg_height-Sus_scrofa -0.2329 4.015000e-01 1.1333 311
## week-Canis_latrans -0.0089 1.293353e+08 1.2923 46045
## week-Lynx_rufus -0.0102 1.519513e+08 1.3560 2247
## week-Didelphis_virginiana -0.0969 1.557971e+08 1.2906 12067
## week-Sylvilagus_floridanus 0.0563 1.783785e+08 1.3626 1062
## week-Meleagris_gallopavo -0.0577 3.535186e+08 1.2904 38861
## week-Sciurus_carolinensis -0.1282 2.605136e+08 1.2904 37134
## week-Vulpes_vulpes 0.0286 9.446542e+07 1.3678 2383
## week-Sus_scrofa 0.0437 1.750562e+08 1.2905 101869
## 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): 5.4432
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6142 1.4350 -4.1928 -1.7151 1.5095 1.4984 96
## Cogon_Patch_Size 0.2326 1.2114 -2.2460 0.2637 2.6474 1.0257 423
## Veg_shannon_index 0.8813 1.0622 -1.4216 0.9483 2.8332 1.0582 256
## total_shrub_cover -1.4245 1.2513 -4.0196 -1.3118 0.8326 1.2587 79
## Avg_Cogongrass_Cover -0.1515 1.2190 -2.4600 -0.2156 2.2738 1.1159 184
## Tree_Density -2.0411 1.1761 -4.2908 -2.0871 0.6126 1.2112 147
## Avg_Canopy_Cover 1.8186 1.4633 -1.6278 2.0164 4.3410 1.4937 60
## I(Avg_Cogongrass_Cover^2) 1.3605 1.0534 -1.1317 1.4257 3.2767 1.0709 288
## avg_veg_height -0.1745 0.9223 -1.8756 -0.2265 1.9744 1.3023 107
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.6065 58.6743 0.0994 9.0043 199.3395 1.5855 33
## Cogon_Patch_Size 57.4250 173.7883 0.2362 8.6350 429.8657 4.6493 23
## Veg_shannon_index 36.1862 116.3248 0.0805 3.3086 334.8444 3.8949 12
## total_shrub_cover 13.1116 27.7389 0.1149 4.9070 74.9281 1.0548 116
## Avg_Cogongrass_Cover 5.1492 17.0302 0.0579 0.9183 44.8010 2.4298 118
## Tree_Density 16.9577 60.6463 0.0599 1.1220 159.1334 3.8128 35
## Avg_Canopy_Cover 219.8330 573.4507 0.8972 15.0935 1904.7876 5.8978 10
## I(Avg_Cogongrass_Cover^2) 19.4761 45.7068 0.0740 2.3320 154.3566 4.5272 19
## avg_veg_height 3.6690 18.1512 0.0547 0.5993 28.6618 2.4382 218
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 50.5322 107.1858 0.0763 3.1609 386.2229 8.2109 5
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1643 0.3168 -5.7571 -5.1713 -4.5299 1.1249 194
## shrub_cover 0.4846 0.4097 -0.3463 0.4802 1.3094 1.0010 208
## veg_height 0.0073 0.2273 -0.4609 0.0104 0.4495 1.0163 402
## week -0.0001 1.6479 -3.3690 0.0755 3.2961 1.0001 548
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.595000e-01 7.666000e-01 0.0670 0.3398 2.266400e+00 1.1793 45
## shrub_cover 1.149200e+00 1.027500e+00 0.2107 0.8582 3.690600e+00 1.0158 89
## veg_height 3.161000e-01 3.087000e-01 0.0623 0.2314 1.084900e+00 1.0702 507
## week 2.362062e+11 3.374026e+12 0.0885 64.7123 4.761158e+10 1.2277 385
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans 0.3832 3.3541 -3.6138
## (Intercept)-Lynx_rufus 0.8946 3.6350 -4.3785
## (Intercept)-Didelphis_virginiana -4.0387 2.9745 -11.0801
## (Intercept)-Sylvilagus_floridanus -2.5515 3.1321 -10.4692
## (Intercept)-Meleagris_gallopavo -1.5789 3.4126 -8.0995
## (Intercept)-Sciurus_carolinensis -4.7968 3.6396 -14.1064
## (Intercept)-Vulpes_vulpes -4.5085 4.1478 -13.8554
## (Intercept)-Sus_scrofa -6.4234 5.5971 -20.9257
## Cogon_Patch_Size-Canis_latrans 4.5842 4.7825 -0.2553
## Cogon_Patch_Size-Lynx_rufus -1.6352 7.4496 -25.2280
## Cogon_Patch_Size-Didelphis_virginiana 4.2483 4.7102 0.1094
## Cogon_Patch_Size-Sylvilagus_floridanus -3.2181 4.7779 -16.2123
## Cogon_Patch_Size-Meleagris_gallopavo 3.5882 6.5045 -1.9652
## Cogon_Patch_Size-Sciurus_carolinensis -1.9335 4.0933 -13.8622
## Cogon_Patch_Size-Vulpes_vulpes -2.7047 7.4227 -25.7204
## Cogon_Patch_Size-Sus_scrofa -0.9861 5.1019 -13.3161
## Veg_shannon_index-Canis_latrans 3.4725 4.8689 -0.3737
## Veg_shannon_index-Lynx_rufus -1.8042 5.0990 -18.8272
## Veg_shannon_index-Didelphis_virginiana 2.5290 3.1227 -0.7084
## Veg_shannon_index-Sylvilagus_floridanus 2.1077 3.0209 -1.5737
## Veg_shannon_index-Meleagris_gallopavo 3.4595 4.4464 -0.8063
## Veg_shannon_index-Sciurus_carolinensis -1.4388 4.5409 -14.1181
## Veg_shannon_index-Vulpes_vulpes -1.6573 5.8241 -22.1437
## Veg_shannon_index-Sus_scrofa 4.3099 4.9461 -0.3770
## total_shrub_cover-Canis_latrans 0.8950 2.4375 -3.9783
## total_shrub_cover-Lynx_rufus -2.9753 2.8641 -9.5728
## total_shrub_cover-Didelphis_virginiana -2.8677 2.6353 -9.6109
## total_shrub_cover-Sylvilagus_floridanus -2.3750 2.8345 -10.3821
## total_shrub_cover-Meleagris_gallopavo -4.2705 3.2962 -13.4877
## total_shrub_cover-Sciurus_carolinensis -1.4926 2.1744 -6.1484
## total_shrub_cover-Vulpes_vulpes -2.9002 3.0487 -11.2092
## total_shrub_cover-Sus_scrofa -1.2129 3.0085 -8.8901
## Avg_Cogongrass_Cover-Canis_latrans -0.1564 2.2343 -4.5826
## Avg_Cogongrass_Cover-Lynx_rufus 0.2027 2.4235 -3.9112
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1507 1.9167 -3.9849
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9047 2.3814 -6.2750
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4460 2.1186 -5.2127
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0124 2.1864 -3.6956
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2449 2.2704 -3.6172
## Avg_Cogongrass_Cover-Sus_scrofa -0.1233 2.3919 -4.3998
## Tree_Density-Canis_latrans -4.3566 4.2034 -18.7718
## Tree_Density-Lynx_rufus -1.2507 3.4623 -5.7584
## Tree_Density-Didelphis_virginiana -2.5664 2.4875 -8.6673
## Tree_Density-Sylvilagus_floridanus -2.0730 3.1260 -7.2717
## Tree_Density-Meleagris_gallopavo -2.3049 2.5472 -7.6792
## Tree_Density-Sciurus_carolinensis -2.4420 2.8955 -8.4204
## Tree_Density-Vulpes_vulpes -3.3106 4.9585 -19.8458
## Tree_Density-Sus_scrofa -1.8417 2.8345 -6.3498
## Avg_Canopy_Cover-Canis_latrans -1.6060 3.0293 -10.8950
## Avg_Canopy_Cover-Lynx_rufus -1.1975 6.3646 -20.6906
## Avg_Canopy_Cover-Didelphis_virginiana 11.0799 13.9534 2.0376
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.6937 14.1939 2.2363
## Avg_Canopy_Cover-Meleagris_gallopavo 6.0473 6.4316 0.5691
## Avg_Canopy_Cover-Sciurus_carolinensis 10.5004 14.1727 1.5309
## Avg_Canopy_Cover-Vulpes_vulpes 7.2227 7.2649 0.8393
## Avg_Canopy_Cover-Sus_scrofa 6.5943 13.4280 -0.5556
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.2118 4.4551 0.6065
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.1842 4.3641 0.1568
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6908 1.9404 -1.0711
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6720 2.3886 -2.2019
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.8303 3.5449 -11.0561
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.8809 3.2497 -0.1364
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.5664 2.5746 -2.1011
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.2525 3.7794 -11.0778
## avg_veg_height-Canis_latrans -0.4145 1.5140 -4.3382
## avg_veg_height-Lynx_rufus -0.3361 1.9040 -4.2926
## avg_veg_height-Didelphis_virginiana -0.5615 1.6078 -4.3884
## avg_veg_height-Sylvilagus_floridanus -0.3052 1.5550 -3.3739
## avg_veg_height-Meleagris_gallopavo 0.0378 2.0062 -3.4726
## avg_veg_height-Sciurus_carolinensis 0.4709 1.7727 -1.9773
## avg_veg_height-Vulpes_vulpes -0.2881 1.7369 -3.8281
## avg_veg_height-Sus_scrofa -0.1926 1.8907 -3.5630
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.2918 10.9829 1.7681 64
## (Intercept)-Lynx_rufus 0.2171 10.6201 1.3428 52
## (Intercept)-Didelphis_virginiana -3.8064 1.4880 1.1875 61
## (Intercept)-Sylvilagus_floridanus -2.3796 3.0287 1.1165 69
## (Intercept)-Meleagris_gallopavo -1.7480 6.0929 1.3915 94
## (Intercept)-Sciurus_carolinensis -4.3760 1.0715 1.2261 35
## (Intercept)-Vulpes_vulpes -4.3169 2.4919 1.4037 46
## (Intercept)-Sus_scrofa -5.3434 1.3523 1.1549 16
## Cogon_Patch_Size-Canis_latrans 3.1896 17.6786 2.8044 31
## Cogon_Patch_Size-Lynx_rufus 0.0055 8.0312 3.7814 20
## Cogon_Patch_Size-Didelphis_virginiana 2.7739 18.6008 4.6441 25
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9167 2.0303 2.0362 54
## Cogon_Patch_Size-Meleagris_gallopavo 1.3299 24.2693 4.7407 25
## Cogon_Patch_Size-Sciurus_carolinensis -1.1234 3.7708 1.8916 99
## Cogon_Patch_Size-Vulpes_vulpes -0.8615 5.6141 3.2088 32
## Cogon_Patch_Size-Sus_scrofa -0.5806 8.8084 1.2364 53
## Veg_shannon_index-Canis_latrans 1.8846 19.4000 4.2842 13
## Veg_shannon_index-Lynx_rufus -0.3902 3.0227 2.1000 12
## Veg_shannon_index-Didelphis_virginiana 1.7506 12.7697 2.2191 42
## Veg_shannon_index-Sylvilagus_floridanus 1.5027 11.3797 2.2400 83
## Veg_shannon_index-Meleagris_gallopavo 2.1215 17.0021 2.2389 17
## Veg_shannon_index-Sciurus_carolinensis -0.3369 2.3722 2.2814 23
## Veg_shannon_index-Vulpes_vulpes 0.0841 3.1661 3.4572 16
## Veg_shannon_index-Sus_scrofa 2.4659 18.5851 2.1809 12
## total_shrub_cover-Canis_latrans 0.9179 5.7684 2.0335 28
## total_shrub_cover-Lynx_rufus -2.6738 1.9384 1.1164 68
## total_shrub_cover-Didelphis_virginiana -2.3365 0.4765 1.1105 100
## total_shrub_cover-Sylvilagus_floridanus -1.8657 1.4419 1.1690 62
## total_shrub_cover-Meleagris_gallopavo -3.5688 0.2685 1.0229 53
## total_shrub_cover-Sciurus_carolinensis -1.3195 2.4772 1.1431 157
## total_shrub_cover-Vulpes_vulpes -2.2363 1.2109 1.0226 73
## total_shrub_cover-Sus_scrofa -0.8709 3.6521 1.1977 93
## Avg_Cogongrass_Cover-Canis_latrans -0.1149 3.8000 1.2067 155
## Avg_Cogongrass_Cover-Lynx_rufus 0.0773 4.8610 1.1135 131
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1535 3.6310 1.0558 310
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7748 2.9870 1.1454 254
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3511 3.5111 1.0957 267
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1424 4.2528 1.3805 244
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0577 5.1756 1.2083 211
## Avg_Cogongrass_Cover-Sus_scrofa -0.2553 5.1325 1.3334 147
## Tree_Density-Canis_latrans -3.0841 -0.8488 3.5402 18
## Tree_Density-Lynx_rufus -1.7847 9.6249 1.9765 129
## Tree_Density-Didelphis_virginiana -2.3638 1.8449 1.3627 184
## Tree_Density-Sylvilagus_floridanus -2.4703 7.5670 2.1510 79
## Tree_Density-Meleagris_gallopavo -2.3120 3.0712 1.1958 116
## Tree_Density-Sciurus_carolinensis -2.4633 4.8947 1.2067 109
## Tree_Density-Vulpes_vulpes -2.3584 2.1390 2.8528 18
## Tree_Density-Sus_scrofa -2.1822 5.8316 1.7341 97
## Avg_Canopy_Cover-Canis_latrans -0.5310 1.1885 5.3728 25
## Avg_Canopy_Cover-Lynx_rufus -0.0410 7.3393 3.9244 18
## Avg_Canopy_Cover-Didelphis_virginiana 5.9049 59.6672 6.2683 7
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.4092 50.4752 5.7609 10
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6207 25.0975 5.7936 14
## Avg_Canopy_Cover-Sciurus_carolinensis 5.5340 59.6264 5.7707 6
## Avg_Canopy_Cover-Vulpes_vulpes 4.4013 28.1288 3.5354 14
## Avg_Canopy_Cover-Sus_scrofa 3.0257 64.9250 4.4151 16
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5600 18.4254 4.9949 14
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7565 17.9451 1.9580 29
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4135 6.6173 1.6029 94
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3956 7.9567 1.4359 83
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1621 3.5096 1.6470 48
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0394 13.5276 3.1800 24
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1160 9.1928 1.5861 56
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.8417 4.2452 1.6624 72
## avg_veg_height-Canis_latrans -0.2476 2.0294 1.4866 75
## avg_veg_height-Lynx_rufus -0.3634 4.0576 1.4134 168
## avg_veg_height-Didelphis_virginiana -0.4060 2.1498 1.2421 132
## avg_veg_height-Sylvilagus_floridanus -0.2872 2.5026 1.2054 286
## avg_veg_height-Meleagris_gallopavo -0.0746 4.1212 1.4990 103
## avg_veg_height-Sciurus_carolinensis 0.2115 4.5877 1.6767 119
## avg_veg_height-Vulpes_vulpes -0.3301 3.2927 1.3453 188
## avg_veg_height-Sus_scrofa -0.1991 3.1192 1.2209 121
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8060 0.1788 -5.1595 -4.8028
## (Intercept)-Lynx_rufus -5.6825 0.2992 -6.2463 -5.6817
## (Intercept)-Didelphis_virginiana -4.8475 0.2856 -5.3831 -4.8584
## (Intercept)-Sylvilagus_floridanus -5.0422 0.2243 -5.4780 -5.0400
## (Intercept)-Meleagris_gallopavo -5.6938 0.5753 -7.4609 -5.6141
## (Intercept)-Sciurus_carolinensis -4.9171 0.2839 -5.4752 -4.9084
## (Intercept)-Vulpes_vulpes -5.9799 0.5893 -7.3175 -5.9310
## (Intercept)-Sus_scrofa -5.4386 0.5475 -6.5342 -5.4205
## shrub_cover-Canis_latrans -0.3169 0.2252 -0.7263 -0.3337
## shrub_cover-Lynx_rufus -0.0145 0.3829 -0.7050 -0.0231
## shrub_cover-Didelphis_virginiana 1.3136 0.4062 0.5561 1.3042
## shrub_cover-Sylvilagus_floridanus 0.8319 0.3830 0.1109 0.8278
## shrub_cover-Meleagris_gallopavo -0.6988 0.5090 -2.1460 -0.6435
## shrub_cover-Sciurus_carolinensis 1.2099 0.3824 0.4742 1.2031
## shrub_cover-Vulpes_vulpes 0.5357 0.5885 -0.5433 0.5144
## shrub_cover-Sus_scrofa 1.1850 0.9156 -0.6218 1.1880
## veg_height-Canis_latrans -0.5323 0.1809 -0.8864 -0.5277
## veg_height-Lynx_rufus 0.2120 0.2401 -0.3041 0.2141
## veg_height-Didelphis_virginiana 0.5315 0.2483 0.0960 0.5041
## veg_height-Sylvilagus_floridanus 0.1841 0.2344 -0.2624 0.1783
## veg_height-Meleagris_gallopavo -0.2500 0.3953 -1.0281 -0.2380
## veg_height-Sciurus_carolinensis 0.3015 0.2296 -0.1408 0.2961
## veg_height-Vulpes_vulpes -0.1472 0.3445 -0.8672 -0.1380
## veg_height-Sus_scrofa -0.2604 0.3386 -1.0031 -0.2386
## week-Canis_latrans 7387.9723 481294.3175 -1655.6082 0.1175
## week-Lynx_rufus -11927.7570 481017.4072 -3339.4973 0.0092
## week-Didelphis_virginiana -13566.2376 446889.1246 -1879.9410 0.1469
## week-Sylvilagus_floridanus 6999.1494 516288.5836 -1467.0128 0.0426
## week-Meleagris_gallopavo 1936.2149 383665.3197 -1880.3645 0.1922
## week-Sciurus_carolinensis 4889.8637 406104.4371 -1936.5883 0.0632
## week-Vulpes_vulpes 4062.3387 492554.2010 -1917.8842 0.3298
## week-Sus_scrofa 611.2731 374825.3938 -1527.1378 0.0983
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4664 1.2003 179
## (Intercept)-Lynx_rufus -5.1296 1.2162 76
## (Intercept)-Didelphis_virginiana -4.2975 1.0143 100
## (Intercept)-Sylvilagus_floridanus -4.6110 1.0020 135
## (Intercept)-Meleagris_gallopavo -4.8205 1.2051 28
## (Intercept)-Sciurus_carolinensis -4.3722 1.2015 77
## (Intercept)-Vulpes_vulpes -4.9784 1.1117 41
## (Intercept)-Sus_scrofa -4.4027 1.0755 67
## shrub_cover-Canis_latrans 0.1627 1.7093 55
## shrub_cover-Lynx_rufus 0.7279 1.2889 51
## shrub_cover-Didelphis_virginiana 2.1178 1.1379 73
## shrub_cover-Sylvilagus_floridanus 1.6211 1.1465 75
## shrub_cover-Meleagris_gallopavo 0.1436 1.1357 40
## shrub_cover-Sciurus_carolinensis 1.9635 1.0493 110
## shrub_cover-Vulpes_vulpes 1.7218 1.1945 109
## shrub_cover-Sus_scrofa 2.8526 1.0270 69
## veg_height-Canis_latrans -0.1965 1.2173 142
## veg_height-Lynx_rufus 0.6561 1.3311 107
## veg_height-Didelphis_virginiana 1.0574 1.1123 222
## veg_height-Sylvilagus_floridanus 0.6411 1.1077 159
## veg_height-Meleagris_gallopavo 0.5210 1.0329 91
## veg_height-Sciurus_carolinensis 0.7729 1.0276 174
## veg_height-Vulpes_vulpes 0.4749 1.0959 111
## veg_height-Sus_scrofa 0.3604 1.0472 234
## week-Canis_latrans 2523.4888 1.1111 6164
## week-Lynx_rufus 1686.1383 1.1294 2859
## week-Didelphis_virginiana 2064.3303 1.2456 1351
## week-Sylvilagus_floridanus 2235.9879 1.1844 6792
## week-Meleagris_gallopavo 1626.9273 1.1352 2954
## week-Sciurus_carolinensis 2309.9150 1.1863 14121
## week-Vulpes_vulpes 2143.1300 1.1154 6657
## week-Sus_scrofa 1860.3448 1.1113 3401
# 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): 3.2292
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4818 1.0210 -3.2376 -1.5602 0.8822 1.0406 454
## Cogon_Patch_Size -0.4426 0.8305 -2.1162 -0.4196 1.1714 1.0263 681
## Veg_shannon_index 0.7469 0.6398 -0.6026 0.7517 1.9816 1.0320 536
## total_shrub_cover -0.6095 0.7742 -2.2585 -0.5573 0.8635 1.0165 802
## Avg_Cogongrass_Cover 1.7791 0.8091 0.1787 1.7900 3.4253 1.0848 207
## Tree_Density -2.0303 0.7774 -3.6848 -1.9693 -0.6887 1.1171 164
## Avg_Canopy_Cover 1.8727 0.7515 0.4313 1.8350 3.5332 1.0336 293
## avg_veg_height -0.7264 0.5924 -1.8046 -0.7551 0.5586 1.0279 123
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9956 21.5521 0.6536 5.9580 54.3763 1.5545 104
## Cogon_Patch_Size 4.6424 6.9480 0.1365 2.5355 21.8314 1.3633 163
## Veg_shannon_index 2.7183 5.1634 0.0745 1.2582 13.8280 1.2412 206
## total_shrub_cover 5.2059 9.8870 0.1571 2.4853 28.2477 1.4014 127
## Avg_Cogongrass_Cover 1.9124 3.3696 0.0629 0.8185 10.3556 1.0137 269
## Tree_Density 1.5343 3.1810 0.0495 0.5071 9.7727 1.0866 390
## Avg_Canopy_Cover 3.1882 4.6144 0.1133 1.6920 15.4935 1.0369 198
## avg_veg_height 0.6250 1.0537 0.0456 0.3075 3.3214 1.1418 402
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7162 3.1427 0.0595 0.7714 10.5157 1.1913 40
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8153 0.3163 -5.3876 -4.8281 -4.1333 1.0231 1369
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5969 0.7409 0.0756 0.3981 2.3548 1.1789 121
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.0144 1.6448 -1.4472 0.8196
## (Intercept)-Lynx_rufus 1.5837 2.7328 -1.7897 1.0324
## (Intercept)-Didelphis_virginiana -3.2755 1.1943 -6.0268 -3.1580
## (Intercept)-Sylvilagus_floridanus -1.7084 1.1168 -3.9845 -1.6657
## (Intercept)-Meleagris_gallopavo -2.2502 1.4136 -5.3809 -2.1338
## (Intercept)-Sciurus_carolinensis -3.4272 1.3034 -6.3830 -3.2685
## (Intercept)-Vulpes_vulpes -2.9917 1.6625 -6.2554 -3.0018
## (Intercept)-Sus_scrofa -5.0418 2.1469 -10.7039 -4.7101
## Cogon_Patch_Size-Canis_latrans 1.0191 1.4524 -0.9330 0.7600
## Cogon_Patch_Size-Lynx_rufus 0.0683 1.7833 -2.8382 -0.1289
## Cogon_Patch_Size-Didelphis_virginiana 1.2292 1.0631 -0.4338 1.0943
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0869 1.7482 -6.2958 -1.7158
## Cogon_Patch_Size-Meleagris_gallopavo -0.2456 1.2329 -2.5781 -0.2803
## Cogon_Patch_Size-Sciurus_carolinensis -1.7414 1.4003 -5.2469 -1.4760
## Cogon_Patch_Size-Vulpes_vulpes -1.2694 1.8642 -5.4168 -1.0822
## Cogon_Patch_Size-Sus_scrofa -1.3386 1.6861 -5.4632 -1.0451
## Veg_shannon_index-Canis_latrans 1.4174 0.8616 0.0164 1.3354
## Veg_shannon_index-Lynx_rufus -0.1438 1.4590 -3.5742 0.1033
## Veg_shannon_index-Didelphis_virginiana 1.1156 0.8532 -0.3823 1.0384
## Veg_shannon_index-Sylvilagus_floridanus 1.0738 0.8842 -0.4130 0.9792
## Veg_shannon_index-Meleagris_gallopavo 1.5783 1.2198 -0.0821 1.3846
## Veg_shannon_index-Sciurus_carolinensis -0.2568 0.9527 -2.3423 -0.1438
## Veg_shannon_index-Vulpes_vulpes -0.2384 1.2835 -3.2114 -0.0320
## Veg_shannon_index-Sus_scrofa 2.0611 1.5145 0.0883 1.7383
## total_shrub_cover-Canis_latrans 0.7396 1.0318 -0.9860 0.6242
## total_shrub_cover-Lynx_rufus -1.8862 2.0242 -6.8145 -1.5786
## total_shrub_cover-Didelphis_virginiana -0.7363 0.9339 -2.7286 -0.6770
## total_shrub_cover-Sylvilagus_floridanus -0.1899 1.0630 -2.2068 -0.2180
## total_shrub_cover-Meleagris_gallopavo -3.3992 1.9436 -8.2777 -3.1294
## total_shrub_cover-Sciurus_carolinensis 0.0939 0.8149 -1.4605 0.0851
## total_shrub_cover-Vulpes_vulpes -0.9923 2.0002 -4.6579 -0.7187
## total_shrub_cover-Sus_scrofa 0.3501 1.2947 -2.0852 0.2612
## Avg_Cogongrass_Cover-Canis_latrans 2.3928 1.0823 0.5146 2.2882
## Avg_Cogongrass_Cover-Lynx_rufus 2.5870 1.3306 0.4228 2.4316
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0933 0.9578 0.3631 2.0236
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2984 1.0361 -0.7485 1.3162
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0424 1.4075 -2.1431 1.1787
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2621 1.0106 0.5490 2.1862
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4041 1.3001 0.2433 2.2569
## Avg_Cogongrass_Cover-Sus_scrofa 1.2435 1.2720 -1.7074 1.3620
## Tree_Density-Canis_latrans -2.5103 1.1426 -5.4059 -2.3206
## Tree_Density-Lynx_rufus -1.3466 1.1482 -3.4763 -1.3808
## Tree_Density-Didelphis_virginiana -2.2203 1.0169 -4.4976 -2.1077
## Tree_Density-Sylvilagus_floridanus -2.4482 1.1756 -5.3106 -2.2995
## Tree_Density-Meleagris_gallopavo -2.1607 1.1437 -4.6790 -2.0672
## Tree_Density-Sciurus_carolinensis -2.4683 1.2917 -5.8326 -2.2570
## Tree_Density-Vulpes_vulpes -2.0589 1.2283 -4.8353 -1.9771
## Tree_Density-Sus_scrofa -2.1997 1.2233 -5.0557 -2.0582
## Avg_Canopy_Cover-Canis_latrans 0.3503 0.8607 -1.3280 0.3358
## Avg_Canopy_Cover-Lynx_rufus 1.0102 1.4113 -1.7207 0.9710
## Avg_Canopy_Cover-Didelphis_virginiana 2.8654 1.0418 1.2486 2.6918
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5217 1.7330 1.1906 3.1785
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2854 1.1215 0.5571 2.0999
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4833 0.9274 1.0087 2.3299
## Avg_Canopy_Cover-Vulpes_vulpes 2.3560 1.2872 0.4688 2.1313
## Avg_Canopy_Cover-Sus_scrofa 2.1823 1.0118 0.5691 2.0411
## avg_veg_height-Canis_latrans -0.6841 0.8088 -2.0296 -0.7294
## avg_veg_height-Lynx_rufus -0.8224 0.9202 -2.6325 -0.8226
## avg_veg_height-Didelphis_virginiana -0.7652 0.7255 -2.1459 -0.7725
## avg_veg_height-Sylvilagus_floridanus -0.8451 0.7574 -2.3758 -0.8514
## avg_veg_height-Meleagris_gallopavo -0.8713 0.8576 -2.6342 -0.8706
## avg_veg_height-Sciurus_carolinensis -0.3193 0.7534 -1.6140 -0.3987
## avg_veg_height-Vulpes_vulpes -0.8459 0.8877 -2.7085 -0.8456
## avg_veg_height-Sus_scrofa -0.7468 0.8017 -2.3649 -0.7554
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.6922 1.2364 88
## (Intercept)-Lynx_rufus 8.8313 1.4607 71
## (Intercept)-Didelphis_virginiana -1.3158 1.0813 290
## (Intercept)-Sylvilagus_floridanus 0.5076 1.0486 490
## (Intercept)-Meleagris_gallopavo 0.1483 1.0343 292
## (Intercept)-Sciurus_carolinensis -1.3387 1.0965 194
## (Intercept)-Vulpes_vulpes 0.5408 1.0371 156
## (Intercept)-Sus_scrofa -1.8978 1.1791 91
## Cogon_Patch_Size-Canis_latrans 4.8173 1.0986 332
## Cogon_Patch_Size-Lynx_rufus 4.1004 1.0552 256
## Cogon_Patch_Size-Didelphis_virginiana 3.5982 1.0133 300
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2888 1.1899 341
## Cogon_Patch_Size-Meleagris_gallopavo 2.3595 1.0367 467
## Cogon_Patch_Size-Sciurus_carolinensis 0.2278 1.0471 376
## Cogon_Patch_Size-Vulpes_vulpes 2.0555 1.0986 229
## Cogon_Patch_Size-Sus_scrofa 1.2242 1.0929 309
## Veg_shannon_index-Canis_latrans 3.4038 1.0190 434
## Veg_shannon_index-Lynx_rufus 1.9753 1.0247 229
## Veg_shannon_index-Didelphis_virginiana 3.0188 1.0659 458
## Veg_shannon_index-Sylvilagus_floridanus 3.1064 1.0513 340
## Veg_shannon_index-Meleagris_gallopavo 4.3038 1.1545 174
## Veg_shannon_index-Sciurus_carolinensis 1.2504 1.0081 438
## Veg_shannon_index-Vulpes_vulpes 1.7596 1.0241 266
## Veg_shannon_index-Sus_scrofa 5.9600 1.0857 153
## total_shrub_cover-Canis_latrans 3.1383 1.0437 346
## total_shrub_cover-Lynx_rufus 0.9629 1.0705 92
## total_shrub_cover-Didelphis_virginiana 0.8746 1.0330 491
## total_shrub_cover-Sylvilagus_floridanus 2.0354 1.0154 598
## total_shrub_cover-Meleagris_gallopavo -0.5699 1.0994 140
## total_shrub_cover-Sciurus_carolinensis 1.7206 1.0081 1025
## total_shrub_cover-Vulpes_vulpes 1.5347 1.4701 77
## total_shrub_cover-Sus_scrofa 3.1502 1.0143 620
## Avg_Cogongrass_Cover-Canis_latrans 4.7543 1.0200 335
## Avg_Cogongrass_Cover-Lynx_rufus 5.6122 1.0015 273
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.1822 1.0152 299
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2599 1.1054 310
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6000 1.1198 299
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4149 1.0028 289
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.6030 1.0032 225
## Avg_Cogongrass_Cover-Sus_scrofa 3.5708 1.0713 384
## Tree_Density-Canis_latrans -0.7366 1.0820 181
## Tree_Density-Lynx_rufus 1.0589 1.0471 249
## Tree_Density-Didelphis_virginiana -0.5045 1.0517 216
## Tree_Density-Sylvilagus_floridanus -0.6625 1.0641 255
## Tree_Density-Meleagris_gallopavo -0.1468 1.1654 199
## Tree_Density-Sciurus_carolinensis -0.7145 1.1682 251
## Tree_Density-Vulpes_vulpes 0.2226 1.0332 271
## Tree_Density-Sus_scrofa -0.1597 1.1133 293
## Avg_Canopy_Cover-Canis_latrans 2.0426 1.0703 258
## Avg_Canopy_Cover-Lynx_rufus 3.9507 1.0368 187
## Avg_Canopy_Cover-Didelphis_virginiana 5.2724 1.0322 182
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.8139 1.0102 177
## Avg_Canopy_Cover-Meleagris_gallopavo 5.0319 1.0793 143
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6908 1.0507 261
## Avg_Canopy_Cover-Vulpes_vulpes 5.6866 1.1274 188
## Avg_Canopy_Cover-Sus_scrofa 4.5369 1.0794 235
## avg_veg_height-Canis_latrans 0.8616 1.0766 142
## avg_veg_height-Lynx_rufus 1.0938 1.0171 254
## avg_veg_height-Didelphis_virginiana 0.6943 1.0158 311
## avg_veg_height-Sylvilagus_floridanus 0.7260 1.0176 285
## avg_veg_height-Meleagris_gallopavo 0.8913 1.0124 340
## avg_veg_height-Sciurus_carolinensis 1.4369 1.0152 449
## avg_veg_height-Vulpes_vulpes 0.9364 1.0188 246
## avg_veg_height-Sus_scrofa 0.9728 1.0195 268
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7067 0.1623 -5.0496 -4.6981 -4.4150 1.0541
## (Intercept)-Lynx_rufus -5.5498 0.2862 -6.1034 -5.5396 -5.0194 1.1686
## (Intercept)-Didelphis_virginiana -4.3139 0.2219 -4.7438 -4.3109 -3.8835 1.0118
## (Intercept)-Sylvilagus_floridanus -4.9506 0.2289 -5.4212 -4.9477 -4.5104 1.0776
## (Intercept)-Meleagris_gallopavo -5.1274 0.2646 -5.6834 -5.1189 -4.6403 1.0283
## (Intercept)-Sciurus_carolinensis -4.3926 0.2394 -4.8651 -4.3815 -3.9529 1.0365
## (Intercept)-Vulpes_vulpes -5.7593 0.6405 -7.1572 -5.7103 -4.7112 1.3451
## (Intercept)-Sus_scrofa -4.7782 0.3493 -5.4918 -4.7643 -4.1172 1.0130
## ESS
## (Intercept)-Canis_latrans 206
## (Intercept)-Lynx_rufus 60
## (Intercept)-Didelphis_virginiana 349
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Meleagris_gallopavo 128
## (Intercept)-Sciurus_carolinensis 316
## (Intercept)-Vulpes_vulpes 53
## (Intercept)-Sus_scrofa 259
# 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): 2.9412
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0488 0.4811 -1.9828 -1.0615 -0.0840 1.0109 540
## Avg_Cogongrass_Cover 0.1935 0.4007 -0.5945 0.1928 1.0251 1.0017 651
## total_shrub_cover -0.3346 0.4162 -1.2220 -0.3188 0.4378 1.0144 1339
## avg_veg_height -0.1240 0.3682 -0.8232 -0.1292 0.6286 1.0043 470
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0781 1.3873 0.0616 0.6292 4.8014 1.0306 492
## Avg_Cogongrass_Cover 0.6386 0.8144 0.0564 0.3699 2.8280 1.0042 877
## total_shrub_cover 1.0366 1.3499 0.0722 0.6197 4.6531 1.0390 651
## avg_veg_height 0.3179 0.4337 0.0379 0.1874 1.4714 1.0084 1142
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9831 1.8236 0.1103 1.5038 6.668 1.0155 162
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7909 0.2726 -5.2788 -4.7991 -4.222 1.0303 903
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4622 1.079 0.0578 0.266 1.8795 1.1213 119
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1480 0.8074 -1.6352 -0.1798
## (Intercept)-Lynx_rufus -0.6216 0.7755 -1.9668 -0.6808
## (Intercept)-Didelphis_virginiana -1.3761 0.6448 -2.7433 -1.3452
## (Intercept)-Sylvilagus_floridanus -0.8890 0.6837 -2.2090 -0.8930
## (Intercept)-Meleagris_gallopavo -1.0995 0.6897 -2.5062 -1.0782
## (Intercept)-Sciurus_carolinensis -1.4260 0.6705 -2.8403 -1.4012
## (Intercept)-Vulpes_vulpes -1.4977 0.8788 -3.2766 -1.4795
## (Intercept)-Sus_scrofa -1.7906 0.7983 -3.4770 -1.7283
## Avg_Cogongrass_Cover-Canis_latrans 0.5529 0.5340 -0.4272 0.5156
## Avg_Cogongrass_Cover-Lynx_rufus 0.6630 0.6020 -0.3357 0.6062
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4730 0.5000 -0.4661 0.4442
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2834 0.5716 -1.5450 -0.2425
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4270 0.6696 -1.9308 -0.3684
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3296 0.4836 -0.6258 0.3319
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4019 0.6118 -0.6815 0.3792
## Avg_Cogongrass_Cover-Sus_scrofa -0.1711 0.7042 -1.7884 -0.1144
## total_shrub_cover-Canis_latrans 0.4488 0.5543 -0.4977 0.3848
## total_shrub_cover-Lynx_rufus -0.9629 0.7593 -2.7777 -0.8561
## total_shrub_cover-Didelphis_virginiana -0.2293 0.4856 -1.2163 -0.2109
## total_shrub_cover-Sylvilagus_floridanus -0.3681 0.5394 -1.4647 -0.3302
## total_shrub_cover-Meleagris_gallopavo -1.4514 0.8019 -3.3966 -1.3513
## total_shrub_cover-Sciurus_carolinensis -0.0761 0.4677 -0.9809 -0.0861
## total_shrub_cover-Vulpes_vulpes -0.3782 0.7193 -1.9098 -0.3302
## total_shrub_cover-Sus_scrofa 0.1632 0.6426 -1.0628 0.1279
## avg_veg_height-Canis_latrans -0.1024 0.4592 -0.9642 -0.1181
## avg_veg_height-Lynx_rufus -0.1225 0.5523 -1.2014 -0.1348
## avg_veg_height-Didelphis_virginiana -0.1434 0.4584 -1.0940 -0.1320
## avg_veg_height-Sylvilagus_floridanus -0.2159 0.4595 -1.1281 -0.2113
## avg_veg_height-Meleagris_gallopavo -0.3518 0.5601 -1.5898 -0.3223
## avg_veg_height-Sciurus_carolinensis 0.2113 0.4823 -0.6776 0.1844
## avg_veg_height-Vulpes_vulpes -0.2079 0.5379 -1.3053 -0.1904
## avg_veg_height-Sus_scrofa -0.1064 0.5121 -1.1322 -0.1022
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4469 1.0303 297
## (Intercept)-Lynx_rufus 1.1258 1.0269 429
## (Intercept)-Didelphis_virginiana -0.2071 1.0022 721
## (Intercept)-Sylvilagus_floridanus 0.4950 1.0105 436
## (Intercept)-Meleagris_gallopavo 0.2483 1.0096 648
## (Intercept)-Sciurus_carolinensis -0.1291 1.0048 666
## (Intercept)-Vulpes_vulpes 0.3464 1.0037 292
## (Intercept)-Sus_scrofa -0.4125 1.0140 458
## Avg_Cogongrass_Cover-Canis_latrans 1.7621 1.0026 807
## Avg_Cogongrass_Cover-Lynx_rufus 1.9897 1.0018 936
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5072 1.0031 954
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7503 1.0013 1042
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7301 1.0040 818
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2770 1.0089 1157
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.6473 1.0119 616
## Avg_Cogongrass_Cover-Sus_scrofa 1.0513 1.0043 745
## total_shrub_cover-Canis_latrans 1.7061 1.0051 862
## total_shrub_cover-Lynx_rufus 0.2139 1.0229 501
## total_shrub_cover-Didelphis_virginiana 0.6734 1.0021 1859
## total_shrub_cover-Sylvilagus_floridanus 0.5697 1.0237 603
## total_shrub_cover-Meleagris_gallopavo -0.1814 1.0302 616
## total_shrub_cover-Sciurus_carolinensis 0.8445 1.0044 1915
## total_shrub_cover-Vulpes_vulpes 0.8958 1.0136 680
## total_shrub_cover-Sus_scrofa 1.5079 1.0003 1146
## avg_veg_height-Canis_latrans 0.8470 1.0063 884
## avg_veg_height-Lynx_rufus 0.9785 1.0034 680
## avg_veg_height-Didelphis_virginiana 0.7499 1.0015 831
## avg_veg_height-Sylvilagus_floridanus 0.6641 1.0018 808
## avg_veg_height-Meleagris_gallopavo 0.6965 1.0063 774
## avg_veg_height-Sciurus_carolinensis 1.2625 1.0011 945
## avg_veg_height-Vulpes_vulpes 0.8232 1.0048 737
## avg_veg_height-Sus_scrofa 0.8873 1.0016 954
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6805 0.1615 -5.0190 -4.6760 -4.3827 1.0436
## (Intercept)-Lynx_rufus -5.2977 0.2383 -5.7879 -5.2862 -4.8451 1.0234
## (Intercept)-Didelphis_virginiana -4.3670 0.2373 -4.8442 -4.3629 -3.9014 1.0172
## (Intercept)-Sylvilagus_floridanus -4.8994 0.2378 -5.3795 -4.8936 -4.4531 1.0761
## (Intercept)-Meleagris_gallopavo -5.0509 0.2662 -5.6365 -5.0444 -4.5819 1.0335
## (Intercept)-Sciurus_carolinensis -4.4224 0.2383 -4.9175 -4.4122 -3.9797 1.0173
## (Intercept)-Vulpes_vulpes -5.5564 0.6490 -7.3704 -5.4129 -4.6298 1.0598
## (Intercept)-Sus_scrofa -4.8020 0.3602 -5.5170 -4.7854 -4.1134 1.0228
## ESS
## (Intercept)-Canis_latrans 256
## (Intercept)-Lynx_rufus 133
## (Intercept)-Didelphis_virginiana 316
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Meleagris_gallopavo 142
## (Intercept)-Sciurus_carolinensis 271
## (Intercept)-Vulpes_vulpes 47
## (Intercept)-Sus_scrofa 215
# 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): 2.9758
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1655 0.5647 -2.2728 -1.1807 0.0538 1.0296 641
## Tree_Density -0.7822 0.4759 -1.7853 -0.7377 0.0393 1.0130 536
## Avg_Canopy_Cover 1.0669 0.4117 0.2874 1.0471 1.9526 1.0029 881
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0011 2.7990 0.1241 1.2713 7.7884 1.0026 410
## Tree_Density 0.8463 1.4702 0.0577 0.4364 4.2667 1.0340 579
## Avg_Canopy_Cover 0.8469 0.9940 0.0716 0.5391 3.5381 1.0051 763
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.908 1.3024 0.0573 0.4957 4.2508 1.2678 160
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7896 0.26 -5.2461 -4.8029 -4.2551 1.0078 1048
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4285 0.5426 0.0663 0.2782 1.7041 1.0404 248
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1197 0.7944 -1.4048 0.1222 1.7349
## (Intercept)-Lynx_rufus -0.0503 1.1206 -1.8916 -0.1892 2.5624
## (Intercept)-Didelphis_virginiana -1.8188 0.6870 -3.2846 -1.7917 -0.5358
## (Intercept)-Sylvilagus_floridanus -1.0240 0.6538 -2.2714 -1.0345 0.2908
## (Intercept)-Meleagris_gallopavo -0.9831 0.6832 -2.4067 -0.9662 0.3119
## (Intercept)-Sciurus_carolinensis -1.9358 0.6987 -3.5416 -1.8797 -0.7014
## (Intercept)-Vulpes_vulpes -1.9114 0.9823 -3.6578 -1.9153 0.0477
## (Intercept)-Sus_scrofa -2.4041 0.8843 -4.2886 -2.3304 -0.8722
## Tree_Density-Canis_latrans -0.9646 0.6026 -2.2908 -0.9018 0.0195
## Tree_Density-Lynx_rufus 0.0440 0.6971 -1.0663 -0.0409 1.6465
## Tree_Density-Didelphis_virginiana -1.0290 0.7726 -2.9125 -0.8994 0.1067
## Tree_Density-Sylvilagus_floridanus -1.1059 0.7622 -2.9219 -0.9827 0.0712
## Tree_Density-Meleagris_gallopavo -0.9339 0.7364 -2.6634 -0.8506 0.2837
## Tree_Density-Sciurus_carolinensis -0.9478 0.7553 -2.8239 -0.8489 0.2208
## Tree_Density-Vulpes_vulpes -0.6188 0.8628 -2.2566 -0.6230 0.8446
## Tree_Density-Sus_scrofa -0.9723 0.8284 -2.9369 -0.8772 0.3401
## Avg_Canopy_Cover-Canis_latrans 0.0771 0.5092 -0.8906 0.0681 1.0879
## Avg_Canopy_Cover-Lynx_rufus 0.7365 0.6993 -0.5758 0.7070 2.2007
## Avg_Canopy_Cover-Didelphis_virginiana 1.3138 0.5059 0.4177 1.2714 2.4156
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7590 0.7512 0.6038 1.6455 3.6000
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4643 0.7304 0.3183 1.3495 3.1969
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2583 0.4982 0.3865 1.2219 2.3460
## Avg_Canopy_Cover-Vulpes_vulpes 1.0375 0.6195 -0.0292 0.9871 2.3786
## Avg_Canopy_Cover-Sus_scrofa 1.2522 0.5725 0.2745 1.2174 2.5195
## Rhat ESS
## (Intercept)-Canis_latrans 1.0201 333
## (Intercept)-Lynx_rufus 1.0195 186
## (Intercept)-Didelphis_virginiana 1.0132 956
## (Intercept)-Sylvilagus_floridanus 1.0042 728
## (Intercept)-Meleagris_gallopavo 1.0122 585
## (Intercept)-Sciurus_carolinensis 1.0100 886
## (Intercept)-Vulpes_vulpes 1.0614 188
## (Intercept)-Sus_scrofa 1.0417 636
## Tree_Density-Canis_latrans 1.0023 944
## Tree_Density-Lynx_rufus 1.0098 552
## Tree_Density-Didelphis_virginiana 1.0173 717
## Tree_Density-Sylvilagus_floridanus 1.0051 648
## Tree_Density-Meleagris_gallopavo 1.0013 712
## Tree_Density-Sciurus_carolinensis 1.0010 783
## Tree_Density-Vulpes_vulpes 1.1106 370
## Tree_Density-Sus_scrofa 1.0103 600
## Avg_Canopy_Cover-Canis_latrans 1.0083 981
## Avg_Canopy_Cover-Lynx_rufus 1.0116 670
## Avg_Canopy_Cover-Didelphis_virginiana 1.0071 1490
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0008 619
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0122 582
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0002 1283
## Avg_Canopy_Cover-Vulpes_vulpes 1.0118 749
## Avg_Canopy_Cover-Sus_scrofa 1.0209 1239
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6937 0.1619 -5.0222 -4.6894 -4.3952 1.0168
## (Intercept)-Lynx_rufus -5.4337 0.2992 -6.0200 -5.4264 -4.8803 1.0209
## (Intercept)-Didelphis_virginiana -4.3396 0.2298 -4.8024 -4.3317 -3.9087 1.0094
## (Intercept)-Sylvilagus_floridanus -4.8713 0.2331 -5.3541 -4.8586 -4.4471 1.0606
## (Intercept)-Meleagris_gallopavo -5.0812 0.2476 -5.5675 -5.0874 -4.6036 1.0116
## (Intercept)-Sciurus_carolinensis -4.3943 0.2217 -4.8337 -4.3873 -3.9713 1.0248
## (Intercept)-Vulpes_vulpes -5.4531 0.5431 -6.6646 -5.3678 -4.6335 1.0612
## (Intercept)-Sus_scrofa -4.8047 0.3350 -5.4815 -4.7930 -4.1807 1.0192
## ESS
## (Intercept)-Canis_latrans 226
## (Intercept)-Lynx_rufus 77
## (Intercept)-Didelphis_virginiana 325
## (Intercept)-Sylvilagus_floridanus 184
## (Intercept)-Meleagris_gallopavo 175
## (Intercept)-Sciurus_carolinensis 342
## (Intercept)-Vulpes_vulpes 55
## (Intercept)-Sus_scrofa 302
# 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): 2.8792
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1192 0.4998 -2.1162 -1.1215 -0.1112 1.0295 732
## Cogon_Patch_Size -0.2332 0.5275 -1.4038 -0.2076 0.7934 1.0024 1109
## Avg_Cogongrass_Cover 0.1591 0.3759 -0.6106 0.1700 0.8869 1.0076 1151
## total_shrub_cover -0.2752 0.4002 -1.0859 -0.2709 0.5068 1.0016 1498
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3749 1.9970 0.0706 0.7964 6.2856 1.0070 600
## Cogon_Patch_Size 1.5824 2.0156 0.0978 0.8904 7.2702 1.0118 692
## Avg_Cogongrass_Cover 0.6809 1.0171 0.0476 0.3765 3.2277 1.0128 756
## total_shrub_cover 0.8639 1.0626 0.0679 0.5237 3.6370 1.0058 682
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9824 1.7058 0.1667 1.5279 6.2932 1.0097 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7911 0.2419 -5.249 -4.7971 -4.2763 1.0357 755
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3611 0.4184 0.0549 0.2495 1.3315 1.1309 527
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0144 0.8578 -1.5449 -0.0379
## (Intercept)-Lynx_rufus -0.6256 0.8353 -2.1611 -0.6744
## (Intercept)-Didelphis_virginiana -1.4256 0.6952 -2.8967 -1.4027
## (Intercept)-Sylvilagus_floridanus -0.9943 0.7013 -2.3633 -0.9989
## (Intercept)-Meleagris_gallopavo -1.1438 0.6956 -2.5496 -1.1358
## (Intercept)-Sciurus_carolinensis -1.5861 0.7262 -3.1925 -1.5340
## (Intercept)-Vulpes_vulpes -1.7195 0.9049 -3.6662 -1.6656
## (Intercept)-Sus_scrofa -1.9811 0.9062 -3.9555 -1.8863
## Cogon_Patch_Size-Canis_latrans 0.8184 0.8140 -0.3951 0.6790
## Cogon_Patch_Size-Lynx_rufus -0.4081 0.8941 -2.0316 -0.4589
## Cogon_Patch_Size-Didelphis_virginiana 0.7679 0.5849 -0.2185 0.7141
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1300 0.9873 -3.6924 -0.9410
## Cogon_Patch_Size-Meleagris_gallopavo -0.0534 0.6908 -1.3503 -0.0694
## Cogon_Patch_Size-Sciurus_carolinensis -0.9444 0.8091 -2.8450 -0.8269
## Cogon_Patch_Size-Vulpes_vulpes -0.7127 0.9475 -2.9935 -0.6013
## Cogon_Patch_Size-Sus_scrofa -0.5103 0.9856 -2.9194 -0.3751
## Avg_Cogongrass_Cover-Canis_latrans 0.3387 0.4571 -0.5374 0.3155
## Avg_Cogongrass_Cover-Lynx_rufus 0.7402 0.6449 -0.2498 0.6541
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2050 0.4791 -0.7499 0.2100
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1660 0.5181 -1.3050 -0.1270
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5325 0.7189 -2.2036 -0.4159
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5703 0.4666 -0.2713 0.5412
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3519 0.5280 -0.6753 0.3390
## Avg_Cogongrass_Cover-Sus_scrofa -0.2102 0.7194 -1.8506 -0.1013
## total_shrub_cover-Canis_latrans 0.3974 0.5527 -0.5466 0.3503
## total_shrub_cover-Lynx_rufus -0.7391 0.7056 -2.3936 -0.6568
## total_shrub_cover-Didelphis_virginiana -0.3203 0.4903 -1.3281 -0.3138
## total_shrub_cover-Sylvilagus_floridanus -0.2210 0.5379 -1.2802 -0.2193
## total_shrub_cover-Meleagris_gallopavo -1.3091 0.7671 -3.1281 -1.1871
## total_shrub_cover-Sciurus_carolinensis 0.0084 0.4765 -0.9188 -0.0008
## total_shrub_cover-Vulpes_vulpes -0.2669 0.6338 -1.5916 -0.2451
## total_shrub_cover-Sus_scrofa 0.1920 0.6337 -0.9464 0.1399
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8440 1.0169 304
## (Intercept)-Lynx_rufus 1.2055 1.0616 340
## (Intercept)-Didelphis_virginiana -0.0693 1.0253 759
## (Intercept)-Sylvilagus_floridanus 0.3719 1.0075 661
## (Intercept)-Meleagris_gallopavo 0.2399 1.0043 839
## (Intercept)-Sciurus_carolinensis -0.3346 1.0057 574
## (Intercept)-Vulpes_vulpes -0.1243 1.0225 417
## (Intercept)-Sus_scrofa -0.4208 1.0324 414
## Cogon_Patch_Size-Canis_latrans 2.7287 1.0197 756
## Cogon_Patch_Size-Lynx_rufus 1.5962 1.0179 571
## Cogon_Patch_Size-Didelphis_virginiana 2.0379 1.0080 815
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2969 1.0107 603
## Cogon_Patch_Size-Meleagris_gallopavo 1.3196 1.0223 877
## Cogon_Patch_Size-Sciurus_carolinensis 0.2546 1.0116 781
## Cogon_Patch_Size-Vulpes_vulpes 0.7823 1.0221 609
## Cogon_Patch_Size-Sus_scrofa 1.0489 1.0146 938
## Avg_Cogongrass_Cover-Canis_latrans 1.3078 1.0000 1608
## Avg_Cogongrass_Cover-Lynx_rufus 2.2870 1.0049 757
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1516 1.0022 1866
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7320 1.0011 1174
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6126 1.0068 635
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.5294 1.0199 1062
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3869 1.0097 1430
## Avg_Cogongrass_Cover-Sus_scrofa 0.8970 1.0065 765
## total_shrub_cover-Canis_latrans 1.6062 1.0038 781
## total_shrub_cover-Lynx_rufus 0.4512 1.0069 679
## total_shrub_cover-Didelphis_virginiana 0.6058 1.0002 1893
## total_shrub_cover-Sylvilagus_floridanus 0.8154 1.0074 1336
## total_shrub_cover-Meleagris_gallopavo -0.1176 1.0135 492
## total_shrub_cover-Sciurus_carolinensis 1.0160 1.0116 1591
## total_shrub_cover-Vulpes_vulpes 0.9362 1.0125 1148
## total_shrub_cover-Sus_scrofa 1.6191 1.0010 929
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6611 0.1531 -4.9709 -4.6593 -4.3569 1.0296
## (Intercept)-Lynx_rufus -5.3151 0.2445 -5.8476 -5.3022 -4.8830 1.1930
## (Intercept)-Didelphis_virginiana -4.3906 0.2262 -4.8552 -4.3871 -3.9636 1.0067
## (Intercept)-Sylvilagus_floridanus -4.9114 0.2258 -5.3774 -4.9058 -4.5011 1.0029
## (Intercept)-Meleagris_gallopavo -5.0406 0.2719 -5.6595 -5.0264 -4.5505 1.0715
## (Intercept)-Sciurus_carolinensis -4.4066 0.2412 -4.9163 -4.3937 -3.9614 1.0197
## (Intercept)-Vulpes_vulpes -5.3982 0.4623 -6.3905 -5.3491 -4.6348 1.1512
## (Intercept)-Sus_scrofa -4.7911 0.3528 -5.5679 -4.7796 -4.1396 1.0269
## ESS
## (Intercept)-Canis_latrans 261
## (Intercept)-Lynx_rufus 118
## (Intercept)-Didelphis_virginiana 314
## (Intercept)-Sylvilagus_floridanus 194
## (Intercept)-Meleagris_gallopavo 140
## (Intercept)-Sciurus_carolinensis 304
## (Intercept)-Vulpes_vulpes 121
## (Intercept)-Sus_scrofa 237
# 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): 2.8272
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1283 0.4732 -2.1155 -1.1104 -0.2125 1.0068 462
## Veg_shannon_index 0.3241 0.3434 -0.3724 0.3293 1.0290 1.0023 1565
## Avg_Cogongrass_Cover 0.1996 0.3215 -0.4601 0.2024 0.8134 1.0307 1051
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0659 1.3236 0.0715 0.6973 4.2908 1.0246 824
## Veg_shannon_index 0.5636 0.7462 0.0472 0.3408 2.4811 1.0110 997
## Avg_Cogongrass_Cover 0.4870 0.6990 0.0469 0.2878 2.0996 1.0156 661
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2506 1.2046 0.0999 0.8682 4.4311 1.05 152
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7595 0.2299 -5.1948 -4.7678 -4.2771 1.0099 1093
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3252 0.4421 0.0525 0.2167 1.183 1.1225 158
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1524 0.7197 -1.5227 -0.1737
## (Intercept)-Lynx_rufus -0.6315 0.7207 -2.0014 -0.6452
## (Intercept)-Didelphis_virginiana -1.4847 0.6226 -2.8125 -1.4601
## (Intercept)-Sylvilagus_floridanus -0.9630 0.6284 -2.2692 -0.9478
## (Intercept)-Meleagris_gallopavo -1.0489 0.6616 -2.4273 -1.0094
## (Intercept)-Sciurus_carolinensis -1.4775 0.6115 -2.8127 -1.4444
## (Intercept)-Vulpes_vulpes -1.7046 0.7637 -3.4021 -1.6692
## (Intercept)-Sus_scrofa -1.9995 0.8026 -3.8317 -1.9134
## Veg_shannon_index-Canis_latrans 0.7902 0.4468 0.0063 0.7605
## Veg_shannon_index-Lynx_rufus -0.1980 0.6070 -1.6161 -0.1226
## Veg_shannon_index-Didelphis_virginiana 0.5237 0.4291 -0.2470 0.4934
## Veg_shannon_index-Sylvilagus_floridanus 0.4304 0.4444 -0.4520 0.4218
## Veg_shannon_index-Meleagris_gallopavo 0.4593 0.4808 -0.4416 0.4391
## Veg_shannon_index-Sciurus_carolinensis -0.0988 0.4476 -1.0140 -0.0833
## Veg_shannon_index-Vulpes_vulpes -0.0639 0.5251 -1.2229 -0.0237
## Veg_shannon_index-Sus_scrofa 0.7941 0.6394 -0.2336 0.7068
## Avg_Cogongrass_Cover-Canis_latrans 0.5460 0.4332 -0.1934 0.5123
## Avg_Cogongrass_Cover-Lynx_rufus 0.5916 0.4964 -0.2107 0.5384
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4663 0.4140 -0.3332 0.4580
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2392 0.4791 -1.3388 -0.2035
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2619 0.5178 -1.4142 -0.2167
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3897 0.4039 -0.3718 0.3713
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2936 0.4833 -0.6105 0.2888
## Avg_Cogongrass_Cover-Sus_scrofa -0.1328 0.6370 -1.4793 -0.0656
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2963 1.0194 441
## (Intercept)-Lynx_rufus 0.7899 1.0114 524
## (Intercept)-Didelphis_virginiana -0.3414 1.0078 752
## (Intercept)-Sylvilagus_floridanus 0.2418 1.0066 468
## (Intercept)-Meleagris_gallopavo 0.1864 1.0017 593
## (Intercept)-Sciurus_carolinensis -0.3539 1.0102 630
## (Intercept)-Vulpes_vulpes -0.2856 1.0049 384
## (Intercept)-Sus_scrofa -0.6901 1.0455 558
## Veg_shannon_index-Canis_latrans 1.7560 1.0006 1262
## Veg_shannon_index-Lynx_rufus 0.8256 1.0096 829
## Veg_shannon_index-Didelphis_virginiana 1.4453 1.0052 1833
## Veg_shannon_index-Sylvilagus_floridanus 1.3274 1.0063 1670
## Veg_shannon_index-Meleagris_gallopavo 1.4957 1.0044 1507
## Veg_shannon_index-Sciurus_carolinensis 0.7207 1.0095 1356
## Veg_shannon_index-Vulpes_vulpes 0.9003 1.0005 1289
## Veg_shannon_index-Sus_scrofa 2.2560 1.0088 938
## Avg_Cogongrass_Cover-Canis_latrans 1.4711 1.0028 1349
## Avg_Cogongrass_Cover-Lynx_rufus 1.7219 1.0133 983
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3384 1.0012 1757
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6087 1.0262 1077
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6695 1.0108 1146
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2260 1.0063 1812
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2884 1.0116 1417
## Avg_Cogongrass_Cover-Sus_scrofa 0.8706 1.0205 683
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6543 0.1511 -4.9551 -4.6506 -4.3691 1.0605
## (Intercept)-Lynx_rufus -5.2655 0.2462 -5.7509 -5.2629 -4.7971 1.2481
## (Intercept)-Didelphis_virginiana -4.3778 0.2195 -4.8289 -4.3718 -3.9727 1.0064
## (Intercept)-Sylvilagus_floridanus -4.8568 0.2078 -5.2945 -4.8506 -4.4665 1.0421
## (Intercept)-Meleagris_gallopavo -5.0152 0.2548 -5.5368 -4.9981 -4.5490 1.0291
## (Intercept)-Sciurus_carolinensis -4.4378 0.2315 -4.9021 -4.4335 -4.0035 1.0209
## (Intercept)-Vulpes_vulpes -5.3150 0.4518 -6.3945 -5.2733 -4.5419 1.0523
## (Intercept)-Sus_scrofa -4.7759 0.3369 -5.4870 -4.7600 -4.1658 1.0360
## ESS
## (Intercept)-Canis_latrans 267
## (Intercept)-Lynx_rufus 126
## (Intercept)-Didelphis_virginiana 361
## (Intercept)-Sylvilagus_floridanus 228
## (Intercept)-Meleagris_gallopavo 168
## (Intercept)-Sciurus_carolinensis 301
## (Intercept)-Vulpes_vulpes 114
## (Intercept)-Sus_scrofa 263
# 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): 2.8327
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6552 0.5289 -2.7697 -1.6537 -0.6561 1.0779 536
## Avg_Cogongrass_Cover -0.7205 0.4495 -1.6244 -0.7137 0.1518 1.0722 493
## I(Avg_Cogongrass_Cover^2) 0.6899 0.3748 -0.0402 0.6713 1.4737 1.0370 548
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1469 1.7685 0.0619 0.6381 5.1683 1.1363 317
## Avg_Cogongrass_Cover 0.5788 1.0505 0.0431 0.3040 2.8803 1.0096 861
## I(Avg_Cogongrass_Cover^2) 0.5998 0.9182 0.0430 0.3083 2.7800 1.0077 473
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.098 1.0992 0.0668 0.7607 4.0271 1.1001 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7799 0.226 -5.2302 -4.7838 -4.3168 1.0105 692
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3155 0.3384 0.0519 0.2176 1.1323 1.1027 242
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7763 0.7636 -2.2176 -0.7818
## (Intercept)-Lynx_rufus -1.3264 0.7850 -2.7699 -1.3566
## (Intercept)-Didelphis_virginiana -1.9402 0.7216 -3.4526 -1.8954
## (Intercept)-Sylvilagus_floridanus -1.5069 0.6615 -2.8421 -1.4954
## (Intercept)-Meleagris_gallopavo -1.4384 0.7002 -2.9001 -1.4249
## (Intercept)-Sciurus_carolinensis -2.2701 0.7709 -3.9180 -2.2127
## (Intercept)-Vulpes_vulpes -2.3380 0.9699 -4.4562 -2.2571
## (Intercept)-Sus_scrofa -2.3345 0.9321 -4.5174 -2.1940
## Avg_Cogongrass_Cover-Canis_latrans -0.4409 0.5790 -1.4807 -0.4607
## Avg_Cogongrass_Cover-Lynx_rufus -0.4855 0.6336 -1.6831 -0.5052
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3667 0.6103 -1.4748 -0.3984
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1873 0.6411 -2.6661 -1.1248
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0045 0.6247 -2.3449 -0.9529
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7578 0.5838 -1.9810 -0.7413
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7088 0.6467 -2.0277 -0.6944
## Avg_Cogongrass_Cover-Sus_scrofa -0.9979 0.7173 -2.5825 -0.9162
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2553 0.7800 0.1838 1.0819
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1051 0.5497 0.2063 1.0421
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5130 0.4439 -0.3514 0.5026
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7148 0.4676 -0.1333 0.6837
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1413 0.5689 -1.1917 0.1960
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9193 0.4128 0.1916 0.8992
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8254 0.4701 0.0110 0.7828
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1939 0.6450 -1.4204 0.2854
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7905 1.0157 494
## (Intercept)-Lynx_rufus 0.4157 1.0199 369
## (Intercept)-Didelphis_virginiana -0.6717 1.0521 501
## (Intercept)-Sylvilagus_floridanus -0.1598 1.0183 681
## (Intercept)-Meleagris_gallopavo -0.0831 1.0594 554
## (Intercept)-Sciurus_carolinensis -0.9647 1.0595 458
## (Intercept)-Vulpes_vulpes -0.7822 1.1064 365
## (Intercept)-Sus_scrofa -0.8858 1.1237 287
## Avg_Cogongrass_Cover-Canis_latrans 0.7811 1.0043 737
## Avg_Cogongrass_Cover-Lynx_rufus 0.8850 1.0176 683
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9131 1.0384 716
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1377 1.0507 726
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1166 1.0303 698
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3059 1.0394 709
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5955 1.0400 657
## Avg_Cogongrass_Cover-Sus_scrofa 0.1783 1.0371 513
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1589 1.0111 361
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4243 1.0109 683
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4713 1.0543 683
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7326 1.0663 519
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.1220 1.0212 653
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8233 1.0470 639
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8610 1.0065 575
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2512 1.0366 532
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6857 0.1547 -4.9860 -4.6836 -4.3790 1.0075
## (Intercept)-Lynx_rufus -5.2898 0.2805 -5.9143 -5.2670 -4.7973 1.0715
## (Intercept)-Didelphis_virginiana -4.3895 0.2371 -4.8761 -4.3854 -3.9301 1.0534
## (Intercept)-Sylvilagus_floridanus -4.8797 0.2191 -5.3219 -4.8686 -4.4709 1.0040
## (Intercept)-Meleagris_gallopavo -4.9762 0.2540 -5.4879 -4.9559 -4.5358 1.0025
## (Intercept)-Sciurus_carolinensis -4.4131 0.2190 -4.8766 -4.4080 -4.0072 1.0048
## (Intercept)-Vulpes_vulpes -5.3383 0.4901 -6.5586 -5.2884 -4.5404 1.1319
## (Intercept)-Sus_scrofa -4.7913 0.3349 -5.4666 -4.7714 -4.1886 1.0208
## ESS
## (Intercept)-Canis_latrans 233
## (Intercept)-Lynx_rufus 81
## (Intercept)-Didelphis_virginiana 286
## (Intercept)-Sylvilagus_floridanus 212
## (Intercept)-Meleagris_gallopavo 169
## (Intercept)-Sciurus_carolinensis 363
## (Intercept)-Vulpes_vulpes 97
## (Intercept)-Sus_scrofa 277
# 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): 3.2493
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2851 1.0968 -4.2513 -2.3477 0.0695 1.0078 554
## Cogon_Patch_Size -0.0195 0.9306 -1.8731 -0.0110 1.8112 1.0165 415
## Veg_shannon_index 0.8599 0.7213 -0.6046 0.8629 2.2466 1.0247 827
## total_shrub_cover -0.7376 0.8173 -2.4081 -0.7007 0.8322 1.0087 1083
## Avg_Cogongrass_Cover 0.3331 1.0604 -1.8151 0.3479 2.3431 1.0218 145
## Tree_Density -2.3237 0.9050 -4.1272 -2.2816 -0.6584 1.0286 187
## Avg_Canopy_Cover 1.9656 0.8110 0.3562 1.9408 3.6493 1.0027 819
## I(Avg_Cogongrass_Cover^2) 1.0790 0.7637 -0.4711 1.0750 2.6013 1.0176 354
## avg_veg_height -0.6510 0.5874 -1.8098 -0.6581 0.5124 1.0242 236
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.1967 14.3844 0.2857 6.5581 49.1444 1.0122 150
## Cogon_Patch_Size 7.0029 15.4920 0.2255 3.4597 34.5332 1.0876 469
## Veg_shannon_index 3.4788 5.4819 0.0905 1.8025 18.3339 1.1463 158
## total_shrub_cover 5.4245 6.9557 0.2329 3.3512 23.4354 1.0431 220
## Avg_Cogongrass_Cover 1.4705 3.3897 0.0510 0.5433 8.6300 1.0636 679
## Tree_Density 2.1362 7.6015 0.0498 0.5275 13.2278 1.2678 222
## Avg_Canopy_Cover 4.9772 9.2325 0.1492 2.4273 25.6251 1.2350 133
## I(Avg_Cogongrass_Cover^2) 4.0520 9.5808 0.0701 1.3957 22.4050 1.1561 133
## avg_veg_height 0.5765 0.9567 0.0438 0.2976 2.8647 1.0105 774
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.5073 5.5146 0.0623 0.8314 17.0423 2.4937 29
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8316 0.2936 -5.3712 -4.8466 -4.1982 1.0034 1184
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5736 0.7217 0.0964 0.3917 2.1292 1.0647 448
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6912 1.4426 -3.3927 -0.7286
## (Intercept)-Lynx_rufus 0.1327 2.5891 -3.5602 -0.3508
## (Intercept)-Didelphis_virginiana -4.2243 1.5952 -7.7485 -4.0852
## (Intercept)-Sylvilagus_floridanus -2.7867 1.4297 -6.0095 -2.6847
## (Intercept)-Meleagris_gallopavo -2.6995 1.6582 -6.4212 -2.5472
## (Intercept)-Sciurus_carolinensis -4.8250 1.7103 -8.5706 -4.6986
## (Intercept)-Vulpes_vulpes -4.7390 2.0650 -9.2006 -4.5210
## (Intercept)-Sus_scrofa -5.7151 2.3276 -11.1339 -5.4200
## Cogon_Patch_Size-Canis_latrans 2.1791 1.9765 -0.4080 1.7921
## Cogon_Patch_Size-Lynx_rufus 0.1729 1.8893 -3.3363 0.0595
## Cogon_Patch_Size-Didelphis_virginiana 2.0166 1.3311 -0.0080 1.8361
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9815 2.0852 -7.4389 -1.5015
## Cogon_Patch_Size-Meleagris_gallopavo 0.3735 1.4244 -2.0619 0.2026
## Cogon_Patch_Size-Sciurus_carolinensis -1.3588 1.4718 -5.0417 -1.1477
## Cogon_Patch_Size-Vulpes_vulpes -0.7139 2.0123 -4.9790 -0.6027
## Cogon_Patch_Size-Sus_scrofa -0.9434 1.8477 -5.3274 -0.6187
## Veg_shannon_index-Canis_latrans 1.9057 1.0971 0.2436 1.7353
## Veg_shannon_index-Lynx_rufus -0.4051 1.6004 -4.0885 -0.1794
## Veg_shannon_index-Didelphis_virginiana 1.2191 0.9595 -0.4776 1.1361
## Veg_shannon_index-Sylvilagus_floridanus 1.1350 0.9085 -0.5473 1.0756
## Veg_shannon_index-Meleagris_gallopavo 1.7442 1.2638 -0.1936 1.5418
## Veg_shannon_index-Sciurus_carolinensis -0.3006 1.1096 -2.7686 -0.2043
## Veg_shannon_index-Vulpes_vulpes 0.0732 1.3776 -3.1452 0.2062
## Veg_shannon_index-Sus_scrofa 2.3446 1.6063 0.1987 2.0428
## total_shrub_cover-Canis_latrans 0.6470 1.0247 -1.1087 0.5346
## total_shrub_cover-Lynx_rufus -2.3699 1.8986 -6.6169 -2.1042
## total_shrub_cover-Didelphis_virginiana -0.9890 1.0465 -3.2581 -0.9100
## total_shrub_cover-Sylvilagus_floridanus -0.2787 1.1200 -2.6527 -0.2494
## total_shrub_cover-Meleagris_gallopavo -3.8617 2.0325 -8.5680 -3.6200
## total_shrub_cover-Sciurus_carolinensis 0.2160 0.8705 -1.4591 0.1973
## total_shrub_cover-Vulpes_vulpes -1.0767 1.5004 -4.6031 -0.8972
## total_shrub_cover-Sus_scrofa 0.3707 1.3313 -2.0262 0.2604
## Avg_Cogongrass_Cover-Canis_latrans 0.4913 1.3671 -2.2646 0.4950
## Avg_Cogongrass_Cover-Lynx_rufus 0.7378 1.4503 -2.0790 0.6897
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6212 1.3391 -2.0042 0.5898
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1397 1.4159 -3.3255 -0.0460
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0053 1.4459 -3.1190 0.1144
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4214 1.3512 -2.3059 0.4452
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5172 1.4639 -2.3651 0.4969
## Avg_Cogongrass_Cover-Sus_scrofa 0.0309 1.4560 -3.1277 0.1444
## Tree_Density-Canis_latrans -2.9993 1.4795 -6.4863 -2.7577
## Tree_Density-Lynx_rufus -1.8309 1.3401 -4.2009 -1.8957
## Tree_Density-Didelphis_virginiana -2.4938 1.1168 -4.9108 -2.3874
## Tree_Density-Sylvilagus_floridanus -2.7454 1.3688 -5.9949 -2.5528
## Tree_Density-Meleagris_gallopavo -2.4197 1.3003 -5.2186 -2.3447
## Tree_Density-Sciurus_carolinensis -2.8358 1.6285 -6.3463 -2.5788
## Tree_Density-Vulpes_vulpes -2.3967 1.4242 -5.5656 -2.3286
## Tree_Density-Sus_scrofa -2.4509 1.3573 -5.5431 -2.3357
## Avg_Canopy_Cover-Canis_latrans 0.1584 0.9609 -1.9215 0.1780
## Avg_Canopy_Cover-Lynx_rufus 1.1981 1.6923 -2.1267 1.1713
## Avg_Canopy_Cover-Didelphis_virginiana 3.0568 1.1785 1.3485 2.8659
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2158 2.2250 1.3424 3.7566
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5957 1.4637 0.6788 2.2896
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6229 1.0336 0.9973 2.4800
## Avg_Canopy_Cover-Vulpes_vulpes 2.7046 1.6817 0.5273 2.3603
## Avg_Canopy_Cover-Sus_scrofa 2.3278 1.0834 0.6087 2.1859
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3837 1.6528 0.4077 1.9803
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5507 1.7407 0.1620 2.1848
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9262 0.8250 -0.5884 0.8887
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0217 0.9587 -0.5698 0.9412
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.3741 1.6806 -4.7645 -0.0973
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5200 0.8598 0.0608 1.4435
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8668 1.2366 0.1140 1.6614
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.0668 1.8964 -4.2125 0.2629
## avg_veg_height-Canis_latrans -0.6816 0.6968 -2.0766 -0.6731
## avg_veg_height-Lynx_rufus -0.7609 0.8763 -2.6117 -0.7436
## avg_veg_height-Didelphis_virginiana -0.6902 0.7572 -2.2840 -0.6783
## avg_veg_height-Sylvilagus_floridanus -0.7135 0.7660 -2.2991 -0.6951
## avg_veg_height-Meleagris_gallopavo -0.7119 0.8508 -2.4846 -0.6814
## avg_veg_height-Sciurus_carolinensis -0.2963 0.7810 -1.7371 -0.3263
## avg_veg_height-Vulpes_vulpes -0.7878 0.8384 -2.6057 -0.7451
## avg_veg_height-Sus_scrofa -0.6545 0.8011 -2.3025 -0.6490
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2641 1.0070 300
## (Intercept)-Lynx_rufus 6.5504 1.1332 128
## (Intercept)-Didelphis_virginiana -1.4612 1.0135 180
## (Intercept)-Sylvilagus_floridanus -0.1223 1.0155 362
## (Intercept)-Meleagris_gallopavo 0.1916 1.0063 312
## (Intercept)-Sciurus_carolinensis -1.8963 1.0286 203
## (Intercept)-Vulpes_vulpes -1.1954 1.0075 178
## (Intercept)-Sus_scrofa -2.0180 1.0344 154
## Cogon_Patch_Size-Canis_latrans 7.2310 1.0854 119
## Cogon_Patch_Size-Lynx_rufus 4.1821 1.0236 265
## Cogon_Patch_Size-Didelphis_virginiana 5.3197 1.2321 135
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9239 1.0293 309
## Cogon_Patch_Size-Meleagris_gallopavo 3.6701 1.1173 319
## Cogon_Patch_Size-Sciurus_carolinensis 0.8910 1.0175 336
## Cogon_Patch_Size-Vulpes_vulpes 3.0073 1.0336 245
## Cogon_Patch_Size-Sus_scrofa 1.7914 1.0131 259
## Veg_shannon_index-Canis_latrans 4.5250 1.0245 359
## Veg_shannon_index-Lynx_rufus 2.1418 1.0950 174
## Veg_shannon_index-Didelphis_virginiana 3.3612 1.0078 504
## Veg_shannon_index-Sylvilagus_floridanus 3.1999 1.0311 763
## Veg_shannon_index-Meleagris_gallopavo 4.8062 1.0093 233
## Veg_shannon_index-Sciurus_carolinensis 1.5622 1.0384 365
## Veg_shannon_index-Vulpes_vulpes 2.4798 1.0672 303
## Veg_shannon_index-Sus_scrofa 6.0314 1.0607 319
## total_shrub_cover-Canis_latrans 2.9826 1.0266 463
## total_shrub_cover-Lynx_rufus 0.7766 1.2007 196
## total_shrub_cover-Didelphis_virginiana 0.8279 1.0399 501
## total_shrub_cover-Sylvilagus_floridanus 2.0129 1.0200 522
## total_shrub_cover-Meleagris_gallopavo -0.7655 1.0269 204
## total_shrub_cover-Sciurus_carolinensis 1.9477 1.0052 996
## total_shrub_cover-Vulpes_vulpes 1.4432 1.0316 300
## total_shrub_cover-Sus_scrofa 3.3282 1.0064 458
## Avg_Cogongrass_Cover-Canis_latrans 3.1453 1.0070 209
## Avg_Cogongrass_Cover-Lynx_rufus 3.8546 1.0141 178
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2739 1.0150 231
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4347 1.0095 179
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.5134 1.0231 278
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.0172 1.0194 202
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.6016 1.0241 212
## Avg_Cogongrass_Cover-Sus_scrofa 2.5981 1.0249 232
## Tree_Density-Canis_latrans -0.9841 1.0444 185
## Tree_Density-Lynx_rufus 1.0877 1.0632 199
## Tree_Density-Didelphis_virginiana -0.5824 1.0338 245
## Tree_Density-Sylvilagus_floridanus -0.6601 1.0449 209
## Tree_Density-Meleagris_gallopavo -0.0235 1.0344 291
## Tree_Density-Sciurus_carolinensis -0.7878 1.1135 165
## Tree_Density-Vulpes_vulpes 0.2592 1.0056 223
## Tree_Density-Sus_scrofa -0.0560 1.0087 262
## Avg_Canopy_Cover-Canis_latrans 2.0266 1.1118 217
## Avg_Canopy_Cover-Lynx_rufus 4.6933 1.1844 183
## Avg_Canopy_Cover-Didelphis_virginiana 5.9043 1.1951 110
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.9417 1.0950 168
## Avg_Canopy_Cover-Meleagris_gallopavo 6.3947 1.0777 243
## Avg_Canopy_Cover-Sciurus_carolinensis 5.0564 1.0668 280
## Avg_Canopy_Cover-Vulpes_vulpes 7.3633 1.1201 114
## Avg_Canopy_Cover-Sus_scrofa 4.8573 1.0298 483
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.1119 1.0886 99
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.1805 1.0650 146
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6471 1.0760 226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2523 1.0405 261
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1144 1.0591 115
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.4726 1.0163 297
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.1187 1.0064 164
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.3488 1.1792 164
## avg_veg_height-Canis_latrans 0.6574 1.0107 343
## avg_veg_height-Lynx_rufus 0.9458 1.0082 354
## avg_veg_height-Didelphis_virginiana 0.7600 1.0474 302
## avg_veg_height-Sylvilagus_floridanus 0.7429 1.0291 500
## avg_veg_height-Meleagris_gallopavo 0.9436 1.0037 380
## avg_veg_height-Sciurus_carolinensis 1.3946 1.0382 372
## avg_veg_height-Vulpes_vulpes 0.8020 1.0152 278
## avg_veg_height-Sus_scrofa 0.9102 1.0114 353
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6954 0.1498 -4.9924 -4.6953 -4.4001 1.0355
## (Intercept)-Lynx_rufus -5.5687 0.2708 -6.0949 -5.5614 -5.0478 1.2332
## (Intercept)-Didelphis_virginiana -4.3124 0.2337 -4.7924 -4.3076 -3.8841 1.0045
## (Intercept)-Sylvilagus_floridanus -4.9501 0.2299 -5.4413 -4.9407 -4.5407 1.0800
## (Intercept)-Meleagris_gallopavo -5.1055 0.2687 -5.6536 -5.0942 -4.6115 1.0244
## (Intercept)-Sciurus_carolinensis -4.3978 0.2474 -4.9001 -4.3806 -3.9347 1.0318
## (Intercept)-Vulpes_vulpes -5.7953 0.5964 -7.1061 -5.7296 -4.7558 1.0331
## (Intercept)-Sus_scrofa -4.8196 0.3679 -5.5617 -4.8111 -4.1302 1.0388
## ESS
## (Intercept)-Canis_latrans 267
## (Intercept)-Lynx_rufus 75
## (Intercept)-Didelphis_virginiana 284
## (Intercept)-Sylvilagus_floridanus 176
## (Intercept)-Meleagris_gallopavo 146
## (Intercept)-Sciurus_carolinensis 276
## (Intercept)-Vulpes_vulpes 45
## (Intercept)-Sus_scrofa 254
# 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): 4.8077
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8362 0.4570 -1.7421 -0.8445 0.1290 1.0845 374
## Avg_Cogongrass_Cover 0.1504 0.3151 -0.5140 0.1567 0.7439 1.0174 943
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8226 1.0916 0.0572 0.4891 3.5202 1.0158 695
## Avg_Cogongrass_Cover 0.4326 0.6395 0.0450 0.2525 1.8321 1.0382 984
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6235 1.6014 0.1072 1.1877 5.8358 1.0247 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0255 0.2673 -5.5432 -5.0285 -4.4882 1.0590 514
## shrub_cover 0.2372 0.3449 -0.4521 0.2366 0.9370 1.0521 479
## veg_height -0.0210 0.2137 -0.4489 -0.0179 0.4039 1.0029 722
## week 0.0272 1.6002 -3.2227 0.1077 3.0059 1.0004 713
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.150000e-01 5.002000e-01 0.0537 0.2737 1.708800e+00 1.3381 169
## shrub_cover 8.205000e-01 8.233000e-01 0.1412 0.5978 2.992900e+00 1.0437 547
## veg_height 2.739000e-01 2.375000e-01 0.0550 0.2028 9.023000e-01 1.0123 766
## week 2.062695e+19 2.980404e+20 0.1081 895.3356 3.968673e+17 1.7035 200
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1153 0.7257 -1.4728 -0.1399
## (Intercept)-Lynx_rufus -0.5144 0.7228 -1.8494 -0.5469
## (Intercept)-Didelphis_virginiana -1.1130 0.5600 -2.2515 -1.0989
## (Intercept)-Sylvilagus_floridanus -0.7513 0.5802 -1.8771 -0.7769
## (Intercept)-Meleagris_gallopavo -0.5858 0.7316 -1.8933 -0.6306
## (Intercept)-Sciurus_carolinensis -1.1819 0.6011 -2.4271 -1.1495
## (Intercept)-Vulpes_vulpes -1.2779 0.7189 -2.7789 -1.2559
## (Intercept)-Sus_scrofa -1.4342 0.6960 -2.9710 -1.3732
## Avg_Cogongrass_Cover-Canis_latrans 0.4331 0.3987 -0.2448 0.4070
## Avg_Cogongrass_Cover-Lynx_rufus 0.4664 0.4376 -0.2891 0.4258
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3215 0.3987 -0.3950 0.3013
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2523 0.4486 -1.2620 -0.2168
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3088 0.5845 -1.6015 -0.2537
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3444 0.3901 -0.3871 0.3356
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2942 0.4618 -0.5861 0.2768
## Avg_Cogongrass_Cover-Sus_scrofa -0.0828 0.5788 -1.3910 -0.0235
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4039 1.0403 396
## (Intercept)-Lynx_rufus 0.9672 1.0625 248
## (Intercept)-Didelphis_virginiana -0.0336 1.0208 566
## (Intercept)-Sylvilagus_floridanus 0.4610 1.0286 870
## (Intercept)-Meleagris_gallopavo 1.0801 1.1587 250
## (Intercept)-Sciurus_carolinensis -0.0761 1.0154 699
## (Intercept)-Vulpes_vulpes 0.1362 1.0935 362
## (Intercept)-Sus_scrofa -0.1952 1.0041 567
## Avg_Cogongrass_Cover-Canis_latrans 1.2891 1.0017 1628
## Avg_Cogongrass_Cover-Lynx_rufus 1.4414 1.0053 1176
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1514 1.0022 1713
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5002 1.0010 1230
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7072 1.0132 560
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1618 1.0149 1582
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2972 1.0057 1203
## Avg_Cogongrass_Cover-Sus_scrofa 0.9128 1.0217 935
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.783900e+00 1.745000e-01 -5.140800e+00
## (Intercept)-Lynx_rufus -5.426100e+00 2.860000e-01 -5.982800e+00
## (Intercept)-Didelphis_virginiana -4.708000e+00 2.609000e-01 -5.218600e+00
## (Intercept)-Sylvilagus_floridanus -4.937600e+00 2.142000e-01 -5.389100e+00
## (Intercept)-Meleagris_gallopavo -5.583400e+00 4.518000e-01 -6.661800e+00
## (Intercept)-Sciurus_carolinensis -4.694200e+00 3.047000e-01 -5.338500e+00
## (Intercept)-Vulpes_vulpes -5.604800e+00 5.150000e-01 -6.636500e+00
## (Intercept)-Sus_scrofa -5.166100e+00 4.120000e-01 -6.009700e+00
## shrub_cover-Canis_latrans -2.593000e-01 2.088000e-01 -6.808000e-01
## shrub_cover-Lynx_rufus -1.509000e-01 3.566000e-01 -8.438000e-01
## shrub_cover-Didelphis_virginiana 1.041000e+00 3.612000e-01 3.687000e-01
## shrub_cover-Sylvilagus_floridanus 4.749000e-01 4.035000e-01 -3.154000e-01
## shrub_cover-Meleagris_gallopavo -6.731000e-01 4.015000e-01 -1.555800e+00
## shrub_cover-Sciurus_carolinensis 8.789000e-01 3.826000e-01 1.124000e-01
## shrub_cover-Vulpes_vulpes -1.040000e-02 6.616000e-01 -1.402000e+00
## shrub_cover-Sus_scrofa 6.845000e-01 6.932000e-01 -7.319000e-01
## veg_height-Canis_latrans -5.736000e-01 1.790000e-01 -9.295000e-01
## veg_height-Lynx_rufus 6.910000e-02 2.384000e-01 -4.062000e-01
## veg_height-Didelphis_virginiana 4.962000e-01 2.465000e-01 3.660000e-02
## veg_height-Sylvilagus_floridanus 1.588000e-01 2.248000e-01 -2.756000e-01
## veg_height-Meleagris_gallopavo -2.046000e-01 3.622000e-01 -9.017000e-01
## veg_height-Sciurus_carolinensis 2.153000e-01 2.132000e-01 -1.758000e-01
## veg_height-Vulpes_vulpes -1.091000e-01 2.955000e-01 -7.554000e-01
## veg_height-Sus_scrofa -2.374000e-01 3.448000e-01 -9.905000e-01
## week-Canis_latrans 5.282315e+07 3.371999e+09 -5.848120e+06
## week-Lynx_rufus -1.784533e+06 4.353372e+09 -4.959371e+06
## week-Didelphis_virginiana -1.399759e+07 5.035928e+09 -5.760031e+06
## week-Sylvilagus_floridanus 2.164009e+08 5.500292e+09 -4.151332e+06
## week-Meleagris_gallopavo -6.226795e+07 4.423849e+09 -5.164073e+06
## week-Sciurus_carolinensis -1.041919e+08 5.016220e+09 -2.851442e+06
## week-Vulpes_vulpes 7.321386e+07 3.142694e+09 -3.507015e+06
## week-Sus_scrofa -1.166289e+08 4.203232e+09 -1.607213e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7766 -4.4610 1.0750 146
## (Intercept)-Lynx_rufus -5.4130 -4.8857 1.0784 103
## (Intercept)-Didelphis_virginiana -4.7091 -4.1909 1.0154 213
## (Intercept)-Sylvilagus_floridanus -4.9308 -4.5448 1.0655 220
## (Intercept)-Meleagris_gallopavo -5.5356 -4.8034 2.0852 59
## (Intercept)-Sciurus_carolinensis -4.6801 -4.1380 1.0338 164
## (Intercept)-Vulpes_vulpes -5.5648 -4.7282 1.3922 84
## (Intercept)-Sus_scrofa -5.1500 -4.3532 1.0355 383
## shrub_cover-Canis_latrans -0.2584 0.1323 1.1100 203
## shrub_cover-Lynx_rufus -0.1611 0.5973 1.0279 105
## shrub_cover-Didelphis_virginiana 1.0399 1.7771 1.0035 203
## shrub_cover-Sylvilagus_floridanus 0.4965 1.2546 1.0673 126
## shrub_cover-Meleagris_gallopavo -0.6497 0.1060 1.7322 82
## shrub_cover-Sciurus_carolinensis 0.8831 1.6348 1.0394 209
## shrub_cover-Vulpes_vulpes 0.0040 1.2464 1.0792 133
## shrub_cover-Sus_scrofa 0.7229 2.0042 1.0242 370
## veg_height-Canis_latrans -0.5700 -0.2377 1.0565 193
## veg_height-Lynx_rufus 0.0713 0.5306 1.0291 190
## veg_height-Didelphis_virginiana 0.4865 0.9943 1.0046 263
## veg_height-Sylvilagus_floridanus 0.1539 0.6039 1.0626 227
## veg_height-Meleagris_gallopavo -0.2094 0.5114 1.0412 149
## veg_height-Sciurus_carolinensis 0.2095 0.6663 1.0176 242
## veg_height-Vulpes_vulpes -0.0883 0.4226 1.0521 184
## veg_height-Sus_scrofa -0.2133 0.3933 1.0107 256
## week-Canis_latrans 0.1262 3475627.0929 1.3146 3688
## week-Lynx_rufus 0.1786 5678762.6813 1.2904 551368
## week-Didelphis_virginiana 0.2189 7134823.0735 1.2911 120311
## week-Sylvilagus_floridanus 0.2140 7034866.6249 1.4366 692
## week-Meleagris_gallopavo -0.0149 7600809.8628 1.3100 4612
## week-Sciurus_carolinensis -0.0066 3940418.4428 1.3327 2283
## week-Vulpes_vulpes 0.2442 15277175.1941 1.3434 2626
## week-Sus_scrofa -0.0326 1391807.4720 1.3650 1274
# 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): 2.8298
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0152 0.4379 -1.8793 -1.0151 -0.1450 1.0024 1019
## Avg_Cogongrass_Cover 0.1214 0.3180 -0.5297 0.1265 0.7213 1.0053 1437
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9862 1.2747 0.0737 0.6255 3.9938 1.0273 730
## Avg_Cogongrass_Cover 0.4506 0.5751 0.0463 0.2740 1.9099 1.0087 1061
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1913 1.1154 0.086 0.9385 3.6704 1.0462 309
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7601 0.2347 -5.1938 -4.7715 -4.2725 1.0257 582
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3378 0.3805 0.0578 0.2345 1.2446 1.0182 315
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0666 0.6941 -1.3503 -0.0893
## (Intercept)-Lynx_rufus -0.5688 0.6922 -1.8262 -0.5983
## (Intercept)-Didelphis_virginiana -1.3184 0.5604 -2.4959 -1.2995
## (Intercept)-Sylvilagus_floridanus -0.8615 0.5767 -1.9936 -0.8592
## (Intercept)-Meleagris_gallopavo -0.9592 0.5924 -2.1242 -0.9571
## (Intercept)-Sciurus_carolinensis -1.4334 0.6289 -2.8061 -1.3884
## (Intercept)-Vulpes_vulpes -1.5605 0.7194 -3.0647 -1.5212
## (Intercept)-Sus_scrofa -1.7144 0.7469 -3.3294 -1.6473
## Avg_Cogongrass_Cover-Canis_latrans 0.3671 0.3842 -0.3108 0.3531
## Avg_Cogongrass_Cover-Lynx_rufus 0.5357 0.4664 -0.2517 0.4856
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3523 0.3866 -0.3767 0.3440
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2854 0.4371 -1.2671 -0.2437
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3540 0.5143 -1.5487 -0.3017
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3476 0.3783 -0.3938 0.3469
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2678 0.4509 -0.6297 0.2649
## Avg_Cogongrass_Cover-Sus_scrofa -0.1839 0.5886 -1.5997 -0.0967
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3503 1.0021 601
## (Intercept)-Lynx_rufus 0.8594 1.0115 517
## (Intercept)-Didelphis_virginiana -0.2801 1.0035 1091
## (Intercept)-Sylvilagus_floridanus 0.2852 1.0030 925
## (Intercept)-Meleagris_gallopavo 0.1940 1.0175 827
## (Intercept)-Sciurus_carolinensis -0.3223 1.0021 817
## (Intercept)-Vulpes_vulpes -0.2466 1.0039 641
## (Intercept)-Sus_scrofa -0.4665 1.0064 707
## Avg_Cogongrass_Cover-Canis_latrans 1.1898 1.0045 1952
## Avg_Cogongrass_Cover-Lynx_rufus 1.5576 1.0022 1247
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1327 1.0056 1880
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4772 1.0124 1228
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4803 1.0094 900
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1149 1.0008 2057
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1421 1.0076 1377
## Avg_Cogongrass_Cover-Sus_scrofa 0.7789 1.0060 826
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6617 0.1611 -4.9978 -4.6586 -4.3616 1.0167
## (Intercept)-Lynx_rufus -5.2804 0.2554 -5.8218 -5.2834 -4.7901 1.1445
## (Intercept)-Didelphis_virginiana -4.3623 0.2195 -4.8056 -4.3594 -3.9544 1.0155
## (Intercept)-Sylvilagus_floridanus -4.8476 0.2251 -5.3172 -4.8424 -4.4140 1.0187
## (Intercept)-Meleagris_gallopavo -4.9944 0.2418 -5.4994 -4.9806 -4.5579 1.0652
## (Intercept)-Sciurus_carolinensis -4.3913 0.2272 -4.8382 -4.3900 -3.9669 1.0082
## (Intercept)-Vulpes_vulpes -5.3503 0.4793 -6.4155 -5.2876 -4.5801 1.0741
## (Intercept)-Sus_scrofa -4.7881 0.3493 -5.5177 -4.7714 -4.1428 1.0074
## ESS
## (Intercept)-Canis_latrans 223
## (Intercept)-Lynx_rufus 115
## (Intercept)-Didelphis_virginiana 350
## (Intercept)-Sylvilagus_floridanus 179
## (Intercept)-Meleagris_gallopavo 188
## (Intercept)-Sciurus_carolinensis 346
## (Intercept)-Vulpes_vulpes 94
## (Intercept)-Sus_scrofa 256
# 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): 3.9682
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9956 0.4358 -1.8543 -1.0013 -0.1239 1.0402 723
## Avg_Cogongrass_Cover 0.1231 0.3050 -0.4810 0.1244 0.6968 1.0071 1076
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9297 1.1368 0.0745 0.5881 3.5478 1.002 844
## Avg_Cogongrass_Cover 0.4179 0.5278 0.0444 0.2646 1.6571 1.032 1310
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3313 1.6893 0.1107 0.9597 4.9121 1.1015 212
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7868 0.2388 -5.2460 -4.7889 -4.3092 1.0461 616
## week 0.0573 1.6807 -3.4568 0.1480 3.1844 1.0025 452
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.702000e-01 5.075000e-01 0.0554 0.2529 1.37030e+00 1.0839 247
## week 1.530397e+12 3.579688e+13 0.1028 92.5581 1.34677e+12 1.4615 779
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0729 0.6977 -1.2983 -0.1098
## (Intercept)-Lynx_rufus -0.5464 0.7029 -1.7539 -0.5873
## (Intercept)-Didelphis_virginiana -1.3232 0.6027 -2.5976 -1.2895
## (Intercept)-Sylvilagus_floridanus -0.8253 0.5879 -1.9554 -0.8411
## (Intercept)-Meleagris_gallopavo -0.9471 0.6050 -2.1834 -0.9405
## (Intercept)-Sciurus_carolinensis -1.3861 0.5775 -2.6005 -1.3583
## (Intercept)-Vulpes_vulpes -1.4751 0.7419 -3.0651 -1.4479
## (Intercept)-Sus_scrofa -1.6677 0.7085 -3.2626 -1.6008
## Avg_Cogongrass_Cover-Canis_latrans 0.3481 0.3942 -0.3668 0.3267
## Avg_Cogongrass_Cover-Lynx_rufus 0.5112 0.4510 -0.2491 0.4711
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3522 0.4016 -0.4253 0.3314
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2705 0.4550 -1.2877 -0.2391
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3475 0.5120 -1.5127 -0.2948
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3580 0.3757 -0.3637 0.3445
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2531 0.4634 -0.6320 0.2339
## Avg_Cogongrass_Cover-Sus_scrofa -0.1651 0.5434 -1.3755 -0.1100
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3658 1.0470 479
## (Intercept)-Lynx_rufus 0.9698 1.0333 409
## (Intercept)-Didelphis_virginiana -0.2292 1.0023 886
## (Intercept)-Sylvilagus_floridanus 0.3449 1.0110 776
## (Intercept)-Meleagris_gallopavo 0.2244 1.0208 924
## (Intercept)-Sciurus_carolinensis -0.3221 1.0096 834
## (Intercept)-Vulpes_vulpes -0.0055 1.1056 231
## (Intercept)-Sus_scrofa -0.4696 1.0013 654
## Avg_Cogongrass_Cover-Canis_latrans 1.1953 1.0008 1677
## Avg_Cogongrass_Cover-Lynx_rufus 1.4895 1.0018 1373
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2232 1.0062 1741
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5141 1.0023 1327
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4983 1.0021 1010
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1418 1.0055 1815
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2165 1.0051 1206
## Avg_Cogongrass_Cover-Sus_scrofa 0.7794 1.0140 1090
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6879 0.1513 -4.9997 -4.6803
## (Intercept)-Lynx_rufus -5.2968 0.2758 -5.9086 -5.2763
## (Intercept)-Didelphis_virginiana -4.3554 0.2193 -4.8110 -4.3480
## (Intercept)-Sylvilagus_floridanus -4.8827 0.2436 -5.4384 -4.8544
## (Intercept)-Meleagris_gallopavo -5.0315 0.2645 -5.5788 -5.0166
## (Intercept)-Sciurus_carolinensis -4.4001 0.2170 -4.8557 -4.3948
## (Intercept)-Vulpes_vulpes -5.4334 0.5453 -6.7130 -5.3488
## (Intercept)-Sus_scrofa -4.8173 0.3341 -5.5287 -4.8006
## week-Canis_latrans -16184.1488 1232767.3432 -238584.6146 -0.0197
## week-Lynx_rufus -6306.2169 957422.1142 -316938.0603 0.1087
## week-Didelphis_virginiana -39196.6186 1391173.4518 -340660.2354 0.0748
## week-Sylvilagus_floridanus 15309.0897 1104212.4597 -225670.2541 0.1744
## week-Meleagris_gallopavo 31475.9909 1756247.0035 -201822.0637 0.2821
## week-Sciurus_carolinensis 30667.6232 1743596.1423 -214528.0433 0.2518
## week-Vulpes_vulpes 16689.2975 1257290.4471 -169114.0990 0.3120
## week-Sus_scrofa -8976.7984 1154815.3763 -266186.6502 0.0674
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4096 1.0082 243
## (Intercept)-Lynx_rufus -4.8189 1.0774 107
## (Intercept)-Didelphis_virginiana -3.9578 1.0029 333
## (Intercept)-Sylvilagus_floridanus -4.4771 1.0982 173
## (Intercept)-Meleagris_gallopavo -4.5335 1.0406 155
## (Intercept)-Sciurus_carolinensis -3.9981 1.0079 380
## (Intercept)-Vulpes_vulpes -4.6033 1.3525 87
## (Intercept)-Sus_scrofa -4.2096 1.0077 217
## week-Canis_latrans 193470.7748 1.3076 8318
## week-Lynx_rufus 166630.4416 1.2947 1758
## week-Didelphis_virginiana 143189.3239 1.3675 2118
## week-Sylvilagus_floridanus 221699.8012 1.3091 6342
## week-Meleagris_gallopavo 210509.4578 1.3218 4859
## week-Sciurus_carolinensis 223423.7504 1.3205 3827
## week-Vulpes_vulpes 304573.1917 1.3080 5674
## week-Sus_scrofa 171390.7312 1.2963 25350
# 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): 4.3333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5519 0.9683 -3.3327 -1.6016 0.5419 1.0072 1119
## Cogon_Patch_Size -0.4693 0.8496 -2.2312 -0.4646 1.2135 1.0089 588
## Veg_shannon_index 0.7528 0.6786 -0.6743 0.7792 2.0598 1.0088 654
## total_shrub_cover -0.6478 0.7458 -2.3035 -0.6139 0.7417 1.0053 720
## Avg_Cogongrass_Cover 1.7761 0.7799 0.2650 1.7888 3.2859 1.0422 200
## Tree_Density -1.9694 0.7277 -3.4485 -1.9344 -0.5930 1.0203 147
## Avg_Canopy_Cover 1.8503 0.7085 0.5070 1.8103 3.3260 1.0157 485
## avg_veg_height -0.7215 0.5288 -1.7420 -0.7174 0.2777 1.0277 181
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.4732 11.8926 0.4840 5.9940 38.1530 1.1200 170
## Cogon_Patch_Size 5.6898 8.1391 0.1984 2.9741 27.1980 1.0772 162
## Veg_shannon_index 2.8695 4.7471 0.0760 1.3974 15.3231 1.0553 276
## total_shrub_cover 4.5484 5.9175 0.1596 2.6628 20.3510 1.0150 321
## Avg_Cogongrass_Cover 1.7715 3.2151 0.0550 0.7149 9.5093 1.1204 302
## Tree_Density 1.6389 4.4899 0.0542 0.5723 9.6784 1.1771 334
## Avg_Canopy_Cover 3.4365 5.0810 0.1507 1.9658 15.9506 1.0355 228
## avg_veg_height 0.5270 0.8205 0.0441 0.2693 2.4621 1.1019 667
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1998 3.4144 0.0649 1.0445 10.9928 1.1691 50
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8202 0.2791 -5.3375 -4.8335 -4.2305 1.0009 687
## week -0.0042 1.6458 -3.3041 0.0172 3.1025 1.0013 492
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.21700e-01 5.771000e-01 0.0917 0.3639 1.835000e+00 1.0435 226
## week 3.92265e+13 7.976296e+14 0.0794 21.3133 4.323053e+12 1.5128 464
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8421 1.3245 -1.7975 0.7585
## (Intercept)-Lynx_rufus 1.2213 2.1031 -2.2702 0.9370
## (Intercept)-Didelphis_virginiana -3.1457 1.2094 -5.8346 -3.0550
## (Intercept)-Sylvilagus_floridanus -1.8548 1.2336 -4.5047 -1.8162
## (Intercept)-Meleagris_gallopavo -2.3524 1.4759 -5.6184 -2.2314
## (Intercept)-Sciurus_carolinensis -3.4697 1.3746 -6.7179 -3.2986
## (Intercept)-Vulpes_vulpes -3.3047 1.7333 -7.1119 -3.1787
## (Intercept)-Sus_scrofa -4.9112 1.9293 -9.0139 -4.6734
## Cogon_Patch_Size-Canis_latrans 1.1704 1.6148 -0.9809 0.8491
## Cogon_Patch_Size-Lynx_rufus -0.0341 1.8039 -3.2783 -0.2213
## Cogon_Patch_Size-Didelphis_virginiana 1.3258 1.0845 -0.3978 1.1902
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3856 1.9044 -7.2136 -1.9672
## Cogon_Patch_Size-Meleagris_gallopavo -0.1311 1.3135 -2.3795 -0.2562
## Cogon_Patch_Size-Sciurus_carolinensis -1.9111 1.5826 -6.0109 -1.5916
## Cogon_Patch_Size-Vulpes_vulpes -1.4842 1.9040 -5.9353 -1.2235
## Cogon_Patch_Size-Sus_scrofa -1.3382 1.5899 -5.3554 -1.0584
## Veg_shannon_index-Canis_latrans 1.4517 0.7996 0.0764 1.3711
## Veg_shannon_index-Lynx_rufus -0.1693 1.4402 -3.8054 0.0579
## Veg_shannon_index-Didelphis_virginiana 1.2277 0.9366 -0.3449 1.1311
## Veg_shannon_index-Sylvilagus_floridanus 1.1258 0.8876 -0.4467 1.0609
## Veg_shannon_index-Meleagris_gallopavo 1.6348 1.1842 -0.2423 1.4548
## Veg_shannon_index-Sciurus_carolinensis -0.3192 0.9975 -2.5634 -0.2532
## Veg_shannon_index-Vulpes_vulpes -0.3730 1.3242 -3.6632 -0.1802
## Veg_shannon_index-Sus_scrofa 2.0757 1.4275 -0.0141 1.8008
## total_shrub_cover-Canis_latrans 0.7534 0.9438 -0.8239 0.6597
## total_shrub_cover-Lynx_rufus -1.9784 1.7012 -5.8751 -1.7456
## total_shrub_cover-Didelphis_virginiana -0.8142 0.9000 -2.7835 -0.7547
## total_shrub_cover-Sylvilagus_floridanus -0.2032 1.1387 -2.3463 -0.2244
## total_shrub_cover-Meleagris_gallopavo -3.4205 1.8501 -7.7331 -3.1812
## total_shrub_cover-Sciurus_carolinensis 0.1201 0.8452 -1.4659 0.0907
## total_shrub_cover-Vulpes_vulpes -0.9393 1.3934 -4.0937 -0.7735
## total_shrub_cover-Sus_scrofa 0.3262 1.1325 -1.7556 0.2442
## Avg_Cogongrass_Cover-Canis_latrans 2.3276 1.0244 0.5600 2.2318
## Avg_Cogongrass_Cover-Lynx_rufus 2.5329 1.2583 0.4838 2.3973
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0862 0.9188 0.3916 2.0527
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3284 1.0345 -0.8295 1.3604
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0825 1.4130 -2.1950 1.2526
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2683 1.0217 0.5448 2.1821
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3433 1.2321 0.2295 2.2404
## Avg_Cogongrass_Cover-Sus_scrofa 1.3003 1.2052 -1.4020 1.4163
## Tree_Density-Canis_latrans -2.4750 1.2399 -5.3248 -2.2842
## Tree_Density-Lynx_rufus -1.2314 1.1210 -3.2227 -1.3095
## Tree_Density-Didelphis_virginiana -2.1814 1.0536 -4.5138 -2.0718
## Tree_Density-Sylvilagus_floridanus -2.4026 1.2571 -5.1770 -2.2333
## Tree_Density-Meleagris_gallopavo -2.1519 1.1342 -4.6301 -2.0343
## Tree_Density-Sciurus_carolinensis -2.3583 1.2216 -5.2244 -2.1873
## Tree_Density-Vulpes_vulpes -1.9996 1.1055 -4.3132 -1.9459
## Tree_Density-Sus_scrofa -2.0917 1.2350 -4.9948 -1.9794
## Avg_Canopy_Cover-Canis_latrans 0.2055 0.7655 -1.3329 0.2160
## Avg_Canopy_Cover-Lynx_rufus 0.9091 1.4511 -1.8011 0.8589
## Avg_Canopy_Cover-Didelphis_virginiana 2.9046 1.0439 1.2811 2.7632
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6378 1.7444 1.3376 3.3304
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3749 1.3288 0.5666 2.1241
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5221 0.9590 1.0549 2.4010
## Avg_Canopy_Cover-Vulpes_vulpes 2.4246 1.3402 0.3751 2.1758
## Avg_Canopy_Cover-Sus_scrofa 2.1557 0.9122 0.5887 2.0596
## avg_veg_height-Canis_latrans -0.7401 0.6269 -1.9725 -0.7298
## avg_veg_height-Lynx_rufus -0.8376 0.8196 -2.5635 -0.8232
## avg_veg_height-Didelphis_virginiana -0.7739 0.6912 -2.1507 -0.7746
## avg_veg_height-Sylvilagus_floridanus -0.8196 0.7241 -2.2288 -0.8062
## avg_veg_height-Meleagris_gallopavo -0.8491 0.8025 -2.5074 -0.8360
## avg_veg_height-Sciurus_carolinensis -0.3511 0.7201 -1.6608 -0.3824
## avg_veg_height-Vulpes_vulpes -0.8101 0.7983 -2.3795 -0.8041
## avg_veg_height-Sus_scrofa -0.7275 0.7031 -2.1074 -0.7163
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.8817 1.0727 197
## (Intercept)-Lynx_rufus 6.1394 1.0651 97
## (Intercept)-Didelphis_virginiana -1.0257 1.0419 380
## (Intercept)-Sylvilagus_floridanus 0.4964 1.0190 433
## (Intercept)-Meleagris_gallopavo 0.2320 1.0149 266
## (Intercept)-Sciurus_carolinensis -1.2184 1.0101 160
## (Intercept)-Vulpes_vulpes -0.1109 1.0231 136
## (Intercept)-Sus_scrofa -1.8223 1.0480 189
## Cogon_Patch_Size-Canis_latrans 5.1543 1.1154 203
## Cogon_Patch_Size-Lynx_rufus 4.1178 1.0465 266
## Cogon_Patch_Size-Didelphis_virginiana 3.8811 1.0407 239
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1858 1.0295 140
## Cogon_Patch_Size-Meleagris_gallopavo 2.8923 1.0398 432
## Cogon_Patch_Size-Sciurus_carolinensis 0.3203 1.1021 196
## Cogon_Patch_Size-Vulpes_vulpes 1.6825 1.0105 290
## Cogon_Patch_Size-Sus_scrofa 1.0336 1.0179 305
## Veg_shannon_index-Canis_latrans 3.2278 1.0089 656
## Veg_shannon_index-Lynx_rufus 2.0503 1.0794 239
## Veg_shannon_index-Didelphis_virginiana 3.4329 1.0068 506
## Veg_shannon_index-Sylvilagus_floridanus 3.0817 1.0033 432
## Veg_shannon_index-Meleagris_gallopavo 4.4409 1.0076 340
## Veg_shannon_index-Sciurus_carolinensis 1.3637 1.0219 380
## Veg_shannon_index-Vulpes_vulpes 1.6212 1.0034 234
## Veg_shannon_index-Sus_scrofa 5.4990 1.0078 224
## total_shrub_cover-Canis_latrans 2.9475 1.0397 499
## total_shrub_cover-Lynx_rufus 0.6461 1.0477 198
## total_shrub_cover-Didelphis_virginiana 0.7532 1.0006 620
## total_shrub_cover-Sylvilagus_floridanus 1.8212 1.0366 304
## total_shrub_cover-Meleagris_gallopavo -0.4566 1.0166 206
## total_shrub_cover-Sciurus_carolinensis 1.8710 1.0146 788
## total_shrub_cover-Vulpes_vulpes 1.3124 1.0683 284
## total_shrub_cover-Sus_scrofa 2.7096 1.0045 644
## Avg_Cogongrass_Cover-Canis_latrans 4.6062 1.0109 362
## Avg_Cogongrass_Cover-Lynx_rufus 5.4477 1.0085 218
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0655 1.0090 343
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2649 1.0563 356
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3374 1.1413 300
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4927 1.0073 326
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.1523 1.0030 272
## Avg_Cogongrass_Cover-Sus_scrofa 3.4471 1.0627 419
## Tree_Density-Canis_latrans -0.7088 1.0545 252
## Tree_Density-Lynx_rufus 1.2406 1.0287 290
## Tree_Density-Didelphis_virginiana -0.5074 1.0085 268
## Tree_Density-Sylvilagus_floridanus -0.5776 1.0380 226
## Tree_Density-Meleagris_gallopavo -0.2098 1.0369 234
## Tree_Density-Sciurus_carolinensis -0.5290 1.0546 191
## Tree_Density-Vulpes_vulpes -0.0605 1.0094 273
## Tree_Density-Sus_scrofa -0.0198 1.0088 317
## Avg_Canopy_Cover-Canis_latrans 1.7339 1.0268 524
## Avg_Canopy_Cover-Lynx_rufus 3.9826 1.0137 274
## Avg_Canopy_Cover-Didelphis_virginiana 5.3085 1.0129 222
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.0230 1.0416 190
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5510 1.0537 138
## Avg_Canopy_Cover-Sciurus_carolinensis 4.7627 1.0011 272
## Avg_Canopy_Cover-Vulpes_vulpes 5.7552 1.0339 237
## Avg_Canopy_Cover-Sus_scrofa 4.2224 1.0314 345
## avg_veg_height-Canis_latrans 0.4825 1.0066 461
## avg_veg_height-Lynx_rufus 0.7744 1.0205 423
## avg_veg_height-Didelphis_virginiana 0.5601 1.0010 373
## avg_veg_height-Sylvilagus_floridanus 0.5627 1.0159 319
## avg_veg_height-Meleagris_gallopavo 0.6760 1.0186 417
## avg_veg_height-Sciurus_carolinensis 1.1612 1.0493 415
## avg_veg_height-Vulpes_vulpes 0.7193 1.0224 378
## avg_veg_height-Sus_scrofa 0.6775 1.0116 424
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6821 0.1688 -5.0492 -4.6710
## (Intercept)-Lynx_rufus -5.5413 0.2709 -6.1593 -5.5187
## (Intercept)-Didelphis_virginiana -4.3182 0.2273 -4.7900 -4.3119
## (Intercept)-Sylvilagus_floridanus -4.9705 0.2422 -5.4483 -4.9676
## (Intercept)-Meleagris_gallopavo -5.1265 0.2483 -5.5945 -5.1281
## (Intercept)-Sciurus_carolinensis -4.3911 0.2324 -4.8650 -4.3859
## (Intercept)-Vulpes_vulpes -5.6678 0.5688 -6.8648 -5.6037
## (Intercept)-Sus_scrofa -4.7367 0.3439 -5.4437 -4.7197
## week-Canis_latrans -77393.2093 5676079.9413 -560839.6221 0.0554
## week-Lynx_rufus -71820.0867 5161043.6001 -430518.7463 0.0422
## week-Didelphis_virginiana -33778.7565 3720676.5008 -369604.2688 0.0168
## week-Sylvilagus_floridanus -72665.0790 5174861.1060 -524783.6098 -0.0240
## week-Meleagris_gallopavo 58730.5148 5037686.7753 -499828.6757 0.0615
## week-Sciurus_carolinensis -75025.5177 3853860.4293 -456874.8777 0.0216
## week-Vulpes_vulpes 3416.7218 5063477.7346 -388359.6330 0.0398
## week-Sus_scrofa 100427.3397 8573447.6114 -407453.7188 -0.0815
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3852 1.0401 187
## (Intercept)-Lynx_rufus -5.0478 1.1239 88
## (Intercept)-Didelphis_virginiana -3.9030 1.0314 301
## (Intercept)-Sylvilagus_floridanus -4.5027 1.1019 156
## (Intercept)-Meleagris_gallopavo -4.6387 1.0291 159
## (Intercept)-Sciurus_carolinensis -3.9474 1.0065 323
## (Intercept)-Vulpes_vulpes -4.7335 1.0853 63
## (Intercept)-Sus_scrofa -4.0903 1.0105 279
## week-Canis_latrans 339523.3782 1.3088 3663
## week-Lynx_rufus 309979.7175 1.3095 3012
## week-Didelphis_virginiana 390932.1637 1.2985 4457
## week-Sylvilagus_floridanus 405810.6654 1.3099 3173
## week-Meleagris_gallopavo 354266.4033 1.3038 6554
## week-Sciurus_carolinensis 295460.6972 1.3276 1647
## week-Vulpes_vulpes 365459.3176 1.2904 13747
## week-Sus_scrofa 446741.2087 1.3040 6684
# 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): 4.0523
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0823 0.4853 -2.0847 -1.0692 -0.1507 1.0149 634
## Avg_Cogongrass_Cover 0.1764 0.4043 -0.6293 0.1796 0.9768 1.0111 562
## total_shrub_cover -0.3270 0.4073 -1.1523 -0.3216 0.4527 1.0099 1268
## avg_veg_height -0.1301 0.3414 -0.8406 -0.1220 0.5212 1.0211 536
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0661 1.3961 0.0690 0.6687 4.6595 1.0193 702
## Avg_Cogongrass_Cover 0.6124 0.8129 0.0514 0.3709 2.7122 1.0001 779
## total_shrub_cover 0.8780 1.0345 0.0687 0.5581 3.5387 1.0022 701
## avg_veg_height 0.2679 0.3129 0.0354 0.1667 1.0861 1.0479 1034
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.835 1.7675 0.1486 1.3338 6.7237 1.0805 167
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7850 0.2380 -5.2208 -4.7900 -4.2876 1.0013 823
## week -0.1616 1.6857 -3.5381 -0.1366 3.0312 1.0074 698
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.552000e-01 4.815000e-01 0.0526 0.2338 1.331000e+00 1.0618 370
## week 7.299208e+09 9.711373e+10 0.1208 137.1378 1.678916e+10 1.5303 413
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1516 0.7863 -1.5708 -0.1768
## (Intercept)-Lynx_rufus -0.6437 0.7445 -2.0573 -0.6627
## (Intercept)-Didelphis_virginiana -1.3827 0.6332 -2.7210 -1.3544
## (Intercept)-Sylvilagus_floridanus -0.8717 0.6659 -2.2204 -0.8733
## (Intercept)-Meleagris_gallopavo -1.1462 0.7185 -2.6218 -1.1156
## (Intercept)-Sciurus_carolinensis -1.4860 0.6616 -2.9161 -1.4580
## (Intercept)-Vulpes_vulpes -1.5788 0.7919 -3.2938 -1.5203
## (Intercept)-Sus_scrofa -1.7996 0.7811 -3.5638 -1.7249
## Avg_Cogongrass_Cover-Canis_latrans 0.5282 0.5164 -0.4025 0.4916
## Avg_Cogongrass_Cover-Lynx_rufus 0.6380 0.5860 -0.3485 0.5784
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4457 0.5032 -0.4776 0.4312
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2738 0.5582 -1.5375 -0.2259
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4177 0.6373 -1.8473 -0.3605
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3408 0.4719 -0.5310 0.3326
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3841 0.5671 -0.6889 0.3697
## Avg_Cogongrass_Cover-Sus_scrofa -0.1875 0.6947 -1.7358 -0.1311
## total_shrub_cover-Canis_latrans 0.4173 0.5401 -0.5305 0.3739
## total_shrub_cover-Lynx_rufus -0.8525 0.6802 -2.4386 -0.7763
## total_shrub_cover-Didelphis_virginiana -0.2510 0.4728 -1.2223 -0.2342
## total_shrub_cover-Sylvilagus_floridanus -0.3483 0.5061 -1.4501 -0.3194
## total_shrub_cover-Meleagris_gallopavo -1.3970 0.7641 -3.1957 -1.3019
## total_shrub_cover-Sciurus_carolinensis -0.0791 0.4642 -1.0137 -0.0893
## total_shrub_cover-Vulpes_vulpes -0.3811 0.6084 -1.7214 -0.3640
## total_shrub_cover-Sus_scrofa 0.1214 0.6296 -1.0242 0.0702
## avg_veg_height-Canis_latrans -0.1006 0.4358 -0.9528 -0.1043
## avg_veg_height-Lynx_rufus -0.1317 0.5164 -1.1771 -0.1285
## avg_veg_height-Didelphis_virginiana -0.1442 0.4513 -1.0624 -0.1200
## avg_veg_height-Sylvilagus_floridanus -0.2227 0.4448 -1.1475 -0.2084
## avg_veg_height-Meleagris_gallopavo -0.3216 0.5065 -1.4092 -0.2877
## avg_veg_height-Sciurus_carolinensis 0.1816 0.4664 -0.7329 0.1707
## avg_veg_height-Vulpes_vulpes -0.1901 0.4997 -1.1987 -0.1739
## avg_veg_height-Sus_scrofa -0.1079 0.4894 -1.1107 -0.0904
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4392 1.0095 397
## (Intercept)-Lynx_rufus 0.9322 1.0034 441
## (Intercept)-Didelphis_virginiana -0.1894 1.0154 820
## (Intercept)-Sylvilagus_floridanus 0.3715 1.0311 553
## (Intercept)-Meleagris_gallopavo 0.2402 1.0105 551
## (Intercept)-Sciurus_carolinensis -0.2620 1.0196 674
## (Intercept)-Vulpes_vulpes -0.1837 1.0255 424
## (Intercept)-Sus_scrofa -0.4578 1.0115 456
## Avg_Cogongrass_Cover-Canis_latrans 1.6827 1.0205 783
## Avg_Cogongrass_Cover-Lynx_rufus 1.9691 1.0048 983
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4728 1.0028 892
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6918 1.0055 900
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6853 1.0063 608
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2869 1.0046 1010
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5639 1.0128 862
## Avg_Cogongrass_Cover-Sus_scrofa 1.0332 1.0203 618
## total_shrub_cover-Canis_latrans 1.5963 1.0033 875
## total_shrub_cover-Lynx_rufus 0.2595 1.0086 510
## total_shrub_cover-Didelphis_virginiana 0.6675 1.0060 1989
## total_shrub_cover-Sylvilagus_floridanus 0.5762 1.0000 1457
## total_shrub_cover-Meleagris_gallopavo -0.2066 1.0007 512
## total_shrub_cover-Sciurus_carolinensis 0.8745 1.0054 2066
## total_shrub_cover-Vulpes_vulpes 0.7654 1.0452 802
## total_shrub_cover-Sus_scrofa 1.4981 1.0130 1219
## avg_veg_height-Canis_latrans 0.7360 1.0092 903
## avg_veg_height-Lynx_rufus 0.9076 1.0059 821
## avg_veg_height-Didelphis_virginiana 0.6982 1.0039 1021
## avg_veg_height-Sylvilagus_floridanus 0.6464 1.0180 880
## avg_veg_height-Meleagris_gallopavo 0.5876 1.0326 872
## avg_veg_height-Sciurus_carolinensis 1.1556 1.0079 1006
## avg_veg_height-Vulpes_vulpes 0.7556 1.0125 1037
## avg_veg_height-Sus_scrofa 0.8367 1.0056 1082
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6818 0.1519 -4.9939 -4.6821
## (Intercept)-Lynx_rufus -5.2991 0.2520 -5.7992 -5.2937
## (Intercept)-Didelphis_virginiana -4.3680 0.2309 -4.8310 -4.3546
## (Intercept)-Sylvilagus_floridanus -4.8837 0.2314 -5.3385 -4.8779
## (Intercept)-Meleagris_gallopavo -4.9751 0.2392 -5.4809 -4.9711
## (Intercept)-Sciurus_carolinensis -4.4121 0.2415 -4.9118 -4.4073
## (Intercept)-Vulpes_vulpes -5.3792 0.5095 -6.4555 -5.3224
## (Intercept)-Sus_scrofa -4.8495 0.3577 -5.6018 -4.8305
## week-Canis_latrans -639.9927 67154.9525 -16267.0410 -0.1620
## week-Lynx_rufus 222.2657 81920.6191 -16441.1020 -0.1366
## week-Didelphis_virginiana -2064.9509 82595.0173 -24197.5868 -0.2455
## week-Sylvilagus_floridanus 2098.2149 70186.5981 -17761.7113 -0.1975
## week-Meleagris_gallopavo -930.1706 81288.4018 -18885.9113 -0.3541
## week-Sciurus_carolinensis 1033.7752 85760.6132 -22182.5293 -0.4984
## week-Vulpes_vulpes 1620.4490 76017.9471 -21767.7267 -0.2414
## week-Sus_scrofa -1219.1019 85012.6299 -22593.6097 -0.2674
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3907 1.0097 248
## (Intercept)-Lynx_rufus -4.8379 1.1307 108
## (Intercept)-Didelphis_virginiana -3.9453 1.0085 292
## (Intercept)-Sylvilagus_floridanus -4.4525 1.0213 182
## (Intercept)-Meleagris_gallopavo -4.5247 1.0091 203
## (Intercept)-Sciurus_carolinensis -3.9631 1.0310 269
## (Intercept)-Vulpes_vulpes -4.5299 1.0135 94
## (Intercept)-Sus_scrofa -4.1961 1.0169 201
## week-Canis_latrans 17837.8940 1.1740 1752
## week-Lynx_rufus 22528.0609 1.2813 3376
## week-Didelphis_virginiana 17772.6745 1.2917 2849
## week-Sylvilagus_floridanus 22346.5461 1.3185 1170
## week-Meleagris_gallopavo 17921.8396 1.2335 3278
## week-Sciurus_carolinensis 20401.6977 1.2623 10354
## week-Vulpes_vulpes 14979.7918 1.1490 1725
## week-Sus_scrofa 19093.4210 1.2270 5044
# 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): 3.982
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8269 0.3788 -1.5689 -0.8339 -0.0854 0.9999 1653
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9682 0.9763 0.1478 0.6916 3.3249 1.0145 1329
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7705 0.2708 -5.2404 -4.7817 -4.2514 1.0037 914
## week 0.0850 1.5943 -3.3143 0.1803 3.0434 1.0123 343
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.103000e-01 6.800000e-01 0.0609 0.2663 1.512600e+00 1.0663 708
## week 4.060082e+14 6.151116e+15 0.0740 23.2221 3.168233e+13 1.6703 227
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2343 0.4091 -0.5233 0.2171 1.0587 1.0001
## (Intercept)-Lynx_rufus -0.0879 0.5195 -0.9687 -0.1312 1.1021 1.0246
## (Intercept)-Didelphis_virginiana -1.3111 0.4147 -2.2093 -1.2912 -0.5451 1.0030
## (Intercept)-Sylvilagus_floridanus -0.6073 0.3998 -1.3793 -0.6039 0.1756 1.0056
## (Intercept)-Meleagris_gallopavo -0.6721 0.4246 -1.4800 -0.6801 0.1901 1.0257
## (Intercept)-Sciurus_carolinensis -1.2909 0.4182 -2.1647 -1.2653 -0.5196 1.0007
## (Intercept)-Vulpes_vulpes -1.4243 0.6167 -2.6466 -1.4318 -0.1770 1.0385
## (Intercept)-Sus_scrofa -1.7285 0.5282 -2.8551 -1.6996 -0.7808 1.0111
## ESS
## (Intercept)-Canis_latrans 1633
## (Intercept)-Lynx_rufus 370
## (Intercept)-Didelphis_virginiana 2392
## (Intercept)-Sylvilagus_floridanus 1364
## (Intercept)-Meleagris_gallopavo 1236
## (Intercept)-Sciurus_carolinensis 2113
## (Intercept)-Vulpes_vulpes 278
## (Intercept)-Sus_scrofa 1258
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6543 1.622000e-01 -4.9771
## (Intercept)-Lynx_rufus -5.3087 2.789000e-01 -5.8665
## (Intercept)-Didelphis_virginiana -4.3601 2.336000e-01 -4.8076
## (Intercept)-Sylvilagus_floridanus -4.8741 2.315000e-01 -5.3521
## (Intercept)-Meleagris_gallopavo -5.0270 2.434000e-01 -5.4982
## (Intercept)-Sciurus_carolinensis -4.4059 2.342000e-01 -4.8713
## (Intercept)-Vulpes_vulpes -5.4744 5.357000e-01 -6.5655
## (Intercept)-Sus_scrofa -4.7977 3.551000e-01 -5.5706
## week-Canis_latrans 185989.2735 2.062244e+07 -81747.3056
## week-Lynx_rufus 76753.6148 2.356989e+07 -283492.1639
## week-Didelphis_virginiana 359120.1588 1.918310e+07 -124565.2927
## week-Sylvilagus_floridanus -53296.5492 2.097056e+07 -101797.1887
## week-Meleagris_gallopavo -335714.4396 2.409689e+07 -109920.8184
## week-Sciurus_carolinensis -420003.2576 1.938033e+07 -311693.0106
## week-Vulpes_vulpes -509776.4066 2.265483e+07 -90469.3680
## week-Sus_scrofa -79196.2191 2.372005e+07 -160478.3618
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6527 -4.3476 1.0007 229
## (Intercept)-Lynx_rufus -5.2938 -4.7835 1.0709 97
## (Intercept)-Didelphis_virginiana -4.3597 -3.9173 1.0033 284
## (Intercept)-Sylvilagus_floridanus -4.8612 -4.4443 1.0312 134
## (Intercept)-Meleagris_gallopavo -5.0248 -4.5545 1.1854 180
## (Intercept)-Sciurus_carolinensis -4.4038 -3.9593 1.0437 274
## (Intercept)-Vulpes_vulpes -5.3883 -4.6137 1.1488 73
## (Intercept)-Sus_scrofa -4.7795 -4.1537 1.0047 240
## week-Canis_latrans 0.3368 121094.3019 1.2984 9525
## week-Lynx_rufus 0.3472 87686.9233 1.2914 30287
## week-Didelphis_virginiana 0.3655 114179.4704 1.3248 2976
## week-Sylvilagus_floridanus 0.2844 156350.2037 1.2910 8170
## week-Meleagris_gallopavo 0.3692 115372.5571 1.3096 5981
## week-Sciurus_carolinensis 0.3543 83312.4137 1.3364 1074
## week-Vulpes_vulpes 0.3228 102391.7972 1.3399 2189
## week-Sus_scrofa 0.2976 108381.7453 1.2915 16290
#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): 3.9868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0789 0.4656 -1.9957 -1.0806 -0.1663 1.0121 568
## Veg_shannon_index 0.3315 0.3394 -0.3482 0.3270 0.9934 1.0080 1227
## Avg_Cogongrass_Cover 0.1933 0.3224 -0.4351 0.1897 0.8441 1.0030 1303
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1378 1.3953 0.0712 0.7491 4.8670 1.0229 580
## Veg_shannon_index 0.5447 0.6546 0.0520 0.3391 2.3180 1.0190 778
## Avg_Cogongrass_Cover 0.4754 0.6460 0.0442 0.2940 2.0025 1.0459 815
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1938 1.2023 0.0895 0.8764 4.0082 1.0152 236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7733 0.2450 -5.2632 -4.7688 -4.2782 1.0221 809
## week -0.0196 1.5806 -3.1050 0.0225 3.1046 1.0045 999
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8740e-01 5.003000e-01 0.0544 0.2525 1.53050e+00 1.0725 253
## week 4.2193e+11 1.062929e+13 0.1086 333.1886 2.06003e+10 1.4390 631
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1255 0.7315 -1.5486 -0.1413
## (Intercept)-Lynx_rufus -0.5317 0.7657 -1.8806 -0.5825
## (Intercept)-Didelphis_virginiana -1.4643 0.6247 -2.7946 -1.4502
## (Intercept)-Sylvilagus_floridanus -0.8891 0.6153 -2.1385 -0.8762
## (Intercept)-Meleagris_gallopavo -1.0396 0.6566 -2.3608 -1.0479
## (Intercept)-Sciurus_carolinensis -1.4700 0.6127 -2.8136 -1.4280
## (Intercept)-Vulpes_vulpes -1.6291 0.7457 -3.1376 -1.5933
## (Intercept)-Sus_scrofa -1.9621 0.7889 -3.7512 -1.8983
## Veg_shannon_index-Canis_latrans 0.8000 0.4595 0.0066 0.7576
## Veg_shannon_index-Lynx_rufus -0.1863 0.6072 -1.5515 -0.1297
## Veg_shannon_index-Didelphis_virginiana 0.5284 0.4469 -0.2797 0.4971
## Veg_shannon_index-Sylvilagus_floridanus 0.4417 0.4611 -0.4440 0.4229
## Veg_shannon_index-Meleagris_gallopavo 0.4866 0.4775 -0.4092 0.4610
## Veg_shannon_index-Sciurus_carolinensis -0.0948 0.4487 -1.0332 -0.0728
## Veg_shannon_index-Vulpes_vulpes -0.0642 0.5235 -1.1783 -0.0310
## Veg_shannon_index-Sus_scrofa 0.7747 0.6037 -0.2809 0.7233
## Avg_Cogongrass_Cover-Canis_latrans 0.5584 0.4345 -0.1830 0.5195
## Avg_Cogongrass_Cover-Lynx_rufus 0.5609 0.4764 -0.2432 0.5105
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4466 0.4059 -0.2727 0.4238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2096 0.4539 -1.1823 -0.1730
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2798 0.5250 -1.4294 -0.2330
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3606 0.3965 -0.4406 0.3629
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2829 0.4907 -0.6644 0.2633
## Avg_Cogongrass_Cover-Sus_scrofa -0.1123 0.5925 -1.5028 -0.0557
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3363 1.0093 509
## (Intercept)-Lynx_rufus 0.9934 1.0158 431
## (Intercept)-Didelphis_virginiana -0.3172 1.0163 889
## (Intercept)-Sylvilagus_floridanus 0.3026 1.0031 810
## (Intercept)-Meleagris_gallopavo 0.2925 1.0187 674
## (Intercept)-Sciurus_carolinensis -0.3251 1.0082 937
## (Intercept)-Vulpes_vulpes -0.2009 1.0237 440
## (Intercept)-Sus_scrofa -0.5755 1.0024 481
## Veg_shannon_index-Canis_latrans 1.7881 1.0014 1261
## Veg_shannon_index-Lynx_rufus 0.8668 1.0055 756
## Veg_shannon_index-Didelphis_virginiana 1.5088 1.0051 1940
## Veg_shannon_index-Sylvilagus_floridanus 1.4075 1.0032 1767
## Veg_shannon_index-Meleagris_gallopavo 1.5174 1.0004 1472
## Veg_shannon_index-Sciurus_carolinensis 0.6898 1.0017 1368
## Veg_shannon_index-Vulpes_vulpes 0.9153 1.0035 957
## Veg_shannon_index-Sus_scrofa 2.1810 1.0113 978
## Avg_Cogongrass_Cover-Canis_latrans 1.5106 1.0015 1328
## Avg_Cogongrass_Cover-Lynx_rufus 1.6256 1.0021 1283
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3266 1.0004 1768
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5834 1.0119 1168
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6495 1.0006 781
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1392 1.0007 2031
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2843 1.0037 1218
## Avg_Cogongrass_Cover-Sus_scrofa 0.8790 1.0048 767
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6471 0.1527 -4.9637 -4.6444
## (Intercept)-Lynx_rufus -5.3296 0.2804 -5.8782 -5.3295
## (Intercept)-Didelphis_virginiana -4.3648 0.2360 -4.8340 -4.3658
## (Intercept)-Sylvilagus_floridanus -4.8999 0.2399 -5.4179 -4.8855
## (Intercept)-Meleagris_gallopavo -5.0241 0.2579 -5.5692 -5.0194
## (Intercept)-Sciurus_carolinensis -4.4033 0.2307 -4.8802 -4.4025
## (Intercept)-Vulpes_vulpes -5.3963 0.5527 -6.7629 -5.2970
## (Intercept)-Sus_scrofa -4.7876 0.3489 -5.5324 -4.7662
## week-Canis_latrans -18538.7159 657972.9546 -30476.3288 -0.0059
## week-Lynx_rufus 6629.5298 589862.5586 -30334.2517 -0.1085
## week-Didelphis_virginiana 6130.0192 767620.2343 -24046.5148 -0.1467
## week-Sylvilagus_floridanus 10359.0429 515136.0718 -27904.8349 0.0726
## week-Meleagris_gallopavo -1269.6692 947043.8321 -20725.4598 -0.0171
## week-Sciurus_carolinensis -3593.5175 824594.8669 -33624.3530 0.0100
## week-Vulpes_vulpes 5181.9736 494104.7621 -26634.6969 -0.1753
## week-Sus_scrofa 4241.1216 416302.8741 -28321.4781 0.0393
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3568 1.0139 231
## (Intercept)-Lynx_rufus -4.7838 1.0506 102
## (Intercept)-Didelphis_virginiana -3.9279 1.0090 346
## (Intercept)-Sylvilagus_floridanus -4.4842 1.0921 152
## (Intercept)-Meleagris_gallopavo -4.5334 1.0328 161
## (Intercept)-Sciurus_carolinensis -3.9640 1.0204 314
## (Intercept)-Vulpes_vulpes -4.5812 1.1558 69
## (Intercept)-Sus_scrofa -4.1604 1.0164 248
## week-Canis_latrans 23737.0267 1.3622 1421
## week-Lynx_rufus 34459.0571 1.3016 6599
## week-Didelphis_virginiana 31263.2955 1.2971 19880
## week-Sylvilagus_floridanus 32133.4144 1.3293 2264
## week-Meleagris_gallopavo 31308.4904 1.2905 410141
## week-Sciurus_carolinensis 23556.0964 1.2920 70755
## week-Vulpes_vulpes 24123.5424 1.3008 6724
## week-Sus_scrofa 30545.9163 1.3022 8726
# 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): 4.07
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1480 0.5439 -2.2428 -1.1610 -0.0327 1.0118 474
## Cogon_Patch_Size -0.2595 0.5689 -1.4194 -0.2254 0.8088 1.0033 903
## Avg_Cogongrass_Cover 0.1596 0.4062 -0.6902 0.1641 0.9736 1.0134 1086
## total_shrub_cover -0.2979 0.4349 -1.2062 -0.2970 0.5453 1.0235 1051
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4510 1.9216 0.0683 0.8446 6.7110 1.0721 342
## Cogon_Patch_Size 1.9014 2.8427 0.1004 1.0336 8.5771 1.0783 475
## Avg_Cogongrass_Cover 0.7757 1.1961 0.0582 0.4253 3.5225 1.0129 737
## total_shrub_cover 0.9972 1.3664 0.0691 0.5814 4.3476 1.0437 571
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3159 2.0435 0.2926 1.8132 7.4709 1.1675 118
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8018 0.2689 -5.2940 -4.8141 -4.2108 1.0312 1026
## week -0.0319 1.7048 -3.5588 0.0121 3.1751 1.0064 591
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.771000e-01 7.019000e-01 0.0631 0.3027 1.827300e+00 1.4517 99
## week 7.888456e+10 1.127236e+12 0.1481 734.4310 2.476482e+10 1.7076 115
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0374 0.9390 -1.7154 -0.0812
## (Intercept)-Lynx_rufus -0.6319 0.8870 -2.1827 -0.6882
## (Intercept)-Didelphis_virginiana -1.4738 0.7137 -3.0089 -1.4351
## (Intercept)-Sylvilagus_floridanus -1.0449 0.7678 -2.5973 -1.0578
## (Intercept)-Meleagris_gallopavo -1.2375 0.7725 -2.9178 -1.2246
## (Intercept)-Sciurus_carolinensis -1.6924 0.7887 -3.4187 -1.6224
## (Intercept)-Vulpes_vulpes -1.6411 1.0089 -3.7859 -1.5807
## (Intercept)-Sus_scrofa -2.0110 0.9210 -4.0800 -1.9171
## Cogon_Patch_Size-Canis_latrans 0.8423 0.8790 -0.3995 0.6850
## Cogon_Patch_Size-Lynx_rufus -0.4219 1.0036 -2.2803 -0.4849
## Cogon_Patch_Size-Didelphis_virginiana 0.8199 0.6310 -0.2234 0.7757
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2254 1.1215 -3.9640 -1.0038
## Cogon_Patch_Size-Meleagris_gallopavo -0.0080 0.6714 -1.3537 -0.0122
## Cogon_Patch_Size-Sciurus_carolinensis -0.9880 0.8772 -3.1952 -0.8422
## Cogon_Patch_Size-Vulpes_vulpes -0.7977 1.1724 -3.6244 -0.6409
## Cogon_Patch_Size-Sus_scrofa -0.5172 1.0263 -3.0145 -0.3655
## Avg_Cogongrass_Cover-Canis_latrans 0.3635 0.4907 -0.4963 0.3337
## Avg_Cogongrass_Cover-Lynx_rufus 0.7817 0.7277 -0.3480 0.6693
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2177 0.4861 -0.7227 0.2182
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1763 0.5392 -1.3827 -0.1346
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6038 0.7583 -2.3761 -0.4971
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6035 0.4795 -0.2486 0.5765
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3853 0.5749 -0.6849 0.3551
## Avg_Cogongrass_Cover-Sus_scrofa -0.2101 0.7616 -2.0847 -0.1018
## total_shrub_cover-Canis_latrans 0.4119 0.6006 -0.6181 0.3391
## total_shrub_cover-Lynx_rufus -0.7863 0.7521 -2.5095 -0.6823
## total_shrub_cover-Didelphis_virginiana -0.3544 0.4998 -1.3915 -0.3456
## total_shrub_cover-Sylvilagus_floridanus -0.2615 0.5911 -1.4765 -0.2367
## total_shrub_cover-Meleagris_gallopavo -1.3750 0.8221 -3.4312 -1.2592
## total_shrub_cover-Sciurus_carolinensis 0.0325 0.5105 -0.9464 0.0228
## total_shrub_cover-Vulpes_vulpes -0.3360 0.7346 -1.8795 -0.2938
## total_shrub_cover-Sus_scrofa 0.1901 0.6511 -0.9873 0.1442
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9862 1.0434 256
## (Intercept)-Lynx_rufus 1.2682 1.0440 341
## (Intercept)-Didelphis_virginiana -0.1026 1.0151 724
## (Intercept)-Sylvilagus_floridanus 0.4780 1.0226 520
## (Intercept)-Meleagris_gallopavo 0.2905 1.0241 578
## (Intercept)-Sciurus_carolinensis -0.3010 1.0251 406
## (Intercept)-Vulpes_vulpes 0.2369 1.0693 210
## (Intercept)-Sus_scrofa -0.4391 1.0249 474
## Cogon_Patch_Size-Canis_latrans 2.9670 1.0347 627
## Cogon_Patch_Size-Lynx_rufus 1.7495 1.0218 445
## Cogon_Patch_Size-Didelphis_virginiana 2.0945 1.0144 737
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2799 1.0225 418
## Cogon_Patch_Size-Meleagris_gallopavo 1.4036 1.0066 1263
## Cogon_Patch_Size-Sciurus_carolinensis 0.3118 1.0011 553
## Cogon_Patch_Size-Vulpes_vulpes 1.1453 1.0040 387
## Cogon_Patch_Size-Sus_scrofa 1.0710 1.0075 671
## Avg_Cogongrass_Cover-Canis_latrans 1.4537 1.0028 1649
## Avg_Cogongrass_Cover-Lynx_rufus 2.5208 1.0092 704
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1855 1.0062 1581
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7755 1.0210 896
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5317 1.0178 639
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6267 1.0066 1074
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5950 1.0075 1211
## Avg_Cogongrass_Cover-Sus_scrofa 1.0713 1.0098 859
## total_shrub_cover-Canis_latrans 1.7708 1.0252 831
## total_shrub_cover-Lynx_rufus 0.4705 1.0438 489
## total_shrub_cover-Didelphis_virginiana 0.6000 1.0222 1667
## total_shrub_cover-Sylvilagus_floridanus 0.8459 1.0209 687
## total_shrub_cover-Meleagris_gallopavo -0.1104 1.0871 447
## total_shrub_cover-Sciurus_carolinensis 1.0529 1.0026 1755
## total_shrub_cover-Vulpes_vulpes 0.9778 1.1019 616
## total_shrub_cover-Sus_scrofa 1.6275 1.0124 1014
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6609 0.1606 -4.9721 -4.6583
## (Intercept)-Lynx_rufus -5.3642 0.2833 -5.9492 -5.3489
## (Intercept)-Didelphis_virginiana -4.3658 0.2449 -4.8791 -4.3489
## (Intercept)-Sylvilagus_floridanus -4.9442 0.2374 -5.4365 -4.9362
## (Intercept)-Meleagris_gallopavo -5.0186 0.2467 -5.5392 -4.9991
## (Intercept)-Sciurus_carolinensis -4.4065 0.2329 -4.8627 -4.4011
## (Intercept)-Vulpes_vulpes -5.6404 0.6406 -7.0521 -5.5543
## (Intercept)-Sus_scrofa -4.8118 0.3554 -5.5236 -4.7958
## week-Canis_latrans -3958.8986 304159.0560 -20769.7589 -0.0591
## week-Lynx_rufus -2666.6674 340936.3621 -20334.8213 -0.1389
## week-Didelphis_virginiana -7052.7895 202711.3447 -23141.3591 0.3190
## week-Sylvilagus_floridanus 1112.0225 336256.0857 -19627.9149 -0.1337
## week-Meleagris_gallopavo -2018.6588 251486.0482 -17735.2755 0.0392
## week-Sciurus_carolinensis -4503.3271 303230.8566 -22055.2898 -0.0356
## week-Vulpes_vulpes -1138.0824 331523.1472 -16937.7112 -0.0007
## week-Sus_scrofa 10709.1849 299172.0951 -17883.5835 -0.1097
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3561 1.0188 217
## (Intercept)-Lynx_rufus -4.8549 1.2028 89
## (Intercept)-Didelphis_virginiana -3.9199 1.0163 276
## (Intercept)-Sylvilagus_floridanus -4.4777 1.0291 173
## (Intercept)-Meleagris_gallopavo -4.5738 1.0438 190
## (Intercept)-Sciurus_carolinensis -3.9620 1.0279 299
## (Intercept)-Vulpes_vulpes -4.6513 1.2565 46
## (Intercept)-Sus_scrofa -4.1322 1.1143 204
## week-Canis_latrans 21274.3879 1.3038 6964
## week-Lynx_rufus 23752.9583 1.2942 6706
## week-Didelphis_virginiana 22605.9020 1.4306 498
## week-Sylvilagus_floridanus 27179.2925 1.2893 17961
## week-Meleagris_gallopavo 23936.0998 1.3001 5309
## week-Sciurus_carolinensis 17590.9846 1.3075 5137
## week-Vulpes_vulpes 25191.0076 1.2925 5308
## week-Sus_scrofa 22941.5779 1.4086 621
#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): 4.0785
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1609 0.5905 -2.2984 -1.1870 0.1495 1.0005 993
## Tree_Density -0.8298 0.4793 -1.8930 -0.8013 0.0578 1.0585 603
## Avg_Canopy_Cover 1.1128 0.4227 0.3257 1.0838 1.9915 1.0222 657
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1420 2.5009 0.1701 1.4032 8.9992 1.0043 422
## Tree_Density 0.8753 1.4929 0.0542 0.4443 4.3248 1.0399 465
## Avg_Canopy_Cover 0.8992 1.4276 0.0727 0.5712 3.6882 1.0869 908
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9228 1.3344 0.0523 0.4696 4.8059 1.2202 126
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7788 0.2481 -5.2258 -4.7913 -4.2707 1.0127 1142
## week -0.0082 1.6766 -3.3265 -0.0447 3.2750 1.0063 708
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.863000e-01 4.824000e-01 0.0678 0.2620 1.480000e+00 1.0428 424
## week 6.060497e+09 7.908038e+10 0.1191 236.5376 1.043766e+09 1.7839 236
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1652 0.7592 -1.2819 0.1480 1.7405
## (Intercept)-Lynx_rufus -0.0370 1.0757 -1.7511 -0.1528 2.5624
## (Intercept)-Didelphis_virginiana -1.8891 0.7024 -3.3677 -1.8399 -0.5853
## (Intercept)-Sylvilagus_floridanus -1.0674 0.6542 -2.4216 -1.0550 0.1948
## (Intercept)-Meleagris_gallopavo -0.9071 0.6901 -2.2968 -0.9156 0.5007
## (Intercept)-Sciurus_carolinensis -1.9542 0.7204 -3.5279 -1.9110 -0.6295
## (Intercept)-Vulpes_vulpes -2.0763 0.8942 -3.9871 -2.0613 -0.3720
## (Intercept)-Sus_scrofa -2.4751 0.8869 -4.3882 -2.4236 -0.9105
## Tree_Density-Canis_latrans -1.0184 0.6224 -2.4702 -0.9466 -0.0136
## Tree_Density-Lynx_rufus 0.0297 0.6983 -1.1178 -0.0463 1.7435
## Tree_Density-Didelphis_virginiana -1.0978 0.7725 -2.9457 -0.9883 0.0792
## Tree_Density-Sylvilagus_floridanus -1.1820 0.7925 -3.0637 -1.0422 0.0228
## Tree_Density-Meleagris_gallopavo -0.9800 0.7496 -2.6534 -0.8977 0.3022
## Tree_Density-Sciurus_carolinensis -1.0121 0.7832 -2.8800 -0.9073 0.2088
## Tree_Density-Vulpes_vulpes -0.6692 0.7824 -2.2537 -0.6530 0.7398
## Tree_Density-Sus_scrofa -1.0106 0.8260 -2.9583 -0.8886 0.2759
## Avg_Canopy_Cover-Canis_latrans 0.0913 0.5274 -0.9418 0.0878 1.1359
## Avg_Canopy_Cover-Lynx_rufus 0.8049 0.7422 -0.5315 0.7642 2.4276
## Avg_Canopy_Cover-Didelphis_virginiana 1.3681 0.5303 0.4378 1.3326 2.4899
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8175 0.8186 0.6619 1.6665 3.8106
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4800 0.7523 0.3247 1.3669 3.2122
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2997 0.5172 0.3555 1.2669 2.4512
## Avg_Canopy_Cover-Vulpes_vulpes 1.1006 0.6098 0.0096 1.0575 2.4648
## Avg_Canopy_Cover-Sus_scrofa 1.2896 0.5868 0.2640 1.2495 2.5528
## Rhat ESS
## (Intercept)-Canis_latrans 1.0131 444
## (Intercept)-Lynx_rufus 1.0263 171
## (Intercept)-Didelphis_virginiana 1.0072 743
## (Intercept)-Sylvilagus_floridanus 1.0063 915
## (Intercept)-Meleagris_gallopavo 1.0034 677
## (Intercept)-Sciurus_carolinensis 1.0079 743
## (Intercept)-Vulpes_vulpes 1.0164 404
## (Intercept)-Sus_scrofa 1.0051 500
## Tree_Density-Canis_latrans 1.0168 875
## Tree_Density-Lynx_rufus 1.0099 612
## Tree_Density-Didelphis_virginiana 1.0150 729
## Tree_Density-Sylvilagus_floridanus 1.0188 617
## Tree_Density-Meleagris_gallopavo 1.0356 756
## Tree_Density-Sciurus_carolinensis 1.0214 692
## Tree_Density-Vulpes_vulpes 1.0196 631
## Tree_Density-Sus_scrofa 1.0179 633
## Avg_Canopy_Cover-Canis_latrans 1.0019 929
## Avg_Canopy_Cover-Lynx_rufus 1.0131 350
## Avg_Canopy_Cover-Didelphis_virginiana 1.0024 1022
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0067 512
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0574 436
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0090 1428
## Avg_Canopy_Cover-Vulpes_vulpes 1.0411 485
## Avg_Canopy_Cover-Sus_scrofa 1.0068 996
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7063 0.1552 -5.0335 -4.6998
## (Intercept)-Lynx_rufus -5.4014 0.3262 -6.1078 -5.3799
## (Intercept)-Didelphis_virginiana -4.3553 0.2191 -4.8161 -4.3447
## (Intercept)-Sylvilagus_floridanus -4.8575 0.2253 -5.3063 -4.8433
## (Intercept)-Meleagris_gallopavo -5.1119 0.2821 -5.7311 -5.0932
## (Intercept)-Sciurus_carolinensis -4.3915 0.2199 -4.8359 -4.3923
## (Intercept)-Vulpes_vulpes -5.3294 0.5024 -6.4429 -5.2666
## (Intercept)-Sus_scrofa -4.7354 0.3121 -5.3538 -4.7360
## week-Canis_latrans -610.3044 78641.0136 -3162.7652 0.1770
## week-Lynx_rufus 12.4941 65653.3119 -4318.1238 -0.0113
## week-Didelphis_virginiana 652.3667 64063.5417 -4208.0165 0.1941
## week-Sylvilagus_floridanus 923.8931 83050.7960 -3440.2770 0.2655
## week-Meleagris_gallopavo 3541.0960 91049.3835 -2903.3166 0.2032
## week-Sciurus_carolinensis -867.2990 80004.7435 -3062.1074 -0.1017
## week-Vulpes_vulpes -1918.1879 61988.8157 -4085.5978 -0.1069
## week-Sus_scrofa -501.8353 65365.4898 -3466.0804 0.2049
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4185 1.0746 243
## (Intercept)-Lynx_rufus -4.8390 1.0400 69
## (Intercept)-Didelphis_virginiana -3.9592 1.0727 359
## (Intercept)-Sylvilagus_floridanus -4.4323 1.0295 182
## (Intercept)-Meleagris_gallopavo -4.6084 1.0493 131
## (Intercept)-Sciurus_carolinensis -3.9790 1.0039 363
## (Intercept)-Vulpes_vulpes -4.5052 1.0884 103
## (Intercept)-Sus_scrofa -4.1355 1.0091 335
## week-Canis_latrans 5597.7882 1.2969 1639
## week-Lynx_rufus 3085.5074 1.2903 7430
## week-Didelphis_virginiana 4148.8902 1.3010 11550
## week-Sylvilagus_floridanus 5101.2354 1.3021 3874
## week-Meleagris_gallopavo 5835.0689 1.4321 1232
## week-Sciurus_carolinensis 3940.3740 1.3018 2006
## week-Vulpes_vulpes 4083.8301 1.3846 1246
## week-Sus_scrofa 4102.7395 1.2971 11779
# 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): 3.9735
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6214 0.4887 -2.5973 -1.6250 -0.6519 1.0032 443
## Avg_Cogongrass_Cover -0.6927 0.4416 -1.5770 -0.7038 0.2191 1.0023 459
## I(Avg_Cogongrass_Cover^2) 0.6686 0.3684 -0.0356 0.6681 1.4317 1.0214 646
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9868 1.4377 0.0593 0.5751 4.2942 1.0167 609
## Avg_Cogongrass_Cover 0.4870 0.6123 0.0450 0.3044 2.1385 1.0035 1041
## I(Avg_Cogongrass_Cover^2) 0.5443 0.7668 0.0436 0.2829 2.7936 1.0206 553
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0092 0.9663 0.0731 0.724 3.6409 1.0406 224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7881 0.2382 -5.2587 -4.7912 -4.2993 1.0001 618
## week 0.0081 1.6802 -3.4945 0.0621 3.1767 1.0169 806
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.897000e-01 4.778000e-01 0.0542 0.2514 1.591000e+00 1.0116 145
## week 1.177205e+16 1.536922e+17 0.1392 15926.4967 3.888079e+16 1.7699 283
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7533 0.7436 -2.0890 -0.7866
## (Intercept)-Lynx_rufus -1.3647 0.6962 -2.6128 -1.3956
## (Intercept)-Didelphis_virginiana -1.8747 0.6196 -3.1704 -1.8620
## (Intercept)-Sylvilagus_floridanus -1.4838 0.6497 -2.7881 -1.4765
## (Intercept)-Meleagris_gallopavo -1.3737 0.6688 -2.7278 -1.3644
## (Intercept)-Sciurus_carolinensis -2.1566 0.7232 -3.7352 -2.0983
## (Intercept)-Vulpes_vulpes -2.2246 0.8329 -4.0641 -2.1595
## (Intercept)-Sus_scrofa -2.2146 0.7697 -3.9439 -2.1374
## Avg_Cogongrass_Cover-Canis_latrans -0.4263 0.5745 -1.5024 -0.4528
## Avg_Cogongrass_Cover-Lynx_rufus -0.4438 0.5871 -1.5537 -0.4784
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3563 0.5852 -1.4083 -0.3871
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1541 0.5985 -2.4597 -1.1175
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9756 0.6041 -2.2590 -0.9432
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6973 0.5677 -1.8378 -0.6939
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.6838 0.6193 -1.8930 -0.6851
## Avg_Cogongrass_Cover-Sus_scrofa -0.9446 0.6748 -2.4472 -0.8994
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.1905 0.7384 0.1221 1.0460
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.0458 0.5128 0.1869 0.9980
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5023 0.4263 -0.3056 0.5021
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6984 0.4325 -0.0766 0.6664
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1357 0.5523 -1.1455 0.2010
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8788 0.4016 0.1427 0.8623
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.7829 0.4436 -0.0116 0.7553
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1951 0.6364 -1.3627 0.3009
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8022 1.0006 441
## (Intercept)-Lynx_rufus 0.1287 1.0070 470
## (Intercept)-Didelphis_virginiana -0.7297 1.0050 891
## (Intercept)-Sylvilagus_floridanus -0.2340 1.0117 696
## (Intercept)-Meleagris_gallopavo -0.0991 1.0067 598
## (Intercept)-Sciurus_carolinensis -0.8774 1.0040 630
## (Intercept)-Vulpes_vulpes -0.7854 1.0032 319
## (Intercept)-Sus_scrofa -0.8936 1.0022 742
## Avg_Cogongrass_Cover-Canis_latrans 0.7863 1.0010 894
## Avg_Cogongrass_Cover-Lynx_rufus 0.7938 1.0002 851
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8917 1.0019 940
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1194 1.0085 743
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1548 1.0149 873
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4060 1.0055 889
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5703 1.0007 844
## Avg_Cogongrass_Cover-Sus_scrofa 0.2926 1.0054 818
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0855 1.0132 390
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2120 1.0021 646
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3443 1.0185 581
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6513 1.0098 785
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0703 1.0109 540
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7321 1.0206 809
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7420 1.0074 631
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1576 1.0368 541
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6762 1.576000e-01 -5.008300e+00
## (Intercept)-Lynx_rufus -5.2171 2.526000e-01 -5.757300e+00
## (Intercept)-Didelphis_virginiana -4.3847 2.529000e-01 -4.891600e+00
## (Intercept)-Sylvilagus_floridanus -4.8880 2.387000e-01 -5.354700e+00
## (Intercept)-Meleagris_gallopavo -5.0265 2.600000e-01 -5.586200e+00
## (Intercept)-Sciurus_carolinensis -4.4082 2.275000e-01 -4.853000e+00
## (Intercept)-Vulpes_vulpes -5.5142 5.822000e-01 -6.884100e+00
## (Intercept)-Sus_scrofa -4.8143 3.371000e-01 -5.489400e+00
## week-Canis_latrans 128203.7667 8.788590e+07 -4.703496e+07
## week-Lynx_rufus -621658.7985 1.013688e+08 -5.936502e+07
## week-Didelphis_virginiana 1035099.6136 1.003146e+08 -3.116366e+07
## week-Sylvilagus_floridanus 2390806.0471 1.098025e+08 -3.352038e+07
## week-Meleagris_gallopavo 846972.2725 1.172282e+08 -4.358604e+07
## week-Sciurus_carolinensis -582329.5485 8.843120e+07 -4.974288e+07
## week-Vulpes_vulpes 111202.6130 9.076581e+07 -5.035772e+07
## week-Sus_scrofa 525212.8057 1.187807e+08 -4.189105e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6630 -4.3927 1.0102 225
## (Intercept)-Lynx_rufus -5.2045 -4.7588 1.0134 133
## (Intercept)-Didelphis_virginiana -4.3764 -3.9214 1.0100 273
## (Intercept)-Sylvilagus_floridanus -4.8927 -4.4138 1.0185 183
## (Intercept)-Meleagris_gallopavo -5.0041 -4.5523 1.0318 161
## (Intercept)-Sciurus_carolinensis -4.4028 -3.9759 1.0581 330
## (Intercept)-Vulpes_vulpes -5.4443 -4.6110 1.0178 64
## (Intercept)-Sus_scrofa -4.7964 -4.1913 1.0280 276
## week-Canis_latrans 0.1872 38531182.8525 1.2884 8816
## week-Lynx_rufus 0.2410 36256115.1449 1.2860 13725
## week-Didelphis_virginiana 0.3484 57800730.6960 1.2882 26056
## week-Sylvilagus_floridanus 0.1634 51986786.6174 1.3125 3806
## week-Meleagris_gallopavo 0.1075 51308286.2115 1.2897 7036
## week-Sciurus_carolinensis 0.2652 40237701.0052 1.2831 28390
## week-Vulpes_vulpes 0.0163 45561613.8127 1.2835 14529
## week-Sus_scrofa -0.0501 35700342.9692 1.2863 29704
# 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): 4.3395
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3845 1.1033 -4.4043 -2.4602 0.0142 1.0161 542
## Cogon_Patch_Size -0.0761 0.9356 -2.0806 -0.0618 1.7559 1.0637 364
## Veg_shannon_index 0.8145 0.6977 -0.5980 0.8154 2.1815 1.0026 795
## total_shrub_cover -0.7160 0.7920 -2.3983 -0.6963 0.8928 1.0270 928
## Avg_Cogongrass_Cover 0.1347 1.0184 -1.8416 0.1230 2.0960 1.2587 124
## Tree_Density -2.2559 0.8013 -3.9092 -2.2553 -0.6672 1.0502 265
## Avg_Canopy_Cover 1.9011 0.7311 0.5106 1.8866 3.3918 1.0242 685
## I(Avg_Cogongrass_Cover^2) 1.0323 0.7583 -0.5215 1.0393 2.5080 1.0661 524
## avg_veg_height -0.5504 0.5942 -1.7510 -0.5649 0.6135 1.0758 241
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9662 15.1664 0.2523 6.3781 48.6237 1.0667 159
## Cogon_Patch_Size 7.7442 12.2963 0.2777 4.1079 36.7260 1.1333 302
## Veg_shannon_index 3.4251 5.2423 0.0848 1.8990 15.7740 1.1081 257
## total_shrub_cover 5.3120 6.7492 0.2024 3.1718 23.4817 1.2405 70
## Avg_Cogongrass_Cover 1.5035 3.1618 0.0547 0.5747 8.7364 1.0413 596
## Tree_Density 1.5083 3.0147 0.0520 0.5406 8.8990 1.0147 294
## Avg_Canopy_Cover 3.8005 6.1859 0.1158 1.9307 20.9442 1.0169 163
## I(Avg_Cogongrass_Cover^2) 4.0178 7.0790 0.1251 1.9131 20.4692 1.0681 250
## avg_veg_height 0.6056 1.0088 0.0404 0.2878 3.1852 1.0303 437
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6301 2.5678 0.0609 0.8047 8.7788 1.3584 84
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8033 0.2861 -5.3489 -4.8161 -4.1948 1.0132 953
## week -0.0349 1.6353 -3.3545 0.0075 3.1537 1.0552 451
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5203 5.798000e-01 0.0865 0.3567 1.918400e+00 1.0242 219
## week 9250256.2143 7.684982e+07 0.0881 19.0098 5.986645e+07 1.3309 305
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7603 1.4898 -3.4331 -0.8309
## (Intercept)-Lynx_rufus -0.1509 2.4363 -3.6971 -0.5926
## (Intercept)-Didelphis_virginiana -4.2916 1.5377 -7.8521 -4.1385
## (Intercept)-Sylvilagus_floridanus -2.8098 1.4008 -5.8153 -2.7638
## (Intercept)-Meleagris_gallopavo -2.6706 1.5621 -5.9759 -2.6377
## (Intercept)-Sciurus_carolinensis -4.9755 1.6485 -8.6191 -4.7578
## (Intercept)-Vulpes_vulpes -4.9597 2.2388 -9.8593 -4.7468
## (Intercept)-Sus_scrofa -5.7295 2.3645 -11.1599 -5.3395
## Cogon_Patch_Size-Canis_latrans 2.3332 1.9386 -0.3120 1.9838
## Cogon_Patch_Size-Lynx_rufus 0.2836 2.1015 -3.4289 0.0987
## Cogon_Patch_Size-Didelphis_virginiana 2.0743 1.2620 0.0291 1.9513
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0826 2.0714 -7.5140 -1.6411
## Cogon_Patch_Size-Meleagris_gallopavo 0.3553 1.2701 -1.8049 0.2458
## Cogon_Patch_Size-Sciurus_carolinensis -1.5935 1.8306 -6.3270 -1.1994
## Cogon_Patch_Size-Vulpes_vulpes -0.9212 2.2785 -5.5631 -0.7902
## Cogon_Patch_Size-Sus_scrofa -1.1217 1.9918 -6.2857 -0.7329
## Veg_shannon_index-Canis_latrans 1.9100 1.1100 0.2340 1.7313
## Veg_shannon_index-Lynx_rufus -0.4322 1.5045 -3.8754 -0.2652
## Veg_shannon_index-Didelphis_virginiana 1.1901 0.9693 -0.5503 1.1057
## Veg_shannon_index-Sylvilagus_floridanus 1.0520 0.9723 -0.7085 0.9680
## Veg_shannon_index-Meleagris_gallopavo 1.7114 1.2026 -0.1752 1.5484
## Veg_shannon_index-Sciurus_carolinensis -0.3398 1.0556 -2.6238 -0.2505
## Veg_shannon_index-Vulpes_vulpes -0.0381 1.3358 -3.0062 0.1097
## Veg_shannon_index-Sus_scrofa 2.2755 1.5454 0.0732 1.9952
## total_shrub_cover-Canis_latrans 0.6864 1.0400 -1.0585 0.5810
## total_shrub_cover-Lynx_rufus -2.3071 1.9048 -7.1171 -1.9953
## total_shrub_cover-Didelphis_virginiana -0.8786 0.9754 -2.9661 -0.8233
## total_shrub_cover-Sylvilagus_floridanus -0.3463 1.1559 -2.7175 -0.3158
## total_shrub_cover-Meleagris_gallopavo -3.7791 1.9644 -8.3872 -3.5133
## total_shrub_cover-Sciurus_carolinensis 0.1778 0.8604 -1.4620 0.1494
## total_shrub_cover-Vulpes_vulpes -1.0454 1.5040 -4.5754 -0.8707
## total_shrub_cover-Sus_scrofa 0.2839 1.2226 -1.8855 0.1752
## Avg_Cogongrass_Cover-Canis_latrans 0.2652 1.3048 -2.1671 0.2377
## Avg_Cogongrass_Cover-Lynx_rufus 0.5614 1.4464 -2.1113 0.4684
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4074 1.3530 -2.1747 0.3775
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3268 1.3595 -3.3151 -0.2866
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1830 1.3848 -3.1794 -0.1261
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2253 1.2974 -2.4456 0.2641
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3377 1.4119 -2.4169 0.3015
## Avg_Cogongrass_Cover-Sus_scrofa -0.1659 1.4480 -3.3900 -0.1030
## Tree_Density-Canis_latrans -2.8370 1.1954 -5.6791 -2.6581
## Tree_Density-Lynx_rufus -1.8599 1.1955 -4.1220 -1.9232
## Tree_Density-Didelphis_virginiana -2.4282 1.0558 -4.7598 -2.3397
## Tree_Density-Sylvilagus_floridanus -2.5659 1.1708 -5.1749 -2.4336
## Tree_Density-Meleagris_gallopavo -2.3001 1.1645 -4.7781 -2.2702
## Tree_Density-Sciurus_carolinensis -2.6712 1.1772 -5.4796 -2.5335
## Tree_Density-Vulpes_vulpes -2.2077 1.2425 -4.6686 -2.2100
## Tree_Density-Sus_scrofa -2.3936 1.2472 -5.1466 -2.3434
## Avg_Canopy_Cover-Canis_latrans 0.2888 0.9074 -1.5313 0.2630
## Avg_Canopy_Cover-Lynx_rufus 0.9984 1.6581 -2.6457 1.0218
## Avg_Canopy_Cover-Didelphis_virginiana 2.9505 1.1731 1.2550 2.7501
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7796 1.8699 1.2156 3.4364
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3017 1.1621 0.6706 2.1165
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4429 1.0028 0.9291 2.2905
## Avg_Canopy_Cover-Vulpes_vulpes 2.4521 1.2868 0.5164 2.2556
## Avg_Canopy_Cover-Sus_scrofa 2.1794 0.9657 0.5746 2.0659
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4491 1.3876 0.4952 2.1901
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7241 1.7077 0.3185 2.3485
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9244 0.8435 -0.6731 0.8943
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0215 0.9100 -0.5260 0.9435
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.5428 1.5712 -4.3694 -0.3551
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5852 0.8804 0.1158 1.4842
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7566 1.1040 -0.0775 1.6286
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.2987 1.7192 -4.6588 0.0230
## avg_veg_height-Canis_latrans -0.5928 0.7073 -2.0586 -0.5785
## avg_veg_height-Lynx_rufus -0.6708 0.9194 -2.6767 -0.6488
## avg_veg_height-Didelphis_virginiana -0.5935 0.7503 -2.0597 -0.5740
## avg_veg_height-Sylvilagus_floridanus -0.5593 0.7662 -2.0374 -0.5768
## avg_veg_height-Meleagris_gallopavo -0.6715 0.8732 -2.4977 -0.6306
## avg_veg_height-Sciurus_carolinensis -0.1942 0.7406 -1.5389 -0.2214
## avg_veg_height-Vulpes_vulpes -0.7242 0.8922 -2.5563 -0.6976
## avg_veg_height-Sus_scrofa -0.5408 0.7690 -2.1167 -0.5422
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4257 1.0463 269
## (Intercept)-Lynx_rufus 6.0176 1.0699 105
## (Intercept)-Didelphis_virginiana -1.8424 1.0892 307
## (Intercept)-Sylvilagus_floridanus -0.2202 1.0229 409
## (Intercept)-Meleagris_gallopavo 0.1822 1.0780 337
## (Intercept)-Sciurus_carolinensis -2.3347 1.0525 155
## (Intercept)-Vulpes_vulpes -0.8984 1.0508 149
## (Intercept)-Sus_scrofa -2.1205 1.1065 119
## Cogon_Patch_Size-Canis_latrans 7.3150 1.1102 339
## Cogon_Patch_Size-Lynx_rufus 5.1409 1.0966 177
## Cogon_Patch_Size-Didelphis_virginiana 4.8656 1.1283 218
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7638 1.0229 304
## Cogon_Patch_Size-Meleagris_gallopavo 3.2964 1.0724 443
## Cogon_Patch_Size-Sciurus_carolinensis 0.8427 1.0227 250
## Cogon_Patch_Size-Vulpes_vulpes 3.3072 1.0391 162
## Cogon_Patch_Size-Sus_scrofa 1.7092 1.0133 361
## Veg_shannon_index-Canis_latrans 4.6409 1.0581 327
## Veg_shannon_index-Lynx_rufus 2.1170 1.0637 215
## Veg_shannon_index-Didelphis_virginiana 3.3025 1.0049 587
## Veg_shannon_index-Sylvilagus_floridanus 3.2188 1.0100 646
## Veg_shannon_index-Meleagris_gallopavo 4.2748 1.0508 292
## Veg_shannon_index-Sciurus_carolinensis 1.4638 1.0584 488
## Veg_shannon_index-Vulpes_vulpes 2.3348 1.0136 455
## Veg_shannon_index-Sus_scrofa 6.0663 1.1159 246
## total_shrub_cover-Canis_latrans 3.0240 1.0764 476
## total_shrub_cover-Lynx_rufus 0.4809 1.1595 158
## total_shrub_cover-Didelphis_virginiana 0.8972 1.0533 590
## total_shrub_cover-Sylvilagus_floridanus 1.8930 1.0191 467
## total_shrub_cover-Meleagris_gallopavo -0.8733 1.1858 107
## total_shrub_cover-Sciurus_carolinensis 1.9129 1.0238 1008
## total_shrub_cover-Vulpes_vulpes 1.5729 1.0356 310
## total_shrub_cover-Sus_scrofa 2.9807 1.0157 581
## Avg_Cogongrass_Cover-Canis_latrans 2.9394 1.1505 244
## Avg_Cogongrass_Cover-Lynx_rufus 3.7628 1.1477 222
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.3179 1.1626 253
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0524 1.1439 244
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2787 1.1573 193
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7596 1.2182 159
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3308 1.1650 218
## Avg_Cogongrass_Cover-Sus_scrofa 2.3957 1.1506 203
## Tree_Density-Canis_latrans -0.9398 1.0677 348
## Tree_Density-Lynx_rufus 0.5969 1.0070 339
## Tree_Density-Didelphis_virginiana -0.6106 1.0556 300
## Tree_Density-Sylvilagus_floridanus -0.5693 1.0480 360
## Tree_Density-Meleagris_gallopavo 0.0109 1.0334 436
## Tree_Density-Sciurus_carolinensis -0.7681 1.0501 417
## Tree_Density-Vulpes_vulpes 0.3334 1.0298 264
## Tree_Density-Sus_scrofa 0.0093 1.0236 418
## Avg_Canopy_Cover-Canis_latrans 2.0711 1.0487 348
## Avg_Canopy_Cover-Lynx_rufus 4.2696 1.0338 191
## Avg_Canopy_Cover-Didelphis_virginiana 5.8840 1.0980 136
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.6248 1.0471 177
## Avg_Canopy_Cover-Meleagris_gallopavo 5.0179 1.0500 222
## Avg_Canopy_Cover-Sciurus_carolinensis 4.8657 1.0353 244
## Avg_Canopy_Cover-Vulpes_vulpes 5.4611 1.0421 224
## Avg_Canopy_Cover-Sus_scrofa 4.4033 1.0185 673
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8853 1.0281 302
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.9513 1.0361 157
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6337 1.1372 172
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0784 1.1217 323
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.7966 1.0085 198
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5819 1.0671 490
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.4645 1.1039 338
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.1321 1.0173 202
## avg_veg_height-Canis_latrans 0.7384 1.0459 286
## avg_veg_height-Lynx_rufus 1.0141 1.0479 370
## avg_veg_height-Didelphis_virginiana 0.7850 1.0804 354
## avg_veg_height-Sylvilagus_floridanus 0.9308 1.0620 467
## avg_veg_height-Meleagris_gallopavo 0.8979 1.0537 355
## avg_veg_height-Sciurus_carolinensis 1.3782 1.0434 560
## avg_veg_height-Vulpes_vulpes 0.9039 1.0271 334
## avg_veg_height-Sus_scrofa 0.9780 1.0487 310
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6855 0.1720 -5.0627 -4.6762
## (Intercept)-Lynx_rufus -5.5460 0.2865 -6.1448 -5.5529
## (Intercept)-Didelphis_virginiana -4.3245 0.2188 -4.7602 -4.3205
## (Intercept)-Sylvilagus_floridanus -4.9721 0.2458 -5.5237 -4.9585
## (Intercept)-Meleagris_gallopavo -5.0539 0.2535 -5.5915 -5.0392
## (Intercept)-Sciurus_carolinensis -4.3940 0.2429 -4.8895 -4.3865
## (Intercept)-Vulpes_vulpes -5.6814 0.5883 -7.0263 -5.5836
## (Intercept)-Sus_scrofa -4.7598 0.3569 -5.5053 -4.7366
## week-Canis_latrans 62.8857 2776.5136 -2029.8434 -0.0153
## week-Lynx_rufus 169.8081 3314.0776 -1816.6170 0.0123
## week-Didelphis_virginiana -5.1336 3080.6696 -1853.8964 -0.0858
## week-Sylvilagus_floridanus 33.2922 2928.3138 -2529.0415 -0.0130
## week-Meleagris_gallopavo 17.6763 2839.4136 -2143.1713 0.0384
## week-Sciurus_carolinensis -40.5605 2489.2489 -2578.9658 0.0036
## week-Vulpes_vulpes 89.0716 2637.0906 -1935.6489 -0.0208
## week-Sus_scrofa -9.5905 2548.2873 -2234.6107 -0.1963
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3841 1.1232 142
## (Intercept)-Lynx_rufus -5.0044 1.2209 84
## (Intercept)-Didelphis_virginiana -3.9170 1.0452 336
## (Intercept)-Sylvilagus_floridanus -4.5305 1.0002 166
## (Intercept)-Meleagris_gallopavo -4.5830 1.0380 170
## (Intercept)-Sciurus_carolinensis -3.9188 1.0209 297
## (Intercept)-Vulpes_vulpes -4.7570 1.0868 49
## (Intercept)-Sus_scrofa -4.1118 1.0149 237
## week-Canis_latrans 2211.4424 1.1453 5378
## week-Lynx_rufus 2897.2898 1.1842 1309
## week-Didelphis_virginiana 2546.2238 1.1488 4170
## week-Sylvilagus_floridanus 2022.4521 1.1119 5851
## week-Meleagris_gallopavo 1985.8852 1.1541 3593
## week-Sciurus_carolinensis 1856.0198 1.1907 3231
## week-Vulpes_vulpes 2513.1764 1.1673 2173
## week-Sus_scrofa 2119.9485 1.1987 14860
# 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): 3.4068
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7799 0.4781 -1.6837 -0.7923 0.2060 1.0595 412
## Avg_Cogongrass_Cover 0.1892 0.3298 -0.4855 0.1975 0.8273 1.0240 726
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8916 1.3296 0.0597 0.4989 4.0351 1.0655 780
## Avg_Cogongrass_Cover 0.4731 0.6184 0.0398 0.2708 2.1393 1.0084 843
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8604 1.8249 0.1343 1.3617 6.4372 1.2288 185
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0407 0.3072 -5.6081 -5.0454 -4.3985 1.0992 495
## shrub_cover 0.2484 0.3823 -0.5197 0.2360 1.0214 1.0215 484
## veg_height -0.0525 0.2175 -0.4849 -0.0606 0.3879 1.0562 351
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5347 0.8611 0.0602 0.3341 2.1241 1.2572 256
## shrub_cover 0.9876 1.0391 0.1377 0.6812 3.6050 1.3424 95
## veg_height 0.3058 0.2628 0.0633 0.2318 1.0011 1.0485 751
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0336 0.7523 -1.4057 -0.0546
## (Intercept)-Lynx_rufus -0.4405 0.7344 -1.7693 -0.4902
## (Intercept)-Didelphis_virginiana -1.0606 0.6411 -2.3393 -1.0536
## (Intercept)-Sylvilagus_floridanus -0.7043 0.6306 -1.9423 -0.7066
## (Intercept)-Meleagris_gallopavo -0.4871 0.7570 -1.8610 -0.5392
## (Intercept)-Sciurus_carolinensis -1.1276 0.6360 -2.3974 -1.1143
## (Intercept)-Vulpes_vulpes -1.2404 0.7839 -2.8721 -1.2020
## (Intercept)-Sus_scrofa -1.3414 0.7434 -2.9043 -1.3080
## Avg_Cogongrass_Cover-Canis_latrans 0.4829 0.4305 -0.2559 0.4450
## Avg_Cogongrass_Cover-Lynx_rufus 0.5284 0.4793 -0.2561 0.4759
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3641 0.4176 -0.4113 0.3566
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2345 0.4703 -1.2692 -0.1943
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2645 0.6898 -1.8193 -0.1880
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3751 0.3888 -0.3752 0.3656
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3385 0.4834 -0.5668 0.3253
## Avg_Cogongrass_Cover-Sus_scrofa -0.0505 0.6084 -1.4889 -0.0011
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4977 1.0267 322
## (Intercept)-Lynx_rufus 1.1945 1.0051 455
## (Intercept)-Didelphis_virginiana 0.2166 1.0232 606
## (Intercept)-Sylvilagus_floridanus 0.5284 1.0033 629
## (Intercept)-Meleagris_gallopavo 1.1712 1.1233 308
## (Intercept)-Sciurus_carolinensis 0.1319 1.0168 600
## (Intercept)-Vulpes_vulpes 0.2571 1.0639 433
## (Intercept)-Sus_scrofa 0.0440 1.0662 555
## Avg_Cogongrass_Cover-Canis_latrans 1.4448 1.0071 1140
## Avg_Cogongrass_Cover-Lynx_rufus 1.6532 0.9998 948
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2441 1.0411 1023
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5902 1.0068 849
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9022 1.0510 329
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1491 1.0031 1487
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3521 1.0201 853
## Avg_Cogongrass_Cover-Sus_scrofa 0.9792 1.0162 808
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8251 0.1828 -5.1934 -4.8206 -4.4848 1.1170
## (Intercept)-Lynx_rufus -5.4546 0.2960 -6.0126 -5.4473 -4.8932 1.1485
## (Intercept)-Didelphis_virginiana -4.7013 0.2687 -5.2168 -4.6933 -4.2136 1.0421
## (Intercept)-Sylvilagus_floridanus -4.9015 0.2176 -5.3321 -4.8987 -4.4838 1.0361
## (Intercept)-Meleagris_gallopavo -5.7481 0.5208 -6.8811 -5.7261 -4.8540 1.9376
## (Intercept)-Sciurus_carolinensis -4.6927 0.2824 -5.2401 -4.6864 -4.1386 1.0020
## (Intercept)-Vulpes_vulpes -5.6926 0.5815 -7.1223 -5.6004 -4.8008 1.2246
## (Intercept)-Sus_scrofa -5.2735 0.5215 -6.4781 -5.2278 -4.3695 1.4343
## shrub_cover-Canis_latrans -0.2679 0.2060 -0.6962 -0.2622 0.1208 1.0492
## shrub_cover-Lynx_rufus -0.2336 0.3388 -0.9145 -0.2331 0.4056 1.0533
## shrub_cover-Didelphis_virginiana 1.1021 0.3635 0.4483 1.0951 1.8780 1.2497
## shrub_cover-Sylvilagus_floridanus 0.4894 0.3873 -0.2994 0.5072 1.1955 1.0317
## shrub_cover-Meleagris_gallopavo -0.7864 0.4402 -1.6917 -0.7773 0.0235 1.7420
## shrub_cover-Sciurus_carolinensis 0.8875 0.3706 0.1943 0.8812 1.6322 1.0738
## shrub_cover-Vulpes_vulpes -0.0830 0.5947 -1.3054 -0.0816 1.1023 1.0339
## shrub_cover-Sus_scrofa 0.8822 0.8996 -0.7779 0.8338 2.9510 1.3453
## veg_height-Canis_latrans -0.6178 0.1809 -0.9775 -0.6068 -0.2836 1.0449
## veg_height-Lynx_rufus 0.0602 0.2286 -0.3826 0.0588 0.5102 1.0105
## veg_height-Didelphis_virginiana 0.4689 0.2523 -0.0046 0.4687 0.9719 1.0743
## veg_height-Sylvilagus_floridanus 0.1567 0.2317 -0.2708 0.1497 0.6073 1.0170
## veg_height-Meleagris_gallopavo -0.2817 0.4454 -1.1828 -0.2728 0.5730 1.2191
## veg_height-Sciurus_carolinensis 0.2104 0.2146 -0.1855 0.2077 0.6297 1.0066
## veg_height-Vulpes_vulpes -0.1818 0.2992 -0.8093 -0.1730 0.3642 1.0301
## veg_height-Sus_scrofa -0.2560 0.3415 -0.9454 -0.2449 0.3734 1.0735
## ESS
## (Intercept)-Canis_latrans 162
## (Intercept)-Lynx_rufus 98
## (Intercept)-Didelphis_virginiana 168
## (Intercept)-Sylvilagus_floridanus 193
## (Intercept)-Meleagris_gallopavo 33
## (Intercept)-Sciurus_carolinensis 207
## (Intercept)-Vulpes_vulpes 76
## (Intercept)-Sus_scrofa 174
## shrub_cover-Canis_latrans 190
## shrub_cover-Lynx_rufus 96
## shrub_cover-Didelphis_virginiana 122
## shrub_cover-Sylvilagus_floridanus 148
## shrub_cover-Meleagris_gallopavo 48
## shrub_cover-Sciurus_carolinensis 248
## shrub_cover-Vulpes_vulpes 168
## shrub_cover-Sus_scrofa 129
## veg_height-Canis_latrans 189
## veg_height-Lynx_rufus 222
## veg_height-Didelphis_virginiana 227
## veg_height-Sylvilagus_floridanus 218
## veg_height-Meleagris_gallopavo 93
## veg_height-Sciurus_carolinensis 249
## veg_height-Vulpes_vulpes 175
## veg_height-Sus_scrofa 304
# 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): 4.0457
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1398 1.0690 -3.1934 -1.1751 1.1401 1.0562 386
## Cogon_Patch_Size -0.0752 1.0515 -2.1763 -0.0969 2.1721 1.2467 206
## Veg_shannon_index 0.8080 0.8860 -1.0676 0.8130 2.5296 1.0238 429
## total_shrub_cover -1.3063 0.9967 -3.3091 -1.2915 0.6442 1.0711 302
## Avg_Cogongrass_Cover 1.4680 0.9707 -0.4464 1.4803 3.4037 1.0449 240
## Tree_Density -1.7359 1.0665 -3.7729 -1.7403 0.4224 1.0117 180
## Avg_Canopy_Cover 1.9849 1.1349 -0.4734 2.0232 4.1449 1.1212 442
## avg_veg_height -0.2696 0.7763 -1.6835 -0.3116 1.4458 1.3509 160
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.7494 23.2811 0.2285 5.9965 74.4455 1.8537 56
## Cogon_Patch_Size 11.3786 33.1210 0.1084 2.6599 90.5200 1.9290 52
## Veg_shannon_index 6.5069 13.5971 0.1252 2.6030 34.6824 1.8061 106
## total_shrub_cover 8.9232 15.0539 0.2516 4.7918 42.2669 1.6796 107
## Avg_Cogongrass_Cover 3.0754 6.8362 0.0552 1.0028 18.3922 1.4638 153
## Tree_Density 4.5658 13.0016 0.0529 0.8911 34.2711 1.7054 158
## Avg_Canopy_Cover 26.3905 58.2111 0.6647 7.4121 172.5562 3.9374 30
## avg_veg_height 1.3342 3.6034 0.0467 0.4138 8.1439 1.7451 147
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 13.894 34.398 0.1025 3.6853 110.7267 4.1945 27
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1654 0.3206 -5.7007 -5.1732 -4.5301 1.0530 710
## shrub_cover 0.4790 0.4054 -0.3208 0.4822 1.2725 1.0532 352
## veg_height -0.0303 0.2339 -0.5134 -0.0305 0.4264 1.0951 508
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5296 0.7230 0.0630 0.3343 2.2218 1.1931 286
## shrub_cover 1.1948 1.3776 0.2337 0.8702 4.1360 1.0964 415
## veg_height 0.3265 0.3311 0.0702 0.2431 1.1314 1.0869 842
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.1601 2.1592 -2.1615 0.9135
## (Intercept)-Lynx_rufus 1.6995 3.3598 -2.8187 1.0343
## (Intercept)-Didelphis_virginiana -2.8239 2.0977 -7.9976 -2.5179
## (Intercept)-Sylvilagus_floridanus -1.2269 2.1022 -6.1465 -1.1874
## (Intercept)-Meleagris_gallopavo -1.4746 2.1945 -5.7722 -1.5055
## (Intercept)-Sciurus_carolinensis -3.3662 2.8034 -11.0585 -2.7515
## (Intercept)-Vulpes_vulpes -2.7372 2.4106 -8.2444 -2.4292
## (Intercept)-Sus_scrofa -4.1771 2.7735 -10.8930 -3.7911
## Cogon_Patch_Size-Canis_latrans 1.5627 2.3082 -0.9206 1.0033
## Cogon_Patch_Size-Lynx_rufus 0.1589 2.3377 -4.0300 0.0916
## Cogon_Patch_Size-Didelphis_virginiana 1.7214 1.8259 -0.6387 1.3525
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2746 3.5246 -12.8119 -1.4276
## Cogon_Patch_Size-Meleagris_gallopavo 1.1061 3.2980 -2.1634 0.3878
## Cogon_Patch_Size-Sciurus_carolinensis -1.4353 2.4762 -8.0294 -0.9579
## Cogon_Patch_Size-Vulpes_vulpes -0.8889 2.4971 -6.8951 -0.6167
## Cogon_Patch_Size-Sus_scrofa -1.0608 2.7380 -8.2876 -0.6575
## Veg_shannon_index-Canis_latrans 1.7257 1.1651 -0.2550 1.6005
## Veg_shannon_index-Lynx_rufus -0.4437 1.9669 -5.2644 -0.1561
## Veg_shannon_index-Didelphis_virginiana 1.9012 1.5930 -0.3400 1.5928
## Veg_shannon_index-Sylvilagus_floridanus 1.4898 1.4112 -0.8538 1.2868
## Veg_shannon_index-Meleagris_gallopavo 2.1624 1.9527 -0.6456 1.8752
## Veg_shannon_index-Sciurus_carolinensis -0.7301 1.7524 -5.2452 -0.4284
## Veg_shannon_index-Vulpes_vulpes -0.7080 2.1513 -6.4350 -0.2927
## Veg_shannon_index-Sus_scrofa 2.5487 2.0127 -0.2865 2.1273
## total_shrub_cover-Canis_latrans 1.4687 1.6481 -1.2823 1.2048
## total_shrub_cover-Lynx_rufus -2.9332 2.5884 -9.0930 -2.5187
## total_shrub_cover-Didelphis_virginiana -2.3670 2.0899 -7.8065 -1.9586
## total_shrub_cover-Sylvilagus_floridanus -2.1428 2.2052 -7.6383 -1.7726
## total_shrub_cover-Meleagris_gallopavo -3.4041 1.9495 -7.6468 -3.2178
## total_shrub_cover-Sciurus_carolinensis -1.6679 1.9366 -5.8456 -1.4420
## total_shrub_cover-Vulpes_vulpes -2.1391 2.5441 -7.9133 -1.8552
## total_shrub_cover-Sus_scrofa -1.1324 2.1507 -5.8011 -0.9997
## Avg_Cogongrass_Cover-Canis_latrans 2.2469 1.3693 0.0667 2.0918
## Avg_Cogongrass_Cover-Lynx_rufus 2.2343 1.5285 -0.2029 2.0951
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7899 1.2988 -0.5186 1.6977
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7126 1.4944 -2.5511 0.8416
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7849 2.0300 -3.9453 1.0685
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8644 1.3948 -0.6863 1.7704
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3238 1.7478 -0.2755 2.0683
## Avg_Cogongrass_Cover-Sus_scrofa 1.0760 1.7413 -3.1958 1.2546
## Tree_Density-Canis_latrans -2.6841 1.8029 -7.5360 -2.3396
## Tree_Density-Lynx_rufus -0.8550 1.7411 -3.7840 -1.0314
## Tree_Density-Didelphis_virginiana -1.9852 1.7598 -5.7773 -1.9324
## Tree_Density-Sylvilagus_floridanus -2.6214 2.3715 -9.3548 -2.2610
## Tree_Density-Meleagris_gallopavo -2.1378 1.6628 -6.0346 -2.0052
## Tree_Density-Sciurus_carolinensis -2.1316 1.6984 -6.0839 -2.0409
## Tree_Density-Vulpes_vulpes -1.7959 2.1226 -6.5232 -1.7046
## Tree_Density-Sus_scrofa -2.0175 1.8636 -6.5706 -1.8584
## Avg_Canopy_Cover-Canis_latrans -0.5044 1.4040 -4.5246 -0.2574
## Avg_Canopy_Cover-Lynx_rufus 0.4048 2.7093 -5.9640 0.5020
## Avg_Canopy_Cover-Didelphis_virginiana 5.5561 3.7865 1.6462 4.5851
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1634 5.0781 1.7008 5.5875
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6602 2.8221 0.5578 2.9396
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1318 3.4025 1.4041 4.2239
## Avg_Canopy_Cover-Vulpes_vulpes 4.2367 3.2717 0.5744 3.2405
## Avg_Canopy_Cover-Sus_scrofa 2.7817 1.7832 0.1151 2.5220
## avg_veg_height-Canis_latrans -0.2437 0.8868 -1.8370 -0.3068
## avg_veg_height-Lynx_rufus -0.4916 1.3865 -3.3339 -0.4930
## avg_veg_height-Didelphis_virginiana -0.5319 1.0780 -2.7160 -0.5315
## avg_veg_height-Sylvilagus_floridanus -0.4328 0.9958 -2.3583 -0.4498
## avg_veg_height-Meleagris_gallopavo -0.3530 1.5143 -3.1144 -0.3362
## avg_veg_height-Sciurus_carolinensis 0.2569 1.1316 -1.5406 0.0978
## avg_veg_height-Vulpes_vulpes -0.2750 1.1482 -2.3539 -0.3537
## avg_veg_height-Sus_scrofa -0.2458 1.1078 -2.3465 -0.2898
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.6719 1.5979 80
## (Intercept)-Lynx_rufus 10.4818 1.2825 72
## (Intercept)-Didelphis_virginiana 0.8363 1.1506 152
## (Intercept)-Sylvilagus_floridanus 2.9516 1.1183 138
## (Intercept)-Meleagris_gallopavo 3.0987 1.1241 171
## (Intercept)-Sciurus_carolinensis 0.3181 1.4961 46
## (Intercept)-Vulpes_vulpes 1.3704 1.1214 171
## (Intercept)-Sus_scrofa -0.0115 1.1530 99
## Cogon_Patch_Size-Canis_latrans 8.1809 1.5838 74
## Cogon_Patch_Size-Lynx_rufus 4.9696 1.0595 168
## Cogon_Patch_Size-Didelphis_virginiana 6.7612 1.6636 93
## Cogon_Patch_Size-Sylvilagus_floridanus 1.6974 1.2772 59
## Cogon_Patch_Size-Meleagris_gallopavo 10.4591 2.0326 41
## Cogon_Patch_Size-Sciurus_carolinensis 2.0596 1.0775 131
## Cogon_Patch_Size-Vulpes_vulpes 3.6346 1.0972 157
## Cogon_Patch_Size-Sus_scrofa 2.4461 1.1261 118
## Veg_shannon_index-Canis_latrans 4.5702 1.2542 227
## Veg_shannon_index-Lynx_rufus 2.8826 1.0498 125
## Veg_shannon_index-Didelphis_virginiana 6.0185 1.3694 196
## Veg_shannon_index-Sylvilagus_floridanus 4.8835 1.1883 177
## Veg_shannon_index-Meleagris_gallopavo 6.7220 1.2792 124
## Veg_shannon_index-Sciurus_carolinensis 1.9417 1.4160 116
## Veg_shannon_index-Vulpes_vulpes 2.1535 1.4113 77
## Veg_shannon_index-Sus_scrofa 7.4564 1.1720 214
## total_shrub_cover-Canis_latrans 5.3293 1.7632 102
## total_shrub_cover-Lynx_rufus 1.2308 1.3993 112
## total_shrub_cover-Didelphis_virginiana 0.5775 1.3189 128
## total_shrub_cover-Sylvilagus_floridanus 1.1855 1.4061 119
## total_shrub_cover-Meleagris_gallopavo -0.1548 1.1505 240
## total_shrub_cover-Sciurus_carolinensis 1.5111 1.1299 184
## total_shrub_cover-Vulpes_vulpes 2.7667 1.2871 141
## total_shrub_cover-Sus_scrofa 3.0078 1.0510 150
## Avg_Cogongrass_Cover-Canis_latrans 5.3063 1.0716 191
## Avg_Cogongrass_Cover-Lynx_rufus 5.7508 1.0326 357
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.7034 1.0487 361
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3578 1.0726 170
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.0924 1.1559 132
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.9487 1.0340 285
## Avg_Cogongrass_Cover-Vulpes_vulpes 6.8725 1.1312 170
## Avg_Cogongrass_Cover-Sus_scrofa 3.9609 1.0402 278
## Tree_Density-Canis_latrans -0.3010 1.1682 160
## Tree_Density-Lynx_rufus 3.5205 1.0517 167
## Tree_Density-Didelphis_virginiana 1.3895 1.1370 151
## Tree_Density-Sylvilagus_floridanus 0.6592 1.2248 94
## Tree_Density-Meleagris_gallopavo 0.8238 1.0673 196
## Tree_Density-Sciurus_carolinensis 0.9480 1.0452 240
## Tree_Density-Vulpes_vulpes 1.8206 1.1817 123
## Tree_Density-Sus_scrofa 1.2238 1.0713 196
## Avg_Canopy_Cover-Canis_latrans 1.3382 1.8806 81
## Avg_Canopy_Cover-Lynx_rufus 5.5666 1.4858 61
## Avg_Canopy_Cover-Didelphis_virginiana 16.5516 2.8906 42
## Avg_Canopy_Cover-Sylvilagus_floridanus 20.4284 2.3545 33
## Avg_Canopy_Cover-Meleagris_gallopavo 11.4569 2.2306 78
## Avg_Canopy_Cover-Sciurus_carolinensis 14.5605 2.6453 44
## Avg_Canopy_Cover-Vulpes_vulpes 13.7739 3.1176 46
## Avg_Canopy_Cover-Sus_scrofa 6.8476 1.2364 204
## avg_veg_height-Canis_latrans 1.7429 1.0954 326
## avg_veg_height-Lynx_rufus 2.2100 1.2537 152
## avg_veg_height-Didelphis_virginiana 1.5674 1.1533 192
## avg_veg_height-Sylvilagus_floridanus 1.5995 1.1388 247
## avg_veg_height-Meleagris_gallopavo 2.3200 1.1860 164
## avg_veg_height-Sciurus_carolinensis 3.0851 1.3548 242
## avg_veg_height-Vulpes_vulpes 2.1018 1.4339 183
## avg_veg_height-Sus_scrofa 2.1704 1.2745 183
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8148 0.1715 -5.1676 -4.8113 -4.5017 1.0287
## (Intercept)-Lynx_rufus -5.6183 0.2841 -6.1819 -5.6130 -5.0865 1.4353
## (Intercept)-Didelphis_virginiana -4.8100 0.2865 -5.3620 -4.8117 -4.2357 1.1046
## (Intercept)-Sylvilagus_floridanus -5.0781 0.2189 -5.5315 -5.0599 -4.6854 1.0456
## (Intercept)-Meleagris_gallopavo -5.7040 0.4953 -6.9952 -5.6681 -4.8810 1.5233
## (Intercept)-Sciurus_carolinensis -4.9120 0.2775 -5.4334 -4.9085 -4.3739 1.1796
## (Intercept)-Vulpes_vulpes -5.9376 0.5168 -6.9331 -5.9454 -4.9978 1.1319
## (Intercept)-Sus_scrofa -5.4563 0.5242 -6.4928 -5.4296 -4.4495 1.0123
## shrub_cover-Canis_latrans -0.3740 0.2132 -0.7704 -0.3821 0.0742 1.1376
## shrub_cover-Lynx_rufus 0.0424 0.3160 -0.6041 0.0395 0.6440 1.1204
## shrub_cover-Didelphis_virginiana 1.2968 0.3875 0.5572 1.2868 2.1038 1.1261
## shrub_cover-Sylvilagus_floridanus 0.8539 0.3588 0.1635 0.8399 1.6164 1.1082
## shrub_cover-Meleagris_gallopavo -0.6964 0.4636 -1.8519 -0.6788 0.0957 1.2768
## shrub_cover-Sciurus_carolinensis 1.2512 0.3659 0.5162 1.2518 1.9675 1.1401
## shrub_cover-Vulpes_vulpes 0.4152 0.6765 -1.1069 0.4691 1.6925 1.0689
## shrub_cover-Sus_scrofa 1.2566 0.8990 -0.5144 1.2175 2.9428 1.0383
## veg_height-Canis_latrans -0.5774 0.1695 -0.9262 -0.5712 -0.2601 1.1370
## veg_height-Lynx_rufus 0.1639 0.2127 -0.2689 0.1672 0.5776 1.0259
## veg_height-Didelphis_virginiana 0.5171 0.2308 0.0644 0.5123 0.9692 1.0213
## veg_height-Sylvilagus_floridanus 0.1379 0.2318 -0.2878 0.1380 0.6241 1.0166
## veg_height-Meleagris_gallopavo -0.3005 0.4400 -1.1775 -0.2905 0.5543 1.3730
## veg_height-Sciurus_carolinensis 0.2752 0.2156 -0.1646 0.2788 0.6825 1.0373
## veg_height-Vulpes_vulpes -0.2174 0.3659 -1.0532 -0.1741 0.3907 1.4379
## veg_height-Sus_scrofa -0.2890 0.3280 -0.9431 -0.2816 0.3167 1.0560
## ESS
## (Intercept)-Canis_latrans 202
## (Intercept)-Lynx_rufus 97
## (Intercept)-Didelphis_virginiana 124
## (Intercept)-Sylvilagus_floridanus 176
## (Intercept)-Meleagris_gallopavo 65
## (Intercept)-Sciurus_carolinensis 141
## (Intercept)-Vulpes_vulpes 76
## (Intercept)-Sus_scrofa 67
## shrub_cover-Canis_latrans 166
## shrub_cover-Lynx_rufus 114
## shrub_cover-Didelphis_virginiana 109
## shrub_cover-Sylvilagus_floridanus 139
## shrub_cover-Meleagris_gallopavo 80
## shrub_cover-Sciurus_carolinensis 129
## shrub_cover-Vulpes_vulpes 77
## shrub_cover-Sus_scrofa 95
## veg_height-Canis_latrans 205
## veg_height-Lynx_rufus 156
## veg_height-Didelphis_virginiana 207
## veg_height-Sylvilagus_floridanus 221
## veg_height-Meleagris_gallopavo 104
## veg_height-Sciurus_carolinensis 180
## veg_height-Vulpes_vulpes 103
## veg_height-Sus_scrofa 273
# 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): 4.0758
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6963 0.6050 -1.9622 -0.6761 0.4354 1.1865 187
## Avg_Cogongrass_Cover 0.0657 0.4695 -0.8860 0.0667 0.9548 1.0018 470
## total_shrub_cover -0.9839 0.6385 -2.3468 -0.9334 0.2116 1.0250 181
## avg_veg_height 0.0562 0.4659 -0.8388 0.0625 0.9616 1.0434 289
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1623 2.2297 0.0504 0.5305 6.2528 1.1183 107
## Avg_Cogongrass_Cover 0.7585 1.0804 0.0534 0.4032 3.4234 1.0182 711
## total_shrub_cover 2.1672 2.9142 0.1265 1.2447 10.1922 1.0077 206
## avg_veg_height 0.4758 0.7083 0.0427 0.2549 2.2882 1.0100 485
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.2976 5.0156 0.3145 2.7952 19.0063 1.0836 67
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1144 0.2628 -5.6033 -5.1241 -4.5766 1.0782 330
## shrub_cover 0.5346 0.4029 -0.2717 0.5346 1.3080 1.0453 291
## veg_height -0.0210 0.2157 -0.4532 -0.0208 0.4139 1.0859 436
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3641 0.5904 0.0412 0.2190 1.4640 1.0422 250
## shrub_cover 1.0388 0.9491 0.2073 0.7869 3.4904 1.1255 300
## veg_height 0.2753 0.2632 0.0605 0.2068 0.8598 1.0337 743
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0627 0.9251 -1.7823 -0.1103
## (Intercept)-Lynx_rufus -0.3666 0.9384 -2.0991 -0.4177
## (Intercept)-Didelphis_virginiana -0.8763 0.8994 -2.8162 -0.8279
## (Intercept)-Sylvilagus_floridanus -0.3865 0.8987 -2.0657 -0.4240
## (Intercept)-Meleagris_gallopavo -0.9666 0.9731 -3.1658 -0.8639
## (Intercept)-Sciurus_carolinensis -0.9453 0.9285 -3.1277 -0.8778
## (Intercept)-Vulpes_vulpes -1.0473 0.9328 -3.0862 -0.9990
## (Intercept)-Sus_scrofa -1.1756 1.0014 -3.2977 -1.0906
## Avg_Cogongrass_Cover-Canis_latrans 0.4928 0.6082 -0.6110 0.4607
## Avg_Cogongrass_Cover-Lynx_rufus 0.5775 0.7197 -0.6577 0.5149
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2771 0.5989 -0.9287 0.2688
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4832 0.7119 -2.0798 -0.4002
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4940 0.8275 -2.4594 -0.4021
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0659 0.5764 -1.1416 0.0800
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2799 0.6505 -0.9660 0.2634
## Avg_Cogongrass_Cover-Sus_scrofa -0.2229 0.8040 -2.0886 -0.1394
## total_shrub_cover-Canis_latrans 0.5711 0.7777 -0.7678 0.4933
## total_shrub_cover-Lynx_rufus -1.6868 1.1376 -4.3571 -1.5486
## total_shrub_cover-Didelphis_virginiana -1.1427 0.8985 -3.3592 -1.0081
## total_shrub_cover-Sylvilagus_floridanus -1.6582 1.2160 -4.6209 -1.4299
## total_shrub_cover-Meleagris_gallopavo -1.9567 1.0227 -4.3895 -1.8475
## total_shrub_cover-Sciurus_carolinensis -1.1487 0.9487 -3.3373 -1.0200
## total_shrub_cover-Vulpes_vulpes -1.1244 1.4104 -4.2694 -1.0116
## total_shrub_cover-Sus_scrofa -0.6245 1.0805 -2.9317 -0.5660
## avg_veg_height-Canis_latrans 0.1328 0.5515 -0.9248 0.1069
## avg_veg_height-Lynx_rufus 0.0145 0.7344 -1.4121 0.0170
## avg_veg_height-Didelphis_virginiana -0.0648 0.5959 -1.2782 -0.0481
## avg_veg_height-Sylvilagus_floridanus 0.0146 0.6231 -1.1840 0.0042
## avg_veg_height-Meleagris_gallopavo -0.2356 0.7971 -2.0026 -0.1698
## avg_veg_height-Sciurus_carolinensis 0.4735 0.6131 -0.6035 0.4345
## avg_veg_height-Vulpes_vulpes 0.0311 0.6245 -1.1627 0.0307
## avg_veg_height-Sus_scrofa 0.1157 0.6329 -1.0987 0.1198
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8966 1.0625 217
## (Intercept)-Lynx_rufus 1.7106 1.1299 242
## (Intercept)-Didelphis_virginiana 0.7325 1.1301 134
## (Intercept)-Sylvilagus_floridanus 1.5059 1.1211 149
## (Intercept)-Meleagris_gallopavo 0.6081 1.0968 185
## (Intercept)-Sciurus_carolinensis 0.7803 1.1415 215
## (Intercept)-Vulpes_vulpes 0.7412 1.1876 252
## (Intercept)-Sus_scrofa 0.5249 1.0447 292
## Avg_Cogongrass_Cover-Canis_latrans 1.8178 0.9996 667
## Avg_Cogongrass_Cover-Lynx_rufus 2.2903 1.0043 743
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4798 1.0019 777
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7241 1.0117 531
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8498 1.0055 342
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1222 1.0032 559
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5936 1.0099 660
## Avg_Cogongrass_Cover-Sus_scrofa 1.1646 1.0163 448
## total_shrub_cover-Canis_latrans 2.2918 1.0091 296
## total_shrub_cover-Lynx_rufus 0.0994 1.0549 161
## total_shrub_cover-Didelphis_virginiana 0.2529 1.0590 169
## total_shrub_cover-Sylvilagus_floridanus 0.0344 1.0276 131
## total_shrub_cover-Meleagris_gallopavo -0.2764 1.0220 286
## total_shrub_cover-Sciurus_carolinensis 0.3471 1.0098 149
## total_shrub_cover-Vulpes_vulpes 1.5267 1.0821 116
## total_shrub_cover-Sus_scrofa 1.3839 1.0464 235
## avg_veg_height-Canis_latrans 1.3091 1.0143 526
## avg_veg_height-Lynx_rufus 1.5043 1.0301 410
## avg_veg_height-Didelphis_virginiana 1.0626 1.0586 478
## avg_veg_height-Sylvilagus_floridanus 1.2581 1.0197 323
## avg_veg_height-Meleagris_gallopavo 1.2547 1.0133 262
## avg_veg_height-Sciurus_carolinensis 1.7970 1.0095 443
## avg_veg_height-Vulpes_vulpes 1.2849 1.0530 437
## avg_veg_height-Sus_scrofa 1.3459 1.0434 570
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8154 0.1738 -5.1471 -4.8148 -4.4794 1.0866
## (Intercept)-Lynx_rufus -5.4299 0.2681 -6.0331 -5.4117 -4.9466 1.1800
## (Intercept)-Didelphis_virginiana -4.8848 0.2690 -5.4019 -4.8992 -4.3344 1.0881
## (Intercept)-Sylvilagus_floridanus -5.1039 0.2299 -5.5584 -5.1073 -4.6354 1.0296
## (Intercept)-Meleagris_gallopavo -5.3971 0.3934 -6.2067 -5.3760 -4.6588 1.2678
## (Intercept)-Sciurus_carolinensis -4.8856 0.2882 -5.4784 -4.8887 -4.3189 1.0399
## (Intercept)-Vulpes_vulpes -5.7321 0.5726 -7.0068 -5.6423 -4.8279 1.1842
## (Intercept)-Sus_scrofa -5.3634 0.3876 -6.1202 -5.3609 -4.5826 1.0555
## shrub_cover-Canis_latrans -0.3119 0.2169 -0.7330 -0.3128 0.1321 1.0132
## shrub_cover-Lynx_rufus 0.1696 0.3484 -0.5890 0.1914 0.7905 1.1334
## shrub_cover-Didelphis_virginiana 1.3836 0.3915 0.6323 1.3902 2.1173 1.3183
## shrub_cover-Sylvilagus_floridanus 0.9532 0.4563 0.0216 0.9588 1.7982 1.0950
## shrub_cover-Meleagris_gallopavo -0.4243 0.3913 -1.1912 -0.4161 0.3137 1.3079
## shrub_cover-Sciurus_carolinensis 1.2488 0.4154 0.4324 1.2319 2.1225 1.0441
## shrub_cover-Vulpes_vulpes 0.3724 0.7866 -1.1955 0.3887 1.9002 1.2004
## shrub_cover-Sus_scrofa 1.0972 0.7578 -0.6016 1.1147 2.4797 1.0404
## veg_height-Canis_latrans -0.5659 0.1696 -0.8833 -0.5736 -0.2272 1.0582
## veg_height-Lynx_rufus 0.1081 0.2337 -0.3483 0.1079 0.5557 1.0824
## veg_height-Didelphis_virginiana 0.4896 0.2454 0.0486 0.4775 0.9694 1.1073
## veg_height-Sylvilagus_floridanus 0.0862 0.2457 -0.3747 0.0797 0.5622 1.1549
## veg_height-Meleagris_gallopavo -0.1764 0.4264 -1.0147 -0.1619 0.6238 1.0460
## veg_height-Sciurus_carolinensis 0.2425 0.2110 -0.1613 0.2351 0.6651 1.0399
## veg_height-Vulpes_vulpes -0.1196 0.3151 -0.7696 -0.1174 0.4970 1.0527
## veg_height-Sus_scrofa -0.2426 0.3142 -0.9070 -0.2305 0.3388 1.1035
## ESS
## (Intercept)-Canis_latrans 192
## (Intercept)-Lynx_rufus 94
## (Intercept)-Didelphis_virginiana 110
## (Intercept)-Sylvilagus_floridanus 114
## (Intercept)-Meleagris_gallopavo 119
## (Intercept)-Sciurus_carolinensis 101
## (Intercept)-Vulpes_vulpes 60
## (Intercept)-Sus_scrofa 160
## shrub_cover-Canis_latrans 156
## shrub_cover-Lynx_rufus 89
## shrub_cover-Didelphis_virginiana 89
## shrub_cover-Sylvilagus_floridanus 61
## shrub_cover-Meleagris_gallopavo 164
## shrub_cover-Sciurus_carolinensis 100
## shrub_cover-Vulpes_vulpes 49
## shrub_cover-Sus_scrofa 133
## veg_height-Canis_latrans 206
## veg_height-Lynx_rufus 153
## veg_height-Didelphis_virginiana 206
## veg_height-Sylvilagus_floridanus 153
## veg_height-Meleagris_gallopavo 164
## veg_height-Sciurus_carolinensis 225
## veg_height-Vulpes_vulpes 142
## veg_height-Sus_scrofa 236
# 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): 3.4753
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5187 0.4269 -1.3362 -0.5282 0.3998 1.0088 346
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0816 1.1925 0.1399 0.7692 3.9752 1.0109 626
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0685 0.3862 -5.7377 -5.0931 -4.3072 1.0037 607
## shrub_cover 0.1780 0.3948 -0.5994 0.1691 0.9948 1.0105 582
## veg_height -0.0628 0.2211 -0.5003 -0.0632 0.3603 1.0136 443
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8086 1.5774 0.0988 0.4946 3.2061 1.1299 239
## shrub_cover 1.1764 1.2989 0.1967 0.8576 4.0793 1.1579 163
## veg_height 0.3019 0.3282 0.0623 0.2213 0.9359 1.0369 341
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3479 0.4067 -0.4003 0.3321 1.1726 1.0018
## (Intercept)-Lynx_rufus 0.2036 0.6309 -0.7787 0.1376 1.6115 1.0124
## (Intercept)-Didelphis_virginiana -1.0477 0.4470 -1.9397 -1.0425 -0.1935 1.0103
## (Intercept)-Sylvilagus_floridanus -0.4865 0.4053 -1.2731 -0.4972 0.3425 1.0015
## (Intercept)-Meleagris_gallopavo 0.1409 0.7856 -1.0118 0.0305 1.8966 1.0410
## (Intercept)-Sciurus_carolinensis -1.0810 0.4416 -1.9995 -1.0701 -0.2494 1.0033
## (Intercept)-Vulpes_vulpes -1.0474 0.8497 -2.4657 -1.1237 0.7111 1.2046
## (Intercept)-Sus_scrofa -1.3961 0.5912 -2.6633 -1.3666 -0.3249 1.0095
## ESS
## (Intercept)-Canis_latrans 1324
## (Intercept)-Lynx_rufus 433
## (Intercept)-Didelphis_virginiana 723
## (Intercept)-Sylvilagus_floridanus 1711
## (Intercept)-Meleagris_gallopavo 153
## (Intercept)-Sciurus_carolinensis 1255
## (Intercept)-Vulpes_vulpes 87
## (Intercept)-Sus_scrofa 441
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7809 0.1771 -5.1500 -4.7689 -4.4710 1.0150
## (Intercept)-Lynx_rufus -5.5595 0.2930 -6.1207 -5.5695 -4.9749 1.1692
## (Intercept)-Didelphis_virginiana -4.7339 0.3170 -5.4019 -4.7154 -4.1704 1.0437
## (Intercept)-Sylvilagus_floridanus -4.9261 0.2347 -5.3984 -4.9267 -4.4871 1.0420
## (Intercept)-Meleagris_gallopavo -5.9844 0.4708 -6.9291 -5.9817 -5.0891 1.0138
## (Intercept)-Sciurus_carolinensis -4.6398 0.2916 -5.2655 -4.6207 -4.1048 1.0175
## (Intercept)-Vulpes_vulpes -5.9898 0.7341 -7.6954 -5.9031 -4.8414 1.2084
## (Intercept)-Sus_scrofa -5.3626 0.5465 -6.5385 -5.3341 -4.3327 1.0309
## shrub_cover-Canis_latrans -0.3174 0.2050 -0.7213 -0.3164 0.0995 1.0882
## shrub_cover-Lynx_rufus -0.3930 0.3384 -1.0698 -0.3831 0.2582 1.0789
## shrub_cover-Didelphis_virginiana 1.1534 0.4109 0.4101 1.1409 2.0390 1.0197
## shrub_cover-Sylvilagus_floridanus 0.4730 0.4143 -0.3521 0.4766 1.2831 1.0462
## shrub_cover-Meleagris_gallopavo -0.9693 0.4104 -1.7387 -0.9888 -0.1960 1.0555
## shrub_cover-Sciurus_carolinensis 0.8998 0.4133 0.1263 0.8873 1.7895 1.0520
## shrub_cover-Vulpes_vulpes -0.2663 0.6361 -1.4680 -0.2718 1.0809 1.0083
## shrub_cover-Sus_scrofa 0.9012 0.9699 -1.0101 0.8804 2.8841 1.0899
## veg_height-Canis_latrans -0.5779 0.1737 -0.9205 -0.5792 -0.2376 1.0039
## veg_height-Lynx_rufus 0.1090 0.2276 -0.3296 0.1133 0.5341 1.0069
## veg_height-Didelphis_virginiana 0.4850 0.2572 0.0097 0.4749 1.0384 1.0298
## veg_height-Sylvilagus_floridanus 0.1338 0.2394 -0.3418 0.1350 0.5917 1.0036
## veg_height-Meleagris_gallopavo -0.3962 0.3433 -1.1059 -0.3842 0.2806 1.0161
## veg_height-Sciurus_carolinensis 0.1735 0.2122 -0.2270 0.1607 0.6199 1.0454
## veg_height-Vulpes_vulpes -0.1719 0.3268 -0.8616 -0.1555 0.4289 1.0723
## veg_height-Sus_scrofa -0.2884 0.3543 -1.0565 -0.2666 0.3432 1.0038
## ESS
## (Intercept)-Canis_latrans 174
## (Intercept)-Lynx_rufus 90
## (Intercept)-Didelphis_virginiana 123
## (Intercept)-Sylvilagus_floridanus 195
## (Intercept)-Meleagris_gallopavo 48
## (Intercept)-Sciurus_carolinensis 212
## (Intercept)-Vulpes_vulpes 49
## (Intercept)-Sus_scrofa 108
## shrub_cover-Canis_latrans 188
## shrub_cover-Lynx_rufus 109
## shrub_cover-Didelphis_virginiana 122
## shrub_cover-Sylvilagus_floridanus 122
## shrub_cover-Meleagris_gallopavo 74
## shrub_cover-Sciurus_carolinensis 181
## shrub_cover-Vulpes_vulpes 127
## shrub_cover-Sus_scrofa 155
## veg_height-Canis_latrans 169
## veg_height-Lynx_rufus 184
## veg_height-Didelphis_virginiana 179
## veg_height-Sylvilagus_floridanus 191
## veg_height-Meleagris_gallopavo 153
## veg_height-Sciurus_carolinensis 298
## veg_height-Vulpes_vulpes 138
## veg_height-Sus_scrofa 209
#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): 3.452
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8089 0.4853 -1.7708 -0.8152 0.1772 1.0280 408
## Veg_shannon_index 0.3294 0.3768 -0.4400 0.3346 1.0633 1.0091 1009
## Avg_Cogongrass_Cover 0.2509 0.3711 -0.5180 0.2562 0.9773 1.0369 660
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1018 1.3884 0.0642 0.6758 4.7533 1.0787 485
## Veg_shannon_index 0.6668 0.9057 0.0542 0.3973 2.9630 1.0064 618
## Avg_Cogongrass_Cover 0.5651 0.7411 0.0479 0.3165 2.4884 1.0695 705
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.639 1.6581 0.0666 1.1653 5.9066 1.0912 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0557 0.2897 -5.5830 -5.0696 -4.4444 1.0107 941
## shrub_cover 0.2412 0.3674 -0.4729 0.2344 0.9769 1.0282 519
## veg_height -0.0572 0.2148 -0.4845 -0.0559 0.3823 1.0215 556
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5464 0.5987 0.0694 0.3750 2.0431 1.0675 264
## shrub_cover 0.9041 0.8592 0.1437 0.6709 3.0220 1.0320 209
## veg_height 0.2769 0.2503 0.0579 0.2088 0.8996 1.0209 371
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0280 0.7254 -1.3996 -0.0463
## (Intercept)-Lynx_rufus -0.2906 0.8161 -1.6938 -0.3663
## (Intercept)-Didelphis_virginiana -1.1919 0.6318 -2.5505 -1.1689
## (Intercept)-Sylvilagus_floridanus -0.7372 0.6322 -1.9853 -0.7442
## (Intercept)-Meleagris_gallopavo -0.4691 0.7869 -1.8875 -0.5264
## (Intercept)-Sciurus_carolinensis -1.2028 0.6595 -2.6372 -1.1612
## (Intercept)-Vulpes_vulpes -1.2737 0.8083 -2.9750 -1.2565
## (Intercept)-Sus_scrofa -1.6435 0.8238 -3.4963 -1.5461
## Veg_shannon_index-Canis_latrans 0.8242 0.4858 -0.0147 0.7855
## Veg_shannon_index-Lynx_rufus -0.2109 0.7216 -1.9662 -0.1076
## Veg_shannon_index-Didelphis_virginiana 0.5499 0.4636 -0.2893 0.5306
## Veg_shannon_index-Sylvilagus_floridanus 0.3970 0.4663 -0.5098 0.3944
## Veg_shannon_index-Meleagris_gallopavo 0.5616 0.6194 -0.5863 0.5164
## Veg_shannon_index-Sciurus_carolinensis -0.1817 0.5087 -1.2448 -0.1530
## Veg_shannon_index-Vulpes_vulpes -0.0669 0.6062 -1.3714 -0.0284
## Veg_shannon_index-Sus_scrofa 0.8149 0.6757 -0.2947 0.7369
## Avg_Cogongrass_Cover-Canis_latrans 0.7033 0.4982 -0.1017 0.6456
## Avg_Cogongrass_Cover-Lynx_rufus 0.5742 0.5411 -0.2938 0.5115
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4678 0.4403 -0.3691 0.4475
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2300 0.5132 -1.3273 -0.1897
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1715 0.7575 -1.9084 -0.1094
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3904 0.4114 -0.4115 0.3848
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3832 0.5543 -0.6666 0.3625
## Avg_Cogongrass_Cover-Sus_scrofa -0.0490 0.6889 -1.6982 0.0396
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4824 1.0397 391
## (Intercept)-Lynx_rufus 1.5222 1.0565 331
## (Intercept)-Didelphis_virginiana -0.0038 1.0226 844
## (Intercept)-Sylvilagus_floridanus 0.5668 1.0086 802
## (Intercept)-Meleagris_gallopavo 1.2737 1.0881 207
## (Intercept)-Sciurus_carolinensis -0.0174 1.0015 772
## (Intercept)-Vulpes_vulpes 0.2858 1.0694 402
## (Intercept)-Sus_scrofa -0.2339 1.0192 474
## Veg_shannon_index-Canis_latrans 1.8878 0.9997 1232
## Veg_shannon_index-Lynx_rufus 0.9444 1.0155 488
## Veg_shannon_index-Didelphis_virginiana 1.5127 1.0024 1644
## Veg_shannon_index-Sylvilagus_floridanus 1.3173 1.0042 1436
## Veg_shannon_index-Meleagris_gallopavo 1.8639 1.0110 799
## Veg_shannon_index-Sciurus_carolinensis 0.6674 1.0033 695
## Veg_shannon_index-Vulpes_vulpes 1.0023 1.0131 698
## Veg_shannon_index-Sus_scrofa 2.3339 1.0010 1031
## Avg_Cogongrass_Cover-Canis_latrans 1.8370 1.0158 945
## Avg_Cogongrass_Cover-Lynx_rufus 1.8149 1.0292 1017
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3878 1.0070 972
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6752 1.0313 798
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1406 1.1202 427
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2157 1.0047 1445
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5513 1.0218 1043
## Avg_Cogongrass_Cover-Sus_scrofa 1.1159 1.0706 763
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7716 0.1841 -5.1560 -4.7647 -4.4334 1.1118
## (Intercept)-Lynx_rufus -5.5366 0.2931 -6.1384 -5.5315 -4.9682 1.0274
## (Intercept)-Didelphis_virginiana -4.6730 0.2653 -5.2124 -4.6662 -4.1638 1.0127
## (Intercept)-Sylvilagus_floridanus -4.9374 0.2209 -5.3920 -4.9316 -4.5104 1.0642
## (Intercept)-Meleagris_gallopavo -5.7281 0.4043 -6.5975 -5.7346 -4.9320 1.3412
## (Intercept)-Sciurus_carolinensis -4.6535 0.3049 -5.2768 -4.6404 -4.1086 1.0005
## (Intercept)-Vulpes_vulpes -5.8155 0.5491 -6.9719 -5.7660 -4.8756 1.3227
## (Intercept)-Sus_scrofa -5.2835 0.4749 -6.2284 -5.2650 -4.3729 1.0071
## shrub_cover-Canis_latrans -0.2825 0.2071 -0.6908 -0.2826 0.1185 1.0956
## shrub_cover-Lynx_rufus -0.1889 0.3837 -0.9878 -0.1776 0.5296 1.0963
## shrub_cover-Didelphis_virginiana 1.0608 0.3691 0.3639 1.0471 1.8331 1.0208
## shrub_cover-Sylvilagus_floridanus 0.4889 0.4018 -0.3087 0.5001 1.2904 1.1383
## shrub_cover-Meleagris_gallopavo -0.7573 0.3937 -1.5171 -0.7669 0.0770 1.2476
## shrub_cover-Sciurus_carolinensis 0.9225 0.4046 0.1995 0.9006 1.7653 1.0353
## shrub_cover-Vulpes_vulpes -0.0484 0.5785 -1.2699 -0.0486 1.0946 1.0460
## shrub_cover-Sus_scrofa 0.8163 0.8288 -0.6612 0.7755 2.4753 1.0093
## veg_height-Canis_latrans -0.5769 0.1838 -0.9429 -0.5768 -0.2109 1.1214
## veg_height-Lynx_rufus 0.0795 0.2485 -0.4170 0.0780 0.5698 1.0844
## veg_height-Didelphis_virginiana 0.4694 0.2349 0.0306 0.4580 0.9550 1.0079
## veg_height-Sylvilagus_floridanus 0.1485 0.2411 -0.3112 0.1536 0.6243 1.0040
## veg_height-Meleagris_gallopavo -0.2865 0.3661 -1.0013 -0.2887 0.4069 1.0771
## veg_height-Sciurus_carolinensis 0.1587 0.2166 -0.2437 0.1536 0.6040 1.0052
## veg_height-Vulpes_vulpes -0.1799 0.2959 -0.8066 -0.1662 0.3655 1.0207
## veg_height-Sus_scrofa -0.2863 0.3425 -0.9428 -0.2730 0.3373 1.0217
## ESS
## (Intercept)-Canis_latrans 183
## (Intercept)-Lynx_rufus 83
## (Intercept)-Didelphis_virginiana 199
## (Intercept)-Sylvilagus_floridanus 211
## (Intercept)-Meleagris_gallopavo 54
## (Intercept)-Sciurus_carolinensis 167
## (Intercept)-Vulpes_vulpes 97
## (Intercept)-Sus_scrofa 256
## shrub_cover-Canis_latrans 195
## shrub_cover-Lynx_rufus 79
## shrub_cover-Didelphis_virginiana 164
## shrub_cover-Sylvilagus_floridanus 114
## shrub_cover-Meleagris_gallopavo 81
## shrub_cover-Sciurus_carolinensis 157
## shrub_cover-Vulpes_vulpes 137
## shrub_cover-Sus_scrofa 249
## veg_height-Canis_latrans 185
## veg_height-Lynx_rufus 154
## veg_height-Didelphis_virginiana 211
## veg_height-Sylvilagus_floridanus 198
## veg_height-Meleagris_gallopavo 159
## veg_height-Sciurus_carolinensis 222
## veg_height-Vulpes_vulpes 164
## veg_height-Sus_scrofa 315
# 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): 3.8435
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7537 0.6174 -1.9947 -0.7386 0.4697 1.0687 249
## Cogon_Patch_Size -0.2551 0.6117 -1.6039 -0.2202 0.8444 1.0127 814
## Avg_Cogongrass_Cover 0.1998 0.4809 -0.7246 0.1883 1.1914 1.0208 421
## total_shrub_cover -0.8535 0.6327 -2.1885 -0.8165 0.2855 1.1685 165
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3826 2.4058 0.0571 0.6722 7.0817 1.0248 309
## Cogon_Patch_Size 2.2820 4.0376 0.0888 1.0923 11.4403 1.1219 235
## Avg_Cogongrass_Cover 0.8226 1.1388 0.0502 0.4316 4.0131 1.0319 534
## total_shrub_cover 1.7035 2.7559 0.0760 0.9298 7.9738 1.1388 193
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.2526 4.4904 0.3871 3.0309 15.7867 1.4918 72
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1049 0.2850 -5.6158 -5.1196 -4.5189 1.1944 285
## shrub_cover 0.4952 0.4072 -0.3149 0.4883 1.3342 1.0368 262
## veg_height -0.0413 0.2258 -0.5106 -0.0394 0.4076 1.0406 400
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4339 0.7833 0.0552 0.2677 1.7490 1.3315 172
## shrub_cover 1.0341 0.9720 0.1822 0.7599 3.4009 1.1451 303
## veg_height 0.3000 0.2748 0.0622 0.2211 1.0559 1.0493 371
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1019 1.0221 -1.6642 -0.0101
## (Intercept)-Lynx_rufus -0.4398 0.9789 -2.1892 -0.4899
## (Intercept)-Didelphis_virginiana -0.8900 0.9234 -2.7121 -0.8851
## (Intercept)-Sylvilagus_floridanus -0.5656 0.9242 -2.3571 -0.5868
## (Intercept)-Meleagris_gallopavo -0.8820 0.9227 -2.8594 -0.8334
## (Intercept)-Sciurus_carolinensis -1.1314 0.9449 -3.2009 -1.0657
## (Intercept)-Vulpes_vulpes -1.2376 1.0477 -3.5290 -1.1832
## (Intercept)-Sus_scrofa -1.3612 1.0376 -3.6378 -1.2362
## Cogon_Patch_Size-Canis_latrans 0.8731 1.0557 -0.5175 0.6790
## Cogon_Patch_Size-Lynx_rufus -0.3187 1.0337 -2.2930 -0.3554
## Cogon_Patch_Size-Didelphis_virginiana 0.7898 0.6462 -0.3280 0.7438
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2756 1.3528 -4.8564 -0.9602
## Cogon_Patch_Size-Meleagris_gallopavo 0.0805 0.9732 -1.6872 0.0215
## Cogon_Patch_Size-Sciurus_carolinensis -1.0281 1.0018 -3.4642 -0.8468
## Cogon_Patch_Size-Vulpes_vulpes -0.8554 1.2975 -4.0886 -0.6398
## Cogon_Patch_Size-Sus_scrofa -0.4799 1.0889 -3.0023 -0.3691
## Avg_Cogongrass_Cover-Canis_latrans 0.4652 0.5810 -0.5276 0.4064
## Avg_Cogongrass_Cover-Lynx_rufus 0.7449 0.7641 -0.4978 0.6458
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1516 0.6019 -1.0238 0.1557
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1914 0.7083 -1.7220 -0.1709
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3943 0.9215 -2.4049 -0.3392
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5173 0.5890 -0.5764 0.4946
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4861 0.6654 -0.7113 0.4438
## Avg_Cogongrass_Cover-Sus_scrofa -0.0866 0.8929 -2.1367 -0.0179
## total_shrub_cover-Canis_latrans 0.3963 0.7950 -1.0173 0.3170
## total_shrub_cover-Lynx_rufus -1.2527 1.0942 -3.6660 -1.1400
## total_shrub_cover-Didelphis_virginiana -1.1507 0.9443 -3.5964 -0.9737
## total_shrub_cover-Sylvilagus_floridanus -1.3803 1.0612 -3.9586 -1.2207
## total_shrub_cover-Meleagris_gallopavo -1.7067 1.0894 -4.4673 -1.5528
## total_shrub_cover-Sciurus_carolinensis -0.8379 0.8966 -3.0136 -0.7339
## total_shrub_cover-Vulpes_vulpes -0.8436 1.1902 -3.4447 -0.7675
## total_shrub_cover-Sus_scrofa -0.6461 0.9935 -2.8646 -0.5899
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4676 1.0105 266
## (Intercept)-Lynx_rufus 1.6059 1.0473 279
## (Intercept)-Didelphis_virginiana 0.9604 1.0811 376
## (Intercept)-Sylvilagus_floridanus 1.3432 1.0634 325
## (Intercept)-Meleagris_gallopavo 0.8518 1.0212 319
## (Intercept)-Sciurus_carolinensis 0.6468 1.0548 258
## (Intercept)-Vulpes_vulpes 0.7580 1.0236 286
## (Intercept)-Sus_scrofa 0.2974 1.0603 319
## Cogon_Patch_Size-Canis_latrans 3.5624 1.0669 314
## Cogon_Patch_Size-Lynx_rufus 1.9703 1.0667 454
## Cogon_Patch_Size-Didelphis_virginiana 2.2238 1.0035 510
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4935 1.0290 304
## Cogon_Patch_Size-Meleagris_gallopavo 2.2136 1.0329 430
## Cogon_Patch_Size-Sciurus_carolinensis 0.4542 1.0087 493
## Cogon_Patch_Size-Vulpes_vulpes 0.9378 1.0906 200
## Cogon_Patch_Size-Sus_scrofa 1.3571 1.0031 605
## Avg_Cogongrass_Cover-Canis_latrans 1.7048 1.0036 716
## Avg_Cogongrass_Cover-Lynx_rufus 2.5900 1.0109 449
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3291 1.0130 513
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1403 1.0099 431
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2311 1.0547 267
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6963 1.0051 471
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.0065 1.0098 684
## Avg_Cogongrass_Cover-Sus_scrofa 1.5092 1.0253 555
## total_shrub_cover-Canis_latrans 2.1802 1.0159 307
## total_shrub_cover-Lynx_rufus 0.6357 1.2180 148
## total_shrub_cover-Didelphis_virginiana 0.1775 1.2048 113
## total_shrub_cover-Sylvilagus_floridanus 0.2874 1.2940 110
## total_shrub_cover-Meleagris_gallopavo -0.0761 1.1548 144
## total_shrub_cover-Sciurus_carolinensis 0.6225 1.1437 182
## total_shrub_cover-Vulpes_vulpes 1.3304 1.0608 189
## total_shrub_cover-Sus_scrofa 1.2034 1.1253 359
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8100 0.1786 -5.1612 -4.8116 -4.4780 1.1303
## (Intercept)-Lynx_rufus -5.4485 0.2855 -6.0964 -5.4362 -4.9168 1.2153
## (Intercept)-Didelphis_virginiana -4.8251 0.3025 -5.5056 -4.8026 -4.2792 1.3899
## (Intercept)-Sylvilagus_floridanus -5.0601 0.2329 -5.4845 -5.0688 -4.6006 1.2557
## (Intercept)-Meleagris_gallopavo -5.5381 0.4636 -6.5528 -5.5094 -4.6885 1.3459
## (Intercept)-Sciurus_carolinensis -4.8785 0.3225 -5.4663 -4.8922 -4.1735 1.1332
## (Intercept)-Vulpes_vulpes -5.7271 0.5887 -7.1713 -5.6344 -4.8257 1.1756
## (Intercept)-Sus_scrofa -5.3978 0.4472 -6.3542 -5.3877 -4.5574 1.3891
## shrub_cover-Canis_latrans -0.2875 0.2247 -0.7293 -0.2805 0.1390 1.0330
## shrub_cover-Lynx_rufus 0.0887 0.3852 -0.6843 0.1111 0.8071 1.1540
## shrub_cover-Didelphis_virginiana 1.2881 0.4262 0.4737 1.2669 2.1774 1.2387
## shrub_cover-Sylvilagus_floridanus 0.8916 0.4147 -0.0654 0.9091 1.6875 1.2764
## shrub_cover-Meleagris_gallopavo -0.5450 0.4325 -1.4498 -0.5238 0.2719 1.2226
## shrub_cover-Sciurus_carolinensis 1.1939 0.4147 0.3929 1.1835 2.0347 1.1165
## shrub_cover-Vulpes_vulpes 0.3488 0.6669 -0.9924 0.3513 1.6416 1.0464
## shrub_cover-Sus_scrofa 1.1577 0.7776 -0.3680 1.1341 2.8060 1.2757
## veg_height-Canis_latrans -0.5870 0.1766 -0.9456 -0.5795 -0.2635 1.0299
## veg_height-Lynx_rufus 0.1247 0.2423 -0.3580 0.1313 0.5950 1.0578
## veg_height-Didelphis_virginiana 0.4862 0.2443 0.0507 0.4655 1.0181 1.0408
## veg_height-Sylvilagus_floridanus 0.0760 0.2209 -0.3542 0.0835 0.5075 1.0374
## veg_height-Meleagris_gallopavo -0.3163 0.4516 -1.2294 -0.2969 0.5336 1.2613
## veg_height-Sciurus_carolinensis 0.2623 0.2240 -0.1640 0.2589 0.7105 1.1215
## veg_height-Vulpes_vulpes -0.1306 0.3015 -0.6889 -0.1412 0.4787 1.0858
## veg_height-Sus_scrofa -0.2451 0.3243 -0.9062 -0.2319 0.3586 1.1336
## ESS
## (Intercept)-Canis_latrans 174
## (Intercept)-Lynx_rufus 119
## (Intercept)-Didelphis_virginiana 112
## (Intercept)-Sylvilagus_floridanus 121
## (Intercept)-Meleagris_gallopavo 69
## (Intercept)-Sciurus_carolinensis 116
## (Intercept)-Vulpes_vulpes 71
## (Intercept)-Sus_scrofa 99
## shrub_cover-Canis_latrans 148
## shrub_cover-Lynx_rufus 91
## shrub_cover-Didelphis_virginiana 102
## shrub_cover-Sylvilagus_floridanus 90
## shrub_cover-Meleagris_gallopavo 82
## shrub_cover-Sciurus_carolinensis 121
## shrub_cover-Vulpes_vulpes 107
## shrub_cover-Sus_scrofa 117
## veg_height-Canis_latrans 169
## veg_height-Lynx_rufus 147
## veg_height-Didelphis_virginiana 260
## veg_height-Sylvilagus_floridanus 188
## veg_height-Meleagris_gallopavo 103
## veg_height-Sciurus_carolinensis 193
## veg_height-Vulpes_vulpes 138
## veg_height-Sus_scrofa 252
#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): 3.6125
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9000 0.6057 -2.0990 -0.9031 0.3522 1.0204 545
## Tree_Density -0.8178 0.5100 -1.9544 -0.7870 0.0972 1.0294 386
## Avg_Canopy_Cover 1.3487 0.5834 0.3239 1.3087 2.6422 1.0195 503
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2733 2.7944 0.1082 1.4639 9.3606 1.0131 314
## Tree_Density 0.9013 1.4435 0.0494 0.4151 4.5331 1.0115 453
## Avg_Canopy_Cover 1.8944 2.2467 0.1452 1.2148 7.9071 1.0964 388
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2559 1.6453 0.0552 0.666 5.7209 1.3243 125
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1032 0.2930 -5.6663 -5.1043 -4.5156 1.0118 488
## shrub_cover 0.2634 0.3693 -0.4613 0.2634 0.9811 1.0221 475
## veg_height -0.0175 0.2247 -0.4879 -0.0131 0.4226 1.0019 630
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4876 0.5893 0.0639 0.3149 2.0791 1.0445 202
## shrub_cover 1.0118 0.9672 0.1999 0.7413 3.3989 1.0376 288
## veg_height 0.3234 0.2655 0.0728 0.2515 1.0482 1.0160 825
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1654 0.7813 -1.4281 0.1687 1.7405
## (Intercept)-Lynx_rufus 0.3664 1.2901 -1.7120 0.2030 3.3579
## (Intercept)-Didelphis_virginiana -1.5398 0.7616 -3.0984 -1.5220 -0.0677
## (Intercept)-Sylvilagus_floridanus -0.8228 0.7294 -2.2659 -0.8256 0.6374
## (Intercept)-Meleagris_gallopavo -0.3973 0.9328 -1.9792 -0.4711 1.7062
## (Intercept)-Sciurus_carolinensis -1.5926 0.8294 -3.5212 -1.5521 -0.1457
## (Intercept)-Vulpes_vulpes -1.8520 0.9681 -3.9193 -1.8117 -0.0510
## (Intercept)-Sus_scrofa -2.1521 1.0078 -4.3577 -2.0662 -0.3744
## Tree_Density-Canis_latrans -1.0261 0.6411 -2.4338 -0.9563 0.0339
## Tree_Density-Lynx_rufus 0.0385 0.7753 -1.2194 -0.0531 2.0332
## Tree_Density-Didelphis_virginiana -1.0412 0.8321 -3.0015 -0.9293 0.2717
## Tree_Density-Sylvilagus_floridanus -1.1140 0.8618 -3.2806 -0.9848 0.2521
## Tree_Density-Meleagris_gallopavo -1.0870 0.9242 -3.3431 -0.9688 0.3089
## Tree_Density-Sciurus_carolinensis -0.8851 0.8016 -2.8358 -0.8024 0.5098
## Tree_Density-Vulpes_vulpes -0.6855 0.7446 -2.2835 -0.6335 0.6631
## Tree_Density-Sus_scrofa -0.9698 0.8965 -3.0458 -0.8643 0.4873
## Avg_Canopy_Cover-Canis_latrans -0.1539 0.5356 -1.2111 -0.1532 0.9286
## Avg_Canopy_Cover-Lynx_rufus 1.0146 1.0040 -0.7072 0.9039 3.3153
## Avg_Canopy_Cover-Didelphis_virginiana 1.9394 0.8946 0.5897 1.8012 4.0733
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6414 1.2445 0.8888 2.3961 5.6446
## Avg_Canopy_Cover-Meleagris_gallopavo 1.8551 0.9885 0.3422 1.6822 4.3832
## Avg_Canopy_Cover-Sciurus_carolinensis 1.8070 0.8213 0.5325 1.6848 3.7565
## Avg_Canopy_Cover-Vulpes_vulpes 1.2306 0.8234 -0.0846 1.1429 2.8858
## Avg_Canopy_Cover-Sus_scrofa 1.4213 0.7331 0.1351 1.3523 3.0545
## Rhat ESS
## (Intercept)-Canis_latrans 1.0451 305
## (Intercept)-Lynx_rufus 1.0757 160
## (Intercept)-Didelphis_virginiana 1.0191 622
## (Intercept)-Sylvilagus_floridanus 1.0112 641
## (Intercept)-Meleagris_gallopavo 1.0267 210
## (Intercept)-Sciurus_carolinensis 1.0228 514
## (Intercept)-Vulpes_vulpes 1.0431 281
## (Intercept)-Sus_scrofa 1.0260 378
## Tree_Density-Canis_latrans 1.0075 523
## Tree_Density-Lynx_rufus 1.0250 420
## Tree_Density-Didelphis_virginiana 1.0373 405
## Tree_Density-Sylvilagus_floridanus 1.0082 481
## Tree_Density-Meleagris_gallopavo 1.0201 432
## Tree_Density-Sciurus_carolinensis 1.0071 532
## Tree_Density-Vulpes_vulpes 1.0072 753
## Tree_Density-Sus_scrofa 1.0013 448
## Avg_Canopy_Cover-Canis_latrans 1.0152 909
## Avg_Canopy_Cover-Lynx_rufus 1.0177 303
## Avg_Canopy_Cover-Didelphis_virginiana 1.0276 400
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0965 271
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0311 303
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0141 628
## Avg_Canopy_Cover-Vulpes_vulpes 1.0574 244
## Avg_Canopy_Cover-Sus_scrofa 1.0024 718
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8259 0.1821 -5.1786 -4.8245 -4.4606 1.0418
## (Intercept)-Lynx_rufus -5.6508 0.3412 -6.4274 -5.6151 -5.0883 1.1017
## (Intercept)-Didelphis_virginiana -4.7750 0.2770 -5.3656 -4.7583 -4.2679 1.0633
## (Intercept)-Sylvilagus_floridanus -4.9497 0.2235 -5.3904 -4.9484 -4.5304 1.0082
## (Intercept)-Meleagris_gallopavo -5.7323 0.4460 -6.6330 -5.7084 -4.8925 1.1039
## (Intercept)-Sciurus_carolinensis -4.8069 0.2928 -5.3596 -4.8118 -4.2247 1.0814
## (Intercept)-Vulpes_vulpes -5.6718 0.5466 -7.0297 -5.5872 -4.8024 1.1596
## (Intercept)-Sus_scrofa -5.2743 0.4728 -6.2676 -5.2687 -4.3376 1.0179
## shrub_cover-Canis_latrans -0.3008 0.2063 -0.7175 -0.3007 0.0971 1.0184
## shrub_cover-Lynx_rufus -0.2587 0.3778 -1.0553 -0.2527 0.4664 1.1073
## shrub_cover-Didelphis_virginiana 1.1375 0.3643 0.4802 1.1144 1.8806 1.0147
## shrub_cover-Sylvilagus_floridanus 0.6183 0.3740 -0.1020 0.6113 1.3576 1.0946
## shrub_cover-Meleagris_gallopavo -0.7791 0.4043 -1.6139 -0.7918 -0.0251 1.1630
## shrub_cover-Sciurus_carolinensis 1.0509 0.3743 0.3331 1.0574 1.7619 1.0541
## shrub_cover-Vulpes_vulpes -0.0641 0.6628 -1.4561 -0.0448 1.2632 1.0855
## shrub_cover-Sus_scrofa 0.8014 0.8168 -0.7984 0.7819 2.4327 1.1005
## veg_height-Canis_latrans -0.6154 0.1769 -0.9615 -0.6076 -0.2766 1.0043
## veg_height-Lynx_rufus 0.1753 0.2364 -0.2900 0.1757 0.6362 1.0186
## veg_height-Didelphis_virginiana 0.5627 0.2376 0.1013 0.5581 1.0316 1.0113
## veg_height-Sylvilagus_floridanus 0.1925 0.2189 -0.2476 0.1941 0.6143 1.0130
## veg_height-Meleagris_gallopavo -0.2796 0.3478 -1.0336 -0.2605 0.3795 1.0471
## veg_height-Sciurus_carolinensis 0.2568 0.2251 -0.1717 0.2517 0.7101 1.0699
## veg_height-Vulpes_vulpes -0.1460 0.3208 -0.8016 -0.1209 0.4366 1.0342
## veg_height-Sus_scrofa -0.2647 0.3546 -1.0256 -0.2527 0.4118 1.0865
## ESS
## (Intercept)-Canis_latrans 169
## (Intercept)-Lynx_rufus 48
## (Intercept)-Didelphis_virginiana 137
## (Intercept)-Sylvilagus_floridanus 208
## (Intercept)-Meleagris_gallopavo 47
## (Intercept)-Sciurus_carolinensis 176
## (Intercept)-Vulpes_vulpes 82
## (Intercept)-Sus_scrofa 259
## shrub_cover-Canis_latrans 226
## shrub_cover-Lynx_rufus 73
## shrub_cover-Didelphis_virginiana 150
## shrub_cover-Sylvilagus_floridanus 156
## shrub_cover-Meleagris_gallopavo 54
## shrub_cover-Sciurus_carolinensis 161
## shrub_cover-Vulpes_vulpes 140
## shrub_cover-Sus_scrofa 235
## veg_height-Canis_latrans 162
## veg_height-Lynx_rufus 137
## veg_height-Didelphis_virginiana 270
## veg_height-Sylvilagus_floridanus 248
## veg_height-Meleagris_gallopavo 152
## veg_height-Sciurus_carolinensis 196
## veg_height-Vulpes_vulpes 147
## veg_height-Sus_scrofa 331
# 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): 3.4203
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3939 0.5487 -2.4848 -1.3908 -0.2715 1.0435 301
## Avg_Cogongrass_Cover -0.7489 0.4820 -1.7451 -0.7372 0.1653 1.0198 467
## I(Avg_Cogongrass_Cover^2) 0.7709 0.3816 0.0850 0.7568 1.5891 1.0278 465
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1623 1.4877 0.0649 0.7011 4.9551 1.0328 405
## Avg_Cogongrass_Cover 0.6390 0.8697 0.0492 0.3645 2.8403 1.0480 459
## I(Avg_Cogongrass_Cover^2) 0.4790 0.7668 0.0416 0.2513 2.2788 1.0444 362
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.346 1.5866 0.0682 0.8232 5.9992 1.06 126
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0619 0.2939 -5.6143 -5.0683 -4.4649 1.0062 273
## shrub_cover 0.2160 0.3834 -0.5400 0.2184 0.9859 1.0062 517
## veg_height -0.0195 0.2245 -0.4656 -0.0184 0.4190 1.0037 595
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5165 0.6815 0.0565 0.3349 2.0537 1.0623 216
## shrub_cover 0.9502 0.9835 0.1667 0.6947 3.2993 1.0207 507
## veg_height 0.2867 0.2915 0.0587 0.2139 0.8946 1.0413 593
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6661 0.7811 -2.1966 -0.6770
## (Intercept)-Lynx_rufus -1.1023 0.8229 -2.6907 -1.1280
## (Intercept)-Didelphis_virginiana -1.6868 0.6796 -3.1055 -1.6634
## (Intercept)-Sylvilagus_floridanus -1.3397 0.6876 -2.6948 -1.3490
## (Intercept)-Meleagris_gallopavo -0.8523 0.9484 -2.5021 -0.9298
## (Intercept)-Sciurus_carolinensis -2.0178 0.7762 -3.7222 -1.9535
## (Intercept)-Vulpes_vulpes -2.0454 0.9223 -4.0126 -1.9890
## (Intercept)-Sus_scrofa -2.0836 0.8288 -3.9610 -2.0117
## Avg_Cogongrass_Cover-Canis_latrans -0.3067 0.6401 -1.4056 -0.3391
## Avg_Cogongrass_Cover-Lynx_rufus -0.5537 0.7070 -1.8998 -0.5604
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4114 0.6175 -1.6001 -0.4391
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2681 0.6720 -2.7543 -1.2032
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1408 0.7990 -2.8625 -1.0677
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7784 0.6094 -2.0663 -0.7682
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7303 0.7050 -2.1413 -0.7285
## Avg_Cogongrass_Cover-Sus_scrofa -0.9592 0.7602 -2.6818 -0.9170
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2216 0.7219 0.1675 1.0788
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.0992 0.5573 0.2217 1.0262
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5825 0.4749 -0.2519 0.5441
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7439 0.4564 -0.0880 0.7113
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4067 0.6970 -0.9197 0.3838
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9591 0.4347 0.2174 0.9221
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8748 0.5076 0.0398 0.8313
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4111 0.6220 -0.9340 0.4494
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8592 1.0235 493
## (Intercept)-Lynx_rufus 0.5971 1.0686 204
## (Intercept)-Didelphis_virginiana -0.4296 1.0285 669
## (Intercept)-Sylvilagus_floridanus 0.0293 1.0373 684
## (Intercept)-Meleagris_gallopavo 1.1802 1.0110 181
## (Intercept)-Sciurus_carolinensis -0.6267 1.0533 477
## (Intercept)-Vulpes_vulpes -0.3667 1.0577 298
## (Intercept)-Sus_scrofa -0.6925 1.0366 577
## Avg_Cogongrass_Cover-Canis_latrans 1.0441 1.0092 666
## Avg_Cogongrass_Cover-Lynx_rufus 0.8923 1.0031 463
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8570 1.0006 855
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1108 1.0356 534
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2459 1.0168 377
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3710 1.0084 702
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.6936 1.0494 701
## Avg_Cogongrass_Cover-Sus_scrofa 0.3948 1.0287 598
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0728 1.0099 346
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4292 1.0149 623
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6663 1.0206 509
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7424 1.0171 733
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8238 1.0427 294
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8985 1.0066 647
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0305 1.0394 414
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5247 1.0121 549
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7908 0.1652 -5.1197 -4.7855 -4.4877 1.0095
## (Intercept)-Lynx_rufus -5.4941 0.3090 -6.1608 -5.4855 -4.8996 1.0994
## (Intercept)-Didelphis_virginiana -4.7262 0.2818 -5.3064 -4.7233 -4.2093 1.0158
## (Intercept)-Sylvilagus_floridanus -4.9763 0.2113 -5.4247 -4.9604 -4.5817 1.0387
## (Intercept)-Meleagris_gallopavo -5.7739 0.4771 -6.8324 -5.7629 -4.9284 1.0802
## (Intercept)-Sciurus_carolinensis -4.6977 0.3089 -5.3475 -4.6877 -4.1175 1.0856
## (Intercept)-Vulpes_vulpes -5.7448 0.6034 -7.1326 -5.6439 -4.8022 1.0374
## (Intercept)-Sus_scrofa -5.2421 0.4699 -6.1968 -5.2316 -4.3103 1.0425
## shrub_cover-Canis_latrans -0.2734 0.2172 -0.6936 -0.2697 0.1591 1.1755
## shrub_cover-Lynx_rufus -0.2494 0.3588 -0.9256 -0.2466 0.4497 1.0519
## shrub_cover-Didelphis_virginiana 1.1325 0.3815 0.4213 1.1221 1.8814 1.0380
## shrub_cover-Sylvilagus_floridanus 0.4804 0.4258 -0.3020 0.4636 1.3365 1.1650
## shrub_cover-Meleagris_gallopavo -0.8248 0.4075 -1.7806 -0.8086 -0.0756 1.0780
## shrub_cover-Sciurus_carolinensis 0.8873 0.3782 0.1906 0.8850 1.6619 1.0503
## shrub_cover-Vulpes_vulpes -0.0997 0.6089 -1.3165 -0.1147 1.1131 1.0681
## shrub_cover-Sus_scrofa 0.7735 0.7600 -0.8057 0.7744 2.3288 1.0103
## veg_height-Canis_latrans -0.5754 0.1710 -0.9345 -0.5703 -0.2694 1.0051
## veg_height-Lynx_rufus 0.1520 0.2292 -0.3244 0.1583 0.5806 1.0149
## veg_height-Didelphis_virginiana 0.4790 0.2634 -0.0019 0.4743 1.0146 1.0044
## veg_height-Sylvilagus_floridanus 0.1885 0.2287 -0.2529 0.1841 0.6229 1.0228
## veg_height-Meleagris_gallopavo -0.2113 0.3769 -0.9144 -0.2234 0.5606 1.0362
## veg_height-Sciurus_carolinensis 0.2053 0.2086 -0.1997 0.2007 0.6264 1.0496
## veg_height-Vulpes_vulpes -0.1750 0.3200 -0.8774 -0.1537 0.3938 1.0108
## veg_height-Sus_scrofa -0.2320 0.3381 -0.9083 -0.2294 0.4352 1.0248
## ESS
## (Intercept)-Canis_latrans 214
## (Intercept)-Lynx_rufus 79
## (Intercept)-Didelphis_virginiana 145
## (Intercept)-Sylvilagus_floridanus 218
## (Intercept)-Meleagris_gallopavo 48
## (Intercept)-Sciurus_carolinensis 174
## (Intercept)-Vulpes_vulpes 51
## (Intercept)-Sus_scrofa 259
## shrub_cover-Canis_latrans 168
## shrub_cover-Lynx_rufus 101
## shrub_cover-Didelphis_virginiana 151
## shrub_cover-Sylvilagus_floridanus 121
## shrub_cover-Meleagris_gallopavo 65
## shrub_cover-Sciurus_carolinensis 183
## shrub_cover-Vulpes_vulpes 172
## shrub_cover-Sus_scrofa 270
## veg_height-Canis_latrans 161
## veg_height-Lynx_rufus 166
## veg_height-Didelphis_virginiana 220
## veg_height-Sylvilagus_floridanus 210
## veg_height-Meleagris_gallopavo 127
## veg_height-Sciurus_carolinensis 203
## veg_height-Vulpes_vulpes 154
## veg_height-Sus_scrofa 302
# 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): 3.9348
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9578 1.1883 -4.0498 -2.0370 0.6564 1.1021 353
## Cogon_Patch_Size 0.1667 1.0361 -2.1011 0.2201 2.1438 1.0230 251
## Veg_shannon_index 0.9522 0.8147 -0.6558 0.9659 2.5602 1.0607 378
## total_shrub_cover -1.2445 1.0014 -3.3482 -1.2120 0.6974 1.0289 248
## Avg_Cogongrass_Cover -0.1828 1.1916 -2.4680 -0.2142 2.2869 1.0396 158
## Tree_Density -2.1976 0.9864 -4.1289 -2.2465 -0.2028 1.0815 185
## Avg_Canopy_Cover 2.2216 1.0615 -0.0470 2.2168 4.3737 1.0292 657
## I(Avg_Cogongrass_Cover^2) 1.3119 0.8402 -0.4892 1.3397 2.9234 0.9998 498
## avg_veg_height -0.3431 0.7545 -1.8170 -0.3446 1.1911 1.0087 171
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.1324 23.4364 0.2550 8.4855 68.5369 1.1187 161
## Cogon_Patch_Size 11.2161 24.9823 0.0941 3.5716 76.8797 1.1815 69
## Veg_shannon_index 4.3525 7.8333 0.1056 1.8483 25.6914 1.0118 174
## total_shrub_cover 8.7544 17.4552 0.2884 4.3184 43.5683 1.1040 143
## Avg_Cogongrass_Cover 2.2950 4.7131 0.0525 0.7455 15.0140 1.0853 259
## Tree_Density 4.0031 12.1671 0.0554 0.8716 25.5971 1.1935 147
## Avg_Canopy_Cover 10.9940 15.8788 0.4501 5.5316 54.6556 1.0275 138
## I(Avg_Cogongrass_Cover^2) 6.1265 16.4102 0.0681 1.6409 40.6320 1.1050 103
## avg_veg_height 1.1473 2.3181 0.0484 0.4468 6.3534 1.0302 394
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0937 4.2853 0.0594 0.6582 14.2559 1.0722 65
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1151 0.2914 -5.6620 -5.1205 -4.4865 1.0312 552
## shrub_cover 0.4394 0.3756 -0.2974 0.4372 1.2071 1.0569 314
## veg_height 0.0160 0.2140 -0.4164 0.0206 0.4442 1.0263 432
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4883 0.5280 0.0637 0.3244 1.9517 1.0805 174
## shrub_cover 0.9388 0.8334 0.2023 0.6995 3.1017 1.0610 421
## veg_height 0.2988 0.2809 0.0591 0.2214 0.9869 1.0083 465
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6470 1.5780 -3.5216 -0.6683
## (Intercept)-Lynx_rufus 0.9569 3.1909 -3.2444 0.1977
## (Intercept)-Didelphis_virginiana -3.9137 1.8140 -7.9008 -3.7719
## (Intercept)-Sylvilagus_floridanus -2.4320 1.8551 -6.5126 -2.4344
## (Intercept)-Meleagris_gallopavo -2.2206 2.0600 -6.4214 -2.2367
## (Intercept)-Sciurus_carolinensis -4.5929 2.1589 -9.5707 -4.3180
## (Intercept)-Vulpes_vulpes -5.0005 2.6941 -11.0409 -4.7751
## (Intercept)-Sus_scrofa -5.4679 2.8369 -12.0093 -5.0543
## Cogon_Patch_Size-Canis_latrans 2.6297 2.4955 -0.2415 1.9609
## Cogon_Patch_Size-Lynx_rufus -0.1121 3.2928 -7.8985 0.1387
## Cogon_Patch_Size-Didelphis_virginiana 2.0303 1.5064 -0.1735 1.7935
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7883 2.9047 -9.1216 -1.1567
## Cogon_Patch_Size-Meleagris_gallopavo 1.1866 2.3753 -1.8283 0.7467
## Cogon_Patch_Size-Sciurus_carolinensis -1.1467 2.3259 -7.6612 -0.6803
## Cogon_Patch_Size-Vulpes_vulpes -1.1339 2.8827 -8.8541 -0.4714
## Cogon_Patch_Size-Sus_scrofa -0.7728 2.1419 -6.2438 -0.4391
## Veg_shannon_index-Canis_latrans 1.8123 1.1169 -0.0339 1.6789
## Veg_shannon_index-Lynx_rufus -0.1127 1.7233 -4.1166 0.0654
## Veg_shannon_index-Didelphis_virginiana 1.6559 1.3436 -0.4373 1.5022
## Veg_shannon_index-Sylvilagus_floridanus 1.3159 1.1003 -0.5527 1.2012
## Veg_shannon_index-Meleagris_gallopavo 2.1402 1.5547 -0.1712 1.8543
## Veg_shannon_index-Sciurus_carolinensis -0.3970 1.4528 -3.8680 -0.2437
## Veg_shannon_index-Vulpes_vulpes 0.0424 1.6181 -3.7186 0.2743
## Veg_shannon_index-Sus_scrofa 2.4761 1.7655 0.0478 2.1279
## total_shrub_cover-Canis_latrans 1.1063 1.2990 -0.9145 0.9162
## total_shrub_cover-Lynx_rufus -2.4046 2.5708 -8.1261 -2.2193
## total_shrub_cover-Didelphis_virginiana -2.4558 2.2232 -7.3601 -2.0518
## total_shrub_cover-Sylvilagus_floridanus -1.8675 2.2440 -7.1003 -1.3908
## total_shrub_cover-Meleagris_gallopavo -3.9796 2.4394 -9.9427 -3.5413
## total_shrub_cover-Sciurus_carolinensis -1.3286 1.6905 -5.2674 -1.1046
## total_shrub_cover-Vulpes_vulpes -1.7495 2.2580 -6.8940 -1.5462
## total_shrub_cover-Sus_scrofa -0.7627 1.9697 -5.2451 -0.6510
## Avg_Cogongrass_Cover-Canis_latrans 0.1429 1.5112 -2.5432 0.0445
## Avg_Cogongrass_Cover-Lynx_rufus 0.3333 1.8708 -2.7369 0.1742
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0941 1.5376 -2.9451 -0.1256
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9380 1.7171 -4.9519 -0.7900
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5701 1.7876 -4.2917 -0.5087
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1785 1.6583 -3.4863 -0.2207
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1481 1.6929 -2.8406 0.0634
## Avg_Cogongrass_Cover-Sus_scrofa -0.4878 1.7571 -4.4856 -0.3960
## Tree_Density-Canis_latrans -3.2201 2.0143 -7.9127 -2.8418
## Tree_Density-Lynx_rufus -1.6450 1.8252 -4.4835 -1.8607
## Tree_Density-Didelphis_virginiana -2.3615 1.4338 -5.4259 -2.3420
## Tree_Density-Sylvilagus_floridanus -2.8976 1.8785 -7.6408 -2.6429
## Tree_Density-Meleagris_gallopavo -2.3699 1.7041 -5.9206 -2.3467
## Tree_Density-Sciurus_carolinensis -2.7502 1.8267 -7.1911 -2.5712
## Tree_Density-Vulpes_vulpes -2.0482 1.6328 -5.1276 -2.1739
## Tree_Density-Sus_scrofa -2.5304 1.8540 -6.6592 -2.4335
## Avg_Canopy_Cover-Canis_latrans -0.0996 0.8620 -1.8351 -0.0988
## Avg_Canopy_Cover-Lynx_rufus 1.5714 2.6362 -3.3519 1.3131
## Avg_Canopy_Cover-Didelphis_virginiana 4.5638 2.1843 1.6189 4.1414
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.5819 2.7325 1.7239 5.0100
## Avg_Canopy_Cover-Meleagris_gallopavo 3.1012 1.7269 0.7332 2.7869
## Avg_Canopy_Cover-Sciurus_carolinensis 4.4702 2.3577 1.4220 3.8891
## Avg_Canopy_Cover-Vulpes_vulpes 3.3507 2.0980 0.4124 2.9344
## Avg_Canopy_Cover-Sus_scrofa 2.7254 1.5634 0.4246 2.4955
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5948 1.8074 0.5882 2.1805
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.2758 2.6343 0.5095 2.6037
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2216 1.0131 -0.5245 1.1405
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2593 1.1546 -0.8030 1.1742
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.1956 2.0599 -5.2455 0.1631
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0078 1.2153 0.2288 1.8064
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1128 1.4254 0.0538 1.9196
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4572 2.3449 -5.2490 0.7843
## avg_veg_height-Canis_latrans -0.3036 0.8785 -1.9433 -0.2837
## avg_veg_height-Lynx_rufus -0.6505 1.2765 -3.4972 -0.5870
## avg_veg_height-Didelphis_virginiana -0.5785 1.0067 -2.7083 -0.5286
## avg_veg_height-Sylvilagus_floridanus -0.3451 0.9679 -2.2314 -0.3540
## avg_veg_height-Meleagris_gallopavo -0.3737 1.1944 -2.9732 -0.3721
## avg_veg_height-Sciurus_carolinensis 0.1433 0.9953 -1.6280 0.0617
## avg_veg_height-Vulpes_vulpes -0.5071 1.1928 -3.0711 -0.4762
## avg_veg_height-Sus_scrofa -0.2935 1.0368 -2.4040 -0.3051
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7214 1.0247 225
## (Intercept)-Lynx_rufus 8.7510 1.1574 88
## (Intercept)-Didelphis_virginiana -0.7833 1.0004 237
## (Intercept)-Sylvilagus_floridanus 1.3263 1.0344 210
## (Intercept)-Meleagris_gallopavo 1.7835 1.3292 108
## (Intercept)-Sciurus_carolinensis -1.0969 1.0629 139
## (Intercept)-Vulpes_vulpes -0.2462 1.0134 84
## (Intercept)-Sus_scrofa -1.0575 1.0222 83
## Cogon_Patch_Size-Canis_latrans 9.5430 1.0458 100
## Cogon_Patch_Size-Lynx_rufus 5.7007 1.2054 71
## Cogon_Patch_Size-Didelphis_virginiana 5.7132 1.0296 157
## Cogon_Patch_Size-Sylvilagus_floridanus 1.7402 1.2201 120
## Cogon_Patch_Size-Meleagris_gallopavo 7.8448 1.1291 96
## Cogon_Patch_Size-Sciurus_carolinensis 2.0038 1.1687 122
## Cogon_Patch_Size-Vulpes_vulpes 2.5008 1.0732 47
## Cogon_Patch_Size-Sus_scrofa 2.7034 1.0043 121
## Veg_shannon_index-Canis_latrans 4.5256 1.0048 400
## Veg_shannon_index-Lynx_rufus 2.6788 1.1815 130
## Veg_shannon_index-Didelphis_virginiana 4.7754 1.0122 350
## Veg_shannon_index-Sylvilagus_floridanus 3.7579 1.0791 538
## Veg_shannon_index-Meleagris_gallopavo 6.0860 1.0115 153
## Veg_shannon_index-Sciurus_carolinensis 1.9351 1.0366 194
## Veg_shannon_index-Vulpes_vulpes 2.6369 1.0634 181
## Veg_shannon_index-Sus_scrofa 7.0516 1.0164 167
## total_shrub_cover-Canis_latrans 4.1598 1.0289 249
## total_shrub_cover-Lynx_rufus 2.8832 1.1487 94
## total_shrub_cover-Didelphis_virginiana 0.2796 1.1337 79
## total_shrub_cover-Sylvilagus_floridanus 1.0835 1.0874 110
## total_shrub_cover-Meleagris_gallopavo -0.5433 1.0774 111
## total_shrub_cover-Sciurus_carolinensis 1.4558 1.0615 148
## total_shrub_cover-Vulpes_vulpes 2.1760 1.0742 135
## total_shrub_cover-Sus_scrofa 2.8200 1.2159 121
## Avg_Cogongrass_Cover-Canis_latrans 3.4285 1.0739 279
## Avg_Cogongrass_Cover-Lynx_rufus 4.6630 1.0719 168
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2706 1.0080 263
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0880 1.0113 195
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.9561 1.0246 182
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.3876 1.0634 195
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.8762 1.0751 212
## Avg_Cogongrass_Cover-Sus_scrofa 3.0068 1.0212 157
## Tree_Density-Canis_latrans -0.7483 1.1329 126
## Tree_Density-Lynx_rufus 2.7917 1.1537 186
## Tree_Density-Didelphis_virginiana 0.4100 1.0495 241
## Tree_Density-Sylvilagus_floridanus 0.1245 1.0566 199
## Tree_Density-Meleagris_gallopavo 1.0724 1.0686 232
## Tree_Density-Sciurus_carolinensis 0.2339 1.0537 212
## Tree_Density-Vulpes_vulpes 1.5972 1.0168 143
## Tree_Density-Sus_scrofa 0.6676 1.0481 222
## Avg_Canopy_Cover-Canis_latrans 1.6242 1.0107 275
## Avg_Canopy_Cover-Lynx_rufus 7.7094 1.0894 69
## Avg_Canopy_Cover-Didelphis_virginiana 10.1092 1.0155 168
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.4791 1.0226 118
## Avg_Canopy_Cover-Meleagris_gallopavo 7.4231 1.0127 160
## Avg_Canopy_Cover-Sciurus_carolinensis 10.6356 1.0464 106
## Avg_Canopy_Cover-Vulpes_vulpes 8.7499 1.0269 117
## Avg_Canopy_Cover-Sus_scrofa 6.3746 1.2185 317
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3404 1.0164 113
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 9.8994 1.0571 68
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.6150 1.0425 239
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.1154 1.0317 330
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5693 1.0590 65
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.9518 1.0380 236
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.4239 1.0561 212
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 4.0238 1.0413 123
## avg_veg_height-Canis_latrans 1.4528 1.0269 209
## avg_veg_height-Lynx_rufus 1.7280 1.0057 230
## avg_veg_height-Didelphis_virginiana 1.2485 1.0010 451
## avg_veg_height-Sylvilagus_floridanus 1.5991 1.0219 236
## avg_veg_height-Meleagris_gallopavo 1.9512 1.0161 187
## avg_veg_height-Sciurus_carolinensis 2.4394 1.0021 280
## avg_veg_height-Vulpes_vulpes 1.8573 1.0036 236
## avg_veg_height-Sus_scrofa 1.7623 1.0035 381
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7474 0.1603 -5.0696 -4.7462 -4.4284 1.0134
## (Intercept)-Lynx_rufus -5.6934 0.3276 -6.4442 -5.6718 -5.0897 1.1747
## (Intercept)-Didelphis_virginiana -4.8108 0.2631 -5.3191 -4.8147 -4.3034 1.0410
## (Intercept)-Sylvilagus_floridanus -5.0324 0.2211 -5.4644 -5.0257 -4.6108 1.0346
## (Intercept)-Meleagris_gallopavo -5.5000 0.3795 -6.2711 -5.5065 -4.7647 1.1570
## (Intercept)-Sciurus_carolinensis -4.8470 0.2973 -5.4213 -4.8511 -4.2608 1.0960
## (Intercept)-Vulpes_vulpes -5.8206 0.5769 -7.1882 -5.7605 -4.8721 1.0233
## (Intercept)-Sus_scrofa -5.3655 0.5175 -6.3486 -5.3511 -4.3647 1.1470
## shrub_cover-Canis_latrans -0.3565 0.2172 -0.7611 -0.3625 0.0679 1.0017
## shrub_cover-Lynx_rufus 0.0077 0.3728 -0.6855 0.0020 0.7308 1.2183
## shrub_cover-Didelphis_virginiana 1.2204 0.3518 0.5301 1.2231 1.9313 1.0474
## shrub_cover-Sylvilagus_floridanus 0.7532 0.3818 0.0475 0.7352 1.5163 1.0526
## shrub_cover-Meleagris_gallopavo -0.5107 0.3692 -1.2316 -0.5211 0.2587 1.1692
## shrub_cover-Sciurus_carolinensis 1.1116 0.3731 0.3255 1.1236 1.8280 1.1934
## shrub_cover-Vulpes_vulpes 0.3411 0.6031 -0.8857 0.3530 1.5225 1.0522
## shrub_cover-Sus_scrofa 1.0318 0.8266 -0.6398 1.0172 2.5186 1.1846
## veg_height-Canis_latrans -0.5146 0.1632 -0.8307 -0.5138 -0.1994 1.0865
## veg_height-Lynx_rufus 0.2261 0.2343 -0.2722 0.2315 0.6571 1.0108
## veg_height-Didelphis_virginiana 0.5527 0.2362 0.1149 0.5379 1.0566 1.0074
## veg_height-Sylvilagus_floridanus 0.1711 0.2391 -0.2996 0.1751 0.6382 1.0256
## veg_height-Meleagris_gallopavo -0.1087 0.3889 -0.8430 -0.1205 0.6709 1.0784
## veg_height-Sciurus_carolinensis 0.2783 0.2198 -0.1484 0.2758 0.7331 1.0155
## veg_height-Vulpes_vulpes -0.2052 0.3043 -0.8124 -0.2051 0.3866 1.0157
## veg_height-Sus_scrofa -0.2369 0.3345 -0.9651 -0.2298 0.3791 1.0203
## ESS
## (Intercept)-Canis_latrans 248
## (Intercept)-Lynx_rufus 65
## (Intercept)-Didelphis_virginiana 141
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Meleagris_gallopavo 111
## (Intercept)-Sciurus_carolinensis 106
## (Intercept)-Vulpes_vulpes 52
## (Intercept)-Sus_scrofa 97
## shrub_cover-Canis_latrans 170
## shrub_cover-Lynx_rufus 44
## shrub_cover-Didelphis_virginiana 134
## shrub_cover-Sylvilagus_floridanus 108
## shrub_cover-Meleagris_gallopavo 168
## shrub_cover-Sciurus_carolinensis 118
## shrub_cover-Vulpes_vulpes 99
## shrub_cover-Sus_scrofa 133
## veg_height-Canis_latrans 224
## veg_height-Lynx_rufus 130
## veg_height-Didelphis_virginiana 244
## veg_height-Sylvilagus_floridanus 183
## veg_height-Meleagris_gallopavo 162
## veg_height-Sciurus_carolinensis 225
## veg_height-Vulpes_vulpes 147
## veg_height-Sus_scrofa 213
#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): 4.0552
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.853 0.3752 -1.6087 -0.8602 -0.1082 1.0024 1329
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0142 1.0958 0.1583 0.7109 3.7816 1.0571 1114
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7686 0.2306 -5.2083 -4.7755 -4.3010 1.0009 582
## week 0.0882 1.7541 -3.3212 0.0943 3.5674 1.0166 482
## I(week^2) -0.0010 1.6519 -3.1738 -0.0302 3.2528 1.0150 869
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.268000e-01 3.726000e-01 0.0513 0.2222 1.218600e+00 1.0022 508
## week 1.711089e+12 1.832616e+13 0.1295 129.6526 1.549347e+12 1.9806 121
## I(week^2) 5.470897e+30 7.459523e+31 0.1277 6393.4837 3.072309e+30 1.7478 208
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2346 0.4157 -0.5500 0.2266 1.0916 1.0182
## (Intercept)-Lynx_rufus -0.1091 0.5460 -0.9822 -0.1649 1.1097 1.0520
## (Intercept)-Didelphis_virginiana -1.3083 0.4119 -2.1324 -1.2906 -0.5412 1.0045
## (Intercept)-Sylvilagus_floridanus -0.6277 0.3929 -1.3915 -0.6344 0.1328 1.0077
## (Intercept)-Meleagris_gallopavo -0.6910 0.4173 -1.4763 -0.6985 0.1576 1.0055
## (Intercept)-Sciurus_carolinensis -1.3163 0.4126 -2.1545 -1.3066 -0.5404 1.0025
## (Intercept)-Vulpes_vulpes -1.5329 0.5834 -2.7368 -1.5109 -0.4488 1.0557
## (Intercept)-Sus_scrofa -1.7323 0.5309 -2.8934 -1.6925 -0.7767 1.0159
## ESS
## (Intercept)-Canis_latrans 1540
## (Intercept)-Lynx_rufus 430
## (Intercept)-Didelphis_virginiana 2071
## (Intercept)-Sylvilagus_floridanus 1433
## (Intercept)-Meleagris_gallopavo 1366
## (Intercept)-Sciurus_carolinensis 1734
## (Intercept)-Vulpes_vulpes 509
## (Intercept)-Sus_scrofa 1129
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.671100e+00 1.597000e-01 -4.990800e+00
## (Intercept)-Lynx_rufus -5.261100e+00 2.857000e-01 -5.843400e+00
## (Intercept)-Didelphis_virginiana -4.361500e+00 2.078000e-01 -4.774600e+00
## (Intercept)-Sylvilagus_floridanus -4.828200e+00 2.180000e-01 -5.304900e+00
## (Intercept)-Meleagris_gallopavo -5.016900e+00 2.443000e-01 -5.511000e+00
## (Intercept)-Sciurus_carolinensis -4.427500e+00 2.281000e-01 -4.878300e+00
## (Intercept)-Vulpes_vulpes -5.314900e+00 4.812000e-01 -6.338000e+00
## (Intercept)-Sus_scrofa -4.840200e+00 3.384000e-01 -5.591200e+00
## week-Canis_latrans 1.322732e+04 1.053003e+06 -1.071144e+05
## week-Lynx_rufus -8.088090e+02 1.399713e+06 -1.188995e+05
## week-Didelphis_virginiana 1.718701e+03 1.124269e+06 -1.113052e+05
## week-Sylvilagus_floridanus -4.886605e+04 1.276879e+06 -1.895190e+05
## week-Meleagris_gallopavo -4.213215e+03 1.127772e+06 -7.728379e+04
## week-Sciurus_carolinensis -2.752102e+04 1.289028e+06 -1.609708e+05
## week-Vulpes_vulpes -2.270983e+04 1.574696e+06 -7.361314e+04
## week-Sus_scrofa 1.430504e+04 1.198406e+06 -9.070825e+04
## I(week^2)-Canis_latrans -3.724773e+13 2.541732e+15 -1.557072e+12
## I(week^2)-Lynx_rufus 4.443411e+13 2.569978e+15 -1.654928e+12
## I(week^2)-Didelphis_virginiana 2.129408e+13 1.933277e+15 -1.228374e+11
## I(week^2)-Sylvilagus_floridanus -5.293396e+13 2.641521e+15 -1.614026e+12
## I(week^2)-Meleagris_gallopavo -2.904382e+13 1.929488e+15 -4.570451e+12
## I(week^2)-Sciurus_carolinensis 3.286199e+13 2.414227e+15 -2.212281e+12
## I(week^2)-Vulpes_vulpes 5.199152e+11 2.682168e+15 -1.250883e+11
## I(week^2)-Sus_scrofa 3.620352e+13 3.005495e+15 -2.872555e+12
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6669 -4.363400e+00 1.0382 255
## (Intercept)-Lynx_rufus -5.2456 -4.719300e+00 1.0750 97
## (Intercept)-Didelphis_virginiana -4.3635 -3.959300e+00 1.0079 383
## (Intercept)-Sylvilagus_floridanus -4.8152 -4.427200e+00 1.1195 214
## (Intercept)-Meleagris_gallopavo -5.0138 -4.558400e+00 1.0100 175
## (Intercept)-Sciurus_carolinensis -4.4270 -3.986100e+00 1.0007 271
## (Intercept)-Vulpes_vulpes -5.2560 -4.520900e+00 1.1006 93
## (Intercept)-Sus_scrofa -4.8102 -4.242500e+00 1.0220 195
## week-Canis_latrans 0.1005 7.662529e+04 1.3063 3940
## week-Lynx_rufus -0.1041 4.509224e+04 1.2904 29906
## week-Didelphis_virginiana 0.3195 8.133676e+04 1.2906 57898
## week-Sylvilagus_floridanus -0.0496 5.759475e+04 1.4283 771
## week-Meleagris_gallopavo 0.1370 1.787654e+05 1.2917 15211
## week-Sciurus_carolinensis 0.1072 8.669266e+04 1.3354 1284
## week-Vulpes_vulpes 0.3121 1.890953e+05 1.3108 2792
## week-Sus_scrofa 0.2429 1.448395e+05 1.3045 15100
## I(week^2)-Canis_latrans -0.3595 3.757146e+12 1.3116 1998
## I(week^2)-Lynx_rufus -0.0931 2.441153e+12 1.3198 2595
## I(week^2)-Didelphis_virginiana -0.0964 4.439834e+12 1.3024 4385
## I(week^2)-Sylvilagus_floridanus -0.1639 3.037633e+12 1.3298 3127
## I(week^2)-Meleagris_gallopavo -0.1602 1.229825e+11 1.3128 8178
## I(week^2)-Sciurus_carolinensis -0.1177 1.406618e+12 1.3087 4808
## I(week^2)-Vulpes_vulpes -0.0150 4.859273e+12 1.2904 37289
## I(week^2)-Sus_scrofa -0.1308 1.691798e+12 1.3047 3115
#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): 4.3992
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5718 0.9700 -3.3717 -1.6180 0.5174 1.0089 662
## Cogon_Patch_Size -0.4338 0.8285 -2.0858 -0.4203 1.2104 1.0021 734
## Veg_shannon_index 0.7540 0.6451 -0.5003 0.7494 2.0571 1.0259 559
## total_shrub_cover -0.5544 0.7457 -2.1818 -0.5271 0.9025 1.0215 900
## Avg_Cogongrass_Cover 1.8004 0.7678 0.3196 1.7845 3.4171 0.9999 281
## Tree_Density -2.0343 0.7568 -3.6670 -1.9646 -0.7074 1.1052 175
## Avg_Canopy_Cover 1.8411 0.6779 0.4865 1.8058 3.2929 1.0080 580
## avg_veg_height -0.7509 0.5344 -1.7444 -0.7702 0.3730 1.0094 300
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.9804 11.3573 0.7635 5.6886 36.7757 1.1050 142
## Cogon_Patch_Size 5.2572 8.1040 0.2106 2.8786 25.3652 1.0219 232
## Veg_shannon_index 2.1768 3.8061 0.0766 1.1657 9.7465 1.0149 582
## total_shrub_cover 4.2698 5.9397 0.1888 2.4753 19.0882 1.0910 227
## Avg_Cogongrass_Cover 2.0305 3.8208 0.0592 0.7529 11.8969 1.0606 230
## Tree_Density 1.5468 3.4581 0.0490 0.5587 9.0920 1.1482 336
## Avg_Canopy_Cover 2.9134 4.5256 0.1457 1.5575 14.2603 1.0609 225
## avg_veg_height 0.5350 0.8475 0.0411 0.2717 2.7143 1.0257 820
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.886 2.5241 0.0566 0.8588 9.3118 1.026 62
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7987 0.2858 -5.3111 -4.8201 -4.2288 1.0100 1203
## week 0.0277 1.6673 -3.2497 0.0565 3.1492 1.0085 456
## I(week^2) 0.1162 1.6833 -3.2102 0.0686 3.3768 1.0406 436
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.151000e-01 6.635000e-01 0.0852 0.3477 1.978500e+00 1.1088 338
## week 1.837283e+21 3.297818e+22 0.1007 432.1686 1.766753e+19 1.5703 335
## I(week^2) 4.137276e+19 8.578706e+20 0.1280 92.4242 1.527861e+18 1.5049 472
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7854 1.3183 -1.8395 0.7436
## (Intercept)-Lynx_rufus 1.2793 2.4595 -2.0276 0.9105
## (Intercept)-Didelphis_virginiana -3.0775 1.1323 -5.5673 -3.0075
## (Intercept)-Sylvilagus_floridanus -1.7975 1.2058 -4.2730 -1.7694
## (Intercept)-Meleagris_gallopavo -2.2986 1.3097 -5.0677 -2.2235
## (Intercept)-Sciurus_carolinensis -3.4265 1.2489 -6.3065 -3.2752
## (Intercept)-Vulpes_vulpes -3.2432 1.6949 -6.8550 -3.1548
## (Intercept)-Sus_scrofa -4.8015 1.7438 -8.7718 -4.6452
## Cogon_Patch_Size-Canis_latrans 1.1288 1.5017 -0.8921 0.8196
## Cogon_Patch_Size-Lynx_rufus 0.0032 1.7345 -2.9769 -0.1434
## Cogon_Patch_Size-Didelphis_virginiana 1.3162 1.1479 -0.4102 1.1671
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2791 1.9344 -7.0958 -1.7889
## Cogon_Patch_Size-Meleagris_gallopavo -0.1468 1.2038 -2.2867 -0.2674
## Cogon_Patch_Size-Sciurus_carolinensis -1.8918 1.4686 -5.4007 -1.6018
## Cogon_Patch_Size-Vulpes_vulpes -1.3976 1.8664 -5.5955 -1.1875
## Cogon_Patch_Size-Sus_scrofa -1.2166 1.5122 -5.0245 -0.9906
## Veg_shannon_index-Canis_latrans 1.4044 0.7650 0.0738 1.3470
## Veg_shannon_index-Lynx_rufus -0.0265 1.2759 -3.0644 0.1002
## Veg_shannon_index-Didelphis_virginiana 1.0824 0.8539 -0.4331 1.0234
## Veg_shannon_index-Sylvilagus_floridanus 1.0660 0.8378 -0.3619 0.9891
## Veg_shannon_index-Meleagris_gallopavo 1.4853 0.9787 -0.1255 1.3716
## Veg_shannon_index-Sciurus_carolinensis -0.1985 0.9166 -2.1810 -0.1413
## Veg_shannon_index-Vulpes_vulpes -0.1826 1.1690 -2.6518 -0.0434
## Veg_shannon_index-Sus_scrofa 1.9019 1.2849 -0.0016 1.6774
## total_shrub_cover-Canis_latrans 0.8287 1.0546 -0.6847 0.6648
## total_shrub_cover-Lynx_rufus -1.8993 1.6821 -6.0183 -1.6309
## total_shrub_cover-Didelphis_virginiana -0.7480 0.8956 -2.7073 -0.6897
## total_shrub_cover-Sylvilagus_floridanus -0.1540 1.0882 -2.5746 -0.1443
## total_shrub_cover-Meleagris_gallopavo -3.2640 1.7462 -7.3222 -3.0901
## total_shrub_cover-Sciurus_carolinensis 0.1521 0.8258 -1.3811 0.1170
## total_shrub_cover-Vulpes_vulpes -0.8721 1.2841 -4.0429 -0.7137
## total_shrub_cover-Sus_scrofa 0.3615 1.0827 -1.6810 0.3129
## Avg_Cogongrass_Cover-Canis_latrans 2.3948 1.0360 0.6934 2.2685
## Avg_Cogongrass_Cover-Lynx_rufus 2.6331 1.3217 0.6717 2.4187
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1268 0.9730 0.5059 2.0440
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3408 0.9866 -0.6683 1.3443
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0449 1.4068 -2.1595 1.2470
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3182 1.0110 0.6898 2.1922
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4759 1.2746 0.4898 2.2773
## Avg_Cogongrass_Cover-Sus_scrofa 1.2767 1.2771 -1.8791 1.3795
## Tree_Density-Canis_latrans -2.5185 1.1551 -5.4548 -2.3348
## Tree_Density-Lynx_rufus -1.3125 1.1288 -3.3687 -1.4127
## Tree_Density-Didelphis_virginiana -2.2470 1.0217 -4.7040 -2.1268
## Tree_Density-Sylvilagus_floridanus -2.3951 1.1735 -5.2878 -2.2386
## Tree_Density-Meleagris_gallopavo -2.1902 1.0600 -4.5239 -2.1102
## Tree_Density-Sciurus_carolinensis -2.3827 1.1559 -5.1952 -2.2163
## Tree_Density-Vulpes_vulpes -2.1034 1.2099 -4.8771 -1.9891
## Tree_Density-Sus_scrofa -2.1607 1.2955 -5.0695 -2.0291
## Avg_Canopy_Cover-Canis_latrans 0.3362 0.7949 -1.2179 0.3221
## Avg_Canopy_Cover-Lynx_rufus 0.9749 1.4259 -1.8336 0.9388
## Avg_Canopy_Cover-Didelphis_virginiana 2.8357 1.0311 1.3172 2.6774
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4364 1.6611 1.2834 3.0734
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2564 1.0569 0.6578 2.0975
## Avg_Canopy_Cover-Sciurus_carolinensis 2.4011 0.8931 1.0100 2.2819
## Avg_Canopy_Cover-Vulpes_vulpes 2.2866 1.1552 0.6168 2.0902
## Avg_Canopy_Cover-Sus_scrofa 2.1587 0.9240 0.6159 2.0625
## avg_veg_height-Canis_latrans -0.7274 0.6454 -1.9963 -0.7321
## avg_veg_height-Lynx_rufus -0.8544 0.8093 -2.5642 -0.8582
## avg_veg_height-Didelphis_virginiana -0.8287 0.6942 -2.2391 -0.8181
## avg_veg_height-Sylvilagus_floridanus -0.8241 0.6865 -2.1942 -0.8121
## avg_veg_height-Meleagris_gallopavo -0.8795 0.8053 -2.5726 -0.8608
## avg_veg_height-Sciurus_carolinensis -0.3610 0.6936 -1.6311 -0.4094
## avg_veg_height-Vulpes_vulpes -0.8534 0.7950 -2.4997 -0.8464
## avg_veg_height-Sus_scrofa -0.7865 0.7245 -2.2186 -0.7793
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.4675 1.0242 171
## (Intercept)-Lynx_rufus 6.7224 1.2581 55
## (Intercept)-Didelphis_virginiana -1.1105 1.0063 501
## (Intercept)-Sylvilagus_floridanus 0.6554 1.0202 358
## (Intercept)-Meleagris_gallopavo 0.1245 1.0316 311
## (Intercept)-Sciurus_carolinensis -1.3810 1.0203 389
## (Intercept)-Vulpes_vulpes -0.0860 1.0101 195
## (Intercept)-Sus_scrofa -1.8815 1.0430 269
## Cogon_Patch_Size-Canis_latrans 5.0761 1.0291 388
## Cogon_Patch_Size-Lynx_rufus 3.7824 1.0151 259
## Cogon_Patch_Size-Didelphis_virginiana 3.8206 1.0281 268
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0706 1.0264 127
## Cogon_Patch_Size-Meleagris_gallopavo 2.6228 1.0118 480
## Cogon_Patch_Size-Sciurus_carolinensis 0.1328 1.0398 340
## Cogon_Patch_Size-Vulpes_vulpes 1.6760 1.0157 235
## Cogon_Patch_Size-Sus_scrofa 1.1900 1.0239 437
## Veg_shannon_index-Canis_latrans 3.0682 1.0004 579
## Veg_shannon_index-Lynx_rufus 2.2401 1.1057 280
## Veg_shannon_index-Didelphis_virginiana 2.9269 1.0054 855
## Veg_shannon_index-Sylvilagus_floridanus 2.9832 1.0191 574
## Veg_shannon_index-Meleagris_gallopavo 3.8233 1.0063 376
## Veg_shannon_index-Sciurus_carolinensis 1.3512 1.0199 405
## Veg_shannon_index-Vulpes_vulpes 1.7929 1.0501 311
## Veg_shannon_index-Sus_scrofa 5.0104 1.0149 404
## total_shrub_cover-Canis_latrans 3.2158 1.0438 214
## total_shrub_cover-Lynx_rufus 0.5777 1.0928 190
## total_shrub_cover-Didelphis_virginiana 0.8595 1.0211 550
## total_shrub_cover-Sylvilagus_floridanus 2.0977 1.0187 578
## total_shrub_cover-Meleagris_gallopavo -0.5849 1.0344 173
## total_shrub_cover-Sciurus_carolinensis 1.8140 1.0087 919
## total_shrub_cover-Vulpes_vulpes 1.2140 1.0939 425
## total_shrub_cover-Sus_scrofa 2.6272 0.9999 864
## Avg_Cogongrass_Cover-Canis_latrans 4.7688 1.0228 303
## Avg_Cogongrass_Cover-Lynx_rufus 5.8240 1.0553 247
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.3097 1.0073 338
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2821 1.0119 497
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4425 1.0050 309
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5809 1.0137 229
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.6873 1.0666 248
## Avg_Cogongrass_Cover-Sus_scrofa 3.4963 1.0149 373
## Tree_Density-Canis_latrans -0.7518 1.0596 243
## Tree_Density-Lynx_rufus 1.0490 1.0224 236
## Tree_Density-Didelphis_virginiana -0.5564 1.1051 260
## Tree_Density-Sylvilagus_floridanus -0.6054 1.0929 271
## Tree_Density-Meleagris_gallopavo -0.2710 1.0724 349
## Tree_Density-Sciurus_carolinensis -0.7333 1.1211 242
## Tree_Density-Vulpes_vulpes 0.0262 1.0687 286
## Tree_Density-Sus_scrofa -0.0215 1.1174 336
## Avg_Canopy_Cover-Canis_latrans 1.9174 1.0034 347
## Avg_Canopy_Cover-Lynx_rufus 3.9753 1.0041 226
## Avg_Canopy_Cover-Didelphis_virginiana 5.3437 1.0155 287
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.5945 1.0245 193
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8610 1.0228 300
## Avg_Canopy_Cover-Sciurus_carolinensis 4.4588 1.0122 423
## Avg_Canopy_Cover-Vulpes_vulpes 5.2117 1.1406 236
## Avg_Canopy_Cover-Sus_scrofa 4.1701 1.0363 464
## avg_veg_height-Canis_latrans 0.5529 1.0011 555
## avg_veg_height-Lynx_rufus 0.7785 1.0060 388
## avg_veg_height-Didelphis_virginiana 0.5249 1.0134 574
## avg_veg_height-Sylvilagus_floridanus 0.5606 1.0075 485
## avg_veg_height-Meleagris_gallopavo 0.6959 1.0143 406
## avg_veg_height-Sciurus_carolinensis 1.1336 1.0068 475
## avg_veg_height-Vulpes_vulpes 0.6545 1.0075 418
## avg_veg_height-Sus_scrofa 0.6213 1.0032 490
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.680200e+00 1.614000e-01 -5.020000e+00
## (Intercept)-Lynx_rufus -5.539500e+00 2.879000e-01 -6.087900e+00
## (Intercept)-Didelphis_virginiana -4.313400e+00 2.211000e-01 -4.770500e+00
## (Intercept)-Sylvilagus_floridanus -4.932000e+00 2.276000e-01 -5.403300e+00
## (Intercept)-Meleagris_gallopavo -5.103600e+00 2.580000e-01 -5.653400e+00
## (Intercept)-Sciurus_carolinensis -4.383700e+00 2.259000e-01 -4.843900e+00
## (Intercept)-Vulpes_vulpes -5.606000e+00 5.264000e-01 -6.667900e+00
## (Intercept)-Sus_scrofa -4.766200e+00 3.609000e-01 -5.503600e+00
## week-Canis_latrans 3.563269e+08 3.643567e+10 -3.581930e+08
## week-Lynx_rufus 1.789553e+09 4.666896e+10 -1.295699e+08
## week-Didelphis_virginiana -1.573222e+08 4.418806e+10 -2.928113e+08
## week-Sylvilagus_floridanus -1.013642e+09 3.001810e+10 -1.975747e+08
## week-Meleagris_gallopavo 7.629637e+08 3.917553e+10 -1.462040e+08
## week-Sciurus_carolinensis 1.212906e+09 4.056958e+10 -1.563112e+08
## week-Vulpes_vulpes -1.522750e+09 4.934153e+10 -2.676124e+08
## week-Sus_scrofa -1.481397e+08 3.918221e+10 -3.030846e+08
## I(week^2)-Canis_latrans -1.077959e+08 6.409847e+09 -2.138488e+07
## I(week^2)-Lynx_rufus -1.276966e+08 5.805552e+09 -1.683600e+07
## I(week^2)-Didelphis_virginiana -1.118987e+08 7.157481e+09 -3.805102e+07
## I(week^2)-Sylvilagus_floridanus -1.503289e+08 4.927232e+09 -4.759364e+07
## I(week^2)-Meleagris_gallopavo -4.573702e+07 6.482389e+09 -1.986408e+07
## I(week^2)-Sciurus_carolinensis 2.170554e+07 4.888916e+09 -2.822147e+07
## I(week^2)-Vulpes_vulpes 3.871306e+06 6.049225e+09 -3.085048e+07
## I(week^2)-Sus_scrofa -4.511884e+06 6.134990e+09 -2.849168e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6763 -4.378400e+00 1.0340 215
## (Intercept)-Lynx_rufus -5.5314 -4.990900e+00 1.1107 77
## (Intercept)-Didelphis_virginiana -4.3075 -3.903500e+00 1.0127 301
## (Intercept)-Sylvilagus_floridanus -4.9238 -4.502900e+00 1.0029 192
## (Intercept)-Meleagris_gallopavo -5.0898 -4.633500e+00 1.0759 155
## (Intercept)-Sciurus_carolinensis -4.3754 -3.956400e+00 1.0000 323
## (Intercept)-Vulpes_vulpes -5.5413 -4.711300e+00 1.1554 59
## (Intercept)-Sus_scrofa -4.7469 -4.125000e+00 1.0085 257
## week-Canis_latrans -0.0323 1.676718e+08 1.2999 11296
## week-Lynx_rufus -0.0272 5.489351e+08 1.4297 736
## week-Didelphis_virginiana -0.0979 1.038291e+08 1.2916 65560
## week-Sylvilagus_floridanus -0.1183 3.145279e+08 1.3996 841
## week-Meleagris_gallopavo 0.0579 3.274458e+08 1.3277 2801
## week-Sciurus_carolinensis 0.0536 3.518276e+08 1.3767 1103
## week-Vulpes_vulpes 0.1720 1.497652e+08 1.3822 1161
## week-Sus_scrofa -0.0190 1.207090e+08 1.2918 78113
## I(week^2)-Canis_latrans 0.3922 3.630638e+07 1.3182 3004
## I(week^2)-Lynx_rufus 0.2747 3.434782e+07 1.3378 2165
## I(week^2)-Didelphis_virginiana 0.0968 1.425479e+07 1.3145 3869
## I(week^2)-Sylvilagus_floridanus 0.2375 1.956901e+07 1.3800 1613
## I(week^2)-Meleagris_gallopavo 0.2640 4.108642e+07 1.2953 21328
## I(week^2)-Sciurus_carolinensis 0.2552 2.451143e+07 1.2923 26054
## I(week^2)-Vulpes_vulpes 0.3434 2.772058e+07 1.2904 58169
## I(week^2)-Sus_scrofa 0.1590 2.783228e+07 1.2904 99485
#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): 4.1553
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1309 0.4944 -2.1860 -1.1122 -0.1910 1.0240 405
## Avg_Cogongrass_Cover 0.1653 0.4085 -0.6772 0.1564 0.9495 1.0231 695
## total_shrub_cover -0.3469 0.4161 -1.2298 -0.3232 0.4610 1.0009 1191
## avg_veg_height -0.0909 0.3379 -0.7814 -0.0810 0.5513 1.0189 573
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0161 1.4537 0.0542 0.6239 4.4244 1.0351 596
## Avg_Cogongrass_Cover 0.6226 0.9483 0.0491 0.3314 2.7518 1.0306 895
## total_shrub_cover 1.0017 1.2188 0.0736 0.6214 4.1272 1.0048 721
## avg_veg_height 0.2916 0.3425 0.0363 0.1881 1.2027 1.0038 1362
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0527 2.0443 0.1475 1.4656 7.6489 1.1158 121
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7752 0.2604 -5.2461 -4.7879 -4.2487 1.0085 906
## week 0.0148 1.7014 -3.3499 -0.0171 3.2529 1.0272 599
## I(week^2) -0.1019 1.6982 -3.4755 -0.1463 3.2509 1.0369 352
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.930000e-01 5.308000e-01 0.0555 0.2584 1.460300e+00 1.1285 260
## week 9.766930e+14 1.220570e+16 0.1084 404.0213 6.932805e+14 1.8147 141
## I(week^2) 2.753246e+06 3.518142e+07 0.0652 11.8121 9.065118e+06 1.7091 229
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2731 0.8075 -1.8788 -0.3032
## (Intercept)-Lynx_rufus -0.6837 0.7946 -2.1912 -0.7229
## (Intercept)-Didelphis_virginiana -1.3917 0.6308 -2.7785 -1.3498
## (Intercept)-Sylvilagus_floridanus -0.9655 0.6744 -2.3769 -0.9520
## (Intercept)-Meleagris_gallopavo -1.2112 0.6807 -2.6601 -1.1774
## (Intercept)-Sciurus_carolinensis -1.5393 0.6762 -3.0178 -1.4898
## (Intercept)-Vulpes_vulpes -1.5390 0.8425 -3.2841 -1.5100
## (Intercept)-Sus_scrofa -1.8177 0.8019 -3.5876 -1.7511
## Avg_Cogongrass_Cover-Canis_latrans 0.5222 0.5227 -0.4299 0.4890
## Avg_Cogongrass_Cover-Lynx_rufus 0.6145 0.6028 -0.3886 0.5569
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4427 0.4935 -0.4696 0.4212
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2794 0.5681 -1.5833 -0.2410
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4065 0.6659 -2.0053 -0.3266
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3108 0.4699 -0.5919 0.3044
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3553 0.5681 -0.7191 0.3295
## Avg_Cogongrass_Cover-Sus_scrofa -0.1706 0.6929 -1.7826 -0.1031
## total_shrub_cover-Canis_latrans 0.4563 0.5391 -0.4953 0.4121
## total_shrub_cover-Lynx_rufus -0.9656 0.7390 -2.6813 -0.8608
## total_shrub_cover-Didelphis_virginiana -0.2348 0.4646 -1.2064 -0.2203
## total_shrub_cover-Sylvilagus_floridanus -0.3727 0.5233 -1.4977 -0.3431
## total_shrub_cover-Meleagris_gallopavo -1.4803 0.7908 -3.2636 -1.3702
## total_shrub_cover-Sciurus_carolinensis -0.0644 0.4652 -0.9659 -0.0723
## total_shrub_cover-Vulpes_vulpes -0.4369 0.6809 -1.9648 -0.3798
## total_shrub_cover-Sus_scrofa 0.1263 0.6093 -1.0036 0.1033
## avg_veg_height-Canis_latrans -0.0632 0.4442 -0.9383 -0.0542
## avg_veg_height-Lynx_rufus -0.1035 0.5172 -1.1297 -0.0930
## avg_veg_height-Didelphis_virginiana -0.1031 0.4524 -1.0363 -0.0927
## avg_veg_height-Sylvilagus_floridanus -0.2070 0.4575 -1.1540 -0.1953
## avg_veg_height-Meleagris_gallopavo -0.3090 0.5169 -1.4411 -0.2745
## avg_veg_height-Sciurus_carolinensis 0.2462 0.4714 -0.6361 0.2310
## avg_veg_height-Vulpes_vulpes -0.1626 0.5091 -1.2403 -0.1488
## avg_veg_height-Sus_scrofa -0.0652 0.4824 -1.0114 -0.0637
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3167 1.0304 294
## (Intercept)-Lynx_rufus 1.0173 1.0384 365
## (Intercept)-Didelphis_virginiana -0.2240 1.0301 440
## (Intercept)-Sylvilagus_floridanus 0.3360 1.0051 447
## (Intercept)-Meleagris_gallopavo 0.0864 1.0073 572
## (Intercept)-Sciurus_carolinensis -0.3623 1.0034 543
## (Intercept)-Vulpes_vulpes 0.1271 1.0147 469
## (Intercept)-Sus_scrofa -0.4293 1.0000 615
## Avg_Cogongrass_Cover-Canis_latrans 1.6396 1.0039 1059
## Avg_Cogongrass_Cover-Lynx_rufus 1.9561 1.0019 997
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5068 1.0019 1092
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7065 1.0172 893
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6951 1.0354 822
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2400 1.0066 1126
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5195 1.0062 1015
## Avg_Cogongrass_Cover-Sus_scrofa 1.0148 1.0182 771
## total_shrub_cover-Canis_latrans 1.5999 1.0016 1089
## total_shrub_cover-Lynx_rufus 0.2372 1.0036 531
## total_shrub_cover-Didelphis_virginiana 0.6562 0.9999 2107
## total_shrub_cover-Sylvilagus_floridanus 0.6029 1.0033 1294
## total_shrub_cover-Meleagris_gallopavo -0.2164 1.0145 460
## total_shrub_cover-Sciurus_carolinensis 0.8651 1.0007 2207
## total_shrub_cover-Vulpes_vulpes 0.7516 1.0043 671
## total_shrub_cover-Sus_scrofa 1.3920 1.0126 1270
## avg_veg_height-Canis_latrans 0.8079 1.0115 1042
## avg_veg_height-Lynx_rufus 0.9450 1.0134 1116
## avg_veg_height-Didelphis_virginiana 0.7491 1.0022 1013
## avg_veg_height-Sylvilagus_floridanus 0.6787 1.0157 937
## avg_veg_height-Meleagris_gallopavo 0.6428 1.0175 935
## avg_veg_height-Sciurus_carolinensis 1.2318 1.0066 1030
## avg_veg_height-Vulpes_vulpes 0.8048 1.0135 954
## avg_veg_height-Sus_scrofa 0.9059 1.0157 1112
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6700 1.568000e-01 -4.9752
## (Intercept)-Lynx_rufus -5.3176 2.432000e-01 -5.7877
## (Intercept)-Didelphis_virginiana -4.3477 2.365000e-01 -4.8420
## (Intercept)-Sylvilagus_floridanus -4.9278 2.300000e-01 -5.4109
## (Intercept)-Meleagris_gallopavo -4.9766 2.313000e-01 -5.4420
## (Intercept)-Sciurus_carolinensis -4.3963 2.296000e-01 -4.8470
## (Intercept)-Vulpes_vulpes -5.4880 5.599000e-01 -6.7529
## (Intercept)-Sus_scrofa -4.7944 3.691000e-01 -5.5976
## week-Canis_latrans 378106.1651 2.755898e+07 -633791.2532
## week-Lynx_rufus 776621.0824 3.371390e+07 -453648.4203
## week-Didelphis_virginiana 418058.5448 3.030260e+07 -425882.3884
## week-Sylvilagus_floridanus -408881.0505 3.283451e+07 -886518.3053
## week-Meleagris_gallopavo -66046.3968 2.588054e+07 -1065438.7395
## week-Sciurus_carolinensis 113908.6769 2.917338e+07 -689100.1358
## week-Vulpes_vulpes 933395.2416 2.759822e+07 -364927.2184
## week-Sus_scrofa 505552.3063 2.746021e+07 -320547.9253
## I(week^2)-Canis_latrans 40.9930 1.610036e+03 -827.6991
## I(week^2)-Lynx_rufus 66.5181 1.300698e+03 -648.8755
## I(week^2)-Didelphis_virginiana -2.9568 1.246559e+03 -769.9779
## I(week^2)-Sylvilagus_floridanus -27.5436 1.514399e+03 -1030.1613
## I(week^2)-Meleagris_gallopavo 14.8914 1.462576e+03 -836.9914
## I(week^2)-Sciurus_carolinensis 5.7498 1.494713e+03 -769.8485
## I(week^2)-Vulpes_vulpes 47.2431 1.538320e+03 -761.9720
## I(week^2)-Sus_scrofa -26.2282 1.336564e+03 -1005.4709
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6664 -4.3680 1.0300 236
## (Intercept)-Lynx_rufus -5.3178 -4.8593 1.0269 132
## (Intercept)-Didelphis_virginiana -4.3375 -3.9147 1.0394 268
## (Intercept)-Sylvilagus_floridanus -4.9193 -4.5103 1.0056 162
## (Intercept)-Meleagris_gallopavo -4.9667 -4.5341 1.0247 212
## (Intercept)-Sciurus_carolinensis -4.3932 -3.9692 1.0471 338
## (Intercept)-Vulpes_vulpes -5.4037 -4.6047 1.1599 63
## (Intercept)-Sus_scrofa -4.7745 -4.1113 1.1076 240
## week-Canis_latrans -0.4146 963582.6710 1.3035 10505
## week-Lynx_rufus -0.0620 951107.8122 1.3290 4506
## week-Didelphis_virginiana -0.2603 556882.8931 1.3150 2827
## week-Sylvilagus_floridanus -0.1393 416816.6875 1.2982 3249
## week-Meleagris_gallopavo 0.1442 631490.2469 1.2899 108828
## week-Sciurus_carolinensis -0.1964 385111.0299 1.2871 9210
## week-Vulpes_vulpes -0.3939 1388016.7051 1.3965 728
## week-Sus_scrofa -0.3099 1135331.2151 1.3139 7593
## I(week^2)-Canis_latrans -0.2443 933.0981 1.2803 2133
## I(week^2)-Lynx_rufus -0.1820 1135.2176 1.3489 1000
## I(week^2)-Didelphis_virginiana -0.1972 861.9675 1.2783 1556
## I(week^2)-Sylvilagus_floridanus -0.3113 782.6381 1.2634 4359
## I(week^2)-Meleagris_gallopavo -0.1725 1011.8508 1.2735 4379
## I(week^2)-Sciurus_carolinensis -0.2627 933.1009 1.2428 9206
## I(week^2)-Vulpes_vulpes -0.2764 898.9105 1.3013 2156
## I(week^2)-Sus_scrofa -0.3553 738.6195 1.2832 3988
#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): 4.2325
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1785 0.5676 -2.3040 -1.1851 0.0250 1.0039 603
## Tree_Density -0.8046 0.4667 -1.8422 -0.7629 -0.0114 1.0313 612
## Avg_Canopy_Cover 1.0826 0.4359 0.2990 1.0622 2.0355 1.0099 580
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0165 2.1710 0.1579 1.3872 7.7878 1.0053 351
## Tree_Density 0.7841 1.2702 0.0482 0.4075 3.6960 1.0041 661
## Avg_Canopy_Cover 0.9082 1.0905 0.0893 0.5842 3.6676 1.0251 808
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8816 1.3246 0.0468 0.4901 4.0288 1.1708 145
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7863 0.2536 -5.2636 -4.7924 -4.2673 1.0043 1139
## week 0.1116 1.6386 -2.9590 0.0786 3.4137 1.0385 520
## I(week^2) 0.0243 1.6597 -3.1224 -0.0439 3.3210 1.0080 717
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.167000e-01 5.647000e-01 0.0679 0.2838 1.458500e+00 1.1013 596
## week 2.588474e+09 5.251076e+10 0.0866 47.9189 4.349198e+08 1.5138 564
## I(week^2) 1.519510e+22 4.465845e+23 0.1527 11957.8453 8.146464e+20 1.4011 980
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1453 0.7259 -1.3448 0.1455 1.5904
## (Intercept)-Lynx_rufus -0.0773 1.0745 -1.8251 -0.2171 2.4343
## (Intercept)-Didelphis_virginiana -1.8795 0.7138 -3.4589 -1.8299 -0.6024
## (Intercept)-Sylvilagus_floridanus -1.0460 0.6760 -2.4197 -1.0504 0.2889
## (Intercept)-Meleagris_gallopavo -0.9219 0.7069 -2.2489 -0.9445 0.5068
## (Intercept)-Sciurus_carolinensis -1.9580 0.7417 -3.5593 -1.9019 -0.6478
## (Intercept)-Vulpes_vulpes -2.0061 0.9295 -3.9817 -1.9603 -0.3397
## (Intercept)-Sus_scrofa -2.4691 0.8662 -4.3724 -2.3965 -0.9793
## Tree_Density-Canis_latrans -0.9833 0.5975 -2.3209 -0.9131 0.0028
## Tree_Density-Lynx_rufus 0.0147 0.6589 -1.0412 -0.0454 1.5759
## Tree_Density-Didelphis_virginiana -1.0378 0.7383 -2.7757 -0.9346 0.1268
## Tree_Density-Sylvilagus_floridanus -1.1416 0.7914 -3.0584 -1.0058 0.0077
## Tree_Density-Meleagris_gallopavo -0.9309 0.6991 -2.5786 -0.8446 0.1695
## Tree_Density-Sciurus_carolinensis -0.9609 0.7281 -2.7846 -0.8491 0.1589
## Tree_Density-Vulpes_vulpes -0.6719 0.7123 -2.2063 -0.6450 0.6849
## Tree_Density-Sus_scrofa -0.9687 0.8147 -3.0144 -0.8527 0.2593
## Avg_Canopy_Cover-Canis_latrans 0.0423 0.4874 -0.9152 0.0454 0.9915
## Avg_Canopy_Cover-Lynx_rufus 0.7560 0.7588 -0.4927 0.6562 2.6332
## Avg_Canopy_Cover-Didelphis_virginiana 1.3341 0.5505 0.3944 1.2866 2.5877
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8164 0.8355 0.6183 1.6633 4.0177
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4606 0.7362 0.3211 1.3630 3.1634
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2794 0.5227 0.3730 1.2343 2.4054
## Avg_Canopy_Cover-Vulpes_vulpes 1.0549 0.6226 -0.0638 1.0114 2.4345
## Avg_Canopy_Cover-Sus_scrofa 1.2403 0.5722 0.2716 1.1783 2.5086
## Rhat ESS
## (Intercept)-Canis_latrans 1.0493 369
## (Intercept)-Lynx_rufus 1.0416 102
## (Intercept)-Didelphis_virginiana 1.0004 807
## (Intercept)-Sylvilagus_floridanus 1.0074 788
## (Intercept)-Meleagris_gallopavo 1.0199 569
## (Intercept)-Sciurus_carolinensis 1.0074 908
## (Intercept)-Vulpes_vulpes 1.0142 351
## (Intercept)-Sus_scrofa 1.0118 512
## Tree_Density-Canis_latrans 1.0194 853
## Tree_Density-Lynx_rufus 1.0037 760
## Tree_Density-Didelphis_virginiana 1.0011 670
## Tree_Density-Sylvilagus_floridanus 1.0056 688
## Tree_Density-Meleagris_gallopavo 1.0059 807
## Tree_Density-Sciurus_carolinensis 1.0071 771
## Tree_Density-Vulpes_vulpes 1.0182 811
## Tree_Density-Sus_scrofa 1.0176 823
## Avg_Canopy_Cover-Canis_latrans 1.0084 963
## Avg_Canopy_Cover-Lynx_rufus 1.0023 371
## Avg_Canopy_Cover-Didelphis_virginiana 1.0031 911
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0190 558
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0385 585
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0105 1095
## Avg_Canopy_Cover-Vulpes_vulpes 1.0138 910
## Avg_Canopy_Cover-Sus_scrofa 1.0161 916
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.671700e+00 1.569000e-01 -5.000600e+00
## (Intercept)-Lynx_rufus -5.378500e+00 3.305000e-01 -6.060600e+00
## (Intercept)-Didelphis_virginiana -4.363700e+00 2.306000e-01 -4.838900e+00
## (Intercept)-Sylvilagus_floridanus -4.863600e+00 2.287000e-01 -5.347800e+00
## (Intercept)-Meleagris_gallopavo -5.082300e+00 2.571000e-01 -5.605800e+00
## (Intercept)-Sciurus_carolinensis -4.390100e+00 2.355000e-01 -4.861900e+00
## (Intercept)-Vulpes_vulpes -5.482200e+00 4.882000e-01 -6.549300e+00
## (Intercept)-Sus_scrofa -4.755000e+00 3.393000e-01 -5.473800e+00
## week-Canis_latrans -2.656516e+02 3.600601e+04 -3.621652e+03
## week-Lynx_rufus -3.939472e+02 3.492707e+04 -2.787059e+03
## week-Didelphis_virginiana 1.115400e+03 3.683636e+04 -3.542345e+03
## week-Sylvilagus_floridanus -4.761641e+02 3.900261e+04 -2.796793e+03
## week-Meleagris_gallopavo -1.290681e+02 4.933831e+04 -2.380171e+03
## week-Sciurus_carolinensis -3.722147e+02 5.489380e+04 -2.808745e+03
## week-Vulpes_vulpes 8.629732e+02 4.888150e+04 -2.619541e+03
## week-Sus_scrofa 1.688994e+03 5.249791e+04 -2.240224e+03
## I(week^2)-Canis_latrans 5.229102e+08 1.172426e+11 -3.799466e+09
## I(week^2)-Lynx_rufus -2.914520e+09 9.440073e+10 -2.596990e+09
## I(week^2)-Didelphis_virginiana -3.690893e+08 1.949747e+11 -2.915791e+09
## I(week^2)-Sylvilagus_floridanus 1.035877e+09 1.217625e+11 -2.596425e+09
## I(week^2)-Meleagris_gallopavo 3.712731e+09 1.185754e+11 -1.690862e+09
## I(week^2)-Sciurus_carolinensis 2.676286e+09 1.547192e+11 -2.527032e+09
## I(week^2)-Vulpes_vulpes -2.355334e+09 1.318076e+11 -3.608594e+09
## I(week^2)-Sus_scrofa -2.484112e+09 2.054537e+11 -2.599799e+09
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6630 -4.386300e+00 1.0776 225
## (Intercept)-Lynx_rufus -5.3563 -4.782300e+00 1.1292 68
## (Intercept)-Didelphis_virginiana -4.3533 -3.938600e+00 1.0001 282
## (Intercept)-Sylvilagus_floridanus -4.8548 -4.453600e+00 1.0309 177
## (Intercept)-Meleagris_gallopavo -5.0700 -4.614100e+00 1.0419 155
## (Intercept)-Sciurus_carolinensis -4.3772 -3.947400e+00 0.9998 292
## (Intercept)-Vulpes_vulpes -5.4391 -4.667100e+00 1.0787 113
## (Intercept)-Sus_scrofa -4.7458 -4.143300e+00 1.0253 293
## week-Canis_latrans 0.2695 1.807043e+03 1.2955 24125
## week-Lynx_rufus 0.3042 2.966870e+03 1.3033 21801
## week-Didelphis_virginiana 0.1492 1.942277e+03 1.3792 772
## week-Sylvilagus_floridanus 0.2577 2.320223e+03 1.3047 7892
## week-Meleagris_gallopavo 0.1044 2.161212e+03 1.2910 176329
## week-Sciurus_carolinensis 0.4263 3.195897e+03 1.2948 12846
## week-Vulpes_vulpes 0.1409 3.387131e+03 1.3217 3312
## week-Sus_scrofa 0.3333 4.706225e+03 1.3892 780
## I(week^2)-Canis_latrans -0.5390 1.893329e+09 1.2922 19597
## I(week^2)-Lynx_rufus -0.2105 2.486435e+09 1.3847 1018
## I(week^2)-Didelphis_virginiana -0.2570 2.969881e+09 1.2907 130432
## I(week^2)-Sylvilagus_floridanus 0.0513 2.646716e+09 1.2975 13884
## I(week^2)-Meleagris_gallopavo -0.1873 4.275047e+09 1.3837 848
## I(week^2)-Sciurus_carolinensis -0.0914 2.683169e+09 1.3201 4324
## I(week^2)-Vulpes_vulpes 0.0384 1.793563e+09 1.3219 3889
## I(week^2)-Sus_scrofa -0.1603 2.253124e+09 1.3049 6448
#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): 4.1392
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1818 0.5282 -2.2262 -1.1791 -0.0911 1.0042 467
## Cogon_Patch_Size -0.2582 0.5321 -1.3820 -0.2241 0.7233 1.0192 1044
## Avg_Cogongrass_Cover 0.1790 0.3957 -0.5911 0.1713 0.9865 1.0138 981
## total_shrub_cover -0.3012 0.4026 -1.1462 -0.2888 0.4554 1.0086 1301
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2505 1.6015 0.0742 0.7658 5.6285 1.0819 429
## Cogon_Patch_Size 1.6903 2.2870 0.1008 0.9520 8.0186 1.0393 463
## Avg_Cogongrass_Cover 0.6396 0.9099 0.0504 0.3713 2.7308 1.0183 799
## total_shrub_cover 0.8980 1.2327 0.0703 0.5364 3.8595 1.0460 555
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2082 1.8811 0.2267 1.7075 7.1813 1.0169 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7915 0.2435 -5.2499 -4.7972 -4.2847 1.0050 931
## week -0.0766 1.6319 -3.3505 -0.0577 3.1533 1.0243 701
## I(week^2) 0.0459 1.6450 -3.3120 0.0284 3.2369 1.0092 705
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.678000e-01 4.087000e-01 0.0596 0.2488 1.409000e+00 1.0129 258
## week 8.192143e+19 1.192196e+21 0.1215 534.2700 6.022069e+19 1.6984 224
## I(week^2) 5.362891e+07 9.817784e+08 0.0920 179.1217 4.709672e+07 1.4626 427
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1121 0.8983 -1.7814 -0.1429
## (Intercept)-Lynx_rufus -0.6937 0.8282 -2.1708 -0.7297
## (Intercept)-Didelphis_virginiana -1.4557 0.6979 -2.9464 -1.4208
## (Intercept)-Sylvilagus_floridanus -1.1037 0.7398 -2.6094 -1.0977
## (Intercept)-Meleagris_gallopavo -1.2756 0.7382 -2.9244 -1.2316
## (Intercept)-Sciurus_carolinensis -1.6565 0.7374 -3.2598 -1.5993
## (Intercept)-Vulpes_vulpes -1.7061 0.8921 -3.5809 -1.6652
## (Intercept)-Sus_scrofa -1.9811 0.8826 -4.0237 -1.8938
## Cogon_Patch_Size-Canis_latrans 0.7847 0.8194 -0.4378 0.6569
## Cogon_Patch_Size-Lynx_rufus -0.4334 0.8893 -2.0392 -0.4837
## Cogon_Patch_Size-Didelphis_virginiana 0.7641 0.5634 -0.2175 0.7182
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1741 1.0045 -3.5072 -1.0051
## Cogon_Patch_Size-Meleagris_gallopavo -0.0589 0.6691 -1.4118 -0.0444
## Cogon_Patch_Size-Sciurus_carolinensis -1.0081 0.9311 -3.4185 -0.8231
## Cogon_Patch_Size-Vulpes_vulpes -0.7931 1.0544 -3.4515 -0.6234
## Cogon_Patch_Size-Sus_scrofa -0.5138 0.9770 -2.7311 -0.3690
## Avg_Cogongrass_Cover-Canis_latrans 0.3482 0.4604 -0.4911 0.3270
## Avg_Cogongrass_Cover-Lynx_rufus 0.7152 0.6360 -0.3035 0.6299
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2118 0.4832 -0.7558 0.2174
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1591 0.5327 -1.2877 -0.1248
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5035 0.6713 -2.0341 -0.4301
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5943 0.4772 -0.2563 0.5651
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3940 0.5615 -0.6363 0.3649
## Avg_Cogongrass_Cover-Sus_scrofa -0.1593 0.7148 -1.7564 -0.0706
## total_shrub_cover-Canis_latrans 0.3698 0.5539 -0.5367 0.3165
## total_shrub_cover-Lynx_rufus -0.7471 0.7272 -2.5506 -0.6481
## total_shrub_cover-Didelphis_virginiana -0.3411 0.4853 -1.3489 -0.3258
## total_shrub_cover-Sylvilagus_floridanus -0.2386 0.5642 -1.4120 -0.2123
## total_shrub_cover-Meleagris_gallopavo -1.3380 0.7813 -3.1255 -1.2203
## total_shrub_cover-Sciurus_carolinensis -0.0041 0.4922 -0.9717 -0.0179
## total_shrub_cover-Vulpes_vulpes -0.2959 0.6344 -1.6824 -0.2749
## total_shrub_cover-Sus_scrofa 0.1394 0.6356 -1.0002 0.1068
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7420 1.0780 337
## (Intercept)-Lynx_rufus 1.0644 1.0126 363
## (Intercept)-Didelphis_virginiana -0.1943 1.0462 658
## (Intercept)-Sylvilagus_floridanus 0.3914 1.0124 687
## (Intercept)-Meleagris_gallopavo 0.0933 1.0085 658
## (Intercept)-Sciurus_carolinensis -0.3307 1.0153 473
## (Intercept)-Vulpes_vulpes -0.0657 1.0333 443
## (Intercept)-Sus_scrofa -0.5485 1.0250 482
## Cogon_Patch_Size-Canis_latrans 2.8046 1.0050 793
## Cogon_Patch_Size-Lynx_rufus 1.5016 1.0059 602
## Cogon_Patch_Size-Didelphis_virginiana 1.9614 1.0082 1085
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2776 1.0272 582
## Cogon_Patch_Size-Meleagris_gallopavo 1.2299 1.0056 1364
## Cogon_Patch_Size-Sciurus_carolinensis 0.2916 1.0089 572
## Cogon_Patch_Size-Vulpes_vulpes 0.7561 1.0136 540
## Cogon_Patch_Size-Sus_scrofa 1.0604 1.0114 617
## Avg_Cogongrass_Cover-Canis_latrans 1.3322 1.0014 1433
## Avg_Cogongrass_Cover-Lynx_rufus 2.1908 1.0098 731
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1342 1.0134 1610
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8310 1.0065 1116
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6027 1.0022 777
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6241 1.0141 1019
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5747 1.0027 1261
## Avg_Cogongrass_Cover-Sus_scrofa 1.0348 1.0105 781
## total_shrub_cover-Canis_latrans 1.6630 1.0073 954
## total_shrub_cover-Lynx_rufus 0.3729 1.0313 474
## total_shrub_cover-Didelphis_virginiana 0.5590 1.0042 1496
## total_shrub_cover-Sylvilagus_floridanus 0.8439 1.0202 733
## total_shrub_cover-Meleagris_gallopavo -0.1082 1.0136 474
## total_shrub_cover-Sciurus_carolinensis 0.9758 1.0086 1652
## total_shrub_cover-Vulpes_vulpes 0.9188 1.0032 719
## total_shrub_cover-Sus_scrofa 1.5022 1.0119 1072
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.668400e+00 1.483000e-01 -4.970600e+00
## (Intercept)-Lynx_rufus -5.303900e+00 2.602000e-01 -5.829800e+00
## (Intercept)-Didelphis_virginiana -4.374300e+00 2.291000e-01 -4.805300e+00
## (Intercept)-Sylvilagus_floridanus -4.918900e+00 2.470000e-01 -5.461400e+00
## (Intercept)-Meleagris_gallopavo -5.019100e+00 2.362000e-01 -5.534100e+00
## (Intercept)-Sciurus_carolinensis -4.413100e+00 2.240000e-01 -4.851000e+00
## (Intercept)-Vulpes_vulpes -5.450100e+00 4.986000e-01 -6.491900e+00
## (Intercept)-Sus_scrofa -4.810300e+00 3.475000e-01 -5.559000e+00
## week-Canis_latrans 6.978944e+07 5.920786e+09 -1.124688e+09
## week-Lynx_rufus 2.353307e+08 9.600224e+09 -9.990634e+08
## week-Didelphis_virginiana -1.157535e+08 1.026082e+10 -8.293863e+08
## week-Sylvilagus_floridanus -5.868479e+07 6.236345e+09 -1.212929e+09
## week-Meleagris_gallopavo -1.277408e+08 5.969733e+09 -1.014710e+09
## week-Sciurus_carolinensis -3.201975e+08 8.283567e+09 -1.674820e+09
## week-Vulpes_vulpes 1.399741e+08 7.868662e+09 -1.461360e+09
## week-Sus_scrofa -1.844116e+06 9.422964e+09 -9.557244e+08
## I(week^2)-Canis_latrans 1.123260e+02 6.659872e+03 -2.478937e+03
## I(week^2)-Lynx_rufus 1.200683e+02 7.199552e+03 -2.980684e+03
## I(week^2)-Didelphis_virginiana 8.011670e+01 5.202364e+03 -2.766805e+03
## I(week^2)-Sylvilagus_floridanus 8.154230e+01 5.784836e+03 -2.954154e+03
## I(week^2)-Meleagris_gallopavo -2.004469e+02 6.409565e+03 -2.666248e+03
## I(week^2)-Sciurus_carolinensis -2.400379e+02 7.360157e+03 -3.395319e+03
## I(week^2)-Vulpes_vulpes -3.371160e+01 5.374917e+03 -2.969245e+03
## I(week^2)-Sus_scrofa 3.485012e+02 9.393439e+03 -2.599016e+03
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6611 -4.396200e+00 1.0743 293
## (Intercept)-Lynx_rufus -5.2864 -4.843100e+00 1.0190 113
## (Intercept)-Didelphis_virginiana -4.3712 -3.929600e+00 1.0204 315
## (Intercept)-Sylvilagus_floridanus -4.9066 -4.448400e+00 1.0277 157
## (Intercept)-Meleagris_gallopavo -5.0085 -4.571600e+00 1.0158 195
## (Intercept)-Sciurus_carolinensis -4.4134 -3.993500e+00 1.0039 320
## (Intercept)-Vulpes_vulpes -5.3942 -4.651400e+00 1.0564 81
## (Intercept)-Sus_scrofa -4.7953 -4.167600e+00 1.0042 263
## week-Canis_latrans -0.4760 1.069909e+09 1.3041 9593
## week-Lynx_rufus -0.2872 1.138560e+09 1.3490 2632
## week-Didelphis_virginiana -0.0346 1.127583e+09 1.3030 3745
## week-Sylvilagus_floridanus -0.2903 8.787181e+08 1.2992 5339
## week-Meleagris_gallopavo -0.0310 1.005044e+09 1.3353 1803
## week-Sciurus_carolinensis -0.4148 9.879955e+08 1.4318 749
## week-Vulpes_vulpes -0.0860 9.078762e+08 1.3215 2624
## week-Sus_scrofa 0.0208 1.005375e+09 1.2904 51114
## I(week^2)-Canis_latrans 0.3222 2.731628e+03 1.2327 5077
## I(week^2)-Lynx_rufus 0.4825 2.754861e+03 1.2551 2963
## I(week^2)-Didelphis_virginiana 0.4447 3.080486e+03 1.2055 1702
## I(week^2)-Sylvilagus_floridanus 0.2741 2.884257e+03 1.2110 3090
## I(week^2)-Meleagris_gallopavo 0.3298 2.898579e+03 1.2047 1441
## I(week^2)-Sciurus_carolinensis 0.2318 2.699608e+03 1.2497 1975
## I(week^2)-Vulpes_vulpes 0.1058 2.902768e+03 1.1844 3301
## I(week^2)-Sus_scrofa 0.3649 3.204447e+03 1.3405 623
#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%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_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): 4.1107
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0724 0.4844 -2.0120 -1.0806 -0.0312 1.0188 733
## Veg_shannon_index 0.3142 0.3497 -0.3882 0.3220 1.0188 1.0006 1299
## Avg_Cogongrass_Cover 0.1834 0.3318 -0.4789 0.1849 0.8659 1.0010 892
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2361 1.4286 0.1000 0.8215 5.1454 1.0025 858
## Veg_shannon_index 0.5942 0.8227 0.0500 0.3509 2.4632 1.0249 965
## Avg_Cogongrass_Cover 0.4881 0.6708 0.0489 0.2988 1.9927 1.0547 1199
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1078 1.0756 0.0755 0.7888 4.1299 1.0206 285
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7699 0.2485 -5.2379 -4.7753 -4.2694 1.0183 589
## week -0.0641 1.6454 -3.2998 -0.0144 3.0544 1.0120 705
## I(week^2) 0.0169 1.5923 -3.0476 0.0104 3.0689 1.0137 470
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 3.419000e-01 5.006000e-01 0.0512 0.2211 1.315300e+00 1.0668
## week 3.116666e+16 7.482405e+17 0.1962 1893011.6932 2.029727e+15 1.4533
## I(week^2) 4.416732e+08 9.376646e+09 0.0818 11.4120 1.496786e+08 1.1674
## ESS
## (Intercept) 124
## week 601
## I(week^2) 944
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0254 0.6974 -1.3895 -0.0301
## (Intercept)-Lynx_rufus -0.5321 0.7449 -1.8813 -0.5580
## (Intercept)-Didelphis_virginiana -1.4856 0.6069 -2.7784 -1.4571
## (Intercept)-Sylvilagus_floridanus -0.8712 0.6489 -2.1688 -0.8518
## (Intercept)-Meleagris_gallopavo -1.0056 0.6459 -2.2800 -1.0038
## (Intercept)-Sciurus_carolinensis -1.4795 0.6209 -2.7598 -1.4500
## (Intercept)-Vulpes_vulpes -1.7170 0.8092 -3.4365 -1.6906
## (Intercept)-Sus_scrofa -2.0479 0.8231 -3.8712 -1.9763
## Veg_shannon_index-Canis_latrans 0.7848 0.4606 -0.0297 0.7542
## Veg_shannon_index-Lynx_rufus -0.2180 0.6015 -1.6121 -0.1513
## Veg_shannon_index-Didelphis_virginiana 0.5383 0.4550 -0.2862 0.5181
## Veg_shannon_index-Sylvilagus_floridanus 0.4212 0.4564 -0.4338 0.3998
## Veg_shannon_index-Meleagris_gallopavo 0.4452 0.5002 -0.4568 0.4136
## Veg_shannon_index-Sciurus_carolinensis -0.1097 0.4490 -1.0942 -0.0837
## Veg_shannon_index-Vulpes_vulpes -0.1006 0.5475 -1.2875 -0.0568
## Veg_shannon_index-Sus_scrofa 0.8097 0.6577 -0.2575 0.7274
## Avg_Cogongrass_Cover-Canis_latrans 0.5466 0.4386 -0.2386 0.5126
## Avg_Cogongrass_Cover-Lynx_rufus 0.5374 0.4658 -0.2884 0.4990
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4511 0.4173 -0.3376 0.4255
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2311 0.4599 -1.2770 -0.1899
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3024 0.5410 -1.5099 -0.2596
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3609 0.4048 -0.4305 0.3533
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2782 0.5032 -0.6639 0.2680
## Avg_Cogongrass_Cover-Sus_scrofa -0.1405 0.6162 -1.5149 -0.0732
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3483 1.0149 431
## (Intercept)-Lynx_rufus 1.0465 1.0230 351
## (Intercept)-Didelphis_virginiana -0.4021 1.0124 859
## (Intercept)-Sylvilagus_floridanus 0.3972 1.0080 642
## (Intercept)-Meleagris_gallopavo 0.2671 1.0335 716
## (Intercept)-Sciurus_carolinensis -0.2997 1.0187 1160
## (Intercept)-Vulpes_vulpes -0.1673 1.0551 448
## (Intercept)-Sus_scrofa -0.6501 1.0121 702
## Veg_shannon_index-Canis_latrans 1.7540 1.0067 1471
## Veg_shannon_index-Lynx_rufus 0.8189 1.0156 931
## Veg_shannon_index-Didelphis_virginiana 1.4794 1.0037 1578
## Veg_shannon_index-Sylvilagus_floridanus 1.3635 1.0020 1613
## Veg_shannon_index-Meleagris_gallopavo 1.5022 1.0023 1380
## Veg_shannon_index-Sciurus_carolinensis 0.7165 1.0000 1518
## Veg_shannon_index-Vulpes_vulpes 0.8733 1.0077 1040
## Veg_shannon_index-Sus_scrofa 2.3424 1.0013 751
## Avg_Cogongrass_Cover-Canis_latrans 1.4765 1.0025 1251
## Avg_Cogongrass_Cover-Lynx_rufus 1.5788 1.0115 1172
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3256 1.0011 1679
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5694 1.0010 1114
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6055 1.0064 838
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1740 1.0007 2182
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2910 1.0065 1175
## Avg_Cogongrass_Cover-Sus_scrofa 0.9216 1.0026 1006
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6762 1.502000e-01 -4.9644
## (Intercept)-Lynx_rufus -5.2715 2.734000e-01 -5.8663
## (Intercept)-Didelphis_virginiana -4.3728 2.313000e-01 -4.8457
## (Intercept)-Sylvilagus_floridanus -4.8848 2.484000e-01 -5.4475
## (Intercept)-Meleagris_gallopavo -5.0214 2.757000e-01 -5.6765
## (Intercept)-Sciurus_carolinensis -4.4267 2.374000e-01 -4.9130
## (Intercept)-Vulpes_vulpes -5.3301 5.236000e-01 -6.7016
## (Intercept)-Sus_scrofa -4.7674 3.167000e-01 -5.4227
## week-Canis_latrans 2105552.4423 1.626768e+08 -5781180.3411
## week-Lynx_rufus -6279748.8919 1.712065e+08 -4474760.4120
## week-Didelphis_virginiana -5664228.5781 1.828738e+08 -5833930.2565
## week-Sylvilagus_floridanus 734599.3102 1.291860e+08 -4057386.0843
## week-Meleagris_gallopavo -4165901.2593 1.498291e+08 -5107718.8032
## week-Sciurus_carolinensis -171285.9288 1.557426e+08 -5274194.9617
## week-Vulpes_vulpes -5463991.8202 1.687884e+08 -5641911.2624
## week-Sus_scrofa 2757787.4002 1.083456e+08 -4469975.8814
## I(week^2)-Canis_latrans 218.7917 1.872762e+04 -537.5842
## I(week^2)-Lynx_rufus -545.9436 1.796302e+04 -1342.3644
## I(week^2)-Didelphis_virginiana -501.2538 2.102958e+04 -708.4021
## I(week^2)-Sylvilagus_floridanus 273.3892 1.930059e+04 -540.9045
## I(week^2)-Meleagris_gallopavo 317.0202 1.770389e+04 -644.1591
## I(week^2)-Sciurus_carolinensis -486.9305 2.060944e+04 -1722.6276
## I(week^2)-Vulpes_vulpes 84.1394 1.632839e+04 -1171.1241
## I(week^2)-Sus_scrofa -72.2458 1.827665e+04 -767.4016
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6786 -4.3719 1.0371 290
## (Intercept)-Lynx_rufus -5.2600 -4.7734 1.1087 96
## (Intercept)-Didelphis_virginiana -4.3612 -3.9265 1.0159 264
## (Intercept)-Sylvilagus_floridanus -4.8689 -4.4473 1.0359 143
## (Intercept)-Meleagris_gallopavo -5.0007 -4.5488 1.0755 137
## (Intercept)-Sciurus_carolinensis -4.4166 -3.9749 1.0413 302
## (Intercept)-Vulpes_vulpes -5.2398 -4.5505 1.1518 69
## (Intercept)-Sus_scrofa -4.7568 -4.1803 1.0277 270
## week-Canis_latrans -0.5555 4071417.6420 1.3071 4976
## week-Lynx_rufus -0.4760 4100092.2527 1.4195 769
## week-Didelphis_virginiana -0.8620 3183094.3411 1.3829 1001
## week-Sylvilagus_floridanus -0.1411 5401360.3591 1.2934 57616
## week-Meleagris_gallopavo -0.1811 4451486.0273 1.3668 1226
## week-Sciurus_carolinensis -0.6239 4354386.1644 1.2904 37599
## week-Vulpes_vulpes -0.3818 5389999.8861 1.3901 873
## week-Sus_scrofa -0.7698 4012051.4563 1.3538 1147
## I(week^2)-Canis_latrans 0.0680 951.7271 1.1201 10081
## I(week^2)-Lynx_rufus -0.0408 671.7064 1.1413 2072
## I(week^2)-Didelphis_virginiana 0.0131 732.4659 1.1127 2252
## I(week^2)-Sylvilagus_floridanus -0.0629 1206.9653 1.1729 1473
## I(week^2)-Meleagris_gallopavo -0.0834 795.4777 1.1302 3919
## I(week^2)-Sciurus_carolinensis -0.0462 376.3700 1.1136 3827
## I(week^2)-Vulpes_vulpes 0.1092 627.6312 1.1530 2134
## I(week^2)-Sus_scrofa -0.0249 878.4007 1.1068 46603
#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): 4.0793
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0202 0.4455 -1.9329 -1.0173 -0.1134 1.0114 567
## Avg_Cogongrass_Cover 0.1389 0.3009 -0.4749 0.1397 0.7228 1.0158 1438
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9067 1.1318 0.0616 0.5669 3.7348 1.0146 471
## Avg_Cogongrass_Cover 0.4456 0.7064 0.0477 0.2606 1.7991 1.0733 1239
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4113 1.5022 0.1095 0.9764 5.4113 1.16 154
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7722 0.2602 -5.2445 -4.7823 -4.2428 1.0127 1000
## week -0.1308 1.5932 -3.3501 -0.0738 2.9670 1.0225 421
## I(week^2) 0.0338 1.6432 -3.2330 0.0307 3.2792 1.0044 1054
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.933000e-01 1.461600e+00 0.0586 0.2501 1.327200e+00 1.2801 1799
## week 1.258494e+12 1.872570e+13 0.0758 68.5316 1.015379e+12 1.6157 284
## I(week^2) 5.306770e+14 8.979646e+15 0.1457 3727.3281 3.671658e+10 1.6019 246
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1344 0.7052 -1.4624 -0.1491
## (Intercept)-Lynx_rufus -0.5746 0.7851 -1.8938 -0.6188
## (Intercept)-Didelphis_virginiana -1.3352 0.6004 -2.6011 -1.3018
## (Intercept)-Sylvilagus_floridanus -0.8807 0.6026 -2.1434 -0.8576
## (Intercept)-Meleagris_gallopavo -0.9583 0.6188 -2.2303 -0.9533
## (Intercept)-Sciurus_carolinensis -1.4025 0.6120 -2.7382 -1.3475
## (Intercept)-Vulpes_vulpes -1.5175 0.7520 -3.1841 -1.4615
## (Intercept)-Sus_scrofa -1.6822 0.7073 -3.2889 -1.6237
## Avg_Cogongrass_Cover-Canis_latrans 0.3741 0.3856 -0.3292 0.3531
## Avg_Cogongrass_Cover-Lynx_rufus 0.5179 0.4610 -0.2292 0.4636
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3701 0.3806 -0.3573 0.3649
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2641 0.4412 -1.2337 -0.2345
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3424 0.5020 -1.4572 -0.2963
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3693 0.3809 -0.3511 0.3570
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2871 0.4460 -0.5714 0.2789
## Avg_Cogongrass_Cover-Sus_scrofa -0.1651 0.5520 -1.4099 -0.1145
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2681 1.0285 436
## (Intercept)-Lynx_rufus 0.9705 1.0450 217
## (Intercept)-Didelphis_virginiana -0.2365 1.0033 807
## (Intercept)-Sylvilagus_floridanus 0.2514 1.0036 739
## (Intercept)-Meleagris_gallopavo 0.2703 1.0029 696
## (Intercept)-Sciurus_carolinensis -0.3130 1.0164 898
## (Intercept)-Vulpes_vulpes -0.1871 1.0275 527
## (Intercept)-Sus_scrofa -0.4974 1.0086 618
## Avg_Cogongrass_Cover-Canis_latrans 1.2163 1.0006 1697
## Avg_Cogongrass_Cover-Lynx_rufus 1.5691 1.0036 1370
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1495 1.0057 1773
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5157 1.0049 1267
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5216 1.0082 987
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1578 1.0010 1702
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2214 1.0130 1632
## Avg_Cogongrass_Cover-Sus_scrofa 0.7829 1.0239 1159
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6717 1.445000e-01 -4.9784
## (Intercept)-Lynx_rufus -5.3265 2.867000e-01 -5.9276
## (Intercept)-Didelphis_virginiana -4.3658 2.313000e-01 -4.8080
## (Intercept)-Sylvilagus_floridanus -4.8678 2.081000e-01 -5.2804
## (Intercept)-Meleagris_gallopavo -5.0437 2.588000e-01 -5.5812
## (Intercept)-Sciurus_carolinensis -4.4115 2.256000e-01 -4.8547
## (Intercept)-Vulpes_vulpes -5.3749 4.818000e-01 -6.5478
## (Intercept)-Sus_scrofa -4.7833 3.556000e-01 -5.4951
## week-Canis_latrans -30735.6303 1.156244e+06 -123410.3886
## week-Lynx_rufus -18616.6910 1.240470e+06 -208574.6944
## week-Didelphis_virginiana -25109.4563 1.066712e+06 -191791.9707
## week-Sylvilagus_floridanus 9815.9784 1.280931e+06 -117833.3604
## week-Meleagris_gallopavo 2837.3522 1.082466e+06 -157904.2074
## week-Sciurus_carolinensis 3656.0286 8.528097e+05 -136412.1777
## week-Vulpes_vulpes -3099.5629 1.151554e+06 -103101.5262
## week-Sus_scrofa 10344.7574 7.795916e+05 -146862.4499
## I(week^2)-Canis_latrans 95657.2441 2.628034e+07 -20800.4631
## I(week^2)-Lynx_rufus 92515.7504 1.667188e+07 -24500.8899
## I(week^2)-Didelphis_virginiana 324736.1896 2.397749e+07 -18205.3636
## I(week^2)-Sylvilagus_floridanus -148252.5664 1.692147e+07 -21841.5240
## I(week^2)-Meleagris_gallopavo 493920.9813 1.747731e+07 -16775.7408
## I(week^2)-Sciurus_carolinensis 26239.6883 1.901692e+07 -21522.6225
## I(week^2)-Vulpes_vulpes -856361.1078 2.547207e+07 -29859.6547
## I(week^2)-Sus_scrofa 520474.5388 2.254076e+07 -23774.9211
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6643 -4.4022 1.0150 271
## (Intercept)-Lynx_rufus -5.3259 -4.7959 1.0127 91
## (Intercept)-Didelphis_virginiana -4.3629 -3.9141 1.0104 285
## (Intercept)-Sylvilagus_floridanus -4.8645 -4.4780 1.0070 244
## (Intercept)-Meleagris_gallopavo -5.0277 -4.5913 1.0638 167
## (Intercept)-Sciurus_carolinensis -4.4097 -3.9864 1.0281 323
## (Intercept)-Vulpes_vulpes -5.3247 -4.5712 1.1506 79
## (Intercept)-Sus_scrofa -4.7770 -4.1267 1.0112 252
## week-Canis_latrans -0.1180 213419.7275 1.3239 1602
## week-Lynx_rufus -0.2508 127141.4415 1.2823 8384
## week-Didelphis_virginiana -0.2586 75409.0450 1.3165 1488
## week-Sylvilagus_floridanus -0.3448 151098.2748 1.2754 13242
## week-Meleagris_gallopavo -0.0476 123785.6846 1.2793 15480
## week-Sciurus_carolinensis -0.1640 153939.8344 1.2603 9518
## week-Vulpes_vulpes -0.1262 188998.0557 1.2776 6317
## week-Sus_scrofa -0.1903 183272.3746 1.2501 5797
## I(week^2)-Canis_latrans 0.1573 24929.9199 1.2917 73046
## I(week^2)-Lynx_rufus 0.0939 21172.5352 1.2934 24811
## I(week^2)-Didelphis_virginiana 0.0857 28857.1621 1.3085 4330
## I(week^2)-Sylvilagus_floridanus 0.0174 24735.6733 1.2980 13327
## I(week^2)-Meleagris_gallopavo 0.2301 35073.1401 1.3676 1187
## I(week^2)-Sciurus_carolinensis -0.0538 27207.3176 1.2905 18078
## I(week^2)-Vulpes_vulpes 0.1165 19662.0318 1.3986 689
## I(week^2)-Sus_scrofa 0.0940 31958.8375 1.3424 2554
# 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): 4.075
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6581 0.5081 -2.6544 -1.6487 -0.6628 1.0035 452
## Avg_Cogongrass_Cover -0.6979 0.4435 -1.6285 -0.6835 0.1360 1.0147 492
## I(Avg_Cogongrass_Cover^2) 0.6624 0.3706 -0.0194 0.6473 1.4110 1.0035 603
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0227 1.2384 0.0712 0.6483 4.3307 1.0249 748
## Avg_Cogongrass_Cover 0.4937 0.6942 0.0460 0.2895 2.0704 1.0310 1032
## I(Avg_Cogongrass_Cover^2) 0.5563 0.9816 0.0402 0.2613 2.9898 1.1003 386
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9258 0.8663 0.0612 0.6641 3.2711 1.0076 195
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.7567 0.2201 -5.1661 -4.7599 -4.2954 1.0011 674
## week 0.0917 1.6853 -3.2246 0.0978 3.4272 1.0109 915
## I(week^2) 0.0538 1.5986 -3.1404 0.1347 3.1075 1.0026 624
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.931000e-01 3.164000e-01 0.0487 0.2135 9.800000e-01 1.0413 431
## week 3.453870e+08 3.771659e+09 0.0902 217.4728 1.886111e+09 1.7213 199
## I(week^2) 2.076683e+08 3.712608e+09 0.1011 142.7478 8.817683e+06 1.5721 295
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7595 0.7461 -2.1932 -0.7550
## (Intercept)-Lynx_rufus -1.3723 0.6943 -2.7618 -1.3736
## (Intercept)-Didelphis_virginiana -1.9179 0.6445 -3.3098 -1.8908
## (Intercept)-Sylvilagus_floridanus -1.4762 0.6625 -2.8563 -1.4617
## (Intercept)-Meleagris_gallopavo -1.4053 0.6642 -2.7927 -1.4015
## (Intercept)-Sciurus_carolinensis -2.2331 0.7160 -3.8348 -2.1707
## (Intercept)-Vulpes_vulpes -2.3598 0.8267 -4.1347 -2.2903
## (Intercept)-Sus_scrofa -2.3161 0.8165 -4.1529 -2.2331
## Avg_Cogongrass_Cover-Canis_latrans -0.4296 0.5652 -1.4993 -0.4382
## Avg_Cogongrass_Cover-Lynx_rufus -0.4611 0.5791 -1.5557 -0.4739
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3682 0.5675 -1.4662 -0.3816
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1369 0.6173 -2.5348 -1.0742
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9877 0.6197 -2.3219 -0.9441
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7212 0.5668 -1.8933 -0.6982
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7046 0.6095 -2.0173 -0.6798
## Avg_Cogongrass_Cover-Sus_scrofa -0.9720 0.6873 -2.4428 -0.9102
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.1834 0.7919 0.1819 1.0017
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.0580 0.5431 0.1933 0.9814
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4951 0.3928 -0.2680 0.4849
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6802 0.4296 -0.0769 0.6477
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1509 0.5665 -1.0791 0.2050
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8929 0.4079 0.1995 0.8609
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.7627 0.4593 -0.0073 0.7156
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2045 0.6289 -1.2522 0.2881
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6747 1.0066 509
## (Intercept)-Lynx_rufus 0.0283 1.0039 510
## (Intercept)-Didelphis_virginiana -0.7154 1.0009 680
## (Intercept)-Sylvilagus_floridanus -0.2369 1.0041 663
## (Intercept)-Meleagris_gallopavo -0.1061 1.0011 583
## (Intercept)-Sciurus_carolinensis -1.0081 1.0066 561
## (Intercept)-Vulpes_vulpes -0.9175 1.0314 389
## (Intercept)-Sus_scrofa -0.9717 1.0088 564
## Avg_Cogongrass_Cover-Canis_latrans 0.7446 1.0098 891
## Avg_Cogongrass_Cover-Lynx_rufus 0.7171 1.0085 751
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8029 1.0045 684
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0851 1.0140 623
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1054 1.0059 604
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3547 1.0029 630
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4511 1.0110 614
## Avg_Cogongrass_Cover-Sus_scrofa 0.2199 1.0199 616
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2998 1.0102 315
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3424 1.0017 552
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3027 1.0048 736
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6499 1.0091 646
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0908 1.0022 537
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7802 1.0001 662
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7574 1.0132 456
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2003 1.0157 487
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6863 0.1606 -5.0105 -4.6787
## (Intercept)-Lynx_rufus -5.2344 0.2633 -5.8087 -5.2138
## (Intercept)-Didelphis_virginiana -4.3665 0.2233 -4.8108 -4.3679
## (Intercept)-Sylvilagus_floridanus -4.8857 0.2202 -5.3664 -4.8762
## (Intercept)-Meleagris_gallopavo -4.9942 0.2617 -5.5614 -4.9802
## (Intercept)-Sciurus_carolinensis -4.4185 0.2269 -4.8756 -4.4125
## (Intercept)-Vulpes_vulpes -5.2715 0.4334 -6.2590 -5.2250
## (Intercept)-Sus_scrofa -4.7516 0.3097 -5.3904 -4.7418
## week-Canis_latrans -264.0781 15655.9079 -15430.3151 0.5539
## week-Lynx_rufus -241.4650 17883.5387 -16333.8341 0.0698
## week-Didelphis_virginiana 461.1605 21442.4971 -11820.3215 0.2743
## week-Sylvilagus_floridanus 142.2911 17747.7102 -13750.0888 0.2495
## week-Meleagris_gallopavo 145.0262 19122.3513 -13098.3569 0.1961
## week-Sciurus_carolinensis -263.2315 16886.9185 -13685.0551 0.2190
## week-Vulpes_vulpes 340.0310 17244.2158 -12935.5233 0.2865
## week-Sus_scrofa -212.2783 22225.0648 -12968.1647 0.4064
## I(week^2)-Canis_latrans -125.2429 16267.7850 -766.4547 0.3869
## I(week^2)-Lynx_rufus 64.0070 6594.8898 -738.9584 0.2177
## I(week^2)-Didelphis_virginiana -326.0548 11372.6248 -1009.4959 0.2101
## I(week^2)-Sylvilagus_floridanus 362.8427 17096.8367 -966.8164 0.1878
## I(week^2)-Meleagris_gallopavo 174.9206 11028.2056 -1169.3920 0.1242
## I(week^2)-Sciurus_carolinensis 72.1484 20969.6302 -906.5824 0.1690
## I(week^2)-Vulpes_vulpes -503.3706 18334.7762 -1023.0318 0.1475
## I(week^2)-Sus_scrofa -307.0256 12239.8018 -1099.3505 0.0967
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3872 1.0054 214
## (Intercept)-Lynx_rufus -4.7711 1.0480 115
## (Intercept)-Didelphis_virginiana -3.9279 1.0320 286
## (Intercept)-Sylvilagus_floridanus -4.4914 1.0164 203
## (Intercept)-Meleagris_gallopavo -4.5251 1.0125 156
## (Intercept)-Sciurus_carolinensis -3.9904 1.0003 353
## (Intercept)-Vulpes_vulpes -4.5547 1.2161 144
## (Intercept)-Sus_scrofa -4.1722 1.0242 338
## week-Canis_latrans 14633.9053 1.2124 3160
## week-Lynx_rufus 13119.0207 1.2349 6769
## week-Didelphis_virginiana 17842.2589 1.2270 2809
## week-Sylvilagus_floridanus 16485.1043 1.2051 17671
## week-Meleagris_gallopavo 15751.4697 1.2480 19992
## week-Sciurus_carolinensis 15383.0984 1.2318 3947
## week-Vulpes_vulpes 14787.7797 1.2219 2741
## week-Sus_scrofa 17356.3566 1.2592 3604
## I(week^2)-Canis_latrans 1060.3241 1.2968 10258
## I(week^2)-Lynx_rufus 882.8246 1.2970 6702
## I(week^2)-Didelphis_virginiana 880.0599 1.3715 1469
## I(week^2)-Sylvilagus_floridanus 792.7774 1.3358 2151
## I(week^2)-Meleagris_gallopavo 950.5518 1.3173 3954
## I(week^2)-Sciurus_carolinensis 917.0117 1.2916 63994
## I(week^2)-Vulpes_vulpes 889.1742 1.3615 1408
## I(week^2)-Sus_scrofa 799.9031 1.3527 1756
# 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): 4.4447
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2574 1.1107 -4.2227 -2.3527 0.3081 1.0028 644
## Cogon_Patch_Size 0.0093 0.9418 -1.9137 0.0493 1.9666 1.0138 501
## Veg_shannon_index 0.8876 0.7446 -0.6291 0.8984 2.3702 1.0161 545
## total_shrub_cover -0.6729 0.7804 -2.3543 -0.6443 0.7961 1.0010 1037
## Avg_Cogongrass_Cover 0.2801 1.0355 -1.7222 0.2503 2.3311 1.1143 132
## Tree_Density -2.4107 0.8986 -4.1770 -2.3546 -0.7462 1.0015 139
## Avg_Canopy_Cover 1.9073 0.7934 0.3296 1.8909 3.5365 1.0106 734
## I(Avg_Cogongrass_Cover^2) 1.0830 0.7594 -0.5421 1.0988 2.5357 1.1016 242
## avg_veg_height -0.6058 0.6226 -1.8657 -0.5854 0.5892 1.0589 154
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.7721 21.9024 0.3643 7.1156 62.0588 1.2934 220
## Cogon_Patch_Size 7.5401 12.3763 0.2916 3.8760 38.4747 1.0561 255
## Veg_shannon_index 3.3002 4.7348 0.1133 1.7851 15.8020 1.0634 302
## total_shrub_cover 5.5458 8.8330 0.2579 2.9815 27.7537 1.0285 194
## Avg_Cogongrass_Cover 1.3749 2.7622 0.0492 0.4936 7.9399 1.0429 514
## Tree_Density 1.6324 3.9528 0.0551 0.5591 10.2393 1.1066 223
## Avg_Canopy_Cover 4.3152 7.2835 0.1252 2.1922 22.1141 1.0687 277
## I(Avg_Cogongrass_Cover^2) 3.6591 7.5402 0.0668 1.2798 21.5202 1.3009 75
## avg_veg_height 0.6097 1.0438 0.0436 0.2976 3.0315 1.1377 385
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6908 2.6408 0.0553 0.7478 9.283 1.0879 116
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8182 0.3003 -5.3431 -4.8439 -4.1458 1.0069 1004
## week -0.0624 1.6702 -3.3258 -0.0641 3.5046 1.0147 666
## I(week^2) 0.0728 1.6352 -3.0250 0.0750 3.3584 1.0881 448
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.711000e-01 6.759000e-01 0.0967 0.3865 2.198900e+00 1.0136 240
## week 2.362186e+10 2.027315e+11 0.1400 1106.1658 1.473333e+11 1.5061 74
## I(week^2) 3.143370e+07 8.909574e+08 0.0965 16.8892 3.217551e+06 1.4085 768
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6727 1.4586 -3.2926 -0.7400
## (Intercept)-Lynx_rufus 0.3714 2.7107 -3.5339 -0.0712
## (Intercept)-Didelphis_virginiana -4.3655 1.5330 -7.7765 -4.2046
## (Intercept)-Sylvilagus_floridanus -2.8233 1.3775 -5.8861 -2.7150
## (Intercept)-Meleagris_gallopavo -2.6297 1.5221 -5.9215 -2.5924
## (Intercept)-Sciurus_carolinensis -5.0463 1.7414 -9.1899 -4.8216
## (Intercept)-Vulpes_vulpes -4.9272 2.1181 -9.4161 -4.8160
## (Intercept)-Sus_scrofa -5.9673 2.5111 -12.1066 -5.5808
## Cogon_Patch_Size-Canis_latrans 2.3131 1.8951 -0.2306 1.9628
## Cogon_Patch_Size-Lynx_rufus 0.6619 2.2970 -3.0758 0.4451
## Cogon_Patch_Size-Didelphis_virginiana 2.0814 1.2876 0.0177 1.9278
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9428 1.8515 -6.4917 -1.6078
## Cogon_Patch_Size-Meleagris_gallopavo 0.4508 1.5029 -1.9886 0.2265
## Cogon_Patch_Size-Sciurus_carolinensis -1.5024 1.7454 -5.5793 -1.1874
## Cogon_Patch_Size-Vulpes_vulpes -0.9527 2.1087 -5.4442 -0.7822
## Cogon_Patch_Size-Sus_scrofa -1.0381 1.9646 -6.0176 -0.6591
## Veg_shannon_index-Canis_latrans 1.9098 1.0665 0.2462 1.7670
## Veg_shannon_index-Lynx_rufus -0.2766 1.6599 -4.3028 -0.0750
## Veg_shannon_index-Didelphis_virginiana 1.2721 0.9957 -0.4740 1.1948
## Veg_shannon_index-Sylvilagus_floridanus 1.1550 0.9454 -0.5089 1.0831
## Veg_shannon_index-Meleagris_gallopavo 1.8048 1.2316 -0.1482 1.6240
## Veg_shannon_index-Sciurus_carolinensis -0.2983 1.0725 -2.6511 -0.2209
## Veg_shannon_index-Vulpes_vulpes 0.1053 1.3243 -2.8548 0.2187
## Veg_shannon_index-Sus_scrofa 2.3220 1.4608 0.0988 2.0819
## total_shrub_cover-Canis_latrans 0.7004 1.0157 -1.0140 0.5795
## total_shrub_cover-Lynx_rufus -2.2847 1.9612 -7.3663 -1.9487
## total_shrub_cover-Didelphis_virginiana -0.9020 0.9928 -3.0540 -0.8320
## total_shrub_cover-Sylvilagus_floridanus -0.2920 1.1628 -2.5472 -0.2814
## total_shrub_cover-Meleagris_gallopavo -3.7192 1.9097 -8.3523 -3.4555
## total_shrub_cover-Sciurus_carolinensis 0.2004 0.8903 -1.4622 0.1686
## total_shrub_cover-Vulpes_vulpes -0.9537 1.7184 -5.1757 -0.7659
## total_shrub_cover-Sus_scrofa 0.4559 1.3523 -1.9929 0.3443
## Avg_Cogongrass_Cover-Canis_latrans 0.3968 1.2622 -2.0513 0.3678
## Avg_Cogongrass_Cover-Lynx_rufus 0.7288 1.4072 -1.7390 0.6278
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5241 1.3493 -2.0997 0.4832
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1637 1.3735 -3.2409 -0.0965
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0351 1.3970 -3.0976 0.0432
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3505 1.2834 -2.1695 0.3320
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4187 1.3833 -2.3143 0.3719
## Avg_Cogongrass_Cover-Sus_scrofa 0.0240 1.4574 -3.2461 0.0894
## Tree_Density-Canis_latrans -2.9806 1.3391 -6.2648 -2.8104
## Tree_Density-Lynx_rufus -1.9777 1.4023 -4.4196 -2.0442
## Tree_Density-Didelphis_virginiana -2.5160 1.1416 -4.8684 -2.4345
## Tree_Density-Sylvilagus_floridanus -2.7516 1.2154 -5.5289 -2.6196
## Tree_Density-Meleagris_gallopavo -2.4974 1.2435 -5.0489 -2.4864
## Tree_Density-Sciurus_carolinensis -2.8517 1.2748 -5.7933 -2.7017
## Tree_Density-Vulpes_vulpes -2.3907 1.4081 -5.2371 -2.3825
## Tree_Density-Sus_scrofa -2.5641 1.3785 -5.6932 -2.4687
## Avg_Canopy_Cover-Canis_latrans 0.2240 0.8908 -1.5410 0.2159
## Avg_Canopy_Cover-Lynx_rufus 1.1533 1.6998 -2.0496 1.1089
## Avg_Canopy_Cover-Didelphis_virginiana 2.9844 1.1282 1.2920 2.8041
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0484 2.0907 1.2508 3.5940
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4189 1.3178 0.5773 2.1629
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6074 1.1318 0.9853 2.4346
## Avg_Canopy_Cover-Vulpes_vulpes 2.5536 1.5325 0.5086 2.3071
## Avg_Canopy_Cover-Sus_scrofa 2.2228 1.0302 0.5662 2.0951
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2700 1.2891 0.4921 2.0481
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5250 1.7322 0.3411 2.1664
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9577 0.7949 -0.5384 0.9091
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0293 0.8685 -0.5241 0.9959
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.3070 1.6670 -4.2265 -0.1044
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5738 0.8465 0.1322 1.5109
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8696 1.1338 0.2666 1.6583
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.1644 1.9984 -5.6278 0.2692
## avg_veg_height-Canis_latrans -0.6465 0.7026 -2.0895 -0.6368
## avg_veg_height-Lynx_rufus -0.6729 0.9409 -2.6214 -0.6540
## avg_veg_height-Didelphis_virginiana -0.6468 0.7894 -2.2138 -0.6411
## avg_veg_height-Sylvilagus_floridanus -0.6432 0.7551 -2.1690 -0.6141
## avg_veg_height-Meleagris_gallopavo -0.7249 0.9265 -2.6035 -0.6908
## avg_veg_height-Sciurus_carolinensis -0.2757 0.7593 -1.7544 -0.2951
## avg_veg_height-Vulpes_vulpes -0.7786 0.8884 -2.5853 -0.7524
## avg_veg_height-Sus_scrofa -0.6334 0.8120 -2.3282 -0.5997
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3402 1.0367 352
## (Intercept)-Lynx_rufus 6.7542 1.0585 109
## (Intercept)-Didelphis_virginiana -1.7936 1.0555 331
## (Intercept)-Sylvilagus_floridanus -0.4002 1.0493 396
## (Intercept)-Meleagris_gallopavo 0.2910 1.0396 303
## (Intercept)-Sciurus_carolinensis -2.3816 1.0799 182
## (Intercept)-Vulpes_vulpes -1.1635 1.1138 143
## (Intercept)-Sus_scrofa -2.3329 1.1854 141
## Cogon_Patch_Size-Canis_latrans 7.1014 1.0738 239
## Cogon_Patch_Size-Lynx_rufus 6.6485 1.1950 175
## Cogon_Patch_Size-Didelphis_virginiana 5.1865 1.0574 207
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8297 1.0191 376
## Cogon_Patch_Size-Meleagris_gallopavo 4.2740 1.0189 346
## Cogon_Patch_Size-Sciurus_carolinensis 0.9122 1.0402 268
## Cogon_Patch_Size-Vulpes_vulpes 2.7478 1.0267 245
## Cogon_Patch_Size-Sus_scrofa 1.7200 1.0831 282
## Veg_shannon_index-Canis_latrans 4.4659 1.0405 421
## Veg_shannon_index-Lynx_rufus 2.4520 1.0222 191
## Veg_shannon_index-Didelphis_virginiana 3.4806 1.0126 606
## Veg_shannon_index-Sylvilagus_floridanus 3.1523 1.0131 730
## Veg_shannon_index-Meleagris_gallopavo 4.7432 1.0130 417
## Veg_shannon_index-Sciurus_carolinensis 1.5825 1.0425 444
## Veg_shannon_index-Vulpes_vulpes 2.4465 1.0551 383
## Veg_shannon_index-Sus_scrofa 5.7194 1.0223 351
## total_shrub_cover-Canis_latrans 3.1004 1.0242 319
## total_shrub_cover-Lynx_rufus 0.6326 1.0057 78
## total_shrub_cover-Didelphis_virginiana 0.8577 1.0120 658
## total_shrub_cover-Sylvilagus_floridanus 1.8961 1.0480 508
## total_shrub_cover-Meleagris_gallopavo -0.7559 1.0004 153
## total_shrub_cover-Sciurus_carolinensis 2.0179 1.0248 892
## total_shrub_cover-Vulpes_vulpes 1.6117 1.0101 190
## total_shrub_cover-Sus_scrofa 3.3195 1.0565 417
## Avg_Cogongrass_Cover-Canis_latrans 2.9549 1.0867 232
## Avg_Cogongrass_Cover-Lynx_rufus 3.9030 1.0234 252
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2664 1.0712 203
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.3106 1.0720 232
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.5067 1.0786 234
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8428 1.0898 187
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2127 1.0678 215
## Avg_Cogongrass_Cover-Sus_scrofa 2.7224 1.0674 248
## Tree_Density-Canis_latrans -0.9798 1.0533 164
## Tree_Density-Lynx_rufus 0.8548 1.0137 170
## Tree_Density-Didelphis_virginiana -0.5837 1.0174 292
## Tree_Density-Sylvilagus_floridanus -0.7374 1.0135 186
## Tree_Density-Meleagris_gallopavo -0.1375 1.0007 318
## Tree_Density-Sciurus_carolinensis -0.8438 1.0272 220
## Tree_Density-Vulpes_vulpes 0.4882 1.0375 167
## Tree_Density-Sus_scrofa -0.0651 1.0067 235
## Avg_Canopy_Cover-Canis_latrans 1.9862 1.0765 382
## Avg_Canopy_Cover-Lynx_rufus 4.7800 1.0139 179
## Avg_Canopy_Cover-Didelphis_virginiana 5.6847 1.0357 219
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.1530 1.0652 150
## Avg_Canopy_Cover-Meleagris_gallopavo 5.6678 1.1463 307
## Avg_Canopy_Cover-Sciurus_carolinensis 5.4305 1.0133 246
## Avg_Canopy_Cover-Vulpes_vulpes 6.2004 1.0358 238
## Avg_Canopy_Cover-Sus_scrofa 4.6059 1.0265 409
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4862 1.0431 152
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 7.1554 1.0872 120
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6204 1.0905 286
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9215 1.0452 300
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2723 1.1622 101
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5342 1.0259 296
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.8023 1.0266 146
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.4223 1.1401 88
## avg_veg_height-Canis_latrans 0.6924 1.0381 231
## avg_veg_height-Lynx_rufus 1.1991 1.0311 360
## avg_veg_height-Didelphis_virginiana 0.9163 1.0282 317
## avg_veg_height-Sylvilagus_floridanus 0.8177 1.0373 327
## avg_veg_height-Meleagris_gallopavo 1.0648 1.0362 307
## avg_veg_height-Sciurus_carolinensis 1.2985 1.0808 345
## avg_veg_height-Vulpes_vulpes 0.8607 1.0509 369
## avg_veg_height-Sus_scrofa 0.9040 1.0244 399
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6933 0.1595 -5.0254 -4.6913
## (Intercept)-Lynx_rufus -5.5812 0.2944 -6.2234 -5.5628
## (Intercept)-Didelphis_virginiana -4.3584 0.2297 -4.8379 -4.3523
## (Intercept)-Sylvilagus_floridanus -4.9486 0.2163 -5.3510 -4.9442
## (Intercept)-Meleagris_gallopavo -5.1088 0.2640 -5.6574 -5.0996
## (Intercept)-Sciurus_carolinensis -4.3763 0.2402 -4.8804 -4.3653
## (Intercept)-Vulpes_vulpes -5.7412 0.5792 -7.0905 -5.6640
## (Intercept)-Sus_scrofa -4.7450 0.3477 -5.4674 -4.7318
## week-Canis_latrans 3239.0945 122984.6407 -110233.1944 -0.0593
## week-Lynx_rufus -7905.8003 157187.8339 -141170.6824 -0.1396
## week-Didelphis_virginiana -3676.0471 156318.7725 -139629.6474 -0.1354
## week-Sylvilagus_floridanus -2475.8404 157744.1091 -112516.7503 0.0762
## week-Meleagris_gallopavo -1396.9376 143588.0451 -92103.2363 -0.1146
## week-Sciurus_carolinensis -1104.5450 128211.6111 -94463.3862 -0.1324
## week-Vulpes_vulpes -8264.5507 135526.8437 -157028.4742 -0.2281
## week-Sus_scrofa 2144.8295 170443.8494 -62752.7856 -0.1841
## I(week^2)-Canis_latrans 7.6585 4165.0972 -470.9510 0.1374
## I(week^2)-Lynx_rufus -42.9022 2926.1947 -617.9963 0.1707
## I(week^2)-Didelphis_virginiana 38.6121 2991.6856 -516.0291 0.0735
## I(week^2)-Sylvilagus_floridanus -17.7132 3773.5890 -417.8591 0.2369
## I(week^2)-Meleagris_gallopavo 21.1179 7267.8965 -433.6246 0.1257
## I(week^2)-Sciurus_carolinensis -4.2431 3891.5011 -509.4128 0.0793
## I(week^2)-Vulpes_vulpes -52.9131 4430.1483 -672.6989 0.1730
## I(week^2)-Sus_scrofa 14.1369 4593.2046 -544.7896 0.1758
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3841 1.0184 215
## (Intercept)-Lynx_rufus -5.0560 1.1533 71
## (Intercept)-Didelphis_virginiana -3.9241 1.0160 347
## (Intercept)-Sylvilagus_floridanus -4.5348 1.0499 212
## (Intercept)-Meleagris_gallopavo -4.6085 1.0433 154
## (Intercept)-Sciurus_carolinensis -3.9528 1.0325 289
## (Intercept)-Vulpes_vulpes -4.8141 1.0876 57
## (Intercept)-Sus_scrofa -4.0941 1.0262 275
## week-Canis_latrans 94067.5003 1.1772 1188
## week-Lynx_rufus 74280.5103 1.1688 606
## week-Didelphis_virginiana 60779.2327 1.1835 4434
## week-Sylvilagus_floridanus 80011.5957 1.1874 3808
## week-Meleagris_gallopavo 84750.9025 1.2155 3215
## week-Sciurus_carolinensis 70362.5089 1.1321 1820
## week-Vulpes_vulpes 56073.5362 1.1694 308
## week-Sus_scrofa 135780.8836 1.2151 8803
## I(week^2)-Canis_latrans 589.0438 1.2887 88097
## I(week^2)-Lynx_rufus 588.2374 1.3101 6569
## I(week^2)-Didelphis_virginiana 556.6803 1.3074 7526
## I(week^2)-Sylvilagus_floridanus 760.0434 1.2931 15911
## I(week^2)-Meleagris_gallopavo 577.7345 1.2902 180815
## I(week^2)-Sciurus_carolinensis 682.5174 1.2896 17890
## I(week^2)-Vulpes_vulpes 541.5991 1.3011 7253
## I(week^2)-Sus_scrofa 547.4284 1.2907 51057
#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): 5.0193
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5919 0.4097 -1.3766 -0.6054 0.2778 1.0706 344
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0904 1.2336 0.1461 0.7483 4.08 1.0596 1347
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0107 0.3335 -5.5981 -5.0155 -4.3151 1.0945 530
## shrub_cover 0.1670 0.3917 -0.6421 0.1752 0.9259 1.0835 486
## veg_height -0.0551 0.2187 -0.5054 -0.0552 0.3733 1.0443 593
## week -0.1121 1.6342 -3.3992 -0.0799 3.0501 1.0076 635
## I(week^2) -0.1634 1.6071 -3.2986 -0.1401 2.9275 1.0072 413
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.429000e-01 1.114900e+00 0.0674 0.3872 2.727300e+00 1.4561 131
## shrub_cover 9.958000e-01 1.019800e+00 0.1664 0.7173 3.515400e+00 1.3155 118
## veg_height 2.815000e-01 2.760000e-01 0.0622 0.2056 9.520000e-01 1.0443 562
## week 5.197074e+09 6.857384e+10 0.0909 256.9952 7.899123e+09 1.3505 310
## I(week^2) 1.647032e+08 1.859522e+09 0.0759 27.6562 3.040526e+08 1.7450 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3346 0.4168 -0.4292 0.3138 1.2093 1.0160
## (Intercept)-Lynx_rufus 0.1427 0.5831 -0.8071 0.0659 1.4509 1.2336
## (Intercept)-Didelphis_virginiana -1.1090 0.4425 -2.0098 -1.1023 -0.2864 1.0018
## (Intercept)-Sylvilagus_floridanus -0.5089 0.4033 -1.2942 -0.5028 0.2904 1.0082
## (Intercept)-Meleagris_gallopavo -0.0286 0.6672 -1.1522 -0.0936 1.4922 1.3413
## (Intercept)-Sciurus_carolinensis -1.1163 0.4437 -2.0336 -1.0916 -0.2718 1.0063
## (Intercept)-Vulpes_vulpes -1.2671 0.6991 -2.5086 -1.2845 0.1578 1.1269
## (Intercept)-Sus_scrofa -1.4699 0.5670 -2.6672 -1.4435 -0.3890 1.0394
## ESS
## (Intercept)-Canis_latrans 1038
## (Intercept)-Lynx_rufus 188
## (Intercept)-Didelphis_virginiana 1652
## (Intercept)-Sylvilagus_floridanus 1428
## (Intercept)-Meleagris_gallopavo 152
## (Intercept)-Sciurus_carolinensis 996
## (Intercept)-Vulpes_vulpes 113
## (Intercept)-Sus_scrofa 899
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7739 0.1648 -5.1156 -4.7700
## (Intercept)-Lynx_rufus -5.5194 0.3192 -6.1766 -5.5097
## (Intercept)-Didelphis_virginiana -4.6335 0.2678 -5.2069 -4.6184
## (Intercept)-Sylvilagus_floridanus -4.9149 0.2335 -5.3866 -4.9095
## (Intercept)-Meleagris_gallopavo -5.8403 0.5659 -7.1850 -5.7822
## (Intercept)-Sciurus_carolinensis -4.6256 0.2924 -5.2035 -4.6273
## (Intercept)-Vulpes_vulpes -5.7192 0.6413 -7.3164 -5.6228
## (Intercept)-Sus_scrofa -5.2701 0.4918 -6.3186 -5.2593
## shrub_cover-Canis_latrans -0.2934 0.2069 -0.6969 -0.2946
## shrub_cover-Lynx_rufus -0.2731 0.3483 -1.0182 -0.2603
## shrub_cover-Didelphis_virginiana 1.0283 0.3476 0.3707 1.0324
## shrub_cover-Sylvilagus_floridanus 0.4569 0.4019 -0.3203 0.4444
## shrub_cover-Meleagris_gallopavo -0.8764 0.4821 -1.9423 -0.8614
## shrub_cover-Sciurus_carolinensis 0.8704 0.3819 0.1306 0.8768
## shrub_cover-Vulpes_vulpes -0.1651 0.7167 -1.6421 -0.1634
## shrub_cover-Sus_scrofa 0.7694 0.8439 -0.8411 0.7612
## veg_height-Canis_latrans -0.5793 0.1718 -0.9022 -0.5736
## veg_height-Lynx_rufus 0.0832 0.2232 -0.3690 0.0904
## veg_height-Didelphis_virginiana 0.4689 0.2234 0.0649 0.4539
## veg_height-Sylvilagus_floridanus 0.1295 0.2267 -0.3206 0.1333
## veg_height-Meleagris_gallopavo -0.3414 0.3947 -1.1788 -0.3346
## veg_height-Sciurus_carolinensis 0.1698 0.2001 -0.1916 0.1617
## veg_height-Vulpes_vulpes -0.0956 0.2933 -0.6834 -0.0939
## veg_height-Sus_scrofa -0.2715 0.3264 -0.9341 -0.2593
## week-Canis_latrans -1167.0082 65344.1146 -11928.2465 -0.2013
## week-Lynx_rufus -1364.4145 59039.3894 -17012.7714 -0.2296
## week-Didelphis_virginiana 221.6351 64747.5942 -13930.9242 -0.1632
## week-Sylvilagus_floridanus -522.4784 77851.4040 -12483.8235 -0.1493
## week-Meleagris_gallopavo -1233.3672 68263.7444 -16374.6461 -0.1701
## week-Sciurus_carolinensis 314.8790 61692.5376 -12405.6704 -0.0650
## week-Vulpes_vulpes 237.3455 90790.3381 -13550.7487 -0.1107
## week-Sus_scrofa -119.1964 78417.8911 -11863.3566 -0.2308
## I(week^2)-Canis_latrans 79.9568 10764.7967 -3676.9441 -0.2558
## I(week^2)-Lynx_rufus 38.5426 10421.1848 -4590.1247 -0.2630
## I(week^2)-Didelphis_virginiana -421.8552 16832.0214 -4009.1983 -0.3769
## I(week^2)-Sylvilagus_floridanus 300.9999 10617.1971 -4522.8536 -0.3620
## I(week^2)-Meleagris_gallopavo -231.5704 11197.8175 -5059.0131 -0.2930
## I(week^2)-Sciurus_carolinensis 297.3800 10852.9226 -4151.3878 -0.3614
## I(week^2)-Vulpes_vulpes -202.1743 10156.0622 -3725.9558 -0.3705
## I(week^2)-Sus_scrofa 132.0412 13315.9630 -4637.3625 -0.3462
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4654 1.0170 197
## (Intercept)-Lynx_rufus -4.9280 1.8247 80
## (Intercept)-Didelphis_virginiana -4.1340 1.0082 190
## (Intercept)-Sylvilagus_floridanus -4.4800 1.0647 196
## (Intercept)-Meleagris_gallopavo -4.8987 2.1439 30
## (Intercept)-Sciurus_carolinensis -4.0552 1.0319 204
## (Intercept)-Vulpes_vulpes -4.7257 1.6131 69
## (Intercept)-Sus_scrofa -4.3235 1.2127 198
## shrub_cover-Canis_latrans 0.1056 1.0966 171
## shrub_cover-Lynx_rufus 0.4023 1.3090 104
## shrub_cover-Didelphis_virginiana 1.7133 1.0598 165
## shrub_cover-Sylvilagus_floridanus 1.2470 1.0375 124
## shrub_cover-Meleagris_gallopavo -0.0160 1.9793 37
## shrub_cover-Sciurus_carolinensis 1.6121 1.0043 211
## shrub_cover-Vulpes_vulpes 1.2476 1.2019 124
## shrub_cover-Sus_scrofa 2.6369 1.2010 178
## veg_height-Canis_latrans -0.2438 1.0130 183
## veg_height-Lynx_rufus 0.5161 1.0896 192
## veg_height-Didelphis_virginiana 0.9430 1.0325 268
## veg_height-Sylvilagus_floridanus 0.5624 1.0137 228
## veg_height-Meleagris_gallopavo 0.4380 1.3188 85
## veg_height-Sciurus_carolinensis 0.6083 1.0214 294
## veg_height-Vulpes_vulpes 0.4742 1.0421 185
## veg_height-Sus_scrofa 0.3695 1.0712 232
## week-Canis_latrans 14079.2259 1.1488 1526
## week-Lynx_rufus 11752.0913 1.1559 3865
## week-Didelphis_virginiana 13614.9538 1.1338 37437
## week-Sylvilagus_floridanus 11608.2751 1.2259 3762
## week-Meleagris_gallopavo 11107.1272 1.2056 4796
## week-Sciurus_carolinensis 13368.0625 1.1438 11896
## week-Vulpes_vulpes 11992.6668 1.1927 5713
## week-Sus_scrofa 15252.3255 1.1641 6056
## I(week^2)-Canis_latrans 4794.6359 1.2459 11140
## I(week^2)-Lynx_rufus 4129.5921 1.2611 5486
## I(week^2)-Didelphis_virginiana 3851.1433 1.2306 2988
## I(week^2)-Sylvilagus_floridanus 4048.3313 1.2188 1387
## I(week^2)-Meleagris_gallopavo 2942.2413 1.2850 1649
## I(week^2)-Sciurus_carolinensis 4789.4612 1.2619 890
## I(week^2)-Vulpes_vulpes 4282.3633 1.2178 3229
## I(week^2)-Sus_scrofa 4487.2052 1.2458 3910
#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): 5.5778
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0011 1.1224 -3.0837 -1.0557 1.5099 1.0695 222
## Cogon_Patch_Size -0.2194 1.0320 -2.2741 -0.2457 1.9558 1.0403 303
## Veg_shannon_index 0.9501 0.8620 -0.8695 0.9369 2.7633 1.0145 513
## total_shrub_cover -1.2818 1.1484 -3.5873 -1.2280 1.0300 1.0208 163
## Avg_Cogongrass_Cover 1.4497 1.0458 -0.8498 1.5394 3.2972 1.0824 198
## Tree_Density -1.8457 1.0073 -3.8799 -1.8250 0.0903 1.0255 223
## Avg_Canopy_Cover 2.0428 1.1687 -0.5269 2.0516 4.4214 1.0124 882
## avg_veg_height -0.2368 0.8189 -1.6644 -0.3380 1.7285 1.0654 103
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.5660 44.7336 0.3733 8.0582 138.1976 2.2643 23
## Cogon_Patch_Size 12.6479 27.9063 0.1145 3.7904 83.4732 1.0128 114
## Veg_shannon_index 6.0882 10.5204 0.1117 2.4463 34.8786 1.3584 54
## total_shrub_cover 25.3427 57.6933 0.3977 7.0314 162.8933 1.4308 32
## Avg_Cogongrass_Cover 3.7071 7.4724 0.0722 1.3266 22.5082 1.0104 173
## Tree_Density 4.3262 13.3493 0.0579 0.9658 31.9179 1.4734 45
## Avg_Canopy_Cover 21.1655 45.7697 0.6076 8.2045 109.8748 1.5995 125
## avg_veg_height 1.3942 4.3640 0.0481 0.4438 8.8241 1.3115 175
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 8.7488 15.6282 0.0786 3.0358 54.4845 1.7124 18
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1678 0.2843 -5.7217 -5.1681 -4.5811 1.0557 202
## shrub_cover 0.5101 0.4166 -0.3386 0.5040 1.3690 1.0081 345
## veg_height -0.0107 0.2203 -0.4514 -0.0073 0.4231 1.0232 430
## week -0.0137 1.5900 -3.1389 -0.0236 3.0370 1.0172 710
## I(week^2) 0.0163 1.7325 -3.3202 0.0191 3.4330 1.0107 460
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.686000e-01 6.072000e-01 0.0698 0.3004 1.865900e+00 1.0535 241
## shrub_cover 1.119500e+00 1.088200e+00 0.2292 0.8246 3.875300e+00 1.1005 157
## veg_height 3.049000e-01 2.801000e-01 0.0651 0.2303 9.887000e-01 1.0091 622
## week 8.514196e+16 1.382688e+18 0.0890 118.7069 1.186687e+15 1.6259 194
## I(week^2) 1.635412e+07 2.508901e+08 0.1166 71.0542 2.778204e+07 1.4871 330
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.5284 2.1376 -2.1907 1.2620
## (Intercept)-Lynx_rufus 3.3675 5.7384 -2.2166 1.4872
## (Intercept)-Didelphis_virginiana -2.7434 1.9860 -7.5199 -2.5242
## (Intercept)-Sylvilagus_floridanus -0.9866 1.9734 -4.9146 -1.0692
## (Intercept)-Meleagris_gallopavo -2.3423 2.6792 -9.6721 -2.0176
## (Intercept)-Sciurus_carolinensis -2.7881 2.2387 -7.8377 -2.6197
## (Intercept)-Vulpes_vulpes -3.2516 3.0560 -10.9426 -2.8079
## (Intercept)-Sus_scrofa -4.6411 3.0086 -11.5657 -4.2975
## Cogon_Patch_Size-Canis_latrans 1.6343 2.1885 -1.0315 1.1039
## Cogon_Patch_Size-Lynx_rufus -0.1558 3.1447 -6.5090 -0.1434
## Cogon_Patch_Size-Didelphis_virginiana 1.8474 1.8921 -0.6226 1.4285
## Cogon_Patch_Size-Sylvilagus_floridanus -2.7577 3.9555 -13.8145 -1.7591
## Cogon_Patch_Size-Meleagris_gallopavo 0.5499 2.3135 -2.8813 0.1944
## Cogon_Patch_Size-Sciurus_carolinensis -1.8473 2.6917 -9.2907 -1.2277
## Cogon_Patch_Size-Vulpes_vulpes -1.2216 2.8972 -8.8326 -0.8129
## Cogon_Patch_Size-Sus_scrofa -1.5171 2.4805 -8.2549 -1.0244
## Veg_shannon_index-Canis_latrans 1.6600 1.1547 -0.3955 1.5934
## Veg_shannon_index-Lynx_rufus -0.1919 1.8772 -4.6642 0.0529
## Veg_shannon_index-Didelphis_virginiana 2.0252 1.7923 -0.3784 1.6289
## Veg_shannon_index-Sylvilagus_floridanus 1.5692 1.3161 -0.6219 1.4098
## Veg_shannon_index-Meleagris_gallopavo 2.4565 1.9860 -0.0908 2.0036
## Veg_shannon_index-Sciurus_carolinensis -0.5927 1.7830 -5.2714 -0.2883
## Veg_shannon_index-Vulpes_vulpes -0.3950 1.8640 -5.0862 -0.0949
## Veg_shannon_index-Sus_scrofa 2.7091 2.2007 -0.0859 2.2241
## total_shrub_cover-Canis_latrans 2.0621 2.0992 -0.7265 1.5912
## total_shrub_cover-Lynx_rufus -2.1843 5.4189 -12.5450 -2.3202
## total_shrub_cover-Didelphis_virginiana -3.0194 2.9031 -11.6511 -2.2749
## total_shrub_cover-Sylvilagus_floridanus -2.8322 3.5897 -13.0560 -1.8161
## total_shrub_cover-Meleagris_gallopavo -4.7039 3.3330 -13.6564 -3.9909
## total_shrub_cover-Sciurus_carolinensis -2.8805 3.7734 -12.8208 -1.8302
## total_shrub_cover-Vulpes_vulpes -4.4518 5.2391 -19.8175 -2.9788
## total_shrub_cover-Sus_scrofa -1.0503 2.4847 -6.9999 -0.7657
## Avg_Cogongrass_Cover-Canis_latrans 2.2657 1.3803 -0.1135 2.1627
## Avg_Cogongrass_Cover-Lynx_rufus 2.3917 1.8573 -0.9701 2.2347
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7190 1.4278 -1.1156 1.6866
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6260 1.5700 -2.8155 0.8038
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5973 2.1017 -4.3943 0.9630
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.9583 1.5763 -1.0987 1.9093
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4172 1.9182 -0.8585 2.1879
## Avg_Cogongrass_Cover-Sus_scrofa 1.0985 1.7759 -2.9442 1.2541
## Tree_Density-Canis_latrans -2.7917 1.7762 -7.1445 -2.5251
## Tree_Density-Lynx_rufus -1.1493 1.4583 -3.8680 -1.2076
## Tree_Density-Didelphis_virginiana -2.0543 1.7112 -5.6047 -2.0361
## Tree_Density-Sylvilagus_floridanus -2.3942 1.8498 -6.7324 -2.1821
## Tree_Density-Meleagris_gallopavo -2.4034 1.9335 -6.7945 -2.1293
## Tree_Density-Sciurus_carolinensis -2.2901 2.1368 -7.7233 -2.0151
## Tree_Density-Vulpes_vulpes -1.6718 2.1391 -5.2936 -1.8017
## Tree_Density-Sus_scrofa -2.2201 1.8303 -6.3376 -2.0532
## Avg_Canopy_Cover-Canis_latrans -0.3789 1.0380 -2.7967 -0.2871
## Avg_Canopy_Cover-Lynx_rufus 0.7184 3.2559 -5.2131 0.4869
## Avg_Canopy_Cover-Didelphis_virginiana 5.3072 2.8902 1.7008 4.6082
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.2997 3.5419 1.5431 5.4628
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6770 2.3596 0.6622 3.1600
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2421 3.1069 1.4773 4.3438
## Avg_Canopy_Cover-Vulpes_vulpes 4.3559 3.3054 0.5999 3.4768
## Avg_Canopy_Cover-Sus_scrofa 2.8500 1.8217 0.2176 2.5158
## avg_veg_height-Canis_latrans -0.1833 0.9067 -1.7972 -0.2652
## avg_veg_height-Lynx_rufus -0.4015 1.3253 -2.9942 -0.4641
## avg_veg_height-Didelphis_virginiana -0.5079 1.0433 -2.7154 -0.5170
## avg_veg_height-Sylvilagus_floridanus -0.3425 1.0457 -2.2538 -0.3995
## avg_veg_height-Meleagris_gallopavo -0.4054 1.3420 -3.1207 -0.3875
## avg_veg_height-Sciurus_carolinensis 0.3727 1.3475 -1.4731 0.1184
## avg_veg_height-Vulpes_vulpes -0.2242 1.2718 -2.5567 -0.3410
## avg_veg_height-Sus_scrofa -0.2061 1.1195 -2.2165 -0.2814
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.7763 1.0972 115
## (Intercept)-Lynx_rufus 20.9260 1.9439 16
## (Intercept)-Didelphis_virginiana 0.5318 1.0832 158
## (Intercept)-Sylvilagus_floridanus 3.5009 1.1141 129
## (Intercept)-Meleagris_gallopavo 2.1139 1.0638 85
## (Intercept)-Sciurus_carolinensis 1.4520 1.1081 131
## (Intercept)-Vulpes_vulpes 1.6883 1.1580 87
## (Intercept)-Sus_scrofa 0.0871 1.0815 76
## Cogon_Patch_Size-Canis_latrans 7.4351 1.0820 140
## Cogon_Patch_Size-Lynx_rufus 5.8417 1.1762 84
## Cogon_Patch_Size-Didelphis_virginiana 6.5088 1.1182 107
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4206 1.0651 100
## Cogon_Patch_Size-Meleagris_gallopavo 6.4543 1.1653 136
## Cogon_Patch_Size-Sciurus_carolinensis 1.8231 1.1085 108
## Cogon_Patch_Size-Vulpes_vulpes 3.5175 1.0749 176
## Cogon_Patch_Size-Sus_scrofa 1.8810 1.0633 170
## Veg_shannon_index-Canis_latrans 4.1699 1.0247 371
## Veg_shannon_index-Lynx_rufus 2.9516 1.0209 153
## Veg_shannon_index-Didelphis_virginiana 6.9390 1.1757 88
## Veg_shannon_index-Sylvilagus_floridanus 4.8257 1.0393 176
## Veg_shannon_index-Meleagris_gallopavo 7.8194 1.1262 64
## Veg_shannon_index-Sciurus_carolinensis 2.0708 1.2188 90
## Veg_shannon_index-Vulpes_vulpes 2.5575 1.0944 189
## Veg_shannon_index-Sus_scrofa 8.3949 1.1768 58
## total_shrub_cover-Canis_latrans 7.7480 1.1415 81
## total_shrub_cover-Lynx_rufus 14.2117 1.9224 25
## total_shrub_cover-Didelphis_virginiana 0.4854 1.1198 71
## total_shrub_cover-Sylvilagus_floridanus 1.0501 1.2459 64
## total_shrub_cover-Meleagris_gallopavo -0.1128 1.0974 74
## total_shrub_cover-Sciurus_carolinensis 1.2677 1.2318 30
## total_shrub_cover-Vulpes_vulpes 1.3510 1.1979 37
## total_shrub_cover-Sus_scrofa 2.9948 1.0311 114
## Avg_Cogongrass_Cover-Canis_latrans 5.4811 1.0380 272
## Avg_Cogongrass_Cover-Lynx_rufus 6.8044 1.1111 104
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.8289 1.0383 235
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2247 1.0653 235
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.7457 1.1154 171
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.3699 1.0557 211
## Avg_Cogongrass_Cover-Vulpes_vulpes 7.1215 1.0409 152
## Avg_Cogongrass_Cover-Sus_scrofa 4.2384 1.0492 265
## Tree_Density-Canis_latrans -0.4246 1.1021 124
## Tree_Density-Lynx_rufus 1.9367 1.0537 293
## Tree_Density-Didelphis_virginiana 1.3003 1.0493 261
## Tree_Density-Sylvilagus_floridanus 0.5844 1.0333 293
## Tree_Density-Meleagris_gallopavo 0.4024 1.2115 121
## Tree_Density-Sciurus_carolinensis 0.9338 1.1264 67
## Tree_Density-Vulpes_vulpes 3.0622 1.1129 125
## Tree_Density-Sus_scrofa 0.9212 1.0431 234
## Avg_Canopy_Cover-Canis_latrans 1.3667 1.0688 157
## Avg_Canopy_Cover-Lynx_rufus 10.2278 1.5274 57
## Avg_Canopy_Cover-Didelphis_virginiana 12.8023 1.0573 62
## Avg_Canopy_Cover-Sylvilagus_floridanus 14.8828 1.0447 67
## Avg_Canopy_Cover-Meleagris_gallopavo 9.7412 1.1858 168
## Avg_Canopy_Cover-Sciurus_carolinensis 12.9778 1.2514 43
## Avg_Canopy_Cover-Vulpes_vulpes 12.9198 1.3673 47
## Avg_Canopy_Cover-Sus_scrofa 7.5272 1.0408 206
## avg_veg_height-Canis_latrans 1.9164 1.0609 189
## avg_veg_height-Lynx_rufus 2.3132 1.0451 287
## avg_veg_height-Didelphis_virginiana 1.6923 1.0246 375
## avg_veg_height-Sylvilagus_floridanus 1.9686 1.0603 243
## avg_veg_height-Meleagris_gallopavo 2.0315 1.1237 129
## avg_veg_height-Sciurus_carolinensis 3.8938 1.0942 136
## avg_veg_height-Vulpes_vulpes 2.5240 1.0482 214
## avg_veg_height-Sus_scrofa 2.2437 1.0678 139
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8117 1.887000e-01 -5.2132
## (Intercept)-Lynx_rufus -5.6592 3.276000e-01 -6.3414
## (Intercept)-Didelphis_virginiana -4.8366 2.704000e-01 -5.3822
## (Intercept)-Sylvilagus_floridanus -5.0685 2.334000e-01 -5.5837
## (Intercept)-Meleagris_gallopavo -5.5935 4.095000e-01 -6.4430
## (Intercept)-Sciurus_carolinensis -4.9436 2.987000e-01 -5.5119
## (Intercept)-Vulpes_vulpes -5.9032 4.966000e-01 -6.9934
## (Intercept)-Sus_scrofa -5.3838 5.318000e-01 -6.5483
## shrub_cover-Canis_latrans -0.3769 2.154000e-01 -0.7557
## shrub_cover-Lynx_rufus 0.0298 4.250000e-01 -0.8660
## shrub_cover-Didelphis_virginiana 1.2940 3.652000e-01 0.5466
## shrub_cover-Sylvilagus_floridanus 0.8935 3.841000e-01 0.1693
## shrub_cover-Meleagris_gallopavo -0.5626 4.533000e-01 -1.4827
## shrub_cover-Sciurus_carolinensis 1.2752 4.036000e-01 0.4121
## shrub_cover-Vulpes_vulpes 0.5715 6.291000e-01 -0.7611
## shrub_cover-Sus_scrofa 1.1892 9.274000e-01 -0.5772
## veg_height-Canis_latrans -0.5677 1.762000e-01 -0.9150
## veg_height-Lynx_rufus 0.1266 2.257000e-01 -0.3171
## veg_height-Didelphis_virginiana 0.5478 2.386000e-01 0.0684
## veg_height-Sylvilagus_floridanus 0.1752 2.434000e-01 -0.3052
## veg_height-Meleagris_gallopavo -0.1848 3.773000e-01 -0.9408
## veg_height-Sciurus_carolinensis 0.2877 2.236000e-01 -0.1353
## veg_height-Vulpes_vulpes -0.1963 3.369000e-01 -0.9342
## veg_height-Sus_scrofa -0.2353 3.295000e-01 -0.9033
## week-Canis_latrans -5365274.2879 3.307840e+08 -1015866.7026
## week-Lynx_rufus 2577461.1507 3.222270e+08 -902771.6254
## week-Didelphis_virginiana -6197365.7546 2.929806e+08 -946861.2993
## week-Sylvilagus_floridanus -938275.2631 3.104861e+08 -494321.3223
## week-Meleagris_gallopavo -7334581.2261 2.140603e+08 -1562382.8936
## week-Sciurus_carolinensis 2115509.5259 2.392078e+08 -885308.7211
## week-Vulpes_vulpes 3158820.9300 2.858931e+08 -1283741.0526
## week-Sus_scrofa -4658093.0467 2.669344e+08 -1222621.3758
## I(week^2)-Canis_latrans -19.4283 3.499029e+03 -1316.1051
## I(week^2)-Lynx_rufus 33.1023 2.767671e+03 -1183.1367
## I(week^2)-Didelphis_virginiana -18.6960 4.683502e+03 -1318.7169
## I(week^2)-Sylvilagus_floridanus 5.5516 3.271369e+03 -1807.3754
## I(week^2)-Meleagris_gallopavo -88.1686 3.734037e+03 -1775.0292
## I(week^2)-Sciurus_carolinensis 90.4937 3.566820e+03 -1262.0632
## I(week^2)-Vulpes_vulpes -2.4279 4.300326e+03 -1606.6649
## I(week^2)-Sus_scrofa 47.6225 4.695149e+03 -1807.7486
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8022 -4.4600 1.0170 133
## (Intercept)-Lynx_rufus -5.6182 -5.1023 1.1284 64
## (Intercept)-Didelphis_virginiana -4.8345 -4.3084 1.1020 158
## (Intercept)-Sylvilagus_floridanus -5.0598 -4.6313 1.0306 116
## (Intercept)-Meleagris_gallopavo -5.5677 -4.8385 1.1164 98
## (Intercept)-Sciurus_carolinensis -4.9536 -4.3247 1.0365 125
## (Intercept)-Vulpes_vulpes -5.8670 -5.0333 1.1355 70
## (Intercept)-Sus_scrofa -5.3650 -4.3721 1.0999 72
## shrub_cover-Canis_latrans -0.3860 0.0743 1.0081 151
## shrub_cover-Lynx_rufus 0.0704 0.7475 1.3345 58
## shrub_cover-Didelphis_virginiana 1.3044 1.9661 1.0296 116
## shrub_cover-Sylvilagus_floridanus 0.8777 1.7138 1.0156 96
## shrub_cover-Meleagris_gallopavo -0.5492 0.2930 1.2423 92
## shrub_cover-Sciurus_carolinensis 1.3067 2.0252 1.0565 86
## shrub_cover-Vulpes_vulpes 0.6092 1.7046 1.2072 89
## shrub_cover-Sus_scrofa 1.1721 3.0909 1.0498 104
## veg_height-Canis_latrans -0.5612 -0.2278 1.1293 144
## veg_height-Lynx_rufus 0.1232 0.5694 1.1267 134
## veg_height-Didelphis_virginiana 0.5448 1.0279 1.0077 252
## veg_height-Sylvilagus_floridanus 0.1735 0.6628 1.0143 205
## veg_height-Meleagris_gallopavo -0.1665 0.5437 1.0244 124
## veg_height-Sciurus_carolinensis 0.2832 0.7629 1.0417 220
## veg_height-Vulpes_vulpes -0.1801 0.4122 1.1123 114
## veg_height-Sus_scrofa -0.2286 0.3981 1.0711 215
## week-Canis_latrans 0.1491 1272146.6469 1.3163 4072
## week-Lynx_rufus -0.0404 1129026.1437 1.2967 35035
## week-Didelphis_virginiana 0.0878 1344277.7319 1.3343 2102
## week-Sylvilagus_floridanus -0.0560 1197936.9733 1.2913 70995
## week-Meleagris_gallopavo -0.1265 593274.6933 1.4027 822
## week-Sciurus_carolinensis 0.1412 1068287.0042 1.2981 9578
## week-Vulpes_vulpes -0.0253 581340.9813 1.3025 6799
## week-Sus_scrofa 0.0350 780085.6829 1.3204 3592
## I(week^2)-Canis_latrans -0.0206 1891.4164 1.1042 3255
## I(week^2)-Lynx_rufus 0.0485 1412.8693 1.1011 1838
## I(week^2)-Didelphis_virginiana 0.0337 1637.1356 1.2363 12751
## I(week^2)-Sylvilagus_floridanus 0.1956 1264.8701 1.1190 27517
## I(week^2)-Meleagris_gallopavo -0.0204 1387.0294 1.2119 3759
## I(week^2)-Sciurus_carolinensis 0.3577 1584.9213 1.2194 2491
## I(week^2)-Vulpes_vulpes 0.0166 1624.7740 1.2232 4971
## I(week^2)-Sus_scrofa 0.0976 1377.9495 1.2430 5012
#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): 5.7945
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3141 0.7699 -1.7617 -0.3786 1.3448 1.1866 99
## Avg_Cogongrass_Cover 0.0057 0.5312 -1.0843 0.0251 1.0457 1.0031 352
## total_shrub_cover -1.2122 0.7603 -2.8265 -1.1567 0.2497 1.0952 202
## avg_veg_height 0.1225 0.5261 -0.8399 0.1022 1.3143 1.0406 236
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0631 1.6419 0.0483 0.5057 5.7422 1.0432 353
## Avg_Cogongrass_Cover 1.0044 1.9914 0.0461 0.4338 5.5605 1.0695 254
## total_shrub_cover 2.8929 4.2806 0.1506 1.6426 12.5662 1.1502 173
## avg_veg_height 0.6708 1.3792 0.0467 0.3067 3.4882 1.0634 319
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 5.1243 5.0044 0.3774 3.5576 18.9215 1.0465 81
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.2127 0.3317 -5.8237 -5.2225 -4.5474 1.1654 127
## shrub_cover 0.5954 0.4318 -0.2820 0.5990 1.4798 1.0442 312
## veg_height -0.0213 0.2451 -0.5220 -0.0176 0.4526 1.0871 286
## week 0.0000 1.6153 -3.2235 0.0293 3.1212 1.0043 1088
## I(week^2) -0.0027 1.6248 -3.1779 -0.0184 3.0284 1.0092 642
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.296000e-01 8.965000e-01 0.0532 0.2820 2.736000e+00 1.5034 27
## shrub_cover 1.267000e+00 1.188500e+00 0.2245 0.9530 4.132500e+00 1.1216 444
## veg_height 3.549000e-01 3.926000e-01 0.0652 0.2520 1.257400e+00 1.3077 214
## week 1.536126e+17 2.635359e+18 0.1106 4177.6902 2.100496e+16 1.5943 415
## I(week^2) 1.472170e+06 1.883886e+07 0.0847 28.3925 2.617759e+06 1.3716 320
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2317 1.0141 -1.6415 0.1605
## (Intercept)-Lynx_rufus -0.0305 1.0625 -2.0233 -0.0838
## (Intercept)-Didelphis_virginiana -0.4565 0.9672 -2.3199 -0.5312
## (Intercept)-Sylvilagus_floridanus -0.1018 1.0237 -2.0104 -0.1619
## (Intercept)-Meleagris_gallopavo -0.4428 1.0185 -2.4437 -0.4848
## (Intercept)-Sciurus_carolinensis -0.5181 0.9592 -2.3706 -0.5746
## (Intercept)-Vulpes_vulpes -0.5485 1.1452 -2.7607 -0.6327
## (Intercept)-Sus_scrofa -0.8199 1.1606 -3.2836 -0.7750
## Avg_Cogongrass_Cover-Canis_latrans 0.4952 0.7487 -0.7876 0.4301
## Avg_Cogongrass_Cover-Lynx_rufus 0.5695 0.8987 -0.8864 0.4579
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1801 0.6714 -1.1245 0.1734
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5975 0.8166 -2.4721 -0.4824
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5084 0.9578 -2.6288 -0.3887
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0739 0.6834 -1.4825 -0.0386
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2824 0.8362 -1.1666 0.2169
## Avg_Cogongrass_Cover-Sus_scrofa -0.2741 0.9498 -2.3947 -0.1863
## total_shrub_cover-Canis_latrans 0.6873 0.9816 -0.9156 0.5834
## total_shrub_cover-Lynx_rufus -1.9573 1.2709 -4.9203 -1.8413
## total_shrub_cover-Didelphis_virginiana -1.5239 1.1519 -4.4997 -1.3135
## total_shrub_cover-Sylvilagus_floridanus -2.0881 1.4290 -5.8256 -1.8193
## total_shrub_cover-Meleagris_gallopavo -2.1127 1.2018 -4.8169 -1.9792
## total_shrub_cover-Sciurus_carolinensis -1.5732 1.2129 -4.5938 -1.3559
## total_shrub_cover-Vulpes_vulpes -1.5811 1.4842 -5.1064 -1.3921
## total_shrub_cover-Sus_scrofa -1.1047 1.3139 -3.9958 -0.9907
## avg_veg_height-Canis_latrans 0.1998 0.6229 -0.9258 0.1597
## avg_veg_height-Lynx_rufus 0.0718 0.8204 -1.4817 0.0336
## avg_veg_height-Didelphis_virginiana -0.1279 0.6978 -1.6377 -0.0985
## avg_veg_height-Sylvilagus_floridanus 0.0935 0.7052 -1.2208 0.0554
## avg_veg_height-Meleagris_gallopavo -0.0902 1.0602 -2.1446 -0.0756
## avg_veg_height-Sciurus_carolinensis 0.5710 0.7085 -0.6281 0.4934
## avg_veg_height-Vulpes_vulpes 0.0297 0.7463 -1.4985 0.0172
## avg_veg_height-Sus_scrofa 0.2638 0.7280 -1.0159 0.2168
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4577 1.0574 182
## (Intercept)-Lynx_rufus 2.2006 1.1489 115
## (Intercept)-Didelphis_virginiana 1.6630 1.2149 135
## (Intercept)-Sylvilagus_floridanus 2.0921 1.0728 160
## (Intercept)-Meleagris_gallopavo 1.6106 1.1125 138
## (Intercept)-Sciurus_carolinensis 1.5071 1.2353 164
## (Intercept)-Vulpes_vulpes 1.8671 1.0562 110
## (Intercept)-Sus_scrofa 1.4530 1.1434 129
## Avg_Cogongrass_Cover-Canis_latrans 2.0848 1.0138 305
## Avg_Cogongrass_Cover-Lynx_rufus 2.7144 1.0153 334
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5828 1.0142 451
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6785 1.0120 261
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0329 1.0372 299
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1498 1.0536 375
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.2185 1.0028 307
## Avg_Cogongrass_Cover-Sus_scrofa 1.3021 1.0049 394
## total_shrub_cover-Canis_latrans 2.9466 1.0689 153
## total_shrub_cover-Lynx_rufus 0.3038 1.0517 128
## total_shrub_cover-Didelphis_virginiana 0.0766 1.2608 93
## total_shrub_cover-Sylvilagus_floridanus -0.1070 1.1142 116
## total_shrub_cover-Meleagris_gallopavo -0.1760 1.0284 159
## total_shrub_cover-Sciurus_carolinensis 0.2264 1.0925 156
## total_shrub_cover-Vulpes_vulpes 0.9901 1.0633 133
## total_shrub_cover-Sus_scrofa 1.2544 1.1233 206
## avg_veg_height-Canis_latrans 1.6045 1.0264 359
## avg_veg_height-Lynx_rufus 1.8899 1.0141 261
## avg_veg_height-Didelphis_virginiana 1.1882 1.0186 383
## avg_veg_height-Sylvilagus_floridanus 1.6915 1.0060 270
## avg_veg_height-Meleagris_gallopavo 2.1036 1.1268 195
## avg_veg_height-Sciurus_carolinensis 2.2330 1.0119 444
## avg_veg_height-Vulpes_vulpes 1.5875 1.0412 302
## avg_veg_height-Sus_scrofa 1.8568 1.0254 417
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8557 1.775000e-01 -5.235100e+00
## (Intercept)-Lynx_rufus -5.5268 2.920000e-01 -6.122400e+00
## (Intercept)-Didelphis_virginiana -5.0169 3.015000e-01 -5.606500e+00
## (Intercept)-Sylvilagus_floridanus -5.1241 2.240000e-01 -5.579000e+00
## (Intercept)-Meleagris_gallopavo -5.5750 4.843000e-01 -6.586500e+00
## (Intercept)-Sciurus_carolinensis -5.0082 3.249000e-01 -5.631200e+00
## (Intercept)-Vulpes_vulpes -6.0668 7.941000e-01 -8.101700e+00
## (Intercept)-Sus_scrofa -5.5495 4.572000e-01 -6.475500e+00
## shrub_cover-Canis_latrans -0.3160 2.473000e-01 -7.881000e-01
## shrub_cover-Lynx_rufus 0.1499 3.526000e-01 -5.799000e-01
## shrub_cover-Didelphis_virginiana 1.4774 4.145000e-01 7.739000e-01
## shrub_cover-Sylvilagus_floridanus 1.0004 3.646000e-01 2.738000e-01
## shrub_cover-Meleagris_gallopavo -0.5788 4.591000e-01 -1.472500e+00
## shrub_cover-Sciurus_carolinensis 1.3402 3.767000e-01 5.624000e-01
## shrub_cover-Vulpes_vulpes 0.5644 7.692000e-01 -9.119000e-01
## shrub_cover-Sus_scrofa 1.3908 7.493000e-01 -2.152000e-01
## veg_height-Canis_latrans -0.5744 1.825000e-01 -9.465000e-01
## veg_height-Lynx_rufus 0.1431 2.239000e-01 -2.966000e-01
## veg_height-Didelphis_virginiana 0.5573 2.764000e-01 2.770000e-02
## veg_height-Sylvilagus_floridanus 0.0762 2.448000e-01 -3.930000e-01
## veg_height-Meleagris_gallopavo -0.3007 4.979000e-01 -1.317500e+00
## veg_height-Sciurus_carolinensis 0.3137 2.509000e-01 -1.634000e-01
## veg_height-Vulpes_vulpes -0.1606 3.847000e-01 -9.666000e-01
## veg_height-Sus_scrofa -0.2900 3.248000e-01 -9.799000e-01
## week-Canis_latrans 3717162.6960 2.569679e+08 -2.014266e+07
## week-Lynx_rufus -4906883.1160 3.890495e+08 -2.194407e+07
## week-Didelphis_virginiana -5238689.9321 3.349897e+08 -2.310491e+07
## week-Sylvilagus_floridanus 4372620.1995 4.153245e+08 -1.796485e+07
## week-Meleagris_gallopavo 7282908.0677 4.024358e+08 -2.118571e+07
## week-Sciurus_carolinensis 618730.8731 2.470344e+08 -3.060183e+07
## week-Vulpes_vulpes 9627047.5873 4.115197e+08 -1.313440e+07
## week-Sus_scrofa 10508545.9898 4.555879e+08 -1.887923e+07
## I(week^2)-Canis_latrans -16.7886 1.189487e+03 -3.868017e+02
## I(week^2)-Lynx_rufus 28.7332 1.346418e+03 -4.441546e+02
## I(week^2)-Didelphis_virginiana 4.8423 9.026767e+02 -4.167789e+02
## I(week^2)-Sylvilagus_floridanus -19.1917 8.429672e+02 -5.218988e+02
## I(week^2)-Meleagris_gallopavo 26.9227 1.164102e+03 -3.907371e+02
## I(week^2)-Sciurus_carolinensis 26.0562 9.285245e+02 -3.776932e+02
## I(week^2)-Vulpes_vulpes 12.0314 1.208494e+03 -3.663193e+02
## I(week^2)-Sus_scrofa 18.9901 1.219318e+03 -3.526751e+02
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8434 -4.5371 1.1116 177
## (Intercept)-Lynx_rufus -5.5220 -4.9499 1.0899 110
## (Intercept)-Didelphis_virginiana -5.0043 -4.4347 1.3389 100
## (Intercept)-Sylvilagus_floridanus -5.1184 -4.6931 1.2099 120
## (Intercept)-Meleagris_gallopavo -5.5396 -4.7066 1.0856 62
## (Intercept)-Sciurus_carolinensis -5.0064 -4.3496 1.1223 101
## (Intercept)-Vulpes_vulpes -5.8696 -4.9184 1.5100 36
## (Intercept)-Sus_scrofa -5.5372 -4.6506 1.3289 87
## shrub_cover-Canis_latrans -0.3128 0.1556 1.1414 119
## shrub_cover-Lynx_rufus 0.1568 0.8348 1.1191 108
## shrub_cover-Didelphis_virginiana 1.4372 2.3748 1.6178 57
## shrub_cover-Sylvilagus_floridanus 1.0147 1.6991 1.4758 120
## shrub_cover-Meleagris_gallopavo -0.5721 0.2813 1.1116 93
## shrub_cover-Sciurus_carolinensis 1.3435 2.0737 1.0733 128
## shrub_cover-Vulpes_vulpes 0.5653 2.0669 1.0285 57
## shrub_cover-Sus_scrofa 1.4042 2.7566 1.3790 92
## veg_height-Canis_latrans -0.5723 -0.2173 1.4094 134
## veg_height-Lynx_rufus 0.1445 0.5909 1.0183 163
## veg_height-Didelphis_virginiana 0.5466 1.0920 1.2257 130
## veg_height-Sylvilagus_floridanus 0.0674 0.5764 1.0381 139
## veg_height-Meleagris_gallopavo -0.2918 0.6306 1.2955 91
## veg_height-Sciurus_carolinensis 0.3073 0.8118 1.0719 133
## veg_height-Vulpes_vulpes -0.1269 0.5261 1.2928 94
## veg_height-Sus_scrofa -0.2697 0.3012 1.0612 217
## week-Canis_latrans 0.0211 17201753.2454 1.3119 3577
## week-Lynx_rufus -0.1496 21767928.4335 1.3059 4380
## week-Didelphis_virginiana -0.0408 24101288.6229 1.3145 3859
## week-Sylvilagus_floridanus -0.1793 23172583.4223 1.3012 6242
## week-Meleagris_gallopavo 0.0485 16237198.4693 1.3229 2426
## week-Sciurus_carolinensis -0.2407 22662720.9819 1.2909 15607
## week-Vulpes_vulpes -0.1393 31017602.1802 1.3445 2543
## week-Sus_scrofa 0.0817 22823561.1759 1.3424 1908
## I(week^2)-Canis_latrans -0.0077 349.0541 1.1199 3677
## I(week^2)-Lynx_rufus -0.0903 383.1668 1.1866 2134
## I(week^2)-Didelphis_virginiana 0.0866 457.6912 1.1058 53379
## I(week^2)-Sylvilagus_floridanus -0.0304 384.7599 1.1265 2391
## I(week^2)-Meleagris_gallopavo 0.1004 374.0683 1.1942 1587
## I(week^2)-Sciurus_carolinensis -0.0174 465.4180 1.1101 3590
## I(week^2)-Vulpes_vulpes 0.1526 526.3017 1.2042 16442
## I(week^2)-Sus_scrofa 0.1771 430.5580 1.1725 2212
#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): 5.1912
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8564 0.6071 -2.0234 -0.8941 0.4296 1.0287 415
## Tree_Density -0.8558 0.4986 -1.9498 -0.8388 0.0745 1.0113 510
## Avg_Canopy_Cover 1.3201 0.5496 0.3075 1.2862 2.4514 1.0222 414
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4191 2.9263 0.1377 1.5783 10.3724 1.0314 117
## Tree_Density 0.9396 1.7038 0.0495 0.4461 4.6272 1.1035 425
## Avg_Canopy_Cover 1.8150 2.4470 0.1255 1.1154 7.5690 1.0313 503
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0411 1.2109 0.0525 0.6384 4.5153 1.0535 152
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0844 0.3120 -5.6283 -5.1010 -4.4092 1.0267 563
## shrub_cover 0.2465 0.3823 -0.5018 0.2471 1.0015 1.0290 473
## veg_height 0.0050 0.2154 -0.4200 0.0051 0.4380 1.0497 452
## week -0.0264 1.6907 -3.4272 -0.0199 3.4482 1.0075 558
## I(week^2) -0.0052 1.6746 -3.2251 0.0073 3.2169 1.0105 527
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.639000e-01 6.418000e-01 0.0725 0.3932 2.069200e+00 1.0249 205
## shrub_cover 9.428000e-01 9.235000e-01 0.2019 0.6915 3.117200e+00 1.0172 774
## veg_height 2.712000e-01 2.291000e-01 0.0611 0.2071 8.426000e-01 1.0120 965
## week 6.888106e+24 7.766228e+25 0.0989 128.9378 1.726856e+24 1.9238 147
## I(week^2) 8.331841e+10 9.508776e+11 0.1141 39.6028 5.230324e+10 1.9110 133
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2500 0.7459 -1.2115 0.2349 1.7923
## (Intercept)-Lynx_rufus 0.5690 1.4066 -1.5355 0.3207 4.1623
## (Intercept)-Didelphis_virginiana -1.5630 0.7734 -3.1940 -1.5390 -0.1399
## (Intercept)-Sylvilagus_floridanus -0.8201 0.7162 -2.2697 -0.8250 0.6069
## (Intercept)-Meleagris_gallopavo -0.3748 0.8431 -1.9072 -0.4259 1.4040
## (Intercept)-Sciurus_carolinensis -1.6692 0.7934 -3.3493 -1.6238 -0.2234
## (Intercept)-Vulpes_vulpes -1.6944 1.0692 -3.7998 -1.6727 0.3356
## (Intercept)-Sus_scrofa -2.1616 0.9678 -4.1470 -2.0829 -0.4736
## Tree_Density-Canis_latrans -1.0865 0.6630 -2.5710 -1.0270 -0.0387
## Tree_Density-Lynx_rufus -0.0156 0.7785 -1.2813 -0.0976 1.7378
## Tree_Density-Didelphis_virginiana -1.0668 0.7811 -2.8598 -0.9738 0.2090
## Tree_Density-Sylvilagus_floridanus -1.1803 0.8526 -3.1844 -1.0605 0.1033
## Tree_Density-Meleagris_gallopavo -1.1321 0.8727 -3.2146 -1.0079 0.1748
## Tree_Density-Sciurus_carolinensis -0.9570 0.7744 -2.7864 -0.8879 0.3554
## Tree_Density-Vulpes_vulpes -0.7290 0.7913 -2.4515 -0.6994 0.7546
## Tree_Density-Sus_scrofa -0.9960 0.8912 -3.1669 -0.8806 0.4111
## Avg_Canopy_Cover-Canis_latrans -0.1323 0.5431 -1.2238 -0.1190 0.9102
## Avg_Canopy_Cover-Lynx_rufus 1.0308 1.0520 -0.6824 0.9319 3.4625
## Avg_Canopy_Cover-Didelphis_virginiana 1.8197 0.7903 0.5744 1.6927 3.6510
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.5956 1.2186 0.8428 2.3853 5.4536
## Avg_Canopy_Cover-Meleagris_gallopavo 1.7739 0.9454 0.3188 1.6312 4.1694
## Avg_Canopy_Cover-Sciurus_carolinensis 1.7172 0.7711 0.5092 1.5949 3.5500
## Avg_Canopy_Cover-Vulpes_vulpes 1.1896 0.7385 -0.0953 1.1127 2.8471
## Avg_Canopy_Cover-Sus_scrofa 1.4516 0.7116 0.3021 1.3557 3.1174
## Rhat ESS
## (Intercept)-Canis_latrans 1.0222 367
## (Intercept)-Lynx_rufus 1.0668 146
## (Intercept)-Didelphis_virginiana 1.0314 514
## (Intercept)-Sylvilagus_floridanus 1.0131 820
## (Intercept)-Meleagris_gallopavo 1.0214 413
## (Intercept)-Sciurus_carolinensis 1.0117 597
## (Intercept)-Vulpes_vulpes 1.0076 169
## (Intercept)-Sus_scrofa 1.0187 438
## Tree_Density-Canis_latrans 1.0117 768
## Tree_Density-Lynx_rufus 1.0057 425
## Tree_Density-Didelphis_virginiana 1.0111 654
## Tree_Density-Sylvilagus_floridanus 1.0298 588
## Tree_Density-Meleagris_gallopavo 1.0491 457
## Tree_Density-Sciurus_carolinensis 1.0199 744
## Tree_Density-Vulpes_vulpes 1.0053 697
## Tree_Density-Sus_scrofa 1.0275 555
## Avg_Canopy_Cover-Canis_latrans 1.0130 909
## Avg_Canopy_Cover-Lynx_rufus 1.1433 264
## Avg_Canopy_Cover-Didelphis_virginiana 1.0001 621
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0441 351
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0301 362
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0166 597
## Avg_Canopy_Cover-Vulpes_vulpes 1.0017 652
## Avg_Canopy_Cover-Sus_scrofa 1.0019 1129
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.805800e+00 1.856000e-01 -5.166400e+00
## (Intercept)-Lynx_rufus -5.723000e+00 3.284000e-01 -6.363300e+00
## (Intercept)-Didelphis_virginiana -4.738300e+00 2.726000e-01 -5.272200e+00
## (Intercept)-Sylvilagus_floridanus -4.948700e+00 2.182000e-01 -5.391200e+00
## (Intercept)-Meleagris_gallopavo -5.741800e+00 4.114000e-01 -6.561100e+00
## (Intercept)-Sciurus_carolinensis -4.723400e+00 2.950000e-01 -5.272300e+00
## (Intercept)-Vulpes_vulpes -5.789700e+00 5.781000e-01 -7.067400e+00
## (Intercept)-Sus_scrofa -5.260000e+00 4.891000e-01 -6.261800e+00
## shrub_cover-Canis_latrans -2.540000e-01 2.052000e-01 -6.539000e-01
## shrub_cover-Lynx_rufus -3.189000e-01 3.623000e-01 -1.011400e+00
## shrub_cover-Didelphis_virginiana 1.081300e+00 3.289000e-01 4.667000e-01
## shrub_cover-Sylvilagus_floridanus 6.713000e-01 3.748000e-01 -4.670000e-02
## shrub_cover-Meleagris_gallopavo -7.803000e-01 3.873000e-01 -1.592100e+00
## shrub_cover-Sciurus_carolinensis 9.782000e-01 3.743000e-01 2.582000e-01
## shrub_cover-Vulpes_vulpes -5.110000e-02 5.879000e-01 -1.217200e+00
## shrub_cover-Sus_scrofa 8.016000e-01 7.571000e-01 -7.306000e-01
## veg_height-Canis_latrans -5.709000e-01 1.790000e-01 -9.643000e-01
## veg_height-Lynx_rufus 1.370000e-01 2.489000e-01 -4.097000e-01
## veg_height-Didelphis_virginiana 5.465000e-01 2.419000e-01 1.002000e-01
## veg_height-Sylvilagus_floridanus 1.907000e-01 2.189000e-01 -2.447000e-01
## veg_height-Meleagris_gallopavo -2.163000e-01 3.306000e-01 -8.622000e-01
## veg_height-Sciurus_carolinensis 2.304000e-01 2.088000e-01 -1.950000e-01
## veg_height-Vulpes_vulpes -9.680000e-02 3.002000e-01 -7.002000e-01
## veg_height-Sus_scrofa -1.958000e-01 3.162000e-01 -8.214000e-01
## week-Canis_latrans -2.801346e+09 2.412722e+12 -5.166487e+10
## week-Lynx_rufus 8.552145e+10 2.505241e+12 -5.501081e+10
## week-Didelphis_virginiana 2.109038e+10 2.227057e+12 -5.659499e+10
## week-Sylvilagus_floridanus 1.687708e+10 2.060662e+12 -3.418812e+10
## week-Meleagris_gallopavo 5.759937e+10 2.321761e+12 -4.535564e+10
## week-Sciurus_carolinensis 3.239785e+10 2.431079e+12 -9.507264e+10
## week-Vulpes_vulpes -3.411889e+10 2.687033e+12 -7.616917e+10
## week-Sus_scrofa -7.897833e+10 2.521114e+12 -9.020814e+10
## I(week^2)-Canis_latrans -5.826059e+03 1.894653e+05 -4.116161e+04
## I(week^2)-Lynx_rufus 1.338779e+03 2.277690e+05 -2.262724e+04
## I(week^2)-Didelphis_virginiana 2.784376e+03 2.982582e+05 -1.836221e+04
## I(week^2)-Sylvilagus_floridanus 7.675288e+03 3.170523e+05 -1.907191e+04
## I(week^2)-Meleagris_gallopavo -8.465803e+02 3.349493e+05 -2.508064e+04
## I(week^2)-Sciurus_carolinensis 2.948366e+03 2.945065e+05 -3.371078e+04
## I(week^2)-Vulpes_vulpes -8.503969e+03 3.274214e+05 -2.868817e+04
## I(week^2)-Sus_scrofa 5.768753e+03 2.867427e+05 -1.880480e+04
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8014 -4.447500e+00 1.0154 127
## (Intercept)-Lynx_rufus -5.7141 -5.110800e+00 1.1309 69
## (Intercept)-Didelphis_virginiana -4.7321 -4.237700e+00 1.0282 197
## (Intercept)-Sylvilagus_floridanus -4.9433 -4.543500e+00 1.0035 230
## (Intercept)-Meleagris_gallopavo -5.7352 -4.946400e+00 1.1701 75
## (Intercept)-Sciurus_carolinensis -4.7293 -4.126700e+00 1.0515 144
## (Intercept)-Vulpes_vulpes -5.7355 -4.801500e+00 1.0762 70
## (Intercept)-Sus_scrofa -5.2432 -4.298500e+00 1.0653 245
## shrub_cover-Canis_latrans -0.2602 1.572000e-01 1.0171 177
## shrub_cover-Lynx_rufus -0.3207 3.432000e-01 1.2189 66
## shrub_cover-Didelphis_virginiana 1.0734 1.745000e+00 1.0937 160
## shrub_cover-Sylvilagus_floridanus 0.6619 1.428700e+00 1.0295 153
## shrub_cover-Meleagris_gallopavo -0.7610 -7.740000e-02 1.1261 94
## shrub_cover-Sciurus_carolinensis 0.9728 1.751000e+00 1.0321 198
## shrub_cover-Vulpes_vulpes -0.0661 1.114000e+00 1.0639 172
## shrub_cover-Sus_scrofa 0.7930 2.297900e+00 1.0554 353
## veg_height-Canis_latrans -0.5620 -2.386000e-01 1.0388 154
## veg_height-Lynx_rufus 0.1519 5.697000e-01 1.0201 134
## veg_height-Didelphis_virginiana 0.5340 1.052400e+00 1.0556 232
## veg_height-Sylvilagus_floridanus 0.1878 6.356000e-01 1.0863 249
## veg_height-Meleagris_gallopavo -0.2070 4.129000e-01 1.1385 124
## veg_height-Sciurus_carolinensis 0.2270 6.277000e-01 1.0182 257
## veg_height-Vulpes_vulpes -0.0835 4.346000e-01 1.0206 178
## veg_height-Sus_scrofa -0.1872 4.291000e-01 1.0358 373
## week-Canis_latrans 0.1577 7.156928e+10 1.2905 65496
## week-Lynx_rufus 0.1248 6.673881e+10 1.4019 1260
## week-Didelphis_virginiana -0.1200 8.092964e+10 1.2993 30119
## week-Sylvilagus_floridanus -0.0853 9.737460e+10 1.2970 26412
## week-Meleagris_gallopavo -0.0226 7.049516e+10 1.3504 877
## week-Sciurus_carolinensis 0.0455 4.360705e+10 1.3079 3534
## week-Vulpes_vulpes -0.1709 3.680403e+10 1.3063 8623
## week-Sus_scrofa -0.1431 5.235649e+10 1.3849 1128
## I(week^2)-Canis_latrans -0.0608 2.020381e+04 1.3813 903
## I(week^2)-Lynx_rufus -0.0108 2.826668e+04 1.2939 70821
## I(week^2)-Didelphis_virginiana -0.0424 2.852679e+04 1.2991 31154
## I(week^2)-Sylvilagus_floridanus 0.0962 3.906798e+04 1.3474 1534
## I(week^2)-Meleagris_gallopavo -0.0096 2.159391e+04 1.2910 13616
## I(week^2)-Sciurus_carolinensis 0.1785 2.427979e+04 1.3004 15068
## I(week^2)-Vulpes_vulpes 0.1215 2.046429e+04 1.3563 2330
## I(week^2)-Sus_scrofa 0.1356 3.062678e+04 1.3300 4423
#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): 5.6218
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6156 0.6067 -1.7721 -0.6357 0.5838 1.0118 244
## Cogon_Patch_Size -0.1597 0.6187 -1.4500 -0.1375 1.0130 1.0087 649
## Avg_Cogongrass_Cover 0.1721 0.4521 -0.7254 0.1738 1.0726 1.0410 419
## total_shrub_cover -0.9349 0.7075 -2.6390 -0.8529 0.2260 1.0210 158
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2232 2.0576 0.0575 0.6039 5.9445 1.0105 360
## Cogon_Patch_Size 2.2350 4.3567 0.0844 1.0019 12.8500 1.3246 118
## Avg_Cogongrass_Cover 0.8028 1.3926 0.0501 0.3820 4.0797 1.0111 400
## total_shrub_cover 2.2045 4.3674 0.0805 1.0476 10.7617 1.1344 213
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.3167 4.0105 0.2601 3.1682 15.3775 1.0557 117
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1754 0.2981 -5.7696 -5.1741 -4.5722 1.0479 348
## shrub_cover 0.5075 0.4340 -0.3666 0.5000 1.3837 1.0210 169
## veg_height -0.0518 0.2244 -0.5153 -0.0482 0.3832 1.0215 410
## week 0.0003 1.6769 -3.2874 0.0614 3.3314 1.0127 953
## I(week^2) -0.0891 1.6718 -3.4142 -0.0714 3.0765 1.0047 348
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.818000e-01 7.667000e-01 0.0530 0.2815 2.054800e+00 1.4505 126
## shrub_cover 1.211400e+00 1.138700e+00 0.2275 0.8879 4.145400e+00 1.1573 291
## veg_height 3.012000e-01 2.779000e-01 0.0604 0.2247 1.031200e+00 1.0064 557
## week 1.062242e+24 1.682221e+25 0.1718 14986.4463 9.933390e+21 1.6414 337
## I(week^2) 4.570629e+16 5.329326e+17 0.1035 34.3848 3.680465e+14 1.8889 148
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1255 0.9068 -1.4608 0.0662
## (Intercept)-Lynx_rufus -0.2571 0.9428 -1.9934 -0.2881
## (Intercept)-Didelphis_virginiana -0.7960 0.8237 -2.4892 -0.7844
## (Intercept)-Sylvilagus_floridanus -0.4553 0.8801 -2.1035 -0.4885
## (Intercept)-Meleagris_gallopavo -0.6111 0.9355 -2.5081 -0.6132
## (Intercept)-Sciurus_carolinensis -0.9675 0.8816 -2.7865 -0.9387
## (Intercept)-Vulpes_vulpes -0.9823 1.0139 -2.9949 -0.9857
## (Intercept)-Sus_scrofa -1.1796 1.0743 -3.6601 -1.0688
## Cogon_Patch_Size-Canis_latrans 0.8636 0.9013 -0.4556 0.7012
## Cogon_Patch_Size-Lynx_rufus -0.1315 1.1527 -2.1061 -0.2170
## Cogon_Patch_Size-Didelphis_virginiana 0.7753 0.6333 -0.3403 0.7309
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1378 1.2848 -4.2845 -0.8562
## Cogon_Patch_Size-Meleagris_gallopavo 0.2118 1.0329 -1.4556 0.1142
## Cogon_Patch_Size-Sciurus_carolinensis -0.9218 0.9932 -3.3276 -0.7568
## Cogon_Patch_Size-Vulpes_vulpes -0.6325 1.3337 -3.8352 -0.4793
## Cogon_Patch_Size-Sus_scrofa -0.4861 1.0771 -3.0125 -0.3331
## Avg_Cogongrass_Cover-Canis_latrans 0.4480 0.5486 -0.4937 0.3970
## Avg_Cogongrass_Cover-Lynx_rufus 0.6847 0.7922 -0.5086 0.5656
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1329 0.5711 -1.0632 0.1527
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2416 0.7337 -1.9073 -0.1916
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4035 0.9468 -2.6919 -0.2979
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4283 0.5793 -0.6773 0.4190
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4134 0.6118 -0.7084 0.3730
## Avg_Cogongrass_Cover-Sus_scrofa -0.0599 0.8287 -1.9468 0.0118
## total_shrub_cover-Canis_latrans 0.5125 0.9170 -0.8738 0.3467
## total_shrub_cover-Lynx_rufus -1.4385 1.2093 -4.2970 -1.2379
## total_shrub_cover-Didelphis_virginiana -1.1984 0.8729 -3.3012 -1.0605
## total_shrub_cover-Sylvilagus_floridanus -1.6195 1.2621 -4.8914 -1.3339
## total_shrub_cover-Meleagris_gallopavo -1.7941 1.1618 -4.4961 -1.6092
## total_shrub_cover-Sciurus_carolinensis -1.0188 1.0338 -3.5498 -0.8515
## total_shrub_cover-Vulpes_vulpes -1.1367 1.6533 -5.2842 -0.8723
## total_shrub_cover-Sus_scrofa -0.7489 1.0898 -3.3561 -0.6724
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0429 1.0135 321
## (Intercept)-Lynx_rufus 1.8322 1.0207 370
## (Intercept)-Didelphis_virginiana 0.7098 1.0138 348
## (Intercept)-Sylvilagus_floridanus 1.4525 1.0157 307
## (Intercept)-Meleagris_gallopavo 1.2301 1.0171 293
## (Intercept)-Sciurus_carolinensis 0.7878 1.0117 267
## (Intercept)-Vulpes_vulpes 1.0758 1.1113 258
## (Intercept)-Sus_scrofa 0.5898 1.0202 267
## Cogon_Patch_Size-Canis_latrans 3.1355 1.0525 482
## Cogon_Patch_Size-Lynx_rufus 2.3830 1.1091 194
## Cogon_Patch_Size-Didelphis_virginiana 2.1203 1.0194 542
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5990 1.0934 258
## Cogon_Patch_Size-Meleagris_gallopavo 2.6158 1.1372 308
## Cogon_Patch_Size-Sciurus_carolinensis 0.5450 1.0207 462
## Cogon_Patch_Size-Vulpes_vulpes 1.6844 1.0498 257
## Cogon_Patch_Size-Sus_scrofa 1.2718 1.0160 594
## Avg_Cogongrass_Cover-Canis_latrans 1.7241 1.0094 800
## Avg_Cogongrass_Cover-Lynx_rufus 2.6233 1.0216 447
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2424 1.0229 744
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0091 1.0320 408
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2206 1.0283 238
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6504 1.0303 391
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.7464 1.0095 702
## Avg_Cogongrass_Cover-Sus_scrofa 1.3744 1.0142 397
## total_shrub_cover-Canis_latrans 2.7571 1.0516 213
## total_shrub_cover-Lynx_rufus 0.4508 1.0082 156
## total_shrub_cover-Didelphis_virginiana 0.1082 1.0076 224
## total_shrub_cover-Sylvilagus_floridanus 0.0649 1.0465 99
## total_shrub_cover-Meleagris_gallopavo 0.0228 1.0772 105
## total_shrub_cover-Sciurus_carolinensis 0.5416 1.0725 158
## total_shrub_cover-Vulpes_vulpes 1.4712 1.1270 59
## total_shrub_cover-Sus_scrofa 1.2589 1.0695 264
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.823100e+00 1.768000e-01 -5.226800e+00
## (Intercept)-Lynx_rufus -5.460800e+00 2.807000e-01 -6.012400e+00
## (Intercept)-Didelphis_virginiana -4.830100e+00 3.088000e-01 -5.461600e+00
## (Intercept)-Sylvilagus_floridanus -5.112900e+00 2.312000e-01 -5.584700e+00
## (Intercept)-Meleagris_gallopavo -5.631200e+00 4.814000e-01 -6.682400e+00
## (Intercept)-Sciurus_carolinensis -4.918600e+00 3.140000e-01 -5.519300e+00
## (Intercept)-Vulpes_vulpes -5.887700e+00 6.227000e-01 -7.382800e+00
## (Intercept)-Sus_scrofa -5.523900e+00 4.808000e-01 -6.442900e+00
## shrub_cover-Canis_latrans -3.146000e-01 2.272000e-01 -7.400000e-01
## shrub_cover-Lynx_rufus 9.880000e-02 3.668000e-01 -6.812000e-01
## shrub_cover-Didelphis_virginiana 1.312200e+00 4.050000e-01 5.491000e-01
## shrub_cover-Sylvilagus_floridanus 9.964000e-01 4.018000e-01 1.086000e-01
## shrub_cover-Meleagris_gallopavo -6.272000e-01 4.505000e-01 -1.528400e+00
## shrub_cover-Sciurus_carolinensis 1.259900e+00 4.022000e-01 4.715000e-01
## shrub_cover-Vulpes_vulpes 3.642000e-01 8.745000e-01 -1.521700e+00
## shrub_cover-Sus_scrofa 1.253200e+00 8.496000e-01 -6.005000e-01
## veg_height-Canis_latrans -5.818000e-01 1.891000e-01 -9.739000e-01
## veg_height-Lynx_rufus 1.012000e-01 2.328000e-01 -3.720000e-01
## veg_height-Didelphis_virginiana 4.582000e-01 2.371000e-01 -7.400000e-03
## veg_height-Sylvilagus_floridanus 8.120000e-02 2.397000e-01 -4.008000e-01
## veg_height-Meleagris_gallopavo -3.456000e-01 4.096000e-01 -1.145700e+00
## veg_height-Sciurus_carolinensis 2.540000e-01 2.324000e-01 -1.608000e-01
## veg_height-Vulpes_vulpes -1.225000e-01 3.317000e-01 -8.396000e-01
## veg_height-Sus_scrofa -3.013000e-01 3.424000e-01 -1.077900e+00
## week-Canis_latrans 1.094281e+10 8.147740e+11 -1.251250e+10
## week-Lynx_rufus 1.506778e+10 1.248806e+12 -1.400668e+10
## week-Didelphis_virginiana 2.084832e+10 1.119550e+12 -1.391395e+10
## week-Sylvilagus_floridanus -1.917295e+10 8.738732e+11 -1.013599e+10
## week-Meleagris_gallopavo 2.363655e+10 1.010392e+12 -1.267999e+10
## week-Sciurus_carolinensis 1.273923e+10 1.037075e+12 -1.303946e+10
## week-Vulpes_vulpes 8.458773e+09 1.353873e+12 -1.386086e+10
## week-Sus_scrofa -1.555620e+10 7.794443e+11 -1.370105e+10
## I(week^2)-Canis_latrans -1.477272e+06 2.008397e+08 -3.197103e+04
## I(week^2)-Lynx_rufus 2.961667e+06 2.011557e+08 -2.817862e+04
## I(week^2)-Didelphis_virginiana -6.791725e+06 2.597883e+08 -2.831039e+04
## I(week^2)-Sylvilagus_floridanus -2.309713e+06 1.956572e+08 -2.877720e+04
## I(week^2)-Meleagris_gallopavo 7.560909e+06 2.621110e+08 -2.729757e+04
## I(week^2)-Sciurus_carolinensis -1.461195e+06 2.238092e+08 -6.767338e+04
## I(week^2)-Vulpes_vulpes -1.112387e+06 2.115427e+08 -2.954968e+04
## I(week^2)-Sus_scrofa -2.932939e+06 1.761580e+08 -3.458048e+04
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8096 -4.509000e+00 1.0140 154
## (Intercept)-Lynx_rufus -5.4455 -4.937400e+00 1.0337 121
## (Intercept)-Didelphis_virginiana -4.8268 -4.197000e+00 1.0243 117
## (Intercept)-Sylvilagus_floridanus -5.1146 -4.662500e+00 1.0095 132
## (Intercept)-Meleagris_gallopavo -5.6023 -4.778200e+00 1.1531 71
## (Intercept)-Sciurus_carolinensis -4.9186 -4.311900e+00 1.0942 95
## (Intercept)-Vulpes_vulpes -5.7923 -4.930000e+00 1.5831 58
## (Intercept)-Sus_scrofa -5.5018 -4.549300e+00 1.1219 120
## shrub_cover-Canis_latrans -0.3167 1.364000e-01 1.0347 142
## shrub_cover-Lynx_rufus 0.0943 8.122000e-01 1.0140 100
## shrub_cover-Didelphis_virginiana 1.3026 2.106100e+00 1.0226 83
## shrub_cover-Sylvilagus_floridanus 1.0194 1.752200e+00 1.1338 79
## shrub_cover-Meleagris_gallopavo -0.6350 2.682000e-01 1.1065 121
## shrub_cover-Sciurus_carolinensis 1.2708 2.048600e+00 1.1999 102
## shrub_cover-Vulpes_vulpes 0.4085 2.006400e+00 1.3351 29
## shrub_cover-Sus_scrofa 1.2762 2.917800e+00 1.1944 101
## veg_height-Canis_latrans -0.5735 -2.247000e-01 1.0156 159
## veg_height-Lynx_rufus 0.1073 5.364000e-01 1.0470 154
## veg_height-Didelphis_virginiana 0.4530 9.466000e-01 1.0041 210
## veg_height-Sylvilagus_floridanus 0.0898 5.416000e-01 1.1333 154
## veg_height-Meleagris_gallopavo -0.3239 4.504000e-01 1.1434 143
## veg_height-Sciurus_carolinensis 0.2477 7.289000e-01 1.0892 149
## veg_height-Vulpes_vulpes -0.0960 4.954000e-01 1.0149 127
## veg_height-Sus_scrofa -0.2705 2.879000e-01 1.0712 200
## week-Canis_latrans -0.0665 1.243260e+10 1.3082 5458
## week-Lynx_rufus 0.2735 1.115270e+10 1.3048 6615
## week-Didelphis_virginiana 0.2739 8.648923e+09 1.3245 2622
## week-Sylvilagus_floridanus -0.0830 9.759576e+09 1.3375 2762
## week-Meleagris_gallopavo -0.4148 8.548186e+09 1.3439 2981
## week-Sciurus_carolinensis 0.0459 1.568293e+10 1.3053 4551
## week-Vulpes_vulpes 0.3628 7.607835e+09 1.2942 39080
## week-Sus_scrofa -0.1797 8.756249e+09 1.3295 4786
## I(week^2)-Canis_latrans -0.2995 3.356897e+04 1.2957 10121
## I(week^2)-Lynx_rufus -0.1049 3.362668e+04 1.3118 3381
## I(week^2)-Didelphis_virginiana -0.3889 4.369539e+04 1.3568 1268
## I(week^2)-Sylvilagus_floridanus -0.2363 5.306358e+04 1.3042 5297
## I(week^2)-Meleagris_gallopavo -0.2821 6.268650e+04 1.3709 1266
## I(week^2)-Sciurus_carolinensis -0.3523 1.871561e+04 1.2946 30284
## I(week^2)-Vulpes_vulpes -0.2152 3.972298e+04 1.2931 28541
## I(week^2)-Sus_scrofa -0.0953 2.633026e+04 1.3177 4155
#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): 5.0092
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8393 0.4843 -1.8188 -0.8259 0.1151 1.0988 237
## Veg_shannon_index 0.3120 0.3549 -0.4212 0.3207 0.9865 1.0016 1196
## Avg_Cogongrass_Cover 0.2293 0.3488 -0.4559 0.2277 0.9329 1.0113 1009
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0235 1.3385 0.0648 0.6138 4.2468 1.0295 582
## Veg_shannon_index 0.6297 0.8589 0.0510 0.3810 2.6757 1.0477 738
## Avg_Cogongrass_Cover 0.5245 0.7134 0.0464 0.3022 2.3932 1.0147 839
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4247 1.44 0.0708 1.0145 5.1813 1.0239 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0150 0.3189 -5.6073 -5.0278 -4.2986 1.0414 601
## shrub_cover 0.2110 0.3770 -0.5602 0.2138 0.9450 1.0053 523
## veg_height -0.0319 0.2192 -0.4888 -0.0283 0.3944 1.0597 522
## week -0.0443 1.5295 -3.0263 -0.0041 2.8755 1.0164 410
## I(week^2) 0.0204 1.6792 -3.2658 0.0099 3.3593 1.0230 1342
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 5.572000e-01 7.635000e-01 0.0650 3.431000e-01 2.395300e+00 1.3757
## shrub_cover 8.944000e-01 8.378000e-01 0.1561 6.679000e-01 2.842100e+00 1.0661
## veg_height 2.865000e-01 2.459000e-01 0.0592 2.185000e-01 9.677000e-01 1.0187
## week 2.021649e+05 3.489140e+06 0.0811 1.377850e+01 4.199066e+05 1.5372
## I(week^2) 9.635171e+26 1.520023e+28 0.2007 3.100031e+07 2.520115e+27 1.6438
## ESS
## (Intercept) 107
## shrub_cover 323
## veg_height 948
## week 527
## I(week^2) 341
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0576 0.7071 -1.4451 -0.0537
## (Intercept)-Lynx_rufus -0.3711 0.8003 -1.7799 -0.4296
## (Intercept)-Didelphis_virginiana -1.2185 0.6159 -2.5484 -1.1986
## (Intercept)-Sylvilagus_floridanus -0.7797 0.6036 -2.0532 -0.7665
## (Intercept)-Meleagris_gallopavo -0.4772 0.7580 -1.9220 -0.5145
## (Intercept)-Sciurus_carolinensis -1.2028 0.6329 -2.5627 -1.1717
## (Intercept)-Vulpes_vulpes -1.2842 0.8029 -2.8870 -1.2692
## (Intercept)-Sus_scrofa -1.6400 0.8435 -3.5312 -1.5578
## Veg_shannon_index-Canis_latrans 0.7800 0.4751 -0.1029 0.7494
## Veg_shannon_index-Lynx_rufus -0.1840 0.6513 -1.7492 -0.0987
## Veg_shannon_index-Didelphis_virginiana 0.5238 0.4518 -0.3022 0.5010
## Veg_shannon_index-Sylvilagus_floridanus 0.3893 0.4732 -0.5140 0.3903
## Veg_shannon_index-Meleagris_gallopavo 0.5502 0.5731 -0.5303 0.5341
## Veg_shannon_index-Sciurus_carolinensis -0.1821 0.4896 -1.2666 -0.1455
## Veg_shannon_index-Vulpes_vulpes -0.0841 0.5633 -1.2871 -0.0465
## Veg_shannon_index-Sus_scrofa 0.7917 0.6409 -0.2655 0.7202
## Avg_Cogongrass_Cover-Canis_latrans 0.6511 0.4891 -0.1615 0.6096
## Avg_Cogongrass_Cover-Lynx_rufus 0.5526 0.5041 -0.2974 0.4937
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4368 0.4290 -0.3553 0.4271
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2179 0.4923 -1.3146 -0.1616
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2181 0.6655 -1.7341 -0.1565
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3677 0.4102 -0.4287 0.3527
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3378 0.5172 -0.6512 0.3277
## Avg_Cogongrass_Cover-Sus_scrofa -0.0511 0.6228 -1.4234 0.0007
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3754 1.1100 293
## (Intercept)-Lynx_rufus 1.5234 1.1031 238
## (Intercept)-Didelphis_virginiana -0.0965 1.0132 594
## (Intercept)-Sylvilagus_floridanus 0.3729 1.0239 529
## (Intercept)-Meleagris_gallopavo 1.0483 1.0807 173
## (Intercept)-Sciurus_carolinensis -0.0351 1.0219 547
## (Intercept)-Vulpes_vulpes 0.4464 1.0976 200
## (Intercept)-Sus_scrofa -0.2001 1.0226 402
## Veg_shannon_index-Canis_latrans 1.8016 1.0017 1207
## Veg_shannon_index-Lynx_rufus 0.8328 1.0007 792
## Veg_shannon_index-Didelphis_virginiana 1.5187 1.0019 1945
## Veg_shannon_index-Sylvilagus_floridanus 1.3667 1.0005 1046
## Veg_shannon_index-Meleagris_gallopavo 1.7771 1.0043 1042
## Veg_shannon_index-Sciurus_carolinensis 0.7001 1.0145 1214
## Veg_shannon_index-Vulpes_vulpes 0.9187 1.0121 727
## Veg_shannon_index-Sus_scrofa 2.2535 1.0124 874
## Avg_Cogongrass_Cover-Canis_latrans 1.7780 1.0000 1048
## Avg_Cogongrass_Cover-Lynx_rufus 1.6871 1.0141 1146
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3220 1.0062 1613
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6142 1.0124 979
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9064 1.0477 517
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1955 1.0031 1588
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4212 1.0052 1134
## Avg_Cogongrass_Cover-Sus_scrofa 1.0318 1.0127 837
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.778700e+00 1.759000e-01 -5.136800e+00
## (Intercept)-Lynx_rufus -5.461400e+00 3.227000e-01 -6.149300e+00
## (Intercept)-Didelphis_virginiana -4.677500e+00 2.750000e-01 -5.257800e+00
## (Intercept)-Sylvilagus_floridanus -4.913300e+00 2.309000e-01 -5.384000e+00
## (Intercept)-Meleagris_gallopavo -5.776400e+00 4.671000e-01 -6.788200e+00
## (Intercept)-Sciurus_carolinensis -4.654800e+00 2.675000e-01 -5.166600e+00
## (Intercept)-Vulpes_vulpes -5.730900e+00 6.233000e-01 -7.202000e+00
## (Intercept)-Sus_scrofa -5.186900e+00 4.688000e-01 -6.163000e+00
## shrub_cover-Canis_latrans -2.974000e-01 2.174000e-01 -7.296000e-01
## shrub_cover-Lynx_rufus -1.478000e-01 3.359000e-01 -7.736000e-01
## shrub_cover-Didelphis_virginiana 1.074800e+00 3.663000e-01 4.185000e-01
## shrub_cover-Sylvilagus_floridanus 4.852000e-01 3.863000e-01 -2.410000e-01
## shrub_cover-Meleagris_gallopavo -8.164000e-01 4.271000e-01 -1.663300e+00
## shrub_cover-Sciurus_carolinensis 9.011000e-01 3.605000e-01 1.757000e-01
## shrub_cover-Vulpes_vulpes -7.210000e-02 6.648000e-01 -1.388800e+00
## shrub_cover-Sus_scrofa 7.175000e-01 7.537000e-01 -8.179000e-01
## veg_height-Canis_latrans -5.936000e-01 1.780000e-01 -9.446000e-01
## veg_height-Lynx_rufus 9.330000e-02 2.482000e-01 -3.750000e-01
## veg_height-Didelphis_virginiana 4.839000e-01 2.375000e-01 3.650000e-02
## veg_height-Sylvilagus_floridanus 1.677000e-01 2.244000e-01 -2.657000e-01
## veg_height-Meleagris_gallopavo -2.411000e-01 3.949000e-01 -1.017200e+00
## veg_height-Sciurus_carolinensis 2.070000e-01 1.951000e-01 -1.597000e-01
## veg_height-Vulpes_vulpes -1.217000e-01 3.191000e-01 -7.302000e-01
## veg_height-Sus_scrofa -2.665000e-01 3.296000e-01 -9.644000e-01
## week-Canis_latrans 1.797600e+00 3.357923e+02 -1.227152e+02
## week-Lynx_rufus 7.133200e+00 4.308892e+02 -1.667002e+02
## week-Didelphis_virginiana 5.821400e+00 3.330854e+02 -1.300879e+02
## week-Sylvilagus_floridanus 3.408300e+00 3.242685e+02 -1.394475e+02
## week-Meleagris_gallopavo 1.506400e+00 4.238389e+02 -1.143190e+02
## week-Sciurus_carolinensis -6.355500e+00 3.158969e+02 -1.456905e+02
## week-Vulpes_vulpes -1.880600e+00 5.379813e+02 -1.954207e+02
## week-Sus_scrofa 2.114770e+01 5.515136e+02 -1.212696e+02
## I(week^2)-Canis_latrans 4.987988e+11 2.608503e+13 -9.165370e+12
## I(week^2)-Lynx_rufus -3.906879e+11 2.508381e+13 -7.624608e+12
## I(week^2)-Didelphis_virginiana -1.012243e+12 2.756592e+13 -7.822080e+12
## I(week^2)-Sylvilagus_floridanus -3.187084e+11 2.244642e+13 -7.009496e+12
## I(week^2)-Meleagris_gallopavo 3.368259e+11 3.268348e+13 -4.523217e+12
## I(week^2)-Sciurus_carolinensis -9.322109e+11 2.951825e+13 -1.113232e+13
## I(week^2)-Vulpes_vulpes -1.338854e+11 2.485881e+13 -5.961870e+12
## I(week^2)-Sus_scrofa -3.880969e+11 4.080969e+13 -5.704835e+12
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7748 -4.447100e+00 1.0410 190
## (Intercept)-Lynx_rufus -5.4480 -4.884900e+00 1.1905 82
## (Intercept)-Didelphis_virginiana -4.6679 -4.172900e+00 1.0200 201
## (Intercept)-Sylvilagus_floridanus -4.9037 -4.486900e+00 1.0235 192
## (Intercept)-Meleagris_gallopavo -5.7375 -4.963300e+00 1.1727 36
## (Intercept)-Sciurus_carolinensis -4.6517 -4.146300e+00 1.0042 273
## (Intercept)-Vulpes_vulpes -5.6421 -4.729800e+00 1.3931 63
## (Intercept)-Sus_scrofa -5.1894 -4.274100e+00 1.0038 279
## shrub_cover-Canis_latrans -0.2932 1.232000e-01 1.1207 146
## shrub_cover-Lynx_rufus -0.1533 5.422000e-01 1.1323 108
## shrub_cover-Didelphis_virginiana 1.0540 1.837800e+00 1.0165 200
## shrub_cover-Sylvilagus_floridanus 0.4893 1.253600e+00 1.0724 143
## shrub_cover-Meleagris_gallopavo -0.8182 1.290000e-02 1.0630 61
## shrub_cover-Sciurus_carolinensis 0.9111 1.612600e+00 1.0040 168
## shrub_cover-Vulpes_vulpes -0.0739 1.195600e+00 1.0242 114
## shrub_cover-Sus_scrofa 0.7159 2.259500e+00 1.0033 316
## veg_height-Canis_latrans -0.5932 -2.617000e-01 1.1059 179
## veg_height-Lynx_rufus 0.1016 5.689000e-01 1.0685 165
## veg_height-Didelphis_virginiana 0.4778 9.992000e-01 1.0101 217
## veg_height-Sylvilagus_floridanus 0.1702 6.048000e-01 1.0575 180
## veg_height-Meleagris_gallopavo -0.2218 4.976000e-01 1.2697 130
## veg_height-Sciurus_carolinensis 0.2067 6.092000e-01 1.0359 271
## veg_height-Vulpes_vulpes -0.1291 4.933000e-01 1.0518 150
## veg_height-Sus_scrofa -0.2615 3.645000e-01 1.0615 337
## week-Canis_latrans -0.0780 1.673131e+02 1.2392 17438
## week-Lynx_rufus -0.0277 1.320020e+02 1.2588 2159
## week-Didelphis_virginiana -0.0433 1.501408e+02 1.2588 2283
## week-Sylvilagus_floridanus 0.0334 1.326593e+02 1.2791 4632
## week-Meleagris_gallopavo 0.0124 1.437843e+02 1.2676 27808
## week-Sciurus_carolinensis -0.0706 1.387035e+02 1.2704 2071
## week-Vulpes_vulpes -0.0418 1.244673e+02 1.2707 83622
## week-Sus_scrofa 0.0534 1.823929e+02 1.3194 603
## I(week^2)-Canis_latrans -0.1458 9.350704e+12 1.3263 1733
## I(week^2)-Lynx_rufus -0.2610 4.078264e+12 1.3143 4700
## I(week^2)-Didelphis_virginiana -0.5506 5.601727e+12 1.4186 998
## I(week^2)-Sylvilagus_floridanus 0.4855 6.744434e+12 1.3103 1899
## I(week^2)-Meleagris_gallopavo -0.1338 1.215217e+13 1.3009 19040
## I(week^2)-Sciurus_carolinensis -0.4382 3.006368e+12 1.3863 2459
## I(week^2)-Vulpes_vulpes 0.1545 8.547307e+12 1.2932 7257
## I(week^2)-Sus_scrofa 0.2872 6.923616e+12 1.2993 8787
#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): 5.014
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7843 0.4751 -1.6964 -0.7896 0.1666 1.0483 509
## Avg_Cogongrass_Cover 0.1528 0.3355 -0.5330 0.1542 0.8147 1.0395 1060
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9527 1.4047 0.0656 0.5847 3.8179 1.0152 625
## Avg_Cogongrass_Cover 0.4892 0.7724 0.0428 0.2845 2.2829 1.1022 559
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.603 1.5292 0.1034 1.1745 5.7449 1.1968 181
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0341 0.3154 -5.5915 -5.0446 -4.4119 1.0311 876
## shrub_cover 0.2806 0.3617 -0.4493 0.2708 1.0346 1.0102 583
## veg_height -0.0275 0.2208 -0.4873 -0.0179 0.4099 1.0209 372
## week 0.1184 1.6669 -3.1553 0.0952 3.1328 1.0297 655
## I(week^2) 0.0554 1.6192 -3.1354 0.0530 3.1334 1.0060 1335
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 5.458000e-01 1.285000e+00 0.0572 0.3015 2.210500e+00 1.1870
## shrub_cover 9.005000e-01 8.199000e-01 0.1632 0.6676 3.082900e+00 1.1573
## veg_height 2.775000e-01 2.689000e-01 0.0572 0.2108 9.183000e-01 1.0247
## week 2.852357e+05 9.178507e+06 0.1052 48.6317 5.343909e+05 1.3463
## I(week^2) 3.194844e+23 4.599774e+24 0.1602 20559.9962 3.539032e+18 1.7062
## ESS
## (Intercept) 146
## shrub_cover 231
## veg_height 1051
## week 1763
## I(week^2) 210
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0095 0.7332 -1.3747 -0.0218
## (Intercept)-Lynx_rufus -0.4473 0.7234 -1.7679 -0.4901
## (Intercept)-Didelphis_virginiana -1.0890 0.6131 -2.3701 -1.0637
## (Intercept)-Sylvilagus_floridanus -0.6836 0.5988 -1.8763 -0.6852
## (Intercept)-Meleagris_gallopavo -0.4884 0.8187 -1.9278 -0.5704
## (Intercept)-Sciurus_carolinensis -1.1719 0.6221 -2.4646 -1.1412
## (Intercept)-Vulpes_vulpes -1.2410 0.8144 -2.8210 -1.2206
## (Intercept)-Sus_scrofa -1.4241 0.7642 -3.1254 -1.3676
## Avg_Cogongrass_Cover-Canis_latrans 0.4606 0.4294 -0.2550 0.4218
## Avg_Cogongrass_Cover-Lynx_rufus 0.5044 0.4829 -0.2868 0.4586
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3481 0.4138 -0.4179 0.3335
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2637 0.4480 -1.1775 -0.2399
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3390 0.6754 -1.8669 -0.2710
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3558 0.3924 -0.3666 0.3370
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3145 0.4925 -0.6153 0.2902
## Avg_Cogongrass_Cover-Sus_scrofa -0.0865 0.6318 -1.5225 -0.0276
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5101 1.0406 290
## (Intercept)-Lynx_rufus 1.1105 1.0202 334
## (Intercept)-Didelphis_virginiana 0.0955 1.0045 1007
## (Intercept)-Sylvilagus_floridanus 0.5235 1.0140 837
## (Intercept)-Meleagris_gallopavo 1.4430 1.1258 277
## (Intercept)-Sciurus_carolinensis -0.0404 1.0016 890
## (Intercept)-Vulpes_vulpes 0.3412 1.1328 259
## (Intercept)-Sus_scrofa -0.1047 1.0525 464
## Avg_Cogongrass_Cover-Canis_latrans 1.4141 1.0127 1106
## Avg_Cogongrass_Cover-Lynx_rufus 1.5780 1.0009 758
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1959 1.0250 1499
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5320 1.0167 945
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8150 1.0295 364
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1751 1.0116 1826
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3078 1.0231 989
## Avg_Cogongrass_Cover-Sus_scrofa 0.9511 1.0491 696
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.786900e+00 1.687000e-01 -5.1238
## (Intercept)-Lynx_rufus -5.455900e+00 2.812000e-01 -5.9892
## (Intercept)-Didelphis_virginiana -4.691100e+00 2.873000e-01 -5.2675
## (Intercept)-Sylvilagus_floridanus -4.944900e+00 2.256000e-01 -5.3774
## (Intercept)-Meleagris_gallopavo -5.682100e+00 4.538000e-01 -6.6693
## (Intercept)-Sciurus_carolinensis -4.699500e+00 2.915000e-01 -5.2556
## (Intercept)-Vulpes_vulpes -5.677700e+00 6.408000e-01 -7.5850
## (Intercept)-Sus_scrofa -5.236700e+00 4.502000e-01 -6.1518
## shrub_cover-Canis_latrans -2.534000e-01 1.887000e-01 -0.6490
## shrub_cover-Lynx_rufus -2.141000e-01 3.496000e-01 -0.8683
## shrub_cover-Didelphis_virginiana 1.071600e+00 3.998000e-01 0.3658
## shrub_cover-Sylvilagus_floridanus 5.983000e-01 4.012000e-01 -0.1371
## shrub_cover-Meleagris_gallopavo -7.434000e-01 3.956000e-01 -1.5179
## shrub_cover-Sciurus_carolinensis 8.931000e-01 3.885000e-01 0.1834
## shrub_cover-Vulpes_vulpes 7.500000e-02 6.041000e-01 -1.1540
## shrub_cover-Sus_scrofa 8.826000e-01 7.593000e-01 -0.5890
## veg_height-Canis_latrans -5.707000e-01 1.815000e-01 -0.9579
## veg_height-Lynx_rufus 7.060000e-02 2.285000e-01 -0.3764
## veg_height-Didelphis_virginiana 4.786000e-01 2.368000e-01 0.0274
## veg_height-Sylvilagus_floridanus 1.613000e-01 2.304000e-01 -0.2809
## veg_height-Meleagris_gallopavo -2.225000e-01 3.948000e-01 -1.0554
## veg_height-Sciurus_carolinensis 2.069000e-01 2.137000e-01 -0.1827
## veg_height-Vulpes_vulpes -1.322000e-01 3.088000e-01 -0.7554
## veg_height-Sus_scrofa -2.318000e-01 3.379000e-01 -0.9541
## week-Canis_latrans 2.266000e+00 4.822766e+02 -288.5196
## week-Lynx_rufus -7.776200e+00 6.226663e+02 -291.1893
## week-Didelphis_virginiana -8.383400e+00 3.777670e+02 -290.3968
## week-Sylvilagus_floridanus -4.200500e+00 2.660183e+02 -299.0421
## week-Meleagris_gallopavo 1.810000e-01 3.047855e+02 -256.3702
## week-Sciurus_carolinensis -8.291800e+00 3.626561e+02 -261.4216
## week-Vulpes_vulpes 8.859800e+00 4.132617e+02 -302.3163
## week-Sus_scrofa 2.158350e+01 7.090955e+02 -231.7577
## I(week^2)-Canis_latrans -8.963086e+09 5.872242e+11 -653246.1946
## I(week^2)-Lynx_rufus -1.253927e+10 5.344003e+11 -868233.9994
## I(week^2)-Didelphis_virginiana 1.037950e+10 6.712912e+11 -688772.9941
## I(week^2)-Sylvilagus_floridanus 1.664000e+10 5.745258e+11 -544683.6951
## I(week^2)-Meleagris_gallopavo 9.855291e+09 6.848656e+11 -745854.5237
## I(week^2)-Sciurus_carolinensis 2.016213e+09 4.567119e+11 -649168.3737
## I(week^2)-Vulpes_vulpes -7.773874e+09 4.579343e+11 -586243.5468
## I(week^2)-Sus_scrofa 8.195344e+09 4.167912e+11 -474076.2994
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7838 -4.4662 1.0557 199
## (Intercept)-Lynx_rufus -5.4655 -4.9308 1.1079 105
## (Intercept)-Didelphis_virginiana -4.6885 -4.1539 1.0455 164
## (Intercept)-Sylvilagus_floridanus -4.9490 -4.4941 1.0340 217
## (Intercept)-Meleagris_gallopavo -5.6520 -4.8237 1.3790 54
## (Intercept)-Sciurus_carolinensis -4.6946 -4.1403 1.0678 201
## (Intercept)-Vulpes_vulpes -5.5867 -4.8033 1.5174 46
## (Intercept)-Sus_scrofa -5.2262 -4.3772 1.1924 214
## shrub_cover-Canis_latrans -0.2499 0.0997 1.0150 219
## shrub_cover-Lynx_rufus -0.2121 0.4951 1.0278 113
## shrub_cover-Didelphis_virginiana 1.0404 1.9758 1.0194 108
## shrub_cover-Sylvilagus_floridanus 0.5830 1.3941 1.2448 138
## shrub_cover-Meleagris_gallopavo -0.7326 0.0029 1.3576 83
## shrub_cover-Sciurus_carolinensis 0.8771 1.6584 1.0173 211
## shrub_cover-Vulpes_vulpes 0.0872 1.1851 1.1072 168
## shrub_cover-Sus_scrofa 0.8636 2.4407 1.0685 247
## veg_height-Canis_latrans -0.5634 -0.2385 1.0061 163
## veg_height-Lynx_rufus 0.0611 0.5155 1.0074 183
## veg_height-Didelphis_virginiana 0.4678 0.9544 1.0989 252
## veg_height-Sylvilagus_floridanus 0.1597 0.6070 1.0520 208
## veg_height-Meleagris_gallopavo -0.2098 0.4871 1.0849 144
## veg_height-Sciurus_carolinensis 0.1984 0.6382 1.0309 249
## veg_height-Vulpes_vulpes -0.1198 0.4192 1.1675 159
## veg_height-Sus_scrofa -0.2129 0.3899 1.0036 297
## week-Canis_latrans 0.1553 222.3659 1.2253 8046
## week-Lynx_rufus 0.0396 281.9234 1.2523 5493
## week-Didelphis_virginiana 0.1607 246.3063 1.1045 4961
## week-Sylvilagus_floridanus -0.0011 237.5296 1.0971 3268
## week-Meleagris_gallopavo 0.1728 319.4269 1.0688 3213
## week-Sciurus_carolinensis 0.2041 224.5077 1.1913 2249
## week-Vulpes_vulpes 0.1052 262.7758 1.2396 1930
## week-Sus_scrofa 0.1687 318.1223 1.2714 2139
## I(week^2)-Canis_latrans 0.1561 893935.4907 1.3134 3981
## I(week^2)-Lynx_rufus 0.1832 632011.6543 1.3442 2448
## I(week^2)-Didelphis_virginiana 0.0650 701857.3376 1.3140 4258
## I(week^2)-Sylvilagus_floridanus 0.3278 827128.5726 1.3715 1190
## I(week^2)-Meleagris_gallopavo -0.0200 712132.0986 1.3108 5655
## I(week^2)-Sciurus_carolinensis 0.1153 731878.3526 1.2923 46711
## I(week^2)-Vulpes_vulpes 0.5090 863180.6836 1.3188 3410
## I(week^2)-Sus_scrofa 0.0765 1234633.3291 1.3284 2331
# 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): 5.002
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4108 0.5652 -2.5512 -1.4097 -0.2681 1.0242 449
## Avg_Cogongrass_Cover -0.7606 0.4918 -1.7628 -0.7583 0.1583 1.0239 431
## I(Avg_Cogongrass_Cover^2) 0.8009 0.4302 0.0677 0.7615 1.7468 1.0218 297
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2683 1.7294 0.0651 0.6910 5.7580 1.1013 321
## Avg_Cogongrass_Cover 0.6690 1.0603 0.0462 0.3366 3.4732 1.1489 421
## I(Avg_Cogongrass_Cover^2) 0.5945 1.0748 0.0410 0.2716 3.3262 1.0254 348
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3328 1.4365 0.0757 0.8935 5.1799 1.0456 156
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0524 0.3166 -5.6390 -5.0567 -4.4184 1.0741 502
## shrub_cover 0.2282 0.3795 -0.5132 0.2245 0.9793 1.0028 601
## veg_height -0.0238 0.2117 -0.4509 -0.0209 0.3811 1.0010 290
## week 0.1356 1.6381 -3.1142 0.1589 3.4774 1.0065 452
## I(week^2) 0.2091 1.6372 -3.1318 0.2819 3.3368 1.0481 334
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.917000e-01 8.842000e-01 0.0785 0.3897 2.278400e+00 1.1038 395
## shrub_cover 9.662000e-01 1.025800e+00 0.1747 0.7078 3.216500e+00 1.1256 391
## veg_height 2.573000e-01 2.248000e-01 0.0545 0.1953 8.349000e-01 1.0432 535
## week 1.280713e+13 2.989114e+14 0.0857 9.1535 2.045432e+09 1.4623 571
## I(week^2) 5.484201e+16 1.160576e+18 0.0744 20.2677 4.167768e+14 1.4969 428
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7069 0.7497 -2.1156 -0.7190
## (Intercept)-Lynx_rufus -1.0281 0.8634 -2.5599 -1.0971
## (Intercept)-Didelphis_virginiana -1.6964 0.6683 -3.0795 -1.6861
## (Intercept)-Sylvilagus_floridanus -1.3987 0.7131 -2.7919 -1.3873
## (Intercept)-Meleagris_gallopavo -0.8666 0.9476 -2.4612 -0.9657
## (Intercept)-Sciurus_carolinensis -2.0620 0.8097 -3.7962 -1.9843
## (Intercept)-Vulpes_vulpes -2.0636 0.9517 -4.1664 -1.9822
## (Intercept)-Sus_scrofa -2.1291 0.8873 -4.0533 -2.0468
## Avg_Cogongrass_Cover-Canis_latrans -0.3215 0.6296 -1.4559 -0.3533
## Avg_Cogongrass_Cover-Lynx_rufus -0.6148 0.7211 -2.0338 -0.6024
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4092 0.6292 -1.5838 -0.4282
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2668 0.7052 -2.9349 -1.1883
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1084 0.8130 -2.9196 -1.0306
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7947 0.6340 -2.1118 -0.7648
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7824 0.7374 -2.3171 -0.7524
## Avg_Cogongrass_Cover-Sus_scrofa -0.9938 0.7516 -2.6753 -0.9314
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3012 0.8339 0.2074 1.1294
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1750 0.6467 0.2094 1.0770
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6184 0.4820 -0.2529 0.5935
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7522 0.4622 -0.0332 0.7159
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3916 0.7268 -1.0340 0.3889
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9730 0.4592 0.2013 0.9297
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9651 0.6408 0.0516 0.8669
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4130 0.7054 -1.2485 0.4644
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7836 1.0380 455
## (Intercept)-Lynx_rufus 0.9473 1.0701 290
## (Intercept)-Didelphis_virginiana -0.3957 1.0074 811
## (Intercept)-Sylvilagus_floridanus -0.0207 1.0029 595
## (Intercept)-Meleagris_gallopavo 1.3004 1.1496 194
## (Intercept)-Sciurus_carolinensis -0.6034 1.0020 585
## (Intercept)-Vulpes_vulpes -0.3289 1.0459 300
## (Intercept)-Sus_scrofa -0.6543 1.0250 536
## Avg_Cogongrass_Cover-Canis_latrans 1.0463 1.0112 698
## Avg_Cogongrass_Cover-Lynx_rufus 0.9137 1.0098 593
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9290 1.0013 827
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1138 1.1006 406
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.3308 1.0521 394
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3336 1.0294 470
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5503 1.0214 535
## Avg_Cogongrass_Cover-Sus_scrofa 0.2978 1.0387 482
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4897 1.0016 360
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7745 1.0102 315
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6707 1.0147 458
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7500 1.0416 547
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8358 1.0384 230
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0141 1.0204 460
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4382 1.0526 247
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.6586 1.0072 343
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7786 1.691000e-01 -5.1288
## (Intercept)-Lynx_rufus -5.5384 2.991000e-01 -6.1154
## (Intercept)-Didelphis_virginiana -4.7185 2.838000e-01 -5.2828
## (Intercept)-Sylvilagus_floridanus -4.9592 2.266000e-01 -5.4003
## (Intercept)-Meleagris_gallopavo -5.7704 4.680000e-01 -6.7809
## (Intercept)-Sciurus_carolinensis -4.6334 2.861000e-01 -5.1931
## (Intercept)-Vulpes_vulpes -5.8494 6.414000e-01 -7.4412
## (Intercept)-Sus_scrofa -5.2525 4.771000e-01 -6.1873
## shrub_cover-Canis_latrans -0.2618 2.044000e-01 -0.6397
## shrub_cover-Lynx_rufus -0.2339 3.035000e-01 -0.8434
## shrub_cover-Didelphis_virginiana 1.1564 3.863000e-01 0.4518
## shrub_cover-Sylvilagus_floridanus 0.4881 4.376000e-01 -0.3022
## shrub_cover-Meleagris_gallopavo -0.8151 4.212000e-01 -1.6885
## shrub_cover-Sciurus_carolinensis 0.8376 3.616000e-01 0.1361
## shrub_cover-Vulpes_vulpes -0.0635 6.084000e-01 -1.2254
## shrub_cover-Sus_scrofa 0.8229 8.092000e-01 -0.7825
## veg_height-Canis_latrans -0.5441 1.675000e-01 -0.8962
## veg_height-Lynx_rufus 0.1280 2.333000e-01 -0.3313
## veg_height-Didelphis_virginiana 0.4292 2.482000e-01 -0.0096
## veg_height-Sylvilagus_floridanus 0.1762 2.454000e-01 -0.2837
## veg_height-Meleagris_gallopavo -0.2222 3.863000e-01 -0.9949
## veg_height-Sciurus_carolinensis 0.1928 2.126000e-01 -0.2113
## veg_height-Vulpes_vulpes -0.1355 3.017000e-01 -0.8061
## veg_height-Sus_scrofa -0.2288 3.223000e-01 -0.8718
## week-Canis_latrans 96473.7434 2.809529e+06 -4562.0885
## week-Lynx_rufus -18387.9317 2.549408e+06 -3712.0185
## week-Didelphis_virginiana -58533.5762 2.103106e+06 -3518.0455
## week-Sylvilagus_floridanus -53092.2804 3.415142e+06 -2580.1994
## week-Meleagris_gallopavo -39516.6792 3.073255e+06 -2915.1557
## week-Sciurus_carolinensis -138901.8572 3.526999e+06 -5153.0696
## week-Vulpes_vulpes -13122.6772 2.880278e+06 -4742.6962
## week-Sus_scrofa 25271.0234 2.062768e+06 -3946.5358
## I(week^2)-Canis_latrans 10176567.7772 2.606543e+08 -883981.9775
## I(week^2)-Lynx_rufus 3489870.1522 1.931497e+08 -222308.0808
## I(week^2)-Didelphis_virginiana 682147.1156 1.898447e+08 -925268.2501
## I(week^2)-Sylvilagus_floridanus 2611447.5583 2.157101e+08 -715648.0886
## I(week^2)-Meleagris_gallopavo 1140979.4346 1.768045e+08 -1373317.6157
## I(week^2)-Sciurus_carolinensis 2211406.4756 2.319199e+08 -1226125.9723
## I(week^2)-Vulpes_vulpes -5599408.9856 1.976442e+08 -1182962.8823
## I(week^2)-Sus_scrofa -2026285.2905 3.161064e+08 -324361.2359
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7694 -4.4624 1.1678 205
## (Intercept)-Lynx_rufus -5.5356 -4.9740 1.0864 92
## (Intercept)-Didelphis_virginiana -4.7170 -4.1809 1.0602 171
## (Intercept)-Sylvilagus_floridanus -4.9536 -4.5086 1.0937 162
## (Intercept)-Meleagris_gallopavo -5.7363 -4.9247 1.3899 47
## (Intercept)-Sciurus_carolinensis -4.6196 -4.0981 1.0158 233
## (Intercept)-Vulpes_vulpes -5.7452 -4.8349 1.1471 55
## (Intercept)-Sus_scrofa -5.2400 -4.3089 1.0268 242
## shrub_cover-Canis_latrans -0.2617 0.1434 1.0185 178
## shrub_cover-Lynx_rufus -0.2238 0.3356 1.0785 140
## shrub_cover-Didelphis_virginiana 1.1377 1.9203 1.0473 139
## shrub_cover-Sylvilagus_floridanus 0.4873 1.3415 1.0190 119
## shrub_cover-Meleagris_gallopavo -0.7885 0.0173 1.3239 53
## shrub_cover-Sciurus_carolinensis 0.8341 1.5639 1.0190 235
## shrub_cover-Vulpes_vulpes -0.0693 1.1796 1.0057 148
## shrub_cover-Sus_scrofa 0.8235 2.3777 1.0292 221
## veg_height-Canis_latrans -0.5399 -0.2392 1.0574 217
## veg_height-Lynx_rufus 0.1332 0.5731 1.0909 170
## veg_height-Didelphis_virginiana 0.4083 0.9722 1.0074 198
## veg_height-Sylvilagus_floridanus 0.1747 0.6584 1.0516 167
## veg_height-Meleagris_gallopavo -0.2219 0.5211 1.0115 108
## veg_height-Sciurus_carolinensis 0.1905 0.6143 1.0124 256
## veg_height-Vulpes_vulpes -0.1178 0.3996 1.0794 170
## veg_height-Sus_scrofa -0.2256 0.3820 1.0060 278
## week-Canis_latrans 0.3098 3067.2333 1.4032 774
## week-Lynx_rufus 0.3140 6039.6281 1.2955 22526
## week-Didelphis_virginiana 0.2051 3478.0459 1.3655 1224
## week-Sylvilagus_floridanus 0.3180 6423.5406 1.3142 3965
## week-Meleagris_gallopavo 0.3926 5146.2723 1.3068 5361
## week-Sciurus_carolinensis 0.3948 3100.8383 1.4368 642
## week-Vulpes_vulpes 0.2717 3453.1762 1.2924 65219
## week-Sus_scrofa 0.2359 3592.4511 1.3052 6473
## I(week^2)-Canis_latrans 0.6246 499279.7005 1.4345 627
## I(week^2)-Lynx_rufus 0.6897 1135921.0901 1.3225 2886
## I(week^2)-Didelphis_virginiana 0.4696 371854.5876 1.2916 9901
## I(week^2)-Sylvilagus_floridanus 0.5643 347564.4732 1.3049 6871
## I(week^2)-Meleagris_gallopavo 0.5768 247183.9154 1.2945 14973
## I(week^2)-Sciurus_carolinensis 0.5650 367993.8302 1.2994 9854
## I(week^2)-Vulpes_vulpes 0.6134 188950.2608 1.3681 1214
## I(week^2)-Sus_scrofa 0.6483 1122110.8527 1.2944 20087
# 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): 5.556
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0616 1.1497 -4.0825 -2.1436 0.3824 1.0002 422
## Cogon_Patch_Size 0.1515 1.0912 -2.0526 0.1722 2.2744 1.1711 233
## Veg_shannon_index 0.9883 0.8181 -0.6785 1.0029 2.5387 1.0257 247
## total_shrub_cover -1.0976 1.0148 -3.2018 -1.0562 0.7561 1.0239 124
## Avg_Cogongrass_Cover -0.2905 1.2420 -2.6245 -0.3080 2.1905 1.0142 145
## Tree_Density -2.2720 1.0788 -4.7662 -2.1900 -0.3270 1.0129 106
## Avg_Canopy_Cover 2.1893 1.0935 -0.2371 2.2345 4.2202 1.0064 785
## I(Avg_Cogongrass_Cover^2) 1.4484 0.8624 -0.3223 1.4549 3.0897 1.0435 194
## avg_veg_height -0.2901 0.7466 -1.8505 -0.2610 1.2070 1.0105 159
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.6484 25.2892 0.1605 7.1111 71.3188 1.1150 100
## Cogon_Patch_Size 9.9452 15.6905 0.1927 4.8512 48.0926 1.0433 96
## Veg_shannon_index 4.0610 7.8858 0.0926 1.4948 24.0610 1.0667 116
## total_shrub_cover 7.4431 12.8558 0.1517 3.7194 38.8554 1.0952 75
## Avg_Cogongrass_Cover 2.5038 5.6860 0.0512 0.7213 16.2229 1.0849 171
## Tree_Density 2.0094 4.7576 0.0493 0.6038 12.6388 1.0600 519
## Avg_Canopy_Cover 15.9811 47.0213 0.5600 6.4717 87.2419 1.2914 155
## I(Avg_Cogongrass_Cover^2) 4.3410 9.1592 0.0695 1.3773 25.1306 1.0682 93
## avg_veg_height 1.0831 2.4665 0.0476 0.4226 5.9177 1.0423 484
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9559 4.5814 0.0577 1.1612 16.5617 1.0093 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1383 0.3008 -5.6952 -5.1561 -4.4826 1.0229 485
## shrub_cover 0.3841 0.3633 -0.3392 0.3750 1.0982 1.0016 530
## veg_height -0.0076 0.2348 -0.4962 0.0031 0.4379 1.0279 238
## week 0.0688 1.6967 -3.2289 0.0205 3.4621 1.0186 314
## I(week^2) 0.1290 1.7097 -3.1821 0.1438 3.5825 1.0151 288
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.379000e-01 5.446000e-01 0.0750 0.3656 1.994800e+00 1.0011 207
## shrub_cover 9.120000e-01 1.014000e+00 0.1881 0.6625 2.963000e+00 1.1103 493
## veg_height 3.277000e-01 2.953000e-01 0.0647 0.2451 1.123400e+00 1.0184 431
## week 4.433265e+09 3.789382e+10 0.0899 53.2348 2.910177e+10 1.4640 191
## I(week^2) 1.793658e+10 2.356987e+11 0.0788 50.6249 2.426549e+10 1.7063 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6821 1.6330 -3.5582 -0.8047
## (Intercept)-Lynx_rufus 0.6550 3.0038 -3.3125 0.1417
## (Intercept)-Didelphis_virginiana -4.0432 1.8153 -8.1500 -3.8194
## (Intercept)-Sylvilagus_floridanus -2.6953 1.8868 -6.7535 -2.6481
## (Intercept)-Meleagris_gallopavo -1.9708 2.2412 -6.2243 -2.1468
## (Intercept)-Sciurus_carolinensis -4.6193 2.0242 -9.2298 -4.3657
## (Intercept)-Vulpes_vulpes -4.9191 2.7593 -11.6641 -4.4784
## (Intercept)-Sus_scrofa -5.5721 2.8233 -12.5792 -5.1433
## Cogon_Patch_Size-Canis_latrans 2.7643 2.2794 -0.1417 2.3078
## Cogon_Patch_Size-Lynx_rufus -0.0969 2.4798 -5.2780 0.0654
## Cogon_Patch_Size-Didelphis_virginiana 2.2794 1.3992 -0.0155 2.0884
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9593 2.4754 -8.1828 -1.4887
## Cogon_Patch_Size-Meleagris_gallopavo 1.3099 2.4686 -2.1379 0.8689
## Cogon_Patch_Size-Sciurus_carolinensis -1.3476 2.3373 -7.0440 -0.9624
## Cogon_Patch_Size-Vulpes_vulpes -0.5229 2.5456 -6.5463 -0.3154
## Cogon_Patch_Size-Sus_scrofa -1.0507 2.2132 -6.5272 -0.7259
## Veg_shannon_index-Canis_latrans 1.7636 0.9880 0.0729 1.6411
## Veg_shannon_index-Lynx_rufus -0.2145 1.8893 -4.8952 0.1066
## Veg_shannon_index-Didelphis_virginiana 1.6131 1.3010 -0.3329 1.4754
## Veg_shannon_index-Sylvilagus_floridanus 1.3800 1.0819 -0.4716 1.2902
## Veg_shannon_index-Meleagris_gallopavo 2.0554 1.4388 -0.1218 1.8128
## Veg_shannon_index-Sciurus_carolinensis -0.1915 1.4298 -3.4740 -0.0111
## Veg_shannon_index-Vulpes_vulpes 0.2378 1.6195 -3.4434 0.4642
## Veg_shannon_index-Sus_scrofa 2.3787 1.8773 0.0419 1.9715
## total_shrub_cover-Canis_latrans 1.0499 1.2361 -1.0417 0.8966
## total_shrub_cover-Lynx_rufus -2.1898 2.6721 -8.1728 -2.0215
## total_shrub_cover-Didelphis_virginiana -1.9380 1.7756 -6.1571 -1.6527
## total_shrub_cover-Sylvilagus_floridanus -1.5330 2.1450 -7.6386 -1.0859
## total_shrub_cover-Meleagris_gallopavo -3.5174 2.4224 -9.4986 -3.2079
## total_shrub_cover-Sciurus_carolinensis -1.1464 1.6031 -4.7339 -0.9683
## total_shrub_cover-Vulpes_vulpes -2.0194 2.4162 -8.1085 -1.5827
## total_shrub_cover-Sus_scrofa -0.5118 1.9633 -4.6264 -0.4678
## Avg_Cogongrass_Cover-Canis_latrans -0.0947 1.5538 -2.9231 -0.1667
## Avg_Cogongrass_Cover-Lynx_rufus 0.2158 1.9593 -3.0760 0.0455
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2537 1.6955 -3.6362 -0.2953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0538 1.8021 -5.3295 -0.9302
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6792 1.8416 -4.7089 -0.6212
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3153 1.6602 -3.6551 -0.3271
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1538 1.8852 -3.0105 0.0009
## Avg_Cogongrass_Cover-Sus_scrofa -0.4960 1.8401 -4.2876 -0.4350
## Tree_Density-Canis_latrans -2.9338 1.4818 -6.3954 -2.7211
## Tree_Density-Lynx_rufus -1.8805 1.5253 -4.9322 -1.9150
## Tree_Density-Didelphis_virginiana -2.4165 1.3442 -5.3659 -2.3222
## Tree_Density-Sylvilagus_floridanus -2.6922 1.4845 -6.0799 -2.5358
## Tree_Density-Meleagris_gallopavo -2.4405 1.4216 -5.4007 -2.3350
## Tree_Density-Sciurus_carolinensis -2.5361 1.5738 -6.0496 -2.4284
## Tree_Density-Vulpes_vulpes -2.2691 1.6205 -5.4227 -2.2489
## Tree_Density-Sus_scrofa -2.4059 1.4929 -5.6569 -2.3083
## Avg_Canopy_Cover-Canis_latrans -0.1290 0.8756 -1.8994 -0.0986
## Avg_Canopy_Cover-Lynx_rufus 1.3882 2.6862 -3.3156 1.0007
## Avg_Canopy_Cover-Didelphis_virginiana 4.9405 2.6822 1.6795 4.3244
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9631 3.2641 1.9122 5.2602
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3930 2.2063 0.4985 2.9302
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6860 2.7559 1.4301 4.0428
## Avg_Canopy_Cover-Vulpes_vulpes 3.9601 2.8118 0.6398 3.1998
## Avg_Canopy_Cover-Sus_scrofa 2.8295 1.9480 0.2827 2.5117
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4870 1.3246 0.5652 2.2498
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0939 2.0144 0.4443 2.6283
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2228 0.9245 -0.6178 1.2165
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2623 1.0511 -0.7875 1.2352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0265 2.2417 -5.7637 0.4043
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0165 1.1917 0.1472 1.8508
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2798 1.4932 0.1730 2.0577
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.6143 1.9380 -4.2067 0.9403
## avg_veg_height-Canis_latrans -0.2495 0.8158 -1.8413 -0.2427
## avg_veg_height-Lynx_rufus -0.6025 1.2585 -3.3850 -0.4790
## avg_veg_height-Didelphis_virginiana -0.5628 1.0053 -2.8855 -0.4800
## avg_veg_height-Sylvilagus_floridanus -0.3483 0.9369 -2.3143 -0.3225
## avg_veg_height-Meleagris_gallopavo -0.2409 1.1932 -2.7624 -0.2165
## avg_veg_height-Sciurus_carolinensis 0.1892 1.0185 -1.5244 0.1160
## avg_veg_height-Vulpes_vulpes -0.3707 1.1092 -2.6533 -0.3359
## avg_veg_height-Sus_scrofa -0.2180 1.0371 -2.2497 -0.2146
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9706 1.0046 201
## (Intercept)-Lynx_rufus 8.0379 1.1383 63
## (Intercept)-Didelphis_virginiana -1.0115 1.0174 128
## (Intercept)-Sylvilagus_floridanus 0.9844 1.0162 165
## (Intercept)-Meleagris_gallopavo 2.7802 1.0809 112
## (Intercept)-Sciurus_carolinensis -1.2761 1.0052 113
## (Intercept)-Vulpes_vulpes -0.6676 1.0374 140
## (Intercept)-Sus_scrofa -1.4864 1.0872 72
## Cogon_Patch_Size-Canis_latrans 8.2662 1.0595 185
## Cogon_Patch_Size-Lynx_rufus 4.7837 1.1972 160
## Cogon_Patch_Size-Didelphis_virginiana 5.5137 1.0008 215
## Cogon_Patch_Size-Sylvilagus_floridanus 1.5810 1.0619 149
## Cogon_Patch_Size-Meleagris_gallopavo 7.8950 1.0123 159
## Cogon_Patch_Size-Sciurus_carolinensis 2.1537 1.1158 146
## Cogon_Patch_Size-Vulpes_vulpes 3.9262 1.2127 142
## Cogon_Patch_Size-Sus_scrofa 2.4012 1.2166 168
## Veg_shannon_index-Canis_latrans 4.0942 1.0001 410
## Veg_shannon_index-Lynx_rufus 2.6020 1.0617 138
## Veg_shannon_index-Didelphis_virginiana 4.5566 1.0637 228
## Veg_shannon_index-Sylvilagus_floridanus 3.8156 1.0133 200
## Veg_shannon_index-Meleagris_gallopavo 5.5750 1.0046 233
## Veg_shannon_index-Sciurus_carolinensis 2.1139 1.0629 125
## Veg_shannon_index-Vulpes_vulpes 2.8180 1.0141 154
## Veg_shannon_index-Sus_scrofa 7.1979 1.0347 126
## total_shrub_cover-Canis_latrans 3.8374 1.0804 153
## total_shrub_cover-Lynx_rufus 3.1513 1.2992 49
## total_shrub_cover-Didelphis_virginiana 0.6123 1.1101 135
## total_shrub_cover-Sylvilagus_floridanus 1.1262 1.2671 95
## total_shrub_cover-Meleagris_gallopavo 0.2324 1.0671 91
## total_shrub_cover-Sciurus_carolinensis 1.5186 1.0418 292
## total_shrub_cover-Vulpes_vulpes 1.4184 1.0729 119
## total_shrub_cover-Sus_scrofa 3.3420 1.0530 198
## Avg_Cogongrass_Cover-Canis_latrans 3.2998 1.0185 167
## Avg_Cogongrass_Cover-Lynx_rufus 4.9527 1.0209 120
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1583 1.0157 175
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0750 1.0382 161
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6088 1.0345 190
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9524 1.0542 150
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4635 1.0205 183
## Avg_Cogongrass_Cover-Sus_scrofa 2.8575 1.0552 141
## Tree_Density-Canis_latrans -0.6071 1.0182 164
## Tree_Density-Lynx_rufus 1.3350 1.0185 171
## Tree_Density-Didelphis_virginiana 0.0267 1.0186 157
## Tree_Density-Sylvilagus_floridanus -0.1193 1.0102 219
## Tree_Density-Meleagris_gallopavo 0.2510 1.0268 175
## Tree_Density-Sciurus_carolinensis 0.3248 1.0442 154
## Tree_Density-Vulpes_vulpes 1.2513 1.0040 149
## Tree_Density-Sus_scrofa 0.4826 1.0127 249
## Avg_Canopy_Cover-Canis_latrans 1.5502 1.0061 296
## Avg_Canopy_Cover-Lynx_rufus 7.4601 1.0944 74
## Avg_Canopy_Cover-Didelphis_virginiana 11.2061 1.0698 94
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.8574 1.0527 78
## Avg_Canopy_Cover-Meleagris_gallopavo 9.5971 1.0834 217
## Avg_Canopy_Cover-Sciurus_carolinensis 12.4836 1.0128 99
## Avg_Canopy_Cover-Vulpes_vulpes 11.1898 1.0467 113
## Avg_Canopy_Cover-Sus_scrofa 7.4936 1.1650 226
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6845 1.0188 200
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.3123 1.0239 101
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1219 1.0077 213
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4957 1.0216 295
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.3554 1.1264 47
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.7926 1.0093 122
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9849 1.0101 225
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.4755 1.0920 66
## avg_veg_height-Canis_latrans 1.3704 1.0080 207
## avg_veg_height-Lynx_rufus 1.5856 1.0133 195
## avg_veg_height-Didelphis_virginiana 1.2079 1.0066 269
## avg_veg_height-Sylvilagus_floridanus 1.4941 1.0083 315
## avg_veg_height-Meleagris_gallopavo 2.1648 1.0321 174
## avg_veg_height-Sciurus_carolinensis 2.5232 1.0006 266
## avg_veg_height-Vulpes_vulpes 1.7847 1.0185 264
## avg_veg_height-Sus_scrofa 1.9594 1.0090 320
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7767 0.1768 -5.1454 -4.7649
## (Intercept)-Lynx_rufus -5.6999 0.3053 -6.2731 -5.7161
## (Intercept)-Didelphis_virginiana -4.7577 0.2878 -5.3240 -4.7597
## (Intercept)-Sylvilagus_floridanus -4.9999 0.2251 -5.4728 -5.0005
## (Intercept)-Meleagris_gallopavo -5.6366 0.4619 -6.6630 -5.6089
## (Intercept)-Sciurus_carolinensis -4.8471 0.3115 -5.4942 -4.8499
## (Intercept)-Vulpes_vulpes -5.9577 0.5168 -7.1083 -5.9119
## (Intercept)-Sus_scrofa -5.3782 0.4775 -6.3442 -5.3737
## shrub_cover-Canis_latrans -0.3246 0.2107 -0.7208 -0.3351
## shrub_cover-Lynx_rufus 0.0046 0.3603 -0.6954 -0.0122
## shrub_cover-Didelphis_virginiana 1.1428 0.3681 0.4689 1.1120
## shrub_cover-Sylvilagus_floridanus 0.7171 0.3755 0.0344 0.6947
## shrub_cover-Meleagris_gallopavo -0.6171 0.4317 -1.5107 -0.6144
## shrub_cover-Sciurus_carolinensis 1.0837 0.3586 0.3462 1.0975
## shrub_cover-Vulpes_vulpes 0.3218 0.5758 -0.8744 0.3578
## shrub_cover-Sus_scrofa 0.9299 0.7742 -0.6702 0.9478
## veg_height-Canis_latrans -0.5550 0.1631 -0.8843 -0.5500
## veg_height-Lynx_rufus 0.1951 0.2402 -0.2833 0.1933
## veg_height-Didelphis_virginiana 0.5560 0.2469 0.0841 0.5518
## veg_height-Sylvilagus_floridanus 0.1706 0.2416 -0.3111 0.1756
## veg_height-Meleagris_gallopavo -0.2143 0.4215 -1.0620 -0.2094
## veg_height-Sciurus_carolinensis 0.2888 0.2223 -0.1219 0.2786
## veg_height-Vulpes_vulpes -0.2244 0.3854 -1.1219 -0.1860
## veg_height-Sus_scrofa -0.2412 0.3456 -0.9697 -0.2148
## week-Canis_latrans 1089.4459 58502.3382 -31266.0757 0.1660
## week-Lynx_rufus -249.9298 69956.6614 -52273.5082 0.1726
## week-Didelphis_virginiana -209.1295 53803.5931 -28326.9009 0.1226
## week-Sylvilagus_floridanus -80.4087 54128.6000 -28009.9757 0.1392
## week-Meleagris_gallopavo -32.0251 66474.7619 -31485.3599 0.1096
## week-Sciurus_carolinensis 1274.2705 66161.7402 -42410.5174 0.0585
## week-Vulpes_vulpes -569.7465 62255.7493 -31471.9025 0.2318
## week-Sus_scrofa -1322.5796 59775.0644 -42675.9225 0.0884
## I(week^2)-Canis_latrans -835.4950 113971.3362 -41521.6389 0.3151
## I(week^2)-Lynx_rufus 375.2720 141921.7865 -42230.3237 0.3456
## I(week^2)-Didelphis_virginiana -699.7343 149758.6996 -49398.0683 0.4464
## I(week^2)-Sylvilagus_floridanus -1066.6689 97559.2036 -48763.4625 0.2991
## I(week^2)-Meleagris_gallopavo 942.5933 109973.3376 -50332.6619 0.3005
## I(week^2)-Sciurus_carolinensis 308.3552 99332.4666 -29562.1302 0.4043
## I(week^2)-Vulpes_vulpes 3064.7616 116303.6825 -39157.6475 0.5259
## I(week^2)-Sus_scrofa -4249.0416 123596.3249 -56459.9255 0.2187
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4553 1.2071 162
## (Intercept)-Lynx_rufus -5.0978 1.1118 63
## (Intercept)-Didelphis_virginiana -4.1627 1.1075 127
## (Intercept)-Sylvilagus_floridanus -4.5686 1.0601 154
## (Intercept)-Meleagris_gallopavo -4.7935 1.1174 59
## (Intercept)-Sciurus_carolinensis -4.2381 1.0129 119
## (Intercept)-Vulpes_vulpes -5.0890 1.0101 60
## (Intercept)-Sus_scrofa -4.4314 1.0173 150
## shrub_cover-Canis_latrans 0.1104 1.0512 170
## shrub_cover-Lynx_rufus 0.6983 1.1278 87
## shrub_cover-Didelphis_virginiana 1.9298 1.1335 142
## shrub_cover-Sylvilagus_floridanus 1.4899 1.1123 88
## shrub_cover-Meleagris_gallopavo 0.2279 1.1351 66
## shrub_cover-Sciurus_carolinensis 1.7499 1.0263 152
## shrub_cover-Vulpes_vulpes 1.4069 1.0023 92
## shrub_cover-Sus_scrofa 2.4413 1.0166 155
## veg_height-Canis_latrans -0.2299 1.0475 206
## veg_height-Lynx_rufus 0.6521 1.0310 118
## veg_height-Didelphis_virginiana 1.0324 1.0111 221
## veg_height-Sylvilagus_floridanus 0.6472 1.0430 167
## veg_height-Meleagris_gallopavo 0.5948 1.1578 86
## veg_height-Sciurus_carolinensis 0.7625 1.0349 137
## veg_height-Vulpes_vulpes 0.4282 1.0280 89
## veg_height-Sus_scrofa 0.3786 1.0284 213
## week-Canis_latrans 45126.2182 1.2271 1825
## week-Lynx_rufus 32599.0997 1.2138 2508
## week-Didelphis_virginiana 39513.3007 1.1513 9331
## week-Sylvilagus_floridanus 35091.1790 1.2421 2040
## week-Meleagris_gallopavo 38119.6216 1.1560 7320
## week-Sciurus_carolinensis 31917.7214 1.1626 2298
## week-Vulpes_vulpes 31715.1869 1.2013 2911
## week-Sus_scrofa 21596.8628 1.1843 2104
## I(week^2)-Canis_latrans 45314.5199 1.2667 6348
## I(week^2)-Lynx_rufus 53105.8252 1.2578 15197
## I(week^2)-Didelphis_virginiana 42149.6283 1.2645 16124
## I(week^2)-Sylvilagus_floridanus 37950.5505 1.2386 10602
## I(week^2)-Meleagris_gallopavo 46094.7739 1.2428 16643
## I(week^2)-Sciurus_carolinensis 54971.9524 1.2837 3639
## I(week^2)-Vulpes_vulpes 50349.8432 1.2882 1388
## I(week^2)-Sus_scrofa 39379.6111 1.3257 741
waicOcc(ms_full_full_T, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -965.5288 49.8007 2030.6589
waicOcc(ms_full_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -996.97054 48.73594 2091.41297
waicOcc(ms_full_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -993.93632 36.72367 2061.31997
waicOcc(ms_full_move_T, by.sp = FALSE)
## elpd pD WAIC
## -993.41596 49.14819 2085.12830
waicOcc(ms_full_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1004.3994 42.0976 2092.9941
waicOcc(ms_full_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1009.27612 36.77617 2092.10458
waicOcc(ms_full_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1023.61394 23.42149 2094.07087
waicOcc(ms_full_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1003.82493 40.29474 2088.23933
waicOcc(ms_full_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -959.58057 52.22239 2023.60592
waicOcc(ms_null_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1047.6698 17.6623 2130.6642
waicOcc(ms_null_full_T, by.sp = FALSE)
## elpd pD WAIC
## -988.4426 46.8631 2070.6113
waicOcc(ms_null_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1022.1464 42.2945 2128.8817
waicOcc(ms_null_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1019.12736 32.12581 2102.50635
waicOcc(ms_null_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1018.34001 40.28638 2117.25277
waicOcc(ms_null_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1027.3140 35.2168 2125.0616
waicOcc(ms_null_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1031.47583 31.22937 2125.41040
waicOcc(ms_null_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1026.41197 34.53829 2121.90052
waicOcc(ms_null_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -982.82381 46.91668 2059.48098
waicOcc(ms_week_full_T, by.sp = FALSE)
## elpd pD WAIC
## -987.10083 47.57303 2069.34771
waicOcc(ms_week_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1023.21359 40.78957 2128.00631
waicOcc(ms_week_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1047.32675 18.36746 2131.38843
waicOcc(ms_week_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1027.25253 36.07496 2126.65497
waicOcc(ms_week_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1017.03477 41.84548 2117.76050
waicOcc(ms_week_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1019.3456 32.0781 2102.8474
waicOcc(ms_week_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1031.30863 31.57621 2125.76968
waicOcc(ms_week_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1026.74692 34.66254 2122.81891
waicOcc(ms_week_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -982.76074 47.17148 2059.86444
waicOcc(ms_cover_full_T, by.sp = FALSE)
## elpd pD WAIC
## -964.88613 49.66181 2029.09588
waicOcc(ms_cover_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -997.12218 48.59273 2091.42982
waicOcc(ms_cover_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1022.64789 24.27981 2093.85540
waicOcc(ms_cover_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1004.31218 41.01043 2090.64521
waicOcc(ms_cover_move_T, by.sp = FALSE)
## elpd pD WAIC
## -993.90997 47.95756 2083.73505
waicOcc(ms_cover_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -993.49097 38.13634 2063.25461
waicOcc(ms_cover_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1007.55104 37.44593 2089.99394
waicOcc(ms_cover_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1003.97294 39.25777 2086.46141
waicOcc(ms_cover_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -963.76184 47.82343 2023.17054
waicOcc(ms_weekQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -987.80547 46.27353 2068.15801
waicOcc(ms_weekQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1022.19257 41.67742 2127.73998
waicOcc(ms_weekQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1047.90059 17.19473 2130.19062
waicOcc(ms_weekQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1027.50980 36.24991 2127.51941
waicOcc(ms_weekQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1017.74679 40.30229 2116.09815
waicOcc(ms_weekQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1019.08767 32.52589 2103.22710
waicOcc(ms_weekQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1030.93775 31.55563 2124.98677
waicOcc(ms_weekQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1027.32856 33.26074 2121.17861
waicOcc(ms_weekQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -982.8632 46.0338 2057.7940
waicOcc(ms_fullQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -965.11912 49.98753 2030.21331
waicOcc(ms_fullQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -995.62750 50.79847 2092.85193
waicOcc(ms_fullQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1023.64579 23.32491 2093.94139
waicOcc(ms_fullQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1004.96764 40.06695 2090.06918
waicOcc(ms_fullQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -993.71013 47.69119 2082.80264
waicOcc(ms_fullQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -993.94197 37.01406 2061.91207
waicOcc(ms_fullQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1008.41983 37.36377 2091.56720
waicOcc(ms_fullQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1003.75735 40.03457 2087.58383
waicOcc(ms_fullQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -963.19750 48.18346 2022.76192
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: 0.4135
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.7227
## Lynx_rufus Bayesian p-value: 0.3273
## Didelphis_virginiana Bayesian p-value: 0.3923
## Sylvilagus_floridanus Bayesian p-value: 0.3683
## Meleagris_gallopavo Bayesian p-value: 0.433
## Sciurus_carolinensis Bayesian p-value: 0.305
## Vulpes_vulpes Bayesian p-value: 0.268
## Sus_scrofa Bayesian p-value: 0.4913
## 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): 5.556
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0616 1.1497 -4.0825 -2.1436 0.3824 1.0002 422
## Cogon_Patch_Size 0.1515 1.0912 -2.0526 0.1722 2.2744 1.1711 233
## Veg_shannon_index 0.9883 0.8181 -0.6785 1.0029 2.5387 1.0257 247
## total_shrub_cover -1.0976 1.0148 -3.2018 -1.0562 0.7561 1.0239 124
## Avg_Cogongrass_Cover -0.2905 1.2420 -2.6245 -0.3080 2.1905 1.0142 145
## Tree_Density -2.2720 1.0788 -4.7662 -2.1900 -0.3270 1.0129 106
## Avg_Canopy_Cover 2.1893 1.0935 -0.2371 2.2345 4.2202 1.0064 785
## I(Avg_Cogongrass_Cover^2) 1.4484 0.8624 -0.3223 1.4549 3.0897 1.0435 194
## avg_veg_height -0.2901 0.7466 -1.8505 -0.2610 1.2070 1.0105 159
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.6484 25.2892 0.1605 7.1111 71.3188 1.1150 100
## Cogon_Patch_Size 9.9452 15.6905 0.1927 4.8512 48.0926 1.0433 96
## Veg_shannon_index 4.0610 7.8858 0.0926 1.4948 24.0610 1.0667 116
## total_shrub_cover 7.4431 12.8558 0.1517 3.7194 38.8554 1.0952 75
## Avg_Cogongrass_Cover 2.5038 5.6860 0.0512 0.7213 16.2229 1.0849 171
## Tree_Density 2.0094 4.7576 0.0493 0.6038 12.6388 1.0600 519
## Avg_Canopy_Cover 15.9811 47.0213 0.5600 6.4717 87.2419 1.2914 155
## I(Avg_Cogongrass_Cover^2) 4.3410 9.1592 0.0695 1.3773 25.1306 1.0682 93
## avg_veg_height 1.0831 2.4665 0.0476 0.4226 5.9177 1.0423 484
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9559 4.5814 0.0577 1.1612 16.5617 1.0093 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1383 0.3008 -5.6952 -5.1561 -4.4826 1.0229 485
## shrub_cover 0.3841 0.3633 -0.3392 0.3750 1.0982 1.0016 530
## veg_height -0.0076 0.2348 -0.4962 0.0031 0.4379 1.0279 238
## week 0.0688 1.6967 -3.2289 0.0205 3.4621 1.0186 314
## I(week^2) 0.1290 1.7097 -3.1821 0.1438 3.5825 1.0151 288
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.379000e-01 5.446000e-01 0.0750 0.3656 1.994800e+00 1.0011 207
## shrub_cover 9.120000e-01 1.014000e+00 0.1881 0.6625 2.963000e+00 1.1103 493
## veg_height 3.277000e-01 2.953000e-01 0.0647 0.2451 1.123400e+00 1.0184 431
## week 4.433265e+09 3.789382e+10 0.0899 53.2348 2.910177e+10 1.4640 191
## I(week^2) 1.793658e+10 2.356987e+11 0.0788 50.6249 2.426549e+10 1.7063 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6821 1.6330 -3.5582 -0.8047
## (Intercept)-Lynx_rufus 0.6550 3.0038 -3.3125 0.1417
## (Intercept)-Didelphis_virginiana -4.0432 1.8153 -8.1500 -3.8194
## (Intercept)-Sylvilagus_floridanus -2.6953 1.8868 -6.7535 -2.6481
## (Intercept)-Meleagris_gallopavo -1.9708 2.2412 -6.2243 -2.1468
## (Intercept)-Sciurus_carolinensis -4.6193 2.0242 -9.2298 -4.3657
## (Intercept)-Vulpes_vulpes -4.9191 2.7593 -11.6641 -4.4784
## (Intercept)-Sus_scrofa -5.5721 2.8233 -12.5792 -5.1433
## Cogon_Patch_Size-Canis_latrans 2.7643 2.2794 -0.1417 2.3078
## Cogon_Patch_Size-Lynx_rufus -0.0969 2.4798 -5.2780 0.0654
## Cogon_Patch_Size-Didelphis_virginiana 2.2794 1.3992 -0.0155 2.0884
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9593 2.4754 -8.1828 -1.4887
## Cogon_Patch_Size-Meleagris_gallopavo 1.3099 2.4686 -2.1379 0.8689
## Cogon_Patch_Size-Sciurus_carolinensis -1.3476 2.3373 -7.0440 -0.9624
## Cogon_Patch_Size-Vulpes_vulpes -0.5229 2.5456 -6.5463 -0.3154
## Cogon_Patch_Size-Sus_scrofa -1.0507 2.2132 -6.5272 -0.7259
## Veg_shannon_index-Canis_latrans 1.7636 0.9880 0.0729 1.6411
## Veg_shannon_index-Lynx_rufus -0.2145 1.8893 -4.8952 0.1066
## Veg_shannon_index-Didelphis_virginiana 1.6131 1.3010 -0.3329 1.4754
## Veg_shannon_index-Sylvilagus_floridanus 1.3800 1.0819 -0.4716 1.2902
## Veg_shannon_index-Meleagris_gallopavo 2.0554 1.4388 -0.1218 1.8128
## Veg_shannon_index-Sciurus_carolinensis -0.1915 1.4298 -3.4740 -0.0111
## Veg_shannon_index-Vulpes_vulpes 0.2378 1.6195 -3.4434 0.4642
## Veg_shannon_index-Sus_scrofa 2.3787 1.8773 0.0419 1.9715
## total_shrub_cover-Canis_latrans 1.0499 1.2361 -1.0417 0.8966
## total_shrub_cover-Lynx_rufus -2.1898 2.6721 -8.1728 -2.0215
## total_shrub_cover-Didelphis_virginiana -1.9380 1.7756 -6.1571 -1.6527
## total_shrub_cover-Sylvilagus_floridanus -1.5330 2.1450 -7.6386 -1.0859
## total_shrub_cover-Meleagris_gallopavo -3.5174 2.4224 -9.4986 -3.2079
## total_shrub_cover-Sciurus_carolinensis -1.1464 1.6031 -4.7339 -0.9683
## total_shrub_cover-Vulpes_vulpes -2.0194 2.4162 -8.1085 -1.5827
## total_shrub_cover-Sus_scrofa -0.5118 1.9633 -4.6264 -0.4678
## Avg_Cogongrass_Cover-Canis_latrans -0.0947 1.5538 -2.9231 -0.1667
## Avg_Cogongrass_Cover-Lynx_rufus 0.2158 1.9593 -3.0760 0.0455
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2537 1.6955 -3.6362 -0.2953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0538 1.8021 -5.3295 -0.9302
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6792 1.8416 -4.7089 -0.6212
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3153 1.6602 -3.6551 -0.3271
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1538 1.8852 -3.0105 0.0009
## Avg_Cogongrass_Cover-Sus_scrofa -0.4960 1.8401 -4.2876 -0.4350
## Tree_Density-Canis_latrans -2.9338 1.4818 -6.3954 -2.7211
## Tree_Density-Lynx_rufus -1.8805 1.5253 -4.9322 -1.9150
## Tree_Density-Didelphis_virginiana -2.4165 1.3442 -5.3659 -2.3222
## Tree_Density-Sylvilagus_floridanus -2.6922 1.4845 -6.0799 -2.5358
## Tree_Density-Meleagris_gallopavo -2.4405 1.4216 -5.4007 -2.3350
## Tree_Density-Sciurus_carolinensis -2.5361 1.5738 -6.0496 -2.4284
## Tree_Density-Vulpes_vulpes -2.2691 1.6205 -5.4227 -2.2489
## Tree_Density-Sus_scrofa -2.4059 1.4929 -5.6569 -2.3083
## Avg_Canopy_Cover-Canis_latrans -0.1290 0.8756 -1.8994 -0.0986
## Avg_Canopy_Cover-Lynx_rufus 1.3882 2.6862 -3.3156 1.0007
## Avg_Canopy_Cover-Didelphis_virginiana 4.9405 2.6822 1.6795 4.3244
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9631 3.2641 1.9122 5.2602
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3930 2.2063 0.4985 2.9302
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6860 2.7559 1.4301 4.0428
## Avg_Canopy_Cover-Vulpes_vulpes 3.9601 2.8118 0.6398 3.1998
## Avg_Canopy_Cover-Sus_scrofa 2.8295 1.9480 0.2827 2.5117
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4870 1.3246 0.5652 2.2498
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0939 2.0144 0.4443 2.6283
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2228 0.9245 -0.6178 1.2165
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2623 1.0511 -0.7875 1.2352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0265 2.2417 -5.7637 0.4043
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0165 1.1917 0.1472 1.8508
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2798 1.4932 0.1730 2.0577
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.6143 1.9380 -4.2067 0.9403
## avg_veg_height-Canis_latrans -0.2495 0.8158 -1.8413 -0.2427
## avg_veg_height-Lynx_rufus -0.6025 1.2585 -3.3850 -0.4790
## avg_veg_height-Didelphis_virginiana -0.5628 1.0053 -2.8855 -0.4800
## avg_veg_height-Sylvilagus_floridanus -0.3483 0.9369 -2.3143 -0.3225
## avg_veg_height-Meleagris_gallopavo -0.2409 1.1932 -2.7624 -0.2165
## avg_veg_height-Sciurus_carolinensis 0.1892 1.0185 -1.5244 0.1160
## avg_veg_height-Vulpes_vulpes -0.3707 1.1092 -2.6533 -0.3359
## avg_veg_height-Sus_scrofa -0.2180 1.0371 -2.2497 -0.2146
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9706 1.0046 201
## (Intercept)-Lynx_rufus 8.0379 1.1383 63
## (Intercept)-Didelphis_virginiana -1.0115 1.0174 128
## (Intercept)-Sylvilagus_floridanus 0.9844 1.0162 165
## (Intercept)-Meleagris_gallopavo 2.7802 1.0809 112
## (Intercept)-Sciurus_carolinensis -1.2761 1.0052 113
## (Intercept)-Vulpes_vulpes -0.6676 1.0374 140
## (Intercept)-Sus_scrofa -1.4864 1.0872 72
## Cogon_Patch_Size-Canis_latrans 8.2662 1.0595 185
## Cogon_Patch_Size-Lynx_rufus 4.7837 1.1972 160
## Cogon_Patch_Size-Didelphis_virginiana 5.5137 1.0008 215
## Cogon_Patch_Size-Sylvilagus_floridanus 1.5810 1.0619 149
## Cogon_Patch_Size-Meleagris_gallopavo 7.8950 1.0123 159
## Cogon_Patch_Size-Sciurus_carolinensis 2.1537 1.1158 146
## Cogon_Patch_Size-Vulpes_vulpes 3.9262 1.2127 142
## Cogon_Patch_Size-Sus_scrofa 2.4012 1.2166 168
## Veg_shannon_index-Canis_latrans 4.0942 1.0001 410
## Veg_shannon_index-Lynx_rufus 2.6020 1.0617 138
## Veg_shannon_index-Didelphis_virginiana 4.5566 1.0637 228
## Veg_shannon_index-Sylvilagus_floridanus 3.8156 1.0133 200
## Veg_shannon_index-Meleagris_gallopavo 5.5750 1.0046 233
## Veg_shannon_index-Sciurus_carolinensis 2.1139 1.0629 125
## Veg_shannon_index-Vulpes_vulpes 2.8180 1.0141 154
## Veg_shannon_index-Sus_scrofa 7.1979 1.0347 126
## total_shrub_cover-Canis_latrans 3.8374 1.0804 153
## total_shrub_cover-Lynx_rufus 3.1513 1.2992 49
## total_shrub_cover-Didelphis_virginiana 0.6123 1.1101 135
## total_shrub_cover-Sylvilagus_floridanus 1.1262 1.2671 95
## total_shrub_cover-Meleagris_gallopavo 0.2324 1.0671 91
## total_shrub_cover-Sciurus_carolinensis 1.5186 1.0418 292
## total_shrub_cover-Vulpes_vulpes 1.4184 1.0729 119
## total_shrub_cover-Sus_scrofa 3.3420 1.0530 198
## Avg_Cogongrass_Cover-Canis_latrans 3.2998 1.0185 167
## Avg_Cogongrass_Cover-Lynx_rufus 4.9527 1.0209 120
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1583 1.0157 175
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0750 1.0382 161
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6088 1.0345 190
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9524 1.0542 150
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4635 1.0205 183
## Avg_Cogongrass_Cover-Sus_scrofa 2.8575 1.0552 141
## Tree_Density-Canis_latrans -0.6071 1.0182 164
## Tree_Density-Lynx_rufus 1.3350 1.0185 171
## Tree_Density-Didelphis_virginiana 0.0267 1.0186 157
## Tree_Density-Sylvilagus_floridanus -0.1193 1.0102 219
## Tree_Density-Meleagris_gallopavo 0.2510 1.0268 175
## Tree_Density-Sciurus_carolinensis 0.3248 1.0442 154
## Tree_Density-Vulpes_vulpes 1.2513 1.0040 149
## Tree_Density-Sus_scrofa 0.4826 1.0127 249
## Avg_Canopy_Cover-Canis_latrans 1.5502 1.0061 296
## Avg_Canopy_Cover-Lynx_rufus 7.4601 1.0944 74
## Avg_Canopy_Cover-Didelphis_virginiana 11.2061 1.0698 94
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.8574 1.0527 78
## Avg_Canopy_Cover-Meleagris_gallopavo 9.5971 1.0834 217
## Avg_Canopy_Cover-Sciurus_carolinensis 12.4836 1.0128 99
## Avg_Canopy_Cover-Vulpes_vulpes 11.1898 1.0467 113
## Avg_Canopy_Cover-Sus_scrofa 7.4936 1.1650 226
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6845 1.0188 200
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.3123 1.0239 101
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1219 1.0077 213
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4957 1.0216 295
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.3554 1.1264 47
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.7926 1.0093 122
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9849 1.0101 225
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.4755 1.0920 66
## avg_veg_height-Canis_latrans 1.3704 1.0080 207
## avg_veg_height-Lynx_rufus 1.5856 1.0133 195
## avg_veg_height-Didelphis_virginiana 1.2079 1.0066 269
## avg_veg_height-Sylvilagus_floridanus 1.4941 1.0083 315
## avg_veg_height-Meleagris_gallopavo 2.1648 1.0321 174
## avg_veg_height-Sciurus_carolinensis 2.5232 1.0006 266
## avg_veg_height-Vulpes_vulpes 1.7847 1.0185 264
## avg_veg_height-Sus_scrofa 1.9594 1.0090 320
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7767 0.1768 -5.1454 -4.7649
## (Intercept)-Lynx_rufus -5.6999 0.3053 -6.2731 -5.7161
## (Intercept)-Didelphis_virginiana -4.7577 0.2878 -5.3240 -4.7597
## (Intercept)-Sylvilagus_floridanus -4.9999 0.2251 -5.4728 -5.0005
## (Intercept)-Meleagris_gallopavo -5.6366 0.4619 -6.6630 -5.6089
## (Intercept)-Sciurus_carolinensis -4.8471 0.3115 -5.4942 -4.8499
## (Intercept)-Vulpes_vulpes -5.9577 0.5168 -7.1083 -5.9119
## (Intercept)-Sus_scrofa -5.3782 0.4775 -6.3442 -5.3737
## shrub_cover-Canis_latrans -0.3246 0.2107 -0.7208 -0.3351
## shrub_cover-Lynx_rufus 0.0046 0.3603 -0.6954 -0.0122
## shrub_cover-Didelphis_virginiana 1.1428 0.3681 0.4689 1.1120
## shrub_cover-Sylvilagus_floridanus 0.7171 0.3755 0.0344 0.6947
## shrub_cover-Meleagris_gallopavo -0.6171 0.4317 -1.5107 -0.6144
## shrub_cover-Sciurus_carolinensis 1.0837 0.3586 0.3462 1.0975
## shrub_cover-Vulpes_vulpes 0.3218 0.5758 -0.8744 0.3578
## shrub_cover-Sus_scrofa 0.9299 0.7742 -0.6702 0.9478
## veg_height-Canis_latrans -0.5550 0.1631 -0.8843 -0.5500
## veg_height-Lynx_rufus 0.1951 0.2402 -0.2833 0.1933
## veg_height-Didelphis_virginiana 0.5560 0.2469 0.0841 0.5518
## veg_height-Sylvilagus_floridanus 0.1706 0.2416 -0.3111 0.1756
## veg_height-Meleagris_gallopavo -0.2143 0.4215 -1.0620 -0.2094
## veg_height-Sciurus_carolinensis 0.2888 0.2223 -0.1219 0.2786
## veg_height-Vulpes_vulpes -0.2244 0.3854 -1.1219 -0.1860
## veg_height-Sus_scrofa -0.2412 0.3456 -0.9697 -0.2148
## week-Canis_latrans 1089.4459 58502.3382 -31266.0757 0.1660
## week-Lynx_rufus -249.9298 69956.6614 -52273.5082 0.1726
## week-Didelphis_virginiana -209.1295 53803.5931 -28326.9009 0.1226
## week-Sylvilagus_floridanus -80.4087 54128.6000 -28009.9757 0.1392
## week-Meleagris_gallopavo -32.0251 66474.7619 -31485.3599 0.1096
## week-Sciurus_carolinensis 1274.2705 66161.7402 -42410.5174 0.0585
## week-Vulpes_vulpes -569.7465 62255.7493 -31471.9025 0.2318
## week-Sus_scrofa -1322.5796 59775.0644 -42675.9225 0.0884
## I(week^2)-Canis_latrans -835.4950 113971.3362 -41521.6389 0.3151
## I(week^2)-Lynx_rufus 375.2720 141921.7865 -42230.3237 0.3456
## I(week^2)-Didelphis_virginiana -699.7343 149758.6996 -49398.0683 0.4464
## I(week^2)-Sylvilagus_floridanus -1066.6689 97559.2036 -48763.4625 0.2991
## I(week^2)-Meleagris_gallopavo 942.5933 109973.3376 -50332.6619 0.3005
## I(week^2)-Sciurus_carolinensis 308.3552 99332.4666 -29562.1302 0.4043
## I(week^2)-Vulpes_vulpes 3064.7616 116303.6825 -39157.6475 0.5259
## I(week^2)-Sus_scrofa -4249.0416 123596.3249 -56459.9255 0.2187
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4553 1.2071 162
## (Intercept)-Lynx_rufus -5.0978 1.1118 63
## (Intercept)-Didelphis_virginiana -4.1627 1.1075 127
## (Intercept)-Sylvilagus_floridanus -4.5686 1.0601 154
## (Intercept)-Meleagris_gallopavo -4.7935 1.1174 59
## (Intercept)-Sciurus_carolinensis -4.2381 1.0129 119
## (Intercept)-Vulpes_vulpes -5.0890 1.0101 60
## (Intercept)-Sus_scrofa -4.4314 1.0173 150
## shrub_cover-Canis_latrans 0.1104 1.0512 170
## shrub_cover-Lynx_rufus 0.6983 1.1278 87
## shrub_cover-Didelphis_virginiana 1.9298 1.1335 142
## shrub_cover-Sylvilagus_floridanus 1.4899 1.1123 88
## shrub_cover-Meleagris_gallopavo 0.2279 1.1351 66
## shrub_cover-Sciurus_carolinensis 1.7499 1.0263 152
## shrub_cover-Vulpes_vulpes 1.4069 1.0023 92
## shrub_cover-Sus_scrofa 2.4413 1.0166 155
## veg_height-Canis_latrans -0.2299 1.0475 206
## veg_height-Lynx_rufus 0.6521 1.0310 118
## veg_height-Didelphis_virginiana 1.0324 1.0111 221
## veg_height-Sylvilagus_floridanus 0.6472 1.0430 167
## veg_height-Meleagris_gallopavo 0.5948 1.1578 86
## veg_height-Sciurus_carolinensis 0.7625 1.0349 137
## veg_height-Vulpes_vulpes 0.4282 1.0280 89
## veg_height-Sus_scrofa 0.3786 1.0284 213
## week-Canis_latrans 45126.2182 1.2271 1825
## week-Lynx_rufus 32599.0997 1.2138 2508
## week-Didelphis_virginiana 39513.3007 1.1513 9331
## week-Sylvilagus_floridanus 35091.1790 1.2421 2040
## week-Meleagris_gallopavo 38119.6216 1.1560 7320
## week-Sciurus_carolinensis 31917.7214 1.1626 2298
## week-Vulpes_vulpes 31715.1869 1.2013 2911
## week-Sus_scrofa 21596.8628 1.1843 2104
## I(week^2)-Canis_latrans 45314.5199 1.2667 6348
## I(week^2)-Lynx_rufus 53105.8252 1.2578 15197
## I(week^2)-Didelphis_virginiana 42149.6283 1.2645 16124
## I(week^2)-Sylvilagus_floridanus 37950.5505 1.2386 10602
## I(week^2)-Meleagris_gallopavo 46094.7739 1.2428 16643
## I(week^2)-Sciurus_carolinensis 54971.9524 1.2837 3639
## I(week^2)-Vulpes_vulpes 50349.8432 1.2882 1388
## I(week^2)-Sus_scrofa 39379.6111 1.3257 741
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] -2.295 -1.602 -1.41 -0.756 -3.73 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1703333
MCMCplot(ms_fullQ_fullQ_T$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] 0.003449 0.000196 0.000729 0.006433 0.051539 ...
## $ 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$psi.0.samples)
## num [1:3000, 1:8, 1:100] 0.003449 0.000196 0.000729 0.006433 0.051539 ...
psi.0.quants <- apply(out.pred$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] 2.59e-05 4.39e-02 9.89e-01 2.90e-05 4.37e-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 0.0439 0.0437 0.0448 0.0437 0.0453 ...
## $ psi.low : num 2.59e-05 2.90e-05 3.26e-05 3.31e-05 3.35e-05 ...
## $ psi.high : num 0.989 0.987 0.985 0.983 0.985 ...
## $ 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): 5.556
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0616 1.1497 -4.0825 -2.1436 0.3824 1.0002 422
## Cogon_Patch_Size 0.1515 1.0912 -2.0526 0.1722 2.2744 1.1711 233
## Veg_shannon_index 0.9883 0.8181 -0.6785 1.0029 2.5387 1.0257 247
## total_shrub_cover -1.0976 1.0148 -3.2018 -1.0562 0.7561 1.0239 124
## Avg_Cogongrass_Cover -0.2905 1.2420 -2.6245 -0.3080 2.1905 1.0142 145
## Tree_Density -2.2720 1.0788 -4.7662 -2.1900 -0.3270 1.0129 106
## Avg_Canopy_Cover 2.1893 1.0935 -0.2371 2.2345 4.2202 1.0064 785
## I(Avg_Cogongrass_Cover^2) 1.4484 0.8624 -0.3223 1.4549 3.0897 1.0435 194
## avg_veg_height -0.2901 0.7466 -1.8505 -0.2610 1.2070 1.0105 159
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.6484 25.2892 0.1605 7.1111 71.3188 1.1150 100
## Cogon_Patch_Size 9.9452 15.6905 0.1927 4.8512 48.0926 1.0433 96
## Veg_shannon_index 4.0610 7.8858 0.0926 1.4948 24.0610 1.0667 116
## total_shrub_cover 7.4431 12.8558 0.1517 3.7194 38.8554 1.0952 75
## Avg_Cogongrass_Cover 2.5038 5.6860 0.0512 0.7213 16.2229 1.0849 171
## Tree_Density 2.0094 4.7576 0.0493 0.6038 12.6388 1.0600 519
## Avg_Canopy_Cover 15.9811 47.0213 0.5600 6.4717 87.2419 1.2914 155
## I(Avg_Cogongrass_Cover^2) 4.3410 9.1592 0.0695 1.3773 25.1306 1.0682 93
## avg_veg_height 1.0831 2.4665 0.0476 0.4226 5.9177 1.0423 484
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9559 4.5814 0.0577 1.1612 16.5617 1.0093 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.1383 0.3008 -5.6952 -5.1561 -4.4826 1.0229 485
## shrub_cover 0.3841 0.3633 -0.3392 0.3750 1.0982 1.0016 530
## veg_height -0.0076 0.2348 -0.4962 0.0031 0.4379 1.0279 238
## week 0.0688 1.6967 -3.2289 0.0205 3.4621 1.0186 314
## I(week^2) 0.1290 1.7097 -3.1821 0.1438 3.5825 1.0151 288
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.379000e-01 5.446000e-01 0.0750 0.3656 1.994800e+00 1.0011 207
## shrub_cover 9.120000e-01 1.014000e+00 0.1881 0.6625 2.963000e+00 1.1103 493
## veg_height 3.277000e-01 2.953000e-01 0.0647 0.2451 1.123400e+00 1.0184 431
## week 4.433265e+09 3.789382e+10 0.0899 53.2348 2.910177e+10 1.4640 191
## I(week^2) 1.793658e+10 2.356987e+11 0.0788 50.6249 2.426549e+10 1.7063 185
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6821 1.6330 -3.5582 -0.8047
## (Intercept)-Lynx_rufus 0.6550 3.0038 -3.3125 0.1417
## (Intercept)-Didelphis_virginiana -4.0432 1.8153 -8.1500 -3.8194
## (Intercept)-Sylvilagus_floridanus -2.6953 1.8868 -6.7535 -2.6481
## (Intercept)-Meleagris_gallopavo -1.9708 2.2412 -6.2243 -2.1468
## (Intercept)-Sciurus_carolinensis -4.6193 2.0242 -9.2298 -4.3657
## (Intercept)-Vulpes_vulpes -4.9191 2.7593 -11.6641 -4.4784
## (Intercept)-Sus_scrofa -5.5721 2.8233 -12.5792 -5.1433
## Cogon_Patch_Size-Canis_latrans 2.7643 2.2794 -0.1417 2.3078
## Cogon_Patch_Size-Lynx_rufus -0.0969 2.4798 -5.2780 0.0654
## Cogon_Patch_Size-Didelphis_virginiana 2.2794 1.3992 -0.0155 2.0884
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9593 2.4754 -8.1828 -1.4887
## Cogon_Patch_Size-Meleagris_gallopavo 1.3099 2.4686 -2.1379 0.8689
## Cogon_Patch_Size-Sciurus_carolinensis -1.3476 2.3373 -7.0440 -0.9624
## Cogon_Patch_Size-Vulpes_vulpes -0.5229 2.5456 -6.5463 -0.3154
## Cogon_Patch_Size-Sus_scrofa -1.0507 2.2132 -6.5272 -0.7259
## Veg_shannon_index-Canis_latrans 1.7636 0.9880 0.0729 1.6411
## Veg_shannon_index-Lynx_rufus -0.2145 1.8893 -4.8952 0.1066
## Veg_shannon_index-Didelphis_virginiana 1.6131 1.3010 -0.3329 1.4754
## Veg_shannon_index-Sylvilagus_floridanus 1.3800 1.0819 -0.4716 1.2902
## Veg_shannon_index-Meleagris_gallopavo 2.0554 1.4388 -0.1218 1.8128
## Veg_shannon_index-Sciurus_carolinensis -0.1915 1.4298 -3.4740 -0.0111
## Veg_shannon_index-Vulpes_vulpes 0.2378 1.6195 -3.4434 0.4642
## Veg_shannon_index-Sus_scrofa 2.3787 1.8773 0.0419 1.9715
## total_shrub_cover-Canis_latrans 1.0499 1.2361 -1.0417 0.8966
## total_shrub_cover-Lynx_rufus -2.1898 2.6721 -8.1728 -2.0215
## total_shrub_cover-Didelphis_virginiana -1.9380 1.7756 -6.1571 -1.6527
## total_shrub_cover-Sylvilagus_floridanus -1.5330 2.1450 -7.6386 -1.0859
## total_shrub_cover-Meleagris_gallopavo -3.5174 2.4224 -9.4986 -3.2079
## total_shrub_cover-Sciurus_carolinensis -1.1464 1.6031 -4.7339 -0.9683
## total_shrub_cover-Vulpes_vulpes -2.0194 2.4162 -8.1085 -1.5827
## total_shrub_cover-Sus_scrofa -0.5118 1.9633 -4.6264 -0.4678
## Avg_Cogongrass_Cover-Canis_latrans -0.0947 1.5538 -2.9231 -0.1667
## Avg_Cogongrass_Cover-Lynx_rufus 0.2158 1.9593 -3.0760 0.0455
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2537 1.6955 -3.6362 -0.2953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0538 1.8021 -5.3295 -0.9302
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6792 1.8416 -4.7089 -0.6212
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.3153 1.6602 -3.6551 -0.3271
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1538 1.8852 -3.0105 0.0009
## Avg_Cogongrass_Cover-Sus_scrofa -0.4960 1.8401 -4.2876 -0.4350
## Tree_Density-Canis_latrans -2.9338 1.4818 -6.3954 -2.7211
## Tree_Density-Lynx_rufus -1.8805 1.5253 -4.9322 -1.9150
## Tree_Density-Didelphis_virginiana -2.4165 1.3442 -5.3659 -2.3222
## Tree_Density-Sylvilagus_floridanus -2.6922 1.4845 -6.0799 -2.5358
## Tree_Density-Meleagris_gallopavo -2.4405 1.4216 -5.4007 -2.3350
## Tree_Density-Sciurus_carolinensis -2.5361 1.5738 -6.0496 -2.4284
## Tree_Density-Vulpes_vulpes -2.2691 1.6205 -5.4227 -2.2489
## Tree_Density-Sus_scrofa -2.4059 1.4929 -5.6569 -2.3083
## Avg_Canopy_Cover-Canis_latrans -0.1290 0.8756 -1.8994 -0.0986
## Avg_Canopy_Cover-Lynx_rufus 1.3882 2.6862 -3.3156 1.0007
## Avg_Canopy_Cover-Didelphis_virginiana 4.9405 2.6822 1.6795 4.3244
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9631 3.2641 1.9122 5.2602
## Avg_Canopy_Cover-Meleagris_gallopavo 3.3930 2.2063 0.4985 2.9302
## Avg_Canopy_Cover-Sciurus_carolinensis 4.6860 2.7559 1.4301 4.0428
## Avg_Canopy_Cover-Vulpes_vulpes 3.9601 2.8118 0.6398 3.1998
## Avg_Canopy_Cover-Sus_scrofa 2.8295 1.9480 0.2827 2.5117
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4870 1.3246 0.5652 2.2498
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.0939 2.0144 0.4443 2.6283
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2228 0.9245 -0.6178 1.2165
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2623 1.0511 -0.7875 1.2352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0265 2.2417 -5.7637 0.4043
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.0165 1.1917 0.1472 1.8508
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2798 1.4932 0.1730 2.0577
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.6143 1.9380 -4.2067 0.9403
## avg_veg_height-Canis_latrans -0.2495 0.8158 -1.8413 -0.2427
## avg_veg_height-Lynx_rufus -0.6025 1.2585 -3.3850 -0.4790
## avg_veg_height-Didelphis_virginiana -0.5628 1.0053 -2.8855 -0.4800
## avg_veg_height-Sylvilagus_floridanus -0.3483 0.9369 -2.3143 -0.3225
## avg_veg_height-Meleagris_gallopavo -0.2409 1.1932 -2.7624 -0.2165
## avg_veg_height-Sciurus_carolinensis 0.1892 1.0185 -1.5244 0.1160
## avg_veg_height-Vulpes_vulpes -0.3707 1.1092 -2.6533 -0.3359
## avg_veg_height-Sus_scrofa -0.2180 1.0371 -2.2497 -0.2146
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9706 1.0046 201
## (Intercept)-Lynx_rufus 8.0379 1.1383 63
## (Intercept)-Didelphis_virginiana -1.0115 1.0174 128
## (Intercept)-Sylvilagus_floridanus 0.9844 1.0162 165
## (Intercept)-Meleagris_gallopavo 2.7802 1.0809 112
## (Intercept)-Sciurus_carolinensis -1.2761 1.0052 113
## (Intercept)-Vulpes_vulpes -0.6676 1.0374 140
## (Intercept)-Sus_scrofa -1.4864 1.0872 72
## Cogon_Patch_Size-Canis_latrans 8.2662 1.0595 185
## Cogon_Patch_Size-Lynx_rufus 4.7837 1.1972 160
## Cogon_Patch_Size-Didelphis_virginiana 5.5137 1.0008 215
## Cogon_Patch_Size-Sylvilagus_floridanus 1.5810 1.0619 149
## Cogon_Patch_Size-Meleagris_gallopavo 7.8950 1.0123 159
## Cogon_Patch_Size-Sciurus_carolinensis 2.1537 1.1158 146
## Cogon_Patch_Size-Vulpes_vulpes 3.9262 1.2127 142
## Cogon_Patch_Size-Sus_scrofa 2.4012 1.2166 168
## Veg_shannon_index-Canis_latrans 4.0942 1.0001 410
## Veg_shannon_index-Lynx_rufus 2.6020 1.0617 138
## Veg_shannon_index-Didelphis_virginiana 4.5566 1.0637 228
## Veg_shannon_index-Sylvilagus_floridanus 3.8156 1.0133 200
## Veg_shannon_index-Meleagris_gallopavo 5.5750 1.0046 233
## Veg_shannon_index-Sciurus_carolinensis 2.1139 1.0629 125
## Veg_shannon_index-Vulpes_vulpes 2.8180 1.0141 154
## Veg_shannon_index-Sus_scrofa 7.1979 1.0347 126
## total_shrub_cover-Canis_latrans 3.8374 1.0804 153
## total_shrub_cover-Lynx_rufus 3.1513 1.2992 49
## total_shrub_cover-Didelphis_virginiana 0.6123 1.1101 135
## total_shrub_cover-Sylvilagus_floridanus 1.1262 1.2671 95
## total_shrub_cover-Meleagris_gallopavo 0.2324 1.0671 91
## total_shrub_cover-Sciurus_carolinensis 1.5186 1.0418 292
## total_shrub_cover-Vulpes_vulpes 1.4184 1.0729 119
## total_shrub_cover-Sus_scrofa 3.3420 1.0530 198
## Avg_Cogongrass_Cover-Canis_latrans 3.2998 1.0185 167
## Avg_Cogongrass_Cover-Lynx_rufus 4.9527 1.0209 120
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1583 1.0157 175
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0750 1.0382 161
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.6088 1.0345 190
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9524 1.0542 150
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4635 1.0205 183
## Avg_Cogongrass_Cover-Sus_scrofa 2.8575 1.0552 141
## Tree_Density-Canis_latrans -0.6071 1.0182 164
## Tree_Density-Lynx_rufus 1.3350 1.0185 171
## Tree_Density-Didelphis_virginiana 0.0267 1.0186 157
## Tree_Density-Sylvilagus_floridanus -0.1193 1.0102 219
## Tree_Density-Meleagris_gallopavo 0.2510 1.0268 175
## Tree_Density-Sciurus_carolinensis 0.3248 1.0442 154
## Tree_Density-Vulpes_vulpes 1.2513 1.0040 149
## Tree_Density-Sus_scrofa 0.4826 1.0127 249
## Avg_Canopy_Cover-Canis_latrans 1.5502 1.0061 296
## Avg_Canopy_Cover-Lynx_rufus 7.4601 1.0944 74
## Avg_Canopy_Cover-Didelphis_virginiana 11.2061 1.0698 94
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.8574 1.0527 78
## Avg_Canopy_Cover-Meleagris_gallopavo 9.5971 1.0834 217
## Avg_Canopy_Cover-Sciurus_carolinensis 12.4836 1.0128 99
## Avg_Canopy_Cover-Vulpes_vulpes 11.1898 1.0467 113
## Avg_Canopy_Cover-Sus_scrofa 7.4936 1.1650 226
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6845 1.0188 200
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.3123 1.0239 101
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.1219 1.0077 213
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4957 1.0216 295
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.3554 1.1264 47
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.7926 1.0093 122
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 5.9849 1.0101 225
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.4755 1.0920 66
## avg_veg_height-Canis_latrans 1.3704 1.0080 207
## avg_veg_height-Lynx_rufus 1.5856 1.0133 195
## avg_veg_height-Didelphis_virginiana 1.2079 1.0066 269
## avg_veg_height-Sylvilagus_floridanus 1.4941 1.0083 315
## avg_veg_height-Meleagris_gallopavo 2.1648 1.0321 174
## avg_veg_height-Sciurus_carolinensis 2.5232 1.0006 266
## avg_veg_height-Vulpes_vulpes 1.7847 1.0185 264
## avg_veg_height-Sus_scrofa 1.9594 1.0090 320
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7767 0.1768 -5.1454 -4.7649
## (Intercept)-Lynx_rufus -5.6999 0.3053 -6.2731 -5.7161
## (Intercept)-Didelphis_virginiana -4.7577 0.2878 -5.3240 -4.7597
## (Intercept)-Sylvilagus_floridanus -4.9999 0.2251 -5.4728 -5.0005
## (Intercept)-Meleagris_gallopavo -5.6366 0.4619 -6.6630 -5.6089
## (Intercept)-Sciurus_carolinensis -4.8471 0.3115 -5.4942 -4.8499
## (Intercept)-Vulpes_vulpes -5.9577 0.5168 -7.1083 -5.9119
## (Intercept)-Sus_scrofa -5.3782 0.4775 -6.3442 -5.3737
## shrub_cover-Canis_latrans -0.3246 0.2107 -0.7208 -0.3351
## shrub_cover-Lynx_rufus 0.0046 0.3603 -0.6954 -0.0122
## shrub_cover-Didelphis_virginiana 1.1428 0.3681 0.4689 1.1120
## shrub_cover-Sylvilagus_floridanus 0.7171 0.3755 0.0344 0.6947
## shrub_cover-Meleagris_gallopavo -0.6171 0.4317 -1.5107 -0.6144
## shrub_cover-Sciurus_carolinensis 1.0837 0.3586 0.3462 1.0975
## shrub_cover-Vulpes_vulpes 0.3218 0.5758 -0.8744 0.3578
## shrub_cover-Sus_scrofa 0.9299 0.7742 -0.6702 0.9478
## veg_height-Canis_latrans -0.5550 0.1631 -0.8843 -0.5500
## veg_height-Lynx_rufus 0.1951 0.2402 -0.2833 0.1933
## veg_height-Didelphis_virginiana 0.5560 0.2469 0.0841 0.5518
## veg_height-Sylvilagus_floridanus 0.1706 0.2416 -0.3111 0.1756
## veg_height-Meleagris_gallopavo -0.2143 0.4215 -1.0620 -0.2094
## veg_height-Sciurus_carolinensis 0.2888 0.2223 -0.1219 0.2786
## veg_height-Vulpes_vulpes -0.2244 0.3854 -1.1219 -0.1860
## veg_height-Sus_scrofa -0.2412 0.3456 -0.9697 -0.2148
## week-Canis_latrans 1089.4459 58502.3382 -31266.0757 0.1660
## week-Lynx_rufus -249.9298 69956.6614 -52273.5082 0.1726
## week-Didelphis_virginiana -209.1295 53803.5931 -28326.9009 0.1226
## week-Sylvilagus_floridanus -80.4087 54128.6000 -28009.9757 0.1392
## week-Meleagris_gallopavo -32.0251 66474.7619 -31485.3599 0.1096
## week-Sciurus_carolinensis 1274.2705 66161.7402 -42410.5174 0.0585
## week-Vulpes_vulpes -569.7465 62255.7493 -31471.9025 0.2318
## week-Sus_scrofa -1322.5796 59775.0644 -42675.9225 0.0884
## I(week^2)-Canis_latrans -835.4950 113971.3362 -41521.6389 0.3151
## I(week^2)-Lynx_rufus 375.2720 141921.7865 -42230.3237 0.3456
## I(week^2)-Didelphis_virginiana -699.7343 149758.6996 -49398.0683 0.4464
## I(week^2)-Sylvilagus_floridanus -1066.6689 97559.2036 -48763.4625 0.2991
## I(week^2)-Meleagris_gallopavo 942.5933 109973.3376 -50332.6619 0.3005
## I(week^2)-Sciurus_carolinensis 308.3552 99332.4666 -29562.1302 0.4043
## I(week^2)-Vulpes_vulpes 3064.7616 116303.6825 -39157.6475 0.5259
## I(week^2)-Sus_scrofa -4249.0416 123596.3249 -56459.9255 0.2187
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4553 1.2071 162
## (Intercept)-Lynx_rufus -5.0978 1.1118 63
## (Intercept)-Didelphis_virginiana -4.1627 1.1075 127
## (Intercept)-Sylvilagus_floridanus -4.5686 1.0601 154
## (Intercept)-Meleagris_gallopavo -4.7935 1.1174 59
## (Intercept)-Sciurus_carolinensis -4.2381 1.0129 119
## (Intercept)-Vulpes_vulpes -5.0890 1.0101 60
## (Intercept)-Sus_scrofa -4.4314 1.0173 150
## shrub_cover-Canis_latrans 0.1104 1.0512 170
## shrub_cover-Lynx_rufus 0.6983 1.1278 87
## shrub_cover-Didelphis_virginiana 1.9298 1.1335 142
## shrub_cover-Sylvilagus_floridanus 1.4899 1.1123 88
## shrub_cover-Meleagris_gallopavo 0.2279 1.1351 66
## shrub_cover-Sciurus_carolinensis 1.7499 1.0263 152
## shrub_cover-Vulpes_vulpes 1.4069 1.0023 92
## shrub_cover-Sus_scrofa 2.4413 1.0166 155
## veg_height-Canis_latrans -0.2299 1.0475 206
## veg_height-Lynx_rufus 0.6521 1.0310 118
## veg_height-Didelphis_virginiana 1.0324 1.0111 221
## veg_height-Sylvilagus_floridanus 0.6472 1.0430 167
## veg_height-Meleagris_gallopavo 0.5948 1.1578 86
## veg_height-Sciurus_carolinensis 0.7625 1.0349 137
## veg_height-Vulpes_vulpes 0.4282 1.0280 89
## veg_height-Sus_scrofa 0.3786 1.0284 213
## week-Canis_latrans 45126.2182 1.2271 1825
## week-Lynx_rufus 32599.0997 1.2138 2508
## week-Didelphis_virginiana 39513.3007 1.1513 9331
## week-Sylvilagus_floridanus 35091.1790 1.2421 2040
## week-Meleagris_gallopavo 38119.6216 1.1560 7320
## week-Sciurus_carolinensis 31917.7214 1.1626 2298
## week-Vulpes_vulpes 31715.1869 1.2013 2911
## week-Sus_scrofa 21596.8628 1.1843 2104
## I(week^2)-Canis_latrans 45314.5199 1.2667 6348
## I(week^2)-Lynx_rufus 53105.8252 1.2578 15197
## I(week^2)-Didelphis_virginiana 42149.6283 1.2645 16124
## I(week^2)-Sylvilagus_floridanus 37950.5505 1.2386 10602
## I(week^2)-Meleagris_gallopavo 46094.7739 1.2428 16643
## I(week^2)-Sciurus_carolinensis 54971.9524 1.2837 3639
## I(week^2)-Vulpes_vulpes 50349.8432 1.2882 1388
## I(week^2)-Sus_scrofa 39379.6111 1.3257 741
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$beta.samples)
## 'mcmc' num [1:3000, 1:72] -2.295 -1.602 -1.41 -0.756 -3.73 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1703333
MCMCplot(ms_fullQ_fullQ_T$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
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/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/Kody Melissa/Downloads/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "CameraLocations")
# First day of the study
study_start <- as.Date("2024-05-10")
# Effort Matrix
effort_matrix <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "Daily_EM")
col_names <- names(effort_matrix)
date_cols <- col_names[-1]
converted_dates <- as.character(as.Date(as.numeric(date_cols), origin = "1899-12-30"))
names(effort_matrix) <- c("Plot", converted_dates)
# Pivot, filter, and add custom study week
effort_matrix <- effort_matrix %>%
pivot_longer(
cols = -Plot,
names_to = "date",
values_to = "effort"
) %>%
filter(effort == 1) %>%
mutate(
date = as.Date(date),
study_week = as.integer(floor((date - study_start) / 7)) + 1 # Week 1 starts on study_start
) %>%
dplyr::select(Plot, date, study_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 > 10) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format daily observation data
observations_daily <- CameraData %>%
mutate(Date = as.Date(Date)) %>%
group_by(Plot, date = Date, Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge observations with daily effort
observations_daily <- effort_matrix %>%
left_join(observations_daily, by = c("Plot", "date")) %>%
replace_na(list(observations = 0))
observations_species <- observations_daily %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-`NA`) # Remove empty species name if present
# Create detection array (site × date × species)
site_names <- sort(unique(observations_species$Plot))
date_names <- sort(unique(observations_species$date))
species_names <- setdiff(colnames(observations_species), c("Plot", "date", "study_week"))
num_sites <- length(site_names)
num_days <- length(date_names)
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_days, num_species))
dimnames(detection_array) <- list(site_names, as.character(date_names), 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(date_names)) {
day <- date_names[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site &
observations_species$date == day &
observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be [num_sites x num_days x num_species]
## [1] 32 258 6
# Backup camera location data
CameraLoc_O2 <- CameraLoc
# Standardize site-level covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, -Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names # site_names should match the detection array
# Scale site covariates
covariates_matrix <- scale(covariates_matrix)
## Detection-level Covariates ----
effort_expanded <- expand.grid(
Plot = site_names,
date = date_names
) %>%
left_join(effort_matrix[, c("Plot", "date", "study_week")], by = c("Plot", "date"))
week_matrix <- matrix(effort_expanded$study_week,
nrow = num_sites,
ncol = num_days,
byrow = FALSE)
# Convert to numeric and scale
week_numeric <- scale(week_matrix)
dim(week_numeric)
## [1] 32 258
# Detection covariates list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_numeric
)
det.covs$week[is.na(det.covs$week)] <- 0
# Check structure
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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258, 1:6] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## .. ..$ : chr [1:6] "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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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 [1:6, 1:32, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:6] "Canis_latrans" "Lynx_rufus" "Didelphis_virginiana" "Sylvilagus_floridanus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## $ 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.4518
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5556 0.3732 -1.2553 -0.5668 0.2386 1.003 1924
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7391 0.9132 0.0763 0.4811 3.0862 1.046 1236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6605 0.275 -5.1359 -4.6769 -4.0598 1.0086 1197
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3428 0.4657 0.0476 0.2087 1.4569 1.0463 753
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2005 0.4027 -0.5348 0.1909 1.0353 1.0033
## (Intercept)-Lynx_rufus -0.1569 0.5099 -0.9554 -0.2035 0.8492 1.0574
## (Intercept)-Didelphis_virginiana -1.1533 0.3998 -1.9744 -1.1285 -0.4273 1.0009
## (Intercept)-Sylvilagus_floridanus -0.5675 0.3706 -1.3008 -0.5713 0.1644 1.0041
## (Intercept)-Meleagris_gallopavo -0.5981 0.4507 -1.3685 -0.6117 0.2374 1.0231
## (Intercept)-Sciurus_carolinensis -1.1553 0.4124 -2.0160 -1.1442 -0.3898 1.0056
## ESS
## (Intercept)-Canis_latrans 1485
## (Intercept)-Lynx_rufus 402
## (Intercept)-Didelphis_virginiana 2016
## (Intercept)-Sylvilagus_floridanus 1741
## (Intercept)-Meleagris_gallopavo 550
## (Intercept)-Sciurus_carolinensis 1860
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6485 0.1492 -4.9514 -4.6469 -4.3626 1.0433
## (Intercept)-Lynx_rufus -5.2047 0.2521 -5.7305 -5.1880 -4.7472 1.0878
## (Intercept)-Didelphis_virginiana -4.3636 0.2196 -4.7963 -4.3587 -3.9392 1.0272
## (Intercept)-Sylvilagus_floridanus -4.8444 0.2224 -5.3270 -4.8305 -4.4324 1.0174
## (Intercept)-Meleagris_gallopavo -5.0085 0.2695 -5.5859 -4.9982 -4.5111 1.0272
## (Intercept)-Sciurus_carolinensis -4.4091 0.2344 -4.9050 -4.3984 -3.9921 1.0021
## ESS
## (Intercept)-Canis_latrans 268
## (Intercept)-Lynx_rufus 102
## (Intercept)-Didelphis_virginiana 331
## (Intercept)-Sylvilagus_floridanus 222
## (Intercept)-Meleagris_gallopavo 149
## (Intercept)-Sciurus_carolinensis 301
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.454
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5910 1.0951 -2.7447 -0.6319 1.7074 1.0167 213
## Cogon_Patch_Size 0.2517 1.1065 -2.0521 0.2547 2.5267 1.0166 498
## Veg_shannon_index 0.9449 0.9299 -0.9398 0.9336 2.8759 1.1649 418
## total_shrub_cover -1.2570 1.1812 -3.5540 -1.2421 1.1396 1.0044 963
## Avg_Cogongrass_Cover 1.2528 1.0438 -0.8787 1.2662 3.3002 1.0339 273
## Tree_Density -1.7324 1.0875 -3.7731 -1.7490 0.6068 1.0198 212
## Avg_Canopy_Cover 1.5867 1.3475 -1.3929 1.6608 4.0882 1.0022 1138
## avg_veg_height -0.2682 0.9521 -2.1426 -0.2911 1.7180 1.1575 217
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.7950 14.2647 0.0804 3.5697 39.7316 1.0993 211
## Cogon_Patch_Size 11.6865 23.6806 0.1296 4.2482 70.1854 1.1095 223
## Veg_shannon_index 4.5013 10.4191 0.0744 1.5776 26.3968 1.0467 282
## total_shrub_cover 19.4457 35.3559 0.3198 8.6323 103.0417 1.3736 110
## Avg_Cogongrass_Cover 5.2456 10.4092 0.0848 1.8634 31.0839 1.0165 415
## Tree_Density 4.4728 12.9287 0.0565 0.9125 34.9556 1.4797 68
## Avg_Canopy_Cover 41.4020 137.5765 1.2645 18.0438 200.8287 1.3631 845
## avg_veg_height 2.7031 6.1852 0.0500 0.7521 17.7250 1.0740 277
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.7507 15.323 0.0884 4.9591 56.8605 2.3405 58
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9827 0.3333 -5.5264 -5.0107 -4.3217 1.0198 541
## shrub_cover 0.3969 0.4344 -0.4756 0.3981 1.2732 1.0025 892
## veg_height 0.0823 0.2817 -0.4833 0.0833 0.6423 1.0145 484
## week -0.0251 1.6549 -3.3269 -0.0035 3.2846 1.0284 1040
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.442000e-01 1.08080e+00 0.0485 0.2261 2.007800e+00 1.1112 888
## shrub_cover 1.219500e+00 2.56360e+00 0.2079 0.7966 4.447700e+00 1.1380 1917
## veg_height 3.971000e-01 3.98300e-01 0.0728 0.2825 1.425600e+00 1.0066 1202
## week 1.880234e+09 3.57784e+10 0.1129 153.2245 2.709592e+09 1.4501 445
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8168 1.6290 -2.1891 0.7617
## (Intercept)-Lynx_rufus 1.1586 2.3295 -2.2970 0.6690
## (Intercept)-Didelphis_virginiana -2.1629 1.6856 -5.6766 -2.0813
## (Intercept)-Sylvilagus_floridanus -0.8467 1.6743 -4.0052 -0.9549
## (Intercept)-Meleagris_gallopavo -1.3623 1.9364 -5.7024 -1.2728
## (Intercept)-Sciurus_carolinensis -2.1656 1.8058 -6.1675 -2.0358
## Cogon_Patch_Size-Canis_latrans 1.8685 1.9475 -0.7068 1.4662
## Cogon_Patch_Size-Lynx_rufus 0.2735 2.4784 -4.9503 0.3421
## Cogon_Patch_Size-Didelphis_virginiana 2.1696 1.8027 -0.2885 1.8172
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9653 2.6763 -8.7145 -1.4894
## Cogon_Patch_Size-Meleagris_gallopavo 1.2405 2.7713 -2.4986 0.7028
## Cogon_Patch_Size-Sciurus_carolinensis -1.4135 2.5078 -7.7532 -0.9790
## Veg_shannon_index-Canis_latrans 1.7576 1.1913 -0.1404 1.6079
## Veg_shannon_index-Lynx_rufus -0.0273 1.7634 -4.2536 0.1639
## Veg_shannon_index-Didelphis_virginiana 1.7793 1.4748 -0.4196 1.5377
## Veg_shannon_index-Sylvilagus_floridanus 1.6219 1.3991 -0.4655 1.3870
## Veg_shannon_index-Meleagris_gallopavo 1.9388 1.7592 -0.5904 1.6618
## Veg_shannon_index-Sciurus_carolinensis -0.2291 1.5532 -3.8370 -0.0539
## total_shrub_cover-Canis_latrans 2.0478 1.9669 -0.6734 1.6882
## total_shrub_cover-Lynx_rufus -4.0707 2.9947 -11.1517 -3.5989
## total_shrub_cover-Didelphis_virginiana -2.9746 3.2238 -11.0799 -2.3100
## total_shrub_cover-Sylvilagus_floridanus -2.1804 2.7818 -9.0704 -1.6449
## total_shrub_cover-Meleagris_gallopavo -4.3695 2.6892 -10.9818 -3.9741
## total_shrub_cover-Sciurus_carolinensis -2.7756 2.9052 -9.8955 -2.0348
## Avg_Cogongrass_Cover-Canis_latrans 2.3002 1.4785 -0.1132 2.1201
## Avg_Cogongrass_Cover-Lynx_rufus 2.5327 1.8445 -0.3785 2.2748
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.6207 1.3965 -1.0372 1.5843
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.3893 1.6818 -3.1625 0.5547
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2251 2.1828 -4.8409 0.5342
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0251 1.6274 -0.6971 1.8825
## Tree_Density-Canis_latrans -2.7104 1.7295 -7.1576 -2.3869
## Tree_Density-Lynx_rufus -0.9339 1.9270 -3.5768 -1.2111
## Tree_Density-Didelphis_virginiana -2.1135 1.7471 -5.8845 -1.9898
## Tree_Density-Sylvilagus_floridanus -2.4504 1.8649 -7.2175 -2.1944
## Tree_Density-Meleagris_gallopavo -2.0449 1.6623 -5.8292 -1.9610
## Tree_Density-Sciurus_carolinensis -2.0949 1.9538 -6.5170 -1.9744
## Avg_Canopy_Cover-Canis_latrans -0.4907 0.9437 -2.6124 -0.4269
## Avg_Canopy_Cover-Lynx_rufus -0.2046 2.4880 -5.6137 -0.1710
## Avg_Canopy_Cover-Didelphis_virginiana 5.9721 2.9766 1.8539 5.4028
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1641 4.5431 2.1627 7.2635
## Avg_Canopy_Cover-Meleagris_gallopavo 3.9141 2.9407 0.3821 3.1596
## Avg_Canopy_Cover-Sciurus_carolinensis 5.9634 3.9886 1.5093 4.9880
## avg_veg_height-Canis_latrans -0.2445 0.9861 -2.1227 -0.2540
## avg_veg_height-Lynx_rufus -0.5591 1.7054 -4.3472 -0.5177
## avg_veg_height-Didelphis_virginiana -0.7025 1.3049 -3.7231 -0.6067
## avg_veg_height-Sylvilagus_floridanus -0.4852 1.2843 -3.1217 -0.4450
## avg_veg_height-Meleagris_gallopavo -0.4085 1.9074 -4.1057 -0.4022
## avg_veg_height-Sciurus_carolinensis 0.6133 1.5459 -1.6147 0.3932
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.3645 1.2367 128
## (Intercept)-Lynx_rufus 6.8863 1.1145 114
## (Intercept)-Didelphis_virginiana 1.1739 1.1662 229
## (Intercept)-Sylvilagus_floridanus 2.9709 1.0073 188
## (Intercept)-Meleagris_gallopavo 2.3168 1.0539 147
## (Intercept)-Sciurus_carolinensis 1.0090 1.0741 170
## Cogon_Patch_Size-Canis_latrans 6.8166 1.0186 302
## Cogon_Patch_Size-Lynx_rufus 5.3691 1.0746 117
## Cogon_Patch_Size-Didelphis_virginiana 6.7308 1.1145 138
## Cogon_Patch_Size-Sylvilagus_floridanus 2.1283 1.1134 119
## Cogon_Patch_Size-Meleagris_gallopavo 8.6605 1.0938 112
## Cogon_Patch_Size-Sciurus_carolinensis 2.3950 1.1226 148
## Veg_shannon_index-Canis_latrans 4.6437 1.0371 277
## Veg_shannon_index-Lynx_rufus 3.0716 1.1100 180
## Veg_shannon_index-Didelphis_virginiana 5.2890 1.1920 239
## Veg_shannon_index-Sylvilagus_floridanus 5.0144 1.0912 314
## Veg_shannon_index-Meleagris_gallopavo 5.9707 1.0352 284
## Veg_shannon_index-Sciurus_carolinensis 2.4594 1.0787 279
## total_shrub_cover-Canis_latrans 6.7291 1.1898 95
## total_shrub_cover-Lynx_rufus 0.3765 1.2554 108
## total_shrub_cover-Didelphis_virginiana 0.7524 1.4871 79
## total_shrub_cover-Sylvilagus_floridanus 1.6567 1.1080 120
## total_shrub_cover-Meleagris_gallopavo -0.3590 1.0832 124
## total_shrub_cover-Sciurus_carolinensis 1.1315 1.2612 75
## Avg_Cogongrass_Cover-Canis_latrans 5.7237 1.0094 305
## Avg_Cogongrass_Cover-Lynx_rufus 6.8213 1.0402 246
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.4198 1.0156 456
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2515 1.0479 364
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8374 1.0816 180
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.8568 1.0470 298
## Tree_Density-Canis_latrans -0.1697 1.0585 228
## Tree_Density-Lynx_rufus 3.4143 1.2341 112
## Tree_Density-Didelphis_virginiana 1.0471 1.0610 193
## Tree_Density-Sylvilagus_floridanus 0.6073 1.1123 158
## Tree_Density-Meleagris_gallopavo 1.1899 1.0506 237
## Tree_Density-Sciurus_carolinensis 1.4365 1.0471 135
## Avg_Canopy_Cover-Canis_latrans 1.0911 1.0962 254
## Avg_Canopy_Cover-Lynx_rufus 5.0098 1.1019 141
## Avg_Canopy_Cover-Didelphis_virginiana 13.3916 1.2656 67
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.4117 1.3611 104
## Avg_Canopy_Cover-Meleagris_gallopavo 11.8819 1.0269 94
## Avg_Canopy_Cover-Sciurus_carolinensis 15.0435 1.2335 103
## avg_veg_height-Canis_latrans 1.7121 1.0366 268
## avg_veg_height-Lynx_rufus 2.6587 1.2189 167
## avg_veg_height-Didelphis_virginiana 1.5297 1.1154 322
## avg_veg_height-Sylvilagus_floridanus 2.0026 1.0274 304
## avg_veg_height-Meleagris_gallopavo 3.7584 1.2718 156
## avg_veg_height-Sciurus_carolinensis 4.6569 1.0703 282
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8016 0.1629 -5.1259 -4.7982
## (Intercept)-Lynx_rufus -5.5494 0.2767 -6.1992 -5.5301
## (Intercept)-Didelphis_virginiana -4.8161 0.2601 -5.3303 -4.8179
## (Intercept)-Sylvilagus_floridanus -5.0226 0.2264 -5.4400 -5.0255
## (Intercept)-Meleagris_gallopavo -5.4824 0.4519 -6.5075 -5.4412
## (Intercept)-Sciurus_carolinensis -4.9244 0.2870 -5.4926 -4.9269
## shrub_cover-Canis_latrans -0.3909 0.2032 -0.7784 -0.3976
## shrub_cover-Lynx_rufus 0.1621 0.3232 -0.4602 0.1624
## shrub_cover-Didelphis_virginiana 1.2464 0.3432 0.5769 1.2453
## shrub_cover-Sylvilagus_floridanus 0.8277 0.3691 0.0941 0.8333
## shrub_cover-Meleagris_gallopavo -0.5227 0.4419 -1.4581 -0.5081
## shrub_cover-Sciurus_carolinensis 1.2276 0.3751 0.5221 1.2290
## veg_height-Canis_latrans -0.5819 0.1688 -0.9224 -0.5794
## veg_height-Lynx_rufus 0.1404 0.2439 -0.3372 0.1416
## veg_height-Didelphis_virginiana 0.5671 0.2294 0.1401 0.5658
## veg_height-Sylvilagus_floridanus 0.2253 0.2265 -0.2005 0.2178
## veg_height-Meleagris_gallopavo -0.1664 0.4408 -1.0883 -0.1638
## veg_height-Sciurus_carolinensis 0.2906 0.2151 -0.1354 0.2826
## week-Canis_latrans 45.4661 29483.5319 -10525.6223 -0.1374
## week-Lynx_rufus 1192.6096 62399.1805 -11233.2552 -0.1074
## week-Didelphis_virginiana 306.6275 34226.3809 -9979.9285 -0.2911
## week-Sylvilagus_floridanus -483.0948 41275.5620 -10719.5582 -0.2201
## week-Meleagris_gallopavo -163.3169 46432.4481 -10533.5021 -0.0874
## week-Sciurus_carolinensis 409.5299 39024.9653 -9080.2470 0.0755
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4941 1.0079 184
## (Intercept)-Lynx_rufus -5.0491 1.0766 110
## (Intercept)-Didelphis_virginiana -4.3080 1.0298 167
## (Intercept)-Sylvilagus_floridanus -4.5834 1.0132 172
## (Intercept)-Meleagris_gallopavo -4.6952 1.1509 74
## (Intercept)-Sciurus_carolinensis -4.3559 1.0561 147
## shrub_cover-Canis_latrans 0.0287 1.0480 154
## shrub_cover-Lynx_rufus 0.7918 1.0475 115
## shrub_cover-Didelphis_virginiana 1.8938 1.0745 117
## shrub_cover-Sylvilagus_floridanus 1.5454 1.0253 133
## shrub_cover-Meleagris_gallopavo 0.3118 1.0745 74
## shrub_cover-Sciurus_carolinensis 1.9751 1.0477 114
## veg_height-Canis_latrans -0.2639 1.0210 177
## veg_height-Lynx_rufus 0.6015 1.0978 122
## veg_height-Didelphis_virginiana 1.0265 1.0031 259
## veg_height-Sylvilagus_floridanus 0.6748 1.0057 231
## veg_height-Meleagris_gallopavo 0.7393 1.2479 63
## veg_height-Sciurus_carolinensis 0.7170 1.0443 222
## week-Canis_latrans 7552.4013 1.1987 4378
## week-Lynx_rufus 13918.7757 1.3023 2161
## week-Didelphis_virginiana 6748.2247 1.2301 5495
## week-Sylvilagus_floridanus 8184.3368 1.2696 6739
## week-Meleagris_gallopavo 9589.7158 1.2260 15988
## week-Sciurus_carolinensis 7988.7294 1.1922 6058
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.0597
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2964 0.4227 -1.0933 -0.313 0.6088 1.0162 876
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8733 1.1107 0.0815 0.5657 3.5536 1.0379 1275
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8649 0.4111 -5.5081 -4.9048 -3.9280 1.0146 856
## shrub_cover 0.1554 0.4262 -0.6916 0.1619 0.9728 1.0113 541
## veg_height 0.0205 0.2868 -0.5522 0.0185 0.5914 1.0048 549
## week -0.0165 1.6247 -3.1757 -0.0492 3.3341 1.0034 952
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.836000e-01 2.154600e+00 0.0611 0.3580 3.001500e+00 1.1761 1414
## shrub_cover 1.029800e+00 1.192000e+00 0.1661 0.6929 3.852200e+00 1.0142 983
## veg_height 4.001000e-01 5.211000e-01 0.0685 0.2707 1.521500e+00 1.0515 708
## week 6.166241e+11 1.013486e+13 0.1099 1193.0231 3.823926e+11 1.6178 442
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3465 0.4190 -0.4083 0.3170 1.2719 1.0146
## (Intercept)-Lynx_rufus 0.1808 0.5595 -0.7536 0.1255 1.4471 1.0437
## (Intercept)-Didelphis_virginiana -0.9716 0.4285 -1.8671 -0.9474 -0.1930 1.0020
## (Intercept)-Sylvilagus_floridanus -0.4342 0.4021 -1.2036 -0.4365 0.3699 1.0095
## (Intercept)-Meleagris_gallopavo -0.0269 0.6469 -1.0833 -0.0998 1.4169 1.0651
## (Intercept)-Sciurus_carolinensis -0.9689 0.4520 -1.9261 -0.9545 -0.1179 1.0021
## ESS
## (Intercept)-Canis_latrans 1084
## (Intercept)-Lynx_rufus 313
## (Intercept)-Didelphis_virginiana 1412
## (Intercept)-Sylvilagus_floridanus 1184
## (Intercept)-Meleagris_gallopavo 163
## (Intercept)-Sciurus_carolinensis 1088
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7883 0.1757 -5.1759 -4.7786
## (Intercept)-Lynx_rufus -5.5241 0.2968 -6.1099 -5.5152
## (Intercept)-Didelphis_virginiana -4.6550 0.2884 -5.2239 -4.6499
## (Intercept)-Sylvilagus_floridanus -4.9078 0.2371 -5.3708 -4.8949
## (Intercept)-Meleagris_gallopavo -5.7270 0.4787 -6.5924 -5.7379
## (Intercept)-Sciurus_carolinensis -4.6559 0.3012 -5.3025 -4.6431
## shrub_cover-Canis_latrans -0.2937 0.2145 -0.7093 -0.2948
## shrub_cover-Lynx_rufus -0.3147 0.3608 -1.0486 -0.3204
## shrub_cover-Didelphis_virginiana 1.0237 0.3517 0.3557 1.0049
## shrub_cover-Sylvilagus_floridanus 0.4482 0.4163 -0.3764 0.4445
## shrub_cover-Meleagris_gallopavo -0.7633 0.4082 -1.5811 -0.7557
## shrub_cover-Sciurus_carolinensis 0.9053 0.3772 0.1815 0.9001
## veg_height-Canis_latrans -0.5802 0.1684 -0.9071 -0.5814
## veg_height-Lynx_rufus 0.1243 0.2428 -0.3547 0.1253
## veg_height-Didelphis_virginiana 0.5265 0.2341 0.0959 0.5211
## veg_height-Sylvilagus_floridanus 0.1486 0.2312 -0.3258 0.1504
## veg_height-Meleagris_gallopavo -0.3029 0.4231 -1.2089 -0.2883
## veg_height-Sciurus_carolinensis 0.1974 0.2252 -0.2146 0.1965
## week-Canis_latrans -999.5436 566724.4449 -147057.3745 -0.1533
## week-Lynx_rufus 15090.1116 660959.2972 -170134.0596 0.0277
## week-Didelphis_virginiana 332.1379 598873.5763 -131381.6957 0.0598
## week-Sylvilagus_floridanus -11448.9312 864009.3844 -170979.2518 0.0123
## week-Meleagris_gallopavo 9828.9051 714252.7125 -129999.8515 -0.0714
## week-Sciurus_carolinensis 13921.0387 662027.2122 -167851.1680 -0.0376
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4664 1.0179 167
## (Intercept)-Lynx_rufus -4.9567 1.0827 88
## (Intercept)-Didelphis_virginiana -4.0911 1.0767 176
## (Intercept)-Sylvilagus_floridanus -4.4721 1.0447 185
## (Intercept)-Meleagris_gallopavo -4.8127 1.1695 60
## (Intercept)-Sciurus_carolinensis -4.1114 1.0320 150
## shrub_cover-Canis_latrans 0.1147 1.0189 177
## shrub_cover-Lynx_rufus 0.3887 1.0802 97
## shrub_cover-Didelphis_virginiana 1.7562 1.0115 163
## shrub_cover-Sylvilagus_floridanus 1.2920 1.0178 94
## shrub_cover-Meleagris_gallopavo 0.0460 1.1578 72
## shrub_cover-Sciurus_carolinensis 1.6645 1.0191 190
## veg_height-Canis_latrans -0.2560 1.0096 222
## veg_height-Lynx_rufus 0.6151 1.0343 161
## veg_height-Didelphis_virginiana 1.0080 1.0485 260
## veg_height-Sylvilagus_floridanus 0.5999 1.0194 197
## veg_height-Meleagris_gallopavo 0.5345 1.0187 115
## veg_height-Sciurus_carolinensis 0.6681 1.0447 178
## week-Canis_latrans 143590.6975 1.2896 17195
## week-Lynx_rufus 178592.3781 1.3347 2750
## week-Didelphis_virginiana 176982.7321 1.2897 12320
## week-Sylvilagus_floridanus 116850.2156 1.3082 16438
## week-Meleagris_gallopavo 160140.1237 1.3092 6353
## week-Sciurus_carolinensis 182960.2037 1.3393 3276
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.5213
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1167 0.6623 -1.3392 -0.1326 1.3349 1.0423 293
## Avg_Cogongrass_Cover 0.0383 0.5903 -1.1886 0.0338 1.2349 1.0032 885
## total_shrub_cover -1.0507 0.7468 -2.6368 -1.0295 0.3827 1.0190 249
## avg_veg_height -0.0062 0.5892 -1.2092 -0.0009 1.0939 1.0078 429
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2164 2.3331 0.0501 0.5551 6.0422 1.0331 788
## Avg_Cogongrass_Cover 1.5395 2.9739 0.0577 0.7389 7.6080 1.1066 558
## total_shrub_cover 3.0077 5.4421 0.1388 1.5237 15.4347 1.0561 417
## avg_veg_height 0.9737 1.8009 0.0502 0.4291 5.5126 1.0003 756
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6814 3.5218 0.1204 1.723 11.4399 1.0445 120
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9854 0.3000 -5.4628 -5.0117 -4.3245 1.0384 347
## shrub_cover 0.4617 0.4522 -0.4709 0.4628 1.3507 1.0032 556
## veg_height 0.0727 0.2848 -0.4915 0.0645 0.6690 1.0052 513
## week -0.0548 1.6134 -3.2380 -0.0633 2.9572 1.0073 689
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.427000e-01 8.186000e-01 0.0344 0.1726 1.709200e+00 1.0644 416
## shrub_cover 1.213300e+00 1.388100e+00 0.2052 0.8318 4.723300e+00 1.0367 1150
## veg_height 4.084000e-01 4.563000e-01 0.0725 0.2833 1.462500e+00 1.0115 1306
## week 7.433361e+10 8.061991e+11 0.1150 703.9206 3.126462e+11 1.9563 174
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4786 0.8201 -0.9830 0.4157
## (Intercept)-Lynx_rufus 0.0845 0.9089 -1.5491 0.0307
## (Intercept)-Didelphis_virginiana -0.4308 0.8848 -2.0538 -0.4458
## (Intercept)-Sylvilagus_floridanus 0.0767 0.8944 -1.5060 0.0199
## (Intercept)-Meleagris_gallopavo -0.4152 0.9778 -2.4223 -0.4218
## (Intercept)-Sciurus_carolinensis -0.5213 0.9535 -2.4313 -0.4983
## Avg_Cogongrass_Cover-Canis_latrans 0.6079 0.7620 -0.6376 0.5113
## Avg_Cogongrass_Cover-Lynx_rufus 0.7960 0.9412 -0.6697 0.6486
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2819 0.7088 -1.0286 0.2465
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6928 0.8083 -2.4757 -0.5922
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.7051 0.9678 -2.8259 -0.6043
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0686 0.7067 -1.5413 -0.0305
## total_shrub_cover-Canis_latrans 0.6339 0.8618 -0.8596 0.5407
## total_shrub_cover-Lynx_rufus -1.7328 1.2214 -4.5083 -1.6178
## total_shrub_cover-Didelphis_virginiana -1.2569 0.9732 -3.6992 -1.1195
## total_shrub_cover-Sylvilagus_floridanus -1.8907 1.2849 -5.1110 -1.6563
## total_shrub_cover-Meleagris_gallopavo -1.9950 1.1545 -4.7948 -1.8404
## total_shrub_cover-Sciurus_carolinensis -1.2804 1.1278 -3.8931 -1.1452
## avg_veg_height-Canis_latrans 0.1145 0.6368 -1.0802 0.0864
## avg_veg_height-Lynx_rufus -0.1733 0.8840 -2.0181 -0.1278
## avg_veg_height-Didelphis_virginiana -0.1762 0.7323 -1.7383 -0.1284
## avg_veg_height-Sylvilagus_floridanus 0.0316 0.6798 -1.3030 0.0384
## avg_veg_height-Meleagris_gallopavo -0.4802 1.1122 -3.0642 -0.3595
## avg_veg_height-Sciurus_carolinensis 0.6004 0.6989 -0.6206 0.5367
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2841 1.0078 409
## (Intercept)-Lynx_rufus 1.9990 1.0134 374
## (Intercept)-Didelphis_virginiana 1.4066 1.0518 303
## (Intercept)-Sylvilagus_floridanus 2.0448 1.0201 268
## (Intercept)-Meleagris_gallopavo 1.5486 1.0612 228
## (Intercept)-Sciurus_carolinensis 1.3673 1.0145 215
## Avg_Cogongrass_Cover-Canis_latrans 2.3674 1.0143 492
## Avg_Cogongrass_Cover-Lynx_rufus 3.0224 1.0236 442
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.8293 1.0049 759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6361 1.0238 519
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8946 1.0406 472
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2313 1.0161 703
## total_shrub_cover-Canis_latrans 2.5153 1.0215 218
## total_shrub_cover-Lynx_rufus 0.3808 1.0116 106
## total_shrub_cover-Didelphis_virginiana 0.2037 1.0308 210
## total_shrub_cover-Sylvilagus_floridanus -0.0885 1.0631 134
## total_shrub_cover-Meleagris_gallopavo -0.1771 1.0069 258
## total_shrub_cover-Sciurus_carolinensis 0.4118 1.0836 119
## avg_veg_height-Canis_latrans 1.4403 1.0131 857
## avg_veg_height-Lynx_rufus 1.4867 1.0037 286
## avg_veg_height-Didelphis_virginiana 1.1925 1.0130 451
## avg_veg_height-Sylvilagus_floridanus 1.4218 1.0117 633
## avg_veg_height-Meleagris_gallopavo 1.4721 1.0153 141
## avg_veg_height-Sciurus_carolinensis 2.1218 1.0051 685
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8485 0.1827 -5.2237 -4.8501
## (Intercept)-Lynx_rufus -5.3923 0.2731 -5.9797 -5.3721
## (Intercept)-Didelphis_virginiana -4.8831 0.3163 -5.5377 -4.8791
## (Intercept)-Sylvilagus_floridanus -5.0656 0.2060 -5.4717 -5.0623
## (Intercept)-Meleagris_gallopavo -5.3879 0.4593 -6.4468 -5.3497
## (Intercept)-Sciurus_carolinensis -4.9010 0.3068 -5.5254 -4.8962
## shrub_cover-Canis_latrans -0.3320 0.2232 -0.7367 -0.3393
## shrub_cover-Lynx_rufus 0.1948 0.3490 -0.5436 0.2163
## shrub_cover-Didelphis_virginiana 1.3518 0.4115 0.6183 1.3336
## shrub_cover-Sylvilagus_floridanus 0.9681 0.3781 0.2135 0.9579
## shrub_cover-Meleagris_gallopavo -0.4402 0.4437 -1.3251 -0.4258
## shrub_cover-Sciurus_carolinensis 1.2379 0.3920 0.4469 1.2460
## veg_height-Canis_latrans -0.6039 0.1800 -0.9846 -0.5978
## veg_height-Lynx_rufus 0.1323 0.2309 -0.3204 0.1362
## veg_height-Didelphis_virginiana 0.5164 0.2617 0.0203 0.5179
## veg_height-Sylvilagus_floridanus 0.1243 0.2276 -0.3294 0.1273
## veg_height-Meleagris_gallopavo -0.0098 0.5109 -0.9086 -0.0149
## veg_height-Sciurus_carolinensis 0.2766 0.2302 -0.2046 0.2872
## week-Canis_latrans -3594.7729 223485.9589 -87693.3807 -0.1624
## week-Lynx_rufus -7319.8337 288876.2484 -68405.2810 -0.3794
## week-Didelphis_virginiana 4034.6177 223137.9124 -60338.6779 -0.1046
## week-Sylvilagus_floridanus 7283.1985 335468.2127 -65769.7989 -0.4158
## week-Meleagris_gallopavo 1221.8834 222573.6949 -90387.4690 -0.2367
## week-Sciurus_carolinensis -806.6858 281680.8472 -83424.9277 -0.0009
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4936 1.0331 145
## (Intercept)-Lynx_rufus -4.9142 1.0260 126
## (Intercept)-Didelphis_virginiana -4.2569 1.1296 92
## (Intercept)-Sylvilagus_floridanus -4.6664 1.0522 232
## (Intercept)-Meleagris_gallopavo -4.6434 1.1185 86
## (Intercept)-Sciurus_carolinensis -4.3149 1.0759 102
## shrub_cover-Canis_latrans 0.1170 1.0581 138
## shrub_cover-Lynx_rufus 0.8078 1.0010 116
## shrub_cover-Didelphis_virginiana 2.2852 1.0711 85
## shrub_cover-Sylvilagus_floridanus 1.7024 1.1579 102
## shrub_cover-Meleagris_gallopavo 0.3419 1.1107 132
## shrub_cover-Sciurus_carolinensis 1.9563 1.0824 122
## veg_height-Canis_latrans -0.2643 1.0582 183
## veg_height-Lynx_rufus 0.5909 1.0121 166
## veg_height-Didelphis_virginiana 1.0290 1.0766 173
## veg_height-Sylvilagus_floridanus 0.5548 1.0113 175
## veg_height-Meleagris_gallopavo 1.0127 1.0539 117
## veg_height-Sciurus_carolinensis 0.7105 1.0196 189
## week-Canis_latrans 85786.1203 1.3157 7107
## week-Lynx_rufus 105558.4874 1.3463 667
## week-Didelphis_virginiana 90517.5371 1.3139 5111
## week-Sylvilagus_floridanus 85916.7189 1.3319 2217
## week-Meleagris_gallopavo 83556.5333 1.2933 1466
## week-Sciurus_carolinensis 111108.5628 1.2875 23263
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.2643
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3986 0.6293 -1.5994 -0.4255 0.9811 1.0137 841
## Tree_Density -0.7742 0.6033 -2.0254 -0.7554 0.3459 1.0045 611
## Avg_Canopy_Cover 1.2885 0.7067 -0.0996 1.2551 2.8259 1.0177 1060
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0010 2.8546 0.0864 1.0864 9.6386 1.0449 288
## Tree_Density 1.4343 2.6129 0.0587 0.6317 7.6647 1.0061 940
## Avg_Canopy_Cover 3.1001 4.8222 0.2126 1.7333 13.9406 1.0778 495
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8289 1.3982 0.0453 0.3812 4.6737 1.1516 177
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9134 0.4907 -5.5329 -4.9806 -3.8076 1.0079 537
## shrub_cover 0.1818 0.4331 -0.6951 0.1941 1.0213 1.0175 372
## veg_height 0.0524 0.2901 -0.5046 0.0534 0.6312 1.0046 860
## week -0.0139 1.6225 -3.1771 0.0261 2.9785 1.0009 686
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.317000e-01 2.78040e+00 0.0782 0.3734 3.856200e+00 1.0097 562
## shrub_cover 1.233200e+00 1.51140e+00 0.1918 0.8347 4.721200e+00 1.0033 781
## veg_height 4.258000e-01 4.71100e-01 0.0734 0.2926 1.648600e+00 1.0034 663
## week 6.688732e+12 1.01649e+14 0.0913 41.1756 3.431607e+12 1.6682 411
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3428 0.6962 -0.9328 0.2881 1.8953
## (Intercept)-Lynx_rufus 0.5161 1.1863 -1.2347 0.3226 3.4352
## (Intercept)-Didelphis_virginiana -1.2896 0.7731 -2.9121 -1.2339 0.0911
## (Intercept)-Sylvilagus_floridanus -0.6226 0.7119 -2.0821 -0.6172 0.7158
## (Intercept)-Meleagris_gallopavo -0.1214 0.9327 -1.7451 -0.2091 1.9989
## (Intercept)-Sciurus_carolinensis -1.4260 0.8151 -3.1653 -1.3685 -0.0088
## Tree_Density-Canis_latrans -1.0986 0.6946 -2.7008 -1.0086 0.0223
## Tree_Density-Lynx_rufus 0.2112 0.8680 -1.1231 0.0899 2.2461
## Tree_Density-Didelphis_virginiana -1.0110 0.8442 -2.9340 -0.9134 0.3785
## Tree_Density-Sylvilagus_floridanus -1.1452 0.9342 -3.2975 -1.0279 0.4023
## Tree_Density-Meleagris_gallopavo -1.1235 0.9711 -3.4153 -0.9859 0.5442
## Tree_Density-Sciurus_carolinensis -0.8539 0.7946 -2.6601 -0.7573 0.4993
## Avg_Canopy_Cover-Canis_latrans -0.2458 0.5198 -1.2961 -0.2475 0.8006
## Avg_Canopy_Cover-Lynx_rufus 0.9254 1.0433 -0.7575 0.7921 3.2797
## Avg_Canopy_Cover-Didelphis_virginiana 2.0182 0.9978 0.5758 1.8407 4.5592
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8158 1.3391 0.9362 2.5705 6.1082
## Avg_Canopy_Cover-Meleagris_gallopavo 1.9251 1.1226 0.3059 1.7100 4.5858
## Avg_Canopy_Cover-Sciurus_carolinensis 1.7754 0.8550 0.4750 1.6347 3.7743
## Rhat ESS
## (Intercept)-Canis_latrans 1.0564 622
## (Intercept)-Lynx_rufus 1.0195 137
## (Intercept)-Didelphis_virginiana 1.0034 494
## (Intercept)-Sylvilagus_floridanus 1.0195 780
## (Intercept)-Meleagris_gallopavo 1.0064 373
## (Intercept)-Sciurus_carolinensis 1.0187 475
## Tree_Density-Canis_latrans 1.0093 964
## Tree_Density-Lynx_rufus 1.0047 456
## Tree_Density-Didelphis_virginiana 1.0072 617
## Tree_Density-Sylvilagus_floridanus 1.0068 578
## Tree_Density-Meleagris_gallopavo 1.0034 459
## Tree_Density-Sciurus_carolinensis 1.0022 839
## Avg_Canopy_Cover-Canis_latrans 1.0059 903
## Avg_Canopy_Cover-Lynx_rufus 1.0302 279
## Avg_Canopy_Cover-Didelphis_virginiana 1.0566 394
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0559 326
## Avg_Canopy_Cover-Meleagris_gallopavo 1.1071 263
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1269 366
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8028 0.1816 -5.1804 -4.7945
## (Intercept)-Lynx_rufus -5.6049 0.3156 -6.2558 -5.6018
## (Intercept)-Didelphis_virginiana -4.7918 0.2886 -5.3616 -4.7990
## (Intercept)-Sylvilagus_floridanus -4.9447 0.2202 -5.3792 -4.9467
## (Intercept)-Meleagris_gallopavo -5.8398 0.4756 -6.9603 -5.8009
## (Intercept)-Sciurus_carolinensis -4.7250 0.2990 -5.2958 -4.7179
## shrub_cover-Canis_latrans -0.3064 0.1921 -0.6829 -0.3076
## shrub_cover-Lynx_rufus -0.2755 0.3128 -0.9411 -0.2647
## shrub_cover-Didelphis_virginiana 1.1437 0.3629 0.4872 1.1259
## shrub_cover-Sylvilagus_floridanus 0.6235 0.3821 -0.1544 0.6259
## shrub_cover-Meleagris_gallopavo -0.8670 0.4260 -1.8283 -0.8266
## shrub_cover-Sciurus_carolinensis 0.9415 0.3812 0.2028 0.9284
## veg_height-Canis_latrans -0.5954 0.1673 -0.9482 -0.5895
## veg_height-Lynx_rufus 0.1769 0.2305 -0.3225 0.1868
## veg_height-Didelphis_virginiana 0.5928 0.2539 0.1229 0.5837
## veg_height-Sylvilagus_floridanus 0.1864 0.2230 -0.2549 0.1861
## veg_height-Meleagris_gallopavo -0.2917 0.3778 -1.0967 -0.2797
## veg_height-Sciurus_carolinensis 0.2409 0.2039 -0.1286 0.2377
## week-Canis_latrans 4212.5436 2059703.1455 -515180.9606 0.1062
## week-Lynx_rufus 63539.6205 2694619.7908 -241293.3099 0.0796
## week-Didelphis_virginiana 46594.6320 2282135.1129 -423300.4671 0.2649
## week-Sylvilagus_floridanus 18408.7149 1873678.2946 -324814.1274 0.0623
## week-Meleagris_gallopavo -1263.0870 1951472.5592 -310553.5868 -0.0162
## week-Sciurus_carolinensis -71142.9260 2431243.9250 -386438.0750 0.0520
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4783 1.0012 138
## (Intercept)-Lynx_rufus -4.9944 1.1387 70
## (Intercept)-Didelphis_virginiana -4.2324 1.0125 139
## (Intercept)-Sylvilagus_floridanus -4.5172 1.0123 184
## (Intercept)-Meleagris_gallopavo -5.0313 1.2326 43
## (Intercept)-Sciurus_carolinensis -4.1569 1.0354 174
## shrub_cover-Canis_latrans 0.0696 1.0230 205
## shrub_cover-Lynx_rufus 0.2967 1.0270 106
## shrub_cover-Didelphis_virginiana 1.8761 1.0436 121
## shrub_cover-Sylvilagus_floridanus 1.3529 1.0514 147
## shrub_cover-Meleagris_gallopavo -0.1482 1.1633 65
## shrub_cover-Sciurus_carolinensis 1.6498 1.0448 142
## veg_height-Canis_latrans -0.2820 1.0367 199
## veg_height-Lynx_rufus 0.6035 1.0201 152
## veg_height-Didelphis_virginiana 1.1042 1.0203 237
## veg_height-Sylvilagus_floridanus 0.6323 1.0067 229
## veg_height-Meleagris_gallopavo 0.4237 1.0145 127
## veg_height-Sciurus_carolinensis 0.6483 1.0237 256
## week-Canis_latrans 294053.0705 1.2908 4671
## week-Lynx_rufus 575481.4613 1.3448 3498
## week-Didelphis_virginiana 482605.2457 1.3313 4997
## week-Sylvilagus_floridanus 321798.9938 1.3001 2381
## week-Meleagris_gallopavo 470260.1581 1.2904 14550
## week-Sciurus_carolinensis 547761.9920 1.3733 1715
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.5075
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1693 0.6809 -1.4144 -0.1910 1.3417 1.1532 235
## Cogon_Patch_Size 0.0034 0.7144 -1.4982 0.0357 1.3943 1.0249 908
## Avg_Cogongrass_Cover 0.0596 0.6060 -1.1358 0.0658 1.3366 1.0736 709
## total_shrub_cover -1.0513 0.7820 -2.6869 -0.9993 0.4115 1.0214 241
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1775 2.3580 0.0508 0.4755 6.3100 1.0181 372
## Cogon_Patch_Size 3.3479 5.8955 0.0988 1.4617 19.0071 1.0468 299
## Avg_Cogongrass_Cover 1.7189 3.9950 0.0679 0.7331 9.3653 1.1920 376
## total_shrub_cover 3.3517 5.9152 0.0987 1.6443 17.3153 1.0935 293
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1528 3.3652 0.149 2.1486 12.5813 1.0303 177
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9994 0.2822 -5.4933 -5.0185 -4.3827 1.0682 383
## shrub_cover 0.4668 0.4714 -0.4825 0.4729 1.4026 1.0302 640
## veg_height 0.0440 0.2888 -0.5596 0.0521 0.6142 1.0379 869
## week -0.1313 1.7271 -3.5221 -0.1629 3.2613 1.0026 456
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.39900e-01 5.723000e-01 0.0393 0.1851 1.593400e+00 1.2214 263
## shrub_cover 1.30670e+00 1.378700e+00 0.2062 0.8950 5.073000e+00 1.0639 759
## veg_height 4.24000e-01 6.210000e-01 0.0665 0.2775 1.688100e+00 1.1113 780
## week 1.06864e+20 2.912398e+21 0.0897 57.1112 1.865733e+19 1.4184 1047
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4085 0.8655 -1.1316 0.3382
## (Intercept)-Lynx_rufus 0.0911 0.9968 -1.5770 -0.0008
## (Intercept)-Didelphis_virginiana -0.4715 0.8528 -2.2048 -0.4637
## (Intercept)-Sylvilagus_floridanus -0.0831 0.8514 -1.7338 -0.1355
## (Intercept)-Meleagris_gallopavo -0.3615 0.9571 -2.2109 -0.4064
## (Intercept)-Sciurus_carolinensis -0.6076 0.9283 -2.4936 -0.5911
## Cogon_Patch_Size-Canis_latrans 1.1086 1.0917 -0.4026 0.8869
## Cogon_Patch_Size-Lynx_rufus -0.1568 1.2432 -2.4676 -0.2152
## Cogon_Patch_Size-Didelphis_virginiana 0.9278 0.6999 -0.2387 0.8458
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2704 1.5021 -5.0604 -0.9411
## Cogon_Patch_Size-Meleagris_gallopavo 0.5273 1.3128 -1.3503 0.3302
## Cogon_Patch_Size-Sciurus_carolinensis -1.0049 1.1597 -3.9019 -0.7979
## Avg_Cogongrass_Cover-Canis_latrans 0.4399 0.5880 -0.5502 0.3787
## Avg_Cogongrass_Cover-Lynx_rufus 0.8864 1.0704 -0.6397 0.6871
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0523 0.6442 -1.4112 -0.0343
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4338 0.8392 -2.2200 -0.4019
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8564 1.1754 -3.6193 -0.7351
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4350 0.7273 -0.8844 0.3882
## total_shrub_cover-Canis_latrans 0.6594 0.9190 -0.7282 0.4971
## total_shrub_cover-Lynx_rufus -1.8274 1.4091 -5.2777 -1.6370
## total_shrub_cover-Didelphis_virginiana -1.4771 1.0654 -4.0934 -1.2829
## total_shrub_cover-Sylvilagus_floridanus -1.9095 1.4377 -5.2209 -1.6405
## total_shrub_cover-Meleagris_gallopavo -1.9420 1.1613 -4.5838 -1.8069
## total_shrub_cover-Sciurus_carolinensis -1.1159 1.0869 -3.7602 -0.9737
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3400 1.0806 335
## (Intercept)-Lynx_rufus 2.4381 1.1428 135
## (Intercept)-Didelphis_virginiana 1.2357 1.1058 316
## (Intercept)-Sylvilagus_floridanus 1.7633 1.0421 322
## (Intercept)-Meleagris_gallopavo 1.6240 1.1625 243
## (Intercept)-Sciurus_carolinensis 1.2034 1.0552 289
## Cogon_Patch_Size-Canis_latrans 3.8862 1.0084 448
## Cogon_Patch_Size-Lynx_rufus 2.7150 1.0442 379
## Cogon_Patch_Size-Didelphis_virginiana 2.5025 1.0176 533
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6907 1.0253 304
## Cogon_Patch_Size-Meleagris_gallopavo 3.9830 1.1163 255
## Cogon_Patch_Size-Sciurus_carolinensis 0.6864 1.0061 414
## Avg_Cogongrass_Cover-Canis_latrans 1.8295 1.0634 848
## Avg_Cogongrass_Cover-Lynx_rufus 3.5311 1.0703 419
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1612 1.0257 552
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1099 1.0572 404
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0829 1.0304 269
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0531 1.0106 498
## total_shrub_cover-Canis_latrans 2.8445 1.1060 254
## total_shrub_cover-Lynx_rufus 0.4344 1.0529 121
## total_shrub_cover-Didelphis_virginiana 0.1209 1.0541 153
## total_shrub_cover-Sylvilagus_floridanus 0.0791 1.0672 87
## total_shrub_cover-Meleagris_gallopavo -0.0820 1.0204 208
## total_shrub_cover-Sciurus_carolinensis 0.5556 1.0302 190
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.806700e+00 1.740000e-01 -5.1640
## (Intercept)-Lynx_rufus -5.379400e+00 2.662000e-01 -5.9765
## (Intercept)-Didelphis_virginiana -4.896900e+00 2.850000e-01 -5.4792
## (Intercept)-Sylvilagus_floridanus -5.102100e+00 2.266000e-01 -5.5897
## (Intercept)-Meleagris_gallopavo -5.441700e+00 4.686000e-01 -6.5007
## (Intercept)-Sciurus_carolinensis -4.946800e+00 3.024000e-01 -5.5347
## shrub_cover-Canis_latrans -3.469000e-01 2.181000e-01 -0.7909
## shrub_cover-Lynx_rufus 2.082000e-01 3.343000e-01 -0.4987
## shrub_cover-Didelphis_virginiana 1.427400e+00 4.290000e-01 0.6597
## shrub_cover-Sylvilagus_floridanus 9.543000e-01 4.167000e-01 0.1101
## shrub_cover-Meleagris_gallopavo -5.079000e-01 4.180000e-01 -1.3648
## shrub_cover-Sciurus_carolinensis 1.213900e+00 3.824000e-01 0.4450
## veg_height-Canis_latrans -5.849000e-01 1.889000e-01 -0.9499
## veg_height-Lynx_rufus 1.063000e-01 2.359000e-01 -0.3682
## veg_height-Didelphis_virginiana 5.229000e-01 2.483000e-01 0.0880
## veg_height-Sylvilagus_floridanus 1.103000e-01 2.432000e-01 -0.3882
## veg_height-Meleagris_gallopavo -1.922000e-01 4.468000e-01 -1.1267
## veg_height-Sciurus_carolinensis 3.177000e-01 2.263000e-01 -0.0995
## week-Canis_latrans 2.116861e+07 7.575197e+09 -526910.1479
## week-Lynx_rufus -2.227826e+08 1.127434e+10 -747538.9052
## week-Didelphis_virginiana 3.400421e+07 9.227266e+09 -844476.6080
## week-Sylvilagus_floridanus -5.414059e+07 8.527185e+09 -606389.8099
## week-Meleagris_gallopavo 1.328589e+08 6.979668e+09 -310739.3683
## week-Sciurus_carolinensis 8.764290e+07 7.269841e+09 -257257.8237
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8020 -4.4664 1.0154 134
## (Intercept)-Lynx_rufus -5.3625 -4.8927 1.1449 133
## (Intercept)-Didelphis_virginiana -4.8943 -4.3335 1.1471 93
## (Intercept)-Sylvilagus_floridanus -5.0969 -4.6581 1.1040 156
## (Intercept)-Meleagris_gallopavo -5.3937 -4.6277 1.5932 84
## (Intercept)-Sciurus_carolinensis -4.9501 -4.3566 1.0914 96
## shrub_cover-Canis_latrans -0.3312 0.0531 1.1808 149
## shrub_cover-Lynx_rufus 0.2350 0.8083 1.0831 130
## shrub_cover-Didelphis_virginiana 1.4031 2.2923 1.1234 76
## shrub_cover-Sylvilagus_floridanus 0.9766 1.7262 1.2115 78
## shrub_cover-Meleagris_gallopavo -0.4920 0.2562 1.3312 160
## shrub_cover-Sciurus_carolinensis 1.2177 1.9576 1.1612 93
## veg_height-Canis_latrans -0.5773 -0.2179 1.0408 164
## veg_height-Lynx_rufus 0.0999 0.5695 1.1058 149
## veg_height-Didelphis_virginiana 0.5064 1.0548 1.0111 214
## veg_height-Sylvilagus_floridanus 0.1157 0.5651 1.0541 152
## veg_height-Meleagris_gallopavo -0.1848 0.6569 1.1956 130
## veg_height-Sciurus_carolinensis 0.3082 0.7871 1.0501 189
## week-Canis_latrans -0.3316 306750.2720 1.2911 21497
## week-Lynx_rufus -0.3526 249211.8099 1.3287 2041
## week-Didelphis_virginiana -0.2765 341216.8615 1.2917 64695
## week-Sylvilagus_floridanus -0.5759 282625.2404 1.2944 10384
## week-Meleagris_gallopavo -0.3302 569064.8194 1.3260 4300
## week-Sciurus_carolinensis -0.2751 1159033.0200 1.3048 9655
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.0748
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4018 0.5330 -1.4233 -0.4103 0.7029 1.0722 708
## Veg_shannon_index 0.3096 0.4285 -0.5290 0.3028 1.1604 1.0094 1053
## Avg_Cogongrass_Cover 0.2676 0.4266 -0.5835 0.2762 1.1036 1.0010 822
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0861 1.8989 0.0568 0.5400 5.4467 1.0903 318
## Veg_shannon_index 0.7224 1.3760 0.0542 0.3652 3.5661 1.0254 1312
## Avg_Cogongrass_Cover 0.8020 1.6024 0.0522 0.4062 3.8336 1.1739 657
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0938 1.2967 0.0619 0.6786 4.6935 1.1245 109
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8477 0.4787 -5.4708 -4.9109 -3.8017 1.0745 393
## shrub_cover 0.1804 0.4347 -0.6922 0.1815 1.0213 1.0513 799
## veg_height 0.0418 0.2815 -0.5067 0.0453 0.5871 1.0230 717
## week -0.0352 1.6935 -3.3725 -0.0642 3.2245 1.0026 565
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.402000e-01 3.878500e+00 0.0587 0.3621 3.879300e+00 1.1404 459
## shrub_cover 1.030100e+00 1.164800e+00 0.1544 0.6930 4.003500e+00 1.0493 350
## veg_height 3.889000e-01 4.788000e-01 0.0629 0.2589 1.467900e+00 1.0564 995
## week 2.209896e+23 6.388662e+24 0.0877 82.0077 1.095544e+22 1.4047 979
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1753 0.6422 -0.9729 0.1385
## (Intercept)-Lynx_rufus 0.0065 0.9157 -1.3398 -0.1294
## (Intercept)-Didelphis_virginiana -0.9631 0.6311 -2.2504 -0.9275
## (Intercept)-Sylvilagus_floridanus -0.5635 0.5589 -1.6994 -0.5511
## (Intercept)-Meleagris_gallopavo -0.1606 0.8556 -1.5663 -0.2577
## (Intercept)-Sciurus_carolinensis -1.0204 0.6243 -2.3669 -0.9774
## Veg_shannon_index-Canis_latrans 0.7925 0.4900 -0.0861 0.7638
## Veg_shannon_index-Lynx_rufus -0.1682 0.7173 -1.8793 -0.0960
## Veg_shannon_index-Didelphis_virginiana 0.5339 0.4570 -0.3210 0.5185
## Veg_shannon_index-Sylvilagus_floridanus 0.3908 0.4729 -0.5262 0.3785
## Veg_shannon_index-Meleagris_gallopavo 0.5573 0.6363 -0.5502 0.5170
## Veg_shannon_index-Sciurus_carolinensis -0.1443 0.4923 -1.2396 -0.1158
## Avg_Cogongrass_Cover-Canis_latrans 0.7320 0.5148 -0.1193 0.6751
## Avg_Cogongrass_Cover-Lynx_rufus 0.6351 0.5397 -0.2741 0.5820
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4597 0.4527 -0.4104 0.4516
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2419 0.5135 -1.3660 -0.2053
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3030 0.8273 -2.3195 -0.2188
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4095 0.4314 -0.4088 0.3932
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4643 1.0289 503
## (Intercept)-Lynx_rufus 2.1646 1.1320 187
## (Intercept)-Didelphis_virginiana 0.2105 1.0112 764
## (Intercept)-Sylvilagus_floridanus 0.4938 1.0240 747
## (Intercept)-Meleagris_gallopavo 1.8647 1.3194 88
## (Intercept)-Sciurus_carolinensis 0.1192 1.0130 896
## Veg_shannon_index-Canis_latrans 1.8464 1.0009 1268
## Veg_shannon_index-Lynx_rufus 1.0067 1.0270 537
## Veg_shannon_index-Didelphis_virginiana 1.5248 1.0029 1293
## Veg_shannon_index-Sylvilagus_floridanus 1.3440 1.0219 1509
## Veg_shannon_index-Meleagris_gallopavo 2.0234 1.0175 758
## Veg_shannon_index-Sciurus_carolinensis 0.7288 1.0008 994
## Avg_Cogongrass_Cover-Canis_latrans 1.9204 1.0111 1229
## Avg_Cogongrass_Cover-Lynx_rufus 1.8765 1.0177 1027
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3715 1.0054 1140
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6601 1.0078 844
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1271 1.0699 281
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3181 1.0023 1755
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.750800e+00 1.663000e-01 -5.083600e+00
## (Intercept)-Lynx_rufus -5.519700e+00 3.567000e-01 -6.341400e+00
## (Intercept)-Didelphis_virginiana -4.702400e+00 2.812000e-01 -5.245000e+00
## (Intercept)-Sylvilagus_floridanus -4.912100e+00 2.393000e-01 -5.418100e+00
## (Intercept)-Meleagris_gallopavo -5.754300e+00 4.825000e-01 -6.869300e+00
## (Intercept)-Sciurus_carolinensis -4.641700e+00 2.888000e-01 -5.239000e+00
## shrub_cover-Canis_latrans -2.578000e-01 1.947000e-01 -6.404000e-01
## shrub_cover-Lynx_rufus -1.739000e-01 3.793000e-01 -9.330000e-01
## shrub_cover-Didelphis_virginiana 1.080900e+00 3.857000e-01 4.287000e-01
## shrub_cover-Sylvilagus_floridanus 4.551000e-01 4.277000e-01 -3.412000e-01
## shrub_cover-Meleagris_gallopavo -7.634000e-01 4.209000e-01 -1.605100e+00
## shrub_cover-Sciurus_carolinensis 8.420000e-01 3.967000e-01 5.260000e-02
## veg_height-Canis_latrans -5.667000e-01 1.721000e-01 -9.136000e-01
## veg_height-Lynx_rufus 7.980000e-02 2.349000e-01 -4.196000e-01
## veg_height-Didelphis_virginiana 5.279000e-01 2.370000e-01 9.300000e-02
## veg_height-Sylvilagus_floridanus 1.835000e-01 2.238000e-01 -2.443000e-01
## veg_height-Meleagris_gallopavo -1.689000e-01 4.427000e-01 -1.045900e+00
## veg_height-Sciurus_carolinensis 1.855000e-01 2.132000e-01 -2.188000e-01
## week-Canis_latrans -1.428642e+10 5.786731e+11 -1.322796e+09
## week-Lynx_rufus -6.761870e+09 3.718043e+11 -3.237462e+09
## week-Didelphis_virginiana 3.434315e+09 3.215841e+11 -3.822270e+09
## week-Sylvilagus_floridanus 9.019104e+08 3.672712e+11 -2.500291e+09
## week-Meleagris_gallopavo 2.629967e+09 2.565326e+11 -1.949207e+09
## week-Sciurus_carolinensis 5.158924e+09 4.379557e+11 -2.987723e+09
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7437 -4.443900e+00 1.0030 216
## (Intercept)-Lynx_rufus -5.4948 -4.887500e+00 1.4328 56
## (Intercept)-Didelphis_virginiana -4.6979 -4.166600e+00 1.0200 162
## (Intercept)-Sylvilagus_floridanus -4.8973 -4.465900e+00 1.1765 187
## (Intercept)-Meleagris_gallopavo -5.7315 -4.907000e+00 1.3485 34
## (Intercept)-Sciurus_carolinensis -4.6226 -4.108900e+00 1.0127 235
## shrub_cover-Canis_latrans -0.2560 1.297000e-01 1.0021 199
## shrub_cover-Lynx_rufus -0.1731 5.472000e-01 1.2629 86
## shrub_cover-Didelphis_virginiana 1.0373 1.968000e+00 1.0041 106
## shrub_cover-Sylvilagus_floridanus 0.4360 1.357800e+00 1.2454 125
## shrub_cover-Meleagris_gallopavo -0.7379 2.330000e-02 1.4676 51
## shrub_cover-Sciurus_carolinensis 0.8429 1.654400e+00 1.0193 200
## veg_height-Canis_latrans -0.5642 -2.407000e-01 1.0061 189
## veg_height-Lynx_rufus 0.0900 5.192000e-01 1.0193 172
## veg_height-Didelphis_virginiana 0.5207 1.015700e+00 1.0388 210
## veg_height-Sylvilagus_floridanus 0.1873 6.131000e-01 1.1485 236
## veg_height-Meleagris_gallopavo -0.1693 6.695000e-01 1.1843 84
## veg_height-Sciurus_carolinensis 0.1831 6.248000e-01 1.0244 264
## week-Canis_latrans -0.0457 2.379607e+09 1.3498 1849
## week-Lynx_rufus -0.0599 1.980714e+09 1.3229 3439
## week-Didelphis_virginiana -0.1655 1.459437e+09 1.3017 7868
## week-Sylvilagus_floridanus 0.0161 2.060138e+09 1.2910 59336
## week-Meleagris_gallopavo -0.0240 2.805580e+09 1.3008 10420
## week-Sciurus_carolinensis -0.1361 1.600025e+09 1.3041 5805
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.0482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0104 0.5361 -2.0480 -1.0220 0.0559 1.0090 645
## Avg_Cogongrass_Cover -0.6127 0.5388 -1.7070 -0.5911 0.3956 1.0229 552
## I(Avg_Cogongrass_Cover^2) 0.7461 0.4819 -0.1734 0.7330 1.7221 1.0126 619
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0848 1.7660 0.0519 0.5417 5.1411 1.0237 690
## Avg_Cogongrass_Cover 0.8405 1.4149 0.0511 0.4205 4.2660 1.0525 1009
## I(Avg_Cogongrass_Cover^2) 0.9046 1.9605 0.0508 0.3762 4.7355 1.3067 472
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7869 1.0508 0.053 0.421 3.6963 1.0654 172
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8614 0.3907 -5.4273 -4.9139 -3.9834 1.0250 1004
## shrub_cover 0.1940 0.4135 -0.6433 0.1989 1.0284 1.0031 791
## veg_height 0.0657 0.2651 -0.4512 0.0643 0.6197 1.0322 567
## week -0.0709 1.6971 -3.3371 -0.0784 3.2419 1.0018 659
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.128000e-01 1.45470e+00 0.0560 0.3021 2.814300e+00 1.1149 223
## shrub_cover 9.980000e-01 1.14800e+00 0.1488 0.6718 3.735200e+00 1.0242 479
## veg_height 3.551000e-01 4.97200e-01 0.0623 0.2380 1.348600e+00 1.0413 1573
## week 2.935093e+21 5.00747e+22 0.1011 837.0509 9.853193e+20 1.3034 566
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4966 0.6789 -1.7595 -0.5165
## (Intercept)-Lynx_rufus -0.8614 0.7234 -2.1630 -0.9071
## (Intercept)-Didelphis_virginiana -1.4671 0.6591 -2.8458 -1.4283
## (Intercept)-Sylvilagus_floridanus -1.1667 0.6290 -2.4414 -1.1612
## (Intercept)-Meleagris_gallopavo -0.6305 0.8484 -2.0931 -0.7174
## (Intercept)-Sciurus_carolinensis -1.8035 0.7550 -3.4926 -1.7192
## Avg_Cogongrass_Cover-Canis_latrans -0.1696 0.6508 -1.3159 -0.1993
## Avg_Cogongrass_Cover-Lynx_rufus -0.4785 0.7179 -1.9045 -0.4860
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2815 0.6351 -1.4693 -0.3037
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1602 0.6910 -2.6733 -1.0957
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0402 0.8552 -3.0152 -0.9368
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6621 0.6264 -1.9542 -0.6381
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3972 0.9202 0.1182 1.1992
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1538 0.6289 0.1718 1.0712
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4698 0.4844 -0.4643 0.4554
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6647 0.4809 -0.2164 0.6354
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2425 0.8989 -1.4096 0.2258
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8769 0.4603 0.0521 0.8449
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9345 1.0136 747
## (Intercept)-Lynx_rufus 0.7362 1.0339 520
## (Intercept)-Didelphis_virginiana -0.2367 1.0013 890
## (Intercept)-Sylvilagus_floridanus 0.0062 1.0009 1077
## (Intercept)-Meleagris_gallopavo 1.2811 1.0109 309
## (Intercept)-Sciurus_carolinensis -0.5313 1.0031 644
## Avg_Cogongrass_Cover-Canis_latrans 1.2472 1.0069 1078
## Avg_Cogongrass_Cover-Lynx_rufus 0.9241 1.0082 559
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1192 1.0324 783
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0445 1.0062 794
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4354 1.0429 374
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4834 1.0043 841
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7012 1.0387 225
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6205 1.0352 675
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4741 1.0169 603
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7314 1.0012 733
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.0181 1.0418 215
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8755 1.0049 483
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.790600e+00 1.602000e-01 -5.101200e+00
## (Intercept)-Lynx_rufus -5.475000e+00 2.919000e-01 -6.056400e+00
## (Intercept)-Didelphis_virginiana -4.690000e+00 2.679000e-01 -5.231800e+00
## (Intercept)-Sylvilagus_floridanus -4.932700e+00 2.314000e-01 -5.425700e+00
## (Intercept)-Meleagris_gallopavo -5.641800e+00 5.211000e-01 -6.839400e+00
## (Intercept)-Sciurus_carolinensis -4.655100e+00 2.846000e-01 -5.185200e+00
## shrub_cover-Canis_latrans -2.543000e-01 1.944000e-01 -6.411000e-01
## shrub_cover-Lynx_rufus -2.003000e-01 3.356000e-01 -8.956000e-01
## shrub_cover-Didelphis_virginiana 1.073200e+00 3.557000e-01 4.582000e-01
## shrub_cover-Sylvilagus_floridanus 4.791000e-01 4.017000e-01 -3.113000e-01
## shrub_cover-Meleagris_gallopavo -7.183000e-01 4.433000e-01 -1.661200e+00
## shrub_cover-Sciurus_carolinensis 8.735000e-01 3.882000e-01 9.420000e-02
## veg_height-Canis_latrans -5.767000e-01 1.644000e-01 -9.029000e-01
## veg_height-Lynx_rufus 1.681000e-01 2.371000e-01 -2.951000e-01
## veg_height-Didelphis_virginiana 4.866000e-01 2.607000e-01 1.410000e-02
## veg_height-Sylvilagus_floridanus 1.730000e-01 2.268000e-01 -2.936000e-01
## veg_height-Meleagris_gallopavo -1.141000e-01 3.913000e-01 -8.743000e-01
## veg_height-Sciurus_carolinensis 2.020000e-01 1.943000e-01 -1.734000e-01
## week-Canis_latrans 3.412745e+08 4.727740e+10 -4.121798e+09
## week-Lynx_rufus -4.248568e+08 5.835286e+10 -4.770646e+09
## week-Didelphis_virginiana -9.149436e+08 6.313000e+10 -4.622326e+09
## week-Sylvilagus_floridanus 9.978281e+08 5.851274e+10 -2.849273e+09
## week-Meleagris_gallopavo 5.702771e+08 4.519604e+10 -4.818853e+09
## week-Sciurus_carolinensis 3.166160e+08 4.855687e+10 -5.053650e+09
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7925 -4.485900e+00 1.1041 227
## (Intercept)-Lynx_rufus -5.4651 -4.937500e+00 1.0210 94
## (Intercept)-Didelphis_virginiana -4.6816 -4.182200e+00 1.0794 210
## (Intercept)-Sylvilagus_floridanus -4.9177 -4.494400e+00 1.0597 143
## (Intercept)-Meleagris_gallopavo -5.5798 -4.775500e+00 1.1227 45
## (Intercept)-Sciurus_carolinensis -4.6597 -4.095300e+00 1.0334 222
## shrub_cover-Canis_latrans -0.2500 1.106000e-01 1.0216 207
## shrub_cover-Lynx_rufus -0.1904 4.362000e-01 1.0086 121
## shrub_cover-Didelphis_virginiana 1.0470 1.823400e+00 1.0144 126
## shrub_cover-Sylvilagus_floridanus 0.4829 1.255300e+00 1.0403 115
## shrub_cover-Meleagris_gallopavo -0.6984 1.027000e-01 1.0881 75
## shrub_cover-Sciurus_carolinensis 0.8788 1.637900e+00 1.0257 228
## veg_height-Canis_latrans -0.5745 -2.670000e-01 1.0628 188
## veg_height-Lynx_rufus 0.1670 6.383000e-01 1.0675 155
## veg_height-Didelphis_virginiana 0.4754 1.035000e+00 1.0795 194
## veg_height-Sylvilagus_floridanus 0.1778 5.973000e-01 1.0128 217
## veg_height-Meleagris_gallopavo -0.1092 6.336000e-01 1.0895 130
## veg_height-Sciurus_carolinensis 0.2055 5.794000e-01 1.0019 288
## week-Canis_latrans -0.4746 4.087231e+09 1.1444 11902
## week-Lynx_rufus -0.2297 2.454280e+09 1.1817 8792
## week-Didelphis_virginiana -0.2924 4.701541e+09 1.1408 4648
## week-Sylvilagus_floridanus -0.1910 5.259595e+09 1.2798 2845
## week-Meleagris_gallopavo -0.1288 4.759515e+09 1.1260 2249
## week-Sciurus_carolinensis -0.3997 4.908738e+09 1.1342 2302
## 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.4772
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3044 1.1861 -3.3345 -1.4192 1.3328 1.0475 284
## Cogon_Patch_Size 0.6723 1.2396 -1.9604 0.6925 3.0906 1.0449 469
## Veg_shannon_index 0.8404 1.0861 -1.3963 0.8569 3.0091 1.0213 494
## total_shrub_cover -1.3087 1.2943 -3.7975 -1.3284 1.4427 1.0216 635
## Avg_Cogongrass_Cover -0.2813 1.3617 -2.8527 -0.2937 2.5480 1.0323 217
## Tree_Density -1.8477 1.2467 -4.1526 -1.8593 0.7308 1.0358 157
## Avg_Canopy_Cover 1.4743 1.5026 -1.8231 1.5754 4.1879 1.0537 448
## I(Avg_Cogongrass_Cover^2) 1.2773 1.0864 -1.0179 1.2963 3.4473 1.0697 242
## avg_veg_height -0.1184 1.0432 -2.1965 -0.1712 2.1669 1.0093 174
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.3930 36.3483 0.0754 3.6026 70.3493 1.0990 168
## Cogon_Patch_Size 23.3429 66.9514 0.1392 6.8900 157.6410 1.9191 65
## Veg_shannon_index 11.4021 23.5203 0.1057 3.5246 71.5611 1.1874 136
## total_shrub_cover 34.8222 83.5855 0.3990 11.2780 216.4017 1.1501 84
## Avg_Cogongrass_Cover 7.8386 24.6764 0.0591 1.2424 60.4352 1.1701 122
## Tree_Density 8.5871 27.9859 0.0620 1.3591 61.6626 1.2714 188
## Avg_Canopy_Cover 148.7293 968.6267 1.1477 23.9437 1043.0604 1.4387 703
## I(Avg_Cogongrass_Cover^2) 9.9469 25.5400 0.0862 2.4146 67.2210 1.1263 181
## avg_veg_height 3.4234 8.9274 0.0634 0.7666 23.0946 1.0220 215
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 29.5044 64.427 0.065 3.2438 230.555 3.076 8
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0080 0.3445 -5.5068 -5.0431 -4.3121 1.0164 359
## shrub_cover 0.4047 0.4481 -0.5020 0.3977 1.3164 1.0096 792
## veg_height 0.1153 0.2808 -0.4269 0.1110 0.6852 1.0043 627
## week 0.0474 1.5770 -3.1353 0.0402 3.2072 1.0489 784
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.15600e-01 1.012700e+00 0.0443 0.2089 1.900600e+00 1.0745 286
## shrub_cover 1.24500e+00 1.395300e+00 0.2058 0.8598 4.742200e+00 1.0643 812
## veg_height 3.94300e-01 4.186000e-01 0.0666 0.2711 1.518600e+00 1.0090 311
## week 6.03947e+08 9.492364e+09 0.0969 26.3103 7.083848e+08 1.4823 369
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4979 2.1836 -3.9645 -0.7621
## (Intercept)-Lynx_rufus 0.3326 3.1760 -3.5436 -0.4076
## (Intercept)-Didelphis_virginiana -3.1238 2.3640 -8.8752 -2.7213
## (Intercept)-Sylvilagus_floridanus -1.8757 2.2811 -6.7526 -1.8634
## (Intercept)-Meleagris_gallopavo -1.9861 2.6462 -7.4979 -1.9290
## (Intercept)-Sciurus_carolinensis -3.6336 2.6883 -9.8988 -3.1610
## Cogon_Patch_Size-Canis_latrans 3.7091 3.2381 -0.0533 2.9562
## Cogon_Patch_Size-Lynx_rufus 0.7356 3.1921 -5.3980 0.5777
## Cogon_Patch_Size-Didelphis_virginiana 3.7349 3.6025 0.1673 2.8020
## Cogon_Patch_Size-Sylvilagus_floridanus -2.1667 3.8317 -12.3033 -1.3541
## Cogon_Patch_Size-Meleagris_gallopavo 1.3552 3.0104 -3.6416 1.0392
## Cogon_Patch_Size-Sciurus_carolinensis -0.8720 3.4066 -9.4234 -0.3039
## Veg_shannon_index-Canis_latrans 2.2728 1.6473 -0.0504 1.9494
## Veg_shannon_index-Lynx_rufus -1.3114 3.0047 -9.3171 -0.7178
## Veg_shannon_index-Didelphis_virginiana 2.2977 2.0947 -0.5634 1.8404
## Veg_shannon_index-Sylvilagus_floridanus 1.8780 2.0518 -1.1792 1.5292
## Veg_shannon_index-Meleagris_gallopavo 2.4162 2.3958 -1.4821 2.0330
## Veg_shannon_index-Sciurus_carolinensis -0.8092 2.5967 -7.6494 -0.3029
## total_shrub_cover-Canis_latrans 1.9466 2.0393 -1.2904 1.5850
## total_shrub_cover-Lynx_rufus -4.4488 4.3257 -14.8040 -3.9390
## total_shrub_cover-Didelphis_virginiana -4.2061 3.5790 -13.7295 -3.3709
## total_shrub_cover-Sylvilagus_floridanus -2.9577 4.3039 -15.7803 -1.9193
## total_shrub_cover-Meleagris_gallopavo -5.4273 3.4431 -14.7009 -4.7952
## total_shrub_cover-Sciurus_carolinensis -3.1197 4.3464 -15.5710 -1.9952
## Avg_Cogongrass_Cover-Canis_latrans -0.0385 2.0922 -3.7791 -0.1380
## Avg_Cogongrass_Cover-Lynx_rufus 0.5305 2.5498 -3.2922 0.1176
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0815 2.1569 -4.0064 -0.2256
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4201 2.4744 -7.4945 -1.0810
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9109 2.8104 -7.2007 -0.8079
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1948 2.8081 -3.8487 -0.1832
## Tree_Density-Canis_latrans -3.3581 2.4911 -9.3572 -2.8771
## Tree_Density-Lynx_rufus -1.3155 2.2400 -5.1477 -1.5081
## Tree_Density-Didelphis_virginiana -2.2394 2.1848 -7.0889 -2.1225
## Tree_Density-Sylvilagus_floridanus -3.0667 2.5311 -9.9363 -2.6161
## Tree_Density-Meleagris_gallopavo -2.1096 2.1889 -6.5781 -2.1006
## Tree_Density-Sciurus_carolinensis -2.0251 2.5532 -7.0900 -2.0968
## Avg_Canopy_Cover-Canis_latrans -0.8544 1.7351 -5.5116 -0.5865
## Avg_Canopy_Cover-Lynx_rufus 2.1272 6.8477 -5.5171 0.4818
## Avg_Canopy_Cover-Didelphis_virginiana 8.2916 5.5997 1.9267 6.3759
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.2866 8.6171 2.0991 7.3988
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5735 6.0579 0.5668 3.4998
## Avg_Canopy_Cover-Sciurus_carolinensis 9.6253 9.7921 1.5523 6.0185
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0679 2.1863 0.3208 2.5454
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.4397 2.7040 0.0000 2.7736
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1862 1.3394 -1.3249 1.1411
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1910 1.3920 -1.4815 1.1695
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.7427 2.8372 -7.4235 -0.2985
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.2030 1.9485 -0.3431 1.8574
## avg_veg_height-Canis_latrans -0.2219 1.2589 -3.0754 -0.2185
## avg_veg_height-Lynx_rufus -0.4723 2.1088 -4.8502 -0.4300
## avg_veg_height-Didelphis_virginiana -0.4179 1.5978 -3.5546 -0.3851
## avg_veg_height-Sylvilagus_floridanus -0.2580 1.4443 -3.2149 -0.2507
## avg_veg_height-Meleagris_gallopavo -0.2065 2.0730 -4.6481 -0.1816
## avg_veg_height-Sciurus_carolinensis 0.6496 1.6893 -1.8686 0.4064
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.8246 1.0437 131
## (Intercept)-Lynx_rufus 7.9250 1.0653 61
## (Intercept)-Didelphis_virginiana 0.3987 1.0225 176
## (Intercept)-Sylvilagus_floridanus 3.1448 1.0628 170
## (Intercept)-Meleagris_gallopavo 2.9792 1.0763 153
## (Intercept)-Sciurus_carolinensis 0.5615 1.0442 153
## Cogon_Patch_Size-Canis_latrans 11.3292 1.1873 116
## Cogon_Patch_Size-Lynx_rufus 8.3732 1.0792 128
## Cogon_Patch_Size-Didelphis_virginiana 14.0831 1.6587 44
## Cogon_Patch_Size-Sylvilagus_floridanus 2.4649 1.4901 46
## Cogon_Patch_Size-Meleagris_gallopavo 7.5309 1.1307 147
## Cogon_Patch_Size-Sciurus_carolinensis 3.9672 1.3386 142
## Veg_shannon_index-Canis_latrans 6.5184 1.1737 191
## Veg_shannon_index-Lynx_rufus 2.6209 1.3008 88
## Veg_shannon_index-Didelphis_virginiana 8.0121 1.1403 78
## Veg_shannon_index-Sylvilagus_floridanus 6.6708 1.1049 188
## Veg_shannon_index-Meleagris_gallopavo 8.3092 1.1433 148
## Veg_shannon_index-Sciurus_carolinensis 2.7956 1.0886 153
## total_shrub_cover-Canis_latrans 6.8750 1.0441 99
## total_shrub_cover-Lynx_rufus 3.0437 1.1355 56
## total_shrub_cover-Didelphis_virginiana 0.3044 1.0545 80
## total_shrub_cover-Sylvilagus_floridanus 2.0810 1.0678 58
## total_shrub_cover-Meleagris_gallopavo -0.5914 1.0523 56
## total_shrub_cover-Sciurus_carolinensis 1.4835 1.3632 61
## Avg_Cogongrass_Cover-Canis_latrans 4.3229 1.0374 207
## Avg_Cogongrass_Cover-Lynx_rufus 6.9224 1.0421 165
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.9868 1.0666 206
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4514 1.0351 242
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.4320 1.1564 144
## Avg_Cogongrass_Cover-Sciurus_carolinensis 7.1795 1.2431 107
## Tree_Density-Canis_latrans -0.1943 1.1830 126
## Tree_Density-Lynx_rufus 3.8601 1.0788 217
## Tree_Density-Didelphis_virginiana 1.9533 1.0823 155
## Tree_Density-Sylvilagus_floridanus 0.6513 1.0857 109
## Tree_Density-Meleagris_gallopavo 2.1858 1.1265 224
## Tree_Density-Sciurus_carolinensis 3.6136 1.1945 102
## Avg_Canopy_Cover-Canis_latrans 1.5391 1.2756 80
## Avg_Canopy_Cover-Lynx_rufus 24.8433 2.6564 22
## Avg_Canopy_Cover-Didelphis_virginiana 22.5383 1.4304 42
## Avg_Canopy_Cover-Sylvilagus_floridanus 37.5002 2.1366 18
## Avg_Canopy_Cover-Meleagris_gallopavo 24.6613 1.9512 21
## Avg_Canopy_Cover-Sciurus_carolinensis 38.5013 1.5286 26
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.8684 1.1010 102
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 10.6516 1.1237 116
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 4.0481 1.0774 213
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.2314 1.0771 240
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.4387 1.1222 68
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 6.8585 1.1446 123
## avg_veg_height-Canis_latrans 2.1366 1.0863 177
## avg_veg_height-Lynx_rufus 3.9983 1.0481 120
## avg_veg_height-Didelphis_virginiana 2.7489 1.0292 291
## avg_veg_height-Sylvilagus_floridanus 2.6650 1.0066 271
## avg_veg_height-Meleagris_gallopavo 4.1125 1.0708 89
## avg_veg_height-Sciurus_carolinensis 4.8479 1.0074 252
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8012 0.1842 -5.1886 -4.7902
## (Intercept)-Lynx_rufus -5.5783 0.3103 -6.2707 -5.5489
## (Intercept)-Didelphis_virginiana -4.8980 0.2535 -5.3900 -4.8970
## (Intercept)-Sylvilagus_floridanus -5.0523 0.2174 -5.4850 -5.0567
## (Intercept)-Meleagris_gallopavo -5.4410 0.3983 -6.2503 -5.4398
## (Intercept)-Sciurus_carolinensis -4.9564 0.2682 -5.4696 -4.9602
## shrub_cover-Canis_latrans -0.3550 0.2148 -0.7825 -0.3658
## shrub_cover-Lynx_rufus 0.1025 0.4079 -0.7219 0.1300
## shrub_cover-Didelphis_virginiana 1.3778 0.3774 0.6517 1.3780
## shrub_cover-Sylvilagus_floridanus 0.7879 0.4181 -0.0172 0.7785
## shrub_cover-Meleagris_gallopavo -0.4886 0.3837 -1.2090 -0.4929
## shrub_cover-Sciurus_carolinensis 1.2466 0.3663 0.5405 1.2463
## veg_height-Canis_latrans -0.5188 0.1817 -0.8746 -0.5194
## veg_height-Lynx_rufus 0.2107 0.2240 -0.2553 0.2158
## veg_height-Didelphis_virginiana 0.5900 0.2421 0.1427 0.5800
## veg_height-Sylvilagus_floridanus 0.1825 0.2635 -0.3358 0.1859
## veg_height-Meleagris_gallopavo -0.0818 0.4697 -0.9656 -0.0966
## veg_height-Sciurus_carolinensis 0.3389 0.2068 -0.0618 0.3412
## week-Canis_latrans 373.8046 33641.0799 -3799.0374 0.0196
## week-Lynx_rufus -42.0257 16310.3454 -4847.6191 -0.0361
## week-Didelphis_virginiana -354.3227 23571.9629 -6592.4056 0.0671
## week-Sylvilagus_floridanus 740.6321 23099.7902 -3916.0718 -0.0245
## week-Meleagris_gallopavo 267.9220 25151.1755 -3853.2761 0.0170
## week-Sciurus_carolinensis 120.5548 23161.0571 -4870.3066 0.0955
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4692 1.0249 152
## (Intercept)-Lynx_rufus -5.0426 1.2410 56
## (Intercept)-Didelphis_virginiana -4.3966 1.0561 163
## (Intercept)-Sylvilagus_floridanus -4.6174 1.0609 172
## (Intercept)-Meleagris_gallopavo -4.6681 1.0454 119
## (Intercept)-Sciurus_carolinensis -4.4273 1.0217 81
## shrub_cover-Canis_latrans 0.1140 1.0705 160
## shrub_cover-Lynx_rufus 0.8505 1.0164 73
## shrub_cover-Didelphis_virginiana 2.1122 1.0633 101
## shrub_cover-Sylvilagus_floridanus 1.6076 1.0216 71
## shrub_cover-Meleagris_gallopavo 0.2849 1.0631 96
## shrub_cover-Sciurus_carolinensis 1.9532 1.0266 87
## veg_height-Canis_latrans -0.1599 1.0117 176
## veg_height-Lynx_rufus 0.6286 1.0406 139
## veg_height-Didelphis_virginiana 1.0876 1.0568 240
## veg_height-Sylvilagus_floridanus 0.6895 1.0532 146
## veg_height-Meleagris_gallopavo 0.8911 1.0502 84
## veg_height-Sciurus_carolinensis 0.7309 1.0055 254
## week-Canis_latrans 4215.5577 1.2701 6777
## week-Lynx_rufus 4479.8554 1.1810 16076
## week-Didelphis_virginiana 3745.5779 1.2383 10325
## week-Sylvilagus_floridanus 4616.3233 1.2571 827
## week-Meleagris_gallopavo 4157.6176 1.2111 10100
## week-Sciurus_carolinensis 5939.3299 1.1736 37006
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.8037
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8228 1.0490 -2.8004 -0.8593 1.4468 1.0072 613
## Cogon_Patch_Size -0.0598 0.9818 -2.0452 -0.0731 1.9586 1.0080 995
## Veg_shannon_index 0.7421 0.7188 -0.7504 0.7427 2.1957 1.0332 584
## total_shrub_cover -0.7644 0.9550 -2.7516 -0.7602 1.0978 1.0409 988
## Avg_Cogongrass_Cover 1.6742 0.8993 -0.1422 1.6789 3.3908 1.0243 450
## Tree_Density -2.0507 0.8702 -3.7976 -2.0286 -0.4031 1.0203 276
## Avg_Canopy_Cover 1.5915 0.9344 -0.4291 1.6275 3.5189 1.0018 1684
## avg_veg_height -0.6284 0.6379 -1.9312 -0.6294 0.5922 1.0062 467
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.4621 15.8896 0.5293 6.6560 49.9456 1.0191 268
## Cogon_Patch_Size 9.4159 20.7146 0.2118 3.8568 54.8009 1.0909 227
## Veg_shannon_index 2.6521 5.2706 0.0693 1.0386 15.8140 1.0398 287
## total_shrub_cover 8.0502 12.2749 0.3040 4.3831 38.8784 1.1100 377
## Avg_Cogongrass_Cover 2.8367 5.9761 0.0721 1.0107 16.5203 1.0325 667
## Tree_Density 2.8122 12.0993 0.0565 0.7094 16.8088 1.3714 244
## Avg_Canopy_Cover 8.0263 13.5959 0.3588 4.0353 41.9166 1.0662 319
## avg_veg_height 0.8966 1.7420 0.0450 0.3839 4.7445 1.0485 951
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.614 3.8149 0.076 1.0666 13.5785 1.0079 72
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6736 0.4055 -5.261 -4.7226 -3.7745 1.0091 1544
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6166 1.4019 0.0867 0.351 2.5208 1.0667 1221
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.2969 1.3521 -0.9741 1.1501
## (Intercept)-Lynx_rufus 1.9685 2.5030 -1.7023 1.5466
## (Intercept)-Didelphis_virginiana -3.1229 1.3283 -5.8158 -3.0300
## (Intercept)-Sylvilagus_floridanus -1.6651 1.4933 -4.8196 -1.6075
## (Intercept)-Meleagris_gallopavo -2.3480 1.5071 -5.6287 -2.2437
## (Intercept)-Sciurus_carolinensis -3.4026 1.4986 -6.9245 -3.2656
## Cogon_Patch_Size-Canis_latrans 1.5592 1.8175 -0.7810 1.1415
## Cogon_Patch_Size-Lynx_rufus 0.4351 2.2629 -3.1809 0.1488
## Cogon_Patch_Size-Didelphis_virginiana 1.6429 1.1667 -0.2052 1.4857
## Cogon_Patch_Size-Sylvilagus_floridanus -2.5158 2.8077 -9.9681 -1.8766
## Cogon_Patch_Size-Meleagris_gallopavo 0.0158 1.4774 -2.6789 -0.1144
## Cogon_Patch_Size-Sciurus_carolinensis -1.9783 1.8476 -6.8653 -1.6028
## Veg_shannon_index-Canis_latrans 1.4824 0.9009 -0.0282 1.3654
## Veg_shannon_index-Lynx_rufus -0.0973 1.5519 -3.8135 0.1750
## Veg_shannon_index-Didelphis_virginiana 1.1747 0.9476 -0.4172 1.0591
## Veg_shannon_index-Sylvilagus_floridanus 1.1110 0.9162 -0.4062 0.9934
## Veg_shannon_index-Meleagris_gallopavo 1.5741 1.2503 -0.3092 1.3580
## Veg_shannon_index-Sciurus_carolinensis -0.1924 0.9771 -2.4528 -0.0702
## total_shrub_cover-Canis_latrans 0.9571 1.1597 -0.7959 0.7568
## total_shrub_cover-Lynx_rufus -2.6570 2.0979 -7.3745 -2.3108
## total_shrub_cover-Didelphis_virginiana -0.8886 1.0479 -3.1591 -0.8266
## total_shrub_cover-Sylvilagus_floridanus -0.2073 1.3461 -3.2308 -0.1745
## total_shrub_cover-Meleagris_gallopavo -3.9312 2.0454 -8.7332 -3.6738
## total_shrub_cover-Sciurus_carolinensis 0.1954 0.9148 -1.5453 0.1756
## Avg_Cogongrass_Cover-Canis_latrans 2.3424 1.1662 0.4254 2.2011
## Avg_Cogongrass_Cover-Lynx_rufus 2.6179 1.5083 0.2411 2.3854
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0729 1.0071 0.2240 1.9939
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2125 1.1706 -1.2208 1.2571
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7994 1.6690 -2.9847 1.0196
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3189 1.1080 0.4792 2.1856
## Tree_Density-Canis_latrans -2.6664 1.3409 -5.8530 -2.4531
## Tree_Density-Lynx_rufus -1.2900 1.7408 -3.7034 -1.4526
## Tree_Density-Didelphis_virginiana -2.3689 1.1321 -4.9574 -2.2412
## Tree_Density-Sylvilagus_floridanus -2.5421 1.3258 -5.6890 -2.3956
## Tree_Density-Meleagris_gallopavo -2.3244 1.3409 -5.3159 -2.1846
## Tree_Density-Sciurus_carolinensis -2.4899 1.2654 -5.5790 -2.3169
## Avg_Canopy_Cover-Canis_latrans -0.0127 0.8093 -1.6418 -0.0036
## Avg_Canopy_Cover-Lynx_rufus 0.4990 1.7065 -2.8505 0.4704
## Avg_Canopy_Cover-Didelphis_virginiana 3.2425 1.2367 1.3261 3.0470
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.4542 2.4299 1.4024 3.8792
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5212 1.5801 0.4613 2.1787
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5801 1.0882 0.9886 2.4038
## avg_veg_height-Canis_latrans -0.7059 0.7412 -2.2194 -0.6987
## avg_veg_height-Lynx_rufus -0.6628 0.9584 -2.6047 -0.6639
## avg_veg_height-Didelphis_virginiana -0.7475 0.8103 -2.4311 -0.7307
## avg_veg_height-Sylvilagus_floridanus -0.7698 0.8273 -2.4674 -0.7413
## avg_veg_height-Meleagris_gallopavo -0.8703 0.9192 -2.8102 -0.8181
## avg_veg_height-Sciurus_carolinensis -0.1700 0.7998 -1.5910 -0.2253
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.4708 1.0701 293
## (Intercept)-Lynx_rufus 8.5077 1.0260 97
## (Intercept)-Didelphis_virginiana -0.6055 1.0861 284
## (Intercept)-Sylvilagus_floridanus 1.1004 1.0546 330
## (Intercept)-Meleagris_gallopavo 0.4174 1.1645 273
## (Intercept)-Sciurus_carolinensis -0.9274 1.1859 287
## Cogon_Patch_Size-Canis_latrans 6.4018 1.0257 424
## Cogon_Patch_Size-Lynx_rufus 6.0432 1.0446 228
## Cogon_Patch_Size-Didelphis_virginiana 4.4927 1.0330 324
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7360 1.0841 168
## Cogon_Patch_Size-Meleagris_gallopavo 3.3367 1.0365 440
## Cogon_Patch_Size-Sciurus_carolinensis 0.4797 1.0791 336
## Veg_shannon_index-Canis_latrans 3.5453 1.0152 416
## Veg_shannon_index-Lynx_rufus 2.1945 1.0113 203
## Veg_shannon_index-Didelphis_virginiana 3.2738 1.0386 681
## Veg_shannon_index-Sylvilagus_floridanus 3.3036 1.0133 531
## Veg_shannon_index-Meleagris_gallopavo 4.6892 1.0567 361
## Veg_shannon_index-Sciurus_carolinensis 1.4113 1.0309 398
## total_shrub_cover-Canis_latrans 3.9207 1.0422 290
## total_shrub_cover-Lynx_rufus 0.5480 1.2543 143
## total_shrub_cover-Didelphis_virginiana 1.0078 1.0263 677
## total_shrub_cover-Sylvilagus_floridanus 2.5795 1.0322 425
## total_shrub_cover-Meleagris_gallopavo -0.7658 1.2550 139
## total_shrub_cover-Sciurus_carolinensis 2.0652 1.0111 783
## Avg_Cogongrass_Cover-Canis_latrans 4.9905 1.0308 396
## Avg_Cogongrass_Cover-Lynx_rufus 6.2161 1.0341 334
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.2308 1.0128 553
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4242 1.0130 410
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4195 1.0051 330
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.8562 1.0360 451
## Tree_Density-Canis_latrans -0.7735 1.0746 247
## Tree_Density-Lynx_rufus 1.7129 1.1290 149
## Tree_Density-Didelphis_virginiana -0.5527 1.0731 315
## Tree_Density-Sylvilagus_floridanus -0.4641 1.0289 427
## Tree_Density-Meleagris_gallopavo -0.0902 1.1050 292
## Tree_Density-Sciurus_carolinensis -0.5636 1.0535 295
## Avg_Canopy_Cover-Canis_latrans 1.5900 1.0062 742
## Avg_Canopy_Cover-Lynx_rufus 4.0558 1.0588 171
## Avg_Canopy_Cover-Didelphis_virginiana 6.2593 1.0310 173
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.6576 1.0209 194
## Avg_Canopy_Cover-Meleagris_gallopavo 6.9312 1.0645 219
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1455 1.0827 252
## avg_veg_height-Canis_latrans 0.7200 1.0069 510
## avg_veg_height-Lynx_rufus 1.2335 1.0072 520
## avg_veg_height-Didelphis_virginiana 0.8187 1.0062 556
## avg_veg_height-Sylvilagus_floridanus 0.8215 1.0132 774
## avg_veg_height-Meleagris_gallopavo 0.7704 1.0152 547
## avg_veg_height-Sciurus_carolinensis 1.6390 1.0087 1047
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6681 0.1569 -4.9861 -4.6628 -4.3743 1.0281
## (Intercept)-Lynx_rufus -5.5780 0.2807 -6.1630 -5.5683 -5.0516 1.1408
## (Intercept)-Didelphis_virginiana -4.3138 0.2253 -4.7999 -4.3060 -3.8912 1.0086
## (Intercept)-Sylvilagus_floridanus -4.9603 0.2326 -5.4405 -4.9544 -4.5316 1.0064
## (Intercept)-Meleagris_gallopavo -5.0836 0.2551 -5.5751 -5.0842 -4.5795 1.2122
## (Intercept)-Sciurus_carolinensis -4.3710 0.2181 -4.8069 -4.3668 -3.9564 1.0032
## ESS
## (Intercept)-Canis_latrans 237
## (Intercept)-Lynx_rufus 78
## (Intercept)-Didelphis_virginiana 332
## (Intercept)-Sylvilagus_floridanus 185
## (Intercept)-Meleagris_gallopavo 156
## (Intercept)-Sciurus_carolinensis 355
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.5602
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7372 0.5346 -1.8477 -0.7359 0.3346 1.0111 608
## Avg_Cogongrass_Cover 0.2391 0.4824 -0.7467 0.2468 1.1803 1.0093 828
## total_shrub_cover -0.4314 0.5177 -1.5222 -0.4023 0.5731 1.0074 1197
## avg_veg_height -0.1659 0.4102 -1.0213 -0.1597 0.6225 1.0105 799
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1112 1.8331 0.0551 0.5740 5.5599 1.0365 965
## Avg_Cogongrass_Cover 0.9117 1.5114 0.0575 0.4687 4.5416 1.0399 869
## total_shrub_cover 1.4486 2.2307 0.0864 0.8004 6.9810 1.0661 735
## avg_veg_height 0.4392 0.7041 0.0400 0.2263 1.9803 1.0481 1665
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5552 1.8876 0.0802 0.9733 6.6894 1.0239 112
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6625 0.326 -5.1595 -4.6895 -3.99 1.0179 1254
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4646 3.1731 0.0574 0.2444 1.6799 1.2826 2683
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0191 0.7323 -1.3973 0.0020
## (Intercept)-Lynx_rufus -0.3531 0.8030 -1.8678 -0.4139
## (Intercept)-Didelphis_virginiana -1.2208 0.6477 -2.6444 -1.1823
## (Intercept)-Sylvilagus_floridanus -0.7023 0.6465 -1.9855 -0.7120
## (Intercept)-Meleagris_gallopavo -1.0124 0.6876 -2.4892 -0.9849
## (Intercept)-Sciurus_carolinensis -1.3278 0.6745 -2.8333 -1.2708
## Avg_Cogongrass_Cover-Canis_latrans 0.6040 0.5737 -0.3892 0.5607
## Avg_Cogongrass_Cover-Lynx_rufus 0.7610 0.6830 -0.3680 0.6735
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5056 0.5214 -0.4653 0.4834
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2581 0.5880 -1.5252 -0.2182
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4579 0.7394 -2.1104 -0.3810
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3546 0.4977 -0.6410 0.3581
## total_shrub_cover-Canis_latrans 0.4916 0.5888 -0.4848 0.4277
## total_shrub_cover-Lynx_rufus -1.0547 0.7811 -2.8490 -0.9385
## total_shrub_cover-Didelphis_virginiana -0.2401 0.4892 -1.2456 -0.2207
## total_shrub_cover-Sylvilagus_floridanus -0.4135 0.5813 -1.6928 -0.3829
## total_shrub_cover-Meleagris_gallopavo -1.5911 0.8595 -3.6809 -1.4763
## total_shrub_cover-Sciurus_carolinensis -0.0720 0.4861 -1.0300 -0.0766
## avg_veg_height-Canis_latrans -0.1328 0.4728 -1.0959 -0.1206
## avg_veg_height-Lynx_rufus -0.1972 0.5887 -1.3683 -0.1899
## avg_veg_height-Didelphis_virginiana -0.1903 0.4841 -1.1787 -0.1799
## avg_veg_height-Sylvilagus_floridanus -0.2743 0.4901 -1.3345 -0.2556
## avg_veg_height-Meleagris_gallopavo -0.4342 0.6026 -1.7739 -0.3778
## avg_veg_height-Sciurus_carolinensis 0.2014 0.5091 -0.7063 0.1648
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5188 1.0064 576
## (Intercept)-Lynx_rufus 1.3695 1.0043 435
## (Intercept)-Didelphis_virginiana -0.0211 1.0149 668
## (Intercept)-Sylvilagus_floridanus 0.6312 1.0192 747
## (Intercept)-Meleagris_gallopavo 0.2365 1.0103 635
## (Intercept)-Sciurus_carolinensis -0.1670 1.0163 625
## Avg_Cogongrass_Cover-Canis_latrans 1.9039 1.0055 921
## Avg_Cogongrass_Cover-Lynx_rufus 2.3868 1.0020 746
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5680 1.0009 555
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7449 1.0185 842
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7221 1.0178 753
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3368 1.0041 1204
## total_shrub_cover-Canis_latrans 1.8285 1.0116 863
## total_shrub_cover-Lynx_rufus 0.1699 1.0153 597
## total_shrub_cover-Didelphis_virginiana 0.6962 1.0059 2137
## total_shrub_cover-Sylvilagus_floridanus 0.6589 1.0091 794
## total_shrub_cover-Meleagris_gallopavo -0.2618 1.0115 443
## total_shrub_cover-Sciurus_carolinensis 0.8861 1.0034 2103
## avg_veg_height-Canis_latrans 0.7928 1.0006 1072
## avg_veg_height-Lynx_rufus 0.9536 1.0015 918
## avg_veg_height-Didelphis_virginiana 0.7514 1.0116 948
## avg_veg_height-Sylvilagus_floridanus 0.6513 1.0071 1036
## avg_veg_height-Meleagris_gallopavo 0.6293 1.0149 802
## avg_veg_height-Sciurus_carolinensis 1.2911 0.9997 1237
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6741 0.1534 -4.9838 -4.6700 -4.3761 1.0359
## (Intercept)-Lynx_rufus -5.3182 0.2677 -5.8678 -5.3054 -4.8189 1.0140
## (Intercept)-Didelphis_virginiana -4.3629 0.2315 -4.8409 -4.3582 -3.9317 1.0119
## (Intercept)-Sylvilagus_floridanus -4.9314 0.2439 -5.4535 -4.9247 -4.4877 1.1639
## (Intercept)-Meleagris_gallopavo -5.0010 0.2500 -5.5061 -4.9880 -4.5468 1.0809
## (Intercept)-Sciurus_carolinensis -4.3885 0.2285 -4.8178 -4.3855 -3.9463 1.0172
## ESS
## (Intercept)-Canis_latrans 253
## (Intercept)-Lynx_rufus 119
## (Intercept)-Didelphis_virginiana 248
## (Intercept)-Sylvilagus_floridanus 157
## (Intercept)-Meleagris_gallopavo 177
## (Intercept)-Sciurus_carolinensis 303
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.6038
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7555 0.5690 -1.8598 -0.7747 0.4241 1.0182 881
## Tree_Density -0.7871 0.5596 -2.0220 -0.7440 0.1982 1.0018 822
## Avg_Canopy_Cover 1.0100 0.5484 -0.0178 0.9882 2.1524 1.0175 1143
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7808 2.5002 0.0956 1.0046 8.7208 1.0169 618
## Tree_Density 1.1510 2.3533 0.0528 0.5235 5.8810 1.0887 843
## Avg_Canopy_Cover 1.5633 2.4722 0.1016 0.8521 8.1233 1.0051 750
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6944 0.9866 0.0473 0.3868 3.2795 1.0429 131
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6619 0.3288 -5.1473 -4.6965 -3.9705 1.0121 1444
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4616 1.356 0.0571 0.2574 1.7533 1.1875 1726
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2143 0.7034 -1.1117 0.1811 1.6635
## (Intercept)-Lynx_rufus 0.0058 0.9275 -1.5038 -0.1212 2.1960
## (Intercept)-Didelphis_virginiana -1.6930 0.7298 -3.3526 -1.6394 -0.4527
## (Intercept)-Sylvilagus_floridanus -0.9136 0.6203 -2.1608 -0.9058 0.3345
## (Intercept)-Meleagris_gallopavo -0.8120 0.6540 -2.1223 -0.7989 0.5186
## (Intercept)-Sciurus_carolinensis -1.7024 0.7064 -3.2861 -1.6498 -0.4716
## Tree_Density-Canis_latrans -0.9969 0.6625 -2.5406 -0.8964 0.0489
## Tree_Density-Lynx_rufus 0.1220 0.7231 -1.0334 0.0211 1.8830
## Tree_Density-Didelphis_virginiana -1.0476 0.8377 -3.1060 -0.9039 0.1959
## Tree_Density-Sylvilagus_floridanus -1.1790 0.8218 -3.1239 -1.0497 0.0779
## Tree_Density-Meleagris_gallopavo -0.9547 0.7781 -2.7853 -0.8636 0.3072
## Tree_Density-Sciurus_carolinensis -0.9540 0.7578 -2.7595 -0.8453 0.2347
## Avg_Canopy_Cover-Canis_latrans -0.0551 0.5094 -1.0873 -0.0588 0.9169
## Avg_Canopy_Cover-Lynx_rufus 0.6110 0.7495 -0.7632 0.5563 2.3240
## Avg_Canopy_Cover-Didelphis_virginiana 1.3253 0.5721 0.3798 1.2721 2.5473
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9620 0.9971 0.6026 1.7619 4.3999
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5013 0.8099 0.2655 1.3947 3.3672
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2638 0.5488 0.3375 1.2059 2.4534
## Rhat ESS
## (Intercept)-Canis_latrans 1.0076 570
## (Intercept)-Lynx_rufus 1.1118 176
## (Intercept)-Didelphis_virginiana 1.0016 773
## (Intercept)-Sylvilagus_floridanus 1.0151 935
## (Intercept)-Meleagris_gallopavo 1.0596 627
## (Intercept)-Sciurus_carolinensis 1.0061 742
## Tree_Density-Canis_latrans 1.0040 706
## Tree_Density-Lynx_rufus 1.0683 586
## Tree_Density-Didelphis_virginiana 1.0132 642
## Tree_Density-Sylvilagus_floridanus 1.0004 705
## Tree_Density-Meleagris_gallopavo 1.0044 786
## Tree_Density-Sciurus_carolinensis 1.0120 844
## Avg_Canopy_Cover-Canis_latrans 1.0028 1195
## Avg_Canopy_Cover-Lynx_rufus 1.0308 506
## Avg_Canopy_Cover-Didelphis_virginiana 1.0014 1105
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0152 435
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0050 624
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0030 1268
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6956 0.1656 -5.0491 -4.6913 -4.3976 1.0294
## (Intercept)-Lynx_rufus -5.3850 0.2989 -6.0056 -5.3597 -4.8501 1.1910
## (Intercept)-Didelphis_virginiana -4.3640 0.2257 -4.8230 -4.3595 -3.9265 1.0014
## (Intercept)-Sylvilagus_floridanus -4.8265 0.2121 -5.2497 -4.8219 -4.4263 1.0460
## (Intercept)-Meleagris_gallopavo -5.0518 0.2432 -5.5567 -5.0495 -4.5898 1.1014
## (Intercept)-Sciurus_carolinensis -4.4003 0.2201 -4.8304 -4.3996 -3.9780 1.0113
## ESS
## (Intercept)-Canis_latrans 214
## (Intercept)-Lynx_rufus 77
## (Intercept)-Didelphis_virginiana 306
## (Intercept)-Sylvilagus_floridanus 245
## (Intercept)-Meleagris_gallopavo 179
## (Intercept)-Sciurus_carolinensis 350
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.5422
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7545 0.5802 -1.9008 -0.7583 0.4135 1.0209 780
## Cogon_Patch_Size -0.0187 0.6471 -1.3253 -0.0138 1.2932 1.0045 817
## Avg_Cogongrass_Cover 0.1166 0.4647 -0.8256 0.1266 1.0580 1.0074 1571
## total_shrub_cover -0.4296 0.5105 -1.5702 -0.4009 0.5437 1.0092 1493
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3033 1.8955 0.0720 0.6809 6.4890 1.0367 586
## Cogon_Patch_Size 2.3463 3.7070 0.1136 1.2057 12.1459 1.0310 595
## Avg_Cogongrass_Cover 1.0104 1.6600 0.0609 0.5219 5.1150 1.0611 879
## total_shrub_cover 1.4016 2.2833 0.0845 0.7456 6.7917 1.0475 782
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6885 1.6842 0.0909 1.1516 6.0786 1.0312 217
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6701 0.3074 -5.1506 -4.7012 -4.038 1.0167 1021
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.407 0.8017 0.0508 0.2474 1.638 1.1011 1440
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1625 0.8404 -1.3912 0.0997
## (Intercept)-Lynx_rufus -0.3402 0.8838 -1.9052 -0.4234
## (Intercept)-Didelphis_virginiana -1.2456 0.6746 -2.5727 -1.2468
## (Intercept)-Sylvilagus_floridanus -0.8865 0.6863 -2.3208 -0.8639
## (Intercept)-Meleagris_gallopavo -1.0199 0.7358 -2.6137 -0.9883
## (Intercept)-Sciurus_carolinensis -1.4538 0.7644 -3.1339 -1.3880
## Cogon_Patch_Size-Canis_latrans 0.9955 0.9676 -0.3048 0.8102
## Cogon_Patch_Size-Lynx_rufus -0.1974 1.1621 -2.0561 -0.3134
## Cogon_Patch_Size-Didelphis_virginiana 0.8586 0.6507 -0.1794 0.8027
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1287 1.1423 -3.8774 -0.9057
## Cogon_Patch_Size-Meleagris_gallopavo 0.1362 0.7758 -1.2524 0.1055
## Cogon_Patch_Size-Sciurus_carolinensis -0.9325 0.9066 -3.1367 -0.7856
## Avg_Cogongrass_Cover-Canis_latrans 0.3025 0.4781 -0.5845 0.2910
## Avg_Cogongrass_Cover-Lynx_rufus 0.7619 0.7341 -0.3768 0.6466
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1544 0.5004 -0.8974 0.1690
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2408 0.5557 -1.4629 -0.1955
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.7434 0.7799 -2.5559 -0.6455
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5705 0.5081 -0.3295 0.5294
## total_shrub_cover-Canis_latrans 0.3946 0.5884 -0.5933 0.3386
## total_shrub_cover-Lynx_rufus -0.9295 0.8479 -2.9758 -0.8146
## total_shrub_cover-Didelphis_virginiana -0.3973 0.5112 -1.4503 -0.3911
## total_shrub_cover-Sylvilagus_floridanus -0.3121 0.6159 -1.6100 -0.3048
## total_shrub_cover-Meleagris_gallopavo -1.5448 0.8503 -3.5679 -1.4148
## total_shrub_cover-Sciurus_carolinensis 0.0097 0.5083 -0.9383 -0.0143
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0259 1.0199 429
## (Intercept)-Lynx_rufus 1.7069 1.0492 360
## (Intercept)-Didelphis_virginiana 0.0721 1.0169 764
## (Intercept)-Sylvilagus_floridanus 0.3949 1.0205 641
## (Intercept)-Meleagris_gallopavo 0.3917 1.0231 647
## (Intercept)-Sciurus_carolinensis -0.0799 1.0087 554
## Cogon_Patch_Size-Canis_latrans 3.5902 1.0108 568
## Cogon_Patch_Size-Lynx_rufus 2.4209 1.0912 378
## Cogon_Patch_Size-Didelphis_virginiana 2.2293 1.0116 638
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4415 1.0132 537
## Cogon_Patch_Size-Meleagris_gallopavo 1.7413 1.0446 717
## Cogon_Patch_Size-Sciurus_carolinensis 0.3934 1.0060 675
## Avg_Cogongrass_Cover-Canis_latrans 1.3279 1.0018 1479
## Avg_Cogongrass_Cover-Lynx_rufus 2.5069 1.0040 862
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1314 1.0039 1791
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7773 1.0018 970
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4706 1.0088 747
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6547 1.0035 1095
## total_shrub_cover-Canis_latrans 1.7835 1.0006 952
## total_shrub_cover-Lynx_rufus 0.3870 1.0502 360
## total_shrub_cover-Didelphis_virginiana 0.5754 1.0008 1764
## total_shrub_cover-Sylvilagus_floridanus 0.8444 1.0157 881
## total_shrub_cover-Meleagris_gallopavo -0.2810 0.9997 569
## total_shrub_cover-Sciurus_carolinensis 1.0750 1.0017 1559
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6477 0.1509 -4.9549 -4.6438 -4.3623 1.0694
## (Intercept)-Lynx_rufus -5.3329 0.2730 -5.8610 -5.3349 -4.8142 1.0194
## (Intercept)-Didelphis_virginiana -4.3766 0.2318 -4.8389 -4.3709 -3.9387 1.0262
## (Intercept)-Sylvilagus_floridanus -4.9200 0.2283 -5.3872 -4.9120 -4.4834 1.0223
## (Intercept)-Meleagris_gallopavo -5.0128 0.2588 -5.5456 -4.9867 -4.5559 1.1188
## (Intercept)-Sciurus_carolinensis -4.3905 0.2258 -4.8690 -4.3858 -3.9694 1.0885
## ESS
## (Intercept)-Canis_latrans 269
## (Intercept)-Lynx_rufus 101
## (Intercept)-Didelphis_virginiana 316
## (Intercept)-Sylvilagus_floridanus 216
## (Intercept)-Meleagris_gallopavo 165
## (Intercept)-Sciurus_carolinensis 309
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.49
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6627 0.4917 -1.5789 -0.6772 0.3891 1.0218 992
## Veg_shannon_index 0.2912 0.3902 -0.4825 0.2909 1.0690 1.0015 1604
## Avg_Cogongrass_Cover 0.2480 0.4003 -0.5499 0.2413 1.0548 1.0083 1535
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0127 1.8010 0.0687 0.5344 4.8008 1.1652 790
## Veg_shannon_index 0.6183 0.9220 0.0490 0.3248 3.0537 1.0039 1011
## Avg_Cogongrass_Cover 0.6996 1.6046 0.0517 0.3587 3.3880 1.1293 890
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8971 1.1992 0.0501 0.5223 3.8822 1.2645 146
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6614 0.2806 -5.1339 -4.6817 -4.0517 1.0109 1162
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3533 0.6555 0.0535 0.2149 1.4412 1.1479 1782
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0972 0.7048 -1.1518 0.0599
## (Intercept)-Lynx_rufus -0.3420 0.6753 -1.5858 -0.3852
## (Intercept)-Didelphis_virginiana -1.2333 0.5963 -2.5199 -1.2167
## (Intercept)-Sylvilagus_floridanus -0.6958 0.5395 -1.7623 -0.7082
## (Intercept)-Meleagris_gallopavo -0.8015 0.5914 -1.9438 -0.7968
## (Intercept)-Sciurus_carolinensis -1.2554 0.6118 -2.5160 -1.2190
## Veg_shannon_index-Canis_latrans 0.7721 0.4705 -0.0405 0.7328
## Veg_shannon_index-Lynx_rufus -0.2410 0.6533 -1.7563 -0.1671
## Veg_shannon_index-Didelphis_virginiana 0.5089 0.4344 -0.2809 0.4811
## Veg_shannon_index-Sylvilagus_floridanus 0.4218 0.4535 -0.4147 0.3991
## Veg_shannon_index-Meleagris_gallopavo 0.4459 0.4745 -0.4308 0.4218
## Veg_shannon_index-Sciurus_carolinensis -0.0956 0.4514 -1.0597 -0.0743
## Avg_Cogongrass_Cover-Canis_latrans 0.6087 0.4767 -0.2123 0.5582
## Avg_Cogongrass_Cover-Lynx_rufus 0.6239 0.5433 -0.2402 0.5542
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4791 0.4204 -0.2985 0.4650
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2630 0.4891 -1.3333 -0.2352
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3240 0.5758 -1.6693 -0.2635
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3881 0.4035 -0.3610 0.3724
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5694 1.0646 461
## (Intercept)-Lynx_rufus 1.1249 1.0161 591
## (Intercept)-Didelphis_virginiana -0.1024 1.0038 797
## (Intercept)-Sylvilagus_floridanus 0.3731 1.0214 893
## (Intercept)-Meleagris_gallopavo 0.3664 1.0218 709
## (Intercept)-Sciurus_carolinensis -0.1431 1.0192 708
## Veg_shannon_index-Canis_latrans 1.8071 1.0014 1257
## Veg_shannon_index-Lynx_rufus 0.7581 1.0082 741
## Veg_shannon_index-Didelphis_virginiana 1.4374 1.0028 1879
## Veg_shannon_index-Sylvilagus_floridanus 1.3659 1.0000 1821
## Veg_shannon_index-Meleagris_gallopavo 1.4897 1.0012 1397
## Veg_shannon_index-Sciurus_carolinensis 0.7348 1.0004 1353
## Avg_Cogongrass_Cover-Canis_latrans 1.6734 1.0188 1372
## Avg_Cogongrass_Cover-Lynx_rufus 1.8418 1.0538 395
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3552 1.0063 1710
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6107 1.0000 1382
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6784 1.0038 1064
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2200 1.0058 1879
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6460 0.1566 -4.9710 -4.6403 -4.3565 1.0168
## (Intercept)-Lynx_rufus -5.2439 0.2543 -5.7649 -5.2430 -4.7727 1.0097
## (Intercept)-Didelphis_virginiana -4.3636 0.2389 -4.8557 -4.3616 -3.9205 1.0250
## (Intercept)-Sylvilagus_floridanus -4.8772 0.2190 -5.3038 -4.8775 -4.4687 1.0445
## (Intercept)-Meleagris_gallopavo -5.0024 0.2633 -5.5292 -4.9920 -4.5129 1.0050
## (Intercept)-Sciurus_carolinensis -4.4135 0.2222 -4.8641 -4.4076 -3.9890 1.1065
## ESS
## (Intercept)-Canis_latrans 239
## (Intercept)-Lynx_rufus 108
## (Intercept)-Didelphis_virginiana 321
## (Intercept)-Sylvilagus_floridanus 257
## (Intercept)-Meleagris_gallopavo 148
## (Intercept)-Sciurus_carolinensis 335
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.483
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2175 0.5017 -2.1489 -1.2296 -0.1921 1.0186 663
## Avg_Cogongrass_Cover -0.5402 0.4809 -1.4536 -0.5353 0.4078 1.0192 723
## I(Avg_Cogongrass_Cover^2) 0.6663 0.4662 -0.2211 0.6455 1.6345 1.0058 756
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8861 1.6260 0.0524 0.4729 3.9790 1.0360 1517
## Avg_Cogongrass_Cover 0.7227 1.3581 0.0502 0.3668 3.4920 1.0937 1422
## I(Avg_Cogongrass_Cover^2) 0.9712 2.9917 0.0482 0.4000 4.8329 1.1201 1112
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6366 0.7124 0.0482 0.4081 2.564 1.0286 309
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6622 0.2687 -5.1159 -4.6845 -4.0983 1.0229 1177
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.324 0.5211 0.0466 0.1996 1.289 1.0016 622
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6250 0.6707 -1.7995 -0.6525
## (Intercept)-Lynx_rufus -1.1348 0.6430 -2.3171 -1.1485
## (Intercept)-Didelphis_virginiana -1.6226 0.6076 -2.8783 -1.5981
## (Intercept)-Sylvilagus_floridanus -1.2725 0.6031 -2.5295 -1.2545
## (Intercept)-Meleagris_gallopavo -1.0817 0.6305 -2.3084 -1.1028
## (Intercept)-Sciurus_carolinensis -1.9284 0.6850 -3.4588 -1.8524
## Avg_Cogongrass_Cover-Canis_latrans -0.3074 0.6019 -1.4407 -0.3428
## Avg_Cogongrass_Cover-Lynx_rufus -0.3163 0.6223 -1.4897 -0.3565
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1766 0.5894 -1.2414 -0.2096
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1030 0.6669 -2.5963 -1.0409
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8687 0.6336 -2.1808 -0.8422
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5933 0.6039 -1.8542 -0.5717
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3636 0.9547 0.1194 1.1319
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.0820 0.6110 0.1167 1.0130
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.3930 0.4628 -0.4426 0.3737
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6338 0.4660 -0.2025 0.5917
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0098 0.6310 -1.4710 0.0776
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8091 0.4236 0.0635 0.7803
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7894 1.0159 803
## (Intercept)-Lynx_rufus 0.2349 1.0338 628
## (Intercept)-Didelphis_virginiana -0.5128 1.0113 1072
## (Intercept)-Sylvilagus_floridanus -0.1215 1.0051 756
## (Intercept)-Meleagris_gallopavo 0.1939 1.0248 597
## (Intercept)-Sciurus_carolinensis -0.7661 1.0017 821
## Avg_Cogongrass_Cover-Canis_latrans 0.9864 1.0154 907
## Avg_Cogongrass_Cover-Lynx_rufus 0.9158 1.0216 892
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0619 1.0168 928
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0486 1.0129 750
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.3150 1.0237 906
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5397 1.0061 920
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7173 1.0153 339
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5509 1.0069 598
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3860 1.0155 598
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7151 1.0057 741
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0284 1.0027 628
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7434 1.0034 755
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6728 0.1569 -4.9951 -4.6689 -4.3811 1.0092
## (Intercept)-Lynx_rufus -5.1822 0.2564 -5.7188 -5.1706 -4.7250 1.0186
## (Intercept)-Didelphis_virginiana -4.3814 0.2441 -4.8802 -4.3755 -3.9273 1.0894
## (Intercept)-Sylvilagus_floridanus -4.8494 0.2328 -5.3242 -4.8355 -4.4363 1.0494
## (Intercept)-Meleagris_gallopavo -5.0145 0.2503 -5.5325 -5.0009 -4.5668 1.0529
## (Intercept)-Sciurus_carolinensis -4.4116 0.2249 -4.8566 -4.4100 -3.9927 1.0210
## ESS
## (Intercept)-Canis_latrans 229
## (Intercept)-Lynx_rufus 108
## (Intercept)-Didelphis_virginiana 248
## (Intercept)-Sylvilagus_floridanus 163
## (Intercept)-Meleagris_gallopavo 181
## (Intercept)-Sciurus_carolinensis 325
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.8302
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6452 1.1719 -3.6910 -1.7368 0.9225 1.0986 432
## Cogon_Patch_Size 0.3441 1.1161 -2.0034 0.3795 2.4665 1.0164 725
## Veg_shannon_index 0.7778 0.8665 -0.9546 0.7739 2.6153 1.0675 405
## total_shrub_cover -0.8531 1.0034 -2.9386 -0.8409 1.1496 1.0192 835
## Avg_Cogongrass_Cover -0.0131 1.1024 -2.2019 0.0305 2.0638 1.0133 266
## Tree_Density -2.1843 1.0997 -4.1911 -2.2257 0.3664 1.0841 126
## Avg_Canopy_Cover 1.4985 1.1441 -1.2079 1.6078 3.4395 1.1384 277
## I(Avg_Cogongrass_Cover^2) 1.0374 1.0108 -1.2045 1.0819 2.9285 1.0914 419
## avg_veg_height -0.4197 0.7334 -1.8815 -0.4033 0.9843 1.0407 297
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.5454 17.7735 0.1471 5.0034 54.2262 1.1368 314
## Cogon_Patch_Size 32.7820 122.7913 0.3173 5.6215 283.4595 2.9083 50
## Veg_shannon_index 4.0892 8.3659 0.0893 1.5464 23.7616 1.3435 151
## total_shrub_cover 10.7122 19.0279 0.2639 5.1761 62.0053 1.2960 213
## Avg_Cogongrass_Cover 2.5515 6.4540 0.0599 0.8457 15.5582 1.0509 568
## Tree_Density 10.6510 51.1668 0.0489 0.7573 101.4100 2.7556 47
## Avg_Canopy_Cover 79.9690 315.8139 0.2915 5.3327 835.3989 3.9609 11
## I(Avg_Cogongrass_Cover^2) 74.5725 389.7613 0.0929 2.8432 804.2772 3.1695 22
## avg_veg_height 1.1523 2.8561 0.0460 0.3826 7.6414 1.2196 252
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 14.9236 44.6444 0.0752 1.4813 158.8526 4.8287 11
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6954 0.3828 -5.2834 -4.7383 -3.7847 1.0493 1114
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6064 1.1879 0.083 0.3517 2.598 1.0347 1507
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.6363 1.7408 -3.7282
## (Intercept)-Lynx_rufus 0.0000 2.5652 -3.9032
## (Intercept)-Didelphis_virginiana -3.9828 2.0026 -8.7267
## (Intercept)-Sylvilagus_floridanus -2.6199 1.7159 -6.2506
## (Intercept)-Meleagris_gallopavo -2.3972 1.8076 -6.2116
## (Intercept)-Sciurus_carolinensis -4.4429 2.1576 -8.9644
## Cogon_Patch_Size-Canis_latrans 3.5613 4.6832 -0.0652
## Cogon_Patch_Size-Lynx_rufus -0.6683 5.8213 -17.8195
## Cogon_Patch_Size-Didelphis_virginiana 3.1926 2.9372 0.2781
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6642 3.4884 -13.3855
## Cogon_Patch_Size-Meleagris_gallopavo 1.1939 3.0566 -2.1369
## Cogon_Patch_Size-Sciurus_carolinensis -1.6469 3.3377 -11.0821
## Veg_shannon_index-Canis_latrans 1.9411 1.3235 0.0327
## Veg_shannon_index-Lynx_rufus -0.2840 1.7908 -4.6729
## Veg_shannon_index-Didelphis_virginiana 1.2309 1.1913 -0.6979
## Veg_shannon_index-Sylvilagus_floridanus 1.3381 1.5727 -0.6056
## Veg_shannon_index-Meleagris_gallopavo 1.7146 1.5244 -0.5761
## Veg_shannon_index-Sciurus_carolinensis -0.2295 1.4670 -3.0729
## total_shrub_cover-Canis_latrans 0.8921 1.4066 -1.5715
## total_shrub_cover-Lynx_rufus -2.9890 2.5985 -9.5368
## total_shrub_cover-Didelphis_virginiana -1.1698 1.3226 -4.4079
## total_shrub_cover-Sylvilagus_floridanus -0.3230 1.5416 -3.5267
## total_shrub_cover-Meleagris_gallopavo -4.2690 2.4627 -10.0183
## total_shrub_cover-Sciurus_carolinensis 0.0244 1.5672 -3.8365
## Avg_Cogongrass_Cover-Canis_latrans -0.0210 1.5508 -3.2671
## Avg_Cogongrass_Cover-Lynx_rufus 0.4701 1.7490 -2.6601
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3213 1.4938 -2.5000
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5520 1.5635 -4.2079
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4077 1.7666 -4.1070
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1911 1.5832 -2.9577
## Tree_Density-Canis_latrans -3.4185 2.8383 -12.4831
## Tree_Density-Lynx_rufus -1.4071 2.9151 -4.6827
## Tree_Density-Didelphis_virginiana -2.7963 1.8496 -7.6578
## Tree_Density-Sylvilagus_floridanus -3.5731 3.3647 -14.8081
## Tree_Density-Meleagris_gallopavo -2.3840 1.8633 -6.1045
## Tree_Density-Sciurus_carolinensis -2.5069 1.9608 -6.0442
## Avg_Canopy_Cover-Canis_latrans -0.4148 1.6842 -5.2710
## Avg_Canopy_Cover-Lynx_rufus -0.4529 4.4790 -14.4156
## Avg_Canopy_Cover-Didelphis_virginiana 4.1944 3.4320 1.2941
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.4318 8.4095 1.4175
## Avg_Canopy_Cover-Meleagris_gallopavo 4.1516 6.4318 0.5342
## Avg_Canopy_Cover-Sciurus_carolinensis 6.0338 10.7292 0.9410
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7115 7.1360 0.5835
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.6205 12.3843 0.2704
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2199 1.3131 -0.6011
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8874 1.3583 -2.3982
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -1.5500 3.3960 -12.5460
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.4175 2.9252 -0.0062
## avg_veg_height-Canis_latrans -0.5775 0.8639 -2.3689
## avg_veg_height-Lynx_rufus -0.5055 1.2078 -2.9368
## avg_veg_height-Didelphis_virginiana -0.4948 0.9591 -2.3152
## avg_veg_height-Sylvilagus_floridanus -0.5038 0.9747 -2.4515
## avg_veg_height-Meleagris_gallopavo -0.5288 1.1731 -2.8550
## avg_veg_height-Sciurus_carolinensis -0.0167 1.0161 -1.7761
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.7544 3.2085 1.0541 318
## (Intercept)-Lynx_rufus -0.3922 6.2941 1.1103 143
## (Intercept)-Didelphis_virginiana -3.7756 -0.4306 1.0712 208
## (Intercept)-Sylvilagus_floridanus -2.5360 0.6836 1.0584 230
## (Intercept)-Meleagris_gallopavo -2.3642 1.2823 1.1191 203
## (Intercept)-Sciurus_carolinensis -4.3243 -0.4886 1.0762 146
## Cogon_Patch_Size-Canis_latrans 2.3932 14.9898 2.2527 42
## Cogon_Patch_Size-Lynx_rufus 0.2633 6.1110 2.4811 23
## Cogon_Patch_Size-Didelphis_virginiana 2.3961 12.3750 3.2380 16
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6791 1.0991 1.9717 29
## Cogon_Patch_Size-Meleagris_gallopavo 0.5881 10.8528 2.0554 29
## Cogon_Patch_Size-Sciurus_carolinensis -0.9780 1.8184 1.6726 122
## Veg_shannon_index-Canis_latrans 1.7021 5.3150 1.1937 312
## Veg_shannon_index-Lynx_rufus -0.1025 2.7360 1.0623 286
## Veg_shannon_index-Didelphis_virginiana 1.0922 3.9755 1.1600 290
## Veg_shannon_index-Sylvilagus_floridanus 1.0842 4.8728 1.7161 137
## Veg_shannon_index-Meleagris_gallopavo 1.5042 5.1658 1.0774 336
## Veg_shannon_index-Sciurus_carolinensis -0.1454 2.2970 1.1296 278
## total_shrub_cover-Canis_latrans 0.7431 3.9493 1.1201 205
## total_shrub_cover-Lynx_rufus -2.5411 0.6091 1.0575 138
## total_shrub_cover-Didelphis_virginiana -0.9897 0.8091 1.3611 221
## total_shrub_cover-Sylvilagus_floridanus -0.2830 2.7173 1.1224 288
## total_shrub_cover-Meleagris_gallopavo -3.9256 -0.6793 1.2520 122
## total_shrub_cover-Sciurus_carolinensis 0.1617 2.3605 1.4510 177
## Avg_Cogongrass_Cover-Canis_latrans 0.0889 2.9054 1.0621 323
## Avg_Cogongrass_Cover-Lynx_rufus 0.4419 4.2329 1.0179 419
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3411 3.3441 1.0288 448
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4421 2.1723 1.0095 365
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3070 2.5465 1.0362 386
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2122 3.0941 1.0431 370
## Tree_Density-Canis_latrans -2.8508 -0.6105 1.9452 100
## Tree_Density-Lynx_rufus -1.9293 8.0256 2.0447 48
## Tree_Density-Didelphis_virginiana -2.5120 -0.2310 1.8810 82
## Tree_Density-Sylvilagus_floridanus -2.7892 -0.5578 3.0897 16
## Tree_Density-Meleagris_gallopavo -2.3247 1.1042 1.1483 384
## Tree_Density-Sciurus_carolinensis -2.5415 1.8118 1.1742 98
## Avg_Canopy_Cover-Canis_latrans -0.1452 1.7167 2.1194 62
## Avg_Canopy_Cover-Lynx_rufus 0.3720 5.2571 2.5343 31
## Avg_Canopy_Cover-Didelphis_virginiana 3.2642 14.6377 3.8835 20
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.8018 36.4081 4.6158 9
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4800 26.2782 3.9641 15
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6165 44.1623 5.6024 5
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4602 28.3086 5.1997 12
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7248 51.6102 4.7821 8
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0023 4.9633 1.9836 29
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9443 3.4591 1.3147 152
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.6968 1.8843 2.7704 18
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5815 12.2929 3.9076 14
## avg_veg_height-Canis_latrans -0.5381 0.9746 1.1137 321
## avg_veg_height-Lynx_rufus -0.4643 1.7501 1.0577 442
## avg_veg_height-Didelphis_virginiana -0.4846 1.2357 1.0582 681
## avg_veg_height-Sylvilagus_floridanus -0.4679 1.3470 1.0614 427
## avg_veg_height-Meleagris_gallopavo -0.5235 1.7071 1.0889 343
## avg_veg_height-Sciurus_carolinensis -0.0817 2.1285 1.0646 385
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6831 0.1635 -5.0252 -4.6795 -4.3792 1.0664
## (Intercept)-Lynx_rufus -5.6106 0.3156 -6.2377 -5.6080 -5.0183 1.2474
## (Intercept)-Didelphis_virginiana -4.3262 0.2233 -4.7658 -4.3232 -3.9039 1.0750
## (Intercept)-Sylvilagus_floridanus -4.9610 0.2247 -5.4487 -4.9437 -4.5541 1.0107
## (Intercept)-Meleagris_gallopavo -5.0979 0.2551 -5.6289 -5.0933 -4.6416 1.0208
## (Intercept)-Sciurus_carolinensis -4.4706 0.3226 -5.2887 -4.4125 -3.9763 1.9524
## ESS
## (Intercept)-Canis_latrans 213
## (Intercept)-Lynx_rufus 64
## (Intercept)-Didelphis_virginiana 312
## (Intercept)-Sylvilagus_floridanus 180
## (Intercept)-Meleagris_gallopavo 143
## (Intercept)-Sciurus_carolinensis 20
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.0203
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4261 0.4993 -1.3959 -0.4211 0.5439 1.0202 582
## Avg_Cogongrass_Cover 0.1474 0.3961 -0.6284 0.1507 0.8804 1.0117 795
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9262 1.7229 0.0632 0.5032 4.4277 1.1975 759
## Avg_Cogongrass_Cover 0.7343 1.3731 0.0505 0.3691 3.6459 1.0138 612
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0116 1.9308 0.0618 0.534 4.6766 1.2911 172
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8557 0.3868 -5.4513 -4.8963 -3.9320 1.0104 1044
## shrub_cover 0.1971 0.4327 -0.6709 0.2052 1.0742 1.0032 865
## veg_height 0.0358 0.2620 -0.4942 0.0393 0.5468 1.0074 638
## week -0.0305 1.7450 -3.4863 0.0600 3.2246 1.0099 408
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.456000e-01 1.345700e+00 0.0619 0.3563 2.798000e+00 1.0856 561
## shrub_cover 1.046800e+00 1.491500e+00 0.1689 0.7044 3.741100e+00 1.0811 801
## veg_height 3.662000e-01 4.312000e-01 0.0587 0.2432 1.433200e+00 1.0198 1281
## week 5.461809e+14 9.267204e+15 0.0789 33.1113 3.734231e+12 1.6004 290
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2122 0.6506 -1.0029 0.1948
## (Intercept)-Lynx_rufus -0.1520 0.6910 -1.4742 -0.1816
## (Intercept)-Didelphis_virginiana -0.9464 0.5987 -2.2011 -0.9216
## (Intercept)-Sylvilagus_floridanus -0.5127 0.5858 -1.6338 -0.5028
## (Intercept)-Meleagris_gallopavo -0.2516 0.7159 -1.6660 -0.2698
## (Intercept)-Sciurus_carolinensis -1.0063 0.6177 -2.3034 -0.9821
## Avg_Cogongrass_Cover-Canis_latrans 0.4813 0.4668 -0.2995 0.4304
## Avg_Cogongrass_Cover-Lynx_rufus 0.5408 0.5199 -0.2880 0.4738
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3210 0.4108 -0.4670 0.3088
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3313 0.4850 -1.4095 -0.2899
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5011 0.7754 -2.1902 -0.4119
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3554 0.4097 -0.4386 0.3483
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5427 1.0299 536
## (Intercept)-Lynx_rufus 1.3001 1.0578 258
## (Intercept)-Didelphis_virginiana 0.1263 1.0085 751
## (Intercept)-Sylvilagus_floridanus 0.6206 1.0225 504
## (Intercept)-Meleagris_gallopavo 1.2423 1.0239 230
## (Intercept)-Sciurus_carolinensis 0.1015 1.0032 520
## Avg_Cogongrass_Cover-Canis_latrans 1.5379 1.0044 1046
## Avg_Cogongrass_Cover-Lynx_rufus 1.7860 1.0063 809
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1834 1.0010 1573
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5221 1.0381 825
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7163 1.0609 429
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1768 1.0015 1624
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7690 1.605000e-01 -5.0787
## (Intercept)-Lynx_rufus -5.4945 3.013000e-01 -6.1021
## (Intercept)-Didelphis_virginiana -4.6477 2.767000e-01 -5.2079
## (Intercept)-Sylvilagus_floridanus -4.9580 2.364000e-01 -5.4116
## (Intercept)-Meleagris_gallopavo -5.7137 4.723000e-01 -6.6171
## (Intercept)-Sciurus_carolinensis -4.6580 2.904000e-01 -5.2404
## shrub_cover-Canis_latrans -0.2591 1.912000e-01 -0.6390
## shrub_cover-Lynx_rufus -0.1991 3.333000e-01 -0.8632
## shrub_cover-Didelphis_virginiana 1.0696 3.702000e-01 0.4074
## shrub_cover-Sylvilagus_floridanus 0.4697 4.145000e-01 -0.3398
## shrub_cover-Meleagris_gallopavo -0.7777 4.198000e-01 -1.6515
## shrub_cover-Sciurus_carolinensis 0.8686 3.852000e-01 0.1544
## veg_height-Canis_latrans -0.5662 1.731000e-01 -0.9279
## veg_height-Lynx_rufus 0.0694 2.450000e-01 -0.4252
## veg_height-Didelphis_virginiana 0.5022 2.558000e-01 0.0005
## veg_height-Sylvilagus_floridanus 0.1716 2.180000e-01 -0.2642
## veg_height-Meleagris_gallopavo -0.1716 4.236000e-01 -0.9643
## veg_height-Sciurus_carolinensis 0.1849 2.118000e-01 -0.2252
## week-Canis_latrans -419968.6738 2.163046e+07 -85417.4024
## week-Lynx_rufus -352356.7092 2.343524e+07 -139834.5905
## week-Didelphis_virginiana 130450.7088 1.701860e+07 -91714.9793
## week-Sylvilagus_floridanus 228551.0915 2.173121e+07 -82718.3812
## week-Meleagris_gallopavo -849670.3237 2.228729e+07 -171958.5492
## week-Sciurus_carolinensis -302868.9385 1.595348e+07 -125078.8727
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7694 -4.4593 1.0909 216
## (Intercept)-Lynx_rufus -5.5006 -4.9359 1.0807 99
## (Intercept)-Didelphis_virginiana -4.6500 -4.1298 1.0796 160
## (Intercept)-Sylvilagus_floridanus -4.9598 -4.5076 1.0401 182
## (Intercept)-Meleagris_gallopavo -5.6922 -4.7981 1.1140 63
## (Intercept)-Sciurus_carolinensis -4.6400 -4.1010 1.0136 222
## shrub_cover-Canis_latrans -0.2586 0.1096 1.0398 216
## shrub_cover-Lynx_rufus -0.2174 0.5040 1.1082 114
## shrub_cover-Didelphis_virginiana 1.0500 1.8361 1.0150 112
## shrub_cover-Sylvilagus_floridanus 0.4574 1.2584 1.0738 116
## shrub_cover-Meleagris_gallopavo -0.7766 0.0599 1.0572 68
## shrub_cover-Sciurus_carolinensis 0.8460 1.6954 1.0169 174
## veg_height-Canis_latrans -0.5653 -0.2364 1.0046 195
## veg_height-Lynx_rufus 0.0713 0.5349 1.0456 162
## veg_height-Didelphis_virginiana 0.4985 1.0165 1.0772 194
## veg_height-Sylvilagus_floridanus 0.1822 0.5813 1.0620 265
## veg_height-Meleagris_gallopavo -0.1838 0.6588 1.0413 115
## veg_height-Sciurus_carolinensis 0.1814 0.6032 1.0205 236
## week-Canis_latrans 0.2431 152556.5330 1.3274 3220
## week-Lynx_rufus -0.0488 103480.6244 1.3127 3773
## week-Didelphis_virginiana 0.2848 134529.8278 1.2962 7262
## week-Sylvilagus_floridanus 0.1907 152435.0112 1.3014 12704
## week-Meleagris_gallopavo 0.1558 91662.3505 1.4282 700
## week-Sciurus_carolinensis 0.0111 96824.1921 1.3259 2111
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.494
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6396 0.4634 -1.5723 -0.6454 0.2851 1.0040 874
## Avg_Cogongrass_Cover 0.1626 0.3762 -0.5807 0.1645 0.8891 1.0066 1729
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8605 1.3688 0.0585 0.4623 4.0393 1.0117 1054
## Avg_Cogongrass_Cover 0.6037 0.9176 0.0522 0.3442 2.8764 1.0313 900
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9015 0.8813 0.0759 0.6374 3.3189 1.0047 340
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6492 0.3077 -5.1528 -4.6718 -3.9943 1.0367 1338
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.413 0.7612 0.0523 0.2389 1.7321 1.1219 379
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0432 0.5844 -0.9984 0.0080
## (Intercept)-Lynx_rufus -0.3700 0.6847 -1.6110 -0.4096
## (Intercept)-Didelphis_virginiana -1.1308 0.5761 -2.3306 -1.0966
## (Intercept)-Sylvilagus_floridanus -0.6633 0.5630 -1.7659 -0.6686
## (Intercept)-Meleagris_gallopavo -0.7732 0.5943 -2.0080 -0.7633
## (Intercept)-Sciurus_carolinensis -1.1776 0.5810 -2.4165 -1.1375
## Avg_Cogongrass_Cover-Canis_latrans 0.3866 0.4015 -0.3472 0.3653
## Avg_Cogongrass_Cover-Lynx_rufus 0.5850 0.5041 -0.2373 0.5327
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3644 0.4041 -0.4154 0.3553
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3186 0.4669 -1.3166 -0.2864
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4029 0.5469 -1.5892 -0.3538
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3605 0.3817 -0.3539 0.3446
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2943 1.0046 799
## (Intercept)-Lynx_rufus 1.0853 1.0083 470
## (Intercept)-Didelphis_virginiana -0.0348 0.9999 978
## (Intercept)-Sylvilagus_floridanus 0.4624 1.0024 773
## (Intercept)-Meleagris_gallopavo 0.3687 1.0082 748
## (Intercept)-Sciurus_carolinensis -0.1459 1.0026 825
## Avg_Cogongrass_Cover-Canis_latrans 1.2561 1.0003 2145
## Avg_Cogongrass_Cover-Lynx_rufus 1.7467 1.0046 1070
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1501 1.0020 1927
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4748 1.0031 1505
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5089 1.0032 1180
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1175 1.0027 1986
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6585 0.1561 -4.9865 -4.6530 -4.3663 1.0440
## (Intercept)-Lynx_rufus -5.2678 0.2939 -5.9716 -5.2473 -4.7833 1.1253
## (Intercept)-Didelphis_virginiana -4.3438 0.2253 -4.7835 -4.3424 -3.9158 1.0153
## (Intercept)-Sylvilagus_floridanus -4.8681 0.2295 -5.3567 -4.8565 -4.4453 1.1320
## (Intercept)-Meleagris_gallopavo -5.0302 0.2798 -5.6254 -5.0102 -4.5235 1.0118
## (Intercept)-Sciurus_carolinensis -4.3752 0.2231 -4.8182 -4.3706 -3.9595 1.0259
## ESS
## (Intercept)-Canis_latrans 230
## (Intercept)-Lynx_rufus 72
## (Intercept)-Didelphis_virginiana 331
## (Intercept)-Sylvilagus_floridanus 188
## (Intercept)-Meleagris_gallopavo 138
## (Intercept)-Sciurus_carolinensis 354
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.336
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6617 0.4272 -1.5391 -0.6582 0.1787 1.0226 863
## Avg_Cogongrass_Cover 0.1544 0.3533 -0.5745 0.1631 0.8393 1.0062 1647
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7578 1.0879 0.0492 0.4174 3.5355 1.0104 1034
## Avg_Cogongrass_Cover 0.5976 0.9716 0.0514 0.3376 2.6693 1.0089 1144
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9316 1.069 0.0615 0.6088 3.6049 1.0458 277
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6537 0.3124 -5.1756 -4.6728 -3.9757 1.0072 1135
## week 0.0870 1.6318 -3.2912 0.0957 3.3189 1.0054 510
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.096000e-01 8.204000e-01 0.0547 0.2404 1.595400e+00 1.0110 1237
## week 1.891299e+11 4.555207e+12 0.0829 13.2916 4.156968e+08 1.4523 595
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0117 0.5859 -1.0711 -0.0408
## (Intercept)-Lynx_rufus -0.4240 0.6092 -1.5196 -0.4541
## (Intercept)-Didelphis_virginiana -1.1246 0.5619 -2.3900 -1.0750
## (Intercept)-Sylvilagus_floridanus -0.6612 0.5307 -1.7304 -0.6651
## (Intercept)-Meleagris_gallopavo -0.7868 0.5363 -1.9146 -0.7789
## (Intercept)-Sciurus_carolinensis -1.1531 0.5779 -2.4271 -1.1244
## Avg_Cogongrass_Cover-Canis_latrans 0.3800 0.3925 -0.3259 0.3504
## Avg_Cogongrass_Cover-Lynx_rufus 0.5706 0.4778 -0.2009 0.5146
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3693 0.3892 -0.3736 0.3573
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3171 0.4498 -1.2809 -0.2853
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3889 0.5038 -1.5142 -0.3349
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3580 0.3937 -0.3971 0.3504
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2314 1.0079 695
## (Intercept)-Lynx_rufus 0.9032 1.0187 514
## (Intercept)-Didelphis_virginiana -0.1261 1.0056 856
## (Intercept)-Sylvilagus_floridanus 0.4200 1.0017 902
## (Intercept)-Meleagris_gallopavo 0.2616 1.0310 739
## (Intercept)-Sciurus_carolinensis -0.0965 1.0222 866
## Avg_Cogongrass_Cover-Canis_latrans 1.2178 1.0069 1777
## Avg_Cogongrass_Cover-Lynx_rufus 1.7214 1.0080 1138
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1537 1.0009 1976
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4612 1.0022 1513
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4547 1.0104 1206
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1613 1.0009 2062
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6432 0.1506 -4.9630 -4.6394
## (Intercept)-Lynx_rufus -5.3164 0.2725 -5.8989 -5.3118
## (Intercept)-Didelphis_virginiana -4.3550 0.2196 -4.7979 -4.3479
## (Intercept)-Sylvilagus_floridanus -4.8437 0.2360 -5.3267 -4.8253
## (Intercept)-Meleagris_gallopavo -5.0044 0.2615 -5.5573 -4.9837
## (Intercept)-Sciurus_carolinensis -4.4092 0.2511 -4.9181 -4.4032
## week-Canis_latrans -5024.6223 278780.4952 -3388.4332 0.1515
## week-Lynx_rufus 4022.8651 271413.2403 -1897.9314 0.2227
## week-Didelphis_virginiana -150.0254 452489.3729 -993.9289 0.2572
## week-Sylvilagus_floridanus -5766.5531 317222.4165 -1947.1449 0.2515
## week-Meleagris_gallopavo 3077.1137 252188.4838 -1720.5793 0.2784
## week-Sciurus_carolinensis 6858.6289 470080.4373 -2026.4335 0.1588
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3674 1.0179 284
## (Intercept)-Lynx_rufus -4.8146 1.1601 126
## (Intercept)-Didelphis_virginiana -3.9305 1.0038 289
## (Intercept)-Sylvilagus_floridanus -4.4170 1.0115 181
## (Intercept)-Meleagris_gallopavo -4.5323 1.0406 135
## (Intercept)-Sciurus_carolinensis -3.9380 1.0023 254
## week-Canis_latrans 721.1203 1.3223 2467
## week-Lynx_rufus 1143.8609 1.3120 4494
## week-Didelphis_virginiana 2683.3328 1.2904 416147
## week-Sylvilagus_floridanus 1113.5415 1.3228 2869
## week-Meleagris_gallopavo 1271.2786 1.3051 6152
## week-Sciurus_carolinensis 1921.4962 1.3114 4796
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.6002
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9873 0.9776 -2.8554 -1.0005 1.1114 1.0207 1367
## Cogon_Patch_Size -0.0919 0.9355 -1.8739 -0.1165 1.8853 1.0036 974
## Veg_shannon_index 0.7204 0.6524 -0.6119 0.7113 2.0221 1.0033 595
## total_shrub_cover -0.6741 0.8678 -2.4259 -0.6621 1.0526 1.0061 1001
## Avg_Cogongrass_Cover 1.5473 0.8207 -0.1008 1.5566 3.2150 1.0204 420
## Tree_Density -1.8340 0.8535 -3.5668 -1.8188 -0.1215 1.0085 385
## Avg_Canopy_Cover 1.5195 0.8919 -0.3675 1.5229 3.2932 1.0025 1205
## avg_veg_height -0.5945 0.6091 -1.8207 -0.5810 0.6126 1.0028 410
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.1850 14.1229 0.5439 4.6642 35.6613 1.0821 441
## Cogon_Patch_Size 7.2015 14.6650 0.2155 3.2389 36.6868 1.1116 333
## Veg_shannon_index 1.8841 4.6668 0.0554 0.7333 9.3565 1.0600 414
## total_shrub_cover 6.7291 25.1082 0.2138 3.0702 31.1208 1.2807 983
## Avg_Cogongrass_Cover 2.4675 5.3904 0.0648 0.9292 13.6579 1.0677 474
## Tree_Density 2.9437 8.2014 0.0544 0.7381 23.3957 1.1546 285
## Avg_Canopy_Cover 6.3273 10.9866 0.2578 3.1981 32.9616 1.0749 300
## avg_veg_height 0.8251 1.7082 0.0455 0.3588 4.2508 1.0000 864
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1021 3.6501 0.0771 0.8761 12.7711 1.2291 85
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6686 0.3397 -5.2006 -4.7080 -3.9292 1.0113 1602
## week 0.0262 1.6434 -3.1897 0.0731 3.1885 1.0452 483
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5178 9.044000e-01 0.0758 0.3132 2.1054 1.0226 1269
## week 1629157.2213 2.314852e+07 0.0858 17.0759 1175913.1690 1.6453 239
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.9480 1.1939 -1.1154 0.8287
## (Intercept)-Lynx_rufus 0.9967 1.7182 -1.7517 0.7854
## (Intercept)-Didelphis_virginiana -2.9910 1.2416 -5.8321 -2.9170
## (Intercept)-Sylvilagus_floridanus -1.6638 1.2161 -4.2432 -1.6152
## (Intercept)-Meleagris_gallopavo -2.0388 1.4094 -5.0742 -1.9384
## (Intercept)-Sciurus_carolinensis -3.2271 1.4751 -6.3825 -3.0181
## Cogon_Patch_Size-Canis_latrans 1.3734 1.5602 -0.7568 1.0782
## Cogon_Patch_Size-Lynx_rufus -0.0689 2.0034 -3.7555 -0.2065
## Cogon_Patch_Size-Didelphis_virginiana 1.4944 1.1503 -0.2989 1.3669
## Cogon_Patch_Size-Sylvilagus_floridanus -2.1029 2.0439 -7.3483 -1.6929
## Cogon_Patch_Size-Meleagris_gallopavo 0.0959 1.4771 -2.2247 -0.1040
## Cogon_Patch_Size-Sciurus_carolinensis -1.7645 1.6200 -6.0925 -1.3943
## Veg_shannon_index-Canis_latrans 1.3457 0.8321 -0.0675 1.2494
## Veg_shannon_index-Lynx_rufus 0.0285 1.1323 -2.6966 0.1603
## Veg_shannon_index-Didelphis_virginiana 1.0522 0.8389 -0.3993 0.9850
## Veg_shannon_index-Sylvilagus_floridanus 1.0233 0.8573 -0.4065 0.9488
## Veg_shannon_index-Meleagris_gallopavo 1.3860 1.1245 -0.1641 1.1825
## Veg_shannon_index-Sciurus_carolinensis -0.0490 0.8657 -1.9997 0.0594
## total_shrub_cover-Canis_latrans 0.8337 1.0503 -0.7938 0.6591
## total_shrub_cover-Lynx_rufus -2.2198 2.0169 -7.2101 -1.8097
## total_shrub_cover-Didelphis_virginiana -0.7943 0.9862 -3.0073 -0.7262
## total_shrub_cover-Sylvilagus_floridanus -0.1803 1.1201 -2.4583 -0.1905
## total_shrub_cover-Meleagris_gallopavo -3.3408 1.9115 -7.6961 -3.0693
## total_shrub_cover-Sciurus_carolinensis 0.1177 0.8215 -1.5422 0.1102
## Avg_Cogongrass_Cover-Canis_latrans 2.1509 1.0649 0.3699 2.0490
## Avg_Cogongrass_Cover-Lynx_rufus 2.5445 1.5610 0.4309 2.2611
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9407 0.9656 0.2497 1.8637
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1099 1.0370 -0.9424 1.1406
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7474 1.4808 -2.5767 0.9053
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1561 1.0516 0.3711 2.0636
## Tree_Density-Canis_latrans -2.5594 1.4137 -6.1943 -2.2935
## Tree_Density-Lynx_rufus -0.9624 1.5500 -3.3192 -1.1630
## Tree_Density-Didelphis_virginiana -2.1851 1.0497 -4.6399 -2.0605
## Tree_Density-Sylvilagus_floridanus -2.4709 1.3903 -6.0309 -2.2420
## Tree_Density-Meleagris_gallopavo -2.0634 1.2414 -4.6871 -1.9794
## Tree_Density-Sciurus_carolinensis -2.3175 1.1890 -5.2657 -2.1592
## Avg_Canopy_Cover-Canis_latrans 0.0142 0.7982 -1.7780 0.0500
## Avg_Canopy_Cover-Lynx_rufus 0.3473 1.5001 -2.8581 0.3689
## Avg_Canopy_Cover-Didelphis_virginiana 2.9924 1.1820 1.2351 2.8105
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0057 2.0758 1.2204 3.5841
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2759 1.2559 0.3964 2.0393
## Avg_Canopy_Cover-Sciurus_carolinensis 2.3624 0.9556 0.8494 2.2149
## avg_veg_height-Canis_latrans -0.6335 0.6991 -2.1015 -0.6091
## avg_veg_height-Lynx_rufus -0.6911 0.9284 -2.6335 -0.6666
## avg_veg_height-Didelphis_virginiana -0.6861 0.7517 -2.1986 -0.6633
## avg_veg_height-Sylvilagus_floridanus -0.7225 0.7623 -2.3267 -0.6866
## avg_veg_height-Meleagris_gallopavo -0.8166 0.8969 -2.7560 -0.7623
## avg_veg_height-Sciurus_carolinensis -0.1325 0.7630 -1.5572 -0.1695
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.6716 1.0122 298
## (Intercept)-Lynx_rufus 5.0140 1.1013 158
## (Intercept)-Didelphis_virginiana -0.7903 1.0206 488
## (Intercept)-Sylvilagus_floridanus 0.6772 1.0107 399
## (Intercept)-Meleagris_gallopavo 0.6434 1.0347 266
## (Intercept)-Sciurus_carolinensis -1.0973 1.0720 231
## Cogon_Patch_Size-Canis_latrans 5.3638 1.1161 275
## Cogon_Patch_Size-Lynx_rufus 4.1763 1.1438 152
## Cogon_Patch_Size-Didelphis_virginiana 4.1416 1.0830 349
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6689 1.0511 278
## Cogon_Patch_Size-Meleagris_gallopavo 3.7320 1.0152 341
## Cogon_Patch_Size-Sciurus_carolinensis 0.3318 1.0233 328
## Veg_shannon_index-Canis_latrans 3.3167 1.0022 372
## Veg_shannon_index-Lynx_rufus 1.9002 1.0162 398
## Veg_shannon_index-Didelphis_virginiana 2.9190 1.0025 437
## Veg_shannon_index-Sylvilagus_floridanus 2.9423 1.0032 421
## Veg_shannon_index-Meleagris_gallopavo 3.9627 1.0607 266
## Veg_shannon_index-Sciurus_carolinensis 1.3911 1.0356 554
## total_shrub_cover-Canis_latrans 3.4110 1.0462 284
## total_shrub_cover-Lynx_rufus 0.4320 1.1133 108
## total_shrub_cover-Didelphis_virginiana 1.0124 1.0285 633
## total_shrub_cover-Sylvilagus_floridanus 2.1652 1.0413 393
## total_shrub_cover-Meleagris_gallopavo -0.4414 1.0325 179
## total_shrub_cover-Sciurus_carolinensis 1.7693 1.0028 922
## Avg_Cogongrass_Cover-Canis_latrans 4.5470 1.0024 424
## Avg_Cogongrass_Cover-Lynx_rufus 6.4232 1.0952 308
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.1227 1.0046 581
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.0884 1.0158 533
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3480 1.0403 334
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.6359 1.0426 535
## Tree_Density-Canis_latrans -0.6558 1.0788 198
## Tree_Density-Lynx_rufus 2.9000 1.1227 135
## Tree_Density-Didelphis_virginiana -0.5003 1.0055 551
## Tree_Density-Sylvilagus_floridanus -0.6005 1.0757 274
## Tree_Density-Meleagris_gallopavo 0.1491 1.0906 301
## Tree_Density-Sciurus_carolinensis -0.5132 1.0625 443
## Avg_Canopy_Cover-Canis_latrans 1.4558 1.0185 305
## Avg_Canopy_Cover-Lynx_rufus 3.3046 1.0879 200
## Avg_Canopy_Cover-Didelphis_virginiana 5.5596 1.0721 155
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.2759 1.0273 196
## Avg_Canopy_Cover-Meleagris_gallopavo 5.2612 1.0020 293
## Avg_Canopy_Cover-Sciurus_carolinensis 4.5813 1.0164 640
## avg_veg_height-Canis_latrans 0.6458 1.0136 527
## avg_veg_height-Lynx_rufus 1.0189 1.0125 583
## avg_veg_height-Didelphis_virginiana 0.7575 1.0059 664
## avg_veg_height-Sylvilagus_floridanus 0.7061 1.0061 699
## avg_veg_height-Meleagris_gallopavo 0.8486 1.0070 442
## avg_veg_height-Sciurus_carolinensis 1.5117 1.0049 637
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -4.6688 0.1602 -4.9981 -4.6653 -4.3687
## (Intercept)-Lynx_rufus -5.4600 0.2696 -6.0577 -5.4487 -4.9656
## (Intercept)-Didelphis_virginiana -4.3366 0.2237 -4.7921 -4.3253 -3.9142
## (Intercept)-Sylvilagus_floridanus -4.9386 0.2194 -5.3731 -4.9378 -4.5305
## (Intercept)-Meleagris_gallopavo -5.1171 0.2853 -5.7130 -5.0965 -4.6192
## (Intercept)-Sciurus_carolinensis -4.3849 0.2328 -4.8745 -4.3749 -3.9528
## week-Canis_latrans -1.9021 1265.9631 -272.8781 0.1839 263.8213
## week-Lynx_rufus -0.5669 958.5299 -296.3272 0.1167 247.4063
## week-Didelphis_virginiana -4.8468 1288.6329 -305.5202 0.1332 306.2369
## week-Sylvilagus_floridanus 2.2250 1399.1711 -269.4254 0.1801 314.6050
## week-Meleagris_gallopavo 9.5866 1177.9579 -312.7772 0.2161 297.2665
## week-Sciurus_carolinensis -3.9140 1324.9356 -303.1150 0.2629 273.5959
## Rhat ESS
## (Intercept)-Canis_latrans 1.0064 200
## (Intercept)-Lynx_rufus 1.0611 86
## (Intercept)-Didelphis_virginiana 1.0096 353
## (Intercept)-Sylvilagus_floridanus 1.0107 199
## (Intercept)-Meleagris_gallopavo 1.1544 135
## (Intercept)-Sciurus_carolinensis 1.0103 310
## week-Canis_latrans 1.2664 119444
## week-Lynx_rufus 1.2551 15881
## week-Didelphis_virginiana 1.2720 7004
## week-Sylvilagus_floridanus 1.2703 42407
## week-Meleagris_gallopavo 1.2688 11517
## week-Sciurus_carolinensis 1.2735 53800
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7132 0.5330 -1.7814 -0.7115 0.3845 1.0216 818
## Avg_Cogongrass_Cover 0.2013 0.4722 -0.7793 0.1982 1.1190 1.0016 872
## total_shrub_cover -0.4308 0.5237 -1.4722 -0.4158 0.6082 1.0085 1682
## avg_veg_height -0.1270 0.4014 -0.9268 -0.1243 0.6521 1.0080 796
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1890 1.8474 0.0695 0.6427 5.3587 1.0218 782
## Avg_Cogongrass_Cover 0.9181 1.5892 0.0516 0.4615 4.7733 1.0716 948
## total_shrub_cover 1.4185 2.2358 0.0786 0.7468 6.8239 1.0464 663
## avg_veg_height 0.4279 0.6542 0.0374 0.2247 2.1182 1.0537 1316
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2041 1.2841 0.0791 0.8243 4.6846 1.0679 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6423 0.3196 -5.1406 -4.6738 -3.9582 1.0114 915
## week -0.0359 1.6312 -3.3915 -0.0002 3.1545 0.9999 917
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.268000e-01 1.053300e+00 0.0557 0.2385 1.748400e+00 1.0944 789
## week 5.911196e+08 1.062307e+10 0.0979 54.6236 7.913446e+07 1.4798 438
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1090 0.7212 -1.2218 0.1024
## (Intercept)-Lynx_rufus -0.2988 0.7525 -1.6389 -0.3619
## (Intercept)-Didelphis_virginiana -1.2839 0.6294 -2.6207 -1.2536
## (Intercept)-Sylvilagus_floridanus -0.7042 0.6144 -1.9522 -0.6876
## (Intercept)-Meleagris_gallopavo -0.9959 0.6913 -2.4404 -0.9638
## (Intercept)-Sciurus_carolinensis -1.3494 0.6582 -2.7484 -1.3066
## Avg_Cogongrass_Cover-Canis_latrans 0.5761 0.5563 -0.3773 0.5341
## Avg_Cogongrass_Cover-Lynx_rufus 0.7203 0.6934 -0.3833 0.6391
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4632 0.4915 -0.4871 0.4617
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3135 0.5924 -1.6226 -0.2651
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5262 0.8147 -2.3522 -0.4209
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3210 0.4915 -0.6487 0.3261
## total_shrub_cover-Canis_latrans 0.4600 0.5806 -0.4924 0.3969
## total_shrub_cover-Lynx_rufus -1.0233 0.8108 -3.0155 -0.9241
## total_shrub_cover-Didelphis_virginiana -0.2563 0.4781 -1.2326 -0.2470
## total_shrub_cover-Sylvilagus_floridanus -0.3819 0.5147 -1.4613 -0.3674
## total_shrub_cover-Meleagris_gallopavo -1.5926 0.8961 -3.6085 -1.4640
## total_shrub_cover-Sciurus_carolinensis -0.0822 0.4747 -1.0146 -0.0861
## avg_veg_height-Canis_latrans -0.1039 0.4591 -0.9937 -0.1044
## avg_veg_height-Lynx_rufus -0.1520 0.5911 -1.3192 -0.1486
## avg_veg_height-Didelphis_virginiana -0.1429 0.4742 -1.1186 -0.1316
## avg_veg_height-Sylvilagus_floridanus -0.2260 0.4803 -1.1973 -0.2193
## avg_veg_height-Meleagris_gallopavo -0.3773 0.5938 -1.6984 -0.3490
## avg_veg_height-Sciurus_carolinensis 0.2264 0.4941 -0.6954 0.1972
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5677 1.0153 485
## (Intercept)-Lynx_rufus 1.3649 1.0133 472
## (Intercept)-Didelphis_virginiana -0.1559 1.0067 681
## (Intercept)-Sylvilagus_floridanus 0.5128 1.0063 849
## (Intercept)-Meleagris_gallopavo 0.2771 1.0015 639
## (Intercept)-Sciurus_carolinensis -0.1855 1.0177 638
## Avg_Cogongrass_Cover-Canis_latrans 1.7973 1.0014 953
## Avg_Cogongrass_Cover-Lynx_rufus 2.2841 1.0074 773
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4707 1.0007 1099
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7676 1.0006 1033
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7024 1.0159 435
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2956 1.0022 1538
## total_shrub_cover-Canis_latrans 1.8227 1.0078 831
## total_shrub_cover-Lynx_rufus 0.2747 1.0663 503
## total_shrub_cover-Didelphis_virginiana 0.6655 1.0019 2099
## total_shrub_cover-Sylvilagus_floridanus 0.5783 1.0127 1511
## total_shrub_cover-Meleagris_gallopavo -0.2573 1.0397 456
## total_shrub_cover-Sciurus_carolinensis 0.8488 1.0026 1463
## avg_veg_height-Canis_latrans 0.8231 1.0025 1040
## avg_veg_height-Lynx_rufus 1.0496 1.0053 853
## avg_veg_height-Didelphis_virginiana 0.7737 1.0015 1310
## avg_veg_height-Sylvilagus_floridanus 0.6895 1.0042 1042
## avg_veg_height-Meleagris_gallopavo 0.6868 1.0018 937
## avg_veg_height-Sciurus_carolinensis 1.2454 0.9998 1230
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6605 0.1576 -4.9631 -4.6590
## (Intercept)-Lynx_rufus -5.3132 0.2684 -5.8515 -5.3006
## (Intercept)-Didelphis_virginiana -4.3416 0.2247 -4.7719 -4.3376
## (Intercept)-Sylvilagus_floridanus -4.8582 0.2281 -5.3080 -4.8534
## (Intercept)-Meleagris_gallopavo -4.9785 0.2441 -5.5048 -4.9614
## (Intercept)-Sciurus_carolinensis -4.3954 0.2249 -4.8599 -4.3924
## week-Canis_latrans 684.9308 26418.5511 -1581.1350 -0.1573
## week-Lynx_rufus 371.7592 21951.0269 -1328.8088 0.0623
## week-Didelphis_virginiana -333.2226 19007.8000 -1449.3531 -0.0486
## week-Sylvilagus_floridanus 255.6274 22084.6835 -1741.0020 -0.1779
## week-Meleagris_gallopavo 328.8685 23714.3795 -1285.8562 0.0078
## week-Sciurus_carolinensis 326.2877 19368.5430 -1535.3506 -0.1514
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3515 1.0149 244
## (Intercept)-Lynx_rufus -4.8158 1.0847 103
## (Intercept)-Didelphis_virginiana -3.9079 1.0127 353
## (Intercept)-Sylvilagus_floridanus -4.4260 1.1394 183
## (Intercept)-Meleagris_gallopavo -4.5291 1.0399 180
## (Intercept)-Sciurus_carolinensis -3.9672 1.0232 372
## week-Canis_latrans 1114.6617 1.3409 1231
## week-Lynx_rufus 1045.2279 1.2773 4952
## week-Didelphis_virginiana 1174.1738 1.2730 4665
## week-Sylvilagus_floridanus 1018.8714 1.2643 7491
## week-Meleagris_gallopavo 1824.0926 1.2578 9247
## week-Sciurus_carolinensis 1280.2884 1.2523 6095
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.3265
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5535 0.3916 -1.3213 -0.5504 0.2222 1.0033 2109
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8084 1.0707 0.1022 0.5022 3.3478 1.0143 1899
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6440 0.2846 -5.1059 -4.6684 -4.0392 1.0009 1330
## week 0.0626 1.6697 -3.1649 0.0525 3.3856 1.0108 862
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.58900e-01 5.048000e-01 0.0540 0.2266 1.4436e+00 1.0001 877
## week 1.14833e+14 2.652251e+15 0.1129 508.6461 5.3680e+13 1.4657 986
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2241 0.4006 -0.5183 0.2033 1.0665 1.0001
## (Intercept)-Lynx_rufus -0.1015 0.4691 -0.9256 -0.1386 0.9298 1.0012
## (Intercept)-Didelphis_virginiana -1.1885 0.4140 -2.0566 -1.1684 -0.4384 1.0075
## (Intercept)-Sylvilagus_floridanus -0.5813 0.3817 -1.3218 -0.5825 0.1968 1.0011
## (Intercept)-Meleagris_gallopavo -0.6105 0.4032 -1.4349 -0.6141 0.1917 1.0032
## (Intercept)-Sciurus_carolinensis -1.1839 0.3997 -2.0336 -1.1677 -0.4708 1.0007
## ESS
## (Intercept)-Canis_latrans 1544
## (Intercept)-Lynx_rufus 680
## (Intercept)-Didelphis_virginiana 1859
## (Intercept)-Sylvilagus_floridanus 1779
## (Intercept)-Meleagris_gallopavo 1249
## (Intercept)-Sciurus_carolinensis 2259
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6481 1.498000e-01 -4.9487
## (Intercept)-Lynx_rufus -5.2668 2.616000e-01 -5.8362
## (Intercept)-Didelphis_virginiana -4.3534 2.206000e-01 -4.7948
## (Intercept)-Sylvilagus_floridanus -4.8239 2.167000e-01 -5.2796
## (Intercept)-Meleagris_gallopavo -4.9725 2.482000e-01 -5.4978
## (Intercept)-Sciurus_carolinensis -4.4019 2.302000e-01 -4.8770
## week-Canis_latrans 239355.6315 1.021033e+07 -613933.8318
## week-Lynx_rufus -72786.9881 8.051854e+06 -585449.9464
## week-Didelphis_virginiana 33001.8634 1.384105e+07 -666141.0421
## week-Sylvilagus_floridanus -219088.0609 8.493366e+06 -1181218.9882
## week-Meleagris_gallopavo -29696.9615 1.202262e+07 -1083010.8934
## week-Sciurus_carolinensis 65422.9996 7.712318e+06 -743403.3614
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6434 -4.3689 1.0516 260
## (Intercept)-Lynx_rufus -5.2521 -4.8172 1.0295 114
## (Intercept)-Didelphis_virginiana -4.3499 -3.9355 1.0053 354
## (Intercept)-Sylvilagus_floridanus -4.8136 -4.4318 1.0253 222
## (Intercept)-Meleagris_gallopavo -4.9564 -4.5157 1.0077 172
## (Intercept)-Sciurus_carolinensis -4.3982 -3.9545 1.0051 324
## week-Canis_latrans 0.1180 748843.5487 1.3433 3639
## week-Lynx_rufus 0.1863 931479.8537 1.2989 3419
## week-Didelphis_virginiana 0.3048 777391.7841 1.2909 4015
## week-Sylvilagus_floridanus 0.3218 665335.7009 1.3561 2910
## week-Meleagris_gallopavo 0.1464 655086.4778 1.2910 15468
## week-Sciurus_carolinensis 0.2465 877167.4367 1.2973 4734
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.344
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6906 0.4599 -1.5937 -0.7050 0.2737 1.0197 932
## Veg_shannon_index 0.3140 0.3671 -0.4481 0.3192 1.0283 1.0195 1515
## Avg_Cogongrass_Cover 0.2439 0.3758 -0.5244 0.2407 1.0358 1.0014 1563
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8723 1.3070 0.0575 0.4931 3.9456 1.0271 1116
## Veg_shannon_index 0.6140 1.4658 0.0488 0.3273 2.8206 1.1485 2004
## Avg_Cogongrass_Cover 0.6139 0.8851 0.0482 0.3367 2.9718 1.0114 1326
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8241 0.9736 0.0574 0.5035 3.452 1.1132 350
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6544 0.3194 -5.1439 -4.6872 -3.9961 1.0242 889
## week 0.1320 1.5483 -2.9742 0.1376 3.0653 1.0057 873
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4308 1.272800e+00 0.0545 0.2263 1.6728 1.0077 1053
## week 1440751.6220 1.228402e+07 0.0920 26.4194 8901211.9009 1.1456 257
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0039 0.6471 -1.1963 -0.0214
## (Intercept)-Lynx_rufus -0.3385 0.6765 -1.5397 -0.3789
## (Intercept)-Didelphis_virginiana -1.2343 0.5691 -2.4378 -1.2036
## (Intercept)-Sylvilagus_floridanus -0.7388 0.5375 -1.8212 -0.7368
## (Intercept)-Meleagris_gallopavo -0.7990 0.5773 -1.9693 -0.7970
## (Intercept)-Sciurus_carolinensis -1.2395 0.5466 -2.3942 -1.2096
## Veg_shannon_index-Canis_latrans 0.7866 0.4524 0.0061 0.7489
## Veg_shannon_index-Lynx_rufus -0.2068 0.6245 -1.5354 -0.1387
## Veg_shannon_index-Didelphis_virginiana 0.5128 0.4463 -0.2845 0.4930
## Veg_shannon_index-Sylvilagus_floridanus 0.4367 0.4571 -0.4097 0.4235
## Veg_shannon_index-Meleagris_gallopavo 0.4602 0.4834 -0.4412 0.4380
## Veg_shannon_index-Sciurus_carolinensis -0.0833 0.4366 -0.9912 -0.0640
## Avg_Cogongrass_Cover-Canis_latrans 0.5930 0.4456 -0.1883 0.5469
## Avg_Cogongrass_Cover-Lynx_rufus 0.6096 0.4852 -0.1965 0.5624
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4774 0.4199 -0.3076 0.4656
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2165 0.4726 -1.2643 -0.1857
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2966 0.5628 -1.5830 -0.2476
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3903 0.4063 -0.3747 0.3803
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3242 1.0438 567
## (Intercept)-Lynx_rufus 1.2033 1.0205 412
## (Intercept)-Didelphis_virginiana -0.2128 1.0073 1130
## (Intercept)-Sylvilagus_floridanus 0.3500 1.0038 822
## (Intercept)-Meleagris_gallopavo 0.3643 1.0100 793
## (Intercept)-Sciurus_carolinensis -0.2091 1.0060 886
## Veg_shannon_index-Canis_latrans 1.7629 1.0020 1389
## Veg_shannon_index-Lynx_rufus 0.8147 1.0063 715
## Veg_shannon_index-Didelphis_virginiana 1.4833 1.0023 1726
## Veg_shannon_index-Sylvilagus_floridanus 1.3899 1.0003 1513
## Veg_shannon_index-Meleagris_gallopavo 1.4924 1.0119 1426
## Veg_shannon_index-Sciurus_carolinensis 0.7394 1.0079 1466
## Avg_Cogongrass_Cover-Canis_latrans 1.5450 1.0004 1296
## Avg_Cogongrass_Cover-Lynx_rufus 1.6891 1.0035 1109
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3331 1.0021 1522
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5966 1.0050 1445
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6572 1.0003 1016
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2276 1.0015 1578
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -4.6469 0.1599 -5.0110 -4.6367 -4.3559
## (Intercept)-Lynx_rufus -5.2768 0.2743 -5.8699 -5.2648 -4.7968
## (Intercept)-Didelphis_virginiana -4.3773 0.2294 -4.8366 -4.3670 -3.9335
## (Intercept)-Sylvilagus_floridanus -4.8382 0.2287 -5.3024 -4.8343 -4.4123
## (Intercept)-Meleagris_gallopavo -5.0489 0.2812 -5.6606 -5.0235 -4.5509
## (Intercept)-Sciurus_carolinensis -4.4079 0.2335 -4.9012 -4.4021 -3.9797
## week-Canis_latrans 6.5414 1046.4561 -493.9616 0.2027 796.5980
## week-Lynx_rufus -0.8887 1019.4475 -650.5772 0.2580 644.9717
## week-Didelphis_virginiana -9.8525 1021.5761 -599.0156 0.1131 448.0978
## week-Sylvilagus_floridanus -6.6615 1067.2109 -570.4034 0.1896 650.2953
## week-Meleagris_gallopavo -27.4018 1132.0871 -717.0620 0.1488 536.4289
## week-Sciurus_carolinensis 6.3411 1125.4639 -580.9218 0.1347 541.4455
## Rhat ESS
## (Intercept)-Canis_latrans 1.0039 229
## (Intercept)-Lynx_rufus 1.0039 104
## (Intercept)-Didelphis_virginiana 1.0216 270
## (Intercept)-Sylvilagus_floridanus 1.1388 191
## (Intercept)-Meleagris_gallopavo 1.0066 129
## (Intercept)-Sciurus_carolinensis 1.0452 273
## week-Canis_latrans 1.1037 6361
## week-Lynx_rufus 1.1010 6146
## week-Didelphis_virginiana 1.1049 5879
## week-Sylvilagus_floridanus 1.1082 10465
## week-Meleagris_gallopavo 1.1037 3423
## week-Sciurus_carolinensis 1.1582 12880
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4097
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7519 0.5656 -1.9112 -0.7492 0.3832 1.0325 793
## Cogon_Patch_Size -0.0414 0.6302 -1.4508 -0.0301 1.2203 1.0097 1163
## Avg_Cogongrass_Cover 0.1431 0.4728 -0.7734 0.1384 1.0826 1.0019 1107
## total_shrub_cover -0.4410 0.5232 -1.5172 -0.4213 0.6086 1.0045 1233
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3235 2.3191 0.0744 0.7063 6.5896 1.0717 711
## Cogon_Patch_Size 2.2101 3.9396 0.0975 1.1046 10.7689 1.0524 473
## Avg_Cogongrass_Cover 1.0179 1.8277 0.0611 0.5258 5.0775 1.0473 817
## total_shrub_cover 1.4189 2.3745 0.0745 0.7339 7.4204 1.0818 712
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6332 1.7948 0.074 1.1343 6.2485 1.056 176
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6590 0.2969 -5.1553 -4.6856 -3.9861 1.0121 1537
## week 0.0594 1.6538 -3.2349 0.0861 3.1721 1.0032 793
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.073000e-01 6.536000e-01 0.0534 0.2580 1.689000e+00 1.0138 1423
## week 5.061649e+09 5.091854e+10 0.0994 200.1412 2.858851e+10 2.0263 289
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1835 0.8404 -1.3256 0.1275
## (Intercept)-Lynx_rufus -0.3572 0.8581 -1.9269 -0.4043
## (Intercept)-Didelphis_virginiana -1.2384 0.6792 -2.6782 -1.2090
## (Intercept)-Sylvilagus_floridanus -0.8360 0.6983 -2.2731 -0.8256
## (Intercept)-Meleagris_gallopavo -1.0110 0.7176 -2.6032 -0.9738
## (Intercept)-Sciurus_carolinensis -1.4273 0.7051 -2.9850 -1.3720
## Cogon_Patch_Size-Canis_latrans 0.9359 0.8746 -0.3468 0.7965
## Cogon_Patch_Size-Lynx_rufus -0.2321 1.0325 -2.1040 -0.3024
## Cogon_Patch_Size-Didelphis_virginiana 0.8574 0.6273 -0.1663 0.8062
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0038 1.0156 -3.5903 -0.8163
## Cogon_Patch_Size-Meleagris_gallopavo 0.1287 0.7206 -1.1942 0.1080
## Cogon_Patch_Size-Sciurus_carolinensis -0.9350 0.9510 -3.2152 -0.7689
## Avg_Cogongrass_Cover-Canis_latrans 0.3248 0.4903 -0.5711 0.2928
## Avg_Cogongrass_Cover-Lynx_rufus 0.7721 0.7456 -0.3991 0.6574
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1454 0.4830 -0.7930 0.1494
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2737 0.5969 -1.5803 -0.2271
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.7281 0.7567 -2.4914 -0.6259
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5849 0.5206 -0.3129 0.5433
## total_shrub_cover-Canis_latrans 0.3955 0.5697 -0.5437 0.3351
## total_shrub_cover-Lynx_rufus -0.9703 0.9408 -3.4325 -0.7966
## total_shrub_cover-Didelphis_virginiana -0.4054 0.5144 -1.4577 -0.3941
## total_shrub_cover-Sylvilagus_floridanus -0.3210 0.6293 -1.6799 -0.2823
## total_shrub_cover-Meleagris_gallopavo -1.5570 0.8901 -3.7073 -1.4320
## total_shrub_cover-Sciurus_carolinensis -0.0176 0.5161 -0.9697 -0.0371
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9274 1.0188 388
## (Intercept)-Lynx_rufus 1.4897 1.0187 395
## (Intercept)-Didelphis_virginiana 0.0422 1.0710 859
## (Intercept)-Sylvilagus_floridanus 0.5590 1.0362 644
## (Intercept)-Meleagris_gallopavo 0.3344 1.0088 687
## (Intercept)-Sciurus_carolinensis -0.1633 1.0502 642
## Cogon_Patch_Size-Canis_latrans 3.0338 1.0254 649
## Cogon_Patch_Size-Lynx_rufus 2.1145 1.0111 570
## Cogon_Patch_Size-Didelphis_virginiana 2.2120 1.0398 901
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4530 1.0598 563
## Cogon_Patch_Size-Meleagris_gallopavo 1.6033 1.0215 743
## Cogon_Patch_Size-Sciurus_carolinensis 0.3782 1.0842 643
## Avg_Cogongrass_Cover-Canis_latrans 1.3942 1.0039 1251
## Avg_Cogongrass_Cover-Lynx_rufus 2.5645 1.0584 534
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0836 1.0023 1676
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7704 1.0091 713
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4857 1.0145 754
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.6893 1.0152 912
## total_shrub_cover-Canis_latrans 1.6776 1.0212 985
## total_shrub_cover-Lynx_rufus 0.3575 1.0740 365
## total_shrub_cover-Didelphis_virginiana 0.5855 1.0039 1597
## total_shrub_cover-Sylvilagus_floridanus 0.7777 1.0118 505
## total_shrub_cover-Meleagris_gallopavo -0.2042 1.0358 457
## total_shrub_cover-Sciurus_carolinensis 1.0636 1.0044 1336
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6667 0.1562 -4.9775 -4.6636
## (Intercept)-Lynx_rufus -5.3324 0.2676 -5.8774 -5.3192
## (Intercept)-Didelphis_virginiana -4.3513 0.2279 -4.8106 -4.3519
## (Intercept)-Sylvilagus_floridanus -4.9063 0.2363 -5.4146 -4.8927
## (Intercept)-Meleagris_gallopavo -4.9873 0.2556 -5.5169 -4.9774
## (Intercept)-Sciurus_carolinensis -4.4029 0.2295 -4.8520 -4.3958
## week-Canis_latrans -1517.5805 70028.8458 -53050.0741 0.1722
## week-Lynx_rufus 1006.7858 51642.6339 -28555.8276 0.0689
## week-Didelphis_virginiana -935.0364 71256.6103 -39991.3129 0.0658
## week-Sylvilagus_floridanus 584.7624 69987.7831 -28221.0661 0.1076
## week-Meleagris_gallopavo 761.7075 59765.9569 -39029.8658 0.2115
## week-Sciurus_carolinensis 1706.9164 66758.5785 -36682.8176 0.0639
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3747 1.0373 217
## (Intercept)-Lynx_rufus -4.8342 1.0705 96
## (Intercept)-Didelphis_virginiana -3.9125 1.0505 278
## (Intercept)-Sylvilagus_floridanus -4.4881 1.0206 168
## (Intercept)-Meleagris_gallopavo -4.5224 1.0520 161
## (Intercept)-Sciurus_carolinensis -3.9698 1.0304 325
## week-Canis_latrans 25383.5850 1.3138 956
## week-Lynx_rufus 35685.1135 1.3133 1791
## week-Didelphis_virginiana 39650.3750 1.2980 1274
## week-Sylvilagus_floridanus 49250.1317 1.2861 14547
## week-Meleagris_gallopavo 34671.9858 1.2793 2842
## week-Sciurus_carolinensis 45865.4398 1.3390 3478
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.5
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7626 0.6066 -1.8456 -0.8002 0.6119 1.0082 833
## Tree_Density -0.8097 0.5453 -1.9470 -0.7869 0.2038 1.0041 840
## Avg_Canopy_Cover 1.0193 0.5305 -0.0677 1.0076 2.1084 1.0077 1005
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1122 4.7158 0.1006 1.0062 10.9381 1.1425 578
## Tree_Density 1.2338 2.3762 0.0563 0.5758 6.6298 1.0168 1239
## Avg_Canopy_Cover 1.4066 2.2676 0.0847 0.7755 6.5238 1.0427 885
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5897 0.9038 0.0446 0.3165 2.6768 1.0462 290
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6525 0.3079 -5.1733 -4.6820 -3.9601 1.0032 1808
## week 0.0405 1.6238 -3.1836 0.0914 3.0885 1.0083 1213
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.732000e-01 8.957000e-01 0.0592 0.2733 2.164100e+00 1.0913 1269
## week 1.336079e+26 3.109135e+27 0.1080 928.0987 5.428142e+25 1.4632 592
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1833 0.7200 -1.1310 0.1513 1.6990
## (Intercept)-Lynx_rufus 0.1782 1.2438 -1.4500 -0.0518 3.2852
## (Intercept)-Didelphis_virginiana -1.7188 0.6807 -3.1859 -1.6819 -0.4963
## (Intercept)-Sylvilagus_floridanus -0.9085 0.6283 -2.1742 -0.8758 0.3473
## (Intercept)-Meleagris_gallopavo -0.8205 0.6449 -2.0552 -0.8231 0.4359
## (Intercept)-Sciurus_carolinensis -1.7485 0.6669 -3.1813 -1.7050 -0.5721
## Tree_Density-Canis_latrans -1.0545 0.6561 -2.6162 -0.9745 -0.0052
## Tree_Density-Lynx_rufus 0.1362 0.8184 -1.1867 0.0282 2.1656
## Tree_Density-Didelphis_virginiana -1.1036 0.8153 -3.0454 -0.9779 0.1389
## Tree_Density-Sylvilagus_floridanus -1.1980 0.8268 -3.2267 -1.0673 0.0942
## Tree_Density-Meleagris_gallopavo -0.9974 0.7626 -2.7501 -0.8857 0.1966
## Tree_Density-Sciurus_carolinensis -0.9724 0.7587 -2.7055 -0.8828 0.2038
## Avg_Canopy_Cover-Canis_latrans -0.0162 0.5118 -0.9998 -0.0259 0.9829
## Avg_Canopy_Cover-Lynx_rufus 0.6772 0.7587 -0.6726 0.6171 2.3888
## Avg_Canopy_Cover-Didelphis_virginiana 1.2835 0.5401 0.3268 1.2441 2.4741
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9209 0.9445 0.6244 1.7300 4.3502
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4800 0.7712 0.2960 1.3700 3.2755
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2586 0.5153 0.3690 1.2227 2.3710
## Rhat ESS
## (Intercept)-Canis_latrans 1.0235 296
## (Intercept)-Lynx_rufus 1.0514 134
## (Intercept)-Didelphis_virginiana 0.9999 794
## (Intercept)-Sylvilagus_floridanus 1.0092 966
## (Intercept)-Meleagris_gallopavo 1.0038 975
## (Intercept)-Sciurus_carolinensis 1.0095 924
## Tree_Density-Canis_latrans 1.0117 1173
## Tree_Density-Lynx_rufus 1.0086 576
## Tree_Density-Didelphis_virginiana 1.0031 810
## Tree_Density-Sylvilagus_floridanus 1.0027 655
## Tree_Density-Meleagris_gallopavo 1.0027 802
## Tree_Density-Sciurus_carolinensis 1.0056 1001
## Avg_Canopy_Cover-Canis_latrans 1.0019 999
## Avg_Canopy_Cover-Lynx_rufus 1.0019 604
## Avg_Canopy_Cover-Didelphis_virginiana 1.0046 1169
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0373 475
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0198 637
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0019 1391
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.679600e+00 1.622000e-01 -5.026500e+00
## (Intercept)-Lynx_rufus -5.381000e+00 3.164000e-01 -6.065100e+00
## (Intercept)-Didelphis_virginiana -4.334000e+00 2.206000e-01 -4.775300e+00
## (Intercept)-Sylvilagus_floridanus -4.855400e+00 2.257000e-01 -5.325300e+00
## (Intercept)-Meleagris_gallopavo -5.074700e+00 2.494000e-01 -5.588400e+00
## (Intercept)-Sciurus_carolinensis -4.386400e+00 2.295000e-01 -4.857300e+00
## week-Canis_latrans 1.636452e+11 1.056511e+13 -2.181776e+11
## week-Lynx_rufus -2.931101e+11 8.314098e+12 -4.384594e+11
## week-Didelphis_virginiana -1.223335e+11 8.430932e+12 -4.287119e+11
## week-Sylvilagus_floridanus -4.254983e+11 1.017162e+13 -4.645430e+11
## week-Meleagris_gallopavo -1.819860e+11 9.840652e+12 -1.909152e+11
## week-Sciurus_carolinensis -1.288244e+11 8.723136e+12 -4.955917e+11
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6764 -4.377300e+00 1.0181 228
## (Intercept)-Lynx_rufus -5.3422 -4.877300e+00 1.0319 69
## (Intercept)-Didelphis_virginiana -4.3339 -3.915100e+00 1.0179 390
## (Intercept)-Sylvilagus_floridanus -4.8461 -4.442800e+00 1.0422 192
## (Intercept)-Meleagris_gallopavo -5.0580 -4.620300e+00 1.0438 168
## (Intercept)-Sciurus_carolinensis -4.3824 -3.959000e+00 1.0316 344
## week-Canis_latrans 0.4266 4.096446e+11 1.3141 5242
## week-Lynx_rufus 0.3225 2.173483e+11 1.4090 1659
## week-Didelphis_virginiana 0.1342 2.641863e+11 1.3112 13537
## week-Sylvilagus_floridanus 0.2101 2.170103e+11 1.4547 765
## week-Meleagris_gallopavo 0.2829 4.773453e+11 1.3240 1761
## week-Sciurus_carolinensis 0.2495 2.257979e+11 1.3119 8868
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.3262
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2237 0.5063 -2.1911 -1.2371 -0.1673 1.0071 724
## Avg_Cogongrass_Cover -0.5370 0.4677 -1.4230 -0.5408 0.4082 1.0056 797
## I(Avg_Cogongrass_Cover^2) 0.6629 0.4476 -0.1432 0.6316 1.6265 1.0168 895
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8401 1.2329 0.0562 0.4616 3.8912 1.0046 1117
## Avg_Cogongrass_Cover 0.6624 1.4115 0.0478 0.3393 3.0996 1.1197 1620
## I(Avg_Cogongrass_Cover^2) 0.8205 1.6281 0.0518 0.3468 4.4946 1.0123 496
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5939 0.6766 0.0456 0.3516 2.5495 1.0131 244
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6554 0.2816 -5.1387 -4.6766 -4.0462 1.006 919
## week 0.0479 1.6733 -3.1104 0.0240 3.5148 1.003 759
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.554000e-01 6.907000e-01 0.0497 0.2074 1.459500e+00 1.0588 1253
## week 8.428615e+24 1.445229e+26 0.2247 6806.5393 1.449588e+23 1.5946 312
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6109 0.6493 -1.8022 -0.6362
## (Intercept)-Lynx_rufus -1.1375 0.6288 -2.3884 -1.1464
## (Intercept)-Didelphis_virginiana -1.6315 0.6111 -2.8929 -1.5972
## (Intercept)-Sylvilagus_floridanus -1.2484 0.5869 -2.4273 -1.2493
## (Intercept)-Meleagris_gallopavo -1.0998 0.6018 -2.2859 -1.1150
## (Intercept)-Sciurus_carolinensis -1.9254 0.6793 -3.4121 -1.8850
## Avg_Cogongrass_Cover-Canis_latrans -0.3072 0.5811 -1.3817 -0.3256
## Avg_Cogongrass_Cover-Lynx_rufus -0.3085 0.6191 -1.4577 -0.3437
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1748 0.5979 -1.2621 -0.2103
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0694 0.6400 -2.4841 -1.0238
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8786 0.6133 -2.1642 -0.8561
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5771 0.5756 -1.7215 -0.5650
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2803 0.9204 0.1102 1.0542
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.0395 0.5520 0.1677 0.9686
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.3600 0.4228 -0.4351 0.3558
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6328 0.4850 -0.1952 0.5906
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0413 0.6290 -1.4038 0.0922
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7865 0.4057 0.0584 0.7642
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7248 1.0250 691
## (Intercept)-Lynx_rufus 0.1422 1.0333 809
## (Intercept)-Didelphis_virginiana -0.4722 1.0070 934
## (Intercept)-Sylvilagus_floridanus -0.1092 1.0136 891
## (Intercept)-Meleagris_gallopavo 0.1220 1.0053 782
## (Intercept)-Sciurus_carolinensis -0.7265 1.0035 896
## Avg_Cogongrass_Cover-Canis_latrans 0.9217 1.0007 1018
## Avg_Cogongrass_Cover-Lynx_rufus 0.9942 1.0110 848
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0676 1.0007 1088
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0464 1.0065 863
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2761 1.0040 986
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5348 1.0051 1064
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7429 1.0472 283
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3317 1.0508 689
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1903 1.0063 874
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6802 1.0306 622
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0666 1.0253 356
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6409 1.0039 1073
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.663100e+00 1.566000e-01 -4.979900e+00
## (Intercept)-Lynx_rufus -5.184900e+00 2.372000e-01 -5.700600e+00
## (Intercept)-Didelphis_virginiana -4.370200e+00 2.260000e-01 -4.837900e+00
## (Intercept)-Sylvilagus_floridanus -4.867500e+00 2.262000e-01 -5.309700e+00
## (Intercept)-Meleagris_gallopavo -4.990000e+00 2.530000e-01 -5.502500e+00
## (Intercept)-Sciurus_carolinensis -4.410500e+00 2.360000e-01 -4.880200e+00
## week-Canis_latrans -8.040033e+09 2.566105e+12 -7.708077e+10
## week-Lynx_rufus 2.206922e+10 1.988842e+12 -6.540833e+10
## week-Didelphis_virginiana 3.731500e+10 2.621128e+12 -5.008526e+10
## week-Sylvilagus_floridanus 9.585902e+10 4.138102e+12 -5.740354e+10
## week-Meleagris_gallopavo -8.047232e+09 3.350466e+12 -4.423605e+10
## week-Sciurus_carolinensis 1.072243e+10 2.578354e+12 -6.428875e+10
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6590 -4.365100e+00 1.0631 228
## (Intercept)-Lynx_rufus -5.1765 -4.749800e+00 1.0823 142
## (Intercept)-Didelphis_virginiana -4.3669 -3.942200e+00 1.0234 312
## (Intercept)-Sylvilagus_floridanus -4.8609 -4.438200e+00 1.0054 192
## (Intercept)-Meleagris_gallopavo -4.9781 -4.531300e+00 1.0308 170
## (Intercept)-Sciurus_carolinensis -4.3960 -3.982600e+00 1.0592 301
## week-Canis_latrans -0.1274 3.859948e+10 1.2913 233925
## week-Lynx_rufus -0.2809 4.906761e+10 1.3026 6712
## week-Didelphis_virginiana 0.0529 5.903672e+10 1.3104 5066
## week-Sylvilagus_floridanus -0.2009 6.520647e+10 1.3428 1856
## week-Meleagris_gallopavo -0.0721 5.338582e+10 1.2909 78759
## week-Sciurus_carolinensis -0.4913 4.670254e+10 1.2921 22399
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.6315
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7756 1.1587 -3.8001 -1.8520 0.7071 1.0108 625
## Cogon_Patch_Size 0.3691 1.0312 -1.7223 0.3781 2.4537 1.0250 659
## Veg_shannon_index 0.7164 0.7477 -0.7889 0.7331 2.1655 1.0354 721
## total_shrub_cover -0.8165 0.9449 -2.6780 -0.8301 1.0791 1.0007 1399
## Avg_Cogongrass_Cover 0.0302 1.1073 -2.0862 0.0484 2.2828 1.0476 274
## Tree_Density -2.2447 0.9386 -4.2210 -2.2357 -0.3703 1.0258 268
## Avg_Canopy_Cover 1.5460 1.0155 -0.6741 1.5831 3.4672 1.0099 1422
## I(Avg_Cogongrass_Cover^2) 1.0539 0.8916 -0.8760 1.0827 2.7632 1.0064 855
## avg_veg_height -0.5038 0.6751 -1.8494 -0.4961 0.7916 1.0287 239
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.1919 16.3787 0.2349 5.1886 52.2602 1.0541 285
## Cogon_Patch_Size 7.8982 12.1496 0.1741 4.0425 38.3842 1.4697 258
## Veg_shannon_index 3.5989 7.1012 0.0855 1.5678 19.2476 1.0431 414
## total_shrub_cover 9.1830 15.6749 0.4607 4.8894 42.9951 1.0613 367
## Avg_Cogongrass_Cover 2.3417 4.8743 0.0555 0.7692 15.9124 1.0203 387
## Tree_Density 3.4326 9.1275 0.0571 0.7522 27.7972 1.4097 201
## Avg_Canopy_Cover 10.5638 22.1786 0.3924 4.7466 56.8806 1.3774 100
## I(Avg_Cogongrass_Cover^2) 6.2147 12.9807 0.0968 2.4800 34.8423 1.0447 235
## avg_veg_height 0.8978 1.7085 0.0487 0.3760 5.0243 1.0813 619
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.88 4.3023 0.0623 0.6874 10.2322 1.4006 105
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6835 0.3268 -5.2313 -4.7127 -3.8584 1.0125 1370
## week -0.0782 1.6442 -3.3511 -0.0892 3.1833 1.0142 584
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.188000e-01 7.579000e-01 0.0724 0.3205 2.332800e+00 1.0150 775
## week 2.091691e+09 4.907703e+10 0.0941 34.1189 5.840426e+08 1.4381 879
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6531 1.5289 -3.4889 -0.6967
## (Intercept)-Lynx_rufus 0.0760 2.6149 -3.4896 -0.3923
## (Intercept)-Didelphis_virginiana -4.1289 1.5798 -7.6941 -3.9600
## (Intercept)-Sylvilagus_floridanus -2.7061 1.4493 -5.6547 -2.6484
## (Intercept)-Meleagris_gallopavo -2.6366 1.5650 -6.0937 -2.5420
## (Intercept)-Sciurus_carolinensis -4.8418 1.8459 -9.0488 -4.6214
## Cogon_Patch_Size-Canis_latrans 2.5499 1.9726 -0.1516 2.1595
## Cogon_Patch_Size-Lynx_rufus 0.4826 2.1201 -3.6851 0.4359
## Cogon_Patch_Size-Didelphis_virginiana 2.2617 1.4963 0.1302 2.0207
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6033 1.8350 -6.1233 -1.2859
## Cogon_Patch_Size-Meleagris_gallopavo 0.5993 1.4471 -1.9647 0.5100
## Cogon_Patch_Size-Sciurus_carolinensis -1.1718 1.6783 -5.5064 -0.8396
## Veg_shannon_index-Canis_latrans 1.8121 1.1948 0.0842 1.6002
## Veg_shannon_index-Lynx_rufus -0.5678 1.6210 -4.3543 -0.3646
## Veg_shannon_index-Didelphis_virginiana 1.1697 1.0331 -0.6283 1.0672
## Veg_shannon_index-Sylvilagus_floridanus 1.1154 1.0863 -0.6919 1.0063
## Veg_shannon_index-Meleagris_gallopavo 1.7980 1.3260 -0.1181 1.5638
## Veg_shannon_index-Sciurus_carolinensis -0.3171 1.0399 -2.6132 -0.2136
## total_shrub_cover-Canis_latrans 0.9213 1.2356 -1.0664 0.7513
## total_shrub_cover-Lynx_rufus -2.9508 2.1775 -8.0268 -2.5953
## total_shrub_cover-Didelphis_virginiana -0.8812 1.0941 -3.2589 -0.8124
## total_shrub_cover-Sylvilagus_floridanus -0.2775 1.1953 -2.6698 -0.2947
## total_shrub_cover-Meleagris_gallopavo -4.3322 2.1777 -9.6993 -3.9491
## total_shrub_cover-Sciurus_carolinensis 0.2672 0.8944 -1.5043 0.2471
## Avg_Cogongrass_Cover-Canis_latrans 0.0741 1.3746 -2.7228 0.0904
## Avg_Cogongrass_Cover-Lynx_rufus 0.6083 1.7351 -2.3364 0.4783
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4820 1.4723 -2.2782 0.4586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5292 1.5637 -3.9933 -0.3921
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4582 1.5935 -4.1430 -0.3002
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1661 1.4580 -2.6674 0.1681
## Tree_Density-Canis_latrans -3.1062 1.6837 -6.8731 -2.7902
## Tree_Density-Lynx_rufus -1.8214 1.5090 -4.5831 -1.9364
## Tree_Density-Didelphis_virginiana -2.4852 1.2224 -5.3587 -2.3216
## Tree_Density-Sylvilagus_floridanus -2.8931 1.5745 -7.0513 -2.6124
## Tree_Density-Meleagris_gallopavo -2.3755 1.5143 -5.6588 -2.2997
## Tree_Density-Sciurus_carolinensis -2.8489 1.4232 -6.5311 -2.6154
## Avg_Canopy_Cover-Canis_latrans -0.1347 0.9119 -2.0357 -0.0909
## Avg_Canopy_Cover-Lynx_rufus 0.7383 2.7359 -2.9873 0.4226
## Avg_Canopy_Cover-Didelphis_virginiana 3.1829 1.2798 1.2507 2.9870
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.9331 2.6550 1.2585 4.4627
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4768 1.3998 0.4980 2.2026
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6310 1.1474 0.9038 2.4661
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6839 1.7236 0.4972 2.2979
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9702 2.0488 0.2780 2.4920
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8412 0.8776 -0.9012 0.8320
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0801 1.0412 -0.7410 1.0020
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.9472 1.9662 -5.9582 -0.6283
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6205 0.9571 -0.0086 1.5271
## avg_veg_height-Canis_latrans -0.5869 0.7645 -2.1804 -0.5782
## avg_veg_height-Lynx_rufus -0.6202 1.0765 -2.9483 -0.5982
## avg_veg_height-Didelphis_virginiana -0.5534 0.8122 -2.2408 -0.5224
## avg_veg_height-Sylvilagus_floridanus -0.5544 0.8696 -2.3407 -0.5325
## avg_veg_height-Meleagris_gallopavo -0.6872 0.9569 -2.6736 -0.6583
## avg_veg_height-Sciurus_carolinensis -0.1088 0.8357 -1.5990 -0.1562
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.6541 1.0420 355
## (Intercept)-Lynx_rufus 6.0547 1.0799 94
## (Intercept)-Didelphis_virginiana -1.5355 1.0194 369
## (Intercept)-Sylvilagus_floridanus 0.0270 1.0061 332
## (Intercept)-Meleagris_gallopavo 0.0873 1.0091 303
## (Intercept)-Sciurus_carolinensis -1.8613 1.0401 237
## Cogon_Patch_Size-Canis_latrans 7.4651 1.0705 241
## Cogon_Patch_Size-Lynx_rufus 5.1210 1.1136 205
## Cogon_Patch_Size-Didelphis_virginiana 5.8479 1.1864 118
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1834 1.1362 317
## Cogon_Patch_Size-Meleagris_gallopavo 3.9680 1.0245 389
## Cogon_Patch_Size-Sciurus_carolinensis 1.2393 1.0672 380
## Veg_shannon_index-Canis_latrans 4.8500 1.0189 304
## Veg_shannon_index-Lynx_rufus 2.0492 1.0247 194
## Veg_shannon_index-Didelphis_virginiana 3.5589 1.0501 444
## Veg_shannon_index-Sylvilagus_floridanus 3.6015 1.0499 418
## Veg_shannon_index-Meleagris_gallopavo 5.0432 1.0182 201
## Veg_shannon_index-Sciurus_carolinensis 1.5030 1.0083 597
## total_shrub_cover-Canis_latrans 3.8021 1.0091 298
## total_shrub_cover-Lynx_rufus 0.2797 1.0312 180
## total_shrub_cover-Didelphis_virginiana 1.0954 1.0061 582
## total_shrub_cover-Sylvilagus_floridanus 2.0961 1.0298 597
## total_shrub_cover-Meleagris_gallopavo -1.1485 1.0581 156
## total_shrub_cover-Sciurus_carolinensis 2.1039 1.0030 900
## Avg_Cogongrass_Cover-Canis_latrans 2.8174 1.0311 385
## Avg_Cogongrass_Cover-Lynx_rufus 4.6502 1.0180 356
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8001 1.0368 310
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.2255 1.0276 394
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1862 1.0181 378
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.1535 1.0463 393
## Tree_Density-Canis_latrans -0.9401 1.1761 298
## Tree_Density-Lynx_rufus 1.4179 1.0587 210
## Tree_Density-Didelphis_virginiana -0.5489 1.0769 351
## Tree_Density-Sylvilagus_floridanus -0.6193 1.1144 231
## Tree_Density-Meleagris_gallopavo 0.2459 1.0637 332
## Tree_Density-Sciurus_carolinensis -0.7284 1.0725 411
## Avg_Canopy_Cover-Canis_latrans 1.5870 1.0242 583
## Avg_Canopy_Cover-Lynx_rufus 5.6061 1.2107 79
## Avg_Canopy_Cover-Didelphis_virginiana 6.2930 1.1215 195
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.2708 1.1102 153
## Avg_Canopy_Cover-Meleagris_gallopavo 5.9603 1.0068 233
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2552 1.0088 253
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.3973 1.1058 144
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.1480 1.0506 126
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5956 1.0301 214
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3223 1.0667 337
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8836 1.0954 153
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.8684 1.0049 513
## avg_veg_height-Canis_latrans 0.8383 1.0487 339
## avg_veg_height-Lynx_rufus 1.5179 1.0122 369
## avg_veg_height-Didelphis_virginiana 0.9434 1.0421 377
## avg_veg_height-Sylvilagus_floridanus 1.0259 1.0087 465
## avg_veg_height-Meleagris_gallopavo 1.1273 1.0089 333
## avg_veg_height-Sciurus_carolinensis 1.7259 1.0119 648
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6849 0.1645 -5.0137 -4.6818
## (Intercept)-Lynx_rufus -5.5218 0.2740 -6.0452 -5.5225
## (Intercept)-Didelphis_virginiana -4.3345 0.2273 -4.7966 -4.3341
## (Intercept)-Sylvilagus_floridanus -4.9516 0.2312 -5.4250 -4.9403
## (Intercept)-Meleagris_gallopavo -5.0391 0.2594 -5.5846 -5.0274
## (Intercept)-Sciurus_carolinensis -4.3780 0.2396 -4.8615 -4.3747
## week-Canis_latrans -1228.1033 62053.6964 -2882.2628 0.0413
## week-Lynx_rufus -919.7156 26070.5712 -4636.5728 -0.2199
## week-Didelphis_virginiana -926.3793 56256.2902 -3930.2068 -0.1141
## week-Sylvilagus_floridanus -970.1651 33643.7269 -7222.3990 -0.2651
## week-Meleagris_gallopavo 411.6177 38596.0358 -3381.0338 -0.0864
## week-Sciurus_carolinensis 53.9627 25577.9898 -4021.5969 -0.1686
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3700 1.0065 210
## (Intercept)-Lynx_rufus -4.9611 1.1011 75
## (Intercept)-Didelphis_virginiana -3.8947 1.0203 320
## (Intercept)-Sylvilagus_floridanus -4.5217 1.0218 163
## (Intercept)-Meleagris_gallopavo -4.5695 1.0989 161
## (Intercept)-Sciurus_carolinensis -3.9248 1.0187 289
## week-Canis_latrans 4428.6669 1.3353 1704
## week-Lynx_rufus 2913.1893 1.3009 834
## week-Didelphis_virginiana 3058.4177 1.2870 5823
## week-Sylvilagus_floridanus 2692.3992 1.2719 3876
## week-Meleagris_gallopavo 4964.9601 1.2875 12197
## week-Sciurus_carolinensis 3368.2476 1.2789 6460
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.0388
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3918 0.4867 -1.3572 -0.3977 0.5815 1.0089 496
## Avg_Cogongrass_Cover 0.1622 0.3964 -0.6310 0.1615 0.9414 1.0074 1270
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8883 1.3558 0.0557 0.4875 4.1776 1.0316 1201
## Avg_Cogongrass_Cover 0.6331 0.9092 0.0470 0.3329 2.9362 1.0134 972
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9433 1.2303 0.0641 0.5707 3.8602 1.1894 248
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8778 0.3755 -5.4386 -4.9293 -3.9679 1.0092 783
## shrub_cover 0.2078 0.4227 -0.6693 0.2037 1.0468 1.0071 844
## veg_height 0.0252 0.2755 -0.5597 0.0348 0.5643 1.0214 815
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5875 1.1189 0.0537 0.3143 2.7829 1.0992 288
## shrub_cover 1.1029 2.6343 0.1640 0.7281 4.1028 1.2562 2422
## veg_height 0.4040 0.5364 0.0707 0.2640 1.6099 1.0268 1228
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2373 0.6392 -0.9149 0.2114
## (Intercept)-Lynx_rufus -0.1203 0.6979 -1.4043 -0.1496
## (Intercept)-Didelphis_virginiana -0.8936 0.5749 -2.1000 -0.8752
## (Intercept)-Sylvilagus_floridanus -0.4805 0.5432 -1.6257 -0.4787
## (Intercept)-Meleagris_gallopavo -0.1971 0.6968 -1.5281 -0.2406
## (Intercept)-Sciurus_carolinensis -0.9565 0.6152 -2.2584 -0.9049
## Avg_Cogongrass_Cover-Canis_latrans 0.4889 0.4593 -0.2560 0.4397
## Avg_Cogongrass_Cover-Lynx_rufus 0.5430 0.5170 -0.3248 0.4888
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3311 0.4209 -0.4353 0.3109
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3235 0.4882 -1.3602 -0.2926
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3997 0.6765 -1.9250 -0.3285
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3579 0.3985 -0.4290 0.3559
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5582 1.0162 556
## (Intercept)-Lynx_rufus 1.3986 1.0064 289
## (Intercept)-Didelphis_virginiana 0.1589 1.0061 882
## (Intercept)-Sylvilagus_floridanus 0.5524 1.0046 665
## (Intercept)-Meleagris_gallopavo 1.3942 1.0393 276
## (Intercept)-Sciurus_carolinensis 0.1266 1.0042 681
## Avg_Cogongrass_Cover-Canis_latrans 1.5016 1.0043 1277
## Avg_Cogongrass_Cover-Lynx_rufus 1.7142 1.0181 1083
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2186 1.0254 1193
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5513 1.0021 1003
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7675 1.0161 464
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1695 1.0126 1119
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7959 0.1817 -5.1629 -4.7952 -4.4456 1.0544
## (Intercept)-Lynx_rufus -5.4686 0.2789 -6.0029 -5.4754 -4.8990 1.1054
## (Intercept)-Didelphis_virginiana -4.6814 0.2708 -5.2128 -4.6796 -4.1644 1.0414
## (Intercept)-Sylvilagus_floridanus -4.9441 0.2426 -5.4370 -4.9354 -4.4888 1.0371
## (Intercept)-Meleagris_gallopavo -5.7029 0.4721 -6.6594 -5.6825 -4.8642 1.3887
## (Intercept)-Sciurus_carolinensis -4.7030 0.3259 -5.3651 -4.6833 -4.1011 1.0058
## shrub_cover-Canis_latrans -0.2913 0.1993 -0.6855 -0.2866 0.0895 1.0518
## shrub_cover-Lynx_rufus -0.2137 0.3724 -0.9499 -0.2038 0.4903 1.1026
## shrub_cover-Didelphis_virginiana 1.0748 0.3562 0.4167 1.0708 1.8108 1.0031
## shrub_cover-Sylvilagus_floridanus 0.5088 0.3987 -0.2602 0.5076 1.3325 1.0834
## shrub_cover-Meleagris_gallopavo -0.7551 0.4077 -1.5226 -0.7351 0.0725 1.2737
## shrub_cover-Sciurus_carolinensis 0.9554 0.3961 0.2087 0.9459 1.7473 1.0748
## veg_height-Canis_latrans -0.6048 0.1888 -0.9955 -0.5980 -0.2410 1.0284
## veg_height-Lynx_rufus 0.0991 0.2346 -0.3649 0.0993 0.5505 1.1586
## veg_height-Didelphis_virginiana 0.5006 0.2367 0.0340 0.5010 0.9501 1.0484
## veg_height-Sylvilagus_floridanus 0.1784 0.2246 -0.2671 0.1833 0.6093 1.0023
## veg_height-Meleagris_gallopavo -0.2251 0.3989 -1.0281 -0.2211 0.5677 1.0776
## veg_height-Sciurus_carolinensis 0.2096 0.2267 -0.1907 0.2013 0.6701 1.0663
## ESS
## (Intercept)-Canis_latrans 172
## (Intercept)-Lynx_rufus 101
## (Intercept)-Didelphis_virginiana 191
## (Intercept)-Sylvilagus_floridanus 166
## (Intercept)-Meleagris_gallopavo 39
## (Intercept)-Sciurus_carolinensis 178
## shrub_cover-Canis_latrans 230
## shrub_cover-Lynx_rufus 96
## shrub_cover-Didelphis_virginiana 177
## shrub_cover-Sylvilagus_floridanus 113
## shrub_cover-Meleagris_gallopavo 74
## shrub_cover-Sciurus_carolinensis 119
## veg_height-Canis_latrans 181
## veg_height-Lynx_rufus 179
## veg_height-Didelphis_virginiana 234
## veg_height-Sylvilagus_floridanus 249
## veg_height-Meleagris_gallopavo 135
## veg_height-Sciurus_carolinensis 227
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4177 1.2469 -2.8828 -0.4335 1.9974 1.0356 177
## Cogon_Patch_Size 0.6206 1.1880 -1.7972 0.6171 2.9525 1.0704 276
## Veg_shannon_index 0.9045 1.1288 -1.3794 0.9091 3.2101 1.0119 287
## total_shrub_cover -1.2560 1.3595 -3.9652 -1.2657 1.6224 1.0089 612
## Avg_Cogongrass_Cover 1.1135 1.2486 -1.3981 1.1598 3.5421 1.0035 260
## Tree_Density -1.6937 1.2268 -3.8767 -1.7814 1.1179 1.0444 259
## Avg_Canopy_Cover 1.4026 1.4959 -1.7853 1.4539 4.1832 1.0068 1222
## avg_veg_height -0.2045 0.9860 -2.1680 -0.1706 1.7656 1.2080 203
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.8604 17.4455 0.1107 4.2931 51.9776 1.0635 225
## Cogon_Patch_Size 14.2395 31.5939 0.0789 4.4199 82.6739 1.0448 207
## Veg_shannon_index 8.7921 24.6729 0.0812 2.8504 50.6589 1.2025 230
## total_shrub_cover 50.5100 115.7225 0.9830 20.2334 263.6484 1.5082 68
## Avg_Cogongrass_Cover 9.0047 19.6303 0.0844 2.9187 52.9267 1.1641 236
## Tree_Density 10.5261 32.6103 0.0623 1.8020 78.5620 1.3299 116
## Avg_Canopy_Cover 86.1889 265.5993 2.0249 35.4149 444.0808 1.6551 347
## avg_veg_height 4.0099 11.0858 0.0524 0.9541 27.6297 1.1022 277
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 27.6153 39.0017 0.2046 13.6363 139.727 1.6956 26
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0140 0.3254 -5.5052 -5.0493 -4.3047 1.0165 939
## shrub_cover 0.4166 0.4485 -0.5269 0.4213 1.2564 1.0009 787
## veg_height 0.0788 0.2731 -0.4538 0.0781 0.6234 1.0412 668
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3836 0.6661 0.0453 0.2095 1.6914 1.0335 1157
## shrub_cover 1.2409 1.2846 0.2403 0.8657 4.7126 1.0218 1418
## veg_height 0.3930 0.4559 0.0771 0.2687 1.5498 1.0092 1239
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.1837 2.3130 -3.0150 1.0709
## (Intercept)-Lynx_rufus 1.3021 2.6709 -3.1300 1.0117
## (Intercept)-Didelphis_virginiana -2.0825 2.2479 -7.2411 -1.9012
## (Intercept)-Sylvilagus_floridanus -0.4757 2.1798 -5.0050 -0.4689
## (Intercept)-Meleagris_gallopavo -1.4238 2.3613 -6.6020 -1.2527
## (Intercept)-Sciurus_carolinensis -2.1334 2.3620 -7.6089 -1.9156
## Cogon_Patch_Size-Canis_latrans 2.3050 2.0410 -0.5697 1.8733
## Cogon_Patch_Size-Lynx_rufus 0.9445 2.6913 -4.2241 0.8605
## Cogon_Patch_Size-Didelphis_virginiana 2.9544 2.7416 -0.2105 2.2841
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6799 3.4240 -10.2353 -0.8347
## Cogon_Patch_Size-Meleagris_gallopavo 1.3859 2.4875 -2.5962 1.0312
## Cogon_Patch_Size-Sciurus_carolinensis -0.8923 2.8272 -8.0226 -0.4514
## Veg_shannon_index-Canis_latrans 2.0633 1.5122 -0.4876 1.8464
## Veg_shannon_index-Lynx_rufus -0.5182 2.4699 -6.4990 -0.0682
## Veg_shannon_index-Didelphis_virginiana 2.2605 2.0170 -0.6226 1.8527
## Veg_shannon_index-Sylvilagus_floridanus 1.9540 1.8380 -1.0190 1.7009
## Veg_shannon_index-Meleagris_gallopavo 2.2310 2.1311 -1.1104 1.9212
## Veg_shannon_index-Sciurus_carolinensis -0.9208 2.6013 -8.0483 -0.4502
## total_shrub_cover-Canis_latrans 3.4444 2.5083 -0.5754 3.1569
## total_shrub_cover-Lynx_rufus -5.9965 3.8260 -15.1490 -5.3598
## total_shrub_cover-Didelphis_virginiana -4.8697 5.1662 -17.3180 -3.5234
## total_shrub_cover-Sylvilagus_floridanus -3.5068 3.8841 -12.8097 -3.0913
## total_shrub_cover-Meleagris_gallopavo -5.9034 4.2399 -17.0350 -5.0210
## total_shrub_cover-Sciurus_carolinensis -4.0773 4.2110 -14.7359 -3.1246
## Avg_Cogongrass_Cover-Canis_latrans 2.5202 1.8990 -0.4950 2.2880
## Avg_Cogongrass_Cover-Lynx_rufus 2.7883 2.4739 -1.1586 2.3895
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7249 2.0942 -2.3421 1.6589
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0190 2.2558 -4.8851 0.2433
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1680 2.6930 -6.3825 0.2041
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.9846 2.2057 -1.8594 1.8213
## Tree_Density-Canis_latrans -3.7482 2.9555 -12.0256 -2.8786
## Tree_Density-Lynx_rufus -1.0202 2.0707 -4.3460 -1.2915
## Tree_Density-Didelphis_virginiana -2.1059 2.1630 -7.2369 -2.0645
## Tree_Density-Sylvilagus_floridanus -2.7797 2.7028 -9.7652 -2.3893
## Tree_Density-Meleagris_gallopavo -2.0695 2.4312 -7.2913 -1.9964
## Tree_Density-Sciurus_carolinensis -2.2579 2.6437 -8.4676 -2.1868
## Avg_Canopy_Cover-Canis_latrans -1.1463 1.4952 -4.8269 -0.8988
## Avg_Canopy_Cover-Lynx_rufus -0.6757 2.7938 -7.1161 -0.4543
## Avg_Canopy_Cover-Didelphis_virginiana 8.4449 5.0403 2.1664 7.2365
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.8198 6.2518 2.6137 9.4064
## Avg_Canopy_Cover-Meleagris_gallopavo 4.7335 3.4668 0.3432 3.9478
## Avg_Canopy_Cover-Sciurus_carolinensis 8.1933 5.6549 1.7871 6.6635
## avg_veg_height-Canis_latrans -0.1606 1.1771 -2.4539 -0.1756
## avg_veg_height-Lynx_rufus -0.3735 2.1206 -4.9909 -0.3261
## avg_veg_height-Didelphis_virginiana -0.5686 1.4938 -3.9038 -0.4521
## avg_veg_height-Sylvilagus_floridanus -0.3506 1.4490 -3.3853 -0.3348
## avg_veg_height-Meleagris_gallopavo -0.6182 1.9509 -5.6796 -0.3618
## avg_veg_height-Sciurus_carolinensis 0.8830 1.9725 -1.7484 0.4776
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.4006 1.1532 108
## (Intercept)-Lynx_rufus 7.6225 1.0970 119
## (Intercept)-Didelphis_virginiana 1.8866 1.1136 143
## (Intercept)-Sylvilagus_floridanus 3.9856 1.0129 204
## (Intercept)-Meleagris_gallopavo 3.0113 1.0286 159
## (Intercept)-Sciurus_carolinensis 1.8680 1.2509 136
## Cogon_Patch_Size-Canis_latrans 7.4171 1.0234 239
## Cogon_Patch_Size-Lynx_rufus 6.6882 1.0541 227
## Cogon_Patch_Size-Didelphis_virginiana 9.4727 1.2820 73
## Cogon_Patch_Size-Sylvilagus_floridanus 3.0707 1.0802 101
## Cogon_Patch_Size-Meleagris_gallopavo 8.1989 1.0356 169
## Cogon_Patch_Size-Sciurus_carolinensis 3.6451 1.0500 161
## Veg_shannon_index-Canis_latrans 5.6935 1.1815 161
## Veg_shannon_index-Lynx_rufus 3.3008 1.0352 140
## Veg_shannon_index-Didelphis_virginiana 7.4141 1.0772 88
## Veg_shannon_index-Sylvilagus_floridanus 6.5721 1.1129 119
## Veg_shannon_index-Meleagris_gallopavo 7.4727 1.0409 231
## Veg_shannon_index-Sciurus_carolinensis 2.7884 1.1069 114
## total_shrub_cover-Canis_latrans 9.1482 1.2517 60
## total_shrub_cover-Lynx_rufus 0.1260 1.2734 60
## total_shrub_cover-Didelphis_virginiana 0.4103 1.3194 51
## total_shrub_cover-Sylvilagus_floridanus 3.5772 1.1034 113
## total_shrub_cover-Meleagris_gallopavo -0.2881 1.3688 57
## total_shrub_cover-Sciurus_carolinensis 1.4376 1.4158 73
## Avg_Cogongrass_Cover-Canis_latrans 6.9992 1.1509 243
## Avg_Cogongrass_Cover-Lynx_rufus 8.7625 1.0495 315
## Avg_Cogongrass_Cover-Didelphis_virginiana 6.3932 1.0209 262
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.9445 1.0704 189
## Avg_Cogongrass_Cover-Meleagris_gallopavo 4.2595 1.1108 116
## Avg_Cogongrass_Cover-Sciurus_carolinensis 6.7783 1.0681 201
## Tree_Density-Canis_latrans -0.3971 1.1633 58
## Tree_Density-Lynx_rufus 3.6928 1.0496 204
## Tree_Density-Didelphis_virginiana 2.3640 1.0322 270
## Tree_Density-Sylvilagus_floridanus 1.4652 1.2097 155
## Tree_Density-Meleagris_gallopavo 2.4648 1.0438 140
## Tree_Density-Sciurus_carolinensis 3.2467 1.1296 100
## Avg_Canopy_Cover-Canis_latrans 0.9425 1.1843 72
## Avg_Canopy_Cover-Lynx_rufus 4.8725 1.0259 132
## Avg_Canopy_Cover-Didelphis_virginiana 22.1093 1.3792 47
## Avg_Canopy_Cover-Sylvilagus_floridanus 26.8712 1.3784 74
## Avg_Canopy_Cover-Meleagris_gallopavo 13.8457 1.3179 111
## Avg_Canopy_Cover-Sciurus_carolinensis 24.5544 1.6497 27
## avg_veg_height-Canis_latrans 2.2427 1.0796 355
## avg_veg_height-Lynx_rufus 4.0250 1.0924 158
## avg_veg_height-Didelphis_virginiana 2.0770 1.1038 298
## avg_veg_height-Sylvilagus_floridanus 2.5700 1.0978 383
## avg_veg_height-Meleagris_gallopavo 2.6582 1.2461 115
## avg_veg_height-Sciurus_carolinensis 5.8634 1.2214 167
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8615 0.1692 -5.2079 -4.8586 -4.5392 1.0836
## (Intercept)-Lynx_rufus -5.5498 0.2863 -6.1678 -5.5326 -5.0240 1.0210
## (Intercept)-Didelphis_virginiana -4.8577 0.2560 -5.3353 -4.8654 -4.3539 1.0124
## (Intercept)-Sylvilagus_floridanus -5.0545 0.1985 -5.4528 -5.0533 -4.6725 1.0033
## (Intercept)-Meleagris_gallopavo -5.4733 0.3656 -6.1975 -5.4631 -4.7796 1.0354
## (Intercept)-Sciurus_carolinensis -4.9370 0.2992 -5.5383 -4.9464 -4.3365 1.1500
## shrub_cover-Canis_latrans -0.4246 0.1937 -0.8222 -0.4142 -0.0622 1.0450
## shrub_cover-Lynx_rufus 0.1738 0.3187 -0.4412 0.1708 0.8113 1.0133
## shrub_cover-Didelphis_virginiana 1.3262 0.3515 0.6204 1.3255 2.0570 1.0157
## shrub_cover-Sylvilagus_floridanus 0.8417 0.3608 0.1346 0.8427 1.5615 1.0849
## shrub_cover-Meleagris_gallopavo -0.5186 0.3451 -1.1770 -0.5098 0.1846 1.0200
## shrub_cover-Sciurus_carolinensis 1.2705 0.3782 0.4261 1.2703 2.0095 1.1334
## veg_height-Canis_latrans -0.5982 0.1797 -0.9565 -0.5967 -0.2520 1.1070
## veg_height-Lynx_rufus 0.1243 0.2352 -0.3411 0.1234 0.6069 1.0303
## veg_height-Didelphis_virginiana 0.5573 0.2318 0.1210 0.5498 1.0379 1.0073
## veg_height-Sylvilagus_floridanus 0.1784 0.2365 -0.2693 0.1738 0.6370 1.1042
## veg_height-Meleagris_gallopavo -0.0691 0.4063 -0.8600 -0.0681 0.7923 1.1084
## veg_height-Sciurus_carolinensis 0.2859 0.2249 -0.1461 0.2826 0.7527 1.0764
## ESS
## (Intercept)-Canis_latrans 189
## (Intercept)-Lynx_rufus 91
## (Intercept)-Didelphis_virginiana 123
## (Intercept)-Sylvilagus_floridanus 248
## (Intercept)-Meleagris_gallopavo 137
## (Intercept)-Sciurus_carolinensis 101
## shrub_cover-Canis_latrans 198
## shrub_cover-Lynx_rufus 123
## shrub_cover-Didelphis_virginiana 124
## shrub_cover-Sylvilagus_floridanus 119
## shrub_cover-Meleagris_gallopavo 181
## shrub_cover-Sciurus_carolinensis 81
## veg_height-Canis_latrans 163
## veg_height-Lynx_rufus 108
## veg_height-Didelphis_virginiana 262
## veg_height-Sylvilagus_floridanus 230
## veg_height-Meleagris_gallopavo 117
## veg_height-Sciurus_carolinensis 201
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.5212
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1113 0.6354 -1.3348 -0.1290 1.1798 1.1011 165
## Avg_Cogongrass_Cover 0.0315 0.6039 -1.2078 0.0411 1.2609 1.0073 817
## total_shrub_cover -1.0536 0.7093 -2.5600 -1.0231 0.3348 1.0079 519
## avg_veg_height 0.0159 0.6001 -1.2127 0.0124 1.1559 1.0834 248
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0717 1.8133 0.0530 0.4890 5.7290 1.0173 427
## Avg_Cogongrass_Cover 1.4619 2.6331 0.0684 0.6969 7.6528 1.0780 620
## total_shrub_cover 2.9420 4.7133 0.1492 1.5985 13.2366 1.0086 477
## avg_veg_height 1.0847 2.3722 0.0476 0.4225 6.3989 1.0866 261
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.5359 3.3246 0.1388 1.5312 11.3372 1.0173 178
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9955 0.3139 -5.4867 -5.0241 -4.3277 1.0538 697
## shrub_cover 0.4675 0.4546 -0.4451 0.4639 1.3843 1.0074 1062
## veg_height 0.0683 0.2796 -0.5001 0.0670 0.6514 1.0427 466
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3563 0.8559 0.0382 0.1813 1.6349 1.1857 882
## shrub_cover 1.2983 1.4878 0.2293 0.8855 4.9318 1.0120 806
## veg_height 0.4051 0.5993 0.0648 0.2644 1.5223 1.0129 1197
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3993 0.7992 -1.0320 0.3549
## (Intercept)-Lynx_rufus 0.1642 0.8850 -1.3330 0.0863
## (Intercept)-Didelphis_virginiana -0.4385 0.8384 -2.0671 -0.4514
## (Intercept)-Sylvilagus_floridanus 0.1090 0.8387 -1.4550 0.0841
## (Intercept)-Meleagris_gallopavo -0.3429 0.9312 -2.1460 -0.3376
## (Intercept)-Sciurus_carolinensis -0.5151 0.8398 -2.2063 -0.4966
## Avg_Cogongrass_Cover-Canis_latrans 0.5546 0.7244 -0.7143 0.5006
## Avg_Cogongrass_Cover-Lynx_rufus 0.7690 0.9327 -0.7042 0.6385
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2486 0.6892 -1.0824 0.2430
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6852 0.8318 -2.4704 -0.5862
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.7578 0.9717 -2.9766 -0.6404
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0755 0.6852 -1.5401 -0.0482
## total_shrub_cover-Canis_latrans 0.6756 0.8779 -0.7318 0.5398
## total_shrub_cover-Lynx_rufus -1.7333 1.1239 -4.2549 -1.6088
## total_shrub_cover-Didelphis_virginiana -1.3059 0.9761 -3.6006 -1.1523
## total_shrub_cover-Sylvilagus_floridanus -1.8717 1.1687 -4.6907 -1.6817
## total_shrub_cover-Meleagris_gallopavo -1.9745 1.0785 -4.5074 -1.8438
## total_shrub_cover-Sciurus_carolinensis -1.2932 1.0207 -3.7651 -1.1456
## avg_veg_height-Canis_latrans 0.1544 0.6316 -1.0687 0.1154
## avg_veg_height-Lynx_rufus -0.1533 0.9112 -2.0915 -0.1285
## avg_veg_height-Didelphis_virginiana -0.1482 0.6741 -1.5103 -0.1381
## avg_veg_height-Sylvilagus_floridanus 0.0590 0.6840 -1.2372 0.0434
## avg_veg_height-Meleagris_gallopavo -0.4891 1.2551 -3.6079 -0.3230
## avg_veg_height-Sciurus_carolinensis 0.6164 0.7291 -0.6269 0.5473
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1750 1.0108 378
## (Intercept)-Lynx_rufus 2.2601 1.0516 264
## (Intercept)-Didelphis_virginiana 1.1866 1.0832 193
## (Intercept)-Sylvilagus_floridanus 1.9486 1.0480 298
## (Intercept)-Meleagris_gallopavo 1.5140 1.1234 199
## (Intercept)-Sciurus_carolinensis 1.0273 1.0722 360
## Avg_Cogongrass_Cover-Canis_latrans 2.1895 1.0260 736
## Avg_Cogongrass_Cover-Lynx_rufus 2.9760 1.0336 386
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.6751 1.0157 607
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7222 1.0071 444
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8176 1.0030 390
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2001 1.0207 545
## total_shrub_cover-Canis_latrans 2.7702 1.0719 268
## total_shrub_cover-Lynx_rufus 0.1431 1.0168 229
## total_shrub_cover-Didelphis_virginiana 0.1484 1.0223 243
## total_shrub_cover-Sylvilagus_floridanus -0.0855 1.0155 189
## total_shrub_cover-Meleagris_gallopavo -0.2515 1.0233 352
## total_shrub_cover-Sciurus_carolinensis 0.2711 1.0332 181
## avg_veg_height-Canis_latrans 1.4846 1.0275 584
## avg_veg_height-Lynx_rufus 1.5587 1.1114 275
## avg_veg_height-Didelphis_virginiana 1.1458 1.0839 506
## avg_veg_height-Sylvilagus_floridanus 1.5117 1.0497 350
## avg_veg_height-Meleagris_gallopavo 1.4405 1.1221 166
## avg_veg_height-Sciurus_carolinensis 2.2084 1.0446 487
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8236 0.1771 -5.1591 -4.8211 -4.4875 1.0156
## (Intercept)-Lynx_rufus -5.4107 0.2846 -5.9801 -5.3982 -4.9118 1.1870
## (Intercept)-Didelphis_virginiana -4.9514 0.2767 -5.4527 -4.9618 -4.3986 1.1047
## (Intercept)-Sylvilagus_floridanus -5.0720 0.2200 -5.5199 -5.0700 -4.6434 1.2116
## (Intercept)-Meleagris_gallopavo -5.4409 0.4109 -6.2524 -5.4232 -4.7132 1.2301
## (Intercept)-Sciurus_carolinensis -4.9090 0.2945 -5.4947 -4.9113 -4.3483 1.1242
## shrub_cover-Canis_latrans -0.3101 0.2172 -0.7443 -0.2986 0.0954 1.2455
## shrub_cover-Lynx_rufus 0.1771 0.3516 -0.5328 0.2004 0.8260 1.0994
## shrub_cover-Didelphis_virginiana 1.4109 0.4091 0.5965 1.4198 2.2091 1.0812
## shrub_cover-Sylvilagus_floridanus 0.9770 0.3675 0.2249 0.9841 1.7061 1.1522
## shrub_cover-Meleagris_gallopavo -0.4709 0.4044 -1.2125 -0.4684 0.3441 1.1701
## shrub_cover-Sciurus_carolinensis 1.2707 0.3898 0.4747 1.2747 2.0051 1.1155
## veg_height-Canis_latrans -0.5794 0.1761 -0.9333 -0.5718 -0.2510 1.0183
## veg_height-Lynx_rufus 0.1243 0.2585 -0.4050 0.1278 0.6121 1.2833
## veg_height-Didelphis_virginiana 0.5415 0.2699 0.0296 0.5427 1.0918 1.0769
## veg_height-Sylvilagus_floridanus 0.1060 0.2181 -0.3064 0.0940 0.5411 1.0800
## veg_height-Meleagris_gallopavo -0.0521 0.4780 -0.9494 -0.0455 0.8857 1.0375
## veg_height-Sciurus_carolinensis 0.2656 0.2306 -0.1910 0.2665 0.7137 1.0310
## ESS
## (Intercept)-Canis_latrans 151
## (Intercept)-Lynx_rufus 100
## (Intercept)-Didelphis_virginiana 123
## (Intercept)-Sylvilagus_floridanus 146
## (Intercept)-Meleagris_gallopavo 142
## (Intercept)-Sciurus_carolinensis 143
## shrub_cover-Canis_latrans 148
## shrub_cover-Lynx_rufus 99
## shrub_cover-Didelphis_virginiana 99
## shrub_cover-Sylvilagus_floridanus 104
## shrub_cover-Meleagris_gallopavo 133
## shrub_cover-Sciurus_carolinensis 116
## veg_height-Canis_latrans 191
## veg_height-Lynx_rufus 118
## veg_height-Didelphis_virginiana 148
## veg_height-Sylvilagus_floridanus 160
## veg_height-Meleagris_gallopavo 95
## veg_height-Sciurus_carolinensis 186
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.9972
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2834 0.4293 -1.0969 -0.2895 0.6778 1.0121 643
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9631 1.4793 0.0769 0.5655 4.1486 1.0288 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8577 0.4126 -5.5132 -4.9063 -3.9018 1.0305 1214
## shrub_cover 0.1723 0.4303 -0.7015 0.1822 1.0362 1.0042 786
## veg_height 0.0121 0.2742 -0.5196 0.0079 0.5671 1.0092 706
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7222 1.3983 0.0721 0.3864 3.3047 1.0691 613
## shrub_cover 1.1113 1.3406 0.1801 0.7572 4.2040 1.0488 730
## veg_height 0.4017 0.4690 0.0664 0.2696 1.5085 1.0136 993
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3362 0.4143 -0.4148 0.3138 1.2150 1.0002
## (Intercept)-Lynx_rufus 0.2153 0.6765 -0.7529 0.1173 1.7374 1.0659
## (Intercept)-Didelphis_virginiana -0.9622 0.4513 -1.9031 -0.9446 -0.1383 1.0117
## (Intercept)-Sylvilagus_floridanus -0.4377 0.3972 -1.2412 -0.4377 0.3375 1.0009
## (Intercept)-Meleagris_gallopavo 0.0463 0.6939 -1.0371 -0.0400 1.6789 1.0514
## (Intercept)-Sciurus_carolinensis -0.9736 0.4615 -1.9213 -0.9566 -0.0974 1.0186
## ESS
## (Intercept)-Canis_latrans 907
## (Intercept)-Lynx_rufus 165
## (Intercept)-Didelphis_virginiana 868
## (Intercept)-Sylvilagus_floridanus 1415
## (Intercept)-Meleagris_gallopavo 198
## (Intercept)-Sciurus_carolinensis 744
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7677 0.1728 -5.1194 -4.7586 -4.4587 1.0114
## (Intercept)-Lynx_rufus -5.5319 0.3086 -6.1877 -5.5298 -4.9489 1.0840
## (Intercept)-Didelphis_virginiana -4.6556 0.2756 -5.2660 -4.6427 -4.1501 1.1415
## (Intercept)-Sylvilagus_floridanus -4.9006 0.2197 -5.3165 -4.9006 -4.4767 1.0541
## (Intercept)-Meleagris_gallopavo -5.7829 0.4647 -6.7394 -5.7838 -4.8981 1.0917
## (Intercept)-Sciurus_carolinensis -4.6652 0.3085 -5.2933 -4.6486 -4.0964 1.0428
## shrub_cover-Canis_latrans -0.3075 0.2089 -0.7207 -0.3052 0.0963 1.0132
## shrub_cover-Lynx_rufus -0.2843 0.3316 -0.9273 -0.2777 0.3396 1.1264
## shrub_cover-Didelphis_virginiana 1.0677 0.3783 0.3888 1.0334 1.8852 1.1068
## shrub_cover-Sylvilagus_floridanus 0.4625 0.4033 -0.2720 0.4425 1.2946 1.0178
## shrub_cover-Meleagris_gallopavo -0.8214 0.3996 -1.6418 -0.8177 -0.0278 1.0774
## shrub_cover-Sciurus_carolinensis 0.9254 0.4058 0.1477 0.9245 1.7451 1.0977
## veg_height-Canis_latrans -0.5794 0.1772 -0.9568 -0.5725 -0.2485 1.0097
## veg_height-Lynx_rufus 0.1404 0.2368 -0.3401 0.1400 0.6116 1.0353
## veg_height-Didelphis_virginiana 0.4907 0.2440 0.0372 0.4789 1.0060 1.0309
## veg_height-Sylvilagus_floridanus 0.1554 0.2264 -0.2906 0.1574 0.6040 1.0317
## veg_height-Meleagris_gallopavo -0.3308 0.3993 -1.0681 -0.3378 0.4871 1.0322
## veg_height-Sciurus_carolinensis 0.1821 0.2121 -0.1987 0.1675 0.6150 1.0252
## ESS
## (Intercept)-Canis_latrans 150
## (Intercept)-Lynx_rufus 84
## (Intercept)-Didelphis_virginiana 206
## (Intercept)-Sylvilagus_floridanus 190
## (Intercept)-Meleagris_gallopavo 59
## (Intercept)-Sciurus_carolinensis 140
## shrub_cover-Canis_latrans 169
## shrub_cover-Lynx_rufus 117
## shrub_cover-Didelphis_virginiana 135
## shrub_cover-Sylvilagus_floridanus 139
## shrub_cover-Meleagris_gallopavo 64
## shrub_cover-Sciurus_carolinensis 167
## veg_height-Canis_latrans 156
## veg_height-Lynx_rufus 168
## veg_height-Didelphis_virginiana 267
## veg_height-Sylvilagus_floridanus 221
## veg_height-Meleagris_gallopavo 103
## veg_height-Sciurus_carolinensis 249
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.9862
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4410 0.4931 -1.4200 -0.4426 0.5013 1.0188 547
## Veg_shannon_index 0.3349 0.4276 -0.5311 0.3434 1.1553 1.0126 981
## Avg_Cogongrass_Cover 0.2888 0.4045 -0.5270 0.2843 1.0961 1.0355 1002
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9558 1.4905 0.0591 0.5165 4.4250 1.0174 785
## Veg_shannon_index 0.6612 1.0413 0.0507 0.3487 3.1249 1.0280 1192
## Avg_Cogongrass_Cover 0.6895 1.0034 0.0508 0.3639 3.2372 1.0255 773
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0246 1.2333 0.067 0.6079 4.7017 1.0481 204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8578 0.4243 -5.4776 -4.9061 -3.9110 1.0328 900
## shrub_cover 0.1726 0.4098 -0.6484 0.1804 0.9794 1.0019 1046
## veg_height 0.0275 0.2747 -0.5162 0.0271 0.5571 1.0229 465
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7594 2.9507 0.0628 0.3565 3.2233 1.2435 1063
## shrub_cover 1.0283 1.2563 0.1674 0.6980 3.7863 1.0479 664
## veg_height 0.3940 0.4792 0.0684 0.2665 1.4300 1.0270 1091
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1489 0.6132 -1.0232 0.1239
## (Intercept)-Lynx_rufus -0.1049 0.7744 -1.4885 -0.1423
## (Intercept)-Didelphis_virginiana -0.9931 0.6232 -2.3413 -0.9580
## (Intercept)-Sylvilagus_floridanus -0.5503 0.5664 -1.7367 -0.5448
## (Intercept)-Meleagris_gallopavo -0.1756 0.7543 -1.5985 -0.2020
## (Intercept)-Sciurus_carolinensis -0.9913 0.6200 -2.3011 -0.9525
## Veg_shannon_index-Canis_latrans 0.7851 0.4724 -0.0694 0.7634
## Veg_shannon_index-Lynx_rufus -0.1436 0.7019 -1.7434 -0.0661
## Veg_shannon_index-Didelphis_virginiana 0.5407 0.4531 -0.2844 0.5188
## Veg_shannon_index-Sylvilagus_floridanus 0.4253 0.4620 -0.4530 0.4129
## Veg_shannon_index-Meleagris_gallopavo 0.5584 0.5772 -0.5189 0.5208
## Veg_shannon_index-Sciurus_carolinensis -0.1341 0.4893 -1.2043 -0.1021
## Avg_Cogongrass_Cover-Canis_latrans 0.7095 0.5106 -0.1102 0.6489
## Avg_Cogongrass_Cover-Lynx_rufus 0.6190 0.5510 -0.2886 0.5710
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4702 0.4397 -0.3462 0.4609
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2479 0.5091 -1.3313 -0.2131
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1987 0.7588 -1.8364 -0.1310
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4025 0.4201 -0.3945 0.3913
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4405 1.0322 549
## (Intercept)-Lynx_rufus 1.5328 1.0200 273
## (Intercept)-Didelphis_virginiana 0.2055 1.0242 668
## (Intercept)-Sylvilagus_floridanus 0.5447 1.0144 745
## (Intercept)-Meleagris_gallopavo 1.4184 1.0426 285
## (Intercept)-Sciurus_carolinensis 0.1374 1.0012 552
## Veg_shannon_index-Canis_latrans 1.8351 1.0075 1303
## Veg_shannon_index-Lynx_rufus 0.9928 1.0272 498
## Veg_shannon_index-Didelphis_virginiana 1.5068 1.0003 1593
## Veg_shannon_index-Sylvilagus_floridanus 1.3425 1.0049 1056
## Veg_shannon_index-Meleagris_gallopavo 1.8432 1.0107 946
## Veg_shannon_index-Sciurus_carolinensis 0.7043 1.0056 1090
## Avg_Cogongrass_Cover-Canis_latrans 1.8861 1.0125 911
## Avg_Cogongrass_Cover-Lynx_rufus 1.9076 1.0133 881
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.4044 1.0088 1534
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6568 1.0310 826
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1700 1.0982 296
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2498 1.0098 1511
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7702 0.1724 -5.1137 -4.7669 -4.4481 1.0153
## (Intercept)-Lynx_rufus -5.4825 0.2965 -6.0565 -5.4650 -4.9455 1.0164
## (Intercept)-Didelphis_virginiana -4.6580 0.2564 -5.1769 -4.6516 -4.1799 1.0369
## (Intercept)-Sylvilagus_floridanus -4.9401 0.2481 -5.4367 -4.9321 -4.4833 1.1386
## (Intercept)-Meleagris_gallopavo -5.7515 0.4957 -6.7877 -5.7398 -4.7510 1.0571
## (Intercept)-Sciurus_carolinensis -4.6663 0.2823 -5.2171 -4.6568 -4.1252 1.0144
## shrub_cover-Canis_latrans -0.2745 0.2012 -0.6759 -0.2736 0.1249 1.1168
## shrub_cover-Lynx_rufus -0.1957 0.3788 -0.9928 -0.1866 0.5217 1.0604
## shrub_cover-Didelphis_virginiana 1.0594 0.3516 0.4245 1.0412 1.8384 1.1172
## shrub_cover-Sylvilagus_floridanus 0.4407 0.4136 -0.3281 0.4261 1.2303 1.0020
## shrub_cover-Meleagris_gallopavo -0.7716 0.4174 -1.6019 -0.7937 0.1218 1.0757
## shrub_cover-Sciurus_carolinensis 0.8768 0.3875 0.1068 0.8673 1.6489 1.0508
## veg_height-Canis_latrans -0.5707 0.1736 -0.9234 -0.5636 -0.2512 1.0213
## veg_height-Lynx_rufus 0.0915 0.2423 -0.3831 0.0916 0.5563 1.0628
## veg_height-Didelphis_virginiana 0.5084 0.2341 0.0606 0.5046 0.9764 1.0441
## veg_height-Sylvilagus_floridanus 0.2045 0.2278 -0.2453 0.1971 0.6519 1.0097
## veg_height-Meleagris_gallopavo -0.2867 0.4286 -1.1041 -0.2984 0.5587 1.1407
## veg_height-Sciurus_carolinensis 0.1949 0.2074 -0.2162 0.1899 0.6027 1.0320
## ESS
## (Intercept)-Canis_latrans 182
## (Intercept)-Lynx_rufus 87
## (Intercept)-Didelphis_virginiana 208
## (Intercept)-Sylvilagus_floridanus 162
## (Intercept)-Meleagris_gallopavo 41
## (Intercept)-Sciurus_carolinensis 247
## shrub_cover-Canis_latrans 194
## shrub_cover-Lynx_rufus 90
## shrub_cover-Didelphis_virginiana 168
## shrub_cover-Sylvilagus_floridanus 127
## shrub_cover-Meleagris_gallopavo 59
## shrub_cover-Sciurus_carolinensis 145
## veg_height-Canis_latrans 199
## veg_height-Lynx_rufus 155
## veg_height-Didelphis_virginiana 268
## veg_height-Sylvilagus_floridanus 223
## veg_height-Meleagris_gallopavo 98
## veg_height-Sciurus_carolinensis 254
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.3273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2396 0.6189 -1.4370 -0.2453 1.0258 1.0330 246
## Cogon_Patch_Size 0.0206 0.6910 -1.4740 0.0499 1.3760 1.0068 955
## Avg_Cogongrass_Cover 0.0611 0.5543 -1.0899 0.0583 1.1615 1.0020 539
## total_shrub_cover -0.9980 0.7365 -2.5954 -0.9547 0.3925 1.0898 418
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1752 2.0503 0.0526 0.5593 6.1453 1.0299 477
## Cogon_Patch_Size 2.4054 4.0345 0.0751 1.1087 13.1063 1.0057 584
## Avg_Cogongrass_Cover 1.2277 2.3630 0.0601 0.5745 6.0110 1.0246 922
## total_shrub_cover 3.1888 8.3393 0.1190 1.4323 15.5181 1.0494 235
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.037 3.673 0.153 1.8751 13.8196 1.055 131
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9633 0.3294 -5.5129 -4.9802 -4.3077 1.0026 558
## shrub_cover 0.4365 0.4382 -0.4468 0.4333 1.3232 1.0447 711
## veg_height 0.0372 0.2794 -0.5205 0.0413 0.5850 1.0065 583
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3910 1.1453 0.0432 0.2052 1.6769 1.1214 1256
## shrub_cover 1.2348 1.6948 0.1821 0.8132 4.8569 1.0850 623
## veg_height 0.4213 0.5247 0.0661 0.2796 1.7322 1.0172 851
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4064 0.8815 -1.1191 0.3268
## (Intercept)-Lynx_rufus -0.0163 0.8456 -1.5341 -0.0506
## (Intercept)-Didelphis_virginiana -0.5950 0.8859 -2.3920 -0.5907
## (Intercept)-Sylvilagus_floridanus -0.1871 0.8833 -1.8586 -0.2055
## (Intercept)-Meleagris_gallopavo -0.4664 0.8429 -2.2509 -0.4516
## (Intercept)-Sciurus_carolinensis -0.7001 0.8757 -2.3793 -0.6755
## Cogon_Patch_Size-Canis_latrans 1.0035 1.0171 -0.3766 0.7845
## Cogon_Patch_Size-Lynx_rufus -0.1056 1.0462 -2.1337 -0.1296
## Cogon_Patch_Size-Didelphis_virginiana 0.8663 0.6679 -0.2671 0.7985
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9947 1.2115 -3.9922 -0.7438
## Cogon_Patch_Size-Meleagris_gallopavo 0.1733 0.8846 -1.6068 0.1676
## Cogon_Patch_Size-Sciurus_carolinensis -0.8486 1.0466 -3.5587 -0.6492
## Avg_Cogongrass_Cover-Canis_latrans 0.4136 0.5771 -0.5780 0.3512
## Avg_Cogongrass_Cover-Lynx_rufus 0.7247 0.8319 -0.5378 0.5927
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0344 0.6075 -1.3069 -0.0145
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4233 0.7824 -2.0497 -0.3959
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6932 1.0325 -2.9381 -0.5913
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4124 0.6399 -0.7935 0.3884
## total_shrub_cover-Canis_latrans 0.5727 0.8506 -0.8306 0.4594
## total_shrub_cover-Lynx_rufus -1.6851 1.2804 -4.7238 -1.5442
## total_shrub_cover-Didelphis_virginiana -1.3414 1.0209 -3.8665 -1.1633
## total_shrub_cover-Sylvilagus_floridanus -1.6968 1.2250 -4.6091 -1.5313
## total_shrub_cover-Meleagris_gallopavo -1.9621 1.2864 -4.9241 -1.7648
## total_shrub_cover-Sciurus_carolinensis -1.0200 0.9923 -3.3391 -0.8544
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3220 1.0513 343
## (Intercept)-Lynx_rufus 1.8387 1.0274 334
## (Intercept)-Didelphis_virginiana 1.2426 1.0686 312
## (Intercept)-Sylvilagus_floridanus 1.6919 1.0374 384
## (Intercept)-Meleagris_gallopavo 1.1926 1.0065 260
## (Intercept)-Sciurus_carolinensis 1.0302 1.0698 259
## Cogon_Patch_Size-Canis_latrans 3.4438 1.0016 646
## Cogon_Patch_Size-Lynx_rufus 2.1643 1.0074 484
## Cogon_Patch_Size-Didelphis_virginiana 2.2725 1.0268 450
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7017 1.0045 472
## Cogon_Patch_Size-Meleagris_gallopavo 1.9495 1.0225 434
## Cogon_Patch_Size-Sciurus_carolinensis 0.6280 1.0072 522
## Avg_Cogongrass_Cover-Canis_latrans 1.7009 1.0065 739
## Avg_Cogongrass_Cover-Lynx_rufus 2.7692 1.0163 712
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1325 1.0037 849
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0707 1.0074 469
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1741 1.0036 325
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7505 1.0193 335
## total_shrub_cover-Canis_latrans 2.5619 1.0129 387
## total_shrub_cover-Lynx_rufus 0.5392 1.0871 135
## total_shrub_cover-Didelphis_virginiana 0.1175 1.1572 206
## total_shrub_cover-Sylvilagus_floridanus 0.1802 1.1029 158
## total_shrub_cover-Meleagris_gallopavo -0.1838 1.1594 167
## total_shrub_cover-Sciurus_carolinensis 0.6010 1.1429 174
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8050 0.1727 -5.1412 -4.8045 -4.4723 1.0193
## (Intercept)-Lynx_rufus -5.3992 0.2835 -6.0434 -5.3879 -4.8888 1.0138
## (Intercept)-Didelphis_virginiana -4.8403 0.2789 -5.4266 -4.8335 -4.3174 1.0901
## (Intercept)-Sylvilagus_floridanus -5.0880 0.2201 -5.5456 -5.0725 -4.6777 1.0144
## (Intercept)-Meleagris_gallopavo -5.4319 0.4981 -6.5123 -5.3901 -4.5878 1.0490
## (Intercept)-Sciurus_carolinensis -4.8769 0.3188 -5.5348 -4.8673 -4.2659 1.0371
## shrub_cover-Canis_latrans -0.3656 0.2148 -0.7975 -0.3689 0.0488 1.0062
## shrub_cover-Lynx_rufus 0.2276 0.3612 -0.6951 0.2482 0.8553 1.0795
## shrub_cover-Didelphis_virginiana 1.2829 0.3736 0.5290 1.2891 1.9955 1.1019
## shrub_cover-Sylvilagus_floridanus 0.9409 0.4015 0.0680 0.9522 1.6922 1.2508
## shrub_cover-Meleagris_gallopavo -0.4684 0.4684 -1.4787 -0.4337 0.3598 1.0499
## shrub_cover-Sciurus_carolinensis 1.2125 0.4463 0.3994 1.1811 2.1442 1.1731
## veg_height-Canis_latrans -0.5872 0.1817 -0.9375 -0.5860 -0.2350 1.0027
## veg_height-Lynx_rufus 0.1454 0.2282 -0.3132 0.1530 0.5979 1.0077
## veg_height-Didelphis_virginiana 0.5385 0.2560 0.0583 0.5274 1.0447 1.0073
## veg_height-Sylvilagus_floridanus 0.1170 0.2463 -0.3425 0.1117 0.6401 1.3165
## veg_height-Meleagris_gallopavo -0.2395 0.4521 -1.0856 -0.2372 0.6462 1.0249
## veg_height-Sciurus_carolinensis 0.2629 0.2248 -0.1699 0.2625 0.6877 1.0201
## ESS
## (Intercept)-Canis_latrans 199
## (Intercept)-Lynx_rufus 92
## (Intercept)-Didelphis_virginiana 159
## (Intercept)-Sylvilagus_floridanus 200
## (Intercept)-Meleagris_gallopavo 81
## (Intercept)-Sciurus_carolinensis 112
## shrub_cover-Canis_latrans 148
## shrub_cover-Lynx_rufus 114
## shrub_cover-Didelphis_virginiana 120
## shrub_cover-Sylvilagus_floridanus 87
## shrub_cover-Meleagris_gallopavo 105
## shrub_cover-Sciurus_carolinensis 101
## veg_height-Canis_latrans 136
## veg_height-Lynx_rufus 173
## veg_height-Didelphis_virginiana 214
## veg_height-Sylvilagus_floridanus 144
## veg_height-Meleagris_gallopavo 130
## veg_height-Sciurus_carolinensis 194
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.2027
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3588 0.6618 -1.6161 -0.3810 1.0589 1.0151 582
## Tree_Density -0.8012 0.5931 -2.0584 -0.7677 0.3116 1.0020 662
## Avg_Canopy_Cover 1.3386 0.7328 -0.1001 1.3151 2.8347 1.0136 1187
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3200 4.4490 0.0766 1.1529 11.2090 1.1773 302
## Tree_Density 1.3464 2.2930 0.0575 0.5923 7.7307 1.0102 598
## Avg_Canopy_Cover 3.5207 6.0332 0.2216 1.9325 15.8881 1.0815 655
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0553 1.5881 0.0556 0.4975 5.3171 1.0406 144
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9221 0.4812 -5.5960 -4.9961 -3.7805 1.0087 536
## shrub_cover 0.2017 0.4436 -0.6859 0.1910 1.1516 1.0074 872
## veg_height 0.0567 0.2943 -0.5243 0.0568 0.6233 1.0047 870
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8428 1.8824 0.0746 0.4087 4.4172 1.0348 285
## shrub_cover 1.2707 1.4411 0.2219 0.8784 4.8404 1.0039 1069
## veg_height 0.4481 0.5947 0.0759 0.2952 1.7277 1.0268 1380
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3004 0.6563 -0.9363 0.2770 1.6976
## (Intercept)-Lynx_rufus 0.7387 1.4974 -1.3139 0.4143 4.2886
## (Intercept)-Didelphis_virginiana -1.2764 0.8045 -2.9980 -1.2228 0.1719
## (Intercept)-Sylvilagus_floridanus -0.6242 0.7155 -2.1132 -0.5968 0.7150
## (Intercept)-Meleagris_gallopavo -0.0568 0.9396 -1.6219 -0.1742 2.1527
## (Intercept)-Sciurus_carolinensis -1.4148 0.8185 -3.1648 -1.3452 -0.0133
## Tree_Density-Canis_latrans -1.0807 0.6817 -2.6569 -0.9965 0.0203
## Tree_Density-Lynx_rufus 0.1295 0.9051 -1.3058 0.0049 2.2932
## Tree_Density-Didelphis_virginiana -1.0135 0.8529 -3.0449 -0.9085 0.4058
## Tree_Density-Sylvilagus_floridanus -1.1562 0.8908 -3.2179 -1.0301 0.3280
## Tree_Density-Meleagris_gallopavo -1.1805 0.9312 -3.3944 -1.0688 0.2802
## Tree_Density-Sciurus_carolinensis -0.9360 0.8683 -3.0022 -0.8252 0.5062
## Avg_Canopy_Cover-Canis_latrans -0.2661 0.5349 -1.3553 -0.2480 0.7662
## Avg_Canopy_Cover-Lynx_rufus 0.9752 1.0882 -0.7990 0.8439 3.5263
## Avg_Canopy_Cover-Didelphis_virginiana 2.0358 0.9740 0.5863 1.8599 4.3143
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0157 1.4770 0.8767 2.7744 6.4315
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0091 1.1372 0.2689 1.8491 4.6217
## Avg_Canopy_Cover-Sciurus_carolinensis 1.8154 0.9247 0.4609 1.6712 3.9653
## Rhat ESS
## (Intercept)-Canis_latrans 1.0119 529
## (Intercept)-Lynx_rufus 1.1042 110
## (Intercept)-Didelphis_virginiana 1.0376 450
## (Intercept)-Sylvilagus_floridanus 1.0350 844
## (Intercept)-Meleagris_gallopavo 1.0104 201
## (Intercept)-Sciurus_carolinensis 1.0405 458
## Tree_Density-Canis_latrans 1.0032 1002
## Tree_Density-Lynx_rufus 1.0128 220
## Tree_Density-Didelphis_virginiana 1.0020 616
## Tree_Density-Sylvilagus_floridanus 1.0198 585
## Tree_Density-Meleagris_gallopavo 1.0027 510
## Tree_Density-Sciurus_carolinensis 1.0015 762
## Avg_Canopy_Cover-Canis_latrans 1.0041 952
## Avg_Canopy_Cover-Lynx_rufus 1.0096 209
## Avg_Canopy_Cover-Didelphis_virginiana 1.0195 390
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0242 298
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0973 405
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0195 552
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7955 0.1665 -5.1308 -4.7901 -4.4783 1.0109
## (Intercept)-Lynx_rufus -5.7508 0.3697 -6.5792 -5.7313 -5.0876 1.3927
## (Intercept)-Didelphis_virginiana -4.7778 0.3011 -5.3675 -4.7706 -4.2085 1.0109
## (Intercept)-Sylvilagus_floridanus -4.9460 0.2183 -5.3877 -4.9375 -4.5294 1.0805
## (Intercept)-Meleagris_gallopavo -5.8362 0.4355 -6.8402 -5.7942 -5.0912 1.0045
## (Intercept)-Sciurus_carolinensis -4.7461 0.2913 -5.3265 -4.7510 -4.1865 1.0128
## shrub_cover-Canis_latrans -0.3184 0.2052 -0.7342 -0.3149 0.0721 1.0717
## shrub_cover-Lynx_rufus -0.3647 0.3578 -1.1202 -0.3564 0.3709 1.3116
## shrub_cover-Didelphis_virginiana 1.1165 0.3694 0.4246 1.1003 1.8523 1.0336
## shrub_cover-Sylvilagus_floridanus 0.6769 0.3671 -0.0842 0.6657 1.3864 1.1770
## shrub_cover-Meleagris_gallopavo -0.8841 0.3799 -1.6395 -0.8643 -0.2046 1.0482
## shrub_cover-Sciurus_carolinensis 1.0005 0.3770 0.2469 1.0148 1.6959 1.0196
## veg_height-Canis_latrans -0.5966 0.1766 -0.9593 -0.5879 -0.2775 1.0212
## veg_height-Lynx_rufus 0.1709 0.2360 -0.2899 0.1720 0.6398 1.0214
## veg_height-Didelphis_virginiana 0.5939 0.2534 0.1368 0.5819 1.1254 1.0162
## veg_height-Sylvilagus_floridanus 0.1785 0.2327 -0.2560 0.1815 0.6501 1.0098
## veg_height-Meleagris_gallopavo -0.2706 0.3606 -1.0086 -0.2825 0.4366 1.0109
## veg_height-Sciurus_carolinensis 0.2701 0.2161 -0.1171 0.2562 0.7423 1.0146
## ESS
## (Intercept)-Canis_latrans 176
## (Intercept)-Lynx_rufus 51
## (Intercept)-Didelphis_virginiana 136
## (Intercept)-Sylvilagus_floridanus 210
## (Intercept)-Meleagris_gallopavo 60
## (Intercept)-Sciurus_carolinensis 136
## shrub_cover-Canis_latrans 162
## shrub_cover-Lynx_rufus 66
## shrub_cover-Didelphis_virginiana 129
## shrub_cover-Sylvilagus_floridanus 150
## shrub_cover-Meleagris_gallopavo 84
## shrub_cover-Sciurus_carolinensis 167
## veg_height-Canis_latrans 168
## veg_height-Lynx_rufus 143
## veg_height-Didelphis_virginiana 172
## veg_height-Sylvilagus_floridanus 229
## veg_height-Meleagris_gallopavo 134
## veg_height-Sciurus_carolinensis 231
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 2.9318
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0328 0.5303 -2.040 -1.0622 0.0652 1.0099 630
## Avg_Cogongrass_Cover -0.6124 0.5234 -1.711 -0.6121 0.4302 1.0064 758
## I(Avg_Cogongrass_Cover^2) 0.7064 0.4622 -0.132 0.6874 1.7254 1.0089 840
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9320 2.2630 0.0495 0.4563 4.5313 1.1455 1504
## Avg_Cogongrass_Cover 0.9100 1.5325 0.0535 0.4476 4.7576 1.0007 649
## I(Avg_Cogongrass_Cover^2) 0.8912 1.9181 0.0522 0.3940 4.7020 1.0214 752
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7517 0.9411 0.0413 0.4251 3.3873 1.0306 228
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8731 0.3356 -5.3935 -4.8989 -4.1648 1.0347 846
## shrub_cover 0.2202 0.3809 -0.5420 0.2140 0.9704 1.0051 529
## veg_height 0.0758 0.2684 -0.4842 0.0751 0.6187 1.0029 755
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4215 0.8677 0.0411 0.2216 2.1681 1.0351 326
## shrub_cover 0.8574 1.1377 0.1311 0.6060 3.0629 1.0668 1055
## veg_height 0.3527 0.3728 0.0653 0.2421 1.3501 1.0153 949
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5174 0.6684 -1.7267 -0.5430
## (Intercept)-Lynx_rufus -0.9770 0.6697 -2.2795 -0.9990
## (Intercept)-Didelphis_virginiana -1.3925 0.6299 -2.7169 -1.3605
## (Intercept)-Sylvilagus_floridanus -1.1494 0.5977 -2.3416 -1.1459
## (Intercept)-Meleagris_gallopavo -0.7614 0.7466 -2.0921 -0.8158
## (Intercept)-Sciurus_carolinensis -1.7499 0.7518 -3.4258 -1.6878
## Avg_Cogongrass_Cover-Canis_latrans -0.1694 0.6286 -1.3039 -0.2147
## Avg_Cogongrass_Cover-Lynx_rufus -0.4368 0.6924 -1.7778 -0.4499
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2404 0.6414 -1.4008 -0.2927
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2095 0.7319 -2.8356 -1.1454
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0888 0.8501 -3.0872 -0.9884
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6432 0.6369 -1.9271 -0.6381
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3353 0.8981 0.1314 1.1535
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1302 0.6458 0.1512 1.0291
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4122 0.4569 -0.4311 0.4033
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6561 0.4601 -0.1743 0.6408
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1018 0.6587 -1.2802 0.1544
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8502 0.4535 0.0444 0.8202
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8493 1.0151 709
## (Intercept)-Lynx_rufus 0.3389 1.0365 457
## (Intercept)-Didelphis_virginiana -0.2347 1.0097 936
## (Intercept)-Sylvilagus_floridanus 0.0154 1.0017 982
## (Intercept)-Meleagris_gallopavo 0.8995 1.0229 367
## (Intercept)-Sciurus_carolinensis -0.4270 1.0231 539
## Avg_Cogongrass_Cover-Canis_latrans 1.1792 1.0034 949
## Avg_Cogongrass_Cover-Lynx_rufus 0.9957 1.0064 763
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1028 1.0203 918
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0632 1.0077 485
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2760 1.0004 409
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5698 1.0018 820
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5657 1.0357 398
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6778 1.0098 560
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4031 1.0065 731
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6493 1.0172 715
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3116 1.0194 729
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8362 1.0054 919
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7769 0.1631 -5.1084 -4.7729 -4.4611 1.0043
## (Intercept)-Lynx_rufus -5.3612 0.3088 -6.0535 -5.3305 -4.8327 1.0203
## (Intercept)-Didelphis_virginiana -4.7096 0.2683 -5.2787 -4.7109 -4.1971 1.0948
## (Intercept)-Sylvilagus_floridanus -4.9381 0.2254 -5.4162 -4.9260 -4.5186 1.0303
## (Intercept)-Meleagris_gallopavo -5.4507 0.4014 -6.2735 -5.4351 -4.6750 1.0368
## (Intercept)-Sciurus_carolinensis -4.6888 0.2769 -5.2334 -4.6877 -4.1345 1.0496
## shrub_cover-Canis_latrans -0.2558 0.1933 -0.6203 -0.2608 0.1359 1.0428
## shrub_cover-Lynx_rufus -0.1629 0.3145 -0.7610 -0.1559 0.4508 1.0128
## shrub_cover-Didelphis_virginiana 1.0647 0.3900 0.3498 1.0475 1.9176 1.0433
## shrub_cover-Sylvilagus_floridanus 0.4364 0.3827 -0.2775 0.4290 1.1830 1.0144
## shrub_cover-Meleagris_gallopavo -0.5497 0.3634 -1.2225 -0.5779 0.1914 1.0343
## shrub_cover-Sciurus_carolinensis 0.8967 0.3850 0.1559 0.8833 1.6611 1.0228
## veg_height-Canis_latrans -0.5619 0.1770 -0.9060 -0.5664 -0.2204 1.0684
## veg_height-Lynx_rufus 0.1485 0.2229 -0.2736 0.1444 0.5885 1.0673
## veg_height-Didelphis_virginiana 0.5101 0.2596 0.0321 0.4948 1.0323 1.0259
## veg_height-Sylvilagus_floridanus 0.1951 0.2250 -0.2334 0.1955 0.6390 1.0493
## veg_height-Meleagris_gallopavo -0.0437 0.3804 -0.8049 -0.0356 0.7048 1.0464
## veg_height-Sciurus_carolinensis 0.2121 0.2063 -0.1752 0.2076 0.6275 1.0351
## ESS
## (Intercept)-Canis_latrans 218
## (Intercept)-Lynx_rufus 92
## (Intercept)-Didelphis_virginiana 152
## (Intercept)-Sylvilagus_floridanus 212
## (Intercept)-Meleagris_gallopavo 92
## (Intercept)-Sciurus_carolinensis 243
## shrub_cover-Canis_latrans 192
## shrub_cover-Lynx_rufus 130
## shrub_cover-Didelphis_virginiana 122
## shrub_cover-Sylvilagus_floridanus 147
## shrub_cover-Meleagris_gallopavo 125
## shrub_cover-Sciurus_carolinensis 202
## veg_height-Canis_latrans 197
## veg_height-Lynx_rufus 203
## veg_height-Didelphis_virginiana 190
## veg_height-Sylvilagus_floridanus 229
## veg_height-Meleagris_gallopavo 204
## veg_height-Sciurus_carolinensis 247
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4165
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2997 1.1466 -3.3457 -1.3710 1.1723 1.0167 300
## Cogon_Patch_Size 0.6436 1.1405 -1.7371 0.6652 2.8834 1.0211 330
## Veg_shannon_index 0.8844 0.8996 -0.9684 0.8867 2.5832 1.0418 428
## total_shrub_cover -1.3390 1.1504 -3.5471 -1.3887 1.0838 1.0096 657
## Avg_Cogongrass_Cover -0.2988 1.2140 -2.5277 -0.3399 2.2557 1.0155 224
## Tree_Density -1.9144 1.1023 -4.1848 -1.8745 0.2200 1.0328 170
## Avg_Canopy_Cover 1.6669 1.2897 -1.2156 1.7539 4.0320 1.0138 1028
## I(Avg_Cogongrass_Cover^2) 1.1497 0.9551 -0.9034 1.1765 2.9246 1.0188 365
## avg_veg_height -0.1252 0.8921 -1.8332 -0.1431 1.6580 1.0389 215
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.8402 25.8831 0.0715 3.0029 55.1142 1.4039 81
## Cogon_Patch_Size 11.8736 25.3058 0.1295 4.2285 67.6951 1.2093 193
## Veg_shannon_index 4.9942 12.7071 0.0812 1.7099 30.2275 1.7897 78
## total_shrub_cover 17.8803 48.0947 0.3803 6.8226 110.3299 1.1999 106
## Avg_Cogongrass_Cover 2.9931 6.9658 0.0595 0.9673 17.1364 1.1211 517
## Tree_Density 5.0740 14.1379 0.0647 1.1161 37.5310 1.1221 248
## Avg_Canopy_Cover 27.2734 60.0669 1.0280 12.0102 147.1863 1.1518 101
## I(Avg_Cogongrass_Cover^2) 7.8445 22.2223 0.0737 2.2513 49.7131 1.4753 86
## avg_veg_height 1.9552 4.4066 0.0494 0.6246 14.0789 1.2855 257
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2467 5.2266 0.0577 1.2597 19.2397 1.1477 57
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9873 0.3281 -5.5079 -5.0127 -4.3447 1.0233 805
## shrub_cover 0.3869 0.4441 -0.5154 0.3979 1.2317 1.0186 462
## veg_height 0.0936 0.2785 -0.4429 0.0916 0.6504 1.0052 419
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4732 1.2212 0.0484 0.2460 1.8938 1.0149 1207
## shrub_cover 1.1540 1.2315 0.1927 0.8009 4.3202 1.0333 409
## veg_height 0.3935 0.4624 0.0716 0.2647 1.5057 1.0101 1181
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5750 1.5578 -3.2408 -0.6933
## (Intercept)-Lynx_rufus 0.4729 2.9670 -3.2470 -0.2618
## (Intercept)-Didelphis_virginiana -3.1584 2.0635 -7.5884 -2.8645
## (Intercept)-Sylvilagus_floridanus -1.6749 1.7866 -5.1756 -1.7072
## (Intercept)-Meleagris_gallopavo -1.9124 1.8637 -6.1674 -1.7671
## (Intercept)-Sciurus_carolinensis -3.4928 2.2182 -8.9701 -3.1657
## Cogon_Patch_Size-Canis_latrans 2.9147 2.3838 -0.0585 2.3791
## Cogon_Patch_Size-Lynx_rufus 0.4951 2.3606 -4.2484 0.5223
## Cogon_Patch_Size-Didelphis_virginiana 2.4429 1.6887 0.0935 2.1279
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5634 2.9497 -8.9314 -0.9652
## Cogon_Patch_Size-Meleagris_gallopavo 1.5650 2.5021 -1.8726 1.0400
## Cogon_Patch_Size-Sciurus_carolinensis -0.6889 2.2217 -6.1606 -0.3557
## Veg_shannon_index-Canis_latrans 1.7802 1.1393 -0.0103 1.6198
## Veg_shannon_index-Lynx_rufus -0.3023 1.8994 -4.6561 -0.0882
## Veg_shannon_index-Didelphis_virginiana 1.7272 1.7661 -0.4589 1.4371
## Veg_shannon_index-Sylvilagus_floridanus 1.4019 1.4374 -0.6785 1.2310
## Veg_shannon_index-Meleagris_gallopavo 2.1545 2.0284 -0.3154 1.7606
## Veg_shannon_index-Sciurus_carolinensis -0.2195 1.4163 -3.4450 -0.0883
## total_shrub_cover-Canis_latrans 1.4030 1.5053 -1.2300 1.2284
## total_shrub_cover-Lynx_rufus -3.7195 3.7926 -12.6959 -3.1540
## total_shrub_cover-Didelphis_virginiana -3.0013 2.7788 -9.7998 -2.4646
## total_shrub_cover-Sylvilagus_floridanus -2.6027 2.6926 -8.9584 -2.0502
## total_shrub_cover-Meleagris_gallopavo -4.3836 2.7917 -10.6613 -3.9162
## total_shrub_cover-Sciurus_carolinensis -2.0098 2.4055 -7.9017 -1.6509
## Avg_Cogongrass_Cover-Canis_latrans -0.1851 1.5976 -3.1353 -0.2273
## Avg_Cogongrass_Cover-Lynx_rufus 0.2782 1.8842 -2.8087 0.0892
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1652 1.7068 -3.5180 -0.2265
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0947 1.7938 -5.0806 -1.0080
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8143 1.9378 -5.1297 -0.7597
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1940 1.6377 -3.3454 -0.2808
## Tree_Density-Canis_latrans -3.1213 2.0098 -8.2126 -2.6671
## Tree_Density-Lynx_rufus -1.4863 1.6099 -4.6076 -1.5363
## Tree_Density-Didelphis_virginiana -2.0965 1.5245 -5.3625 -2.0194
## Tree_Density-Sylvilagus_floridanus -2.8120 2.3026 -8.4817 -2.4481
## Tree_Density-Meleagris_gallopavo -2.2188 1.7791 -6.4209 -2.0517
## Tree_Density-Sciurus_carolinensis -2.3196 1.9031 -6.7495 -2.1578
## Avg_Canopy_Cover-Canis_latrans -0.4021 0.9249 -2.4499 -0.3487
## Avg_Canopy_Cover-Lynx_rufus 0.2455 2.8331 -5.5685 0.1661
## Avg_Canopy_Cover-Didelphis_virginiana 5.3170 3.2011 1.6078 4.6477
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.8059 4.2005 1.6742 5.9287
## Avg_Canopy_Cover-Meleagris_gallopavo 3.1209 1.9306 0.4422 2.7429
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1000 3.1079 1.3965 4.3817
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6552 1.6925 0.4340 2.2705
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.3326 2.7847 0.3016 2.5777
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9496 1.1191 -1.0788 0.9146
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9938 1.2326 -1.4028 0.9610
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.7062 2.3208 -6.2534 -0.3186
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7814 1.3205 -0.2665 1.6031
## avg_veg_height-Canis_latrans -0.1019 0.9269 -1.8940 -0.1284
## avg_veg_height-Lynx_rufus -0.4552 1.5119 -3.8034 -0.3916
## avg_veg_height-Didelphis_virginiana -0.4515 1.1895 -3.0168 -0.3794
## avg_veg_height-Sylvilagus_floridanus -0.1620 1.1319 -2.3137 -0.1769
## avg_veg_height-Meleagris_gallopavo -0.2007 1.6909 -3.3720 -0.2587
## avg_veg_height-Sciurus_carolinensis 0.5232 1.2079 -1.4598 0.4125
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9004 1.0063 194
## (Intercept)-Lynx_rufus 8.3180 1.3301 41
## (Intercept)-Didelphis_virginiana -0.1517 1.1029 80
## (Intercept)-Sylvilagus_floridanus 2.0271 1.0444 197
## (Intercept)-Meleagris_gallopavo 1.4988 1.0691 182
## (Intercept)-Sciurus_carolinensis -0.1271 1.0314 123
## Cogon_Patch_Size-Canis_latrans 9.1025 1.1258 156
## Cogon_Patch_Size-Lynx_rufus 5.3943 1.0253 192
## Cogon_Patch_Size-Didelphis_virginiana 6.2866 1.1456 181
## Cogon_Patch_Size-Sylvilagus_floridanus 2.5010 1.1423 108
## Cogon_Patch_Size-Meleagris_gallopavo 8.0883 1.1782 134
## Cogon_Patch_Size-Sciurus_carolinensis 2.6872 1.0298 215
## Veg_shannon_index-Canis_latrans 4.4443 1.1545 261
## Veg_shannon_index-Lynx_rufus 2.7672 1.1161 107
## Veg_shannon_index-Didelphis_virginiana 5.9916 1.3687 73
## Veg_shannon_index-Sylvilagus_floridanus 4.4367 1.1791 84
## Veg_shannon_index-Meleagris_gallopavo 7.3459 1.5385 92
## Veg_shannon_index-Sciurus_carolinensis 2.1211 1.0006 391
## total_shrub_cover-Canis_latrans 4.6281 1.0680 153
## total_shrub_cover-Lynx_rufus 1.8275 1.2230 42
## total_shrub_cover-Didelphis_virginiana 0.3107 1.0973 112
## total_shrub_cover-Sylvilagus_floridanus 0.8541 1.1794 85
## total_shrub_cover-Meleagris_gallopavo -0.5373 1.1217 79
## total_shrub_cover-Sciurus_carolinensis 1.3502 1.0535 110
## Avg_Cogongrass_Cover-Canis_latrans 3.1728 1.0151 301
## Avg_Cogongrass_Cover-Lynx_rufus 4.6410 1.0382 313
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.4573 1.0025 250
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.1069 1.0171 335
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.8228 1.0060 246
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.1754 1.0086 412
## Tree_Density-Canis_latrans -0.4004 1.0501 170
## Tree_Density-Lynx_rufus 1.9628 1.0628 254
## Tree_Density-Didelphis_virginiana 0.7561 1.0435 330
## Tree_Density-Sylvilagus_floridanus 0.5221 1.0780 105
## Tree_Density-Meleagris_gallopavo 1.0429 1.0253 146
## Tree_Density-Sciurus_carolinensis 1.2437 1.0271 197
## Avg_Canopy_Cover-Canis_latrans 1.2856 1.0244 338
## Avg_Canopy_Cover-Lynx_rufus 5.9961 1.1868 76
## Avg_Canopy_Cover-Didelphis_virginiana 12.6997 1.1137 87
## Avg_Canopy_Cover-Sylvilagus_floridanus 17.8729 1.1762 50
## Avg_Canopy_Cover-Meleagris_gallopavo 8.0498 1.0679 128
## Avg_Canopy_Cover-Sciurus_carolinensis 13.3094 1.0159 104
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.1176 1.2274 98
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 11.8648 1.6524 99
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.9866 1.0617 162
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.6281 1.0308 269
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5781 1.2759 89
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.9974 1.0795 219
## avg_veg_height-Canis_latrans 1.7695 1.0114 357
## avg_veg_height-Lynx_rufus 2.3942 1.0408 210
## avg_veg_height-Didelphis_virginiana 1.6904 1.0962 291
## avg_veg_height-Sylvilagus_floridanus 2.1834 1.0391 238
## avg_veg_height-Meleagris_gallopavo 3.2986 1.0563 104
## avg_veg_height-Sciurus_carolinensis 3.2257 1.0235 251
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7942 0.1784 -5.1674 -4.7825 -4.4685 1.0464
## (Intercept)-Lynx_rufus -5.6122 0.3229 -6.2255 -5.6066 -5.0295 1.1308
## (Intercept)-Didelphis_virginiana -4.8185 0.2636 -5.3575 -4.8095 -4.3165 1.0356
## (Intercept)-Sylvilagus_floridanus -5.0503 0.2132 -5.5154 -5.0434 -4.6589 1.0327
## (Intercept)-Meleagris_gallopavo -5.4755 0.4519 -6.5149 -5.4414 -4.6550 1.1825
## (Intercept)-Sciurus_carolinensis -4.9115 0.2914 -5.4602 -4.9043 -4.3569 1.0019
## shrub_cover-Canis_latrans -0.3260 0.2133 -0.7026 -0.3404 0.1135 1.0219
## shrub_cover-Lynx_rufus 0.1204 0.3930 -0.6113 0.1308 0.8605 1.1215
## shrub_cover-Didelphis_virginiana 1.2681 0.3522 0.6077 1.2563 1.9846 1.0235
## shrub_cover-Sylvilagus_floridanus 0.8481 0.3824 0.0696 0.8579 1.6120 1.0076
## shrub_cover-Meleagris_gallopavo -0.5417 0.4332 -1.4519 -0.5204 0.2839 1.1027
## shrub_cover-Sciurus_carolinensis 1.2144 0.3691 0.4661 1.2218 1.9119 1.0257
## veg_height-Canis_latrans -0.5580 0.1865 -0.9174 -0.5573 -0.2056 1.0771
## veg_height-Lynx_rufus 0.2040 0.2476 -0.2872 0.2072 0.6662 1.1711
## veg_height-Didelphis_virginiana 0.5634 0.2543 0.1030 0.5538 1.0870 1.0092
## veg_height-Sylvilagus_floridanus 0.1643 0.2361 -0.2930 0.1559 0.6350 1.0294
## veg_height-Meleagris_gallopavo -0.0995 0.4734 -1.0884 -0.1089 0.8343 1.0824
## veg_height-Sciurus_carolinensis 0.3047 0.2271 -0.1176 0.2970 0.7789 1.0206
## ESS
## (Intercept)-Canis_latrans 153
## (Intercept)-Lynx_rufus 69
## (Intercept)-Didelphis_virginiana 160
## (Intercept)-Sylvilagus_floridanus 222
## (Intercept)-Meleagris_gallopavo 54
## (Intercept)-Sciurus_carolinensis 84
## shrub_cover-Canis_latrans 115
## shrub_cover-Lynx_rufus 69
## shrub_cover-Didelphis_virginiana 123
## shrub_cover-Sylvilagus_floridanus 121
## shrub_cover-Meleagris_gallopavo 68
## shrub_cover-Sciurus_carolinensis 96
## veg_height-Canis_latrans 153
## veg_height-Lynx_rufus 94
## veg_height-Didelphis_virginiana 221
## veg_height-Sylvilagus_floridanus 197
## veg_height-Meleagris_gallopavo 51
## veg_height-Sciurus_carolinensis 199
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.536 0.385 -1.2125 -0.5559 0.2694 1.0093 1642
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7511 0.9131 0.0812 0.4721 3.1812 1.0045 1622
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6321 0.2891 -5.1257 -4.6609 -3.9523 1.0157 941
## week 0.0318 1.6430 -3.1473 0.0210 3.1448 1.0050 741
## I(week^2) -0.0219 1.6363 -3.2035 -0.0569 3.2791 1.0055 1401
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 3.829000e-01 6.525000e-01 0.0538 0.2268 1.704700e+00 1.0648
## week 3.762958e+14 6.471504e+15 0.1122 1242.7812 4.235011e+13 1.5925
## I(week^2) 5.971934e+14 1.703875e+16 0.1655 11205.1586 4.659320e+12 1.4076
## ESS
## (Intercept) 1291
## week 315
## I(week^2) 844
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2135 0.4016 -0.5243 0.1905 1.0410 1.0125
## (Intercept)-Lynx_rufus -0.1291 0.4771 -0.9105 -0.1805 0.9275 1.0557
## (Intercept)-Didelphis_virginiana -1.1748 0.4023 -1.9934 -1.1572 -0.4345 1.0105
## (Intercept)-Sylvilagus_floridanus -0.5822 0.3722 -1.3110 -0.5834 0.1571 1.0008
## (Intercept)-Meleagris_gallopavo -0.6126 0.4029 -1.3920 -0.6185 0.1921 1.0056
## (Intercept)-Sciurus_carolinensis -1.1710 0.4010 -2.0057 -1.1495 -0.4161 1.0023
## ESS
## (Intercept)-Canis_latrans 1646
## (Intercept)-Lynx_rufus 475
## (Intercept)-Didelphis_virginiana 2082
## (Intercept)-Sylvilagus_floridanus 1560
## (Intercept)-Meleagris_gallopavo 1353
## (Intercept)-Sciurus_carolinensis 2173
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6548 1.512000e-01 -4.9436
## (Intercept)-Lynx_rufus -5.2322 2.573000e-01 -5.7627
## (Intercept)-Didelphis_virginiana -4.3507 2.262000e-01 -4.8121
## (Intercept)-Sylvilagus_floridanus -4.8069 2.100000e-01 -5.2309
## (Intercept)-Meleagris_gallopavo -5.0097 2.449000e-01 -5.5095
## (Intercept)-Sciurus_carolinensis -4.3697 2.335000e-01 -4.8510
## week-Canis_latrans -219670.9425 1.791947e+07 -577928.3204
## week-Lynx_rufus -593285.3800 1.907917e+07 -295153.0759
## week-Didelphis_virginiana -335997.6849 2.272502e+07 -636523.2129
## week-Sylvilagus_floridanus -611095.5369 2.160572e+07 -102626.1948
## week-Meleagris_gallopavo 604327.4732 2.132365e+07 -463554.8842
## week-Sciurus_carolinensis -106472.8693 1.682824e+07 -346547.3292
## I(week^2)-Canis_latrans 448964.0617 3.736439e+07 -351454.0599
## I(week^2)-Lynx_rufus 458732.2403 2.092777e+07 -400802.9435
## I(week^2)-Didelphis_virginiana 222882.1797 1.856109e+07 -457638.1176
## I(week^2)-Sylvilagus_floridanus -437579.7891 2.100082e+07 -553575.2771
## I(week^2)-Meleagris_gallopavo 154042.6906 2.594588e+07 -397133.5058
## I(week^2)-Sciurus_carolinensis -995360.7664 3.336034e+07 -546368.6598
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6504 -4.3728 1.0119 252
## (Intercept)-Lynx_rufus -5.2268 -4.7634 1.1237 122
## (Intercept)-Didelphis_virginiana -4.3423 -3.9393 1.0346 335
## (Intercept)-Sylvilagus_floridanus -4.7961 -4.4178 1.0415 239
## (Intercept)-Meleagris_gallopavo -5.0020 -4.5502 1.0479 192
## (Intercept)-Sciurus_carolinensis -4.3616 -3.9395 1.1569 303
## week-Canis_latrans 0.1072 227457.4403 1.3031 8468
## week-Lynx_rufus -0.1326 443011.9461 1.3780 1478
## week-Didelphis_virginiana -0.2722 137983.2807 1.3119 4944
## week-Sylvilagus_floridanus -0.0469 869216.0352 1.3688 1905
## week-Meleagris_gallopavo -0.1119 449939.7310 1.3697 649
## week-Sciurus_carolinensis 0.0974 382119.0086 1.2932 35733
## I(week^2)-Canis_latrans -0.1564 576712.8861 1.3037 6951
## I(week^2)-Lynx_rufus -0.0558 380060.4150 1.3377 1821
## I(week^2)-Didelphis_virginiana -0.2552 335938.3394 1.3039 6234
## I(week^2)-Sylvilagus_floridanus -0.1668 303291.9924 1.3321 2464
## I(week^2)-Meleagris_gallopavo -0.2935 439557.3222 1.2937 29448
## I(week^2)-Sciurus_carolinensis -0.1396 353627.8743 1.3756 1186
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.7165
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9647 0.9963 -2.8103 -1.0341 1.2183 1.0061 1033
## Cogon_Patch_Size -0.0324 0.9563 -1.9474 -0.0477 1.9589 1.0030 1256
## Veg_shannon_index 0.7875 0.6878 -0.5241 0.7897 2.2069 1.0514 565
## total_shrub_cover -0.7336 0.9282 -2.7336 -0.7236 1.1423 1.0064 1236
## Avg_Cogongrass_Cover 1.6693 0.8903 -0.1885 1.6933 3.3147 1.0104 458
## Tree_Density -2.0396 0.8095 -3.7194 -2.0105 -0.5487 1.0443 212
## Avg_Canopy_Cover 1.5906 0.9594 -0.4622 1.6279 3.4068 1.0201 1263
## avg_veg_height -0.6459 0.6840 -1.9682 -0.6506 0.7634 1.0197 336
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.6861 19.7592 0.2156 5.6364 52.3658 1.1666 262
## Cogon_Patch_Size 8.5834 17.4644 0.2465 3.8372 45.7816 1.1419 385
## Veg_shannon_index 2.3703 6.3500 0.0590 0.8804 14.2419 1.1439 413
## total_shrub_cover 7.7275 12.5172 0.3019 4.1505 36.1533 1.0071 443
## Avg_Cogongrass_Cover 2.9071 6.3878 0.0600 0.9426 18.4583 1.0914 343
## Tree_Density 1.8181 4.4883 0.0516 0.5145 12.7176 1.2643 500
## Avg_Canopy_Cover 8.4036 14.3019 0.3009 4.1761 43.2338 1.2255 537
## avg_veg_height 0.8529 1.4935 0.0536 0.4119 4.1645 1.1359 1059
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.0636 4.6191 0.0744 1.2405 15.7033 1.3627 92
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6749 0.3399 -5.2207 -4.7088 -3.9174 1.0154 1325
## week -0.0786 1.6689 -3.3718 -0.0936 3.1007 1.0037 721
## I(week^2) 0.0303 1.5947 -2.9932 -0.0184 3.2555 1.0103 739
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.388000e-01 9.421000e-01 0.0858 0.3249 2.113700e+00 1.0212 1512
## week 2.360468e+19 5.109373e+20 0.1043 284.1250 1.883606e+17 1.4884 446
## I(week^2) 1.878732e+22 1.938874e+23 0.1198 3395.7312 4.772902e+22 2.0244 282
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.0114 1.3871 -1.4789 0.8903
## (Intercept)-Lynx_rufus 1.6036 2.3148 -1.9516 1.3459
## (Intercept)-Didelphis_virginiana -3.0678 1.3583 -6.1490 -2.9508
## (Intercept)-Sylvilagus_floridanus -1.8626 1.3317 -4.9166 -1.7447
## (Intercept)-Meleagris_gallopavo -2.3166 1.6470 -6.1414 -2.1790
## (Intercept)-Sciurus_carolinensis -3.3970 1.5317 -7.1712 -3.1786
## Cogon_Patch_Size-Canis_latrans 1.5438 1.7131 -0.8423 1.1682
## Cogon_Patch_Size-Lynx_rufus 0.5758 2.0870 -2.9664 0.3174
## Cogon_Patch_Size-Didelphis_virginiana 1.6656 1.2978 -0.2380 1.4725
## Cogon_Patch_Size-Sylvilagus_floridanus -2.3231 2.2866 -8.4044 -1.7666
## Cogon_Patch_Size-Meleagris_gallopavo 0.1534 1.5927 -2.4986 -0.0255
## Cogon_Patch_Size-Sciurus_carolinensis -1.9728 1.6503 -6.2512 -1.6095
## Veg_shannon_index-Canis_latrans 1.4229 0.8626 0.0194 1.3260
## Veg_shannon_index-Lynx_rufus 0.0274 1.4799 -3.5843 0.2430
## Veg_shannon_index-Didelphis_virginiana 1.1247 0.8986 -0.4809 1.0547
## Veg_shannon_index-Sylvilagus_floridanus 1.1384 0.9132 -0.3611 1.0216
## Veg_shannon_index-Meleagris_gallopavo 1.5192 1.1732 -0.2657 1.3399
## Veg_shannon_index-Sciurus_carolinensis -0.1052 0.9919 -2.2883 -0.0193
## total_shrub_cover-Canis_latrans 1.0053 1.1280 -0.7214 0.8257
## total_shrub_cover-Lynx_rufus -2.7601 2.0696 -8.0535 -2.3407
## total_shrub_cover-Didelphis_virginiana -0.8383 1.0390 -3.0936 -0.7885
## total_shrub_cover-Sylvilagus_floridanus -0.1015 1.1754 -2.2908 -0.1479
## total_shrub_cover-Meleagris_gallopavo -3.7827 1.9199 -8.2092 -3.5331
## total_shrub_cover-Sciurus_carolinensis 0.2119 0.9008 -1.5437 0.2002
## Avg_Cogongrass_Cover-Canis_latrans 2.2900 1.0859 0.3967 2.2009
## Avg_Cogongrass_Cover-Lynx_rufus 2.7329 1.6089 0.3617 2.4690
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0486 1.0159 0.2114 2.0032
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1997 1.1188 -1.1889 1.2676
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7809 1.6202 -3.0592 1.0244
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3359 1.1159 0.4934 2.2148
## Tree_Density-Canis_latrans -2.4979 1.1520 -5.1554 -2.3516
## Tree_Density-Lynx_rufus -1.4790 1.2374 -3.8137 -1.5437
## Tree_Density-Didelphis_virginiana -2.2594 0.9698 -4.4691 -2.1617
## Tree_Density-Sylvilagus_floridanus -2.5078 1.2574 -5.4025 -2.3160
## Tree_Density-Meleagris_gallopavo -2.2279 1.1611 -4.8260 -2.1618
## Tree_Density-Sciurus_carolinensis -2.3755 1.1738 -5.1091 -2.2322
## Avg_Canopy_Cover-Canis_latrans -0.0316 0.8221 -1.7817 0.0079
## Avg_Canopy_Cover-Lynx_rufus 0.6552 1.8575 -2.7668 0.5854
## Avg_Canopy_Cover-Didelphis_virginiana 3.2105 1.2586 1.3566 3.0339
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.4285 2.3238 1.3402 3.9562
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5140 1.4329 0.4893 2.2424
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5641 1.0360 0.9965 2.3979
## avg_veg_height-Canis_latrans -0.7130 0.7528 -2.2147 -0.7189
## avg_veg_height-Lynx_rufus -0.8141 1.0707 -3.0789 -0.7740
## avg_veg_height-Didelphis_virginiana -0.7271 0.8295 -2.3749 -0.7431
## avg_veg_height-Sylvilagus_floridanus -0.7949 0.8157 -2.4145 -0.7984
## avg_veg_height-Meleagris_gallopavo -0.9083 1.0208 -3.1498 -0.8718
## avg_veg_height-Sciurus_carolinensis -0.1954 0.7798 -1.6213 -0.2141
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.1774 1.0159 234
## (Intercept)-Lynx_rufus 7.3261 1.1370 108
## (Intercept)-Didelphis_virginiana -0.7525 1.0164 282
## (Intercept)-Sylvilagus_floridanus 0.3224 1.0253 362
## (Intercept)-Meleagris_gallopavo 0.6116 1.0268 218
## (Intercept)-Sciurus_carolinensis -0.9891 1.0316 209
## Cogon_Patch_Size-Canis_latrans 5.8566 1.0305 418
## Cogon_Patch_Size-Lynx_rufus 5.5021 1.0534 310
## Cogon_Patch_Size-Didelphis_virginiana 4.7780 1.1441 278
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4933 1.0740 192
## Cogon_Patch_Size-Meleagris_gallopavo 4.2124 1.0075 314
## Cogon_Patch_Size-Sciurus_carolinensis 0.2759 1.0726 261
## Veg_shannon_index-Canis_latrans 3.4945 1.0350 354
## Veg_shannon_index-Lynx_rufus 2.2559 1.0819 248
## Veg_shannon_index-Didelphis_virginiana 3.0934 1.0373 662
## Veg_shannon_index-Sylvilagus_floridanus 3.2796 1.0785 530
## Veg_shannon_index-Meleagris_gallopavo 4.2901 1.0353 275
## Veg_shannon_index-Sciurus_carolinensis 1.6325 1.0250 404
## total_shrub_cover-Canis_latrans 3.6442 1.0050 283
## total_shrub_cover-Lynx_rufus 0.1184 1.0146 179
## total_shrub_cover-Didelphis_virginiana 1.0463 1.0233 674
## total_shrub_cover-Sylvilagus_floridanus 2.5355 1.0305 562
## total_shrub_cover-Meleagris_gallopavo -0.8495 1.0167 270
## total_shrub_cover-Sciurus_carolinensis 2.0265 1.0190 737
## Avg_Cogongrass_Cover-Canis_latrans 4.7197 1.0044 411
## Avg_Cogongrass_Cover-Lynx_rufus 6.8153 1.0413 256
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.2499 1.0047 517
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2059 1.0065 379
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.2861 1.0312 286
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.9646 1.0790 408
## Tree_Density-Canis_latrans -0.7848 1.0348 342
## Tree_Density-Lynx_rufus 1.1110 1.1110 242
## Tree_Density-Didelphis_virginiana -0.6463 1.0582 322
## Tree_Density-Sylvilagus_floridanus -0.6157 1.0811 332
## Tree_Density-Meleagris_gallopavo -0.1575 1.0363 382
## Tree_Density-Sciurus_carolinensis -0.5852 1.0496 346
## Avg_Canopy_Cover-Canis_latrans 1.5050 1.0335 470
## Avg_Canopy_Cover-Lynx_rufus 4.8376 1.1295 236
## Avg_Canopy_Cover-Didelphis_virginiana 6.1129 1.1989 300
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.2765 1.3068 196
## Avg_Canopy_Cover-Meleagris_gallopavo 6.2357 1.1361 347
## Avg_Canopy_Cover-Sciurus_carolinensis 4.9827 1.0978 296
## avg_veg_height-Canis_latrans 0.8124 1.0137 404
## avg_veg_height-Lynx_rufus 1.1817 1.0251 375
## avg_veg_height-Didelphis_virginiana 0.9100 1.0049 442
## avg_veg_height-Sylvilagus_floridanus 0.7726 1.0099 438
## avg_veg_height-Meleagris_gallopavo 0.9865 1.0266 402
## avg_veg_height-Sciurus_carolinensis 1.4289 1.0090 430
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.688000e+00 1.556000e-01 -5.002000e+00
## (Intercept)-Lynx_rufus -5.548500e+00 2.353000e-01 -6.004400e+00
## (Intercept)-Didelphis_virginiana -4.312600e+00 2.101000e-01 -4.740600e+00
## (Intercept)-Sylvilagus_floridanus -4.922600e+00 2.168000e-01 -5.354800e+00
## (Intercept)-Meleagris_gallopavo -5.072700e+00 2.683000e-01 -5.646600e+00
## (Intercept)-Sciurus_carolinensis -4.383000e+00 2.218000e-01 -4.835000e+00
## week-Canis_latrans -3.527923e+07 3.097921e+09 -3.367569e+07
## week-Lynx_rufus -1.838697e+06 5.773669e+09 -1.379593e+07
## week-Didelphis_virginiana 9.086983e+07 4.184402e+09 -6.612446e+06
## week-Sylvilagus_floridanus 1.052063e+08 4.611178e+09 -4.595485e+06
## week-Meleagris_gallopavo -2.358642e+04 5.248098e+09 -2.843571e+07
## week-Sciurus_carolinensis 4.512565e+07 3.262424e+09 -5.838167e+06
## I(week^2)-Canis_latrans 2.811356e+09 1.364038e+11 -1.282629e+10
## I(week^2)-Lynx_rufus -9.081456e+08 1.252008e+11 -1.488690e+10
## I(week^2)-Didelphis_virginiana -2.331884e+09 1.309537e+11 -2.689994e+10
## I(week^2)-Sylvilagus_floridanus 1.464130e+09 1.551671e+11 -1.081449e+10
## I(week^2)-Meleagris_gallopavo 1.785066e+09 1.341999e+11 -1.277689e+10
## I(week^2)-Sciurus_carolinensis 1.851889e+09 1.306899e+11 -1.375909e+10
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6858 -4.389400e+00 1.0144 247
## (Intercept)-Lynx_rufus -5.5448 -5.077900e+00 1.1322 105
## (Intercept)-Didelphis_virginiana -4.3064 -3.906900e+00 1.0394 362
## (Intercept)-Sylvilagus_floridanus -4.9151 -4.505200e+00 1.1838 210
## (Intercept)-Meleagris_gallopavo -5.0566 -4.578300e+00 1.0880 143
## (Intercept)-Sciurus_carolinensis -4.3777 -3.961000e+00 1.0354 356
## week-Canis_latrans -0.2543 6.457078e+06 1.3032 8188
## week-Lynx_rufus -0.4108 3.978430e+07 1.2904 15227
## week-Didelphis_virginiana -0.3404 3.662364e+07 1.3366 2180
## week-Sylvilagus_floridanus -0.2380 4.364050e+07 1.3413 2536
## week-Meleagris_gallopavo -0.5365 1.353115e+07 1.2903 17808
## week-Sciurus_carolinensis -0.1223 1.908136e+07 1.3093 7653
## I(week^2)-Canis_latrans -0.0275 1.928138e+10 1.3321 1470
## I(week^2)-Lynx_rufus 0.1146 2.265617e+10 1.2956 1623
## I(week^2)-Didelphis_virginiana -0.0506 1.821255e+10 1.3216 2929
## I(week^2)-Sylvilagus_floridanus 0.1468 2.156639e+10 1.2992 887
## I(week^2)-Meleagris_gallopavo 0.1380 2.478057e+10 1.3079 5112
## I(week^2)-Sciurus_carolinensis 0.0511 2.764325e+10 1.3102 2377
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4857
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6766 0.4759 -1.6142 -0.6778 0.2896 1.0061 685
## Avg_Cogongrass_Cover 0.1963 0.4637 -0.7503 0.1978 1.1194 1.0005 951
## total_shrub_cover -0.4294 0.5114 -1.5237 -0.4195 0.5548 1.0034 1492
## avg_veg_height -0.1315 0.4171 -0.9436 -0.1371 0.6988 1.0032 734
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0297 1.6712 0.0683 0.5885 4.5926 1.0591 915
## Avg_Cogongrass_Cover 0.8554 1.2670 0.0549 0.4752 4.0211 1.0065 1412
## total_shrub_cover 1.3655 3.0567 0.0859 0.7770 6.0114 1.1850 1873
## avg_veg_height 0.4245 0.6870 0.0394 0.2245 2.1269 1.0049 1103
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2784 1.4879 0.0804 0.8165 5.4355 1.0814 136
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6638 0.2753 -5.1334 -4.6881 -4.0268 1.0042 974
## week 0.0048 1.5535 -3.0261 0.0301 2.9491 1.0659 443
## I(week^2) -0.1127 1.6584 -3.5573 -0.0828 3.1350 1.0252 522
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.821000e-01 6.893000e-01 0.0520 0.2283 1.523200e+00 1.0927 589
## week 1.282840e+11 2.688626e+12 0.0858 77.1119 1.241712e+11 1.4991 547
## I(week^2) 2.411377e+11 2.436239e+12 0.0837 127.1989 5.967337e+11 1.1577 219
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0945 0.6921 -1.1728 0.0724
## (Intercept)-Lynx_rufus -0.3315 0.7097 -1.6846 -0.3601
## (Intercept)-Didelphis_virginiana -1.1935 0.6308 -2.5004 -1.1664
## (Intercept)-Sylvilagus_floridanus -0.6368 0.6060 -1.8112 -0.6522
## (Intercept)-Meleagris_gallopavo -0.9294 0.6276 -2.1978 -0.8999
## (Intercept)-Sciurus_carolinensis -1.2369 0.6398 -2.5770 -1.2059
## Avg_Cogongrass_Cover-Canis_latrans 0.5714 0.5692 -0.4117 0.5226
## Avg_Cogongrass_Cover-Lynx_rufus 0.7164 0.6479 -0.3761 0.6594
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4659 0.5149 -0.5101 0.4424
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3166 0.5968 -1.5392 -0.2788
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4986 0.7065 -2.0938 -0.4292
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3188 0.4920 -0.6506 0.3011
## total_shrub_cover-Canis_latrans 0.4574 0.5671 -0.5183 0.4058
## total_shrub_cover-Lynx_rufus -1.0149 0.7494 -2.8015 -0.9031
## total_shrub_cover-Didelphis_virginiana -0.2522 0.4897 -1.2543 -0.2389
## total_shrub_cover-Sylvilagus_floridanus -0.4197 0.5425 -1.5749 -0.3884
## total_shrub_cover-Meleagris_gallopavo -1.5776 0.8463 -3.6210 -1.4668
## total_shrub_cover-Sciurus_carolinensis -0.0820 0.4833 -1.0297 -0.0840
## avg_veg_height-Canis_latrans -0.1124 0.4688 -1.0432 -0.1166
## avg_veg_height-Lynx_rufus -0.1601 0.5818 -1.3076 -0.1663
## avg_veg_height-Didelphis_virginiana -0.1611 0.4775 -1.1199 -0.1569
## avg_veg_height-Sylvilagus_floridanus -0.2297 0.4922 -1.2647 -0.2195
## avg_veg_height-Meleagris_gallopavo -0.3812 0.6034 -1.7287 -0.3230
## avg_veg_height-Sciurus_carolinensis 0.2230 0.5028 -0.7163 0.2103
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5343 1.0304 594
## (Intercept)-Lynx_rufus 1.1437 1.0011 464
## (Intercept)-Didelphis_virginiana -0.0254 1.0045 902
## (Intercept)-Sylvilagus_floridanus 0.6037 1.0179 698
## (Intercept)-Meleagris_gallopavo 0.2708 1.0147 862
## (Intercept)-Sciurus_carolinensis -0.0649 1.0004 657
## Avg_Cogongrass_Cover-Canis_latrans 1.8747 1.0005 887
## Avg_Cogongrass_Cover-Lynx_rufus 2.2854 1.0010 864
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.5280 0.9999 1048
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7716 1.0025 957
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6764 1.0064 694
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3115 1.0041 1481
## total_shrub_cover-Canis_latrans 1.7407 1.0070 1061
## total_shrub_cover-Lynx_rufus 0.1922 1.0002 666
## total_shrub_cover-Didelphis_virginiana 0.6821 1.0029 1910
## total_shrub_cover-Sylvilagus_floridanus 0.5336 1.0028 1255
## total_shrub_cover-Meleagris_gallopavo -0.2878 1.0028 600
## total_shrub_cover-Sciurus_carolinensis 0.8663 1.0086 1873
## avg_veg_height-Canis_latrans 0.8133 1.0057 1052
## avg_veg_height-Lynx_rufus 1.0008 1.0127 768
## avg_veg_height-Didelphis_virginiana 0.7587 1.0022 1122
## avg_veg_height-Sylvilagus_floridanus 0.7073 1.0008 1061
## avg_veg_height-Meleagris_gallopavo 0.6645 1.0097 908
## avg_veg_height-Sciurus_carolinensis 1.2612 1.0055 1170
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6626 0.1517 -4.9696 -4.6570
## (Intercept)-Lynx_rufus -5.2944 0.2657 -5.8960 -5.2871
## (Intercept)-Didelphis_virginiana -4.3763 0.2292 -4.8274 -4.3802
## (Intercept)-Sylvilagus_floridanus -4.8826 0.2555 -5.4189 -4.8698
## (Intercept)-Meleagris_gallopavo -4.9794 0.2487 -5.5142 -4.9673
## (Intercept)-Sciurus_carolinensis -4.4217 0.2438 -4.9461 -4.4069
## week-Canis_latrans -1128.0198 258735.2550 -35597.7588 0.1224
## week-Lynx_rufus 10289.7001 275993.3339 -37463.1868 -0.0325
## week-Didelphis_virginiana -8540.6539 254265.1732 -69491.5118 0.0835
## week-Sylvilagus_floridanus 288.5266 326678.8698 -63766.7633 0.0619
## week-Meleagris_gallopavo -527.9631 243172.6870 -65944.2816 0.1214
## week-Sciurus_carolinensis -6128.5084 282155.8505 -52220.9508 0.0424
## I(week^2)-Canis_latrans 12686.0206 515071.6454 -59703.7844 -0.1960
## I(week^2)-Lynx_rufus -2734.4226 416063.2078 -96839.4150 -0.1780
## I(week^2)-Didelphis_virginiana 14012.9761 425545.8606 -88120.3452 -0.3118
## I(week^2)-Sylvilagus_floridanus -10059.6683 441114.5200 -62217.8930 -0.2032
## I(week^2)-Meleagris_gallopavo -252.1339 443244.0493 -91808.8267 -0.2552
## I(week^2)-Sciurus_carolinensis -6555.0010 410363.4490 -76289.8010 -0.2141
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3755 1.0103 255
## (Intercept)-Lynx_rufus -4.8045 1.0424 99
## (Intercept)-Didelphis_virginiana -3.9294 1.0034 301
## (Intercept)-Sylvilagus_floridanus -4.4102 1.0102 113
## (Intercept)-Meleagris_gallopavo -4.5180 1.0152 161
## (Intercept)-Sciurus_carolinensis -3.9747 1.0902 251
## week-Canis_latrans 36834.8866 1.2879 14342
## week-Lynx_rufus 56419.0865 1.4073 1745
## week-Didelphis_virginiana 35511.3648 1.3882 1021
## week-Sylvilagus_floridanus 26819.6732 1.2893 8303
## week-Meleagris_gallopavo 43237.6327 1.2881 8184
## week-Sciurus_carolinensis 36602.1806 1.3320 1643
## I(week^2)-Canis_latrans 109317.0154 1.1209 3791
## I(week^2)-Lynx_rufus 70926.7677 1.1413 2511
## I(week^2)-Didelphis_virginiana 84769.1126 1.1181 1111
## I(week^2)-Sylvilagus_floridanus 75262.7099 1.1115 1832
## I(week^2)-Meleagris_gallopavo 89846.8857 1.1248 3278
## I(week^2)-Sciurus_carolinensis 66986.8736 1.1360 1480
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.5682
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7168 0.6014 -1.8669 -0.7477 0.5793 1.0027 705
## Tree_Density -0.7852 0.5478 -1.9181 -0.7497 0.2577 1.0171 725
## Avg_Canopy_Cover 1.0385 0.5491 -0.0097 1.0150 2.2678 1.0054 1015
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9388 4.4871 0.1091 1.0195 9.2015 1.1450 877
## Tree_Density 1.1704 1.9595 0.0585 0.5377 5.9566 1.0348 683
## Avg_Canopy_Cover 1.5051 2.2024 0.0927 0.8525 6.4164 1.0447 946
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6783 0.7957 0.0521 0.3869 2.9118 1.0207 226
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6785 0.3211 -5.2198 -4.7062 -3.9737 1.0132 1561
## week -0.0747 1.6276 -3.2327 -0.0657 3.1089 1.0034 992
## I(week^2) -0.0322 1.6469 -3.1973 -0.0426 3.2607 1.0032 665
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.740000e-01 7.940000e-01 0.0636 0.2804 1.954800e+00 1.0414 975
## week 1.114189e+17 1.855548e+18 0.1185 935.8862 1.974364e+15 1.4235 391
## I(week^2) 9.368254e+20 1.446161e+22 0.1281 492.2239 7.082942e+18 1.6578 248
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2078 0.6894 -1.0086 0.1776 1.6387
## (Intercept)-Lynx_rufus 0.1138 1.0848 -1.4457 -0.0821 3.1222
## (Intercept)-Didelphis_virginiana -1.6664 0.7042 -3.1637 -1.6374 -0.4151
## (Intercept)-Sylvilagus_floridanus -0.8758 0.6240 -2.0982 -0.8755 0.3067
## (Intercept)-Meleagris_gallopavo -0.7619 0.6577 -2.0273 -0.7753 0.5879
## (Intercept)-Sciurus_carolinensis -1.7268 0.7201 -3.3248 -1.6687 -0.4994
## Tree_Density-Canis_latrans -1.0204 0.6433 -2.5454 -0.9493 0.0513
## Tree_Density-Lynx_rufus 0.1416 0.7875 -1.1507 0.0370 2.1246
## Tree_Density-Didelphis_virginiana -1.0958 0.8294 -3.1691 -0.9561 0.0952
## Tree_Density-Sylvilagus_floridanus -1.1937 0.8267 -3.1772 -1.0538 0.0694
## Tree_Density-Meleagris_gallopavo -0.9492 0.7632 -2.6792 -0.8658 0.3328
## Tree_Density-Sciurus_carolinensis -0.9861 0.7753 -2.7977 -0.8795 0.2062
## Avg_Canopy_Cover-Canis_latrans -0.0584 0.5069 -1.0880 -0.0506 0.9292
## Avg_Canopy_Cover-Lynx_rufus 0.6961 0.8201 -0.6874 0.6186 2.5779
## Avg_Canopy_Cover-Didelphis_virginiana 1.3238 0.5483 0.3971 1.2863 2.5320
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9579 0.9403 0.6217 1.7768 4.2961
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5445 0.8279 0.2746 1.4167 3.5645
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2779 0.5736 0.2979 1.2229 2.5728
## Rhat ESS
## (Intercept)-Canis_latrans 1.0060 583
## (Intercept)-Lynx_rufus 1.0156 182
## (Intercept)-Didelphis_virginiana 1.0041 723
## (Intercept)-Sylvilagus_floridanus 1.0016 1008
## (Intercept)-Meleagris_gallopavo 1.0343 601
## (Intercept)-Sciurus_carolinensis 1.0146 546
## Tree_Density-Canis_latrans 1.0199 769
## Tree_Density-Lynx_rufus 1.0161 585
## Tree_Density-Didelphis_virginiana 1.0182 713
## Tree_Density-Sylvilagus_floridanus 1.0204 703
## Tree_Density-Meleagris_gallopavo 1.0225 809
## Tree_Density-Sciurus_carolinensis 1.0057 775
## Avg_Canopy_Cover-Canis_latrans 1.0018 1081
## Avg_Canopy_Cover-Lynx_rufus 1.0061 545
## Avg_Canopy_Cover-Didelphis_virginiana 1.0020 1067
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0052 592
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0092 521
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0059 1308
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.676500e+00 1.626000e-01 -5.0228
## (Intercept)-Lynx_rufus -5.398200e+00 2.750000e-01 -5.9441
## (Intercept)-Didelphis_virginiana -4.344800e+00 2.188000e-01 -4.7987
## (Intercept)-Sylvilagus_floridanus -4.855900e+00 2.269000e-01 -5.3106
## (Intercept)-Meleagris_gallopavo -5.092300e+00 2.594000e-01 -5.6232
## (Intercept)-Sciurus_carolinensis -4.402800e+00 2.303000e-01 -4.8691
## week-Canis_latrans -4.103748e+06 2.472997e+08 -6572490.5621
## week-Lynx_rufus 1.248021e+06 3.549376e+08 -7772647.5229
## week-Didelphis_virginiana 2.193414e+06 2.478138e+08 -5506718.7483
## week-Sylvilagus_floridanus 5.923820e+06 4.171734e+08 -4062500.9045
## week-Meleagris_gallopavo 4.464564e+06 3.581208e+08 -5744102.6297
## week-Sciurus_carolinensis -9.771957e+06 3.581433e+08 -7853172.8026
## I(week^2)-Canis_latrans -4.978574e+08 2.763053e+10 -8894678.2421
## I(week^2)-Lynx_rufus 1.095369e+09 3.311975e+10 -3284437.6889
## I(week^2)-Didelphis_virginiana 1.287857e+08 3.578425e+10 -8709839.0323
## I(week^2)-Sylvilagus_floridanus 4.871031e+08 2.024531e+10 -4752393.0413
## I(week^2)-Meleagris_gallopavo 5.139372e+08 4.599748e+10 -4095544.5060
## I(week^2)-Sciurus_carolinensis -1.757707e+08 3.287456e+10 -9661303.8033
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6687 -4.3830 1.0113 220
## (Intercept)-Lynx_rufus -5.3976 -4.9026 1.0220 96
## (Intercept)-Didelphis_virginiana -4.3380 -3.9482 1.0437 349
## (Intercept)-Sylvilagus_floridanus -4.8532 -4.4530 1.0923 185
## (Intercept)-Meleagris_gallopavo -5.0834 -4.6038 1.0471 155
## (Intercept)-Sciurus_carolinensis -4.3980 -3.9750 1.0235 330
## week-Canis_latrans -0.3114 5026387.7062 1.1717 5654
## week-Lynx_rufus -0.3483 4285166.8497 1.1891 30225
## week-Didelphis_virginiana -0.3084 5960163.4166 1.2538 23275
## week-Sylvilagus_floridanus -0.3475 7815464.0787 1.2271 7969
## week-Meleagris_gallopavo -0.3473 6066674.8959 1.2606 11625
## week-Sciurus_carolinensis -0.3659 4131069.3312 1.1913 2626
## I(week^2)-Canis_latrans -0.2912 4929010.8901 1.3223 2559
## I(week^2)-Lynx_rufus -0.2341 12762082.4606 1.3953 771
## I(week^2)-Didelphis_virginiana -0.2070 5048438.6228 1.2916 20546
## I(week^2)-Sylvilagus_floridanus -0.0587 6568930.1035 1.3469 1474
## I(week^2)-Meleagris_gallopavo 0.0051 5583629.8524 1.3027 5436
## I(week^2)-Sciurus_carolinensis -0.1898 4139318.7486 1.2932 17446
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.5078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7414 0.5813 -1.8789 -0.7450 0.4387 1.0089 668
## Cogon_Patch_Size -0.0287 0.6428 -1.3738 -0.0272 1.2957 1.0075 1280
## Avg_Cogongrass_Cover 0.1228 0.4775 -0.8816 0.1286 1.0411 1.0050 1225
## total_shrub_cover -0.4483 0.5420 -1.6392 -0.4346 0.5889 1.0196 1214
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4340 2.3009 0.0704 0.7486 7.0099 1.0084 735
## Cogon_Patch_Size 2.2093 3.3862 0.0932 1.1237 10.6426 1.0258 592
## Avg_Cogongrass_Cover 1.0881 1.7851 0.0618 0.5424 5.7317 1.0146 833
## total_shrub_cover 1.4832 2.2050 0.0806 0.8064 6.7876 1.0710 425
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6413 1.6695 0.1266 1.1119 6.1329 1.0735 246
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6664 0.3050 -5.1754 -4.6931 -3.9893 1.0062 1198
## week 0.0328 1.6509 -3.1540 0.0505 3.3031 1.0002 892
## I(week^2) 0.0075 1.7386 -3.4586 0.0167 3.4399 1.0052 534
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.377000e-01 6.851000e-01 0.0643 0.2645 1.800800e+00 1.0210 1038
## week 1.012807e+13 1.636131e+14 0.1087 600.5203 1.648187e+12 1.6291 188
## I(week^2) 5.398111e+11 9.814538e+12 0.0994 1088.8477 2.625999e+11 1.5566 351
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1942 0.8063 -1.2139 0.1399
## (Intercept)-Lynx_rufus -0.3025 0.8748 -1.8459 -0.3692
## (Intercept)-Didelphis_virginiana -1.2643 0.6779 -2.6975 -1.2323
## (Intercept)-Sylvilagus_floridanus -0.8331 0.7370 -2.2956 -0.8092
## (Intercept)-Meleagris_gallopavo -0.9747 0.7734 -2.5262 -0.9739
## (Intercept)-Sciurus_carolinensis -1.4858 0.7865 -3.1933 -1.4047
## Cogon_Patch_Size-Canis_latrans 0.9588 0.9327 -0.3278 0.7693
## Cogon_Patch_Size-Lynx_rufus -0.2145 1.0728 -2.1702 -0.2592
## Cogon_Patch_Size-Didelphis_virginiana 0.8597 0.6141 -0.1510 0.7917
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0844 1.1204 -3.8290 -0.8688
## Cogon_Patch_Size-Meleagris_gallopavo 0.1527 0.9043 -1.3262 0.1064
## Cogon_Patch_Size-Sciurus_carolinensis -0.8955 0.8465 -2.9541 -0.7535
## Avg_Cogongrass_Cover-Canis_latrans 0.3070 0.4913 -0.6071 0.2799
## Avg_Cogongrass_Cover-Lynx_rufus 0.7805 0.7630 -0.3848 0.6629
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1423 0.5012 -0.8843 0.1554
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2724 0.5760 -1.5143 -0.2301
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.7596 0.8182 -2.6503 -0.6466
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6037 0.5066 -0.3046 0.5737
## total_shrub_cover-Canis_latrans 0.4271 0.6338 -0.5956 0.3491
## total_shrub_cover-Lynx_rufus -1.0130 0.8925 -3.2440 -0.8589
## total_shrub_cover-Didelphis_virginiana -0.4027 0.5203 -1.4864 -0.3863
## total_shrub_cover-Sylvilagus_floridanus -0.3709 0.6290 -1.7428 -0.3387
## total_shrub_cover-Meleagris_gallopavo -1.6072 0.8933 -3.7284 -1.4784
## total_shrub_cover-Sciurus_carolinensis 0.0069 0.5103 -0.9599 -0.0072
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9565 1.0133 460
## (Intercept)-Lynx_rufus 1.6532 1.0072 338
## (Intercept)-Didelphis_virginiana -0.0209 1.0188 700
## (Intercept)-Sylvilagus_floridanus 0.6538 1.0065 659
## (Intercept)-Meleagris_gallopavo 0.4403 1.0221 533
## (Intercept)-Sciurus_carolinensis -0.1604 1.0163 469
## Cogon_Patch_Size-Canis_latrans 3.3418 1.0312 612
## Cogon_Patch_Size-Lynx_rufus 2.0572 1.0040 434
## Cogon_Patch_Size-Didelphis_virginiana 2.2002 1.0711 1015
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4653 1.0085 496
## Cogon_Patch_Size-Meleagris_gallopavo 1.8591 1.0237 468
## Cogon_Patch_Size-Sciurus_carolinensis 0.3802 1.0173 767
## Avg_Cogongrass_Cover-Canis_latrans 1.4076 1.0037 1011
## Avg_Cogongrass_Cover-Lynx_rufus 2.6721 1.0018 713
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1146 1.0012 1739
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7837 1.0042 994
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5465 1.0090 605
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.7617 1.0179 817
## total_shrub_cover-Canis_latrans 1.8904 1.0050 926
## total_shrub_cover-Lynx_rufus 0.3282 1.0332 302
## total_shrub_cover-Didelphis_virginiana 0.5676 1.0025 1523
## total_shrub_cover-Sylvilagus_floridanus 0.7613 1.0250 489
## total_shrub_cover-Meleagris_gallopavo -0.2569 1.0258 459
## total_shrub_cover-Sciurus_carolinensis 1.0569 1.0021 1643
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6664 0.1554 -4.9850 -4.6644
## (Intercept)-Lynx_rufus -5.3585 0.2723 -5.8781 -5.3486
## (Intercept)-Didelphis_virginiana -4.3233 0.2202 -4.7694 -4.3174
## (Intercept)-Sylvilagus_floridanus -4.9399 0.2512 -5.4699 -4.9290
## (Intercept)-Meleagris_gallopavo -4.9902 0.2817 -5.5883 -4.9749
## (Intercept)-Sciurus_carolinensis -4.3923 0.2196 -4.8716 -4.3820
## week-Canis_latrans -94206.1731 3218796.4984 -151200.1208 0.0480
## week-Lynx_rufus -1583.7007 4094856.4177 -159455.2451 0.2655
## week-Didelphis_virginiana -17886.6767 2854150.3454 -197379.2134 -0.1372
## week-Sylvilagus_floridanus -13679.4116 2472022.7601 -73880.9615 0.3339
## week-Meleagris_gallopavo 500.1730 2659357.0751 -137956.7441 0.0377
## week-Sciurus_carolinensis -52153.2057 3438047.3184 -150348.7610 -0.0888
## I(week^2)-Canis_latrans 13413.1218 706873.1357 -55380.7532 -0.1125
## I(week^2)-Lynx_rufus 654.5955 838355.1917 -47764.5567 -0.0124
## I(week^2)-Didelphis_virginiana 24768.3383 592648.8130 -71239.3872 -0.1193
## I(week^2)-Sylvilagus_floridanus -5311.8487 473993.2146 -44698.2412 0.0101
## I(week^2)-Meleagris_gallopavo -8343.6183 563621.4126 -91668.9977 -0.1803
## I(week^2)-Sciurus_carolinensis 16968.6536 501777.3954 -58520.4487 -0.2701
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3733 1.0032 244
## (Intercept)-Lynx_rufus -4.8752 1.0214 98
## (Intercept)-Didelphis_virginiana -3.9247 1.0045 285
## (Intercept)-Sylvilagus_floridanus -4.4838 1.0173 121
## (Intercept)-Meleagris_gallopavo -4.4723 1.0832 140
## (Intercept)-Sciurus_carolinensis -3.9851 1.0205 347
## week-Canis_latrans 141834.9976 1.3730 746
## week-Lynx_rufus 115578.2464 1.2903 20344
## week-Didelphis_virginiana 156228.6490 1.2943 15429
## week-Sylvilagus_floridanus 198254.0261 1.2934 26698
## week-Meleagris_gallopavo 108637.4700 1.2903 9522
## week-Sciurus_carolinensis 155907.5589 1.3135 5205
## I(week^2)-Canis_latrans 66963.3917 1.3214 2425
## I(week^2)-Lynx_rufus 74798.5878 1.2851 116098
## I(week^2)-Didelphis_virginiana 42404.1648 1.4671 495
## I(week^2)-Sylvilagus_floridanus 83645.9091 1.2874 9196
## I(week^2)-Meleagris_gallopavo 48735.1296 1.2935 8712
## I(week^2)-Sciurus_carolinensis 45709.5848 1.3836 973
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4198
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6819 0.4475 -1.5288 -0.6878 0.2328 1.0180 977
## Veg_shannon_index 0.3067 0.3677 -0.4438 0.3075 1.0388 1.0024 1326
## Avg_Cogongrass_Cover 0.2628 0.3838 -0.5401 0.2723 1.0213 1.0121 1419
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8618 1.2139 0.0612 0.4935 4.0748 1.0620 991
## Veg_shannon_index 0.5919 1.0141 0.0464 0.3327 2.7381 1.0132 1715
## Avg_Cogongrass_Cover 0.5992 0.8264 0.0491 0.3412 2.8528 1.0039 1545
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7366 0.7917 0.0509 0.4765 2.8315 1.0966 314
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6582 0.2813 -5.1265 -4.6820 -4.0537 1.0099 1235
## week 0.0188 1.7197 -3.2820 -0.0243 3.5771 1.0159 459
## I(week^2) -0.0407 1.6726 -3.3295 -0.0057 3.1138 1.0337 489
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.734000e-01 5.366000e-01 0.0493 0.2348 1.615200e+00 1.0114 1474
## week 7.158156e+11 2.535948e+13 0.1020 22.2845 3.058758e+09 1.3676 1301
## I(week^2) 4.563195e+11 7.238876e+12 0.0848 31.4555 8.837285e+09 1.6403 209
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0326 0.6043 -1.0505 0.0024
## (Intercept)-Lynx_rufus -0.3733 0.6171 -1.4932 -0.4178
## (Intercept)-Didelphis_virginiana -1.2348 0.5586 -2.4401 -1.1987
## (Intercept)-Sylvilagus_floridanus -0.6845 0.5390 -1.7483 -0.6835
## (Intercept)-Meleagris_gallopavo -0.8073 0.5521 -1.8909 -0.8232
## (Intercept)-Sciurus_carolinensis -1.2330 0.5537 -2.4380 -1.1875
## Veg_shannon_index-Canis_latrans 0.7653 0.4429 0.0003 0.7303
## Veg_shannon_index-Lynx_rufus -0.1853 0.5807 -1.5139 -0.1190
## Veg_shannon_index-Didelphis_virginiana 0.5104 0.4285 -0.2580 0.4883
## Veg_shannon_index-Sylvilagus_floridanus 0.4409 0.4592 -0.4166 0.4245
## Veg_shannon_index-Meleagris_gallopavo 0.4685 0.4583 -0.4027 0.4535
## Veg_shannon_index-Sciurus_carolinensis -0.0784 0.4394 -1.0222 -0.0497
## Avg_Cogongrass_Cover-Canis_latrans 0.5869 0.4521 -0.2233 0.5512
## Avg_Cogongrass_Cover-Lynx_rufus 0.6292 0.4892 -0.1870 0.5739
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4782 0.4055 -0.2968 0.4672
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2131 0.4846 -1.2300 -0.1813
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2939 0.5480 -1.5041 -0.2481
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3844 0.4098 -0.4033 0.3750
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2879 1.0152 643
## (Intercept)-Lynx_rufus 0.9670 1.0257 541
## (Intercept)-Didelphis_virginiana -0.2388 1.0062 1141
## (Intercept)-Sylvilagus_floridanus 0.4099 1.0102 1022
## (Intercept)-Meleagris_gallopavo 0.3088 1.0394 770
## (Intercept)-Sciurus_carolinensis -0.2320 1.0130 1127
## Veg_shannon_index-Canis_latrans 1.7403 1.0043 1480
## Veg_shannon_index-Lynx_rufus 0.7867 1.0025 906
## Veg_shannon_index-Didelphis_virginiana 1.4299 1.0048 1760
## Veg_shannon_index-Sylvilagus_floridanus 1.3956 1.0051 1308
## Veg_shannon_index-Meleagris_gallopavo 1.4342 1.0007 1869
## Veg_shannon_index-Sciurus_carolinensis 0.7184 1.0073 1808
## Avg_Cogongrass_Cover-Canis_latrans 1.5324 1.0213 1677
## Avg_Cogongrass_Cover-Lynx_rufus 1.7298 1.0264 1313
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3019 1.0097 1824
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6568 1.0102 1416
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6353 1.0048 1123
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2393 1.0008 1810
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6382 0.1499 -4.9424 -4.6357
## (Intercept)-Lynx_rufus -5.2390 0.2618 -5.7797 -5.2274
## (Intercept)-Didelphis_virginiana -4.3480 0.2255 -4.8109 -4.3390
## (Intercept)-Sylvilagus_floridanus -4.8968 0.2487 -5.4160 -4.8864
## (Intercept)-Meleagris_gallopavo -5.0107 0.2777 -5.6391 -4.9915
## (Intercept)-Sciurus_carolinensis -4.4083 0.2327 -4.8766 -4.4071
## week-Canis_latrans 22699.3928 1185847.1599 -7765.0766 -0.2254
## week-Lynx_rufus 5324.7470 576176.6009 -7340.6574 -0.1436
## week-Didelphis_virginiana 28768.9984 1499007.6406 -4221.7881 -0.2919
## week-Sylvilagus_floridanus -22912.3233 1057732.8462 -3757.9862 -0.2027
## week-Meleagris_gallopavo 8573.4240 791302.6712 -5061.3808 -0.0707
## week-Sciurus_carolinensis -14596.8821 541303.8238 -8702.7463 -0.2140
## I(week^2)-Canis_latrans 11236.0618 665186.5146 -8291.6857 -0.1862
## I(week^2)-Lynx_rufus -12372.6792 784377.7100 -9891.1607 -0.0908
## I(week^2)-Didelphis_virginiana 868.9742 609135.0683 -7552.2284 0.0786
## I(week^2)-Sylvilagus_floridanus -22754.0015 758628.6258 -10126.9748 -0.0869
## I(week^2)-Meleagris_gallopavo -7099.0313 674196.5453 -11312.4576 -0.0779
## I(week^2)-Sciurus_carolinensis 13298.1886 843116.7246 -10386.2995 -0.0772
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3638 1.0762 273
## (Intercept)-Lynx_rufus -4.7713 1.0668 119
## (Intercept)-Didelphis_virginiana -3.9314 1.0350 326
## (Intercept)-Sylvilagus_floridanus -4.4497 1.0154 134
## (Intercept)-Meleagris_gallopavo -4.5262 1.1186 133
## (Intercept)-Sciurus_carolinensis -3.9544 1.0092 314
## week-Canis_latrans 6318.0093 1.3264 2477
## week-Lynx_rufus 5315.5406 1.2989 12432
## week-Didelphis_virginiana 6493.4903 1.3266 2908
## week-Sylvilagus_floridanus 8332.3214 1.3363 2198
## week-Meleagris_gallopavo 7375.9638 1.3020 7727
## week-Sciurus_carolinensis 3195.9686 1.3608 1421
## I(week^2)-Canis_latrans 13441.2565 1.3182 2508
## I(week^2)-Lynx_rufus 11009.8045 1.3152 5073
## I(week^2)-Didelphis_virginiana 11065.2654 1.2905 83700
## I(week^2)-Sylvilagus_floridanus 9704.9435 1.3774 824
## I(week^2)-Meleagris_gallopavo 7059.7922 1.3012 7947
## I(week^2)-Sciurus_carolinensis 7963.6332 1.3152 2327
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4348
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6637 0.4392 -1.5564 -0.6745 0.1802 1.0282 1028
## Avg_Cogongrass_Cover 0.1495 0.3542 -0.5688 0.1662 0.8418 1.0062 1636
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7464 1.0205 0.0591 0.4346 3.2948 1.0287 1129
## Avg_Cogongrass_Cover 0.5645 0.9657 0.0476 0.3179 2.5788 1.0782 1719
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7872 0.8418 0.0569 0.5073 3.1251 1.0282 331
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6542 0.2876 -5.1519 -4.6797 -4.0174 1.0017 1009
## week -0.0644 1.6438 -3.3275 -0.0547 3.0214 1.0069 746
## I(week^2) 0.0126 1.6318 -3.3187 0.0746 3.0641 1.0133 849
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.831000e-01 6.286000e-01 0.0547 0.2276 1.607500e+00 1.0129 1116
## week 1.376136e+11 3.395832e+12 0.1073 110.3311 3.493353e+09 1.4442 598
## I(week^2) 3.029723e+11 7.941308e+12 0.1159 765.5858 1.113413e+11 1.4169 844
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0326 0.5884 -1.0789 0.0002
## (Intercept)-Lynx_rufus -0.3896 0.5922 -1.5081 -0.4141
## (Intercept)-Didelphis_virginiana -1.1197 0.5252 -2.2190 -1.0908
## (Intercept)-Sylvilagus_floridanus -0.6369 0.5147 -1.6192 -0.6388
## (Intercept)-Meleagris_gallopavo -0.7582 0.5284 -1.8593 -0.7555
## (Intercept)-Sciurus_carolinensis -1.1883 0.5513 -2.3688 -1.1521
## Avg_Cogongrass_Cover-Canis_latrans 0.3633 0.3919 -0.3517 0.3450
## Avg_Cogongrass_Cover-Lynx_rufus 0.5636 0.4782 -0.2331 0.5101
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3526 0.3886 -0.4112 0.3480
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2946 0.4614 -1.3225 -0.2627
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3782 0.4999 -1.4793 -0.3271
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3468 0.3736 -0.3449 0.3366
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2285 1.0213 583
## (Intercept)-Lynx_rufus 0.8351 1.0163 619
## (Intercept)-Didelphis_virginiana -0.1522 1.0157 1013
## (Intercept)-Sylvilagus_floridanus 0.3714 1.0304 1001
## (Intercept)-Meleagris_gallopavo 0.2394 1.0176 980
## (Intercept)-Sciurus_carolinensis -0.2097 1.0092 984
## Avg_Cogongrass_Cover-Canis_latrans 1.1916 1.0095 2014
## Avg_Cogongrass_Cover-Lynx_rufus 1.6266 1.0068 1419
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1692 1.0049 1662
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5191 1.0079 1402
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4606 1.0024 1278
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1103 1.0005 2268
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6617 0.1650 -4.9886 -4.6614
## (Intercept)-Lynx_rufus -5.2626 0.2582 -5.7595 -5.2560
## (Intercept)-Didelphis_virginiana -4.3622 0.2206 -4.7961 -4.3576
## (Intercept)-Sylvilagus_floridanus -4.8812 0.2403 -5.4032 -4.8637
## (Intercept)-Meleagris_gallopavo -5.0204 0.2536 -5.5553 -5.0093
## (Intercept)-Sciurus_carolinensis -4.4046 0.2235 -4.8493 -4.4023
## week-Canis_latrans -6288.6159 203164.1383 -8113.5529 -0.1231
## week-Lynx_rufus -3579.5145 388453.5769 -9414.7839 -0.1291
## week-Didelphis_virginiana 15774.1811 420745.6170 -8348.3186 -0.0902
## week-Sylvilagus_floridanus 7896.4215 392962.2345 -7986.9515 0.0247
## week-Meleagris_gallopavo -518.5255 286756.1214 -10199.7402 -0.0190
## week-Sciurus_carolinensis 15634.5319 644029.4478 -6727.3150 0.0314
## I(week^2)-Canis_latrans -4374.5004 612735.7019 -66514.7298 0.1154
## I(week^2)-Lynx_rufus -5344.4304 354489.7186 -82937.8521 0.0661
## I(week^2)-Didelphis_virginiana -19551.4633 390665.1233 -86963.5989 0.0450
## I(week^2)-Sylvilagus_floridanus 17008.4553 567578.0647 -54348.6393 -0.0442
## I(week^2)-Meleagris_gallopavo -12473.9617 384032.1725 -83173.5499 0.2166
## I(week^2)-Sciurus_carolinensis -4277.4169 489362.8091 -90193.6858 0.1420
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3528 1.0113 210
## (Intercept)-Lynx_rufus -4.7708 1.0104 109
## (Intercept)-Didelphis_virginiana -3.9452 1.0239 345
## (Intercept)-Sylvilagus_floridanus -4.4442 1.0664 169
## (Intercept)-Meleagris_gallopavo -4.5642 1.0462 177
## (Intercept)-Sciurus_carolinensis -3.9810 1.0031 328
## week-Canis_latrans 8018.1161 1.3554 1126
## week-Lynx_rufus 9033.1548 1.2987 15085
## week-Didelphis_virginiana 8596.0689 1.4262 695
## week-Sylvilagus_floridanus 9656.4483 1.3328 2341
## week-Meleagris_gallopavo 8768.3194 1.2902 36404
## week-Sciurus_carolinensis 12985.6237 1.3430 1751
## I(week^2)-Canis_latrans 86269.6472 1.2866 22781
## I(week^2)-Lynx_rufus 72912.5641 1.2595 5236
## I(week^2)-Didelphis_virginiana 51101.8899 1.3544 456
## I(week^2)-Sylvilagus_floridanus 110802.9470 1.3640 1011
## I(week^2)-Meleagris_gallopavo 45318.1536 1.2970 1621
## I(week^2)-Sciurus_carolinensis 55540.0505 1.2764 8871
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.4273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2187 0.4802 -2.1557 -1.2277 -0.2445 1.0319 646
## Avg_Cogongrass_Cover -0.5653 0.4816 -1.5136 -0.5614 0.3849 1.0025 764
## I(Avg_Cogongrass_Cover^2) 0.6877 0.4713 -0.1981 0.6554 1.7343 1.0407 746
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7971 1.1632 0.0545 0.4639 3.5126 1.0155 1212
## Avg_Cogongrass_Cover 0.6510 0.9871 0.0509 0.3525 3.1894 1.0135 1291
## I(Avg_Cogongrass_Cover^2) 0.9766 1.7402 0.0484 0.4357 5.1864 1.0447 510
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6201 0.6836 0.0479 0.3869 2.5643 1.0204 312
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6630 0.2944 -5.1434 -4.6881 -4.0543 1.0149 2144
## week -0.0376 1.6443 -3.2221 -0.0315 3.2481 1.0069 722
## I(week^2) 0.0317 1.6069 -3.1436 0.0365 3.1909 1.0036 1398
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.741000e-01 5.429000e-01 0.0545 0.2321 1.547900e+00 1.0852 1000
## week 1.651838e+20 2.308529e+21 0.1261 1125.4454 8.150075e+19 1.7285 360
## I(week^2) 3.809725e+16 7.966496e+17 0.2239 2551.6829 2.509273e+15 1.4979 440
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6543 0.6446 -1.8417 -0.6684
## (Intercept)-Lynx_rufus -1.1284 0.6607 -2.3471 -1.1596
## (Intercept)-Didelphis_virginiana -1.6353 0.5877 -2.8426 -1.6092
## (Intercept)-Sylvilagus_floridanus -1.2835 0.5928 -2.4572 -1.2829
## (Intercept)-Meleagris_gallopavo -1.1107 0.6034 -2.2651 -1.1224
## (Intercept)-Sciurus_carolinensis -1.9262 0.6763 -3.4458 -1.8659
## Avg_Cogongrass_Cover-Canis_latrans -0.3436 0.5860 -1.4618 -0.3527
## Avg_Cogongrass_Cover-Lynx_rufus -0.3809 0.6321 -1.5793 -0.3954
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2030 0.6134 -1.3124 -0.2350
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0750 0.6352 -2.4415 -1.0164
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9023 0.6594 -2.3652 -0.8576
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6129 0.5796 -1.8197 -0.5879
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.4386 1.0153 0.1280 1.1791
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1146 0.6453 0.1904 1.0250
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.3845 0.4500 -0.4338 0.3621
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6210 0.4545 -0.2301 0.5988
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.0164 0.6401 -1.5813 0.0651
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8254 0.4254 0.1037 0.7886
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6328 1.0282 578
## (Intercept)-Lynx_rufus 0.2580 1.0524 573
## (Intercept)-Didelphis_virginiana -0.5323 1.0093 969
## (Intercept)-Sylvilagus_floridanus -0.1143 1.0043 893
## (Intercept)-Meleagris_gallopavo 0.0866 1.0354 684
## (Intercept)-Sciurus_carolinensis -0.7761 1.0149 803
## Avg_Cogongrass_Cover-Canis_latrans 0.8379 1.0039 1116
## Avg_Cogongrass_Cover-Lynx_rufus 0.8838 1.0015 665
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0810 1.0001 992
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0303 1.0093 874
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2693 1.0119 1020
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4787 1.0061 957
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.8772 1.2036 276
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5760 1.0434 423
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2467 1.0106 719
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6238 1.0251 881
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0134 1.0065 482
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7812 1.0272 948
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.707600e+00 1.541000e-01 -5.005200e+00
## (Intercept)-Lynx_rufus -5.255300e+00 2.631000e-01 -5.791800e+00
## (Intercept)-Didelphis_virginiana -4.344900e+00 2.298000e-01 -4.844200e+00
## (Intercept)-Sylvilagus_floridanus -4.862700e+00 2.209000e-01 -5.327100e+00
## (Intercept)-Meleagris_gallopavo -5.011800e+00 2.580000e-01 -5.565700e+00
## (Intercept)-Sciurus_carolinensis -4.389600e+00 2.222000e-01 -4.829200e+00
## week-Canis_latrans -1.905204e+08 9.887288e+09 -2.299607e+08
## week-Lynx_rufus 2.261646e+08 8.612175e+09 -1.059080e+08
## week-Didelphis_virginiana -2.917481e+08 9.131426e+09 -1.136225e+08
## week-Sylvilagus_floridanus -3.224765e+08 1.380273e+10 -1.740190e+08
## week-Meleagris_gallopavo -3.078850e+08 1.249882e+10 -2.060096e+08
## week-Sciurus_carolinensis 1.427888e+08 1.079760e+10 -1.763557e+08
## I(week^2)-Canis_latrans 2.679031e+06 1.641979e+08 -2.031828e+06
## I(week^2)-Lynx_rufus -2.959788e+05 1.348653e+08 -3.164095e+06
## I(week^2)-Didelphis_virginiana 7.985286e+06 1.675594e+08 -3.068068e+06
## I(week^2)-Sylvilagus_floridanus 2.242999e+06 1.590417e+08 -2.708373e+06
## I(week^2)-Meleagris_gallopavo 6.964559e+06 1.558152e+08 -1.903631e+06
## I(week^2)-Sciurus_carolinensis 3.664600e+06 1.430547e+08 -2.353796e+06
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7084 -4.405600e+00 1.0322 243
## (Intercept)-Lynx_rufus -5.2412 -4.782600e+00 1.0914 114
## (Intercept)-Didelphis_virginiana -4.3350 -3.922600e+00 1.0060 320
## (Intercept)-Sylvilagus_floridanus -4.8563 -4.469000e+00 1.0130 199
## (Intercept)-Meleagris_gallopavo -4.9963 -4.537000e+00 1.0712 154
## (Intercept)-Sciurus_carolinensis -4.3870 -3.972900e+00 1.0243 349
## week-Canis_latrans -0.2231 1.038264e+08 1.3269 1315
## week-Lynx_rufus -0.2921 2.032217e+08 1.3574 929
## week-Didelphis_virginiana -0.4072 1.193704e+08 1.3885 1134
## week-Sylvilagus_floridanus -0.1637 1.279853e+08 1.3437 3751
## week-Meleagris_gallopavo -0.3438 1.261105e+08 1.3495 2293
## week-Sciurus_carolinensis -0.4830 1.472184e+08 1.3077 1853
## I(week^2)-Canis_latrans -0.0463 3.573487e+06 1.3033 5328
## I(week^2)-Lynx_rufus 0.0109 2.521698e+06 1.2857 14072
## I(week^2)-Didelphis_virginiana 0.2213 2.189633e+06 1.4781 456
## I(week^2)-Sylvilagus_floridanus 0.1076 3.452790e+06 1.3031 5734
## I(week^2)-Meleagris_gallopavo 0.1504 3.688799e+06 1.4597 507
## I(week^2)-Sciurus_carolinensis 0.2738 1.848332e+06 1.3425 1475
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 3.7205
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8175 1.1577 -3.9121 -1.9122 0.6772 1.0179 646
## Cogon_Patch_Size 0.3632 1.0302 -1.6950 0.3848 2.3235 1.0541 764
## Veg_shannon_index 0.7384 0.8112 -0.9448 0.7467 2.2480 1.0116 817
## total_shrub_cover -0.8071 0.9887 -2.8098 -0.7742 1.2583 1.0135 1468
## Avg_Cogongrass_Cover 0.1104 1.0947 -2.0386 0.0728 2.2720 1.0429 294
## Tree_Density -2.3475 1.0294 -4.4348 -2.3313 -0.3922 1.0364 257
## Avg_Canopy_Cover 1.6130 1.0062 -0.5813 1.6287 3.6078 1.0040 916
## I(Avg_Cogongrass_Cover^2) 1.1293 0.8763 -0.7276 1.1468 2.7968 1.0370 618
## avg_veg_height -0.4910 0.6589 -1.8115 -0.4984 0.9176 1.0092 356
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.7804 14.7054 0.2162 4.8858 48.1594 1.1243 123
## Cogon_Patch_Size 9.7457 18.4076 0.1801 4.9138 50.4292 1.0545 435
## Veg_shannon_index 3.6715 6.3795 0.0929 1.7335 19.2544 1.0777 460
## total_shrub_cover 8.8124 12.3952 0.2799 4.9461 40.9693 1.2509 170
## Avg_Cogongrass_Cover 2.5022 8.4205 0.0514 0.7431 13.9188 1.1344 1080
## Tree_Density 3.4134 12.7074 0.0540 0.7084 22.9879 1.3251 115
## Avg_Canopy_Cover 10.6697 25.8033 0.2340 4.2734 63.0939 1.1606 129
## I(Avg_Cogongrass_Cover^2) 5.9650 11.6212 0.0784 2.2352 33.2303 1.0956 221
## avg_veg_height 0.8224 1.6310 0.0431 0.3379 4.4585 1.0343 927
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7419 4.2253 0.0659 1.1793 15.6911 1.0568 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.6833 0.3781 -5.2422 -4.7229 -3.8465 1.0307 1683
## week 0.0129 1.7067 -3.4903 0.0084 3.4338 1.0030 682
## I(week^2) 0.0392 1.6844 -3.2832 0.0810 3.2757 1.0189 723
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.378000e-01 1.418400e+00 0.0703 0.3020 2.203200e+00 1.2310 1301
## week 1.786873e+19 3.426930e+20 0.1201 764.6572 3.405833e+17 1.5382 361
## I(week^2) 1.810327e+21 1.881828e+22 0.1064 3673.6555 4.985828e+21 2.0157 255
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7360 1.5169 -3.5318 -0.7825
## (Intercept)-Lynx_rufus -0.0918 2.5722 -3.7158 -0.6165
## (Intercept)-Didelphis_virginiana -4.0404 1.5737 -7.5473 -3.8698
## (Intercept)-Sylvilagus_floridanus -2.5836 1.4758 -5.7205 -2.5225
## (Intercept)-Meleagris_gallopavo -2.6419 1.6502 -6.3462 -2.5325
## (Intercept)-Sciurus_carolinensis -4.8171 1.8636 -8.9128 -4.5522
## Cogon_Patch_Size-Canis_latrans 2.6515 2.0861 -0.1269 2.1681
## Cogon_Patch_Size-Lynx_rufus 0.5310 2.3593 -4.1114 0.4693
## Cogon_Patch_Size-Didelphis_virginiana 2.3262 1.4448 0.1382 2.1372
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7486 2.1564 -7.1548 -1.3882
## Cogon_Patch_Size-Meleagris_gallopavo 0.6829 1.6200 -1.9038 0.4827
## Cogon_Patch_Size-Sciurus_carolinensis -1.3319 1.8994 -6.1547 -0.9729
## Veg_shannon_index-Canis_latrans 1.8697 1.1010 0.1352 1.7357
## Veg_shannon_index-Lynx_rufus -0.5259 1.6467 -4.2823 -0.3052
## Veg_shannon_index-Didelphis_virginiana 1.1890 1.0258 -0.5692 1.1044
## Veg_shannon_index-Sylvilagus_floridanus 1.1872 1.1176 -0.6073 1.0603
## Veg_shannon_index-Meleagris_gallopavo 1.8029 1.3168 -0.2529 1.6236
## Veg_shannon_index-Sciurus_carolinensis -0.3375 1.1436 -2.8152 -0.2309
## total_shrub_cover-Canis_latrans 0.8815 1.1629 -0.9982 0.7127
## total_shrub_cover-Lynx_rufus -3.0247 2.0711 -7.9418 -2.6925
## total_shrub_cover-Didelphis_virginiana -0.9946 1.0879 -3.4630 -0.9059
## total_shrub_cover-Sylvilagus_floridanus -0.2627 1.4422 -3.0609 -0.2326
## total_shrub_cover-Meleagris_gallopavo -4.2218 2.1355 -9.0254 -3.9703
## total_shrub_cover-Sciurus_carolinensis 0.3571 0.9724 -1.4544 0.3284
## Avg_Cogongrass_Cover-Canis_latrans 0.1530 1.3862 -2.5691 0.1237
## Avg_Cogongrass_Cover-Lynx_rufus 0.6870 1.7608 -2.2509 0.4478
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5359 1.4823 -2.1145 0.4180
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4563 1.5697 -3.9099 -0.3521
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3471 1.5610 -3.9416 -0.2587
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2778 1.4068 -2.3268 0.1905
## Tree_Density-Canis_latrans -3.1893 1.6433 -7.6661 -2.9231
## Tree_Density-Lynx_rufus -1.8694 1.6270 -4.6687 -2.0127
## Tree_Density-Didelphis_virginiana -2.6024 1.2218 -5.2393 -2.5125
## Tree_Density-Sylvilagus_floridanus -2.9756 1.7388 -6.8041 -2.7227
## Tree_Density-Meleagris_gallopavo -2.5155 1.4133 -5.4745 -2.4599
## Tree_Density-Sciurus_carolinensis -2.8595 1.3741 -6.0832 -2.7129
## Avg_Canopy_Cover-Canis_latrans -0.0848 0.9044 -2.0588 -0.0407
## Avg_Canopy_Cover-Lynx_rufus 0.5588 2.0190 -3.6042 0.5660
## Avg_Canopy_Cover-Didelphis_virginiana 3.2555 1.3279 1.2275 3.0585
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.9189 2.9996 1.2904 4.3246
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6301 1.6471 0.4602 2.2749
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7993 1.3819 0.9000 2.5476
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5752 1.4939 0.5416 2.2778
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 3.1323 2.1774 0.3429 2.6332
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9034 0.8672 -0.8190 0.8944
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0416 0.9908 -0.7259 0.9844
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.8159 2.1537 -6.3431 -0.4669
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6465 0.9907 0.0289 1.5253
## avg_veg_height-Canis_latrans -0.5695 0.7489 -2.1263 -0.5570
## avg_veg_height-Lynx_rufus -0.5804 1.0507 -2.7033 -0.5945
## avg_veg_height-Didelphis_virginiana -0.5782 0.8187 -2.2996 -0.5605
## avg_veg_height-Sylvilagus_floridanus -0.5769 0.8141 -2.2810 -0.5583
## avg_veg_height-Meleagris_gallopavo -0.6415 0.9199 -2.5943 -0.5959
## avg_veg_height-Sciurus_carolinensis -0.1197 0.8365 -1.6141 -0.1684
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3687 1.0290 303
## (Intercept)-Lynx_rufus 6.8066 1.0630 129
## (Intercept)-Didelphis_virginiana -1.3994 1.0804 334
## (Intercept)-Sylvilagus_floridanus 0.3373 1.0247 354
## (Intercept)-Meleagris_gallopavo 0.2546 1.0889 377
## (Intercept)-Sciurus_carolinensis -1.8169 1.1573 163
## Cogon_Patch_Size-Canis_latrans 8.0548 1.0330 275
## Cogon_Patch_Size-Lynx_rufus 5.6067 1.1850 205
## Cogon_Patch_Size-Didelphis_virginiana 5.5063 1.0923 191
## Cogon_Patch_Size-Sylvilagus_floridanus 1.4784 1.0266 264
## Cogon_Patch_Size-Meleagris_gallopavo 4.8661 1.0070 346
## Cogon_Patch_Size-Sciurus_carolinensis 1.1916 1.0394 284
## Veg_shannon_index-Canis_latrans 4.4748 1.0284 447
## Veg_shannon_index-Lynx_rufus 2.1310 1.0715 254
## Veg_shannon_index-Didelphis_virginiana 3.5424 1.0554 569
## Veg_shannon_index-Sylvilagus_floridanus 4.0457 1.1006 272
## Veg_shannon_index-Meleagris_gallopavo 4.9227 1.0688 369
## Veg_shannon_index-Sciurus_carolinensis 1.5982 1.0161 431
## total_shrub_cover-Canis_latrans 3.6227 1.0998 376
## total_shrub_cover-Lynx_rufus 0.0806 1.2752 138
## total_shrub_cover-Didelphis_virginiana 0.8788 1.0364 481
## total_shrub_cover-Sylvilagus_floridanus 2.5240 1.0587 340
## total_shrub_cover-Meleagris_gallopavo -0.7726 1.2653 105
## total_shrub_cover-Sciurus_carolinensis 2.3771 1.0782 474
## Avg_Cogongrass_Cover-Canis_latrans 3.0022 1.0162 334
## Avg_Cogongrass_Cover-Lynx_rufus 4.9481 1.0300 323
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8297 1.0309 356
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4243 1.0539 375
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.5142 1.0239 399
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.3703 1.0494 402
## Tree_Density-Canis_latrans -0.8985 1.0820 244
## Tree_Density-Lynx_rufus 1.9944 1.0505 181
## Tree_Density-Didelphis_virginiana -0.5177 1.0248 328
## Tree_Density-Sylvilagus_floridanus -0.6593 1.1773 187
## Tree_Density-Meleagris_gallopavo 0.1665 1.0748 270
## Tree_Density-Sciurus_carolinensis -0.6586 1.0375 289
## Avg_Canopy_Cover-Canis_latrans 1.6024 1.0687 345
## Avg_Canopy_Cover-Lynx_rufus 4.7915 1.1257 99
## Avg_Canopy_Cover-Didelphis_virginiana 6.3033 1.0582 188
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.3285 1.2531 64
## Avg_Canopy_Cover-Meleagris_gallopavo 6.9307 1.0912 197
## Avg_Canopy_Cover-Sciurus_carolinensis 6.2359 1.1734 218
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.3816 1.0476 171
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 9.0717 1.0925 168
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6145 1.0380 403
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1536 1.0163 471
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1515 1.2324 86
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.8919 1.0875 383
## avg_veg_height-Canis_latrans 0.9116 1.0031 503
## avg_veg_height-Lynx_rufus 1.5031 1.0209 389
## avg_veg_height-Didelphis_virginiana 1.0202 1.0089 588
## avg_veg_height-Sylvilagus_floridanus 1.0595 1.0021 462
## avg_veg_height-Meleagris_gallopavo 1.1449 1.0177 485
## avg_veg_height-Sciurus_carolinensis 1.6827 1.0091 508
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.674800e+00 1.506000e-01 -4.979800e+00
## (Intercept)-Lynx_rufus -5.504400e+00 2.767000e-01 -6.067300e+00
## (Intercept)-Didelphis_virginiana -4.340500e+00 2.237000e-01 -4.781500e+00
## (Intercept)-Sylvilagus_floridanus -4.970900e+00 2.257000e-01 -5.439500e+00
## (Intercept)-Meleagris_gallopavo -5.058700e+00 2.534000e-01 -5.590500e+00
## (Intercept)-Sciurus_carolinensis -4.417500e+00 2.419000e-01 -4.914600e+00
## week-Canis_latrans 3.308738e+07 3.310945e+09 -6.071210e+07
## week-Lynx_rufus 1.217098e+08 4.169931e+09 -1.456809e+07
## week-Didelphis_virginiana -5.031401e+07 4.696385e+09 -8.915764e+06
## week-Sylvilagus_floridanus -3.522919e+07 2.499654e+09 -5.209952e+07
## week-Meleagris_gallopavo 7.120163e+07 3.642633e+09 -3.271390e+07
## week-Sciurus_carolinensis 1.751301e+06 4.487790e+09 -3.096803e+07
## I(week^2)-Canis_latrans 5.876559e+08 4.193450e+10 -1.472447e+10
## I(week^2)-Lynx_rufus -1.311455e+09 4.971366e+10 -1.746424e+10
## I(week^2)-Didelphis_virginiana -1.186394e+09 3.897269e+10 -1.808728e+10
## I(week^2)-Sylvilagus_floridanus -4.753652e+08 3.715922e+10 -2.124407e+10
## I(week^2)-Meleagris_gallopavo -9.378305e+08 3.914228e+10 -2.080078e+10
## I(week^2)-Sciurus_carolinensis 9.013273e+08 3.831295e+10 -1.225363e+10
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6705 -4.391500e+00 1.0554 225
## (Intercept)-Lynx_rufus -5.4985 -4.973100e+00 1.0228 85
## (Intercept)-Didelphis_virginiana -4.3319 -3.920300e+00 1.0071 345
## (Intercept)-Sylvilagus_floridanus -4.9609 -4.552500e+00 1.1106 177
## (Intercept)-Meleagris_gallopavo -5.0389 -4.615900e+00 1.1007 143
## (Intercept)-Sciurus_carolinensis -4.4107 -3.966600e+00 1.0132 258
## week-Canis_latrans -0.2253 2.110068e+07 1.3006 9262
## week-Lynx_rufus 0.0430 5.736738e+07 1.3725 1384
## week-Didelphis_virginiana -0.0234 8.831831e+07 1.3023 6498
## week-Sylvilagus_floridanus -0.1812 3.219485e+07 1.3091 3690
## week-Meleagris_gallopavo 0.1130 2.542572e+07 1.3282 2373
## week-Sciurus_carolinensis -0.0452 2.837960e+07 1.2903 107708
## I(week^2)-Canis_latrans -0.0464 1.470622e+10 1.3098 891
## I(week^2)-Lynx_rufus -0.1430 1.013762e+10 1.3580 2794
## I(week^2)-Didelphis_virginiana -0.1326 1.048162e+10 1.3797 1183
## I(week^2)-Sylvilagus_floridanus -0.0360 1.181541e+10 1.3066 6800
## I(week^2)-Meleagris_gallopavo -0.0075 1.599561e+10 1.3464 3597
## I(week^2)-Sciurus_carolinensis -0.0314 2.420153e+10 1.3445 1431
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.2142
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.264 0.439 -1.1048 -0.2832 0.6817 1.0165 1052
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.938 1.2265 0.0796 0.5619 3.9418 1.1167 913
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8538 0.4537 -5.5093 -4.9188 -3.7673 1.0333 744
## shrub_cover 0.1258 0.4336 -0.7667 0.1251 1.0103 1.0598 784
## veg_height -0.0025 0.2752 -0.5778 0.0007 0.5592 1.0129 871
## week -0.0249 1.6396 -3.4077 0.0066 3.0683 1.0075 773
## I(week^2) 0.0142 1.6757 -3.1217 0.0362 3.2850 1.0060 1385
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 9.049000e-01 2.413400e+00 0.0644 0.4152 4.474900e+00 1.1354
## shrub_cover 1.149300e+00 1.343300e+00 0.1884 0.7848 4.124100e+00 1.0114
## veg_height 3.993000e-01 4.292000e-01 0.0664 0.2719 1.503300e+00 1.0045
## week 5.590335e+13 1.811741e+15 0.1039 64.9649 1.158807e+12 1.3821
## I(week^2) 6.316772e+29 7.657551e+30 0.2094 399884.2184 3.703787e+29 1.8507
## ESS
## (Intercept) 413
## shrub_cover 734
## veg_height 869
## week 1358
## I(week^2) 127
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3261 0.3966 -0.3992 0.3143 1.1232 1.0038
## (Intercept)-Lynx_rufus 0.2119 0.5941 -0.7414 0.1394 1.5894 1.0601
## (Intercept)-Didelphis_virginiana -0.9527 0.4422 -1.9022 -0.9283 -0.1510 1.0249
## (Intercept)-Sylvilagus_floridanus -0.4160 0.3992 -1.1614 -0.4254 0.3728 1.0020
## (Intercept)-Meleagris_gallopavo 0.1284 0.7331 -0.9451 0.0191 1.7757 1.1546
## (Intercept)-Sciurus_carolinensis -0.9599 0.4512 -1.8992 -0.9408 -0.1271 1.0263
## ESS
## (Intercept)-Canis_latrans 1339
## (Intercept)-Lynx_rufus 336
## (Intercept)-Didelphis_virginiana 1046
## (Intercept)-Sylvilagus_floridanus 1205
## (Intercept)-Meleagris_gallopavo 158
## (Intercept)-Sciurus_carolinensis 866
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.773000e+00 1.740000e-01 -5.120000e+00
## (Intercept)-Lynx_rufus -5.544600e+00 3.247000e-01 -6.203800e+00
## (Intercept)-Didelphis_virginiana -4.649900e+00 2.731000e-01 -5.184600e+00
## (Intercept)-Sylvilagus_floridanus -4.940200e+00 2.445000e-01 -5.451100e+00
## (Intercept)-Meleagris_gallopavo -5.880700e+00 5.149000e-01 -6.953900e+00
## (Intercept)-Sciurus_carolinensis -4.681000e+00 2.868000e-01 -5.252100e+00
## shrub_cover-Canis_latrans -3.115000e-01 2.095000e-01 -7.302000e-01
## shrub_cover-Lynx_rufus -3.039000e-01 3.191000e-01 -9.599000e-01
## shrub_cover-Didelphis_virginiana 1.043700e+00 3.598000e-01 3.917000e-01
## shrub_cover-Sylvilagus_floridanus 4.104000e-01 3.918000e-01 -3.919000e-01
## shrub_cover-Meleagris_gallopavo -9.092000e-01 4.518000e-01 -1.830100e+00
## shrub_cover-Sciurus_carolinensis 9.038000e-01 4.200000e-01 9.920000e-02
## veg_height-Canis_latrans -6.038000e-01 1.708000e-01 -9.494000e-01
## veg_height-Lynx_rufus 9.760000e-02 2.552000e-01 -4.499000e-01
## veg_height-Didelphis_virginiana 4.909000e-01 2.457000e-01 5.290000e-02
## veg_height-Sylvilagus_floridanus 1.616000e-01 2.213000e-01 -2.864000e-01
## veg_height-Meleagris_gallopavo -3.551000e-01 3.750000e-01 -1.029800e+00
## veg_height-Sciurus_carolinensis 2.000000e-01 2.140000e-01 -1.841000e-01
## week-Canis_latrans 6.883651e+04 5.110643e+06 -1.652311e+04
## week-Lynx_rufus 2.718590e+02 4.189688e+06 -2.400105e+04
## week-Didelphis_virginiana 1.343208e+05 9.430584e+06 -1.400106e+04
## week-Sylvilagus_floridanus -1.186316e+05 7.060763e+06 -1.821588e+04
## week-Meleagris_gallopavo 3.668641e+04 7.700555e+06 -2.150233e+04
## week-Sciurus_carolinensis -3.265108e+04 4.429947e+06 -1.476585e+04
## I(week^2)-Canis_latrans -1.852228e+13 8.577730e+14 -2.414649e+13
## I(week^2)-Lynx_rufus 2.423398e+12 7.103834e+14 -1.349700e+13
## I(week^2)-Didelphis_virginiana 1.910447e+13 8.128457e+14 -1.799911e+13
## I(week^2)-Sylvilagus_floridanus -4.587658e+11 6.862462e+14 -2.293995e+13
## I(week^2)-Meleagris_gallopavo -8.281386e+12 6.857084e+14 -2.260059e+13
## I(week^2)-Sciurus_carolinensis 3.374676e+12 8.177225e+14 -2.392471e+13
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7708 -4.439900e+00 1.0774 155
## (Intercept)-Lynx_rufus -5.5543 -4.916000e+00 1.0505 70
## (Intercept)-Didelphis_virginiana -4.6401 -4.158100e+00 1.1786 184
## (Intercept)-Sylvilagus_floridanus -4.9256 -4.481500e+00 1.0214 176
## (Intercept)-Meleagris_gallopavo -5.8772 -4.949000e+00 1.4735 34
## (Intercept)-Sciurus_carolinensis -4.6728 -4.150000e+00 1.1928 188
## shrub_cover-Canis_latrans -0.3107 9.070000e-02 1.1095 187
## shrub_cover-Lynx_rufus -0.3006 3.339000e-01 1.0454 117
## shrub_cover-Didelphis_virginiana 1.0358 1.769200e+00 1.0864 161
## shrub_cover-Sylvilagus_floridanus 0.4256 1.146100e+00 1.1948 156
## shrub_cover-Meleagris_gallopavo -0.9049 -7.300000e-02 1.4481 43
## shrub_cover-Sciurus_carolinensis 0.8984 1.726500e+00 1.0654 182
## veg_height-Canis_latrans -0.6022 -2.727000e-01 1.0424 191
## veg_height-Lynx_rufus 0.1068 5.701000e-01 1.0278 140
## veg_height-Didelphis_virginiana 0.4793 1.015500e+00 1.0907 229
## veg_height-Sylvilagus_floridanus 0.1647 5.765000e-01 1.0509 232
## veg_height-Meleagris_gallopavo -0.3831 4.355000e-01 1.1282 129
## veg_height-Sciurus_carolinensis 0.1898 6.561000e-01 1.1185 240
## week-Canis_latrans -0.1584 1.401126e+04 1.3083 2077
## week-Lynx_rufus -0.2455 1.499855e+04 1.2903 28110
## week-Didelphis_virginiana -0.0956 9.859834e+03 1.3104 2709
## week-Sylvilagus_floridanus -0.1193 1.437321e+04 1.3182 1110
## week-Meleagris_gallopavo -0.2580 9.391588e+03 1.2926 27227
## week-Sciurus_carolinensis -0.1016 1.083138e+04 1.2958 2414
## I(week^2)-Canis_latrans 0.4301 1.974781e+13 1.3361 6683
## I(week^2)-Lynx_rufus 0.2337 3.534980e+13 1.2915 3054
## I(week^2)-Didelphis_virginiana 0.1405 3.638095e+13 1.3443 1815
## I(week^2)-Sylvilagus_floridanus 0.1959 2.779926e+13 1.2904 8817
## I(week^2)-Meleagris_gallopavo 0.3574 2.708250e+13 1.3048 4801
## I(week^2)-Sciurus_carolinensis 0.0893 1.222923e+13 1.2920 5561
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.6518
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4245 1.0640 -2.5439 -0.4441 1.7950 1.0263 236
## Cogon_Patch_Size 0.3818 1.0915 -1.7394 0.3645 2.6838 1.1163 118
## Veg_shannon_index 0.8205 0.8826 -1.1402 0.8427 2.5479 1.0118 297
## total_shrub_cover -1.3400 1.1908 -3.7899 -1.3541 1.1158 1.0324 465
## Avg_Cogongrass_Cover 1.0383 1.0856 -1.2383 1.0818 3.1041 1.0179 290
## Tree_Density -1.6484 1.0918 -3.7959 -1.6771 0.6600 1.0348 236
## Avg_Canopy_Cover 1.6519 1.3029 -1.1780 1.7257 4.0813 1.0138 1324
## avg_veg_height -0.1792 0.9199 -2.0068 -0.1707 1.6646 1.0654 249
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9394 14.1739 0.0763 3.1969 37.0388 1.0305 238
## Cogon_Patch_Size 7.3874 16.7444 0.0818 2.2417 47.2076 1.0612 130
## Veg_shannon_index 4.6233 11.6299 0.0700 1.4827 30.3956 1.5582 81
## total_shrub_cover 18.0928 33.5446 0.4385 7.8010 102.0194 1.4194 127
## Avg_Cogongrass_Cover 5.3715 14.0122 0.0657 1.7163 33.0166 1.1757 378
## Tree_Density 6.9516 30.2228 0.0652 1.2435 42.1178 1.3977 206
## Avg_Canopy_Cover 37.6458 74.9389 1.3639 15.3172 217.5498 1.8036 49
## avg_veg_height 2.3538 5.9377 0.0551 0.7305 14.6697 1.1271 271
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 8.0192 18.2443 0.0888 2.8766 43.7353 2.4418 27
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -5.0049 0.3517 -5.5612 -5.0351 -4.2806 1.0287 647
## shrub_cover 0.3936 0.4572 -0.5624 0.3987 1.2758 1.0209 596
## veg_height 0.0778 0.2784 -0.4607 0.0678 0.6385 1.0047 359
## week -0.0009 1.6514 -3.3733 0.0281 3.1944 1.0009 527
## I(week^2) 0.1146 1.6657 -3.1507 0.1256 3.3691 1.0239 581
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.801000e-01 1.130800e+00 0.0502 0.2325 2.435300e+00 1.0183 412
## shrub_cover 1.243800e+00 1.430300e+00 0.2241 0.8656 4.444500e+00 1.0426 1160
## veg_height 4.259000e-01 4.550000e-01 0.0799 0.2958 1.572800e+00 1.0067 1130
## week 5.181179e+06 1.016606e+08 0.0856 18.5442 1.643772e+07 1.4618 840
## I(week^2) 2.712611e+11 3.513553e+12 0.0957 337.5695 2.877268e+11 1.3246 269
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.9215 1.6227 -1.9304 0.8302
## (Intercept)-Lynx_rufus 1.4506 2.3415 -2.1967 1.1214
## (Intercept)-Didelphis_virginiana -1.7819 1.6155 -5.0795 -1.7211
## (Intercept)-Sylvilagus_floridanus -0.5844 1.6720 -3.9573 -0.5799
## (Intercept)-Meleagris_gallopavo -1.4243 1.7896 -5.4868 -1.2800
## (Intercept)-Sciurus_carolinensis -1.9108 1.9165 -6.3668 -1.7437
## Cogon_Patch_Size-Canis_latrans 1.5946 1.7708 -0.6924 1.1876
## Cogon_Patch_Size-Lynx_rufus 0.4659 2.1179 -3.3201 0.3443
## Cogon_Patch_Size-Didelphis_virginiana 1.8882 1.7016 -0.3604 1.5310
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3881 3.0040 -9.5781 -0.7068
## Cogon_Patch_Size-Meleagris_gallopavo 0.8304 1.7620 -2.0775 0.6197
## Cogon_Patch_Size-Sciurus_carolinensis -0.7323 2.1913 -6.0188 -0.4201
## Veg_shannon_index-Canis_latrans 1.5880 1.0088 -0.1319 1.5081
## Veg_shannon_index-Lynx_rufus -0.1461 1.7701 -4.5595 0.1232
## Veg_shannon_index-Didelphis_virginiana 1.6357 1.4448 -0.4978 1.4416
## Veg_shannon_index-Sylvilagus_floridanus 1.3946 1.2685 -0.7136 1.2588
## Veg_shannon_index-Meleagris_gallopavo 1.9232 1.6212 -0.4848 1.6424
## Veg_shannon_index-Sciurus_carolinensis -0.4881 2.0021 -4.9503 -0.1269
## total_shrub_cover-Canis_latrans 1.9109 1.9093 -0.5828 1.4736
## total_shrub_cover-Lynx_rufus -3.3796 3.0605 -11.2739 -2.8498
## total_shrub_cover-Didelphis_virginiana -3.2564 2.9610 -11.2727 -2.5024
## total_shrub_cover-Sylvilagus_floridanus -3.1061 3.2268 -12.3064 -2.4680
## total_shrub_cover-Meleagris_gallopavo -4.1741 2.4203 -9.6438 -3.8301
## total_shrub_cover-Sciurus_carolinensis -2.5701 2.4804 -8.5142 -2.2033
## Avg_Cogongrass_Cover-Canis_latrans 2.0345 1.4277 -0.3489 1.8732
## Avg_Cogongrass_Cover-Lynx_rufus 2.2363 1.8237 -0.7130 1.9879
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3729 1.4961 -1.6124 1.3770
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0231 1.7649 -4.1680 0.2310
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0272 2.2079 -5.1624 0.3372
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8019 1.6919 -1.0365 1.6441
## Tree_Density-Canis_latrans -3.0015 2.6696 -8.3220 -2.4636
## Tree_Density-Lynx_rufus -0.7964 1.6446 -3.5267 -0.9797
## Tree_Density-Didelphis_virginiana -1.8447 1.8030 -5.5620 -1.8881
## Tree_Density-Sylvilagus_floridanus -2.5565 2.4136 -8.8454 -2.2090
## Tree_Density-Meleagris_gallopavo -2.1028 1.7417 -5.7943 -1.9260
## Tree_Density-Sciurus_carolinensis -1.9330 1.8865 -6.0273 -1.8946
## Avg_Canopy_Cover-Canis_latrans -0.4732 0.9775 -2.8608 -0.3784
## Avg_Canopy_Cover-Lynx_rufus 0.0024 2.4665 -5.3417 0.0349
## Avg_Canopy_Cover-Didelphis_virginiana 5.9014 3.3728 1.8031 5.0669
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6980 4.6331 1.9597 6.6435
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6639 2.8791 0.4616 2.9506
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1489 4.4503 1.6092 5.0267
## avg_veg_height-Canis_latrans -0.1156 0.9681 -1.9960 -0.1417
## avg_veg_height-Lynx_rufus -0.5702 1.6194 -4.2635 -0.4690
## avg_veg_height-Didelphis_virginiana -0.5437 1.2178 -3.2899 -0.4493
## avg_veg_height-Sylvilagus_floridanus -0.3147 1.1803 -2.8244 -0.2852
## avg_veg_height-Meleagris_gallopavo -0.3510 1.6411 -3.8708 -0.2857
## avg_veg_height-Sciurus_carolinensis 0.7034 1.3954 -1.5379 0.5013
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.6700 1.0168 173
## (Intercept)-Lynx_rufus 7.4169 1.0537 101
## (Intercept)-Didelphis_virginiana 1.1748 1.0481 178
## (Intercept)-Sylvilagus_floridanus 2.7731 1.0156 146
## (Intercept)-Meleagris_gallopavo 1.7526 1.0124 166
## (Intercept)-Sciurus_carolinensis 1.3137 1.0591 123
## Cogon_Patch_Size-Canis_latrans 6.3416 1.0644 184
## Cogon_Patch_Size-Lynx_rufus 5.4828 1.1406 130
## Cogon_Patch_Size-Didelphis_virginiana 6.2623 1.0458 174
## Cogon_Patch_Size-Sylvilagus_floridanus 2.7901 1.0598 88
## Cogon_Patch_Size-Meleagris_gallopavo 5.0670 1.1009 240
## Cogon_Patch_Size-Sciurus_carolinensis 2.9676 1.0206 135
## Veg_shannon_index-Canis_latrans 3.7693 1.0592 407
## Veg_shannon_index-Lynx_rufus 2.6157 1.1740 160
## Veg_shannon_index-Didelphis_virginiana 4.9144 1.1298 150
## Veg_shannon_index-Sylvilagus_floridanus 4.3762 1.0925 209
## Veg_shannon_index-Meleagris_gallopavo 5.8618 1.0820 113
## Veg_shannon_index-Sciurus_carolinensis 2.1847 1.2416 96
## total_shrub_cover-Canis_latrans 6.4999 1.3088 82
## total_shrub_cover-Lynx_rufus 1.8797 1.5784 83
## total_shrub_cover-Didelphis_virginiana 0.3587 1.2102 100
## total_shrub_cover-Sylvilagus_floridanus 1.2651 1.1388 97
## total_shrub_cover-Meleagris_gallopavo -0.4042 1.0403 162
## total_shrub_cover-Sciurus_carolinensis 1.0916 1.0498 146
## Avg_Cogongrass_Cover-Canis_latrans 5.2559 1.0268 348
## Avg_Cogongrass_Cover-Lynx_rufus 6.7295 1.0311 234
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.5098 1.0237 347
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.9165 1.0111 173
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6733 1.0475 131
## Avg_Cogongrass_Cover-Sciurus_carolinensis 5.5629 1.0424 395
## Tree_Density-Canis_latrans -0.4682 1.3591 103
## Tree_Density-Lynx_rufus 3.3544 1.1093 159
## Tree_Density-Didelphis_virginiana 1.9890 1.0251 227
## Tree_Density-Sylvilagus_floridanus 0.9018 1.1566 125
## Tree_Density-Meleagris_gallopavo 0.7647 1.0078 340
## Tree_Density-Sciurus_carolinensis 1.8673 1.0370 393
## Avg_Canopy_Cover-Canis_latrans 1.1490 1.2359 74
## Avg_Canopy_Cover-Lynx_rufus 5.0257 1.2198 89
## Avg_Canopy_Cover-Didelphis_virginiana 15.2277 1.2623 60
## Avg_Canopy_Cover-Sylvilagus_floridanus 20.8444 1.3749 45
## Avg_Canopy_Cover-Meleagris_gallopavo 11.6389 1.1757 88
## Avg_Canopy_Cover-Sciurus_carolinensis 18.4703 1.5412 42
## avg_veg_height-Canis_latrans 1.8840 1.0476 380
## avg_veg_height-Lynx_rufus 2.3868 1.1157 107
## avg_veg_height-Didelphis_virginiana 1.6487 1.0616 351
## avg_veg_height-Sylvilagus_floridanus 1.9701 1.0302 291
## avg_veg_height-Meleagris_gallopavo 2.7439 1.1099 157
## avg_veg_height-Sciurus_carolinensis 4.0838 1.0247 200
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8118 0.1710 -5.1487 -4.8117
## (Intercept)-Lynx_rufus -5.6108 0.3349 -6.3135 -5.5971
## (Intercept)-Didelphis_virginiana -4.8804 0.2684 -5.4068 -4.8883
## (Intercept)-Sylvilagus_floridanus -5.0606 0.2255 -5.4980 -5.0554
## (Intercept)-Meleagris_gallopavo -5.5367 0.3971 -6.3047 -5.5405
## (Intercept)-Sciurus_carolinensis -4.9367 0.2899 -5.5525 -4.9237
## shrub_cover-Canis_latrans -0.3647 0.2195 -0.7681 -0.3771
## shrub_cover-Lynx_rufus 0.0632 0.4000 -0.7269 0.0627
## shrub_cover-Didelphis_virginiana 1.2952 0.3346 0.6329 1.2916
## shrub_cover-Sylvilagus_floridanus 0.8839 0.3633 0.1241 0.8874
## shrub_cover-Meleagris_gallopavo -0.5565 0.3847 -1.3030 -0.5592
## shrub_cover-Sciurus_carolinensis 1.2188 0.3764 0.4944 1.2214
## veg_height-Canis_latrans -0.5885 0.1778 -0.9351 -0.5884
## veg_height-Lynx_rufus 0.1689 0.2442 -0.3062 0.1813
## veg_height-Didelphis_virginiana 0.6218 0.2364 0.1684 0.6165
## veg_height-Sylvilagus_floridanus 0.1551 0.2432 -0.3226 0.1483
## veg_height-Meleagris_gallopavo -0.2018 0.4591 -1.1027 -0.2065
## veg_height-Sciurus_carolinensis 0.2983 0.2207 -0.1437 0.3004
## week-Canis_latrans 74.3348 2229.3613 -975.9605 0.1921
## week-Lynx_rufus 18.7923 2324.9691 -1000.1771 -0.0243
## week-Didelphis_virginiana -21.8831 1474.5593 -1173.0414 -0.0034
## week-Sylvilagus_floridanus 23.8005 1567.4687 -1097.4648 0.0201
## week-Meleagris_gallopavo 14.0266 1691.1582 -931.2960 -0.0977
## week-Sciurus_carolinensis 40.1684 1917.8116 -1023.7943 -0.0622
## I(week^2)-Canis_latrans 1537.9893 505878.7036 -148535.6542 -0.0363
## I(week^2)-Lynx_rufus 4264.2920 434981.1656 -119167.8624 0.0429
## I(week^2)-Didelphis_virginiana 7723.5208 380089.4806 -101837.1667 0.0270
## I(week^2)-Sylvilagus_floridanus 3490.5551 452176.3431 -98604.0012 0.4372
## I(week^2)-Meleagris_gallopavo -10453.5843 371235.7016 -120171.1687 0.1279
## I(week^2)-Sciurus_carolinensis 9352.6456 566960.2838 -125040.1390 0.1789
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4846 1.0272 201
## (Intercept)-Lynx_rufus -5.0150 1.3596 72
## (Intercept)-Didelphis_virginiana -4.3058 1.1133 125
## (Intercept)-Sylvilagus_floridanus -4.6225 1.1591 196
## (Intercept)-Meleagris_gallopavo -4.7527 1.0690 109
## (Intercept)-Sciurus_carolinensis -4.3681 1.0604 110
## shrub_cover-Canis_latrans 0.0860 1.0203 161
## shrub_cover-Lynx_rufus 0.8222 1.5878 67
## shrub_cover-Didelphis_virginiana 1.9600 1.0526 129
## shrub_cover-Sylvilagus_floridanus 1.6048 1.0781 88
## shrub_cover-Meleagris_gallopavo 0.1974 1.0363 135
## shrub_cover-Sciurus_carolinensis 1.9292 1.1538 112
## veg_height-Canis_latrans -0.2564 1.0419 165
## veg_height-Lynx_rufus 0.6185 1.0114 128
## veg_height-Didelphis_virginiana 1.0913 1.0433 241
## veg_height-Sylvilagus_floridanus 0.6272 1.0344 173
## veg_height-Meleagris_gallopavo 0.7373 1.1471 80
## veg_height-Sciurus_carolinensis 0.7573 1.0091 168
## week-Canis_latrans 1206.9285 1.2738 1077
## week-Lynx_rufus 1001.2425 1.2572 5468
## week-Didelphis_virginiana 1043.8353 1.1644 2963
## week-Sylvilagus_floridanus 1149.0649 1.1966 7998
## week-Meleagris_gallopavo 1264.1757 1.2208 1444
## week-Sciurus_carolinensis 1180.6106 1.2247 2303
## I(week^2)-Canis_latrans 103170.7266 1.1810 13567
## I(week^2)-Lynx_rufus 107886.5568 1.1821 3719
## I(week^2)-Didelphis_virginiana 111772.7363 1.1368 869
## I(week^2)-Sylvilagus_floridanus 108315.6823 1.2413 9862
## I(week^2)-Meleagris_gallopavo 114862.7075 1.2652 940
## I(week^2)-Sciurus_carolinensis 125214.5286 1.1862 4908
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.6698
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1854 0.5974 -1.4222 -0.1809 1.0177 1.0015 329
## Avg_Cogongrass_Cover 0.0365 0.5552 -1.1003 0.0324 1.1299 1.0107 941
## total_shrub_cover -1.0018 0.7178 -2.4867 -0.9745 0.4095 1.0100 538
## avg_veg_height -0.0020 0.5783 -1.1680 0.0105 1.0979 1.0470 371
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0307 2.2816 0.0534 0.4652 5.3143 1.1863 651
## Avg_Cogongrass_Cover 1.4278 2.7205 0.0639 0.6212 8.0943 1.0502 518
## total_shrub_cover 2.6944 5.1801 0.1441 1.4594 12.1729 1.1389 538
## avg_veg_height 0.8755 1.5179 0.0472 0.3884 4.5459 1.0112 356
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1784 2.5089 0.0917 1.3373 8.9828 1.1476 155
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9622 0.2831 -5.4462 -4.9782 -4.3628 1.0133 469
## shrub_cover 0.4770 0.4387 -0.3729 0.4705 1.3851 1.0041 716
## veg_height 0.0492 0.2763 -0.5056 0.0513 0.6122 1.0212 626
## week 0.0791 1.6361 -3.1749 0.0663 3.1417 1.0180 551
## I(week^2) 0.0336 1.6309 -3.1256 0.0342 3.1973 1.0015 661
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.213000e-01 8.278000e-01 0.0373 0.1755 1.224000e+00 1.1285 1298
## shrub_cover 1.153200e+00 1.331400e+00 0.2035 0.7935 4.454200e+00 1.0247 943
## veg_height 3.707000e-01 4.620000e-01 0.0633 0.2498 1.447300e+00 1.0161 1106
## week 5.768676e+06 8.933659e+07 0.0976 46.2620 1.097926e+07 1.2425 424
## I(week^2) 2.604214e+11 7.635143e+12 0.0971 33.4451 4.824247e+09 1.4017 910
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3236 0.7343 -1.0375 0.3013
## (Intercept)-Lynx_rufus 0.0796 0.8063 -1.4630 0.0638
## (Intercept)-Didelphis_virginiana -0.5310 0.7871 -2.1746 -0.5222
## (Intercept)-Sylvilagus_floridanus 0.0395 0.7935 -1.4033 -0.0063
## (Intercept)-Meleagris_gallopavo -0.4894 0.8655 -2.4209 -0.4522
## (Intercept)-Sciurus_carolinensis -0.5868 0.8343 -2.3436 -0.5277
## Avg_Cogongrass_Cover-Canis_latrans 0.5336 0.6780 -0.6272 0.4661
## Avg_Cogongrass_Cover-Lynx_rufus 0.7544 0.8659 -0.5793 0.6247
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2861 0.6781 -0.9809 0.2572
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6641 0.7999 -2.5522 -0.5866
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.6661 0.9567 -2.8796 -0.5473
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0012 0.6301 -1.3633 0.0264
## total_shrub_cover-Canis_latrans 0.6555 0.8702 -0.9004 0.5935
## total_shrub_cover-Lynx_rufus -1.7048 1.0506 -4.0253 -1.6190
## total_shrub_cover-Didelphis_virginiana -1.1396 0.8441 -3.0784 -1.0400
## total_shrub_cover-Sylvilagus_floridanus -1.7333 1.1805 -4.5639 -1.5561
## total_shrub_cover-Meleagris_gallopavo -1.9345 1.0417 -4.3246 -1.8053
## total_shrub_cover-Sciurus_carolinensis -1.1878 0.9348 -3.3311 -1.0619
## avg_veg_height-Canis_latrans 0.1041 0.5890 -1.0203 0.0921
## avg_veg_height-Lynx_rufus -0.1633 0.8873 -1.8985 -0.1439
## avg_veg_height-Didelphis_virginiana -0.1160 0.6535 -1.5150 -0.0950
## avg_veg_height-Sylvilagus_floridanus 0.0426 0.6749 -1.2849 0.0257
## avg_veg_height-Meleagris_gallopavo -0.3758 1.0452 -2.8221 -0.2691
## avg_veg_height-Sciurus_carolinensis 0.5732 0.7072 -0.6187 0.5249
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8819 1.0142 513
## (Intercept)-Lynx_rufus 1.7791 1.0115 471
## (Intercept)-Didelphis_virginiana 0.9435 1.0168 290
## (Intercept)-Sylvilagus_floridanus 1.8719 1.0201 371
## (Intercept)-Meleagris_gallopavo 1.1079 1.0124 370
## (Intercept)-Sciurus_carolinensis 0.9395 1.0304 285
## Avg_Cogongrass_Cover-Canis_latrans 1.9628 1.0205 576
## Avg_Cogongrass_Cover-Lynx_rufus 2.8742 1.0148 446
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.7324 1.0054 656
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6308 1.0028 579
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9031 1.0245 439
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1380 1.0037 837
## total_shrub_cover-Canis_latrans 2.5958 1.0372 192
## total_shrub_cover-Lynx_rufus 0.0954 1.0208 215
## total_shrub_cover-Didelphis_virginiana 0.2373 1.0202 268
## total_shrub_cover-Sylvilagus_floridanus 0.0197 1.0367 192
## total_shrub_cover-Meleagris_gallopavo -0.2425 1.0490 338
## total_shrub_cover-Sciurus_carolinensis 0.3117 1.0067 240
## avg_veg_height-Canis_latrans 1.2855 1.0060 574
## avg_veg_height-Lynx_rufus 1.4137 1.0111 430
## avg_veg_height-Didelphis_virginiana 1.1177 1.0533 645
## avg_veg_height-Sylvilagus_floridanus 1.4636 1.0104 435
## avg_veg_height-Meleagris_gallopavo 1.4180 1.0457 211
## avg_veg_height-Sciurus_carolinensis 2.0355 1.0086 622
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.8117 0.1660 -5.1410 -4.8072
## (Intercept)-Lynx_rufus -5.3491 0.2656 -5.9454 -5.3244
## (Intercept)-Didelphis_virginiana -4.8696 0.2808 -5.3668 -4.8853
## (Intercept)-Sylvilagus_floridanus -5.0719 0.2343 -5.5128 -5.0648
## (Intercept)-Meleagris_gallopavo -5.3568 0.4314 -6.2877 -5.3217
## (Intercept)-Sciurus_carolinensis -4.8747 0.2705 -5.4007 -4.8769
## shrub_cover-Canis_latrans -0.3691 0.2305 -0.8098 -0.3762
## shrub_cover-Lynx_rufus 0.2701 0.3511 -0.4068 0.2957
## shrub_cover-Didelphis_virginiana 1.3682 0.4144 0.6269 1.3504
## shrub_cover-Sylvilagus_floridanus 0.9109 0.3978 0.0671 0.9358
## shrub_cover-Meleagris_gallopavo -0.3943 0.4360 -1.3030 -0.3743
## shrub_cover-Sciurus_carolinensis 1.2557 0.3532 0.5317 1.2638
## veg_height-Canis_latrans -0.5732 0.1720 -0.9298 -0.5661
## veg_height-Lynx_rufus 0.1536 0.2553 -0.3698 0.1698
## veg_height-Didelphis_virginiana 0.4723 0.2452 0.0105 0.4631
## veg_height-Sylvilagus_floridanus 0.1130 0.2328 -0.3311 0.1023
## veg_height-Meleagris_gallopavo -0.1153 0.4732 -1.0470 -0.1235
## veg_height-Sciurus_carolinensis 0.2398 0.2072 -0.1797 0.2441
## week-Canis_latrans 6.0448 1809.6889 -661.7576 0.3715
## week-Lynx_rufus -17.1791 2169.6585 -854.4794 0.2213
## week-Didelphis_virginiana 15.0117 2037.1933 -1136.9206 0.0904
## week-Sylvilagus_floridanus -21.9846 2581.9244 -748.1441 0.2232
## week-Meleagris_gallopavo 50.7295 2574.0001 -941.3774 0.1807
## week-Sciurus_carolinensis 5.3811 2140.4317 -880.7898 0.2505
## I(week^2)-Canis_latrans -1870.7385 282112.7484 -11302.1356 0.0382
## I(week^2)-Lynx_rufus -783.3509 660990.1527 -7584.0719 0.0839
## I(week^2)-Didelphis_virginiana 4137.1315 305707.3217 -8392.1971 0.0141
## I(week^2)-Sylvilagus_floridanus 19584.2507 769426.3632 -5589.8557 0.0754
## I(week^2)-Meleagris_gallopavo -839.8362 191781.0432 -6185.2187 0.0889
## I(week^2)-Sciurus_carolinensis 2292.8769 553033.0102 -8199.7563 0.0389
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.5009 1.0071 204
## (Intercept)-Lynx_rufus -4.8944 1.0947 124
## (Intercept)-Didelphis_virginiana -4.3098 1.0194 121
## (Intercept)-Sylvilagus_floridanus -4.6232 1.1475 154
## (Intercept)-Meleagris_gallopavo -4.6185 1.0072 122
## (Intercept)-Sciurus_carolinensis -4.3396 1.0088 167
## shrub_cover-Canis_latrans 0.1136 1.0099 115
## shrub_cover-Lynx_rufus 0.9350 1.0140 117
## shrub_cover-Didelphis_virginiana 2.2076 1.2420 73
## shrub_cover-Sylvilagus_floridanus 1.6379 1.1732 81
## shrub_cover-Meleagris_gallopavo 0.4075 1.0256 135
## shrub_cover-Sciurus_carolinensis 1.9047 1.0315 140
## veg_height-Canis_latrans -0.2517 1.0961 164
## veg_height-Lynx_rufus 0.6410 1.0520 131
## veg_height-Didelphis_virginiana 0.9527 1.1294 169
## veg_height-Sylvilagus_floridanus 0.5866 1.0069 157
## veg_height-Meleagris_gallopavo 0.7998 1.0480 110
## veg_height-Sciurus_carolinensis 0.6382 1.0080 248
## week-Canis_latrans 853.0356 1.2134 7824
## week-Lynx_rufus 614.0860 1.1630 11263
## week-Didelphis_virginiana 617.6452 1.2003 2350
## week-Sylvilagus_floridanus 728.7724 1.1044 66484
## week-Meleagris_gallopavo 1103.5341 1.1863 3464
## week-Sciurus_carolinensis 734.9980 1.2089 2796
## I(week^2)-Canis_latrans 5519.3042 1.2948 26462
## I(week^2)-Lynx_rufus 9367.4696 1.2905 160124
## I(week^2)-Didelphis_virginiana 7335.4715 1.3078 5529
## I(week^2)-Sylvilagus_floridanus 8233.3185 1.3533 1587
## I(week^2)-Meleagris_gallopavo 7526.7060 1.2924 16238
## I(week^2)-Sciurus_carolinensis 6190.0259 1.2921 54724
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.3732
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4344 0.6325 -1.6331 -0.4739 0.8961 1.0057 777
## Tree_Density -0.7870 0.5522 -1.9184 -0.7683 0.2687 1.0408 832
## Avg_Canopy_Cover 1.3089 0.7364 -0.1187 1.2980 2.8432 1.0194 855
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1006 3.9741 0.0871 1.0208 10.3332 1.0554 485
## Tree_Density 1.2851 3.5303 0.0551 0.5760 6.2142 1.1725 1620
## Avg_Canopy_Cover 3.2829 5.2340 0.2176 1.8098 16.0105 1.0188 821
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1233 1.6666 0.0551 0.5614 5.5075 1.1547 160
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9262 0.4157 -5.5180 -4.9765 -3.9599 1.0231 543
## shrub_cover 0.2049 0.4103 -0.6233 0.2053 0.9930 1.0060 1035
## veg_height 0.0503 0.2816 -0.4977 0.0548 0.5980 1.0102 724
## week -0.0124 1.6243 -3.4783 0.0679 3.0667 0.9999 652
## I(week^2) 0.0822 1.6034 -2.9173 0.0458 3.2665 1.0016 582
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.526000e-01 1.892100e+00 0.0671 0.3318 2.764300e+00 1.0802 438
## shrub_cover 1.071200e+00 1.383800e+00 0.1673 0.7359 4.198400e+00 1.0105 1141
## veg_height 4.260000e-01 4.900000e-01 0.0778 0.2876 1.621700e+00 1.0304 1518
## week 5.954005e+11 1.298538e+13 0.0839 23.8272 3.352178e+10 1.4856 783
## I(week^2) 6.500676e+23 1.239405e+25 0.0839 31.1458 5.662062e+17 1.5409 355
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2901 0.7207 -1.0709 0.2652 1.8332
## (Intercept)-Lynx_rufus 0.5014 1.3485 -1.2715 0.2340 3.9881
## (Intercept)-Didelphis_virginiana -1.3313 0.7713 -2.9726 -1.2715 0.0881
## (Intercept)-Sylvilagus_floridanus -0.6472 0.7223 -2.1625 -0.6325 0.7294
## (Intercept)-Meleagris_gallopavo -0.2204 0.8889 -1.6978 -0.3134 1.8099
## (Intercept)-Sciurus_carolinensis -1.3818 0.7891 -3.0105 -1.3380 0.0641
## Tree_Density-Canis_latrans -1.0700 0.6541 -2.5896 -0.9992 -0.0066
## Tree_Density-Lynx_rufus 0.1390 0.8622 -1.2519 0.0340 2.2052
## Tree_Density-Didelphis_virginiana -1.0203 0.7930 -2.8738 -0.9381 0.3005
## Tree_Density-Sylvilagus_floridanus -1.1417 0.8641 -3.0707 -1.0331 0.2993
## Tree_Density-Meleagris_gallopavo -1.0996 0.8814 -3.1246 -1.0019 0.3286
## Tree_Density-Sciurus_carolinensis -0.8728 0.7719 -2.5712 -0.8199 0.4835
## Avg_Canopy_Cover-Canis_latrans -0.2531 0.5297 -1.3264 -0.2542 0.8467
## Avg_Canopy_Cover-Lynx_rufus 0.9760 1.0858 -0.7730 0.8304 3.4820
## Avg_Canopy_Cover-Didelphis_virginiana 2.0041 0.9712 0.5570 1.8478 4.3163
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8799 1.3396 0.9145 2.6471 6.2787
## Avg_Canopy_Cover-Meleagris_gallopavo 1.9627 1.0746 0.3348 1.7973 4.6771
## Avg_Canopy_Cover-Sciurus_carolinensis 1.8586 0.8946 0.4839 1.7257 3.9880
## Rhat ESS
## (Intercept)-Canis_latrans 1.0107 576
## (Intercept)-Lynx_rufus 1.0579 184
## (Intercept)-Didelphis_virginiana 1.0311 625
## (Intercept)-Sylvilagus_floridanus 1.0317 806
## (Intercept)-Meleagris_gallopavo 1.0515 256
## (Intercept)-Sciurus_carolinensis 1.0734 633
## Tree_Density-Canis_latrans 1.0197 1036
## Tree_Density-Lynx_rufus 1.0192 556
## Tree_Density-Didelphis_virginiana 1.0314 561
## Tree_Density-Sylvilagus_floridanus 1.0347 627
## Tree_Density-Meleagris_gallopavo 1.0316 608
## Tree_Density-Sciurus_carolinensis 1.0365 690
## Avg_Canopy_Cover-Canis_latrans 1.0301 849
## Avg_Canopy_Cover-Lynx_rufus 1.0058 219
## Avg_Canopy_Cover-Didelphis_virginiana 1.0184 390
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1077 311
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0395 456
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0239 389
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.825500e+00 1.696000e-01 -5.157700e+00
## (Intercept)-Lynx_rufus -5.608200e+00 3.071000e-01 -6.200800e+00
## (Intercept)-Didelphis_virginiana -4.708000e+00 2.572000e-01 -5.195900e+00
## (Intercept)-Sylvilagus_floridanus -4.972900e+00 2.164000e-01 -5.389600e+00
## (Intercept)-Meleagris_gallopavo -5.698400e+00 4.054000e-01 -6.479500e+00
## (Intercept)-Sciurus_carolinensis -4.766400e+00 3.084000e-01 -5.419100e+00
## shrub_cover-Canis_latrans -3.327000e-01 2.114000e-01 -7.517000e-01
## shrub_cover-Lynx_rufus -2.333000e-01 2.953000e-01 -7.873000e-01
## shrub_cover-Didelphis_virginiana 1.074200e+00 3.390000e-01 4.514000e-01
## shrub_cover-Sylvilagus_floridanus 6.370000e-01 3.512000e-01 -6.150000e-02
## shrub_cover-Meleagris_gallopavo -7.499000e-01 3.830000e-01 -1.562300e+00
## shrub_cover-Sciurus_carolinensis 9.733000e-01 3.984000e-01 1.856000e-01
## veg_height-Canis_latrans -6.173000e-01 1.774000e-01 -9.654000e-01
## veg_height-Lynx_rufus 2.017000e-01 2.268000e-01 -2.647000e-01
## veg_height-Didelphis_virginiana 5.643000e-01 2.279000e-01 1.138000e-01
## veg_height-Sylvilagus_floridanus 2.035000e-01 2.389000e-01 -2.724000e-01
## veg_height-Meleagris_gallopavo -2.762000e-01 3.759000e-01 -1.005200e+00
## veg_height-Sciurus_carolinensis 2.587000e-01 2.293000e-01 -1.688000e-01
## week-Canis_latrans -6.247310e+02 5.810283e+05 -5.733232e+03
## week-Lynx_rufus -2.544015e+04 7.128103e+05 -8.248432e+03
## week-Didelphis_virginiana 9.776225e+03 7.389881e+05 -7.070066e+03
## week-Sylvilagus_floridanus -2.818406e+04 6.573329e+05 -1.335200e+04
## week-Meleagris_gallopavo -3.876006e+02 5.052135e+05 -6.221669e+03
## week-Sciurus_carolinensis 1.796160e+03 9.809936e+05 -1.018306e+04
## I(week^2)-Canis_latrans 8.571724e+09 8.621440e+11 -1.155573e+08
## I(week^2)-Lynx_rufus -1.542599e+10 9.467963e+11 -1.352526e+08
## I(week^2)-Didelphis_virginiana -1.698343e+10 8.750094e+11 -1.660860e+08
## I(week^2)-Sylvilagus_floridanus 4.918147e+08 7.322197e+11 -1.407404e+08
## I(week^2)-Meleagris_gallopavo -7.321739e+09 5.982597e+11 -1.164339e+08
## I(week^2)-Sciurus_carolinensis -3.910972e+10 8.943354e+11 -1.278425e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8255 -4.503200e+00 1.0415 193
## (Intercept)-Lynx_rufus -5.6127 -5.039100e+00 1.0397 64
## (Intercept)-Didelphis_virginiana -4.7148 -4.200300e+00 1.0095 182
## (Intercept)-Sylvilagus_floridanus -4.9763 -4.513800e+00 1.0282 236
## (Intercept)-Meleagris_gallopavo -5.6870 -4.956000e+00 1.0632 73
## (Intercept)-Sciurus_carolinensis -4.7587 -4.189200e+00 1.0491 140
## shrub_cover-Canis_latrans -0.3316 9.460000e-02 1.0229 168
## shrub_cover-Lynx_rufus -0.2396 3.560000e-01 1.0343 122
## shrub_cover-Didelphis_virginiana 1.0605 1.763300e+00 1.0571 176
## shrub_cover-Sylvilagus_floridanus 0.6357 1.353800e+00 1.0496 130
## shrub_cover-Meleagris_gallopavo -0.7399 -4.780000e-02 1.0718 95
## shrub_cover-Sciurus_carolinensis 0.9724 1.767400e+00 1.0203 161
## veg_height-Canis_latrans -0.6154 -2.815000e-01 1.0208 157
## veg_height-Lynx_rufus 0.2062 6.360000e-01 1.0249 123
## veg_height-Didelphis_virginiana 0.5613 1.018000e+00 1.0040 295
## veg_height-Sylvilagus_floridanus 0.2102 6.604000e-01 1.0092 189
## veg_height-Meleagris_gallopavo -0.2740 4.673000e-01 1.0069 133
## veg_height-Sciurus_carolinensis 0.2455 7.233000e-01 1.0325 168
## week-Canis_latrans -0.0091 1.371541e+04 1.2905 12550
## week-Lynx_rufus 0.0866 9.641296e+03 1.4118 1182
## week-Didelphis_virginiana 0.0578 1.144722e+04 1.3077 1297
## week-Sylvilagus_floridanus 0.0556 5.764793e+03 1.4625 1500
## week-Meleagris_gallopavo 0.2228 9.363678e+03 1.2904 15352
## week-Sciurus_carolinensis 0.0263 8.884146e+03 1.2907 30487
## I(week^2)-Canis_latrans 0.1967 1.173173e+08 1.3002 10434
## I(week^2)-Lynx_rufus 0.2073 1.091427e+08 1.3166 3997
## I(week^2)-Didelphis_virginiana 0.1012 7.754046e+07 1.3274 2662
## I(week^2)-Sylvilagus_floridanus 0.1507 1.074544e+08 1.2904 99913
## I(week^2)-Meleagris_gallopavo 0.2862 1.520449e+08 1.3052 5516
## I(week^2)-Sciurus_carolinensis 0.2051 1.112230e+08 1.4690 561
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.5402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1887 0.6673 -1.4875 -0.2156 1.2158 1.1731 211
## Cogon_Patch_Size 0.0859 0.6978 -1.3661 0.1125 1.4653 1.0068 890
## Avg_Cogongrass_Cover -0.0076 0.5585 -1.1641 -0.0036 1.0759 1.0241 856
## total_shrub_cover -1.1106 0.7720 -2.7915 -1.0521 0.2628 1.0932 280
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2866 2.4742 0.0580 0.5678 6.9137 1.0334 410
## Cogon_Patch_Size 2.8907 7.2342 0.0898 1.2180 15.2822 1.0875 542
## Avg_Cogongrass_Cover 1.4186 3.1871 0.0611 0.6218 7.7824 1.0530 1026
## total_shrub_cover 2.8810 5.0999 0.1179 1.4248 14.9937 1.1182 328
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9692 3.1763 0.1371 1.9886 11.2491 1.1468 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9818 0.3192 -5.5060 -5.0112 -4.2767 1.0561 332
## shrub_cover 0.4574 0.4429 -0.4218 0.4568 1.3525 1.0545 652
## veg_height 0.0516 0.2817 -0.5253 0.0528 0.6087 1.0175 675
## week 0.1214 1.7163 -3.3323 0.1673 3.4290 1.0115 547
## I(week^2) -0.0426 1.6705 -3.4028 -0.0623 3.2767 1.0426 507
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.486000e-01 6.039000e-01 0.0429 0.1984 1.532600e+00 1.0903 747
## shrub_cover 1.226000e+00 1.338200e+00 0.2072 0.8455 4.725300e+00 1.0161 775
## veg_height 4.059000e-01 5.126000e-01 0.0701 0.2633 1.574200e+00 1.0230 1109
## week 1.018822e+11 1.585896e+12 0.0950 448.7960 1.072414e+11 1.2803 418
## I(week^2) 3.215298e+06 5.746952e+07 0.1001 21.1918 8.755137e+06 1.3120 826
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4668 0.8960 -1.0808 0.3854
## (Intercept)-Lynx_rufus 0.1103 0.9751 -1.5965 0.0287
## (Intercept)-Didelphis_virginiana -0.5495 0.8283 -2.2650 -0.5436
## (Intercept)-Sylvilagus_floridanus -0.0298 0.9351 -1.8296 -0.0744
## (Intercept)-Meleagris_gallopavo -0.5218 0.8992 -2.5421 -0.4810
## (Intercept)-Sciurus_carolinensis -0.5952 1.0283 -2.6155 -0.5985
## Cogon_Patch_Size-Canis_latrans 1.1045 1.0168 -0.3435 0.9402
## Cogon_Patch_Size-Lynx_rufus 0.0252 1.2005 -2.1779 -0.0506
## Cogon_Patch_Size-Didelphis_virginiana 0.9172 0.6603 -0.1816 0.8551
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0482 1.4251 -4.3311 -0.7231
## Cogon_Patch_Size-Meleagris_gallopavo 0.3543 0.9953 -1.3033 0.2736
## Cogon_Patch_Size-Sciurus_carolinensis -0.8469 1.0594 -3.5549 -0.6712
## Avg_Cogongrass_Cover-Canis_latrans 0.3621 0.5644 -0.5707 0.3123
## Avg_Cogongrass_Cover-Lynx_rufus 0.7280 0.9564 -0.6701 0.5808
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0897 0.6313 -1.4206 -0.0634
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4872 0.8178 -2.3503 -0.4189
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8505 0.9640 -3.0926 -0.7407
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3127 0.7364 -1.1105 0.2983
## total_shrub_cover-Canis_latrans 0.4537 0.7721 -0.8435 0.3629
## total_shrub_cover-Lynx_rufus -1.7141 1.3385 -4.9817 -1.5117
## total_shrub_cover-Didelphis_virginiana -1.3747 0.9802 -3.6658 -1.2179
## total_shrub_cover-Sylvilagus_floridanus -1.9270 1.4087 -5.3888 -1.6636
## total_shrub_cover-Meleagris_gallopavo -1.8857 1.0419 -4.3085 -1.7516
## total_shrub_cover-Sciurus_carolinensis -1.2834 1.2490 -4.3155 -1.0885
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4642 1.0701 317
## (Intercept)-Lynx_rufus 2.3468 1.0564 210
## (Intercept)-Didelphis_virginiana 1.0700 1.1000 264
## (Intercept)-Sylvilagus_floridanus 2.0493 1.1871 218
## (Intercept)-Meleagris_gallopavo 1.2038 1.0407 448
## (Intercept)-Sciurus_carolinensis 1.4954 1.2052 195
## Cogon_Patch_Size-Canis_latrans 3.5407 1.0273 681
## Cogon_Patch_Size-Lynx_rufus 2.6961 1.0310 396
## Cogon_Patch_Size-Didelphis_virginiana 2.4274 1.0118 576
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7150 1.0205 319
## Cogon_Patch_Size-Meleagris_gallopavo 2.5414 1.0309 351
## Cogon_Patch_Size-Sciurus_carolinensis 0.7111 1.0138 475
## Avg_Cogongrass_Cover-Canis_latrans 1.6881 1.0047 1153
## Avg_Cogongrass_Cover-Lynx_rufus 3.0915 1.0162 568
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1251 1.0123 907
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9753 1.0139 401
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8208 1.0325 385
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.8292 1.0653 390
## total_shrub_cover-Canis_latrans 2.2221 1.0301 426
## total_shrub_cover-Lynx_rufus 0.3216 1.1325 177
## total_shrub_cover-Didelphis_virginiana 0.1105 1.0917 192
## total_shrub_cover-Sylvilagus_floridanus -0.0576 1.3153 178
## total_shrub_cover-Meleagris_gallopavo -0.2467 1.1263 226
## total_shrub_cover-Sciurus_carolinensis 0.5633 1.2263 137
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7933 0.1762 -5.1433 -4.7893
## (Intercept)-Lynx_rufus -5.4317 0.2851 -5.9868 -5.4164
## (Intercept)-Didelphis_virginiana -4.8467 0.2948 -5.4087 -4.8518
## (Intercept)-Sylvilagus_floridanus -5.1234 0.2164 -5.5717 -5.1163
## (Intercept)-Meleagris_gallopavo -5.3555 0.3971 -6.1788 -5.3518
## (Intercept)-Sciurus_carolinensis -4.9503 0.3711 -5.6799 -4.9529
## shrub_cover-Canis_latrans -0.3157 0.1956 -0.7004 -0.3161
## shrub_cover-Lynx_rufus 0.1560 0.3226 -0.5667 0.1804
## shrub_cover-Didelphis_virginiana 1.3065 0.3989 0.5699 1.3053
## shrub_cover-Sylvilagus_floridanus 1.0289 0.3731 0.2991 1.0385
## shrub_cover-Meleagris_gallopavo -0.4323 0.3929 -1.2210 -0.4350
## shrub_cover-Sciurus_carolinensis 1.2973 0.4403 0.4658 1.2991
## veg_height-Canis_latrans -0.5567 0.1796 -0.9152 -0.5500
## veg_height-Lynx_rufus 0.1300 0.2294 -0.3430 0.1384
## veg_height-Didelphis_virginiana 0.5317 0.2419 0.0699 0.5259
## veg_height-Sylvilagus_floridanus 0.1178 0.2281 -0.2861 0.1045
## veg_height-Meleagris_gallopavo -0.1706 0.4686 -1.1392 -0.1657
## veg_height-Sciurus_carolinensis 0.3045 0.2468 -0.1616 0.2988
## week-Canis_latrans 10117.6763 352204.9722 -51408.5008 0.4348
## week-Lynx_rufus 8862.9398 449326.3657 -43893.6916 0.5097
## week-Didelphis_virginiana -3547.8037 243511.0556 -50926.8017 0.4972
## week-Sylvilagus_floridanus -1491.5592 256804.0582 -42566.9046 0.5580
## week-Meleagris_gallopavo -429.1565 325215.0499 -42951.3875 0.4577
## week-Sciurus_carolinensis 11195.3938 312107.1083 -39153.1871 0.5474
## I(week^2)-Canis_latrans -24.5132 1443.2557 -1076.3521 -0.2102
## I(week^2)-Lynx_rufus -14.6466 1975.9971 -791.3019 -0.0035
## I(week^2)-Didelphis_virginiana 0.7581 1743.4367 -591.3450 -0.1254
## I(week^2)-Sylvilagus_floridanus 55.9409 2699.6598 -771.8593 -0.1627
## I(week^2)-Meleagris_gallopavo 7.5006 1843.4800 -742.5490 -0.1262
## I(week^2)-Sciurus_carolinensis -3.3102 1248.2639 -600.7193 -0.0710
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4673 1.0416 176
## (Intercept)-Lynx_rufus -4.8925 1.1162 115
## (Intercept)-Didelphis_virginiana -4.2709 1.1445 95
## (Intercept)-Sylvilagus_floridanus -4.7009 1.1700 164
## (Intercept)-Meleagris_gallopavo -4.5929 1.0692 160
## (Intercept)-Sciurus_carolinensis -4.2447 1.4356 57
## shrub_cover-Canis_latrans 0.0607 1.0359 173
## shrub_cover-Lynx_rufus 0.7289 1.0053 125
## shrub_cover-Didelphis_virginiana 2.1572 1.0323 92
## shrub_cover-Sylvilagus_floridanus 1.7556 1.3512 94
## shrub_cover-Meleagris_gallopavo 0.3243 1.0432 177
## shrub_cover-Sciurus_carolinensis 2.1860 1.2750 57
## veg_height-Canis_latrans -0.2192 1.0281 153
## veg_height-Lynx_rufus 0.5797 1.0306 164
## veg_height-Didelphis_virginiana 1.0242 1.0620 231
## veg_height-Sylvilagus_floridanus 0.6057 1.0330 157
## veg_height-Meleagris_gallopavo 0.7445 1.0449 134
## veg_height-Sciurus_carolinensis 0.7966 1.1705 146
## week-Canis_latrans 57269.1545 1.1333 2511
## week-Lynx_rufus 61403.6145 1.2064 4459
## week-Didelphis_virginiana 43071.7342 1.1921 1725
## week-Sylvilagus_floridanus 72328.2688 1.1055 32272
## week-Meleagris_gallopavo 72837.9863 1.1106 4523
## week-Sciurus_carolinensis 70030.1384 1.1993 1622
## I(week^2)-Canis_latrans 710.8330 1.1083 5899
## I(week^2)-Lynx_rufus 918.0771 1.1132 2390
## I(week^2)-Didelphis_virginiana 716.0549 1.1620 9402
## I(week^2)-Sylvilagus_floridanus 753.0040 1.1900 2041
## I(week^2)-Meleagris_gallopavo 845.5860 1.1457 10747
## I(week^2)-Sciurus_carolinensis 677.8941 1.1201 3660
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.2122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4009 0.5199 -1.3935 -0.4193 0.7201 1.0475 446
## Veg_shannon_index 0.3344 0.4020 -0.4669 0.3363 1.1297 1.0294 1154
## Avg_Cogongrass_Cover 0.2716 0.4296 -0.6087 0.2729 1.0966 1.0157 925
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0902 1.9637 0.0606 0.5662 4.9458 1.0425 1166
## Veg_shannon_index 0.6720 1.0956 0.0473 0.3385 3.4099 1.0167 648
## Avg_Cogongrass_Cover 0.7879 1.3434 0.0502 0.4099 3.9559 1.0564 997
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9396 1.0894 0.0534 0.5917 4.1286 1.0088 240
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8387 0.4642 -5.4876 -4.9064 -3.7102 1.0417 1007
## shrub_cover 0.1808 0.4218 -0.6611 0.1788 1.0692 1.0143 1238
## veg_height 0.0227 0.2661 -0.4967 0.0250 0.5374 1.0025 509
## week 0.1093 1.5931 -3.0247 0.0833 3.2104 1.0116 490
## I(week^2) -0.0701 1.6428 -3.1609 -0.0690 3.1888 1.0199 1008
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.156000e-01 2.352500e+00 0.0689 0.4088 3.938900e+00 1.4135 1072
## shrub_cover 1.105600e+00 1.359400e+00 0.1942 0.7749 3.846100e+00 1.0611 1016
## veg_height 3.543000e-01 4.461000e-01 0.0578 0.2388 1.266100e+00 1.0140 932
## week 1.536181e+10 2.350527e+11 0.0993 24.6306 4.293823e+08 1.6632 345
## I(week^2) 4.194132e+14 6.639000e+15 0.1343 512.8281 3.447693e+14 1.6415 327
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1712 0.6331 -1.0641 0.1519
## (Intercept)-Lynx_rufus 0.0004 0.8306 -1.4202 -0.0958
## (Intercept)-Didelphis_virginiana -1.0260 0.6095 -2.3327 -0.9916
## (Intercept)-Sylvilagus_floridanus -0.5456 0.5791 -1.6915 -0.5291
## (Intercept)-Meleagris_gallopavo -0.1175 0.8127 -1.5616 -0.1663
## (Intercept)-Sciurus_carolinensis -1.0145 0.5860 -2.2479 -0.9887
## Veg_shannon_index-Canis_latrans 0.7893 0.4787 -0.0334 0.7518
## Veg_shannon_index-Lynx_rufus -0.1107 0.7116 -1.7236 -0.0283
## Veg_shannon_index-Didelphis_virginiana 0.5445 0.4685 -0.3473 0.5234
## Veg_shannon_index-Sylvilagus_floridanus 0.4203 0.4496 -0.4189 0.4010
## Veg_shannon_index-Meleagris_gallopavo 0.5452 0.6038 -0.5345 0.4947
## Veg_shannon_index-Sciurus_carolinensis -0.1387 0.4894 -1.2337 -0.1065
## Avg_Cogongrass_Cover-Canis_latrans 0.7309 0.5175 -0.1138 0.6722
## Avg_Cogongrass_Cover-Lynx_rufus 0.6285 0.5470 -0.2865 0.5737
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4602 0.4475 -0.3870 0.4553
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2467 0.5317 -1.3862 -0.2095
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2923 0.8655 -2.1460 -0.2029
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4039 0.4259 -0.4326 0.4012
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4818 1.0589 498
## (Intercept)-Lynx_rufus 2.0282 1.0889 227
## (Intercept)-Didelphis_virginiana 0.0949 1.0012 959
## (Intercept)-Sylvilagus_floridanus 0.5884 1.0281 510
## (Intercept)-Meleagris_gallopavo 1.6342 1.1272 373
## (Intercept)-Sciurus_carolinensis 0.0664 1.0023 906
## Veg_shannon_index-Canis_latrans 1.8487 1.0122 1288
## Veg_shannon_index-Lynx_rufus 1.0494 1.0499 492
## Veg_shannon_index-Didelphis_virginiana 1.5045 1.0109 1485
## Veg_shannon_index-Sylvilagus_floridanus 1.3688 1.0073 1389
## Veg_shannon_index-Meleagris_gallopavo 1.9115 1.0278 744
## Veg_shannon_index-Sciurus_carolinensis 0.7174 1.0090 1101
## Avg_Cogongrass_Cover-Canis_latrans 1.9158 1.0038 1230
## Avg_Cogongrass_Cover-Lynx_rufus 1.8536 1.0046 836
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.3982 1.0060 1224
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7004 1.0151 600
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1586 1.0208 341
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2493 1.0115 1642
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7346 1.709000e-01 -5.0926
## (Intercept)-Lynx_rufus -5.5533 3.270000e-01 -6.2969
## (Intercept)-Didelphis_virginiana -4.6694 2.783000e-01 -5.2160
## (Intercept)-Sylvilagus_floridanus -4.9195 2.386000e-01 -5.4309
## (Intercept)-Meleagris_gallopavo -5.8111 4.183000e-01 -6.6260
## (Intercept)-Sciurus_carolinensis -4.6395 2.973000e-01 -5.2225
## shrub_cover-Canis_latrans -0.2939 1.980000e-01 -0.6658
## shrub_cover-Lynx_rufus -0.1998 3.547000e-01 -0.8363
## shrub_cover-Didelphis_virginiana 1.1089 3.666000e-01 0.4367
## shrub_cover-Sylvilagus_floridanus 0.4827 3.972000e-01 -0.2931
## shrub_cover-Meleagris_gallopavo -0.8596 3.630000e-01 -1.5660
## shrub_cover-Sciurus_carolinensis 0.9207 3.711000e-01 0.2032
## veg_height-Canis_latrans -0.5604 1.835000e-01 -0.9298
## veg_height-Lynx_rufus 0.0839 2.502000e-01 -0.4193
## veg_height-Didelphis_virginiana 0.4622 2.475000e-01 0.0147
## veg_height-Sylvilagus_floridanus 0.1676 2.344000e-01 -0.3177
## veg_height-Meleagris_gallopavo -0.2007 4.286000e-01 -0.9909
## veg_height-Sciurus_carolinensis 0.1808 2.122000e-01 -0.2037
## week-Canis_latrans 2440.9929 1.265148e+05 -955.5880
## week-Lynx_rufus -531.1028 1.537783e+05 -1025.7145
## week-Didelphis_virginiana -707.2632 7.936129e+04 -1095.6569
## week-Sylvilagus_floridanus -2551.7698 1.391526e+05 -1120.7511
## week-Meleagris_gallopavo 2622.2763 1.013319e+05 -959.5892
## week-Sciurus_carolinensis -5443.2805 1.220246e+05 -1295.9710
## I(week^2)-Canis_latrans -131197.1411 1.399457e+07 -2942400.6915
## I(week^2)-Lynx_rufus -183228.9610 1.654846e+07 -2346732.2210
## I(week^2)-Didelphis_virginiana -541963.2808 1.508022e+07 -1839443.7606
## I(week^2)-Sylvilagus_floridanus -120847.6186 2.042279e+07 -1818625.1439
## I(week^2)-Meleagris_gallopavo 386197.2825 2.389107e+07 -1740472.5164
## I(week^2)-Sciurus_carolinensis -269007.3303 1.786281e+07 -1765229.2972
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7249 -4.4225 1.0245 183
## (Intercept)-Lynx_rufus -5.5407 -4.9355 1.3525 75
## (Intercept)-Didelphis_virginiana -4.6568 -4.1551 1.1632 183
## (Intercept)-Sylvilagus_floridanus -4.9027 -4.4861 1.0362 177
## (Intercept)-Meleagris_gallopavo -5.7963 -5.0102 1.3757 63
## (Intercept)-Sciurus_carolinensis -4.6364 -4.0710 1.0370 194
## shrub_cover-Canis_latrans -0.2963 0.1108 1.0265 221
## shrub_cover-Lynx_rufus -0.2107 0.5666 1.0485 96
## shrub_cover-Didelphis_virginiana 1.0964 1.8705 1.0895 155
## shrub_cover-Sylvilagus_floridanus 0.4901 1.2732 1.0133 149
## shrub_cover-Meleagris_gallopavo -0.8581 -0.1326 1.3026 86
## shrub_cover-Sciurus_carolinensis 0.9151 1.6870 1.0325 193
## veg_height-Canis_latrans -0.5541 -0.2127 1.0035 195
## veg_height-Lynx_rufus 0.0791 0.6005 1.0437 145
## veg_height-Didelphis_virginiana 0.4508 0.9510 1.0145 234
## veg_height-Sylvilagus_floridanus 0.1728 0.6136 1.0372 222
## veg_height-Meleagris_gallopavo -0.2134 0.6588 1.0511 102
## veg_height-Sciurus_carolinensis 0.1702 0.6216 1.0113 259
## week-Canis_latrans 0.2195 1137.2086 1.3281 3980
## week-Lynx_rufus 0.3164 2021.0219 1.2917 101479
## week-Didelphis_virginiana 0.2438 1219.3165 1.2980 7321
## week-Sylvilagus_floridanus 0.3170 1131.3876 1.3236 6805
## week-Meleagris_gallopavo 0.2211 1461.3541 1.3543 628
## week-Sciurus_carolinensis 0.3330 1252.0328 1.4774 751
## I(week^2)-Canis_latrans -0.2395 945043.4496 1.2992 10534
## I(week^2)-Lynx_rufus -0.3515 1126868.3320 1.3021 9536
## I(week^2)-Didelphis_virginiana -0.2570 2219136.7278 1.4123 492
## I(week^2)-Sylvilagus_floridanus -0.1404 2201370.3523 1.2937 9025
## I(week^2)-Meleagris_gallopavo -0.1318 2236431.5612 1.3144 2480
## I(week^2)-Sciurus_carolinensis -0.1479 2225956.8529 1.3127 4918
#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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.156
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3954 0.4646 -1.2636 -0.4079 0.6057 1.0495 745
## Avg_Cogongrass_Cover 0.1294 0.4046 -0.7302 0.1392 0.9182 1.0041 1265
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8492 1.3918 0.0555 0.4750 3.9151 1.0562 1155
## Avg_Cogongrass_Cover 0.6545 1.0306 0.0483 0.3599 2.9796 1.0186 685
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9237 0.9936 0.0611 0.6078 3.5648 1.0103 249
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8946 0.3543 -5.4322 -4.9329 -4.0551 1.0277 1012
## shrub_cover 0.2126 0.4217 -0.6283 0.2087 1.0966 1.0640 753
## veg_height 0.0376 0.2824 -0.5059 0.0346 0.5945 1.0222 738
## week -0.0454 1.6623 -3.4222 -0.0382 3.1537 1.0034 608
## I(week^2) -0.0271 1.6242 -3.2048 -0.0148 3.1259 1.0022 770
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.764000e-01 1.954900e+00 0.0516 0.3059 2.428500e+00 1.4451 1389
## shrub_cover 1.048700e+00 1.173500e+00 0.1776 0.7182 3.936300e+00 1.0698 854
## veg_height 4.111000e-01 6.443000e-01 0.0684 0.2685 1.623100e+00 1.0465 2141
## week 1.523045e+16 3.520225e+17 0.1217 1917.9603 5.362850e+15 1.4654 660
## I(week^2) 1.359157e+12 2.052218e+13 0.1032 124.9526 1.938019e+11 1.6720 275
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2284 0.6204 -0.9000 0.1893
## (Intercept)-Lynx_rufus -0.1583 0.6443 -1.3078 -0.2083
## (Intercept)-Didelphis_virginiana -0.8866 0.5723 -2.0866 -0.8644
## (Intercept)-Sylvilagus_floridanus -0.5264 0.5229 -1.5591 -0.5129
## (Intercept)-Meleagris_gallopavo -0.2080 0.7064 -1.4665 -0.2707
## (Intercept)-Sciurus_carolinensis -0.9458 0.5968 -2.1892 -0.9180
## Avg_Cogongrass_Cover-Canis_latrans 0.4681 0.4390 -0.2787 0.4306
## Avg_Cogongrass_Cover-Lynx_rufus 0.5210 0.5088 -0.3005 0.4826
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3156 0.4145 -0.4671 0.2995
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3217 0.4976 -1.4584 -0.2734
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4647 0.7261 -2.1071 -0.3913
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3472 0.4085 -0.4500 0.3410
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5408 1.0399 623
## (Intercept)-Lynx_rufus 1.2751 1.0234 593
## (Intercept)-Didelphis_virginiana 0.1892 1.0151 802
## (Intercept)-Sylvilagus_floridanus 0.4846 1.0145 1122
## (Intercept)-Meleagris_gallopavo 1.4333 1.0925 376
## (Intercept)-Sciurus_carolinensis 0.1385 1.0182 628
## Avg_Cogongrass_Cover-Canis_latrans 1.3880 1.0065 1479
## Avg_Cogongrass_Cover-Lynx_rufus 1.6838 1.0066 894
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1806 1.0033 1128
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5200 1.0018 848
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7579 1.0058 313
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2119 1.0024 1797
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7912 1.785000e-01 -5.1232
## (Intercept)-Lynx_rufus -5.4357 2.888000e-01 -6.0622
## (Intercept)-Didelphis_virginiana -4.6875 2.843000e-01 -5.2736
## (Intercept)-Sylvilagus_floridanus -4.9515 2.200000e-01 -5.3755
## (Intercept)-Meleagris_gallopavo -5.6716 4.586000e-01 -6.7087
## (Intercept)-Sciurus_carolinensis -4.7007 3.084000e-01 -5.3133
## shrub_cover-Canis_latrans -0.3186 2.210000e-01 -0.7493
## shrub_cover-Lynx_rufus -0.2080 3.172000e-01 -0.8043
## shrub_cover-Didelphis_virginiana 1.0868 3.836000e-01 0.4000
## shrub_cover-Sylvilagus_floridanus 0.5518 4.128000e-01 -0.2406
## shrub_cover-Meleagris_gallopavo -0.7458 3.972000e-01 -1.5746
## shrub_cover-Sciurus_carolinensis 0.9124 3.745000e-01 0.1694
## veg_height-Canis_latrans -0.6013 1.822000e-01 -0.9992
## veg_height-Lynx_rufus 0.0754 2.463000e-01 -0.4322
## veg_height-Didelphis_virginiana 0.5025 2.440000e-01 0.0392
## veg_height-Sylvilagus_floridanus 0.1845 2.349000e-01 -0.2589
## veg_height-Meleagris_gallopavo -0.1777 4.229000e-01 -1.0337
## veg_height-Sciurus_carolinensis 0.2095 2.145000e-01 -0.1981
## week-Canis_latrans -795120.0074 1.469793e+08 -4161572.9032
## week-Lynx_rufus 2817814.5535 1.209080e+08 -7545056.7291
## week-Didelphis_virginiana -1596962.7565 9.142226e+07 -6236416.1748
## week-Sylvilagus_floridanus -687295.9900 8.901214e+07 -7836015.5503
## week-Meleagris_gallopavo 5754130.5355 1.309556e+08 -2700447.0824
## week-Sciurus_carolinensis -1738035.3004 9.556931e+07 -5268361.2842
## I(week^2)-Canis_latrans -1668.9829 1.159200e+06 -53574.4349
## I(week^2)-Lynx_rufus -7387.3420 1.275307e+06 -31289.5875
## I(week^2)-Didelphis_virginiana 28002.2567 1.408169e+06 -39539.2633
## I(week^2)-Sylvilagus_floridanus -21912.4035 8.689622e+05 -51614.4868
## I(week^2)-Meleagris_gallopavo -5065.0152 1.232081e+06 -46795.1725
## I(week^2)-Sciurus_carolinensis -7886.1980 9.669746e+05 -44367.7118
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7974 -4.4400 1.0766 191
## (Intercept)-Lynx_rufus -5.4218 -4.9118 1.0486 101
## (Intercept)-Didelphis_virginiana -4.6872 -4.1340 1.0639 111
## (Intercept)-Sylvilagus_floridanus -4.9506 -4.5271 1.0111 217
## (Intercept)-Meleagris_gallopavo -5.6516 -4.8313 1.5928 60
## (Intercept)-Sciurus_carolinensis -4.6957 -4.1048 1.0211 177
## shrub_cover-Canis_latrans -0.3185 0.1101 1.1582 160
## shrub_cover-Lynx_rufus -0.2188 0.4350 1.0389 129
## shrub_cover-Didelphis_virginiana 1.0622 1.8838 1.0619 111
## shrub_cover-Sylvilagus_floridanus 0.5595 1.3130 1.0147 99
## shrub_cover-Meleagris_gallopavo -0.7209 0.0171 1.6977 68
## shrub_cover-Sciurus_carolinensis 0.9114 1.6709 1.0041 211
## veg_height-Canis_latrans -0.5942 -0.2773 1.0281 156
## veg_height-Lynx_rufus 0.0884 0.5295 1.0131 149
## veg_height-Didelphis_virginiana 0.5038 0.9902 1.0096 175
## veg_height-Sylvilagus_floridanus 0.1730 0.6606 1.0189 209
## veg_height-Meleagris_gallopavo -0.1692 0.6400 1.0619 123
## veg_height-Sciurus_carolinensis 0.2032 0.6359 1.0606 246
## week-Canis_latrans -0.0249 11931860.2231 1.2933 6917
## week-Lynx_rufus 0.0603 4569903.4859 1.3435 1653
## week-Didelphis_virginiana -0.0656 6083230.6032 1.3205 1792
## week-Sylvilagus_floridanus -0.0242 3499161.1829 1.2963 23454
## week-Meleagris_gallopavo 0.1281 14548530.4968 1.4706 907
## week-Sciurus_carolinensis 0.2000 5543856.6994 1.3229 4937
## I(week^2)-Canis_latrans -0.2354 50141.2794 1.2905 43813
## I(week^2)-Lynx_rufus 0.1330 55879.1302 1.2941 22892
## I(week^2)-Didelphis_virginiana 0.1286 62560.3222 1.3289 2603
## I(week^2)-Sylvilagus_floridanus -0.0521 35025.1795 1.3570 2249
## I(week^2)-Meleagris_gallopavo -0.0530 46448.6078 1.2916 35406
## I(week^2)-Sciurus_carolinensis -0.0640 59065.0225 1.2964 5883
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.1787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9698 0.5703 -2.0377 -0.9762 0.2006 1.0072 692
## Avg_Cogongrass_Cover -0.5635 0.5355 -1.6496 -0.5368 0.4915 1.0123 578
## I(Avg_Cogongrass_Cover^2) 0.7391 0.4999 -0.1629 0.6912 1.8162 1.0044 418
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2047 4.7305 0.0613 0.5457 5.8438 1.2494 2135
## Avg_Cogongrass_Cover 1.0220 1.7228 0.0530 0.4599 5.4979 1.0512 394
## I(Avg_Cogongrass_Cover^2) 0.9124 1.8252 0.0450 0.3948 4.7133 1.0259 629
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7821 1.0075 0.0428 0.4269 3.6291 1.0481 264
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.8702 0.3752 -5.4243 -4.9102 -4.0110 1.0043 826
## shrub_cover 0.2102 0.3979 -0.5589 0.2022 1.0006 1.0081 904
## veg_height 0.0385 0.2666 -0.4928 0.0363 0.5909 1.0049 616
## week 0.0013 1.7017 -3.3810 -0.0075 3.3130 1.0032 513
## I(week^2) -0.0456 1.6963 -3.2632 -0.0526 3.4421 1.0209 432
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.645000e-01 1.244200e+00 0.0540 0.3017 2.540100e+00 1.0577 681
## shrub_cover 1.004300e+00 1.099700e+00 0.1500 0.6861 3.993500e+00 1.0076 607
## veg_height 3.809000e-01 4.479000e-01 0.0605 0.2497 1.470300e+00 1.0027 1131
## week 4.103204e+07 5.195243e+08 0.1024 52.7069 6.470807e+07 1.7637 268
## I(week^2) 2.256446e+16 4.056564e+17 0.1063 83.8086 1.337822e+16 1.5695 353
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4521 0.7018 -1.7612 -0.4770
## (Intercept)-Lynx_rufus -0.7769 0.8485 -2.2358 -0.8377
## (Intercept)-Didelphis_virginiana -1.4179 0.6542 -2.7960 -1.3831
## (Intercept)-Sylvilagus_floridanus -1.1344 0.6403 -2.4702 -1.1092
## (Intercept)-Meleagris_gallopavo -0.6282 0.8115 -1.9918 -0.7055
## (Intercept)-Sciurus_carolinensis -1.7610 0.7412 -3.4134 -1.6820
## Avg_Cogongrass_Cover-Canis_latrans -0.1153 0.6771 -1.3122 -0.1605
## Avg_Cogongrass_Cover-Lynx_rufus -0.4301 0.7702 -1.9459 -0.4468
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2191 0.6667 -1.4887 -0.2384
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2013 0.7445 -2.9329 -1.1237
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0020 0.8793 -3.0766 -0.9184
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6178 0.6727 -2.0107 -0.5787
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3856 0.9561 0.0619 1.1807
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1486 0.7395 0.0911 1.0409
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4312 0.5020 -0.4770 0.4003
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6633 0.5025 -0.2147 0.6206
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1826 0.7184 -1.3029 0.1833
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8545 0.4834 0.0377 0.8074
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9651 1.0059 709
## (Intercept)-Lynx_rufus 1.0027 1.0323 349
## (Intercept)-Didelphis_virginiana -0.2813 1.0178 752
## (Intercept)-Sylvilagus_floridanus 0.1014 1.0088 836
## (Intercept)-Meleagris_gallopavo 1.2130 1.0159 361
## (Intercept)-Sciurus_carolinensis -0.5279 1.0213 516
## Avg_Cogongrass_Cover-Canis_latrans 1.4154 1.0036 870
## Avg_Cogongrass_Cover-Lynx_rufus 1.1487 1.0152 291
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1781 1.0019 867
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0283 1.0010 523
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4491 1.0181 311
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6102 1.0015 484
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7615 1.0128 317
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9337 1.0175 437
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5800 1.0053 482
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8253 1.0035 599
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5906 1.0081 323
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9484 1.0045 646
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8190 1.721000e-01 -5.158400e+00
## (Intercept)-Lynx_rufus -5.5032 3.383000e-01 -6.197400e+00
## (Intercept)-Didelphis_virginiana -4.6988 2.693000e-01 -5.240900e+00
## (Intercept)-Sylvilagus_floridanus -4.9354 2.289000e-01 -5.402700e+00
## (Intercept)-Meleagris_gallopavo -5.5784 4.415000e-01 -6.475000e+00
## (Intercept)-Sciurus_carolinensis -4.6526 2.892000e-01 -5.255400e+00
## shrub_cover-Canis_latrans -0.2721 2.128000e-01 -6.876000e-01
## shrub_cover-Lynx_rufus -0.2302 3.653000e-01 -9.165000e-01
## shrub_cover-Didelphis_virginiana 1.0919 3.725000e-01 3.956000e-01
## shrub_cover-Sylvilagus_floridanus 0.4652 4.236000e-01 -2.629000e-01
## shrub_cover-Meleagris_gallopavo -0.6722 3.859000e-01 -1.422700e+00
## shrub_cover-Sciurus_carolinensis 0.9066 3.884000e-01 1.307000e-01
## veg_height-Canis_latrans -0.6089 1.862000e-01 -9.744000e-01
## veg_height-Lynx_rufus 0.1454 2.491000e-01 -3.596000e-01
## veg_height-Didelphis_virginiana 0.4911 2.572000e-01 2.900000e-02
## veg_height-Sylvilagus_floridanus 0.1791 2.425000e-01 -2.913000e-01
## veg_height-Meleagris_gallopavo -0.1498 4.077000e-01 -9.356000e-01
## veg_height-Sciurus_carolinensis 0.1788 2.103000e-01 -2.272000e-01
## week-Canis_latrans 8.4752 4.893698e+03 -1.980740e+03
## week-Lynx_rufus -96.1688 6.750009e+03 -2.019054e+03
## week-Didelphis_virginiana -48.5638 6.458718e+03 -2.189186e+03
## week-Sylvilagus_floridanus 150.4647 8.487308e+03 -1.727727e+03
## week-Meleagris_gallopavo 22.3239 6.341286e+03 -2.327560e+03
## week-Sciurus_carolinensis 99.6042 6.059623e+03 -1.599931e+03
## I(week^2)-Canis_latrans 4220125.6140 1.350728e+08 -1.022684e+07
## I(week^2)-Lynx_rufus -792957.4447 1.213555e+08 -1.472373e+07
## I(week^2)-Didelphis_virginiana 4195406.6418 1.412013e+08 -5.595907e+06
## I(week^2)-Sylvilagus_floridanus -1121303.5750 1.188566e+08 -1.396184e+07
## I(week^2)-Meleagris_gallopavo 3821938.7119 1.684596e+08 -1.351261e+07
## I(week^2)-Sciurus_carolinensis -546437.7478 1.423879e+08 -9.598787e+06
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8108 -4.4951 1.0160 195
## (Intercept)-Lynx_rufus -5.4826 -4.8806 1.0389 67
## (Intercept)-Didelphis_virginiana -4.6945 -4.1796 1.0361 211
## (Intercept)-Sylvilagus_floridanus -4.9310 -4.5094 1.0220 173
## (Intercept)-Meleagris_gallopavo -5.5867 -4.7162 1.0205 89
## (Intercept)-Sciurus_carolinensis -4.6517 -4.1087 1.0516 210
## shrub_cover-Canis_latrans -0.2781 0.1431 1.0195 175
## shrub_cover-Lynx_rufus -0.2321 0.5016 1.0211 80
## shrub_cover-Didelphis_virginiana 1.0722 1.8276 1.0256 186
## shrub_cover-Sylvilagus_floridanus 0.4288 1.3038 1.1858 89
## shrub_cover-Meleagris_gallopavo -0.6707 0.0846 1.0247 115
## shrub_cover-Sciurus_carolinensis 0.9124 1.6738 1.0096 195
## veg_height-Canis_latrans -0.6055 -0.2513 1.0374 175
## veg_height-Lynx_rufus 0.1547 0.5996 1.0061 136
## veg_height-Didelphis_virginiana 0.4826 0.9952 1.0125 207
## veg_height-Sylvilagus_floridanus 0.1793 0.6680 1.0331 168
## veg_height-Meleagris_gallopavo -0.1421 0.6839 1.0783 143
## veg_height-Sciurus_carolinensis 0.1765 0.5972 1.0154 239
## week-Canis_latrans 0.1367 1784.8153 1.2618 7225
## week-Lynx_rufus 0.2603 2001.2309 1.2936 8614
## week-Didelphis_virginiana 0.2140 1624.2145 1.2845 4855
## week-Sylvilagus_floridanus 0.1280 1929.4161 1.3259 1364
## week-Meleagris_gallopavo 0.1521 2261.9773 1.2746 4653
## week-Sciurus_carolinensis 0.1354 2308.7019 1.3005 3436
## I(week^2)-Canis_latrans -0.1088 22034108.3277 1.3844 1344
## I(week^2)-Lynx_rufus 0.0745 19207681.1913 1.2946 15932
## I(week^2)-Didelphis_virginiana 0.1785 31748047.3778 1.3757 1906
## I(week^2)-Sylvilagus_floridanus -0.0807 15593192.8745 1.2992 11069
## I(week^2)-Meleagris_gallopavo -0.0246 13416679.5256 1.3407 1945
## I(week^2)-Sciurus_carolinensis 0.0076 18869103.4630 1.2918 23949
# 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 6 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 4.6135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2556 1.1924 -3.4564 -1.3269 1.2381 1.0491 235
## Cogon_Patch_Size 0.7244 1.1328 -1.6417 0.7645 2.8737 1.0595 440
## Veg_shannon_index 0.8328 0.9318 -1.0826 0.8689 2.5702 1.0014 396
## total_shrub_cover -1.2685 1.1696 -3.4757 -1.2785 1.0783 1.0421 342
## Avg_Cogongrass_Cover -0.5290 1.2159 -3.0285 -0.5083 1.6704 1.0462 200
## Tree_Density -1.8893 1.0811 -4.1141 -1.8931 0.3385 1.0592 202
## Avg_Canopy_Cover 1.6677 1.3471 -1.3481 1.7396 4.2481 1.0106 634
## I(Avg_Cogongrass_Cover^2) 1.2866 0.9383 -0.6325 1.2984 3.1139 1.0075 501
## avg_veg_height -0.2499 0.9075 -2.1243 -0.2485 1.5905 1.0436 178
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.6153 17.2842 0.1017 4.6807 57.5414 1.0450 154
## Cogon_Patch_Size 11.5334 22.6414 0.1237 4.9308 61.9000 1.2112 247
## Veg_shannon_index 5.0767 12.1677 0.0734 1.9461 29.3152 1.1031 235
## total_shrub_cover 13.9217 28.4134 0.1905 5.9400 74.9548 1.0866 167
## Avg_Cogongrass_Cover 3.7082 13.3069 0.0560 0.8224 23.9093 1.4834 151
## Tree_Density 4.3617 10.2676 0.0656 1.0742 30.1487 1.0151 300
## Avg_Canopy_Cover 43.8646 114.1458 1.3054 15.1584 283.3446 1.2841 58
## I(Avg_Cogongrass_Cover^2) 5.3725 10.7755 0.0677 1.8412 33.3769 1.0307 194
## avg_veg_height 2.8141 8.4906 0.0524 0.7034 21.8648 1.1895 169
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.4613 12.2545 0.0532 1.0242 29.4221 2.8069 64
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9574 0.4154 -5.5454 -5.0008 -4.0262 1.0121 591
## shrub_cover 0.3780 0.4544 -0.5302 0.3668 1.3041 1.0233 295
## veg_height 0.0956 0.2810 -0.4766 0.0948 0.6730 1.0261 546
## week 0.0448 1.6412 -3.1482 0.0877 3.2019 1.0002 842
## I(week^2) 0.1001 1.6463 -3.1251 0.0816 3.4802 1.0312 567
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.067000e-01 1.708400e+00 0.0563 0.2927 2.943800e+00 1.0839 967
## shrub_cover 1.264000e+00 1.844800e+00 0.1937 0.8162 4.887100e+00 1.0094 578
## veg_height 4.053000e-01 4.600000e-01 0.0721 0.2733 1.597300e+00 1.0056 572
## week 1.097372e+09 1.597582e+10 0.1137 237.3783 7.731858e+08 1.6703 170
## I(week^2) 9.521981e+19 1.423949e+21 0.0935 550.8991 3.315278e+19 1.6792 404
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5521 1.5551 -3.4006 -0.6214
## (Intercept)-Lynx_rufus 0.8784 2.7703 -3.0823 0.2763
## (Intercept)-Didelphis_virginiana -3.4934 1.9895 -8.0871 -3.2566
## (Intercept)-Sylvilagus_floridanus -2.0639 1.9202 -6.3056 -2.0132
## (Intercept)-Meleagris_gallopavo -1.6221 2.1032 -5.8969 -1.6608
## (Intercept)-Sciurus_carolinensis -3.7250 2.2723 -8.6631 -3.5070
## Cogon_Patch_Size-Canis_latrans 3.1213 2.4719 0.0349 2.6246
## Cogon_Patch_Size-Lynx_rufus 0.7852 2.5855 -4.0537 0.7671
## Cogon_Patch_Size-Didelphis_virginiana 2.6248 1.7022 0.1657 2.3175
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4530 2.6426 -8.0686 -0.9025
## Cogon_Patch_Size-Meleagris_gallopavo 1.4801 2.4283 -2.1336 1.0823
## Cogon_Patch_Size-Sciurus_carolinensis -0.5169 2.1635 -5.5570 -0.2571
## Veg_shannon_index-Canis_latrans 1.7972 1.1305 -0.0412 1.6550
## Veg_shannon_index-Lynx_rufus -0.4626 2.0113 -5.0288 -0.1356
## Veg_shannon_index-Didelphis_virginiana 1.6264 1.4143 -0.6694 1.4341
## Veg_shannon_index-Sylvilagus_floridanus 1.3918 1.2530 -0.7865 1.2794
## Veg_shannon_index-Meleagris_gallopavo 2.0966 1.6484 -0.4215 1.8283
## Veg_shannon_index-Sciurus_carolinensis -0.3586 1.5723 -3.9181 -0.1431
## total_shrub_cover-Canis_latrans 1.3295 1.4353 -1.2807 1.1833
## total_shrub_cover-Lynx_rufus -3.0079 2.9040 -9.1717 -2.7468
## total_shrub_cover-Didelphis_virginiana -2.7847 2.5097 -9.2372 -2.3034
## total_shrub_cover-Sylvilagus_floridanus -2.1038 2.1859 -7.2245 -1.7824
## total_shrub_cover-Meleagris_gallopavo -4.0867 2.4229 -9.6801 -3.8403
## total_shrub_cover-Sciurus_carolinensis -1.9066 2.4514 -8.6681 -1.4355
## Avg_Cogongrass_Cover-Canis_latrans -0.2707 1.6929 -3.4324 -0.3116
## Avg_Cogongrass_Cover-Lynx_rufus -0.1350 1.8988 -3.7592 -0.1673
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3463 1.7919 -3.6779 -0.3753
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3032 1.7975 -5.3117 -1.1530
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0867 2.1563 -6.5244 -0.8334
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4262 1.8429 -3.9746 -0.4449
## Tree_Density-Canis_latrans -2.9390 1.7406 -7.2528 -2.6213
## Tree_Density-Lynx_rufus -1.3462 1.7883 -4.3414 -1.5683
## Tree_Density-Didelphis_virginiana -2.2140 1.5710 -5.5091 -2.0906
## Tree_Density-Sylvilagus_floridanus -2.6036 1.8489 -6.9381 -2.3587
## Tree_Density-Meleagris_gallopavo -2.1301 1.6349 -5.3741 -2.0868
## Tree_Density-Sciurus_carolinensis -2.3039 1.8087 -6.4898 -2.1310
## Avg_Canopy_Cover-Canis_latrans -0.4435 0.9069 -2.4500 -0.4091
## Avg_Canopy_Cover-Lynx_rufus 0.8469 3.0771 -4.5120 0.4218
## Avg_Canopy_Cover-Didelphis_virginiana 5.6533 2.7436 1.7879 5.1357
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6553 4.3607 1.9696 6.7411
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6106 2.5262 0.2895 3.0557
## Avg_Canopy_Cover-Sciurus_carolinensis 6.3860 6.7137 1.4001 4.7426
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6845 1.7324 0.5331 2.2751
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9793 1.9830 0.3032 2.5568
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0977 1.0915 -1.0620 1.0934
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1718 1.1510 -0.9800 1.1001
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2029 1.9342 -4.8484 0.0639
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8718 1.2923 -0.1373 1.7173
## avg_veg_height-Canis_latrans -0.1303 0.9348 -1.9043 -0.1539
## avg_veg_height-Lynx_rufus -0.9053 2.0317 -6.0767 -0.5851
## avg_veg_height-Didelphis_virginiana -0.5091 1.2781 -3.3183 -0.4306
## avg_veg_height-Sylvilagus_floridanus -0.3640 1.2506 -3.0555 -0.3129
## avg_veg_height-Meleagris_gallopavo -0.5259 2.0041 -5.4118 -0.3677
## avg_veg_height-Sciurus_carolinensis 0.4849 1.3365 -1.7353 0.3121
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7401 1.0286 294
## (Intercept)-Lynx_rufus 7.6039 1.0164 58
## (Intercept)-Didelphis_virginiana -0.0140 1.0092 196
## (Intercept)-Sylvilagus_floridanus 1.6121 1.1696 207
## (Intercept)-Meleagris_gallopavo 2.6686 1.0064 155
## (Intercept)-Sciurus_carolinensis 0.3782 1.0218 104
## Cogon_Patch_Size-Canis_latrans 9.1163 1.2148 169
## Cogon_Patch_Size-Lynx_rufus 6.4069 1.0054 117
## Cogon_Patch_Size-Didelphis_virginiana 6.8576 1.2309 185
## Cogon_Patch_Size-Sylvilagus_floridanus 2.1266 1.1874 168
## Cogon_Patch_Size-Meleagris_gallopavo 7.6879 1.3343 143
## Cogon_Patch_Size-Sciurus_carolinensis 3.0779 1.0370 214
## Veg_shannon_index-Canis_latrans 4.4476 1.0716 408
## Veg_shannon_index-Lynx_rufus 2.6055 1.0532 147
## Veg_shannon_index-Didelphis_virginiana 5.0607 1.0004 416
## Veg_shannon_index-Sylvilagus_floridanus 4.2246 1.0360 292
## Veg_shannon_index-Meleagris_gallopavo 6.0163 1.0389 256
## Veg_shannon_index-Sciurus_carolinensis 2.2146 1.0056 242
## total_shrub_cover-Canis_latrans 4.5413 1.0549 123
## total_shrub_cover-Lynx_rufus 2.3337 1.1039 70
## total_shrub_cover-Didelphis_virginiana 0.6796 1.0220 120
## total_shrub_cover-Sylvilagus_floridanus 1.3095 1.0775 134
## total_shrub_cover-Meleagris_gallopavo -0.0270 1.0025 149
## total_shrub_cover-Sciurus_carolinensis 1.4888 1.0735 72
## Avg_Cogongrass_Cover-Canis_latrans 3.2653 1.0606 281
## Avg_Cogongrass_Cover-Lynx_rufus 3.8840 1.0310 295
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2030 1.0434 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6495 1.0776 269
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1412 1.1354 168
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.3533 1.0695 328
## Tree_Density-Canis_latrans -0.2402 1.0537 196
## Tree_Density-Lynx_rufus 3.1929 1.0186 214
## Tree_Density-Didelphis_virginiana 0.7970 1.0485 283
## Tree_Density-Sylvilagus_floridanus 0.5418 1.0671 238
## Tree_Density-Meleagris_gallopavo 1.2235 1.0234 263
## Tree_Density-Sciurus_carolinensis 0.7841 1.0818 228
## Avg_Canopy_Cover-Canis_latrans 1.1589 1.0536 246
## Avg_Canopy_Cover-Lynx_rufus 7.8938 1.0610 70
## Avg_Canopy_Cover-Didelphis_virginiana 12.1239 1.0752 140
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.1895 1.0579 78
## Avg_Canopy_Cover-Meleagris_gallopavo 10.0411 1.0540 134
## Avg_Canopy_Cover-Sciurus_carolinensis 26.1615 1.7793 24
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.4377 1.0133 167
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.1168 1.0302 153
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.3199 1.0122 336
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7159 1.0187 299
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7538 1.0103 151
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.8409 1.1228 99
## avg_veg_height-Canis_latrans 1.8168 1.0183 381
## avg_veg_height-Lynx_rufus 2.0647 1.2560 119
## avg_veg_height-Didelphis_virginiana 1.7638 1.0503 307
## avg_veg_height-Sylvilagus_floridanus 1.9292 1.0278 327
## avg_veg_height-Meleagris_gallopavo 2.7739 1.2059 69
## avg_veg_height-Sciurus_carolinensis 3.5882 1.0463 408
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.774600e+00 1.749000e-01 -5.136700e+00
## (Intercept)-Lynx_rufus -5.656100e+00 3.250000e-01 -6.283300e+00
## (Intercept)-Didelphis_virginiana -4.829900e+00 2.744000e-01 -5.391000e+00
## (Intercept)-Sylvilagus_floridanus -5.015100e+00 2.265000e-01 -5.512300e+00
## (Intercept)-Meleagris_gallopavo -5.577600e+00 4.310000e-01 -6.372400e+00
## (Intercept)-Sciurus_carolinensis -4.904600e+00 3.107000e-01 -5.500000e+00
## shrub_cover-Canis_latrans -3.667000e-01 2.015000e-01 -7.391000e-01
## shrub_cover-Lynx_rufus 5.730000e-02 4.289000e-01 -7.295000e-01
## shrub_cover-Didelphis_virginiana 1.260000e+00 3.899000e-01 5.814000e-01
## shrub_cover-Sylvilagus_floridanus 8.017000e-01 3.605000e-01 7.190000e-02
## shrub_cover-Meleagris_gallopavo -5.996000e-01 4.107000e-01 -1.391800e+00
## shrub_cover-Sciurus_carolinensis 1.179000e+00 4.085000e-01 3.735000e-01
## veg_height-Canis_latrans -5.557000e-01 1.758000e-01 -9.251000e-01
## veg_height-Lynx_rufus 1.982000e-01 2.415000e-01 -2.444000e-01
## veg_height-Didelphis_virginiana 5.650000e-01 2.486000e-01 1.102000e-01
## veg_height-Sylvilagus_floridanus 1.755000e-01 2.303000e-01 -2.618000e-01
## veg_height-Meleagris_gallopavo -1.228000e-01 4.302000e-01 -9.728000e-01
## veg_height-Sciurus_carolinensis 3.187000e-01 2.375000e-01 -1.190000e-01
## week-Canis_latrans 5.565714e+02 2.614036e+04 -8.315693e+03
## week-Lynx_rufus -2.388674e+02 3.941004e+04 -8.032669e+03
## week-Didelphis_virginiana -8.588490e+01 3.407243e+04 -8.285568e+03
## week-Sylvilagus_floridanus -5.143875e+02 2.610582e+04 -7.378405e+03
## week-Meleagris_gallopavo 6.039336e+02 3.626392e+04 -1.006223e+04
## week-Sciurus_carolinensis 9.537693e+02 3.449679e+04 -7.512340e+03
## I(week^2)-Canis_latrans 1.133211e+08 9.774388e+09 -3.054657e+08
## I(week^2)-Lynx_rufus -9.026768e+06 8.263168e+09 -3.943464e+08
## I(week^2)-Didelphis_virginiana 4.707234e+07 8.568614e+09 -5.631209e+08
## I(week^2)-Sylvilagus_floridanus 9.638330e+07 1.019263e+10 -5.338157e+08
## I(week^2)-Meleagris_gallopavo 7.020094e+07 8.014711e+09 -7.092978e+08
## I(week^2)-Sciurus_carolinensis -1.620721e+08 9.515915e+09 -3.857481e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7681 -4.454100e+00 1.1192 158
## (Intercept)-Lynx_rufus -5.6432 -5.054100e+00 1.0885 70
## (Intercept)-Didelphis_virginiana -4.8197 -4.328600e+00 1.0079 110
## (Intercept)-Sylvilagus_floridanus -4.9989 -4.618300e+00 1.3167 175
## (Intercept)-Meleagris_gallopavo -5.5928 -4.735900e+00 1.1453 86
## (Intercept)-Sciurus_carolinensis -4.9059 -4.309100e+00 1.0420 114
## shrub_cover-Canis_latrans -0.3724 6.240000e-02 1.0756 183
## shrub_cover-Lynx_rufus 0.0571 8.449000e-01 1.0391 44
## shrub_cover-Didelphis_virginiana 1.2340 2.085100e+00 1.0247 84
## shrub_cover-Sylvilagus_floridanus 0.7983 1.535800e+00 1.1376 116
## shrub_cover-Meleagris_gallopavo -0.6026 2.011000e-01 1.0360 104
## shrub_cover-Sciurus_carolinensis 1.1749 1.968200e+00 1.0414 117
## veg_height-Canis_latrans -0.5497 -2.335000e-01 1.0282 186
## veg_height-Lynx_rufus 0.1871 6.783000e-01 1.0896 122
## veg_height-Didelphis_virginiana 0.5574 1.097800e+00 1.0163 173
## veg_height-Sylvilagus_floridanus 0.1743 6.305000e-01 1.0329 233
## veg_height-Meleagris_gallopavo -0.1396 7.318000e-01 1.0433 112
## veg_height-Sciurus_carolinensis 0.3115 8.129000e-01 1.0437 178
## week-Canis_latrans 0.0901 6.988294e+03 1.3426 1306
## week-Lynx_rufus 0.1238 9.004838e+03 1.2856 8786
## week-Didelphis_virginiana -0.0680 8.440166e+03 1.2817 26620
## week-Sylvilagus_floridanus 0.1343 8.116240e+03 1.3086 2204
## week-Meleagris_gallopavo 0.1473 6.844758e+03 1.3413 1193
## week-Sciurus_carolinensis 0.1760 9.470051e+03 1.3374 1571
## I(week^2)-Canis_latrans 0.2403 6.234965e+08 1.3037 5829
## I(week^2)-Lynx_rufus 0.1139 5.158748e+08 1.2905 5578
## I(week^2)-Didelphis_virginiana 0.1371 4.241491e+08 1.2934 39255
## I(week^2)-Sylvilagus_floridanus 0.2668 4.612801e+08 1.2992 7170
## I(week^2)-Meleagris_gallopavo 0.3141 3.464928e+08 1.2980 1181
## I(week^2)-Sciurus_carolinensis 0.2450 6.455204e+08 1.3190 2665
waicOcc(ms_full_full_T10, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -885.85523 43.18014 1858.07074
waicOcc(ms_full_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -915.37255 40.86191 1912.46892
waicOcc(ms_full_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -911.43570 30.80866 1884.48873
waicOcc(ms_full_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -911.79172 41.02172 1905.62687
waicOcc(ms_full_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -922.55417 35.15846 1915.42526
waicOcc(ms_full_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -926.24512 30.10131 1912.69286
waicOcc(ms_full_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -936.17092 20.33079 1913.00343
waicOcc(ms_full_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -922.75537 32.38746 1910.28566
waicOcc(ms_full_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -882.74575 44.09811 1853.68772
waicOcc(ms_null_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -959.13055 15.19286 1948.64682
waicOcc(ms_null_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -907.60880 41.27246 1897.76254
waicOcc(ms_null_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -938.67643 35.64293 1948.63873
waicOcc(ms_null_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -936.48635 27.41272 1927.79813
waicOcc(ms_null_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -934.50759 34.88012 1938.77542
waicOcc(ms_null_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.2232 30.1354 1948.7171
waicOcc(ms_null_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -947.29754 26.69574 1947.98657
waicOcc(ms_null_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.18038 29.15044 1946.66165
waicOcc(ms_null_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -902.44556 46.19167 1897.27446
waicOcc(ms_week_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -908.76901 41.66917 1900.87637
waicOcc(ms_week_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -939.30107 34.47328 1947.54870
waicOcc(ms_week_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -958.97596 14.96887 1947.88965
waicOcc(ms_week_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.46209 30.08711 1949.09838
waicOcc(ms_week_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -934.58414 35.06545 1939.29917
waicOcc(ms_week_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -936.61794 27.17134 1927.57855
waicOcc(ms_week_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -947.46862 26.14103 1947.21931
waicOcc(ms_week_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.60038 27.99225 1945.18525
waicOcc(ms_week_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -903.67510 41.30553 1889.96126
waicOcc(ms_cover_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -883.90400 42.27327 1852.35455
waicOcc(ms_cover_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -915.70496 39.82909 1911.06810
waicOcc(ms_cover_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -935.88070 19.80176 1911.36493
waicOcc(ms_cover_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -922.84219 34.10114 1913.88667
waicOcc(ms_cover_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -912.60395 40.20203 1905.61196
waicOcc(ms_cover_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -910.60284 31.82255 1884.85078
waicOcc(ms_cover_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -926.25605 29.92805 1912.36821
waicOcc(ms_cover_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -923.53304 32.93069 1912.92746
waicOcc(ms_cover_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -885.2447 42.5301 1855.5496
waicOcc(ms_weekQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -907.57758 40.69959 1896.55432
waicOcc(ms_weekQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -939.3652 35.1405 1949.0113
waicOcc(ms_weekQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -958.98300 15.05907 1948.08414
waicOcc(ms_weekQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.86993 29.51228 1948.76440
waicOcc(ms_weekQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -934.37390 35.06186 1938.87152
waicOcc(ms_weekQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -936.28402 27.51686 1927.60176
waicOcc(ms_weekQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -948.0449 25.6995 1947.4889
waicOcc(ms_weekQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -944.23827 28.00876 1944.49406
waicOcc(ms_weekQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -903.48975 41.76045 1890.50040
waicOcc(ms_fullQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -887.45351 43.07275 1861.05252
waicOcc(ms_fullQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -916.31866 40.02374 1912.68480
waicOcc(ms_fullQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -935.51000 21.16209 1913.34419
waicOcc(ms_fullQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -922.64278 34.27058 1913.82672
waicOcc(ms_fullQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -912.45372 39.53581 1903.97906
waicOcc(ms_fullQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -911.12477 31.16909 1884.58771
waicOcc(ms_fullQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -926.31019 30.26398 1913.14834
waicOcc(ms_fullQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -922.88183 33.40744 1912.57854
waicOcc(ms_fullQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -884.90908 42.83351 1855.48517
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 6
## Currently on species 2 out of 6
## Currently on species 3 out of 6
## Currently on species 4 out of 6
## Currently on species 5 out of 6
## Currently on species 6 out of 6
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: 0.391
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.69
## Lynx_rufus Bayesian p-value: 0.3
## Didelphis_virginiana Bayesian p-value: 0.3677
## Sylvilagus_floridanus Bayesian p-value: 0.3357
## Meleagris_gallopavo Bayesian p-value: 0.405
## Sciurus_carolinensis Bayesian p-value: 0.2477
## 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): 4.6135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2556 1.1924 -3.4564 -1.3269 1.2381 1.0491 235
## Cogon_Patch_Size 0.7244 1.1328 -1.6417 0.7645 2.8737 1.0595 440
## Veg_shannon_index 0.8328 0.9318 -1.0826 0.8689 2.5702 1.0014 396
## total_shrub_cover -1.2685 1.1696 -3.4757 -1.2785 1.0783 1.0421 342
## Avg_Cogongrass_Cover -0.5290 1.2159 -3.0285 -0.5083 1.6704 1.0462 200
## Tree_Density -1.8893 1.0811 -4.1141 -1.8931 0.3385 1.0592 202
## Avg_Canopy_Cover 1.6677 1.3471 -1.3481 1.7396 4.2481 1.0106 634
## I(Avg_Cogongrass_Cover^2) 1.2866 0.9383 -0.6325 1.2984 3.1139 1.0075 501
## avg_veg_height -0.2499 0.9075 -2.1243 -0.2485 1.5905 1.0436 178
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.6153 17.2842 0.1017 4.6807 57.5414 1.0450 154
## Cogon_Patch_Size 11.5334 22.6414 0.1237 4.9308 61.9000 1.2112 247
## Veg_shannon_index 5.0767 12.1677 0.0734 1.9461 29.3152 1.1031 235
## total_shrub_cover 13.9217 28.4134 0.1905 5.9400 74.9548 1.0866 167
## Avg_Cogongrass_Cover 3.7082 13.3069 0.0560 0.8224 23.9093 1.4834 151
## Tree_Density 4.3617 10.2676 0.0656 1.0742 30.1487 1.0151 300
## Avg_Canopy_Cover 43.8646 114.1458 1.3054 15.1584 283.3446 1.2841 58
## I(Avg_Cogongrass_Cover^2) 5.3725 10.7755 0.0677 1.8412 33.3769 1.0307 194
## avg_veg_height 2.8141 8.4906 0.0524 0.7034 21.8648 1.1895 169
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.4613 12.2545 0.0532 1.0242 29.4221 2.8069 64
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9574 0.4154 -5.5454 -5.0008 -4.0262 1.0121 591
## shrub_cover 0.3780 0.4544 -0.5302 0.3668 1.3041 1.0233 295
## veg_height 0.0956 0.2810 -0.4766 0.0948 0.6730 1.0261 546
## week 0.0448 1.6412 -3.1482 0.0877 3.2019 1.0002 842
## I(week^2) 0.1001 1.6463 -3.1251 0.0816 3.4802 1.0312 567
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.067000e-01 1.708400e+00 0.0563 0.2927 2.943800e+00 1.0839 967
## shrub_cover 1.264000e+00 1.844800e+00 0.1937 0.8162 4.887100e+00 1.0094 578
## veg_height 4.053000e-01 4.600000e-01 0.0721 0.2733 1.597300e+00 1.0056 572
## week 1.097372e+09 1.597582e+10 0.1137 237.3783 7.731858e+08 1.6703 170
## I(week^2) 9.521981e+19 1.423949e+21 0.0935 550.8991 3.315278e+19 1.6792 404
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5521 1.5551 -3.4006 -0.6214
## (Intercept)-Lynx_rufus 0.8784 2.7703 -3.0823 0.2763
## (Intercept)-Didelphis_virginiana -3.4934 1.9895 -8.0871 -3.2566
## (Intercept)-Sylvilagus_floridanus -2.0639 1.9202 -6.3056 -2.0132
## (Intercept)-Meleagris_gallopavo -1.6221 2.1032 -5.8969 -1.6608
## (Intercept)-Sciurus_carolinensis -3.7250 2.2723 -8.6631 -3.5070
## Cogon_Patch_Size-Canis_latrans 3.1213 2.4719 0.0349 2.6246
## Cogon_Patch_Size-Lynx_rufus 0.7852 2.5855 -4.0537 0.7671
## Cogon_Patch_Size-Didelphis_virginiana 2.6248 1.7022 0.1657 2.3175
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4530 2.6426 -8.0686 -0.9025
## Cogon_Patch_Size-Meleagris_gallopavo 1.4801 2.4283 -2.1336 1.0823
## Cogon_Patch_Size-Sciurus_carolinensis -0.5169 2.1635 -5.5570 -0.2571
## Veg_shannon_index-Canis_latrans 1.7972 1.1305 -0.0412 1.6550
## Veg_shannon_index-Lynx_rufus -0.4626 2.0113 -5.0288 -0.1356
## Veg_shannon_index-Didelphis_virginiana 1.6264 1.4143 -0.6694 1.4341
## Veg_shannon_index-Sylvilagus_floridanus 1.3918 1.2530 -0.7865 1.2794
## Veg_shannon_index-Meleagris_gallopavo 2.0966 1.6484 -0.4215 1.8283
## Veg_shannon_index-Sciurus_carolinensis -0.3586 1.5723 -3.9181 -0.1431
## total_shrub_cover-Canis_latrans 1.3295 1.4353 -1.2807 1.1833
## total_shrub_cover-Lynx_rufus -3.0079 2.9040 -9.1717 -2.7468
## total_shrub_cover-Didelphis_virginiana -2.7847 2.5097 -9.2372 -2.3034
## total_shrub_cover-Sylvilagus_floridanus -2.1038 2.1859 -7.2245 -1.7824
## total_shrub_cover-Meleagris_gallopavo -4.0867 2.4229 -9.6801 -3.8403
## total_shrub_cover-Sciurus_carolinensis -1.9066 2.4514 -8.6681 -1.4355
## Avg_Cogongrass_Cover-Canis_latrans -0.2707 1.6929 -3.4324 -0.3116
## Avg_Cogongrass_Cover-Lynx_rufus -0.1350 1.8988 -3.7592 -0.1673
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3463 1.7919 -3.6779 -0.3753
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3032 1.7975 -5.3117 -1.1530
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0867 2.1563 -6.5244 -0.8334
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4262 1.8429 -3.9746 -0.4449
## Tree_Density-Canis_latrans -2.9390 1.7406 -7.2528 -2.6213
## Tree_Density-Lynx_rufus -1.3462 1.7883 -4.3414 -1.5683
## Tree_Density-Didelphis_virginiana -2.2140 1.5710 -5.5091 -2.0906
## Tree_Density-Sylvilagus_floridanus -2.6036 1.8489 -6.9381 -2.3587
## Tree_Density-Meleagris_gallopavo -2.1301 1.6349 -5.3741 -2.0868
## Tree_Density-Sciurus_carolinensis -2.3039 1.8087 -6.4898 -2.1310
## Avg_Canopy_Cover-Canis_latrans -0.4435 0.9069 -2.4500 -0.4091
## Avg_Canopy_Cover-Lynx_rufus 0.8469 3.0771 -4.5120 0.4218
## Avg_Canopy_Cover-Didelphis_virginiana 5.6533 2.7436 1.7879 5.1357
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6553 4.3607 1.9696 6.7411
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6106 2.5262 0.2895 3.0557
## Avg_Canopy_Cover-Sciurus_carolinensis 6.3860 6.7137 1.4001 4.7426
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6845 1.7324 0.5331 2.2751
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9793 1.9830 0.3032 2.5568
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0977 1.0915 -1.0620 1.0934
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1718 1.1510 -0.9800 1.1001
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2029 1.9342 -4.8484 0.0639
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8718 1.2923 -0.1373 1.7173
## avg_veg_height-Canis_latrans -0.1303 0.9348 -1.9043 -0.1539
## avg_veg_height-Lynx_rufus -0.9053 2.0317 -6.0767 -0.5851
## avg_veg_height-Didelphis_virginiana -0.5091 1.2781 -3.3183 -0.4306
## avg_veg_height-Sylvilagus_floridanus -0.3640 1.2506 -3.0555 -0.3129
## avg_veg_height-Meleagris_gallopavo -0.5259 2.0041 -5.4118 -0.3677
## avg_veg_height-Sciurus_carolinensis 0.4849 1.3365 -1.7353 0.3121
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7401 1.0286 294
## (Intercept)-Lynx_rufus 7.6039 1.0164 58
## (Intercept)-Didelphis_virginiana -0.0140 1.0092 196
## (Intercept)-Sylvilagus_floridanus 1.6121 1.1696 207
## (Intercept)-Meleagris_gallopavo 2.6686 1.0064 155
## (Intercept)-Sciurus_carolinensis 0.3782 1.0218 104
## Cogon_Patch_Size-Canis_latrans 9.1163 1.2148 169
## Cogon_Patch_Size-Lynx_rufus 6.4069 1.0054 117
## Cogon_Patch_Size-Didelphis_virginiana 6.8576 1.2309 185
## Cogon_Patch_Size-Sylvilagus_floridanus 2.1266 1.1874 168
## Cogon_Patch_Size-Meleagris_gallopavo 7.6879 1.3343 143
## Cogon_Patch_Size-Sciurus_carolinensis 3.0779 1.0370 214
## Veg_shannon_index-Canis_latrans 4.4476 1.0716 408
## Veg_shannon_index-Lynx_rufus 2.6055 1.0532 147
## Veg_shannon_index-Didelphis_virginiana 5.0607 1.0004 416
## Veg_shannon_index-Sylvilagus_floridanus 4.2246 1.0360 292
## Veg_shannon_index-Meleagris_gallopavo 6.0163 1.0389 256
## Veg_shannon_index-Sciurus_carolinensis 2.2146 1.0056 242
## total_shrub_cover-Canis_latrans 4.5413 1.0549 123
## total_shrub_cover-Lynx_rufus 2.3337 1.1039 70
## total_shrub_cover-Didelphis_virginiana 0.6796 1.0220 120
## total_shrub_cover-Sylvilagus_floridanus 1.3095 1.0775 134
## total_shrub_cover-Meleagris_gallopavo -0.0270 1.0025 149
## total_shrub_cover-Sciurus_carolinensis 1.4888 1.0735 72
## Avg_Cogongrass_Cover-Canis_latrans 3.2653 1.0606 281
## Avg_Cogongrass_Cover-Lynx_rufus 3.8840 1.0310 295
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2030 1.0434 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6495 1.0776 269
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1412 1.1354 168
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.3533 1.0695 328
## Tree_Density-Canis_latrans -0.2402 1.0537 196
## Tree_Density-Lynx_rufus 3.1929 1.0186 214
## Tree_Density-Didelphis_virginiana 0.7970 1.0485 283
## Tree_Density-Sylvilagus_floridanus 0.5418 1.0671 238
## Tree_Density-Meleagris_gallopavo 1.2235 1.0234 263
## Tree_Density-Sciurus_carolinensis 0.7841 1.0818 228
## Avg_Canopy_Cover-Canis_latrans 1.1589 1.0536 246
## Avg_Canopy_Cover-Lynx_rufus 7.8938 1.0610 70
## Avg_Canopy_Cover-Didelphis_virginiana 12.1239 1.0752 140
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.1895 1.0579 78
## Avg_Canopy_Cover-Meleagris_gallopavo 10.0411 1.0540 134
## Avg_Canopy_Cover-Sciurus_carolinensis 26.1615 1.7793 24
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.4377 1.0133 167
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.1168 1.0302 153
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.3199 1.0122 336
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7159 1.0187 299
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7538 1.0103 151
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.8409 1.1228 99
## avg_veg_height-Canis_latrans 1.8168 1.0183 381
## avg_veg_height-Lynx_rufus 2.0647 1.2560 119
## avg_veg_height-Didelphis_virginiana 1.7638 1.0503 307
## avg_veg_height-Sylvilagus_floridanus 1.9292 1.0278 327
## avg_veg_height-Meleagris_gallopavo 2.7739 1.2059 69
## avg_veg_height-Sciurus_carolinensis 3.5882 1.0463 408
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.774600e+00 1.749000e-01 -5.136700e+00
## (Intercept)-Lynx_rufus -5.656100e+00 3.250000e-01 -6.283300e+00
## (Intercept)-Didelphis_virginiana -4.829900e+00 2.744000e-01 -5.391000e+00
## (Intercept)-Sylvilagus_floridanus -5.015100e+00 2.265000e-01 -5.512300e+00
## (Intercept)-Meleagris_gallopavo -5.577600e+00 4.310000e-01 -6.372400e+00
## (Intercept)-Sciurus_carolinensis -4.904600e+00 3.107000e-01 -5.500000e+00
## shrub_cover-Canis_latrans -3.667000e-01 2.015000e-01 -7.391000e-01
## shrub_cover-Lynx_rufus 5.730000e-02 4.289000e-01 -7.295000e-01
## shrub_cover-Didelphis_virginiana 1.260000e+00 3.899000e-01 5.814000e-01
## shrub_cover-Sylvilagus_floridanus 8.017000e-01 3.605000e-01 7.190000e-02
## shrub_cover-Meleagris_gallopavo -5.996000e-01 4.107000e-01 -1.391800e+00
## shrub_cover-Sciurus_carolinensis 1.179000e+00 4.085000e-01 3.735000e-01
## veg_height-Canis_latrans -5.557000e-01 1.758000e-01 -9.251000e-01
## veg_height-Lynx_rufus 1.982000e-01 2.415000e-01 -2.444000e-01
## veg_height-Didelphis_virginiana 5.650000e-01 2.486000e-01 1.102000e-01
## veg_height-Sylvilagus_floridanus 1.755000e-01 2.303000e-01 -2.618000e-01
## veg_height-Meleagris_gallopavo -1.228000e-01 4.302000e-01 -9.728000e-01
## veg_height-Sciurus_carolinensis 3.187000e-01 2.375000e-01 -1.190000e-01
## week-Canis_latrans 5.565714e+02 2.614036e+04 -8.315693e+03
## week-Lynx_rufus -2.388674e+02 3.941004e+04 -8.032669e+03
## week-Didelphis_virginiana -8.588490e+01 3.407243e+04 -8.285568e+03
## week-Sylvilagus_floridanus -5.143875e+02 2.610582e+04 -7.378405e+03
## week-Meleagris_gallopavo 6.039336e+02 3.626392e+04 -1.006223e+04
## week-Sciurus_carolinensis 9.537693e+02 3.449679e+04 -7.512340e+03
## I(week^2)-Canis_latrans 1.133211e+08 9.774388e+09 -3.054657e+08
## I(week^2)-Lynx_rufus -9.026768e+06 8.263168e+09 -3.943464e+08
## I(week^2)-Didelphis_virginiana 4.707234e+07 8.568614e+09 -5.631209e+08
## I(week^2)-Sylvilagus_floridanus 9.638330e+07 1.019263e+10 -5.338157e+08
## I(week^2)-Meleagris_gallopavo 7.020094e+07 8.014711e+09 -7.092978e+08
## I(week^2)-Sciurus_carolinensis -1.620721e+08 9.515915e+09 -3.857481e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7681 -4.454100e+00 1.1192 158
## (Intercept)-Lynx_rufus -5.6432 -5.054100e+00 1.0885 70
## (Intercept)-Didelphis_virginiana -4.8197 -4.328600e+00 1.0079 110
## (Intercept)-Sylvilagus_floridanus -4.9989 -4.618300e+00 1.3167 175
## (Intercept)-Meleagris_gallopavo -5.5928 -4.735900e+00 1.1453 86
## (Intercept)-Sciurus_carolinensis -4.9059 -4.309100e+00 1.0420 114
## shrub_cover-Canis_latrans -0.3724 6.240000e-02 1.0756 183
## shrub_cover-Lynx_rufus 0.0571 8.449000e-01 1.0391 44
## shrub_cover-Didelphis_virginiana 1.2340 2.085100e+00 1.0247 84
## shrub_cover-Sylvilagus_floridanus 0.7983 1.535800e+00 1.1376 116
## shrub_cover-Meleagris_gallopavo -0.6026 2.011000e-01 1.0360 104
## shrub_cover-Sciurus_carolinensis 1.1749 1.968200e+00 1.0414 117
## veg_height-Canis_latrans -0.5497 -2.335000e-01 1.0282 186
## veg_height-Lynx_rufus 0.1871 6.783000e-01 1.0896 122
## veg_height-Didelphis_virginiana 0.5574 1.097800e+00 1.0163 173
## veg_height-Sylvilagus_floridanus 0.1743 6.305000e-01 1.0329 233
## veg_height-Meleagris_gallopavo -0.1396 7.318000e-01 1.0433 112
## veg_height-Sciurus_carolinensis 0.3115 8.129000e-01 1.0437 178
## week-Canis_latrans 0.0901 6.988294e+03 1.3426 1306
## week-Lynx_rufus 0.1238 9.004838e+03 1.2856 8786
## week-Didelphis_virginiana -0.0680 8.440166e+03 1.2817 26620
## week-Sylvilagus_floridanus 0.1343 8.116240e+03 1.3086 2204
## week-Meleagris_gallopavo 0.1473 6.844758e+03 1.3413 1193
## week-Sciurus_carolinensis 0.1760 9.470051e+03 1.3374 1571
## I(week^2)-Canis_latrans 0.2403 6.234965e+08 1.3037 5829
## I(week^2)-Lynx_rufus 0.1139 5.158748e+08 1.2905 5578
## I(week^2)-Didelphis_virginiana 0.1371 4.241491e+08 1.2934 39255
## I(week^2)-Sylvilagus_floridanus 0.2668 4.612801e+08 1.2992 7170
## I(week^2)-Meleagris_gallopavo 0.3141 3.464928e+08 1.2980 1181
## I(week^2)-Sciurus_carolinensis 0.2450 6.455204e+08 1.3190 2665
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$beta.samples)
## 'mcmc' num [1:3000, 1:54] -0.4666 -0.0443 -0.8641 0.229 2.1879 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:54] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1953333
MCMCplot(ms_fullQ_fullQ_T10$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, 1:6, 1:100] 0.1113 0.0577 0.0106 0.0133 0.0369 ...
## $ z.0.samples : int [1:3000, 1:6, 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$psi.0.samples)
## num [1:3000, 1:6, 1:100] 0.1113 0.0577 0.0106 0.0133 0.0369 ...
psi.0.quants <- apply(out.pred$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] 5.24e-05 7.66e-02 9.95e-01 6.94e-05 7.85e-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 0.0766 0.0785 0.0798 0.0815 0.0853 ...
## $ psi.low : num 5.24e-05 6.94e-05 6.25e-05 8.44e-05 8.04e-05 ...
## $ psi.high : num 0.995 0.995 0.995 0.995 0.995 ...
## $ 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): 4.6135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2556 1.1924 -3.4564 -1.3269 1.2381 1.0491 235
## Cogon_Patch_Size 0.7244 1.1328 -1.6417 0.7645 2.8737 1.0595 440
## Veg_shannon_index 0.8328 0.9318 -1.0826 0.8689 2.5702 1.0014 396
## total_shrub_cover -1.2685 1.1696 -3.4757 -1.2785 1.0783 1.0421 342
## Avg_Cogongrass_Cover -0.5290 1.2159 -3.0285 -0.5083 1.6704 1.0462 200
## Tree_Density -1.8893 1.0811 -4.1141 -1.8931 0.3385 1.0592 202
## Avg_Canopy_Cover 1.6677 1.3471 -1.3481 1.7396 4.2481 1.0106 634
## I(Avg_Cogongrass_Cover^2) 1.2866 0.9383 -0.6325 1.2984 3.1139 1.0075 501
## avg_veg_height -0.2499 0.9075 -2.1243 -0.2485 1.5905 1.0436 178
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.6153 17.2842 0.1017 4.6807 57.5414 1.0450 154
## Cogon_Patch_Size 11.5334 22.6414 0.1237 4.9308 61.9000 1.2112 247
## Veg_shannon_index 5.0767 12.1677 0.0734 1.9461 29.3152 1.1031 235
## total_shrub_cover 13.9217 28.4134 0.1905 5.9400 74.9548 1.0866 167
## Avg_Cogongrass_Cover 3.7082 13.3069 0.0560 0.8224 23.9093 1.4834 151
## Tree_Density 4.3617 10.2676 0.0656 1.0742 30.1487 1.0151 300
## Avg_Canopy_Cover 43.8646 114.1458 1.3054 15.1584 283.3446 1.2841 58
## I(Avg_Cogongrass_Cover^2) 5.3725 10.7755 0.0677 1.8412 33.3769 1.0307 194
## avg_veg_height 2.8141 8.4906 0.0524 0.7034 21.8648 1.1895 169
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.4613 12.2545 0.0532 1.0242 29.4221 2.8069 64
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -4.9574 0.4154 -5.5454 -5.0008 -4.0262 1.0121 591
## shrub_cover 0.3780 0.4544 -0.5302 0.3668 1.3041 1.0233 295
## veg_height 0.0956 0.2810 -0.4766 0.0948 0.6730 1.0261 546
## week 0.0448 1.6412 -3.1482 0.0877 3.2019 1.0002 842
## I(week^2) 0.1001 1.6463 -3.1251 0.0816 3.4802 1.0312 567
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.067000e-01 1.708400e+00 0.0563 0.2927 2.943800e+00 1.0839 967
## shrub_cover 1.264000e+00 1.844800e+00 0.1937 0.8162 4.887100e+00 1.0094 578
## veg_height 4.053000e-01 4.600000e-01 0.0721 0.2733 1.597300e+00 1.0056 572
## week 1.097372e+09 1.597582e+10 0.1137 237.3783 7.731858e+08 1.6703 170
## I(week^2) 9.521981e+19 1.423949e+21 0.0935 550.8991 3.315278e+19 1.6792 404
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5521 1.5551 -3.4006 -0.6214
## (Intercept)-Lynx_rufus 0.8784 2.7703 -3.0823 0.2763
## (Intercept)-Didelphis_virginiana -3.4934 1.9895 -8.0871 -3.2566
## (Intercept)-Sylvilagus_floridanus -2.0639 1.9202 -6.3056 -2.0132
## (Intercept)-Meleagris_gallopavo -1.6221 2.1032 -5.8969 -1.6608
## (Intercept)-Sciurus_carolinensis -3.7250 2.2723 -8.6631 -3.5070
## Cogon_Patch_Size-Canis_latrans 3.1213 2.4719 0.0349 2.6246
## Cogon_Patch_Size-Lynx_rufus 0.7852 2.5855 -4.0537 0.7671
## Cogon_Patch_Size-Didelphis_virginiana 2.6248 1.7022 0.1657 2.3175
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4530 2.6426 -8.0686 -0.9025
## Cogon_Patch_Size-Meleagris_gallopavo 1.4801 2.4283 -2.1336 1.0823
## Cogon_Patch_Size-Sciurus_carolinensis -0.5169 2.1635 -5.5570 -0.2571
## Veg_shannon_index-Canis_latrans 1.7972 1.1305 -0.0412 1.6550
## Veg_shannon_index-Lynx_rufus -0.4626 2.0113 -5.0288 -0.1356
## Veg_shannon_index-Didelphis_virginiana 1.6264 1.4143 -0.6694 1.4341
## Veg_shannon_index-Sylvilagus_floridanus 1.3918 1.2530 -0.7865 1.2794
## Veg_shannon_index-Meleagris_gallopavo 2.0966 1.6484 -0.4215 1.8283
## Veg_shannon_index-Sciurus_carolinensis -0.3586 1.5723 -3.9181 -0.1431
## total_shrub_cover-Canis_latrans 1.3295 1.4353 -1.2807 1.1833
## total_shrub_cover-Lynx_rufus -3.0079 2.9040 -9.1717 -2.7468
## total_shrub_cover-Didelphis_virginiana -2.7847 2.5097 -9.2372 -2.3034
## total_shrub_cover-Sylvilagus_floridanus -2.1038 2.1859 -7.2245 -1.7824
## total_shrub_cover-Meleagris_gallopavo -4.0867 2.4229 -9.6801 -3.8403
## total_shrub_cover-Sciurus_carolinensis -1.9066 2.4514 -8.6681 -1.4355
## Avg_Cogongrass_Cover-Canis_latrans -0.2707 1.6929 -3.4324 -0.3116
## Avg_Cogongrass_Cover-Lynx_rufus -0.1350 1.8988 -3.7592 -0.1673
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3463 1.7919 -3.6779 -0.3753
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3032 1.7975 -5.3117 -1.1530
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0867 2.1563 -6.5244 -0.8334
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.4262 1.8429 -3.9746 -0.4449
## Tree_Density-Canis_latrans -2.9390 1.7406 -7.2528 -2.6213
## Tree_Density-Lynx_rufus -1.3462 1.7883 -4.3414 -1.5683
## Tree_Density-Didelphis_virginiana -2.2140 1.5710 -5.5091 -2.0906
## Tree_Density-Sylvilagus_floridanus -2.6036 1.8489 -6.9381 -2.3587
## Tree_Density-Meleagris_gallopavo -2.1301 1.6349 -5.3741 -2.0868
## Tree_Density-Sciurus_carolinensis -2.3039 1.8087 -6.4898 -2.1310
## Avg_Canopy_Cover-Canis_latrans -0.4435 0.9069 -2.4500 -0.4091
## Avg_Canopy_Cover-Lynx_rufus 0.8469 3.0771 -4.5120 0.4218
## Avg_Canopy_Cover-Didelphis_virginiana 5.6533 2.7436 1.7879 5.1357
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6553 4.3607 1.9696 6.7411
## Avg_Canopy_Cover-Meleagris_gallopavo 3.6106 2.5262 0.2895 3.0557
## Avg_Canopy_Cover-Sciurus_carolinensis 6.3860 6.7137 1.4001 4.7426
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6845 1.7324 0.5331 2.2751
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.9793 1.9830 0.3032 2.5568
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0977 1.0915 -1.0620 1.0934
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1718 1.1510 -0.9800 1.1001
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2029 1.9342 -4.8484 0.0639
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8718 1.2923 -0.1373 1.7173
## avg_veg_height-Canis_latrans -0.1303 0.9348 -1.9043 -0.1539
## avg_veg_height-Lynx_rufus -0.9053 2.0317 -6.0767 -0.5851
## avg_veg_height-Didelphis_virginiana -0.5091 1.2781 -3.3183 -0.4306
## avg_veg_height-Sylvilagus_floridanus -0.3640 1.2506 -3.0555 -0.3129
## avg_veg_height-Meleagris_gallopavo -0.5259 2.0041 -5.4118 -0.3677
## avg_veg_height-Sciurus_carolinensis 0.4849 1.3365 -1.7353 0.3121
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7401 1.0286 294
## (Intercept)-Lynx_rufus 7.6039 1.0164 58
## (Intercept)-Didelphis_virginiana -0.0140 1.0092 196
## (Intercept)-Sylvilagus_floridanus 1.6121 1.1696 207
## (Intercept)-Meleagris_gallopavo 2.6686 1.0064 155
## (Intercept)-Sciurus_carolinensis 0.3782 1.0218 104
## Cogon_Patch_Size-Canis_latrans 9.1163 1.2148 169
## Cogon_Patch_Size-Lynx_rufus 6.4069 1.0054 117
## Cogon_Patch_Size-Didelphis_virginiana 6.8576 1.2309 185
## Cogon_Patch_Size-Sylvilagus_floridanus 2.1266 1.1874 168
## Cogon_Patch_Size-Meleagris_gallopavo 7.6879 1.3343 143
## Cogon_Patch_Size-Sciurus_carolinensis 3.0779 1.0370 214
## Veg_shannon_index-Canis_latrans 4.4476 1.0716 408
## Veg_shannon_index-Lynx_rufus 2.6055 1.0532 147
## Veg_shannon_index-Didelphis_virginiana 5.0607 1.0004 416
## Veg_shannon_index-Sylvilagus_floridanus 4.2246 1.0360 292
## Veg_shannon_index-Meleagris_gallopavo 6.0163 1.0389 256
## Veg_shannon_index-Sciurus_carolinensis 2.2146 1.0056 242
## total_shrub_cover-Canis_latrans 4.5413 1.0549 123
## total_shrub_cover-Lynx_rufus 2.3337 1.1039 70
## total_shrub_cover-Didelphis_virginiana 0.6796 1.0220 120
## total_shrub_cover-Sylvilagus_floridanus 1.3095 1.0775 134
## total_shrub_cover-Meleagris_gallopavo -0.0270 1.0025 149
## total_shrub_cover-Sciurus_carolinensis 1.4888 1.0735 72
## Avg_Cogongrass_Cover-Canis_latrans 3.2653 1.0606 281
## Avg_Cogongrass_Cover-Lynx_rufus 3.8840 1.0310 295
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.2030 1.0434 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6495 1.0776 269
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1412 1.1354 168
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.3533 1.0695 328
## Tree_Density-Canis_latrans -0.2402 1.0537 196
## Tree_Density-Lynx_rufus 3.1929 1.0186 214
## Tree_Density-Didelphis_virginiana 0.7970 1.0485 283
## Tree_Density-Sylvilagus_floridanus 0.5418 1.0671 238
## Tree_Density-Meleagris_gallopavo 1.2235 1.0234 263
## Tree_Density-Sciurus_carolinensis 0.7841 1.0818 228
## Avg_Canopy_Cover-Canis_latrans 1.1589 1.0536 246
## Avg_Canopy_Cover-Lynx_rufus 7.8938 1.0610 70
## Avg_Canopy_Cover-Didelphis_virginiana 12.1239 1.0752 140
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.1895 1.0579 78
## Avg_Canopy_Cover-Meleagris_gallopavo 10.0411 1.0540 134
## Avg_Canopy_Cover-Sciurus_carolinensis 26.1615 1.7793 24
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.4377 1.0133 167
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 8.1168 1.0302 153
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 3.3199 1.0122 336
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7159 1.0187 299
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.7538 1.0103 151
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 4.8409 1.1228 99
## avg_veg_height-Canis_latrans 1.8168 1.0183 381
## avg_veg_height-Lynx_rufus 2.0647 1.2560 119
## avg_veg_height-Didelphis_virginiana 1.7638 1.0503 307
## avg_veg_height-Sylvilagus_floridanus 1.9292 1.0278 327
## avg_veg_height-Meleagris_gallopavo 2.7739 1.2059 69
## avg_veg_height-Sciurus_carolinensis 3.5882 1.0463 408
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.774600e+00 1.749000e-01 -5.136700e+00
## (Intercept)-Lynx_rufus -5.656100e+00 3.250000e-01 -6.283300e+00
## (Intercept)-Didelphis_virginiana -4.829900e+00 2.744000e-01 -5.391000e+00
## (Intercept)-Sylvilagus_floridanus -5.015100e+00 2.265000e-01 -5.512300e+00
## (Intercept)-Meleagris_gallopavo -5.577600e+00 4.310000e-01 -6.372400e+00
## (Intercept)-Sciurus_carolinensis -4.904600e+00 3.107000e-01 -5.500000e+00
## shrub_cover-Canis_latrans -3.667000e-01 2.015000e-01 -7.391000e-01
## shrub_cover-Lynx_rufus 5.730000e-02 4.289000e-01 -7.295000e-01
## shrub_cover-Didelphis_virginiana 1.260000e+00 3.899000e-01 5.814000e-01
## shrub_cover-Sylvilagus_floridanus 8.017000e-01 3.605000e-01 7.190000e-02
## shrub_cover-Meleagris_gallopavo -5.996000e-01 4.107000e-01 -1.391800e+00
## shrub_cover-Sciurus_carolinensis 1.179000e+00 4.085000e-01 3.735000e-01
## veg_height-Canis_latrans -5.557000e-01 1.758000e-01 -9.251000e-01
## veg_height-Lynx_rufus 1.982000e-01 2.415000e-01 -2.444000e-01
## veg_height-Didelphis_virginiana 5.650000e-01 2.486000e-01 1.102000e-01
## veg_height-Sylvilagus_floridanus 1.755000e-01 2.303000e-01 -2.618000e-01
## veg_height-Meleagris_gallopavo -1.228000e-01 4.302000e-01 -9.728000e-01
## veg_height-Sciurus_carolinensis 3.187000e-01 2.375000e-01 -1.190000e-01
## week-Canis_latrans 5.565714e+02 2.614036e+04 -8.315693e+03
## week-Lynx_rufus -2.388674e+02 3.941004e+04 -8.032669e+03
## week-Didelphis_virginiana -8.588490e+01 3.407243e+04 -8.285568e+03
## week-Sylvilagus_floridanus -5.143875e+02 2.610582e+04 -7.378405e+03
## week-Meleagris_gallopavo 6.039336e+02 3.626392e+04 -1.006223e+04
## week-Sciurus_carolinensis 9.537693e+02 3.449679e+04 -7.512340e+03
## I(week^2)-Canis_latrans 1.133211e+08 9.774388e+09 -3.054657e+08
## I(week^2)-Lynx_rufus -9.026768e+06 8.263168e+09 -3.943464e+08
## I(week^2)-Didelphis_virginiana 4.707234e+07 8.568614e+09 -5.631209e+08
## I(week^2)-Sylvilagus_floridanus 9.638330e+07 1.019263e+10 -5.338157e+08
## I(week^2)-Meleagris_gallopavo 7.020094e+07 8.014711e+09 -7.092978e+08
## I(week^2)-Sciurus_carolinensis -1.620721e+08 9.515915e+09 -3.857481e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7681 -4.454100e+00 1.1192 158
## (Intercept)-Lynx_rufus -5.6432 -5.054100e+00 1.0885 70
## (Intercept)-Didelphis_virginiana -4.8197 -4.328600e+00 1.0079 110
## (Intercept)-Sylvilagus_floridanus -4.9989 -4.618300e+00 1.3167 175
## (Intercept)-Meleagris_gallopavo -5.5928 -4.735900e+00 1.1453 86
## (Intercept)-Sciurus_carolinensis -4.9059 -4.309100e+00 1.0420 114
## shrub_cover-Canis_latrans -0.3724 6.240000e-02 1.0756 183
## shrub_cover-Lynx_rufus 0.0571 8.449000e-01 1.0391 44
## shrub_cover-Didelphis_virginiana 1.2340 2.085100e+00 1.0247 84
## shrub_cover-Sylvilagus_floridanus 0.7983 1.535800e+00 1.1376 116
## shrub_cover-Meleagris_gallopavo -0.6026 2.011000e-01 1.0360 104
## shrub_cover-Sciurus_carolinensis 1.1749 1.968200e+00 1.0414 117
## veg_height-Canis_latrans -0.5497 -2.335000e-01 1.0282 186
## veg_height-Lynx_rufus 0.1871 6.783000e-01 1.0896 122
## veg_height-Didelphis_virginiana 0.5574 1.097800e+00 1.0163 173
## veg_height-Sylvilagus_floridanus 0.1743 6.305000e-01 1.0329 233
## veg_height-Meleagris_gallopavo -0.1396 7.318000e-01 1.0433 112
## veg_height-Sciurus_carolinensis 0.3115 8.129000e-01 1.0437 178
## week-Canis_latrans 0.0901 6.988294e+03 1.3426 1306
## week-Lynx_rufus 0.1238 9.004838e+03 1.2856 8786
## week-Didelphis_virginiana -0.0680 8.440166e+03 1.2817 26620
## week-Sylvilagus_floridanus 0.1343 8.116240e+03 1.3086 2204
## week-Meleagris_gallopavo 0.1473 6.844758e+03 1.3413 1193
## week-Sciurus_carolinensis 0.1760 9.470051e+03 1.3374 1571
## I(week^2)-Canis_latrans 0.2403 6.234965e+08 1.3037 5829
## I(week^2)-Lynx_rufus 0.1139 5.158748e+08 1.2905 5578
## I(week^2)-Didelphis_virginiana 0.1371 4.241491e+08 1.2934 39255
## I(week^2)-Sylvilagus_floridanus 0.2668 4.612801e+08 1.2992 7170
## I(week^2)-Meleagris_gallopavo 0.3141 3.464928e+08 1.2980 1181
## I(week^2)-Sciurus_carolinensis 0.2450 6.455204e+08 1.3190 2665
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$beta.samples)
## 'mcmc' num [1:3000, 1:54] -0.4666 -0.0443 -0.8641 0.229 2.1879 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:54] "(Intercept)-Canis_latrans" "(Intercept)-Lynx_rufus" "(Intercept)-Didelphis_virginiana" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1953333
MCMCplot(ms_fullQ_fullQ_T10$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
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/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/Kody Melissa/Downloads/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/Kody Melissa/Downloads/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "CameraLocations")
# First day of the study
study_start <- as.Date("2024-05-10")
# Effort Matrix
effort_matrix <- read_excel("C:/Users/Kody Melissa/Downloads/CameraLoc.xlsx",
sheet = "Daily_EM")
col_names <- names(effort_matrix)
date_cols <- col_names[-1]
converted_dates <- as.character(as.Date(as.numeric(date_cols), origin = "1899-12-30"))
names(effort_matrix) <- c("Plot", converted_dates)
# Pivot, filter, and add custom study week
effort_matrix <- effort_matrix %>%
pivot_longer(
cols = -Plot,
names_to = "date",
values_to = "effort"
) %>%
filter(effort == 1) %>%
mutate(
date = as.Date(date),
study_week = as.integer(floor((date - study_start) / 7)) + 1 # Week 1 starts on study_start
) %>%
dplyr::select(Plot, date, study_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 daily observation data
observations_daily <- CameraData %>%
mutate(Date = as.Date(Date)) %>%
group_by(Plot, date = Date, Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge observations with daily effort
observations_daily <- effort_matrix %>%
left_join(observations_daily, by = c("Plot", "date")) %>%
replace_na(list(observations = 0))
observations_species <- observations_daily %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-`NA`) # Remove empty species name if present
# Create detection array (site × date × species)
site_names <- sort(unique(observations_species$Plot))
date_names <- sort(unique(observations_species$date))
species_names <- setdiff(colnames(observations_species), c("Plot", "date", "study_week"))
num_sites <- length(site_names)
num_days <- length(date_names)
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_days, num_species))
dimnames(detection_array) <- list(site_names, as.character(date_names), 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(date_names)) {
day <- date_names[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site &
observations_species$date == day &
observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be [num_sites x num_days x num_species]
## [1] 32 258 2
# Backup camera location data
CameraLoc_O2 <- CameraLoc
# Standardize site-level covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, -Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names # site_names should match the detection array
# Scale site covariates
covariates_matrix <- scale(covariates_matrix)
## Detection-level Covariates ----
effort_expanded <- expand.grid(
Plot = site_names,
date = date_names
) %>%
left_join(effort_matrix[, c("Plot", "date", "study_week")], by = c("Plot", "date"))
week_matrix <- matrix(effort_expanded$study_week,
nrow = num_sites,
ncol = num_days,
byrow = FALSE)
# Convert to numeric and scale
week_numeric <- scale(week_matrix)
dim(week_numeric)
## [1] 32 258
# Detection covariates list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_numeric
)
det.covs$week[is.na(det.covs$week)] <- 0
# Check structure
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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258, 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## .. ..$ : 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
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, 1:258] 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 [1:258] "2024-05-10" "2024-05-11" "2024-05-12" "2024-05-13" ...
## $ 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, 1:258] 0 0 0 0 0 0 0 0 0 0 ...
## .. ..- attr(*, "scaled:center")= num [1:258] 1 1 1 1 1 1 1 2 2 2 ...
## .. ..- attr(*, "scaled:scale")= num [1:258] 0 0 0 0 0 0 0 0 0 0 ...
# 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): 1.076
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0106 0.779 -1.6586 0.0021 1.5133 1.0016 2325
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.5963 95.7857 0.0649 0.6772 28.591 1.0162 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6356 1.9218 -5.115 -2.9894 1.4188 1.0027 855
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.2926 870.6063 0.0573 4.3964 253.3972 1.3192 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3981 0.4171 -0.3616 0.3708 1.2930 1.0014
## (Intercept)-Sylvilagus_floridanus -0.3696 0.4672 -1.2442 -0.3847 0.5786 1.0037
## ESS
## (Intercept)-Canis_latrans 1358
## (Intercept)-Sylvilagus_floridanus 680
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6613 0.1689 -5.0013 -4.6504 -4.3479 1.0047
## (Intercept)-Sylvilagus_floridanus -4.9027 0.2412 -5.3993 -4.8914 -4.4614 1.0903
## ESS
## (Intercept)-Canis_latrans 208
## (Intercept)-Sylvilagus_floridanus 163
# 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): 1.7105
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4303 1.4643 -2.4665 0.4107 3.2848 1.0091 612
## Cogon_Patch_Size 0.1109 1.4019 -2.6723 0.0843 2.9170 1.0128 869
## Veg_shannon_index 1.1189 1.3681 -1.8458 1.2138 3.6153 1.0087 410
## total_shrub_cover 0.1561 1.4681 -2.9270 0.1944 2.9513 1.0341 696
## Avg_Cogongrass_Cover 0.6910 1.4753 -2.3742 0.7758 3.4498 1.0079 828
## Tree_Density -1.0670 1.7086 -4.2182 -1.1579 2.4745 1.0247 368
## Avg_Canopy_Cover 0.4122 1.5385 -2.7810 0.4661 3.2887 1.0243 2514
## avg_veg_height -0.2047 1.2326 -2.6361 -0.2335 2.3101 1.0700 671
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 173.4961 1749.5278 0.0743 3.8046 982.1700 1.7106 180
## Cogon_Patch_Size 81.6935 392.8731 0.0866 4.8670 587.1968 1.2949 575
## Veg_shannon_index 33.9796 533.1896 0.0572 1.1974 139.4862 1.3278 3000
## total_shrub_cover 907.7528 24076.2614 0.1329 13.4708 2941.8414 1.2786 3000
## Avg_Cogongrass_Cover 507.9888 18864.8621 0.0845 5.1004 714.6479 1.3364 3000
## Tree_Density 1976.5975 13720.7074 0.0915 27.8069 15601.1990 1.0863 253
## Avg_Canopy_Cover 2383.7356 22216.5687 0.6307 80.2854 13343.2579 1.5360 111
## avg_veg_height 10.5536 95.2047 0.0609 0.9846 62.3794 1.3077 1181
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 12.5786 48.5341 0.0692 1.5565 107.7089 1.0285 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5390 2.0440 -5.2348 -2.7793 1.7948 1.0082 798
## shrub_cover 0.0952 0.8094 -1.6584 0.0936 1.7679 1.0323 1647
## veg_height -0.2075 0.7560 -1.7162 -0.2385 1.4701 1.0218 2766
## week 0.0097 1.6615 -3.1908 0.0251 3.2569 1.0045 1266
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.559890e+01 1.122027e+03 0.0650 5.4451 2.557974e+02 1.1300 2678
## shrub_cover 6.024400e+00 5.545320e+01 0.0831 0.9277 3.059680e+01 1.1519 3000
## veg_height 7.262700e+00 1.799786e+02 0.0666 0.6209 2.091240e+01 1.3114 3000
## week 4.283866e+27 8.613839e+28 0.1091 143.3188 3.887476e+15 1.5175 473
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 3.6765 6.0905 -1.1820 1.7814
## (Intercept)-Sylvilagus_floridanus -1.4029 4.1821 -13.7303 -0.5618
## Cogon_Patch_Size-Canis_latrans 2.5355 4.0152 -1.6408 1.4210
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2497 4.4917 -14.9288 -1.2657
## Veg_shannon_index-Canis_latrans 1.8732 2.2244 -2.1233 1.7133
## Veg_shannon_index-Sylvilagus_floridanus 1.9790 2.5114 -1.9027 1.7046
## total_shrub_cover-Canis_latrans 4.3665 6.3015 -0.5060 2.3741
## total_shrub_cover-Sylvilagus_floridanus -5.5006 10.2402 -36.5477 -2.2651
## Avg_Cogongrass_Cover-Canis_latrans 3.7600 3.9844 -0.6458 2.6691
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5700 4.4273 -12.1828 0.1557
## Tree_Density-Canis_latrans -10.1058 13.8394 -51.9386 -4.9701
## Tree_Density-Sylvilagus_floridanus -14.7778 26.4916 -108.3188 -4.3038
## Avg_Canopy_Cover-Canis_latrans -1.2085 3.1364 -11.3683 -0.4639
## Avg_Canopy_Cover-Sylvilagus_floridanus 19.7922 29.1667 1.0816 10.5208
## avg_veg_height-Canis_latrans -0.4569 1.7920 -4.4548 -0.3048
## avg_veg_height-Sylvilagus_floridanus -0.3870 1.9319 -4.2800 -0.3251
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 24.9492 3.0636 15
## (Intercept)-Sylvilagus_floridanus 4.1211 1.6454 44
## Cogon_Patch_Size-Canis_latrans 13.9157 1.3020 134
## Cogon_Patch_Size-Sylvilagus_floridanus 3.9036 1.0855 126
## Veg_shannon_index-Canis_latrans 7.0856 1.2026 142
## Veg_shannon_index-Sylvilagus_floridanus 8.3052 1.3192 164
## total_shrub_cover-Canis_latrans 25.6290 3.5448 19
## total_shrub_cover-Sylvilagus_floridanus 6.5554 2.1273 19
## Avg_Cogongrass_Cover-Canis_latrans 15.3085 1.1996 149
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.5892 1.0814 152
## Tree_Density-Canis_latrans -0.7240 2.8258 18
## Tree_Density-Sylvilagus_floridanus 1.2847 1.8374 6
## Avg_Canopy_Cover-Canis_latrans 1.3779 2.7773 21
## Avg_Canopy_Cover-Sylvilagus_floridanus 109.8422 2.0744 14
## avg_veg_height-Canis_latrans 2.4509 1.1650 230
## avg_veg_height-Sylvilagus_floridanus 3.1029 1.1248 341
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.819200e+00 1.744000e-01 -5.1531
## (Intercept)-Sylvilagus_floridanus -5.077100e+00 2.390000e-01 -5.5791
## shrub_cover-Canis_latrans -3.963000e-01 1.931000e-01 -0.7517
## shrub_cover-Sylvilagus_floridanus 6.786000e-01 4.740000e-01 -0.2601
## veg_height-Canis_latrans -6.291000e-01 1.618000e-01 -0.9400
## veg_height-Sylvilagus_floridanus 1.365000e-01 2.518000e-01 -0.3642
## week-Canis_latrans 2.812692e+11 5.369462e+13 -164503.7214
## week-Sylvilagus_floridanus -1.761921e+11 6.232799e+13 -107030.8596
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8189 -4.4994 1.0431 156
## (Intercept)-Sylvilagus_floridanus -5.0603 -4.6651 1.0422 95
## shrub_cover-Canis_latrans -0.3947 -0.0120 1.0080 174
## shrub_cover-Sylvilagus_floridanus 0.7142 1.5445 1.5394 58
## veg_height-Canis_latrans -0.6358 -0.3135 1.0050 215
## veg_height-Sylvilagus_floridanus 0.1339 0.6473 1.2154 169
## week-Canis_latrans -0.0244 86171.7956 1.2931 32354
## week-Sylvilagus_floridanus 0.1236 252591.3725 1.2911 35435
#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): 1.5153
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0506 0.758 -1.4946 0.0514 1.6048 1.0044 2495
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.909 23.1533 0.0576 0.6524 24.4282 1.1084 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5507 2.0421 -5.1533 -2.9173 1.7654 1.0113 777
## shrub_cover 0.0003 0.7429 -1.5230 -0.0072 1.5514 1.0151 1807
## veg_height -0.1856 0.7253 -1.6419 -0.2113 1.3852 1.0001 1962
## week 0.1009 1.6211 -3.1303 0.1397 3.1449 1.0014 1436
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.650400e+01 3.145630e+02 0.0602 4.8903 2.570302e+02 1.1278 3000
## shrub_cover 5.049800e+00 5.454990e+01 0.0525 0.5979 2.222760e+01 1.1007 3000
## veg_height 4.123100e+00 3.202550e+01 0.0718 0.6411 2.277750e+01 1.0716 2122
## week 5.313048e+18 1.444596e+20 0.0802 51.4545 1.945652e+15 1.4190 510
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.4452 0.4189 -0.3036 0.4210 1.3298 1.0081
## (Intercept)-Sylvilagus_floridanus -0.3128 0.4390 -1.1757 -0.3069 0.5629 1.0058
## ESS
## (Intercept)-Canis_latrans 1896
## (Intercept)-Sylvilagus_floridanus 926
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.759200e+00 1.755000e-01 -5.1145
## (Intercept)-Sylvilagus_floridanus -4.932900e+00 2.391000e-01 -5.4110
## shrub_cover-Canis_latrans -3.338000e-01 2.064000e-01 -0.7122
## shrub_cover-Sylvilagus_floridanus 3.744000e-01 4.556000e-01 -0.4476
## veg_height-Canis_latrans -6.117000e-01 1.678000e-01 -0.9534
## veg_height-Sylvilagus_floridanus 1.786000e-01 2.727000e-01 -0.3457
## week-Canis_latrans 2.604642e+07 1.943086e+09 -188376.6932
## week-Sylvilagus_floridanus -3.873067e+07 1.372963e+09 -296530.8667
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7527 -4.4405 1.0463 192
## (Intercept)-Sylvilagus_floridanus -4.9249 -4.4794 1.0438 145
## shrub_cover-Canis_latrans -0.3441 0.0917 1.0505 178
## shrub_cover-Sylvilagus_floridanus 0.3707 1.3206 1.0901 102
## veg_height-Canis_latrans -0.6018 -0.3045 1.0027 227
## veg_height-Sylvilagus_floridanus 0.1704 0.7246 1.0839 127
## week-Canis_latrans 0.1998 159610.8992 1.3082 5706
## week-Sylvilagus_floridanus 0.2793 100159.3403 1.3675 981
#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): 1.876
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8935 1.1316 -1.3984 0.9167 3.0572 1.0256 416
## Avg_Cogongrass_Cover -0.1544 1.1772 -2.4971 -0.1675 2.3095 1.0029 1593
## total_shrub_cover -0.1333 1.3997 -2.9568 -0.1139 2.6370 1.1165 248
## avg_veg_height 0.4289 1.0548 -1.7461 0.4424 2.5239 1.0318 214
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.5377 327.0791 0.0583 0.9505 110.8181 1.5504 611
## Avg_Cogongrass_Cover 16.6355 111.0279 0.0944 2.2975 100.8649 1.1649 2610
## total_shrub_cover 215.6970 8444.5532 0.1350 9.5455 371.2674 1.3217 3000
## avg_veg_height 5.3729 31.4932 0.0513 0.5990 37.6868 1.1120 832
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.4917 13.0458 0.0621 1.4731 26.2536 1.1757 454
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5376 2.0526 -5.3490 -2.7777 1.8211 1.0074 815
## shrub_cover 0.1607 0.9185 -1.7790 0.1564 2.1032 1.0161 483
## veg_height -0.2786 0.6996 -1.7241 -0.2948 1.2017 1.0043 1785
## week -0.0418 1.6444 -3.3353 -0.0251 3.0960 1.0028 1390
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.26190e+01 9.824786e+02 0.0695 6.4164 2.509203e+02 1.1423 3000
## shrub_cover 6.69120e+00 3.857600e+01 0.0919 1.3788 3.816200e+01 1.0694 3000
## veg_height 3.10700e+00 1.510730e+01 0.0631 0.5595 2.109780e+01 1.0394 3000
## week 4.38059e+19 1.467674e+21 0.1146 193.6081 6.141007e+14 1.3762 1082
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.8348 2.6495 -0.4568 1.3195
## (Intercept)-Sylvilagus_floridanus 1.2192 1.4475 -1.2100 1.0819
## Avg_Cogongrass_Cover-Canis_latrans 0.7576 1.3261 -1.3239 0.6061
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4445 1.4470 -5.0089 -1.2137
## total_shrub_cover-Canis_latrans 1.7283 1.7292 -1.0396 1.5343
## total_shrub_cover-Sylvilagus_floridanus -3.4512 3.1936 -11.2055 -3.0835
## avg_veg_height-Canis_latrans 0.5012 1.0715 -1.3232 0.4402
## avg_veg_height-Sylvilagus_floridanus 0.6849 1.1676 -1.4477 0.6048
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 8.5041 1.8026 35
## (Intercept)-Sylvilagus_floridanus 4.7369 1.2043 96
## Avg_Cogongrass_Cover-Canis_latrans 3.9057 1.0392 309
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7167 1.1942 184
## total_shrub_cover-Canis_latrans 5.1772 1.2459 210
## total_shrub_cover-Sylvilagus_floridanus 2.1167 1.5919 44
## avg_veg_height-Canis_latrans 2.6941 1.0474 425
## avg_veg_height-Sylvilagus_floridanus 3.1492 1.1516 68
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8832 2.267000e-01 -5.3798
## (Intercept)-Sylvilagus_floridanus -5.1856 2.767000e-01 -5.7411
## shrub_cover-Canis_latrans -0.4377 2.268000e-01 -0.8366
## shrub_cover-Sylvilagus_floridanus 0.9829 6.306000e-01 -0.5128
## veg_height-Canis_latrans -0.6551 1.921000e-01 -1.0632
## veg_height-Sylvilagus_floridanus 0.0250 2.933000e-01 -0.5065
## week-Canis_latrans 35399194.3780 6.680523e+09 -2109638.4487
## week-Sylvilagus_floridanus 35684790.6996 5.556970e+09 -2077151.9643
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8600 -4.4870 1.0500 84
## (Intercept)-Sylvilagus_floridanus -5.1765 -4.6516 1.0623 78
## shrub_cover-Canis_latrans -0.4584 0.0491 1.2547 98
## shrub_cover-Sylvilagus_floridanus 1.0769 1.9942 1.3593 31
## veg_height-Canis_latrans -0.6448 -0.3020 1.0297 159
## veg_height-Sylvilagus_floridanus 0.0017 0.6691 1.1752 69
## week-Canis_latrans -0.0992 2581505.0099 1.2927 33626
## week-Sylvilagus_floridanus -0.0330 2279571.2523 1.2949 19951
#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): 1.5867
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0566 0.9591 -1.7990 0.0373 2.0983 1.0168 1088
## Tree_Density -1.0416 1.0776 -3.0384 -1.0914 1.3452 1.0048 905
## Avg_Canopy_Cover 0.5434 1.3557 -2.3795 0.6026 3.0229 1.0028 2479
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.8044 40.1779 0.0525 0.8188 36.3254 1.0451 1804
## Tree_Density 10.4140 62.3689 0.0533 0.8423 70.6624 1.0192 2085
## Avg_Canopy_Cover 130.2263 990.7427 0.6064 11.2005 810.3696 1.1153 1020
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 8.4509 62.814 0.0479 0.5101 44.4424 2.1183 78
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7559 1.9925 -5.2078 -3.2268 1.6807 1.0010 863
## shrub_cover 0.1625 0.7825 -1.5092 0.1620 1.7853 1.0044 2141
## veg_height -0.1839 0.7083 -1.5292 -0.2178 1.3699 1.0036 2214
## week 0.0525 1.6247 -3.0491 0.0169 3.1899 1.0003 1333
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.800240e+01 2.114851e+02 0.0574 3.9402 2.643883e+02 1.0507 3000
## shrub_cover 4.247300e+00 2.172630e+01 0.0927 0.8629 2.460470e+01 1.1446 1792
## veg_height 3.209900e+00 2.080740e+01 0.0667 0.5839 1.877370e+01 1.1634 3000
## week 2.552905e+09 6.952780e+10 0.0816 19.1621 8.688891e+08 1.4106 1000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4937 0.8188 -0.8868 0.4099 2.4707
## (Intercept)-Sylvilagus_floridanus -0.3666 1.0796 -2.4828 -0.3536 1.6996
## Tree_Density-Canis_latrans -1.3667 0.9052 -3.3892 -1.2568 0.0328
## Tree_Density-Sylvilagus_floridanus -1.6474 1.4685 -4.8730 -1.4639 0.5535
## Avg_Canopy_Cover-Canis_latrans -0.6815 1.1987 -3.2525 -0.4996 0.4729
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.0998 5.1146 1.0469 3.9837 16.5000
## Rhat ESS
## (Intercept)-Canis_latrans 1.0295 540
## (Intercept)-Sylvilagus_floridanus 1.0491 501
## Tree_Density-Canis_latrans 1.0173 662
## Tree_Density-Sylvilagus_floridanus 1.0439 272
## Avg_Canopy_Cover-Canis_latrans 1.7137 56
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8065 44
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7986 0.1742 -5.1655 -4.7896
## (Intercept)-Sylvilagus_floridanus -4.9606 0.2361 -5.4672 -4.9522
## shrub_cover-Canis_latrans -0.3248 0.2172 -0.7726 -0.3165
## shrub_cover-Sylvilagus_floridanus 0.7207 0.3952 -0.0248 0.7064
## veg_height-Canis_latrans -0.6187 0.1742 -0.9721 -0.6128
## veg_height-Sylvilagus_floridanus 0.1457 0.2596 -0.3574 0.1468
## week-Canis_latrans 51.9682 34128.6072 -3143.2102 -0.0301
## week-Sylvilagus_floridanus 280.0521 39540.5149 -3967.2614 -0.0157
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4781 1.0210 132
## (Intercept)-Sylvilagus_floridanus -4.5371 1.0237 190
## shrub_cover-Canis_latrans 0.0717 1.0106 156
## shrub_cover-Sylvilagus_floridanus 1.6226 1.1352 127
## veg_height-Canis_latrans -0.2808 1.0439 191
## veg_height-Sylvilagus_floridanus 0.6595 1.0029 180
## week-Canis_latrans 3252.6590 1.2889 5703
## week-Sylvilagus_floridanus 5054.1954 1.2889 8760
#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): 1.7942
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7961 1.2398 -1.8303 0.7909 3.2690 1.0076 560
## Cogon_Patch_Size 0.0842 1.3177 -2.7201 0.0993 2.7176 1.0004 1781
## Avg_Cogongrass_Cover 0.0431 1.0346 -2.0987 0.0451 2.1387 1.0013 1022
## total_shrub_cover -0.1528 1.3582 -2.8806 -0.1560 2.5313 1.0190 958
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.2641 513.9335 0.0614 1.1829 82.3458 1.2975 3000
## Cogon_Patch_Size 116.0180 1856.7127 0.1161 7.6955 447.8339 1.2059 3000
## Avg_Cogongrass_Cover 15.1514 267.1279 0.0571 0.9527 57.2606 1.1917 3000
## total_shrub_cover 130.5669 3225.0739 0.1454 8.9419 378.1722 1.2735 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.7672 9.7605 0.0789 1.5606 27.6798 1.0383 194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3732 2.0801 -5.2600 -2.5394 1.9697 1.0008 760
## shrub_cover 0.2238 0.9009 -1.6904 0.2065 2.0523 1.0088 887
## veg_height -0.2361 0.7208 -1.6855 -0.2611 1.4319 1.0103 1342
## week -0.0003 1.6578 -3.2284 0.0290 3.1967 1.0065 1416
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.641260e+01 6.602243e+02 0.0776 7.6246 3.56456e+02 1.2347 3000
## shrub_cover 8.111400e+00 7.881340e+01 0.1276 1.3136 3.72878e+01 1.1577 2208
## veg_height 3.164100e+00 2.021840e+01 0.0588 0.5424 1.99067e+01 1.1489 3000
## week 1.289882e+21 3.997897e+22 0.0945 104.4258 3.29191e+12 1.3904 1137
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.8060 1.7528 -0.6197 1.4492
## (Intercept)-Sylvilagus_floridanus 0.7106 1.6093 -2.3671 0.6748
## Cogon_Patch_Size-Canis_latrans 2.7201 3.0595 -0.4523 1.8063
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6204 3.2191 -11.5133 -1.7136
## Avg_Cogongrass_Cover-Canis_latrans 0.5213 0.9133 -0.8745 0.4122
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4513 1.4529 -3.4844 -0.3627
## total_shrub_cover-Canis_latrans 1.6645 1.5029 -0.3830 1.4088
## total_shrub_cover-Sylvilagus_floridanus -3.3254 3.0536 -10.9610 -2.7151
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.3788 1.1455 166
## (Intercept)-Sylvilagus_floridanus 3.9140 1.0259 220
## Cogon_Patch_Size-Canis_latrans 11.2567 1.0960 83
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9753 1.0596 104
## Avg_Cogongrass_Cover-Canis_latrans 2.5683 1.0369 567
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0352 1.0497 324
## total_shrub_cover-Canis_latrans 5.3602 1.0276 195
## total_shrub_cover-Sylvilagus_floridanus 1.4068 1.0974 82
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.800200e+00 1.984000e-01 -5.2011
## (Intercept)-Sylvilagus_floridanus -5.188500e+00 2.471000e-01 -5.6515
## shrub_cover-Canis_latrans -4.356000e-01 2.171000e-01 -0.8451
## shrub_cover-Sylvilagus_floridanus 9.798000e-01 5.041000e-01 -0.3090
## veg_height-Canis_latrans -6.077000e-01 1.884000e-01 -0.9978
## veg_height-Sylvilagus_floridanus 4.520000e-02 2.712000e-01 -0.4520
## week-Canis_latrans 6.645814e+08 2.981907e+10 -26205.6639
## week-Sylvilagus_floridanus -6.059872e+08 2.379605e+10 -41682.8470
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8034 -4.4301 1.0255 144
## (Intercept)-Sylvilagus_floridanus -5.1918 -4.7176 1.0792 111
## shrub_cover-Canis_latrans -0.4391 -0.0040 1.0335 138
## shrub_cover-Sylvilagus_floridanus 1.0509 1.7738 1.2817 50
## veg_height-Canis_latrans -0.6056 -0.2677 1.0366 169
## veg_height-Sylvilagus_floridanus 0.0310 0.6151 1.0105 98
## week-Canis_latrans -0.0003 51531.9981 1.3390 1866
## week-Sylvilagus_floridanus -0.1793 26355.4180 1.3535 1570
#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): 1.5388
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1229 0.9078 -1.7099 0.1042 1.9821 1.0055 1320
## Veg_shannon_index 0.7487 0.8872 -1.2966 0.7885 2.3736 1.0095 1263
## Avg_Cogongrass_Cover 0.2308 1.0430 -1.8307 0.2474 2.3167 1.0028 2090
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.0232 45.2357 0.0642 0.8919 40.5196 1.1198 2361
## Veg_shannon_index 4.4384 26.3208 0.0517 0.6311 34.1258 1.0574 2771
## Avg_Cogongrass_Cover 24.1155 273.5419 0.0889 1.9724 79.5295 1.0834 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5671 3.1224 0.0595 0.6287 8.8448 1.0142 593
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5582 2.0547 -5.1885 -2.8636 1.7759 1.0041 929
## shrub_cover 0.0287 0.7202 -1.4570 0.0176 1.5144 1.0101 1195
## veg_height -0.1864 0.7612 -1.7654 -0.2124 1.5075 1.0028 3000
## week -0.0328 1.6998 -3.4199 -0.0621 3.3757 1.0043 1398
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.462080e+01 6.812790e+02 0.0617 5.2517 3.000125e+02 1.1464 3000
## shrub_cover 3.509900e+00 2.041030e+01 0.0532 0.5391 1.921100e+01 1.0084 3000
## veg_height 5.484500e+00 7.695110e+01 0.0723 0.7055 2.447740e+01 1.2741 3000
## week 6.012047e+22 1.109695e+24 0.1246 4136.3281 1.177669e+22 1.5563 534
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6697 0.8238 -0.8099 0.6239
## (Intercept)-Sylvilagus_floridanus -0.3474 0.8007 -1.8849 -0.3611
## Veg_shannon_index-Canis_latrans 1.2428 0.6736 0.1123 1.1784
## Veg_shannon_index-Sylvilagus_floridanus 0.6588 0.7535 -0.5035 0.6162
## Avg_Cogongrass_Cover-Canis_latrans 1.3258 0.9719 -0.0541 1.1542
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4844 0.7110 -1.8954 -0.4626
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4686 1.0805 470
## (Intercept)-Sylvilagus_floridanus 1.3524 1.0092 377
## Veg_shannon_index-Canis_latrans 2.7441 1.0279 800
## Veg_shannon_index-Sylvilagus_floridanus 1.9862 1.1228 360
## Avg_Cogongrass_Cover-Canis_latrans 3.6579 1.0527 503
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8232 1.0082 600
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.787700e+00 1.865000e-01 -5.174200e+00
## (Intercept)-Sylvilagus_floridanus -4.958800e+00 2.475000e-01 -5.455800e+00
## shrub_cover-Canis_latrans -2.819000e-01 2.002000e-01 -6.771000e-01
## shrub_cover-Sylvilagus_floridanus 3.728000e-01 4.533000e-01 -4.462000e-01
## veg_height-Canis_latrans -6.571000e-01 1.855000e-01 -1.048300e+00
## veg_height-Sylvilagus_floridanus 1.867000e-01 2.588000e-01 -3.253000e-01
## week-Canis_latrans 7.972884e+07 1.712005e+11 -3.702855e+09
## week-Sylvilagus_floridanus -1.732945e+09 1.569333e+11 -1.062751e+09
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7740 -4.432600e+00 1.2028 165
## (Intercept)-Sylvilagus_floridanus -4.9521 -4.481400e+00 1.0255 173
## shrub_cover-Canis_latrans -0.2804 1.155000e-01 1.0080 176
## shrub_cover-Sylvilagus_floridanus 0.3607 1.282600e+00 1.0284 97
## veg_height-Canis_latrans -0.6437 -3.200000e-01 1.3132 171
## veg_height-Sylvilagus_floridanus 0.1913 6.500000e-01 1.0618 157
## week-Canis_latrans -0.2312 3.207584e+09 1.2904 3133
## week-Sylvilagus_floridanus 0.0984 4.883980e+09 1.3025 14753
#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): 1.5683
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5233 1.0286 -2.4492 -0.5806 1.7731 1.0029 1310
## Avg_Cogongrass_Cover -0.4189 1.1289 -2.6260 -0.4675 1.9416 1.0024 2030
## I(Avg_Cogongrass_Cover^2) 0.9772 1.0870 -1.3242 0.9935 3.0487 1.0115 1261
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.2232 38.1031 0.0589 0.9117 49.5706 1.0123 2420
## Avg_Cogongrass_Cover 25.9325 471.0819 0.0882 2.4473 101.9272 1.2995 3000
## I(Avg_Cogongrass_Cover^2) 12.3272 86.2301 0.0611 1.1864 77.8748 1.1363 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1319 5.0141 0.0539 0.7129 13.7327 1.06 300
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6006 2.0361 -5.2257 -2.9369 1.7899 1.0046 849
## shrub_cover 0.0093 0.7160 -1.5560 -0.0051 1.5503 1.0036 1106
## veg_height -0.1647 0.7731 -1.6455 -0.2059 1.6812 1.0148 2414
## week -0.0302 1.6778 -3.3347 -0.0679 3.1535 1.0080 1364
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.591900e+01 3.416050e+02 0.0616 4.6449 4.229991e+02 1.0196 3000
## shrub_cover 4.055200e+00 3.016530e+01 0.0547 0.5411 1.752750e+01 1.0461 3000
## veg_height 3.944600e+00 2.070900e+01 0.0731 0.6982 2.229500e+01 1.0727 3000
## week 2.210678e+14 7.097218e+15 0.1183 360.3400 8.157095e+12 1.2766 1846
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2351 0.9789 -1.9662 -0.3146
## (Intercept)-Sylvilagus_floridanus -1.1730 1.0313 -3.4550 -1.1273
## Avg_Cogongrass_Cover-Canis_latrans 0.3796 1.1095 -1.3095 0.2411
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.7976 1.2097 -4.7285 -1.6292
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0716 1.4318 -0.0020 1.8090
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0065 0.7919 -0.2842 0.9076
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8868 1.0103 580
## (Intercept)-Sylvilagus_floridanus 0.7606 1.0080 549
## Avg_Cogongrass_Cover-Canis_latrans 2.8843 1.0101 393
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0989 1.0499 263
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6133 1.0304 282
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7931 1.0017 321
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7855 1.745000e-01 -5.1522 -4.7819
## (Intercept)-Sylvilagus_floridanus -4.9570 2.545000e-01 -5.4778 -4.9556
## shrub_cover-Canis_latrans -0.2944 1.935000e-01 -0.6750 -0.2954
## shrub_cover-Sylvilagus_floridanus 0.3698 4.582000e-01 -0.3876 0.3287
## veg_height-Canis_latrans -0.6275 1.916000e-01 -1.0216 -0.6218
## veg_height-Sylvilagus_floridanus 0.2159 2.663000e-01 -0.3029 0.2072
## week-Canis_latrans 32780.3349 1.201983e+07 -723541.9372 0.1407
## week-Sylvilagus_floridanus -29376.1889 7.431274e+06 -343597.0772 -0.0906
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4550 1.0260 162
## (Intercept)-Sylvilagus_floridanus -4.4792 1.0505 151
## shrub_cover-Canis_latrans 0.0720 1.0690 163
## shrub_cover-Sylvilagus_floridanus 1.4014 1.0081 80
## veg_height-Canis_latrans -0.2753 1.0049 117
## veg_height-Sylvilagus_floridanus 0.7183 1.1223 140
## week-Canis_latrans 181133.9847 1.1196 6160
## week-Sylvilagus_floridanus 542560.0677 1.1911 15220
## 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): 1.6362
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3397 1.4657 -3.1484 -0.3384 2.5347 1.0055 545
## Cogon_Patch_Size 0.2071 1.5124 -2.8952 0.2465 3.0236 1.0024 1754
## Veg_shannon_index 1.4186 1.3693 -1.4917 1.4649 4.0070 1.0433 841
## total_shrub_cover 0.1067 1.4965 -2.8455 0.1124 3.0777 1.0347 455
## Avg_Cogongrass_Cover -0.0436 1.5012 -2.9454 -0.0565 2.8632 1.0008 859
## Tree_Density -1.0423 1.7260 -4.1396 -1.1572 2.5821 1.0698 321
## Avg_Canopy_Cover 0.3824 1.6396 -2.8532 0.4423 3.5072 1.0039 3000
## I(Avg_Cogongrass_Cover^2) 0.9421 1.4049 -1.9787 1.0016 3.5940 1.0181 961
## avg_veg_height -0.2345 1.2161 -2.5765 -0.2383 2.2251 1.0181 778
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 60.6174 642.6488 0.0654 2.6879 380.8729 1.5660
## Cogon_Patch_Size 262.9879 1346.2288 0.1549 24.5974 1877.3463 1.1817
## Veg_shannon_index 34.2996 238.3965 0.0692 1.7668 234.6307 1.4921
## total_shrub_cover 377.0906 7002.2079 0.1211 14.1477 1414.8322 1.4271
## Avg_Cogongrass_Cover 37.6463 260.6851 0.0661 2.3442 265.0210 1.1587
## Tree_Density 1121.9017 15046.0076 0.1106 32.4717 4571.5413 1.1253
## Avg_Canopy_Cover 923.4260 10631.8784 2.6717 109.5074 4736.5438 1.3004
## I(Avg_Cogongrass_Cover^2) 203.4108 3473.0568 0.0862 3.8481 624.4926 1.1154
## avg_veg_height 52.6511 1462.7093 0.0559 0.8709 85.0631 1.3848
## ESS
## (Intercept) 1115
## Cogon_Patch_Size 223
## Veg_shannon_index 341
## total_shrub_cover 1743
## Avg_Cogongrass_Cover 1854
## Tree_Density 3000
## Avg_Canopy_Cover 2337
## I(Avg_Cogongrass_Cover^2) 3000
## avg_veg_height 1307
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.953 29.4527 0.0642 2.151 92.8412 1.1416 125
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5258 1.9709 -5.2062 -2.7859 1.5721 1.0165 920
## shrub_cover 0.1531 0.8405 -1.6024 0.1424 1.8390 1.0203 1456
## veg_height -0.1948 0.7051 -1.6845 -0.2132 1.3042 1.0084 1504
## week -0.0306 1.6587 -3.2510 -0.0099 3.1502 1.0009 1357
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.196930e+01 2.173124e+03 0.0699 5.4786 3.067403e+02 1.3149 3000
## shrub_cover 1.047350e+01 2.693221e+02 0.0886 0.9747 3.006000e+01 1.3475 3000
## veg_height 3.141400e+00 2.172940e+01 0.0648 0.5640 1.646840e+01 1.0828 3000
## week 3.732888e+13 9.291858e+14 0.0958 142.2279 1.165816e+12 1.4397 683
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans 0.1389 2.8447 -5.2075
## (Intercept)-Sylvilagus_floridanus -2.3510 4.2967 -12.6252
## Cogon_Patch_Size-Canis_latrans 5.9905 7.0488 -0.4379
## Cogon_Patch_Size-Sylvilagus_floridanus -4.4861 6.7334 -19.5855
## Veg_shannon_index-Canis_latrans 2.9141 2.5840 -0.2910
## Veg_shannon_index-Sylvilagus_floridanus 2.4022 1.9737 -0.7501
## total_shrub_cover-Canis_latrans 3.0057 2.9072 -0.9798
## total_shrub_cover-Sylvilagus_floridanus -5.6706 9.1610 -34.3214
## Avg_Cogongrass_Cover-Canis_latrans 0.8127 3.0031 -3.9096
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2754 3.3427 -10.1187
## Tree_Density-Canis_latrans -9.2098 10.9718 -44.1234
## Tree_Density-Sylvilagus_floridanus -6.9763 7.9752 -28.7426
## Avg_Canopy_Cover-Canis_latrans -0.9893 1.7701 -6.2268
## Avg_Canopy_Cover-Sylvilagus_floridanus 14.8582 9.0659 2.8656
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.2611 4.5981 0.0600
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0192 2.3161 -3.3539
## avg_veg_height-Canis_latrans -0.6446 1.9156 -6.8598
## avg_veg_height-Sylvilagus_floridanus -0.4138 2.2940 -4.5225
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.0028 6.3789 1.1227 208
## (Intercept)-Sylvilagus_floridanus -1.5720 2.9211 1.5405 77
## Cogon_Patch_Size-Canis_latrans 4.0592 27.0955 1.5781 48
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8155 2.4322 1.0971 76
## Veg_shannon_index-Canis_latrans 2.3787 10.1686 1.4210 51
## Veg_shannon_index-Sylvilagus_floridanus 2.1893 6.8868 1.0383 207
## total_shrub_cover-Canis_latrans 2.4352 11.0839 1.3241 52
## total_shrub_cover-Sylvilagus_floridanus -2.8094 2.8192 1.6127 22
## Avg_Cogongrass_Cover-Canis_latrans 0.4457 8.1072 1.1245 313
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7312 3.6155 1.0810 264
## Tree_Density-Canis_latrans -5.5219 -0.7845 2.8518 11
## Tree_Density-Sylvilagus_floridanus -4.1005 2.0033 1.8809 33
## Avg_Canopy_Cover-Canis_latrans -0.6794 1.3468 1.4273 88
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.9965 36.3306 1.3100 46
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.8151 19.1068 1.9505 77
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9455 6.5709 1.1918 190
## avg_veg_height-Canis_latrans -0.3894 2.0795 1.5738 60
## avg_veg_height-Sylvilagus_floridanus -0.3143 3.2031 1.2176 147
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7853 0.1738 -5.1529 -4.7773
## (Intercept)-Sylvilagus_floridanus -5.0193 0.2340 -5.5045 -5.0214
## shrub_cover-Canis_latrans -0.3804 0.2024 -0.7855 -0.3808
## shrub_cover-Sylvilagus_floridanus 0.7624 0.4562 -0.1393 0.7598
## veg_height-Canis_latrans -0.5906 0.1790 -0.9661 -0.5854
## veg_height-Sylvilagus_floridanus 0.1153 0.2472 -0.3763 0.1143
## week-Canis_latrans -8143.7111 3564915.4502 -93527.0416 0.0371
## week-Sylvilagus_floridanus -167177.9522 4718758.9055 -99050.8476 0.0755
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4696 1.0692 172
## (Intercept)-Sylvilagus_floridanus -4.5504 1.1021 187
## shrub_cover-Canis_latrans 0.0132 1.0061 164
## shrub_cover-Sylvilagus_floridanus 1.6468 1.3115 62
## veg_height-Canis_latrans -0.2620 1.0378 192
## veg_height-Sylvilagus_floridanus 0.6092 1.1418 184
## week-Canis_latrans 74628.6899 1.2863 67273
## week-Sylvilagus_floridanus 87800.1897 1.3929 843
# 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): 1.2023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1839 1.3397 -2.5297 0.1860 2.7798 1.0023 753
## Cogon_Patch_Size 0.0011 1.3682 -2.7017 -0.0279 2.7520 1.0256 817
## Veg_shannon_index 1.1870 1.2887 -1.6146 1.2442 3.5361 1.0440 846
## total_shrub_cover 0.4949 1.2558 -2.1836 0.5054 3.0152 1.1054 418
## Avg_Cogongrass_Cover 0.8186 1.3996 -1.9307 0.8624 3.4739 1.0037 1113
## Tree_Density -1.2278 1.6578 -4.1880 -1.3479 2.2816 1.0096 805
## Avg_Canopy_Cover 0.5188 1.5755 -2.6156 0.5640 3.5053 1.0062 3000
## avg_veg_height -0.5334 1.1637 -2.8272 -0.5353 1.7862 1.0148 727
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 49.0256 396.6176 0.0836 3.9408 330.3202 1.2726 1753
## Cogon_Patch_Size 106.3552 1000.9273 0.0940 5.1502 660.3514 1.1257 766
## Veg_shannon_index 155.5808 1868.1916 0.0655 1.6158 707.6741 1.6454 226
## total_shrub_cover 26.2655 568.9271 0.0602 0.9683 76.9139 1.2388 3000
## Avg_Cogongrass_Cover 117.3452 3022.1220 0.0806 4.3239 423.5521 1.3462 3000
## Tree_Density 518.4881 5059.2185 0.0864 11.4554 3086.8697 1.1316 1076
## Avg_Canopy_Cover 1138.7044 11236.4221 1.9609 61.6689 5506.8631 1.5863 385
## avg_veg_height 20.4542 511.5309 0.0551 0.9272 53.7748 1.3291 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 10.9387 46.3706 0.0661 1.0575 92.4244 2.6511 33
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5451 2.0413 -5.1795 -2.8443 1.8101 1.0138 877
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 38.0705 165.4145 0.0596 5.265 260.7558 1.0499 1911
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.0352 2.4862 -0.9840 1.4441
## (Intercept)-Sylvilagus_floridanus -1.5798 3.0809 -9.0305 -1.0546
## Cogon_Patch_Size-Canis_latrans 2.3019 3.8643 -1.3628 1.4030
## Cogon_Patch_Size-Sylvilagus_floridanus -2.6294 4.0739 -13.7889 -1.5749
## Veg_shannon_index-Canis_latrans 2.9894 3.2019 0.0102 2.0846
## Veg_shannon_index-Sylvilagus_floridanus 3.1255 6.1110 -0.4309 1.8622
## total_shrub_cover-Canis_latrans 1.0044 1.3914 -1.5916 0.8705
## total_shrub_cover-Sylvilagus_floridanus 0.2919 2.0683 -4.5567 0.4423
## Avg_Cogongrass_Cover-Canis_latrans 3.8595 3.7554 -0.1154 2.7712
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4416 2.6990 -5.1522 0.5640
## Tree_Density-Canis_latrans -5.7304 5.5824 -22.3868 -3.7983
## Tree_Density-Sylvilagus_floridanus -6.8365 11.4278 -48.3954 -3.6265
## Avg_Canopy_Cover-Canis_latrans -0.7964 1.4335 -4.6422 -0.5277
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.7376 15.7290 2.6284 9.2312
## avg_veg_height-Canis_latrans -0.9190 1.4429 -4.6089 -0.7632
## avg_veg_height-Sylvilagus_floridanus -0.6830 1.7249 -4.1756 -0.6187
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 8.5554 1.1625 62
## (Intercept)-Sylvilagus_floridanus 2.8701 1.0936 89
## Cogon_Patch_Size-Canis_latrans 11.5084 1.1946 133
## Cogon_Patch_Size-Sylvilagus_floridanus 2.5735 1.0447 113
## Veg_shannon_index-Canis_latrans 12.6619 2.3602 29
## Veg_shannon_index-Sylvilagus_floridanus 18.1309 2.3422 35
## total_shrub_cover-Canis_latrans 4.0252 1.1967 167
## total_shrub_cover-Sylvilagus_floridanus 3.7387 1.0680 209
## Avg_Cogongrass_Cover-Canis_latrans 15.4144 1.7149 50
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.3916 1.1171 261
## Tree_Density-Canis_latrans -0.5592 1.8619 55
## Tree_Density-Sylvilagus_floridanus 1.1868 2.6534 15
## Avg_Canopy_Cover-Canis_latrans 1.3247 1.1627 43
## Avg_Canopy_Cover-Sylvilagus_floridanus 72.2759 2.1500 14
## avg_veg_height-Canis_latrans 1.5719 1.1428 198
## avg_veg_height-Sylvilagus_floridanus 2.3883 1.0524 398
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7178 0.1598 -5.0396 -4.7115 -4.4202 1.0542
## (Intercept)-Sylvilagus_floridanus -5.0479 0.2480 -5.5298 -5.0417 -4.5850 1.0891
## ESS
## (Intercept)-Canis_latrans 197
## (Intercept)-Sylvilagus_floridanus 134
# 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): 1.1817
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1622 0.9517 -1.7818 0.1495 2.0970 1.0071 995
## Avg_Cogongrass_Cover 0.0782 0.9691 -1.9567 0.0996 2.0097 1.0002 1638
## total_shrub_cover 0.1839 0.9205 -1.7126 0.1901 2.0360 1.0008 1323
## avg_veg_height -0.1356 0.8315 -1.7248 -0.1561 1.6893 1.0106 1360
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.7974 72.7907 0.0581 0.8165 33.0742 1.0276 3000
## Avg_Cogongrass_Cover 10.2598 174.7365 0.0634 1.1366 46.2175 1.3279 3000
## total_shrub_cover 7.7497 63.4030 0.0623 0.9439 44.0959 1.0769 3000
## avg_veg_height 4.6687 50.6910 0.0480 0.5009 19.9037 1.0190 2643
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8134 5.6572 0.061 1.0957 16.4874 1.0392 306
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5606 1.9583 -5.1442 -2.8329 1.6799 1.0105 787
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 47.7927 339.6461 0.0696 5.1302 276.3803 1.1052 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6489 0.9029 -0.9297 0.5506
## (Intercept)-Sylvilagus_floridanus -0.2343 0.9253 -2.0620 -0.2331
## Avg_Cogongrass_Cover-Canis_latrans 0.7697 0.8591 -0.7575 0.7292
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4906 0.8171 -2.2858 -0.4408
## total_shrub_cover-Canis_latrans 0.7949 0.7317 -0.3154 0.6995
## total_shrub_cover-Sylvilagus_floridanus -0.3424 1.0062 -2.7455 -0.2307
## avg_veg_height-Canis_latrans -0.1242 0.6862 -1.4558 -0.1251
## avg_veg_height-Sylvilagus_floridanus -0.1945 0.7009 -1.5052 -0.1998
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.6478 1.0116 410
## (Intercept)-Sylvilagus_floridanus 1.6313 1.0267 397
## Avg_Cogongrass_Cover-Canis_latrans 2.6080 1.0208 795
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0194 1.0074 862
## total_shrub_cover-Canis_latrans 2.5374 1.0031 725
## total_shrub_cover-Sylvilagus_floridanus 1.2588 1.0315 255
## avg_veg_height-Canis_latrans 1.2590 1.0074 1002
## avg_veg_height-Sylvilagus_floridanus 1.1908 1.0098 985
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7055 0.1796 -5.0741 -4.6935 -4.3711 1.0225
## (Intercept)-Sylvilagus_floridanus -4.9929 0.2882 -5.5907 -4.9745 -4.4645 1.2450
## ESS
## (Intercept)-Canis_latrans 174
## (Intercept)-Sylvilagus_floridanus 100
# 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): 1.143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0329 0.9892 -1.8830 0.0149 2.0518 1.0027 731
## Tree_Density -1.1037 1.0708 -3.1459 -1.1197 1.2664 1.0066 971
## Avg_Canopy_Cover 0.5656 1.2879 -2.0926 0.5963 3.1420 1.0039 2698
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.7024 94.6413 0.0658 0.9355 40.5011 1.1666 3000
## Tree_Density 9.5235 78.5977 0.0502 0.7897 54.3939 1.1124 2463
## Avg_Canopy_Cover 362.0368 16645.2072 0.2438 6.8037 336.9070 1.3207 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1052 20.26 0.0488 0.4217 13.7758 1.4662 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6336 1.9685 -5.18 -3.0417 1.6031 1.0009 866
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 35.0214 176.7775 0.0642 4.5351 241.699 1.136 1395
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.5335 0.9188 -0.8623 0.4287 2.6410
## (Intercept)-Sylvilagus_floridanus -0.4175 1.0101 -2.2795 -0.4673 1.8375
## Tree_Density-Canis_latrans -1.3816 0.8926 -3.3917 -1.2624 -0.0403
## Tree_Density-Sylvilagus_floridanus -1.7485 1.2940 -4.9979 -1.5592 0.2002
## Avg_Canopy_Cover-Canis_latrans -0.4506 0.8197 -2.1654 -0.3555 0.6878
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7599 2.5691 0.7120 3.0943 10.9102
## Rhat ESS
## (Intercept)-Canis_latrans 1.0255 294
## (Intercept)-Sylvilagus_floridanus 1.0078 306
## Tree_Density-Canis_latrans 1.0475 383
## Tree_Density-Sylvilagus_floridanus 1.0089 410
## Avg_Canopy_Cover-Canis_latrans 1.0616 113
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0281 114
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7068 0.1688 -5.0666 -4.7023 -4.3966 1.0294
## (Intercept)-Sylvilagus_floridanus -4.9795 0.2609 -5.5052 -4.9703 -4.4837 1.0155
## ESS
## (Intercept)-Canis_latrans 196
## (Intercept)-Sylvilagus_floridanus 137
# 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): 1.1788
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1609 1.0621 -1.9518 0.1411 2.3599 1.0050 793
## Cogon_Patch_Size 0.0833 1.3060 -2.5252 0.0898 2.6736 1.0038 2210
## Avg_Cogongrass_Cover -0.0006 0.8480 -1.7230 -0.0144 1.7575 1.0015 1741
## total_shrub_cover 0.2390 0.9393 -1.6759 0.2396 2.1670 1.0069 1053
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.3494 174.5968 0.0739 1.5545 67.5474 1.0592 3000
## Cogon_Patch_Size 101.9120 1239.6004 0.1511 6.2707 301.3407 1.1314 3000
## Avg_Cogongrass_Cover 3.9809 23.7016 0.0533 0.5651 25.3435 1.0391 3000
## total_shrub_cover 6.1120 42.2772 0.0578 0.7871 39.8287 1.3656 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9966 14.0917 0.061 0.897 17.709 1.3656 556
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6159 1.9872 -5.179 -3.0292 1.5658 1.015 687
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 42.4191 331.9109 0.0658 4.6304 244.3261 1.1539 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.0726 1.2565 -0.9495 0.8918
## (Intercept)-Sylvilagus_floridanus -0.6405 1.1454 -3.2297 -0.5983
## Cogon_Patch_Size-Canis_latrans 2.3286 2.3143 -0.1921 1.6732
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0182 2.0117 -7.2002 -1.5885
## Avg_Cogongrass_Cover-Canis_latrans 0.2096 0.6088 -0.9291 0.1852
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1859 0.6963 -1.6090 -0.1770
## total_shrub_cover-Canis_latrans 0.7352 0.7868 -0.5170 0.6193
## total_shrub_cover-Sylvilagus_floridanus -0.0685 0.9461 -2.0779 -0.0465
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.2481 1.0048 199
## (Intercept)-Sylvilagus_floridanus 1.6267 1.0084 248
## Cogon_Patch_Size-Canis_latrans 8.5979 1.0760 231
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7078 1.0056 302
## Avg_Cogongrass_Cover-Canis_latrans 1.5279 1.0031 1266
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1736 1.0068 1106
## total_shrub_cover-Canis_latrans 2.6162 1.0610 476
## total_shrub_cover-Sylvilagus_floridanus 1.8422 1.0147 332
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6623 0.1640 -4.9884 -4.6630 -4.3423 1.0287
## (Intercept)-Sylvilagus_floridanus -4.9921 0.3249 -5.6906 -4.9578 -4.4605 1.0304
## ESS
## (Intercept)-Canis_latrans 203
## (Intercept)-Sylvilagus_floridanus 57
# 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): 1.1163
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1163 0.9256 -1.7572 0.1110 2.0286 1.0236 853
## Veg_shannon_index 0.7823 0.8574 -1.1979 0.8069 2.4337 1.0048 1469
## Avg_Cogongrass_Cover 0.2134 0.9495 -1.7726 0.2330 2.0917 1.0020 1975
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 63.9360 3193.7576 0.0628 0.7567 33.0014 1.3231 3000
## Veg_shannon_index 4.6491 46.3265 0.0513 0.5651 25.5506 1.2551 3000
## Avg_Cogongrass_Cover 12.4377 199.3281 0.0755 1.1396 41.8375 1.2068 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6804 3.5763 0.0585 0.6345 10.1603 1.0691 350
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6245 2.0079 -5.1289 -3.0749 1.7073 1.0021 747
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 48.0607 484.7123 0.0632 4.3717 241.811 1.1468 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4863 0.7660 -0.9810 0.4708
## (Intercept)-Sylvilagus_floridanus -0.2641 0.8873 -1.9196 -0.3034
## Veg_shannon_index-Canis_latrans 1.1795 0.6049 0.1585 1.1139
## Veg_shannon_index-Sylvilagus_floridanus 0.7439 0.7032 -0.5652 0.7020
## Avg_Cogongrass_Cover-Canis_latrans 0.9335 0.6869 -0.1908 0.8543
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3278 0.6727 -1.6590 -0.3215
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0838 1.0195 442
## (Intercept)-Sylvilagus_floridanus 1.7545 1.0492 247
## Veg_shannon_index-Canis_latrans 2.5347 1.0030 918
## Veg_shannon_index-Sylvilagus_floridanus 2.3643 1.0128 491
## Avg_Cogongrass_Cover-Canis_latrans 2.5993 1.0064 833
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0220 1.0043 827
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6309 0.1485 -4.9264 -4.6262 -4.3598 1.0652
## (Intercept)-Sylvilagus_floridanus -5.0003 0.2994 -5.6537 -4.9809 -4.4688 1.0389
## ESS
## (Intercept)-Canis_latrans 251
## (Intercept)-Sylvilagus_floridanus 105
# 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): 1.169
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5200 1.0695 -2.5906 -0.5864 1.8005 1.0162 581
## Avg_Cogongrass_Cover -0.5244 1.1283 -2.7285 -0.5586 1.8866 0.9998 2020
## I(Avg_Cogongrass_Cover^2) 1.0317 1.1805 -1.6116 1.0321 3.3124 1.0011 1050
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.8873 849.5543 0.0554 0.7312 50.5692 1.3109 3000
## Avg_Cogongrass_Cover 16.5223 155.9275 0.0712 1.8541 98.3131 1.1415 3000
## I(Avg_Cogongrass_Cover^2) 15.6714 148.1995 0.0694 1.3691 92.9316 1.2309 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2684 5.4181 0.0605 0.6657 16.6539 1.0452 283
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6168 2.0038 -5.1981 -2.9845 1.786 1.0023 806
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 48.7906 505.6543 0.0628 4.4503 243.3488 1.2542 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.3289 1.0135 -2.1666 -0.3929
## (Intercept)-Sylvilagus_floridanus -0.9004 1.6605 -3.1452 -1.0540
## Avg_Cogongrass_Cover-Canis_latrans 0.0419 0.9368 -1.6051 -0.0199
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.8222 1.6492 -6.1909 -1.5321
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3645 1.6540 0.1164 2.0324
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1032 1.0064 -0.3103 0.9294
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0997 1.0297 307
## (Intercept)-Sylvilagus_floridanus 1.8416 1.3200 56
## Avg_Cogongrass_Cover-Canis_latrans 2.0965 1.0022 798
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.3121 1.0359 113
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.5394 1.0046 209
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.7691 1.0402 130
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6963 0.1581 -5.0002 -4.6963 -4.3855 1.0348
## (Intercept)-Sylvilagus_floridanus -5.0151 0.3152 -5.7455 -4.9774 -4.4979 1.0730
## ESS
## (Intercept)-Canis_latrans 218
## (Intercept)-Sylvilagus_floridanus 74
# 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): 1.2153
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5466 1.4878 -3.2978 -0.6317 2.6269 1.0250 579
## Cogon_Patch_Size 0.2937 1.5395 -2.7495 0.3309 3.1909 1.0088 1041
## Veg_shannon_index 1.3791 1.4048 -1.6685 1.4742 3.9348 1.0194 1247
## total_shrub_cover 0.3901 1.2999 -2.2766 0.4195 2.8872 1.0047 464
## Avg_Cogongrass_Cover -0.2601 1.5317 -3.3705 -0.2684 2.7495 1.0272 703
## Tree_Density -1.1439 1.6892 -4.2906 -1.2097 2.2741 1.1584 441
## Avg_Canopy_Cover 0.4289 1.6002 -2.7625 0.4681 3.4117 1.0159 1943
## I(Avg_Cogongrass_Cover^2) 1.1352 1.4848 -2.1343 1.2787 3.7659 1.0396 725
## avg_veg_height -0.5147 1.2964 -3.0932 -0.4923 1.9722 1.0474 609
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 71.4905 844.3495 0.0694 2.7953 472.4949 1.3892
## Cogon_Patch_Size 489.5397 14496.2068 0.2038 25.2182 1616.2913 1.3191
## Veg_shannon_index 69.7194 648.5187 0.0777 2.7455 445.3029 1.2940
## total_shrub_cover 44.7881 1355.1708 0.0646 1.2538 124.7191 1.3082
## Avg_Cogongrass_Cover 52.5090 627.2216 0.0744 2.1087 229.8807 1.3522
## Tree_Density 659.0104 6830.1087 0.0977 16.8720 3008.3256 1.4429
## Avg_Canopy_Cover 708.8674 4521.9798 1.3317 102.7713 4598.8619 1.1307
## I(Avg_Cogongrass_Cover^2) 152.7810 1258.6836 0.1013 6.5427 831.2830 1.1390
## avg_veg_height 23.1741 461.0832 0.0581 1.0908 88.7534 1.2851
## ESS
## (Intercept) 534
## Cogon_Patch_Size 3000
## Veg_shannon_index 680
## total_shrub_cover 3000
## Avg_Cogongrass_Cover 3000
## Tree_Density 588
## Avg_Canopy_Cover 1236
## I(Avg_Cogongrass_Cover^2) 672
## avg_veg_height 2461
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 40.566 293.5033 0.0626 1.3174 275.8977 2.2025 98
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4644 2.0373 -5.1785 -2.7196 1.7532 0.9997 873
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 64.5957 577.4333 0.071 5.706 332.4147 1.0778 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.3270 3.2152 -6.4042
## (Intercept)-Sylvilagus_floridanus -2.8116 4.0091 -14.2904
## Cogon_Patch_Size-Canis_latrans 5.9660 5.6358 -0.4982
## Cogon_Patch_Size-Sylvilagus_floridanus -3.6648 5.3332 -18.6385
## Veg_shannon_index-Canis_latrans 3.5275 2.7936 0.2467
## Veg_shannon_index-Sylvilagus_floridanus 3.0196 3.1265 -0.5976
## total_shrub_cover-Canis_latrans 1.0821 1.7293 -1.9900
## total_shrub_cover-Sylvilagus_floridanus -0.0985 2.4510 -6.6279
## Avg_Cogongrass_Cover-Canis_latrans 0.0684 3.4498 -5.9557
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1459 3.0598 -8.4815
## Tree_Density-Canis_latrans -7.1243 7.0565 -25.7776
## Tree_Density-Sylvilagus_floridanus -7.1283 9.2684 -36.2791
## Avg_Canopy_Cover-Canis_latrans -1.0631 2.0249 -6.3320
## Avg_Canopy_Cover-Sylvilagus_floridanus 15.4504 11.0157 2.1493
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5235 5.2422 0.9517
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7610 2.6004 -3.1094
## avg_veg_height-Canis_latrans -1.2266 2.1614 -5.9880
## avg_veg_height-Sylvilagus_floridanus -0.6148 2.1087 -5.4248
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3963 5.5818 1.2573 112
## (Intercept)-Sylvilagus_floridanus -1.9236 2.3851 1.2028 94
## Cogon_Patch_Size-Canis_latrans 4.4293 20.2503 1.0832 127
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4379 3.5316 1.0098 95
## Veg_shannon_index-Canis_latrans 2.7422 11.7139 1.3145 83
## Veg_shannon_index-Sylvilagus_floridanus 2.3186 11.8241 1.2569 118
## total_shrub_cover-Canis_latrans 0.9122 5.3782 1.0429 118
## total_shrub_cover-Sylvilagus_floridanus 0.2436 3.6904 1.0870 151
## Avg_Cogongrass_Cover-Canis_latrans -0.1898 7.9071 1.0884 167
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7886 4.0137 1.0418 398
## Tree_Density-Canis_latrans -4.5579 -0.1127 1.6753 43
## Tree_Density-Sylvilagus_floridanus -3.8089 1.6425 1.6114 24
## Avg_Canopy_Cover-Canis_latrans -0.7734 1.8207 1.3116 87
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.2255 42.1324 1.5117 33
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7388 19.3858 2.1022 40
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5286 8.5323 1.0928 132
## avg_veg_height-Canis_latrans -0.9093 1.4887 1.3019 60
## avg_veg_height-Sylvilagus_floridanus -0.5167 3.1899 1.0514 221
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7188 0.1736 -5.0671 -4.7094 -4.3961 1.1687
## (Intercept)-Sylvilagus_floridanus -5.0831 0.2643 -5.5965 -5.0796 -4.5759 1.0648
## ESS
## (Intercept)-Canis_latrans 190
## (Intercept)-Sylvilagus_floridanus 122
# 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): 1.544
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0604 0.9213 -1.8835 0.0735 1.9174 1.0047 1418
## Avg_Cogongrass_Cover 0.0399 0.8764 -1.7590 0.0316 1.8382 0.9998 2490
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 33.7828 1426.2577 0.0674 0.8739 35.3649 1.3117 3000
## Avg_Cogongrass_Cover 6.6230 69.2016 0.0754 0.9872 30.5439 1.1616 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7457 3.8564 0.0508 0.6141 10.7796 1.0169 464
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7132 1.9803 -5.1572 -3.1736 1.6062 1.0334 796
## shrub_cover 0.0389 0.7515 -1.4594 0.0136 1.6467 1.0094 1314
## veg_height -0.1906 0.7174 -1.6265 -0.2124 1.4060 1.0015 2235
## week -0.0902 1.6453 -3.3686 -0.0812 3.1885 1.0018 1573
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.760990e+01 2.471338e+02 0.0592 3.8291 2.466924e+02 1.0774 3000
## shrub_cover 3.458100e+00 2.602000e+01 0.0572 0.6034 1.874890e+01 1.1331 3000
## veg_height 4.249000e+00 4.695860e+01 0.0715 0.6573 1.876270e+01 1.2222 3000
## week 1.872623e+10 3.894593e+11 0.0938 54.7997 6.707973e+08 1.3581 673
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5817 0.7607 -0.8521 0.5473
## (Intercept)-Sylvilagus_floridanus -0.3633 0.7206 -1.7689 -0.3524
## Avg_Cogongrass_Cover-Canis_latrans 0.6548 0.6685 -0.3535 0.5652
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5246 0.5739 -1.7561 -0.4746
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2185 1.0635 524
## (Intercept)-Sylvilagus_floridanus 1.0936 1.0635 586
## Avg_Cogongrass_Cover-Canis_latrans 2.2323 1.0206 661
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4690 1.0059 1199
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7926 0.1811 -5.1699 -4.7850
## (Intercept)-Sylvilagus_floridanus -4.9168 0.2502 -5.4136 -4.9193
## shrub_cover-Canis_latrans -0.3053 0.2135 -0.6990 -0.3148
## shrub_cover-Sylvilagus_floridanus 0.3989 0.4460 -0.3527 0.3705
## veg_height-Canis_latrans -0.6344 0.1777 -0.9859 -0.6304
## veg_height-Sylvilagus_floridanus 0.1965 0.2442 -0.3073 0.2023
## week-Canis_latrans -1375.7447 87923.3351 -5684.2019 0.1090
## week-Sylvilagus_floridanus 134.2390 138045.1954 -6768.2081 -0.1702
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.4623 1.1095 177
## (Intercept)-Sylvilagus_floridanus -4.4283 1.1335 168
## shrub_cover-Canis_latrans 0.1392 1.0226 157
## shrub_cover-Sylvilagus_floridanus 1.3531 1.0311 105
## veg_height-Canis_latrans -0.2829 1.0693 177
## veg_height-Sylvilagus_floridanus 0.6575 1.0153 196
## week-Canis_latrans 3131.8849 1.1364 7763
## week-Sylvilagus_floridanus 3954.4265 1.1262 1584
# 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): 1.0768
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0326 0.9141 -1.9537 -0.0415 1.8317 1.0004 1570
## Avg_Cogongrass_Cover -0.0281 0.8260 -1.6580 -0.0479 1.6391 1.0220 2632
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.1957 36.2874 0.0555 0.8617 31.9890 1.1650 3000
## Avg_Cogongrass_Cover 5.3925 37.2113 0.0636 0.8450 29.7086 1.0387 2459
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9669 5.636 0.0545 0.6157 11.8072 1.1505 286
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6856 1.9705 -5.1505 -3.1179 1.6929 1.0002 814
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 138.9842 3832.864 0.0599 3.802 255.2738 1.1246 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4119 0.7660 -1.0508 0.3790
## (Intercept)-Sylvilagus_floridanus -0.4779 0.7462 -2.0764 -0.4604
## Avg_Cogongrass_Cover-Canis_latrans 0.4475 0.5497 -0.4928 0.3953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4953 0.5487 -1.6736 -0.4577
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9989 1.0015 411
## (Intercept)-Sylvilagus_floridanus 0.9506 1.0050 594
## Avg_Cogongrass_Cover-Canis_latrans 1.7249 1.0175 1084
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4717 1.0083 1549
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.6745 0.1719 -5.0524 -4.6592 -4.3799 1.0210
## (Intercept)-Sylvilagus_floridanus -4.8871 0.2562 -5.4289 -4.8746 -4.4295 1.0914
## ESS
## (Intercept)-Canis_latrans 204
## (Intercept)-Sylvilagus_floridanus 160
# 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): 1.3635
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0540 0.8808 -1.7823 0.0379 1.9553 1.0038 1630
## Avg_Cogongrass_Cover 0.0111 0.8350 -1.6668 -0.0079 1.7984 1.0010 2424
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.9545 36.2459 0.0610 0.7782 31.4252 1.2828 1182
## Avg_Cogongrass_Cover 5.6098 44.3436 0.0589 0.7764 28.3320 1.0625 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4377 2.6979 0.0542 0.5909 7.9225 1.0369 459
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6401 1.939 -5.1284 -3.0382 1.5141 1.0029 880
## week 0.0003 1.619 -3.1504 -0.0168 3.2036 1.0006 1564
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.560760e+01 4.851108e+02 0.0648 4.0488 2.167120e+02 1.2940 3000
## week 7.486638e+35 1.815686e+37 0.0988 122.7046 1.200716e+33 1.4502 621
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5111 0.7221 -0.7474 0.4503
## (Intercept)-Sylvilagus_floridanus -0.4097 0.6736 -1.7303 -0.4089
## Avg_Cogongrass_Cover-Canis_latrans 0.4873 0.5526 -0.4427 0.4406
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4873 0.5444 -1.6952 -0.4513
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1600 1.0093 513
## (Intercept)-Sylvilagus_floridanus 0.9350 1.0129 604
## Avg_Cogongrass_Cover-Canis_latrans 1.7499 0.9996 1151
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4876 1.0022 1571
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.664600e+00 1.712000e-01 -5.032000e+00
## (Intercept)-Sylvilagus_floridanus -4.884200e+00 2.500000e-01 -5.414900e+00
## week-Canis_latrans -2.481421e+15 7.721223e+17 -2.888581e+14
## week-Sylvilagus_floridanus -2.064584e+16 6.593320e+17 -6.134612e+14
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6588 -4.352400e+00 1.1370 190
## (Intercept)-Sylvilagus_floridanus -4.8647 -4.451200e+00 1.0076 157
## week-Canis_latrans 0.0007 8.655216e+14 1.2914 112057
## week-Sylvilagus_floridanus -0.1787 3.211242e+14 1.3848 990
# 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): 1.4853
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2125 1.2964 -2.5100 0.2465 2.6791 1.0143 618
## Cogon_Patch_Size -0.0076 1.3687 -2.6994 -0.0211 2.8862 1.0094 857
## Veg_shannon_index 1.3905 1.2313 -1.2845 1.4329 3.7233 1.0108 1116
## total_shrub_cover 0.6494 1.1885 -1.7918 0.6610 2.9645 1.0024 693
## Avg_Cogongrass_Cover 0.9069 1.3913 -2.0472 0.9654 3.5188 1.0011 1094
## Tree_Density -1.3830 1.5924 -4.2388 -1.4760 2.0193 1.0374 946
## Avg_Canopy_Cover 0.5570 1.5594 -2.5935 0.6201 3.5188 1.0093 3000
## avg_veg_height -0.3971 1.1173 -2.6380 -0.3750 1.8097 1.0099 613
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.2763 144.0400 0.0782 2.9138 132.0233 1.2418 1005
## Cogon_Patch_Size 203.1445 4459.1414 0.1081 6.2854 551.9099 1.3435 3000
## Veg_shannon_index 27.6159 458.6681 0.0581 1.1030 118.7882 1.1764 3000
## total_shrub_cover 19.4658 309.7816 0.0588 0.9950 71.6553 1.2137 2382
## Avg_Cogongrass_Cover 42.6038 526.7023 0.0753 3.0284 198.8024 1.2784 3000
## Tree_Density 170.0797 2639.4207 0.0865 7.2574 790.3289 1.3358 3000
## Avg_Canopy_Cover 357.6211 3378.5659 1.1082 48.9724 1893.6229 1.1617 3000
## avg_veg_height 5.7178 30.2986 0.0577 0.6915 39.3340 1.0335 2831
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.7449 13.7137 0.0579 0.9395 36.4712 1.0372 132
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6116 2.0161 -5.1876 -2.9744 1.7360 1.0024 806
## week 0.0601 1.6643 -3.3579 0.1031 3.2838 1.0015 1703
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.114310e+01 2.672961e+02 0.0644 4.4209 2.540904e+02 1.1522 3000
## week 1.890061e+11 4.476509e+12 0.0998 74.2745 7.731827e+09 1.1187 1180
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.6643 1.7528 -0.8895 1.3751
## (Intercept)-Sylvilagus_floridanus -1.1433 2.2640 -6.7664 -0.8650
## Cogon_Patch_Size-Canis_latrans 2.0896 2.8797 -1.2313 1.3748
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8142 4.1918 -15.4770 -1.7558
## Veg_shannon_index-Canis_latrans 2.3544 1.6739 0.1647 2.0051
## Veg_shannon_index-Sylvilagus_floridanus 2.1232 1.7224 -0.2439 1.8918
## total_shrub_cover-Canis_latrans 1.2168 1.3154 -0.8930 1.0291
## total_shrub_cover-Sylvilagus_floridanus 0.6910 1.8145 -2.9167 0.6810
## Avg_Cogongrass_Cover-Canis_latrans 3.0305 2.3601 -0.0799 2.5201
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6885 1.9211 -3.5592 0.7595
## Tree_Density-Canis_latrans -4.3862 3.3333 -13.1828 -3.4426
## Tree_Density-Sylvilagus_floridanus -4.4531 3.7912 -15.4450 -3.3083
## Avg_Canopy_Cover-Canis_latrans -0.4997 1.0396 -2.9358 -0.3911
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.1288 6.3373 1.8388 8.6734
## avg_veg_height-Canis_latrans -0.6270 1.1907 -3.2324 -0.5670
## avg_veg_height-Sylvilagus_floridanus -0.4953 1.3879 -3.2655 -0.4531
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.0509 1.0246 224
## (Intercept)-Sylvilagus_floridanus 2.4497 1.0831 112
## Cogon_Patch_Size-Canis_latrans 9.8990 1.0065 264
## Cogon_Patch_Size-Sylvilagus_floridanus 2.3773 1.1176 109
## Veg_shannon_index-Canis_latrans 6.6458 1.1140 187
## Veg_shannon_index-Sylvilagus_floridanus 6.4394 1.2479 257
## total_shrub_cover-Canis_latrans 4.5265 1.0629 242
## total_shrub_cover-Sylvilagus_floridanus 4.2066 1.0318 232
## Avg_Cogongrass_Cover-Canis_latrans 9.0373 1.0881 222
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.2067 1.0119 474
## Tree_Density-Canis_latrans -0.6385 1.1156 160
## Tree_Density-Sylvilagus_floridanus -0.0019 1.4937 86
## Avg_Canopy_Cover-Canis_latrans 1.2488 1.0252 275
## Avg_Canopy_Cover-Sylvilagus_floridanus 25.4531 1.7017 59
## avg_veg_height-Canis_latrans 1.5325 1.0115 482
## avg_veg_height-Sylvilagus_floridanus 2.2508 1.0109 509
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7118 0.1601 -5.0321 -4.7123
## (Intercept)-Sylvilagus_floridanus -5.0425 0.2486 -5.5445 -5.0459
## week-Canis_latrans 8239.1508 596209.8623 -11887.5952 0.3130
## week-Sylvilagus_floridanus 1756.4318 446210.5839 -16310.5363 0.2003
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.402 1.0316 184
## (Intercept)-Sylvilagus_floridanus -4.576 1.1878 111
## week-Canis_latrans 26873.997 1.1101 2704
## week-Sylvilagus_floridanus 22554.968 1.2100 7568
# 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): 1.4793
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2102 1.0047 -1.7966 0.2006 2.3717 1.0179 627
## Avg_Cogongrass_Cover 0.0987 0.9937 -1.9384 0.1082 2.0837 1.0010 1319
## total_shrub_cover 0.2377 0.9224 -1.6851 0.2478 2.0926 1.0027 1487
## avg_veg_height -0.1481 0.8683 -1.8923 -0.1525 1.6627 1.0032 1086
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.6284 304.5360 0.0599 0.8836 47.6816 1.2928 3000
## Avg_Cogongrass_Cover 15.2813 354.6011 0.0738 1.0564 47.9084 1.3175 3000
## total_shrub_cover 7.9080 65.6702 0.0636 0.9262 48.4079 1.2147 3000
## avg_veg_height 5.3147 61.3321 0.0447 0.4926 23.8929 1.0811 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.0867 7.36 0.0565 1.0247 19.7384 1.1949 308
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6538 1.9721 -5.1555 -3.0002 1.4481 1.0132 804
## week -0.0105 1.6280 -3.1623 0.0063 3.1259 1.0041 1374
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.963480e+01 3.944434e+02 0.0622 4.3601 2.312534e+02 1.0657 2511
## week 1.902785e+14 5.847535e+15 0.0949 218.2435 1.974616e+13 1.3692 1122
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7610 1.0903 -0.8768 0.6208
## (Intercept)-Sylvilagus_floridanus -0.1909 1.0167 -2.2266 -0.2151
## Avg_Cogongrass_Cover-Canis_latrans 0.7560 0.8584 -0.8009 0.6939
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4887 0.8788 -2.3782 -0.4375
## total_shrub_cover-Canis_latrans 0.8087 0.7308 -0.3680 0.7111
## total_shrub_cover-Sylvilagus_floridanus -0.2711 1.0224 -2.6216 -0.1904
## avg_veg_height-Canis_latrans -0.1213 0.7467 -1.5809 -0.1389
## avg_veg_height-Sylvilagus_floridanus -0.1956 0.7534 -1.7171 -0.1916
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.2051 1.0302 227
## (Intercept)-Sylvilagus_floridanus 1.9725 1.0735 341
## Avg_Cogongrass_Cover-Canis_latrans 2.7027 1.0021 646
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1121 1.0058 644
## total_shrub_cover-Canis_latrans 2.5538 1.0129 624
## total_shrub_cover-Sylvilagus_floridanus 1.4691 1.0031 218
## avg_veg_height-Canis_latrans 1.4173 1.0014 759
## avg_veg_height-Sylvilagus_floridanus 1.2367 1.0043 601
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.7061 1.744000e-01 -5.0931 -4.7030
## (Intercept)-Sylvilagus_floridanus -5.0179 3.070000e-01 -5.6576 -5.0002
## week-Canis_latrans 78328.3900 9.358834e+06 -422221.3678 0.0809
## week-Sylvilagus_floridanus -9740.1714 1.233333e+07 -642052.8712 0.1683
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3854 1.0687 188
## (Intercept)-Sylvilagus_floridanus -4.4620 1.1055 96
## week-Canis_latrans 608493.7540 1.2641 18535
## week-Sylvilagus_floridanus 540612.0095 1.2353 7011
# 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): 1.3412
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0117 0.7658 -1.6214 -0.0133 1.5358 1.0021 2544
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.6824 75.1403 0.0595 0.6604 20.2583 1.2939 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6754 1.9347 -5.0968 -3.0544 1.4607 1.0013 818
## week 0.0482 1.6475 -3.2645 0.0248 3.4557 1.0099 1148
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.016240e+01 1.195480e+02 0.0630 3.7160 2.294042e+02 1.0717 2785
## week 2.024732e+16 8.358715e+17 0.0855 29.2283 2.973986e+11 1.3476 1846
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3916 0.4193 -0.3680 0.3722 1.2612 1.0000
## (Intercept)-Sylvilagus_floridanus -0.4125 0.4358 -1.2326 -0.4165 0.4680 1.0084
## ESS
## (Intercept)-Canis_latrans 1719
## (Intercept)-Sylvilagus_floridanus 1318
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6660 1.563000e-01 -4.9832 -4.6623
## (Intercept)-Sylvilagus_floridanus -4.8729 2.410000e-01 -5.3613 -4.8599
## week-Canis_latrans 2255027.0365 9.856885e+07 -23815.9790 -0.0272
## week-Sylvilagus_floridanus 1246842.6904 9.525963e+07 -52261.0808 -0.0026
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3708 1.0165 222
## (Intercept)-Sylvilagus_floridanus -4.4390 1.0828 167
## week-Canis_latrans 53768.7762 1.3400 1995
## week-Sylvilagus_floridanus 25485.5540 1.3057 6330
#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): 1.3845
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0331 0.9271 -1.8087 0.0410 1.8898 1.0178 1221
## Veg_shannon_index 0.7236 0.8261 -1.1138 0.7818 2.2096 1.0045 1910
## Avg_Cogongrass_Cover 0.2072 0.9669 -1.7478 0.2204 2.2310 1.0020 2309
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.6992 44.1349 0.0614 0.8466 33.5716 1.0612 3000
## Veg_shannon_index 12.0833 428.3336 0.0501 0.5587 23.4513 1.3176 3000
## Avg_Cogongrass_Cover 8.1714 64.6554 0.0814 1.2982 45.2877 1.1924 2218
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6437 3.8243 0.0516 0.5545 9.823 1.0349 478
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6664 1.9528 -5.1125 -3.0787 1.5401 1.0034 934
## week -0.0349 1.6295 -3.2662 -0.0027 3.2441 1.0171 1673
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.323810e+01 1.523727e+02 0.057 3.9625 2.301053e+02 1.0052 1930
## week 6.907536e+18 3.081833e+20 0.130 558.6178 1.237379e+14 1.3395 1998
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5167 0.7999 -0.9017 0.4694
## (Intercept)-Sylvilagus_floridanus -0.4079 0.7898 -1.9878 -0.4081
## Veg_shannon_index-Canis_latrans 1.1782 0.5915 0.1462 1.1196
## Veg_shannon_index-Sylvilagus_floridanus 0.6954 0.6393 -0.4403 0.6599
## Avg_Cogongrass_Cover-Canis_latrans 0.9941 0.7445 -0.1627 0.8836
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3686 0.6377 -1.6840 -0.3438
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2426 1.0488 567
## (Intercept)-Sylvilagus_floridanus 1.1840 1.0291 355
## Veg_shannon_index-Canis_latrans 2.4694 1.0052 1108
## Veg_shannon_index-Sylvilagus_floridanus 2.0499 1.0081 765
## Avg_Cogongrass_Cover-Canis_latrans 2.8423 1.0053 846
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8254 1.0020 1078
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.647700e+00 1.57400e-01 -4.9604
## (Intercept)-Sylvilagus_floridanus -4.919800e+00 2.77500e-01 -5.5087
## week-Canis_latrans -4.418202e+07 2.24638e+09 -1480256.5550
## week-Sylvilagus_floridanus -1.184029e+06 1.42769e+09 -710313.6798
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6480 -4.3346 1.0008 234
## (Intercept)-Sylvilagus_floridanus -4.9005 -4.4302 1.0036 120
## week-Canis_latrans 0.0811 1143064.2804 1.3284 2729
## week-Sylvilagus_floridanus -0.0052 1148895.7978 1.2904 371583
# 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): 1.456
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2469 1.1890 -2.1242 0.2261 2.6778 1.0249 286
## Cogon_Patch_Size 0.0362 1.2809 -2.5497 0.0496 2.5641 1.0048 2297
## Avg_Cogongrass_Cover 0.0198 0.8295 -1.7316 0.0212 1.7492 1.0057 1447
## total_shrub_cover 0.2692 0.9817 -1.8453 0.2714 2.2101 1.0084 828
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.4962 142.0146 0.0723 1.7035 96.6665 1.1002 2785
## Cogon_Patch_Size 52.1859 555.7198 0.1641 7.1529 331.8493 1.3099 3000
## Avg_Cogongrass_Cover 4.6421 38.6519 0.0510 0.5821 26.6300 1.2442 2816
## total_shrub_cover 10.0496 134.8293 0.0651 0.8589 36.2912 1.1986 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.8318 8.9296 0.0618 1.1656 25.8529 1.2916 146
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5651 1.9842 -5.1402 -2.9103 1.6572 1.0342 871
## week 0.0000 1.6207 -3.0670 -0.0414 3.3259 1.0136 1211
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.419250e+01 4.839996e+02 0.0702 4.8295 2.461266e+02 1.2182 3000
## week 7.053599e+09 1.224244e+11 0.0778 20.8551 1.222505e+09 1.1620 513
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.3142 1.3999 -0.8444 1.0934
## (Intercept)-Sylvilagus_floridanus -0.5425 1.4172 -3.5227 -0.5372
## Cogon_Patch_Size-Canis_latrans 2.5155 2.5662 -0.2976 1.8158
## Cogon_Patch_Size-Sylvilagus_floridanus -2.2058 2.4411 -8.9637 -1.5630
## Avg_Cogongrass_Cover-Canis_latrans 0.2537 0.6472 -0.9474 0.2393
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1886 0.8256 -1.8830 -0.1672
## total_shrub_cover-Canis_latrans 0.7917 0.8130 -0.4570 0.6737
## total_shrub_cover-Sylvilagus_floridanus -0.1050 1.1084 -2.7843 -0.0550
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.7815 1.0296 202
## (Intercept)-Sylvilagus_floridanus 2.4999 1.0798 137
## Cogon_Patch_Size-Canis_latrans 9.2068 1.0355 202
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5347 1.1330 202
## Avg_Cogongrass_Cover-Canis_latrans 1.5839 1.0085 880
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4143 1.0150 464
## total_shrub_cover-Canis_latrans 2.7856 1.0083 504
## total_shrub_cover-Sylvilagus_floridanus 1.9974 1.0539 167
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6623 0.1573 -4.9719 -4.6581
## (Intercept)-Sylvilagus_floridanus -5.0106 0.3253 -5.7978 -4.9825
## week-Canis_latrans 2021.7907 65736.9620 -3376.2492 -0.0634
## week-Sylvilagus_floridanus 690.7496 76920.2728 -2750.0507 -0.1038
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3673 1.0879 175
## (Intercept)-Sylvilagus_floridanus -4.4586 1.5134 77
## week-Canis_latrans 3417.0015 1.0216 1262
## week-Sylvilagus_floridanus 3294.2252 1.1134 1884
#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%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 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): 1.4273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0734 0.9496 -1.9652 -0.0769 1.8605 1.0013 975
## Tree_Density -1.0974 1.0456 -2.9922 -1.1428 1.2008 1.0040 1063
## Avg_Canopy_Cover 0.5932 1.2690 -2.0948 0.6373 3.1179 1.0033 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 67.8940 3371.8508 0.0619 0.9894 35.6720 1.3225 3000
## Tree_Density 7.9468 51.8333 0.0541 0.7716 48.5879 1.0283 3000
## Avg_Canopy_Cover 84.6484 1787.4399 0.2746 6.1311 268.9495 1.1988 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6799 5.5845 0.0444 0.4327 11.8128 1.0587 298
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6499 1.9967 -5.1341 -3.1158 1.6181 1.0068 849
## week 0.0111 1.6748 -3.3386 0.0299 3.1834 1.0003 1893
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.808560e+01 5.106687e+02 0.0637 4.3304 2.507177e+02 1.1664 3000
## week 1.147416e+13 3.757539e+14 0.0924 86.0208 1.434238e+11 1.3803 1126
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4293 0.7873 -0.9089 0.3684 2.1776
## (Intercept)-Sylvilagus_floridanus -0.5784 0.9423 -2.3176 -0.6310 1.4211
## Tree_Density-Canis_latrans -1.3183 0.8233 -3.1542 -1.2652 0.0246
## Tree_Density-Sylvilagus_floridanus -1.7567 1.2690 -4.8214 -1.5433 0.1105
## Avg_Canopy_Cover-Canis_latrans -0.3922 0.5842 -1.6657 -0.3563 0.6623
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5320 2.5474 0.6895 3.0036 9.7479
## Rhat ESS
## (Intercept)-Canis_latrans 1.0010 462
## (Intercept)-Sylvilagus_floridanus 1.0078 390
## Tree_Density-Canis_latrans 1.0033 817
## Tree_Density-Sylvilagus_floridanus 1.0186 400
## Avg_Canopy_Cover-Canis_latrans 1.0031 786
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0267 105
##
## Detection (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -4.6869 0.1676 -5.0549 -4.6799
## (Intercept)-Sylvilagus_floridanus -4.9602 0.2450 -5.4326 -4.9563
## week-Canis_latrans 44799.5959 2393949.0913 -14314.1143 0.0171
## week-Sylvilagus_floridanus 2779.5225 2162969.8488 -21389.7998 -0.0258
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.3834 1.1001 201
## (Intercept)-Sylvilagus_floridanus -4.4791 1.0026 159
## week-Canis_latrans 28374.2511 1.3221 2244
## week-Sylvilagus_floridanus 19275.9146 1.2903 7900
# 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): 1.441
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6151 0.9753 -2.4563 -0.6422 1.4332 1.0053 983
## Avg_Cogongrass_Cover -0.5680 1.0125 -2.4968 -0.6158 1.6130 1.0074 2005
## I(Avg_Cogongrass_Cover^2) 0.9986 1.1226 -1.4517 1.0127 3.1952 1.0021 991
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.4518 32.2644 0.0559 0.7984 31.5030 1.0057 2800
## Avg_Cogongrass_Cover 24.2448 836.1640 0.0690 1.3442 52.5874 1.3252 3000
## I(Avg_Cogongrass_Cover^2) 13.4419 108.4414 0.0560 1.1731 77.2180 1.2032 2832
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6837 4.1251 0.0494 0.586 9.6117 1.0713 507
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6801 1.9315 -5.1485 -3.0146 1.5172 1.0053 949
## week 0.0265 1.6350 -3.0074 -0.0079 3.2555 1.0010 1281
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.713270e+01 1.987528e+02 0.0652 4.0767 2.211036e+02 1.0065 2561
## week 4.461143e+23 1.022554e+25 0.0900 340.8073 2.017761e+20 1.4682 560
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4093 0.8735 -2.1221 -0.4238
## (Intercept)-Sylvilagus_floridanus -1.1840 0.9963 -3.4362 -1.1282
## Avg_Cogongrass_Cover-Canis_latrans -0.0843 0.8544 -1.7767 -0.1060
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5840 1.0747 -4.0118 -1.4364
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1207 1.5912 0.0887 1.7782
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0060 0.8419 -0.2939 0.8641
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3779 1.0029 504
## (Intercept)-Sylvilagus_floridanus 0.5736 1.0147 405
## Avg_Cogongrass_Cover-Canis_latrans 1.6143 1.0025 798
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1530 1.0281 667
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8731 1.0320 238
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0913 1.0102 331
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.699700e+00 1.576000e-01 -5.02660e+00
## (Intercept)-Sylvilagus_floridanus -4.961100e+00 2.822000e-01 -5.59510e+00
## week-Canis_latrans 6.852113e+08 6.256819e+11 -4.13695e+08
## week-Sylvilagus_floridanus 1.964341e+10 7.830755e+11 -2.13715e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6997 -4.392800e+00 1.0285 316
## (Intercept)-Sylvilagus_floridanus -4.9358 -4.459600e+00 1.0847 112
## week-Canis_latrans 0.0034 2.579045e+08 1.2905 76468
## week-Sylvilagus_floridanus 0.0677 5.193656e+08 1.3517 1557
# 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): 1.5422
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4970 1.5051 -3.3503 -0.5175 2.4728 1.0256 581
## Cogon_Patch_Size 0.1288 1.5529 -3.0032 0.1558 3.1039 1.0033 1144
## Veg_shannon_index 1.2048 1.5474 -1.9691 1.2356 4.1206 1.0157 366
## total_shrub_cover 0.3675 1.3709 -2.4566 0.3758 2.9926 1.0204 429
## Avg_Cogongrass_Cover -0.2881 1.5531 -3.1967 -0.3175 2.7550 1.0015 728
## Tree_Density -0.7922 1.7473 -3.9785 -0.8440 2.6894 1.0336 275
## Avg_Canopy_Cover 0.3267 1.6134 -2.8433 0.3642 3.4140 1.0183 900
## I(Avg_Cogongrass_Cover^2) 0.9706 1.5545 -2.3787 1.1249 3.7844 1.0216 499
## avg_veg_height -0.5440 1.3807 -3.3432 -0.5197 2.2561 1.0065 635
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept) 249.0354 2890.8347 0.0757 5.2625 1255.9958
## Cogon_Patch_Size 3779.9196 35647.7553 0.1699 61.1748 22868.9039
## Veg_shannon_index 234.7764 1572.9560 0.0756 4.9695 1564.6856
## total_shrub_cover 62.2494 598.0840 0.0703 1.7239 331.2400
## Avg_Cogongrass_Cover 86.7384 653.8597 0.0643 2.6661 716.8058
## Tree_Density 4619.7944 38836.4513 0.1302 111.8299 32040.5742
## Avg_Canopy_Cover 1508.1970 16068.7316 0.2698 109.4356 6945.2833
## I(Avg_Cogongrass_Cover^2) 731.5730 5000.6402 0.1001 16.4908 4655.7942
## avg_veg_height 169.9672 1497.6788 0.0566 1.2901 1191.1058
## Rhat ESS
## (Intercept) 1.0200 2519
## Cogon_Patch_Size 1.3282 344
## Veg_shannon_index 1.1760 230
## total_shrub_cover 1.1610 1971
## Avg_Cogongrass_Cover 1.1129 777
## Tree_Density 1.7513 240
## Avg_Canopy_Cover 1.1549 1265
## I(Avg_Cogongrass_Cover^2) 1.3278 188
## avg_veg_height 1.8920 137
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 13.5247 33.374 0.0618 1.3984 106.5238 1.2314 57
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4958 2.0092 -5.1675 -2.7631 1.6979 1.0063 957
## week 0.0130 1.5917 -3.1316 0.0241 3.0730 1.0064 1531
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.462000e+01 2.747581e+02 0.0723 5.7862 2.981782e+02 1.0407 2789
## week 2.053485e+31 8.753202e+32 0.1057 239.5147 5.296523e+25 1.3442 1728
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.0087 3.7476 -8.2598
## (Intercept)-Sylvilagus_floridanus -4.5125 8.0417 -30.0706
## Cogon_Patch_Size-Canis_latrans 16.0271 28.3911 -1.2742
## Cogon_Patch_Size-Sylvilagus_floridanus -11.6659 21.2133 -78.5479
## Veg_shannon_index-Canis_latrans 6.1545 7.2298 0.0324
## Veg_shannon_index-Sylvilagus_floridanus 2.6497 5.7168 -9.2407
## total_shrub_cover-Canis_latrans 1.2961 3.4176 -4.1911
## total_shrub_cover-Sylvilagus_floridanus 0.4966 3.8115 -7.9554
## Avg_Cogongrass_Cover-Canis_latrans 0.5551 5.3965 -8.2148
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4996 4.6031 -13.2675
## Tree_Density-Canis_latrans -18.0112 23.0914 -72.2489
## Tree_Density-Sylvilagus_floridanus -18.3887 31.2993 -127.2295
## Avg_Canopy_Cover-Canis_latrans -1.7371 2.8271 -10.5474
## Avg_Canopy_Cover-Sylvilagus_floridanus 17.4471 16.3521 -2.0515
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 9.7775 11.4441 0.8817
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.1033 6.9047 -2.9708
## avg_veg_height-Canis_latrans -2.4800 5.7784 -19.1261
## avg_veg_height-Sylvilagus_floridanus -2.0223 7.0327 -26.1412
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.2049 8.8540 1.1728 183
## (Intercept)-Sylvilagus_floridanus -2.3241 3.5379 1.1430 46
## Cogon_Patch_Size-Canis_latrans 5.9128 106.5291 2.6924 16
## Cogon_Patch_Size-Sylvilagus_floridanus -4.6060 2.1629 1.5979 24
## Veg_shannon_index-Canis_latrans 3.3172 27.6614 1.1341 28
## Veg_shannon_index-Sylvilagus_floridanus 2.2635 15.8153 1.5951 48
## total_shrub_cover-Canis_latrans 0.9122 10.6553 1.5846 55
## total_shrub_cover-Sylvilagus_floridanus 0.3752 9.8478 1.1053 111
## Avg_Cogongrass_Cover-Canis_latrans -0.1449 16.2627 1.0274 82
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9198 5.1240 1.0041 394
## Tree_Density-Canis_latrans -9.3925 -0.8048 1.8524 21
## Tree_Density-Sylvilagus_floridanus -7.7627 1.1271 3.5449 8
## Avg_Canopy_Cover-Canis_latrans -0.9674 1.4997 1.1449 52
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.8674 60.8226 2.1107 15
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.8597 45.0110 1.8320 14
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9178 26.3551 1.5594 19
## avg_veg_height-Canis_latrans -1.0602 1.9482 3.0564 22
## avg_veg_height-Sylvilagus_floridanus -0.7029 4.0588 2.5034 36
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.715300e+00 1.644000e-01 -5.049400e+00
## (Intercept)-Sylvilagus_floridanus -5.144100e+00 2.937000e-01 -5.809800e+00
## week-Canis_latrans -5.302778e+13 2.277719e+15 -4.173582e+10
## week-Sylvilagus_floridanus -6.530692e+13 2.209820e+15 -4.669399e+10
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7091 -4.410700e+00 1.0384 208
## (Intercept)-Sylvilagus_floridanus -5.1014 -4.657700e+00 1.5793 50
## week-Canis_latrans 0.0357 1.329727e+10 1.3434 1876
## week-Sylvilagus_floridanus 0.1281 9.607771e+09 1.3748 1031
# 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): 1.1805
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0814 0.9049 -1.8836 0.0886 1.9582 1.007 1535
## Avg_Cogongrass_Cover 0.0076 0.8671 -1.7884 0.0122 1.8063 1.005 1862
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.3283 387.4253 0.0614 0.8451 29.0905 1.2845 3000
## Avg_Cogongrass_Cover 8.3580 68.7949 0.0730 1.0008 43.6358 1.1627 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.575 3.5427 0.0541 0.607 8.861 1.0329 607
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5922 2.0315 -5.1666 -2.9591 1.6620 1.0235 778
## shrub_cover 0.0418 0.7289 -1.4540 0.0272 1.6209 1.0092 1105
## veg_height -0.1938 0.7446 -1.7037 -0.2066 1.4535 1.0000 2159
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 52.9489 524.6867 0.0615 4.6984 324.9362 1.2373 2598
## shrub_cover 4.5195 44.6947 0.0610 0.6237 20.7095 1.2905 3000
## veg_height 3.6830 24.9786 0.0722 0.6540 18.8167 1.1482 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6021 0.7692 -0.7182 0.5385
## (Intercept)-Sylvilagus_floridanus -0.3245 0.7434 -1.8255 -0.3120
## Avg_Cogongrass_Cover-Canis_latrans 0.6616 0.7502 -0.3367 0.5404
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5495 0.5778 -1.8074 -0.4909
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3508 1.0048 514
## (Intercept)-Sylvilagus_floridanus 1.1216 1.0107 453
## Avg_Cogongrass_Cover-Canis_latrans 2.3463 1.0194 331
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4902 1.0167 844
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8031 0.1853 -5.1652 -4.8065 -4.4291 1.0262
## (Intercept)-Sylvilagus_floridanus -4.9391 0.2493 -5.4527 -4.9257 -4.4837 1.0746
## shrub_cover-Canis_latrans -0.2965 0.1950 -0.6833 -0.2908 0.0712 1.0297
## shrub_cover-Sylvilagus_floridanus 0.4137 0.4546 -0.4180 0.4000 1.3256 1.0676
## veg_height-Canis_latrans -0.6370 0.1855 -1.0481 -0.6284 -0.3094 1.0179
## veg_height-Sylvilagus_floridanus 0.1798 0.2449 -0.3452 0.1946 0.6471 1.0047
## ESS
## (Intercept)-Canis_latrans 166
## (Intercept)-Sylvilagus_floridanus 173
## shrub_cover-Canis_latrans 190
## shrub_cover-Sylvilagus_floridanus 105
## veg_height-Canis_latrans 142
## veg_height-Sylvilagus_floridanus 188
# 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): 1.3237
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4679 1.3762 -2.2960 0.4854 3.1283 1.0068 434
## Cogon_Patch_Size 0.0873 1.4160 -2.7449 0.0866 2.8963 1.0087 860
## Veg_shannon_index 1.2386 1.2309 -1.4326 1.2721 3.5210 1.0034 793
## total_shrub_cover 0.0400 1.5121 -2.8859 0.0122 3.1135 1.0110 500
## Avg_Cogongrass_Cover 0.5895 1.3649 -2.2549 0.6441 3.1183 1.0091 648
## Tree_Density -1.3110 1.6746 -4.2717 -1.4236 2.1706 1.0377 451
## Avg_Canopy_Cover 0.4878 1.5545 -2.6225 0.5337 3.4355 1.0000 2404
## avg_veg_height -0.1425 1.1959 -2.4803 -0.1487 2.2437 1.0140 343
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 137.5088 1372.4395 0.0783 2.5389 527.9990 1.8335 142
## Cogon_Patch_Size 2535.6263 110573.1082 0.1065 10.3143 3847.3042 1.3214 3000
## Veg_shannon_index 16.5865 124.5768 0.0584 1.0397 100.5521 1.1159 566
## total_shrub_cover 331.5431 2293.5284 0.1591 15.7623 2576.2067 2.1417 54
## Avg_Cogongrass_Cover 43.2201 556.8551 0.0789 2.9506 208.8948 1.2849 3000
## Tree_Density 366.0485 12575.9260 0.0780 6.7365 942.3444 1.3395 3000
## Avg_Canopy_Cover 724.6965 7372.9529 0.8927 57.4108 4669.2469 1.1777 1170
## avg_veg_height 7.5272 43.3380 0.0585 0.8134 46.3986 1.0383 1073
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 23.2328 106.2939 0.0626 1.5135 270.3227 1.6746 49
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5382 2.0475 -5.2251 -2.7631 1.5766 1.0068 660
## shrub_cover 0.1499 0.8543 -1.5155 0.1394 1.9713 1.0073 1780
## veg_height -0.2073 0.6941 -1.5739 -0.2250 1.3579 1.0019 2144
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 36.6255 154.6055 0.0649 5.8904 223.2735 1.0471 2798
## shrub_cover 7.0705 71.6263 0.1132 1.2062 34.4145 1.2234 3000
## veg_height 3.4292 28.0378 0.0661 0.5826 17.1654 1.1288 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.6669 4.3332 -0.9485 1.6630
## (Intercept)-Sylvilagus_floridanus -0.3913 3.6390 -8.6545 -0.0198
## Cogon_Patch_Size-Canis_latrans 4.0691 8.8271 -0.9375 1.9607
## Cogon_Patch_Size-Sylvilagus_floridanus -5.8494 13.8818 -51.3397 -2.0340
## Veg_shannon_index-Canis_latrans 1.6233 1.2819 -0.6615 1.5770
## Veg_shannon_index-Sylvilagus_floridanus 2.0975 2.1037 -0.6593 1.7573
## total_shrub_cover-Canis_latrans 2.6441 1.9749 -0.2474 2.3442
## total_shrub_cover-Sylvilagus_floridanus -6.5427 12.5222 -55.5897 -3.3740
## Avg_Cogongrass_Cover-Canis_latrans 2.3139 2.0907 -0.8968 2.0163
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3256 2.4040 -5.7609 -0.0448
## Tree_Density-Canis_latrans -5.1114 5.2417 -18.5675 -3.6920
## Tree_Density-Sylvilagus_floridanus -3.7642 4.2802 -15.0181 -2.9271
## Avg_Canopy_Cover-Canis_latrans -0.7402 1.7572 -6.7605 -0.4783
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.3889 12.4406 1.3275 9.3674
## avg_veg_height-Canis_latrans -0.2587 1.2985 -3.1510 -0.2189
## avg_veg_height-Sylvilagus_floridanus -0.1257 1.7350 -3.3294 -0.1721
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 17.5449 2.2313 31
## (Intercept)-Sylvilagus_floridanus 5.4117 1.1470 41
## Cogon_Patch_Size-Canis_latrans 27.6394 2.2508 22
## Cogon_Patch_Size-Sylvilagus_floridanus 2.9628 2.3676 15
## Veg_shannon_index-Canis_latrans 4.0788 1.0445 351
## Veg_shannon_index-Sylvilagus_floridanus 7.8492 1.1388 87
## total_shrub_cover-Canis_latrans 7.2828 1.1117 148
## total_shrub_cover-Sylvilagus_floridanus 3.3344 2.7162 10
## Avg_Cogongrass_Cover-Canis_latrans 7.2574 1.0356 309
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2869 1.0172 150
## Tree_Density-Canis_latrans -0.3031 1.6492 38
## Tree_Density-Sylvilagus_floridanus 2.1463 1.3083 80
## Avg_Canopy_Cover-Canis_latrans 1.2102 1.5877 28
## Avg_Canopy_Cover-Sylvilagus_floridanus 48.0430 1.6007 21
## avg_veg_height-Canis_latrans 2.1127 1.0512 202
## avg_veg_height-Sylvilagus_floridanus 3.2431 1.0350 374
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8056 0.1717 -5.1603 -4.8034 -4.4725 1.0228
## (Intercept)-Sylvilagus_floridanus -5.0738 0.2179 -5.4934 -5.0749 -4.6460 1.0759
## shrub_cover-Canis_latrans -0.4520 0.1906 -0.8300 -0.4537 -0.0807 1.0173
## shrub_cover-Sylvilagus_floridanus 0.8727 0.4345 -0.1036 0.8914 1.6320 1.1953
## veg_height-Canis_latrans -0.6117 0.1628 -0.9354 -0.6113 -0.2892 1.0103
## veg_height-Sylvilagus_floridanus 0.1158 0.2595 -0.4245 0.1215 0.5913 1.0437
## ESS
## (Intercept)-Canis_latrans 165
## (Intercept)-Sylvilagus_floridanus 214
## shrub_cover-Canis_latrans 259
## shrub_cover-Sylvilagus_floridanus 46
## veg_height-Canis_latrans 240
## veg_height-Sylvilagus_floridanus 167
# 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): 1.4678
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9102 1.1743 -1.5625 0.9162 3.2339 1.0141 451
## Avg_Cogongrass_Cover -0.1886 1.1417 -2.4498 -0.1836 2.2291 1.0043 1645
## total_shrub_cover -0.3130 1.3150 -2.9794 -0.3032 2.2970 1.0050 2443
## avg_veg_height 0.4306 1.0325 -1.6348 0.3716 2.5369 1.0107 294
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.4268 73.6190 0.0537 0.8713 53.1973 1.2145 3000
## Avg_Cogongrass_Cover 113.0286 5202.7038 0.0901 2.5426 107.7456 1.3231 3000
## total_shrub_cover 1073.0028 55043.1703 0.2380 9.2393 366.8859 1.3241 3000
## avg_veg_height 4.0906 20.6735 0.0548 0.6566 28.5375 1.0925 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.6701 8.4015 0.0702 1.2834 22.776 1.0587 225
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5812 2.0158 -5.2717 -2.8385 1.6845 1.0077 828
## shrub_cover 0.2291 0.8923 -1.6585 0.2337 2.0653 1.0016 2179
## veg_height -0.2556 0.6979 -1.6675 -0.2845 1.3042 1.0017 2201
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 60.9201 800.3890 0.0716 5.6617 292.6583 1.3324 3000
## shrub_cover 13.5053 203.5419 0.1511 1.4718 40.9079 1.2155 3000
## veg_height 2.9059 14.5135 0.0627 0.5359 20.4905 1.0587 2780
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.4355 1.1381 -0.5146 1.3153
## (Intercept)-Sylvilagus_floridanus 1.1982 1.4666 -1.4104 1.1098
## Avg_Cogongrass_Cover-Canis_latrans 0.7923 1.1929 -1.1605 0.6768
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.5856 1.5186 -5.4148 -1.3492
## total_shrub_cover-Canis_latrans 1.5435 1.2191 -0.3020 1.3504
## total_shrub_cover-Sylvilagus_floridanus -3.6018 2.8626 -11.7659 -2.9649
## avg_veg_height-Canis_latrans 0.4497 0.9456 -1.2730 0.3916
## avg_veg_height-Sylvilagus_floridanus 0.6797 1.0963 -1.2250 0.5586
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 3.9796 1.0033 222
## (Intercept)-Sylvilagus_floridanus 4.4724 1.0313 112
## Avg_Cogongrass_Cover-Canis_latrans 3.5125 1.0330 615
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6321 1.1114 215
## total_shrub_cover-Canis_latrans 4.3525 1.0109 200
## total_shrub_cover-Sylvilagus_floridanus 0.0690 1.0873 91
## avg_veg_height-Canis_latrans 2.5431 1.0175 696
## avg_veg_height-Sylvilagus_floridanus 3.1557 1.0072 318
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8306 0.1713 -5.1801 -4.8247 -4.5141 1.1026
## (Intercept)-Sylvilagus_floridanus -5.1640 0.2393 -5.6144 -5.1636 -4.6918 1.1082
## shrub_cover-Canis_latrans -0.4300 0.2254 -0.8430 -0.4403 0.0479 1.0817
## shrub_cover-Sylvilagus_floridanus 1.0854 0.4128 0.1549 1.0988 1.8259 1.0924
## veg_height-Canis_latrans -0.6359 0.1826 -0.9817 -0.6278 -0.2902 1.0556
## veg_height-Sylvilagus_floridanus 0.0152 0.2566 -0.4587 0.0143 0.5237 1.1569
## ESS
## (Intercept)-Canis_latrans 166
## (Intercept)-Sylvilagus_floridanus 133
## shrub_cover-Canis_latrans 140
## shrub_cover-Sylvilagus_floridanus 39
## veg_height-Canis_latrans 159
## veg_height-Sylvilagus_floridanus 109
# 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): 1.144
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0359 0.7852 -1.5611 0.0394 1.6645 1.0024 2659
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9766 28.9134 0.0608 0.7045 23.9256 1.2396 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6965 1.9631 -5.1519 -3.1437 1.4055 1.0012 812
## shrub_cover -0.0015 0.7318 -1.4990 -0.0109 1.5610 1.0075 1888
## veg_height -0.2087 0.7052 -1.6394 -0.2369 1.3411 1.0057 2106
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.9594 768.3716 0.0575 4.0759 253.2915 1.1487 3000
## shrub_cover 3.5008 21.9722 0.0590 0.6059 18.1295 1.1759 3000
## veg_height 4.7339 65.3492 0.0700 0.5961 21.4147 1.3429 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.4488 0.418 -0.3184 0.4299 1.345 1.0051
## (Intercept)-Sylvilagus_floridanus -0.3759 0.435 -1.2296 -0.3776 0.499 1.0005
## ESS
## (Intercept)-Canis_latrans 1867
## (Intercept)-Sylvilagus_floridanus 967
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7680 0.1765 -5.1171 -4.7660 -4.4294 1.0649
## (Intercept)-Sylvilagus_floridanus -4.8703 0.2380 -5.3923 -4.8544 -4.4360 1.0083
## shrub_cover-Canis_latrans -0.3174 0.1980 -0.7018 -0.3214 0.0707 1.0227
## shrub_cover-Sylvilagus_floridanus 0.3911 0.4154 -0.3648 0.3849 1.2335 1.0330
## veg_height-Canis_latrans -0.6076 0.1804 -0.9739 -0.6041 -0.2756 1.2249
## veg_height-Sylvilagus_floridanus 0.1411 0.2309 -0.3082 0.1442 0.5849 1.0785
## ESS
## (Intercept)-Canis_latrans 188
## (Intercept)-Sylvilagus_floridanus 181
## shrub_cover-Canis_latrans 199
## shrub_cover-Sylvilagus_floridanus 123
## veg_height-Canis_latrans 183
## veg_height-Sylvilagus_floridanus 210
#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): 1.1782
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1042 0.9454 -1.8453 0.1324 1.9611 1.0072 931
## Veg_shannon_index 0.7061 0.8343 -1.1666 0.7302 2.2636 1.0076 1986
## Avg_Cogongrass_Cover 0.2659 0.9790 -1.7809 0.2704 2.3205 1.0012 2232
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.9394 175.4078 0.0626 0.9271 42.3438 1.2463 3000
## Veg_shannon_index 4.9820 30.3057 0.0544 0.6031 31.4452 1.0780 3000
## Avg_Cogongrass_Cover 14.5950 101.6136 0.0889 1.6820 86.7022 1.1495 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8357 4.0829 0.0544 0.6232 11.7694 1.0289 401
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6065 2.0327 -5.1662 -3.0022 1.7624 1.0170 803
## shrub_cover 0.0352 0.7306 -1.4540 0.0177 1.6117 1.0134 810
## veg_height -0.1864 0.7698 -1.7486 -0.2119 1.5445 1.0013 2418
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 38.3204 257.7363 0.0616 4.5932 260.7040 1.2492 3000
## shrub_cover 4.9299 72.6448 0.0559 0.5364 20.5877 1.2230 3000
## veg_height 3.6437 17.7435 0.0758 0.7127 21.4220 1.0666 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6882 0.9067 -0.9372 0.6414
## (Intercept)-Sylvilagus_floridanus -0.3482 0.8320 -1.9757 -0.3670
## Veg_shannon_index-Canis_latrans 1.2065 0.6799 0.0530 1.1500
## Veg_shannon_index-Sylvilagus_floridanus 0.5952 0.6223 -0.5569 0.5778
## Avg_Cogongrass_Cover-Canis_latrans 1.2907 0.8861 -0.0156 1.1404
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4652 0.6577 -1.8363 -0.4376
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5418 1.0210 305
## (Intercept)-Sylvilagus_floridanus 1.3907 1.0135 464
## Veg_shannon_index-Canis_latrans 2.7435 1.0076 964
## Veg_shannon_index-Sylvilagus_floridanus 1.9632 1.0012 731
## Avg_Cogongrass_Cover-Canis_latrans 3.3750 1.0357 672
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8084 1.0022 810
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7749 0.1717 -5.1281 -4.7671 -4.4623 1.0393
## (Intercept)-Sylvilagus_floridanus -4.9633 0.2501 -5.5073 -4.9501 -4.5125 1.0010
## shrub_cover-Canis_latrans -0.2658 0.2124 -0.6737 -0.2682 0.1529 1.1274
## shrub_cover-Sylvilagus_floridanus 0.3896 0.4431 -0.3536 0.3397 1.3258 1.0329
## veg_height-Canis_latrans -0.6262 0.1743 -0.9974 -0.6210 -0.3126 1.0782
## veg_height-Sylvilagus_floridanus 0.2183 0.2790 -0.3357 0.2112 0.7361 1.0445
## ESS
## (Intercept)-Canis_latrans 165
## (Intercept)-Sylvilagus_floridanus 160
## shrub_cover-Canis_latrans 186
## shrub_cover-Sylvilagus_floridanus 105
## veg_height-Canis_latrans 175
## veg_height-Sylvilagus_floridanus 142
# 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): 1.4495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6011 1.3957 -2.1997 0.5874 3.3627 1.0632 494
## Cogon_Patch_Size 0.0288 1.4069 -2.8346 0.0438 2.8024 1.0063 2079
## Avg_Cogongrass_Cover 0.1446 1.1306 -2.2050 0.1191 2.5375 1.0407 445
## total_shrub_cover -0.1284 1.3715 -3.0064 -0.1159 2.5399 1.0143 1352
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 80.1884 1903.9245 0.0657 1.9370 356.7669 1.3549 3000
## Cogon_Patch_Size 2158.1444 30916.3788 0.1621 16.8752 11458.7981 1.6830 1395
## Avg_Cogongrass_Cover 36.1882 580.2127 0.0592 1.0265 120.5613 1.1776 1064
## total_shrub_cover 957.8767 33110.6022 0.1349 13.4123 2658.9855 1.3142 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 30.2172 93.7471 0.082 3.5126 257.3871 4.1857 34
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5427 2.0555 -5.3331 -2.7839 1.6805 1.0170 874
## shrub_cover 0.1606 0.8692 -1.6786 0.1487 1.9408 1.0089 1793
## veg_height -0.2434 0.7318 -1.7639 -0.2810 1.3215 1.0086 1882
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 45.8041 234.8976 0.0655 6.2627 313.7772 1.0759 2507
## shrub_cover 5.3934 21.6411 0.1192 1.3215 33.5147 1.1572 2485
## veg_height 4.5061 45.6986 0.0668 0.5797 23.6376 1.1468 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 2.6286 3.9621 -1.3388 1.5747
## (Intercept)-Sylvilagus_floridanus 0.9416 2.9533 -3.4475 0.5371
## Cogon_Patch_Size-Canis_latrans 4.7973 7.4851 -0.6245 2.4020
## Cogon_Patch_Size-Sylvilagus_floridanus -12.2830 24.7278 -95.7055 -2.7487
## Avg_Cogongrass_Cover-Canis_latrans 0.9929 2.7643 -1.2609 0.5047
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2871 1.9528 -4.5286 -0.2482
## total_shrub_cover-Canis_latrans 2.4607 2.5432 -0.3416 1.7018
## total_shrub_cover-Sylvilagus_floridanus -7.3124 10.9040 -42.3151 -3.2439
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 14.2969 1.3369 38
## (Intercept)-Sylvilagus_floridanus 8.5746 1.2772 63
## Cogon_Patch_Size-Canis_latrans 22.5761 3.0293 36
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8073 6.8734 9
## Avg_Cogongrass_Cover-Canis_latrans 6.5612 2.0384 49
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4761 1.2399 197
## total_shrub_cover-Canis_latrans 9.1349 2.0525 46
## total_shrub_cover-Sylvilagus_floridanus 0.8363 3.8656 5
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8622 0.1956 -5.3044 -4.8481 -4.5137 1.1854
## (Intercept)-Sylvilagus_floridanus -5.1711 0.2711 -5.7594 -5.1634 -4.6841 1.3163
## shrub_cover-Canis_latrans -0.4532 0.2137 -0.8546 -0.4591 -0.0337 1.2294
## shrub_cover-Sylvilagus_floridanus 0.9289 0.4600 -0.1320 0.9455 1.7892 1.2904
## veg_height-Canis_latrans -0.6571 0.1960 -1.0681 -0.6458 -0.2977 1.1523
## veg_height-Sylvilagus_floridanus 0.0643 0.2732 -0.4508 0.0632 0.6187 1.1283
## ESS
## (Intercept)-Canis_latrans 107
## (Intercept)-Sylvilagus_floridanus 69
## shrub_cover-Canis_latrans 129
## shrub_cover-Sylvilagus_floridanus 46
## veg_height-Canis_latrans 151
## veg_height-Sylvilagus_floridanus 91
#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): 1.2268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1517 0.9733 -1.6889 0.1238 2.2267 1.0070 795
## Tree_Density -1.1016 1.0959 -3.1641 -1.1205 1.3703 1.0238 1082
## Avg_Canopy_Cover 0.5619 1.3683 -2.3276 0.6386 3.1665 1.0029 2704
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.9597 144.7453 0.0548 0.8145 42.8397 1.3256 3000
## Tree_Density 11.8063 232.2209 0.0506 0.8515 62.5356 1.2954 3000
## Avg_Canopy_Cover 60.1347 218.6295 0.5147 11.5463 408.7395 1.0260 1220
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2587 9.5388 0.0497 0.5423 30.4261 1.0952 115
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6867 1.9999 -5.1851 -3.1161 1.6948 1.0104 800
## shrub_cover 0.1197 0.7650 -1.4563 0.0980 1.7872 1.0012 1549
## veg_height -0.1861 0.7175 -1.6528 -0.2059 1.4498 1.0034 2367
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 65.8928 808.0248 0.0595 3.8811 240.9380 1.1060 3000
## shrub_cover 5.5206 68.0754 0.0805 0.7967 29.4242 1.2273 2115
## veg_height 5.1285 60.6987 0.0771 0.6160 26.6520 1.2677 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.5736 0.8729 -0.9310 0.4848 2.8231
## (Intercept)-Sylvilagus_floridanus -0.3023 1.0662 -2.3877 -0.3275 1.9245
## Tree_Density-Canis_latrans -1.4168 0.8871 -3.4759 -1.3194 0.0600
## Tree_Density-Sylvilagus_floridanus -1.7339 1.4688 -5.1294 -1.5550 0.4075
## Avg_Canopy_Cover-Canis_latrans -0.6473 0.7687 -2.7303 -0.5222 0.4325
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7093 2.7410 1.0049 4.1321 11.4107
## Rhat ESS
## (Intercept)-Canis_latrans 1.0135 312
## (Intercept)-Sylvilagus_floridanus 1.0343 315
## Tree_Density-Canis_latrans 1.0381 674
## Tree_Density-Sylvilagus_floridanus 1.0910 363
## Avg_Canopy_Cover-Canis_latrans 1.0690 152
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0737 158
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8181 0.1830 -5.1775 -4.8197 -4.4416 1.0337
## (Intercept)-Sylvilagus_floridanus -4.9742 0.2359 -5.4701 -4.9635 -4.5507 1.0033
## shrub_cover-Canis_latrans -0.3290 0.2014 -0.7386 -0.3288 0.0610 1.0327
## shrub_cover-Sylvilagus_floridanus 0.6342 0.3817 -0.0921 0.6358 1.3883 1.0357
## veg_height-Canis_latrans -0.6387 0.1685 -0.9729 -0.6331 -0.3225 1.0121
## veg_height-Sylvilagus_floridanus 0.1789 0.2470 -0.3055 0.1745 0.6853 1.0673
## ESS
## (Intercept)-Canis_latrans 178
## (Intercept)-Sylvilagus_floridanus 179
## shrub_cover-Canis_latrans 182
## shrub_cover-Sylvilagus_floridanus 135
## veg_height-Canis_latrans 196
## veg_height-Sylvilagus_floridanus 192
# 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): 1.2268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5288 0.9784 -2.3881 -0.5750 1.5087 1.0098 1141
## Avg_Cogongrass_Cover -0.4595 1.1171 -2.6870 -0.4792 1.8737 1.0219 2058
## I(Avg_Cogongrass_Cover^2) 1.0472 1.0848 -1.2707 1.0652 3.0420 1.0133 1055
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.0173 97.8986 0.0622 0.9482 43.0296 1.1052 3000
## Avg_Cogongrass_Cover 19.5600 121.2987 0.1043 2.6562 119.6254 1.0658 1015
## I(Avg_Cogongrass_Cover^2) 12.2639 101.3215 0.0591 0.9500 66.0703 1.1558 2738
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6705 3.8519 0.0463 0.5806 9.3834 1.0305 486
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5764 1.9750 -5.1736 -2.8968 1.5152 1.0009 882
## shrub_cover -0.0205 0.7138 -1.4774 -0.0428 1.4539 1.0076 1629
## veg_height -0.1311 0.7326 -1.5628 -0.1657 1.5075 1.0016 2510
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 42.4680 229.5927 0.0674 5.0748 269.0825 1.0589 2418
## shrub_cover 4.0916 56.3454 0.0559 0.4909 18.2067 1.2909 3000
## veg_height 6.5888 90.1361 0.0862 0.7225 27.3146 1.0623 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2030 0.9582 -1.9206 -0.2518
## (Intercept)-Sylvilagus_floridanus -1.1647 0.9710 -3.2568 -1.1236
## Avg_Cogongrass_Cover-Canis_latrans 0.3880 1.0203 -1.2783 0.2766
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -2.1544 2.0556 -6.6713 -1.7958
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0196 1.4668 0.0275 1.7365
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1203 1.1024 -0.2204 0.9516
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9516 1.0533 547
## (Intercept)-Sylvilagus_floridanus 0.6294 1.0384 545
## Avg_Cogongrass_Cover-Canis_latrans 2.7726 1.0406 535
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0947 1.7243 46
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8218 1.0243 240
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.5165 1.4091 82
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.8157 0.1804 -5.1734 -4.8156 -4.4687 1.0288
## (Intercept)-Sylvilagus_floridanus -4.9658 0.2688 -5.5220 -4.9584 -4.4495 1.1390
## shrub_cover-Canis_latrans -0.2934 0.2039 -0.6921 -0.2901 0.1009 1.1821
## shrub_cover-Sylvilagus_floridanus 0.2724 0.4033 -0.5170 0.2517 1.0885 1.3171
## veg_height-Canis_latrans -0.6493 0.1817 -1.0116 -0.6459 -0.2996 1.0505
## veg_height-Sylvilagus_floridanus 0.2555 0.2481 -0.2475 0.2583 0.7478 1.1224
## ESS
## (Intercept)-Canis_latrans 179
## (Intercept)-Sylvilagus_floridanus 131
## shrub_cover-Canis_latrans 176
## shrub_cover-Sylvilagus_floridanus 106
## veg_height-Canis_latrans 157
## veg_height-Sylvilagus_floridanus 134
# 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): 1.2828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4472 1.4600 -3.2897 -0.4619 2.5926 1.0118 517
## Cogon_Patch_Size 0.2214 1.5051 -2.7843 0.2449 3.1186 1.0121 1216
## Veg_shannon_index 1.2238 1.3325 -1.6471 1.3292 3.6374 1.0716 441
## total_shrub_cover 0.2092 1.4396 -2.7931 0.2160 2.9880 1.0380 374
## Avg_Cogongrass_Cover -0.2679 1.4747 -3.1215 -0.2926 2.6308 1.0496 791
## Tree_Density -1.2366 1.6493 -4.2828 -1.3317 2.1496 1.0011 430
## Avg_Canopy_Cover 0.4819 1.5656 -2.7638 0.5499 3.4272 0.9999 2410
## I(Avg_Cogongrass_Cover^2) 1.0082 1.3817 -2.0274 1.0472 3.4988 1.0145 647
## avg_veg_height -0.2682 1.2418 -2.8277 -0.2190 2.1344 1.0037 296
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 39.3261 308.2597 0.0705 2.3806 239.4200 1.1623
## Cogon_Patch_Size 743.1985 20784.4973 0.1395 20.5913 2444.1295 1.3360
## Veg_shannon_index 148.3387 1604.6765 0.0581 1.2805 909.9293 1.8067
## total_shrub_cover 351.4601 3628.4582 0.0956 7.4315 2133.4109 1.1862
## Avg_Cogongrass_Cover 97.2542 1591.1909 0.0635 1.6700 516.1128 1.3973
## Tree_Density 246.8388 2194.5123 0.0827 9.6560 1499.8512 1.1978
## Avg_Canopy_Cover 556.1192 3421.9958 1.4245 72.3886 3212.2911 1.0645
## I(Avg_Cogongrass_Cover^2) 49.1011 303.5340 0.0818 2.5991 355.6928 1.0907
## avg_veg_height 7.7693 66.5871 0.0583 0.8212 44.1579 1.2970
## ESS
## (Intercept) 1481
## Cogon_Patch_Size 3000
## Veg_shannon_index 132
## total_shrub_cover 1043
## Avg_Cogongrass_Cover 337
## Tree_Density 273
## Avg_Canopy_Cover 1397
## I(Avg_Cogongrass_Cover^2) 233
## avg_veg_height 759
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 52.4286 260.5685 0.0629 1.215 609.1476 2.8833 32
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5926 2.0036 -5.2081 -2.9161 1.6657 1.0023 842
## shrub_cover 0.1001 0.7746 -1.4998 0.0890 1.7442 1.0026 1083
## veg_height -0.1553 0.7431 -1.6759 -0.1643 1.4099 1.0003 1666
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 55.1307 782.1101 0.0637 4.9532 224.5080 1.2883 3000
## shrub_cover 5.1554 40.1868 0.0841 0.8528 30.9362 1.2203 3000
## veg_height 6.6211 193.3150 0.0742 0.6572 19.3408 1.3188 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0608 2.3223 -4.1295 -0.1032
## (Intercept)-Sylvilagus_floridanus -1.9883 3.0063 -9.7793 -1.6014
## Cogon_Patch_Size-Canis_latrans 5.6588 6.1037 -0.2860 3.8308
## Cogon_Patch_Size-Sylvilagus_floridanus -4.8092 7.4626 -28.1088 -2.6037
## Veg_shannon_index-Canis_latrans 2.7117 3.5039 -0.3052 2.0158
## Veg_shannon_index-Sylvilagus_floridanus 3.3004 7.1500 -0.8337 1.8362
## total_shrub_cover-Canis_latrans 2.8703 4.1834 -0.6633 1.7989
## total_shrub_cover-Sylvilagus_floridanus -4.8435 9.9429 -38.7512 -1.4796
## Avg_Cogongrass_Cover-Canis_latrans 0.2165 2.6463 -3.9541 -0.0274
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.8692 5.4075 -13.7454 -0.9176
## Tree_Density-Canis_latrans -5.8121 6.5661 -21.3671 -4.0308
## Tree_Density-Sylvilagus_floridanus -4.7627 6.1131 -22.6685 -3.1304
## Avg_Canopy_Cover-Canis_latrans -0.9832 2.1020 -8.1387 -0.5380
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.9715 9.4259 1.7169 10.2004
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5496 3.8173 0.1118 2.5502
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3580 2.3205 -2.1879 1.1141
## avg_veg_height-Canis_latrans -0.4131 1.4525 -3.6144 -0.2970
## avg_veg_height-Sylvilagus_floridanus -0.3656 1.7509 -4.2975 -0.3289
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.2610 1.1772 195
## (Intercept)-Sylvilagus_floridanus 3.0973 1.0474 105
## Cogon_Patch_Size-Canis_latrans 22.3544 1.4244 53
## Cogon_Patch_Size-Sylvilagus_floridanus 2.4245 1.7943 49
## Veg_shannon_index-Canis_latrans 15.2134 1.8500 20
## Veg_shannon_index-Sylvilagus_floridanus 35.3587 2.9010 18
## total_shrub_cover-Canis_latrans 14.5560 1.6666 38
## total_shrub_cover-Sylvilagus_floridanus 3.3043 1.4518 9
## Avg_Cogongrass_Cover-Canis_latrans 6.3887 1.0946 189
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2780 2.0150 31
## Tree_Density-Canis_latrans -0.5453 1.1239 62
## Tree_Density-Sylvilagus_floridanus 2.1124 1.1452 93
## Avg_Canopy_Cover-Canis_latrans 1.2124 1.9082 27
## Avg_Canopy_Cover-Sylvilagus_floridanus 37.3059 1.2584 32
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 16.1816 1.3712 33
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 7.3635 1.1234 114
## avg_veg_height-Canis_latrans 2.1440 1.0583 173
## avg_veg_height-Sylvilagus_floridanus 2.9380 1.0054 351
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -4.7594 0.1699 -5.0910 -4.7539 -4.4368 1.0671
## (Intercept)-Sylvilagus_floridanus -5.0341 0.2388 -5.5251 -5.0238 -4.5814 1.1241
## shrub_cover-Canis_latrans -0.3967 0.1901 -0.7790 -0.3965 -0.0202 1.1154
## shrub_cover-Sylvilagus_floridanus 0.6890 0.4806 -0.3236 0.7152 1.5319 1.0693
## veg_height-Canis_latrans -0.5902 0.1782 -0.9300 -0.5887 -0.2521 1.0246
## veg_height-Sylvilagus_floridanus 0.2199 0.2676 -0.2800 0.2181 0.7245 1.0238
## ESS
## (Intercept)-Canis_latrans 195
## (Intercept)-Sylvilagus_floridanus 113
## shrub_cover-Canis_latrans 171
## shrub_cover-Sylvilagus_floridanus 64
## veg_height-Canis_latrans 184
## veg_height-Sylvilagus_floridanus 134
#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): 1.3903
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0348 0.7565 -1.4919 0.0312 1.5621 1.006 2427
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.9038 44.817 0.0571 0.6165 20.9711 1.237 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6705 1.9844 -5.1471 -3.1473 1.5942 1.0093 850
## week 0.0035 1.6698 -3.3073 0.0304 3.2684 1.0099 1350
## I(week^2) -0.0276 1.6691 -3.3469 0.0024 3.1925 1.0059 1265
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.616390e+01 1.866898e+02 0.0584 3.8769 2.392940e+02 1.0384 3000
## week 7.657394e+20 2.621838e+22 0.1080 322.5238 3.153824e+16 1.3729 1188
## I(week^2) 3.938382e+12 1.593627e+14 0.0946 32.1352 3.766638e+10 1.3496 1669
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.4010 0.4327 -0.3433 0.3683 1.3349 1.0067
## (Intercept)-Sylvilagus_floridanus -0.3542 0.4610 -1.2014 -0.3705 0.5819 1.0222
## ESS
## (Intercept)-Canis_latrans 1464
## (Intercept)-Sylvilagus_floridanus 657
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.655700e+00 1.676000e-01 -5.0216
## (Intercept)-Sylvilagus_floridanus -4.914900e+00 2.683000e-01 -5.4846
## week-Canis_latrans -4.975220e+08 1.815241e+10 -7398459.3601
## week-Sylvilagus_floridanus -2.742429e+08 2.097174e+10 -7298847.8058
## I(week^2)-Canis_latrans 4.904417e+04 2.789376e+06 -24855.3423
## I(week^2)-Sylvilagus_floridanus 8.274928e+03 4.622906e+05 -14735.1696
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6390 -4.3580 1.0423 198
## (Intercept)-Sylvilagus_floridanus -4.8986 -4.4325 1.0605 152
## week-Canis_latrans -0.1003 5933701.2262 1.3637 1431
## week-Sylvilagus_floridanus 0.0356 6675030.2785 1.3074 6260
## I(week^2)-Canis_latrans -0.0393 11826.0897 1.3160 3552
## I(week^2)-Sylvilagus_floridanus -0.0728 24529.8644 1.2964 4879
#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): 1.5288
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1744 1.3785 -2.6693 0.2038 2.8844 1.0223 534
## Cogon_Patch_Size -0.1125 1.3598 -2.7833 -0.1054 2.7766 1.0302 993
## Veg_shannon_index 1.3119 1.3035 -1.5296 1.3424 3.8022 1.0100 672
## total_shrub_cover 0.6070 1.2437 -1.8930 0.6174 3.0734 1.0052 571
## Avg_Cogongrass_Cover 0.9122 1.4178 -2.0366 0.9538 3.6020 1.0130 1122
## Tree_Density -1.3625 1.6382 -4.4476 -1.4764 2.0808 1.0059 742
## Avg_Canopy_Cover 0.4341 1.5818 -2.6876 0.4943 3.5055 1.0061 2831
## avg_veg_height -0.5116 1.2246 -2.9971 -0.4961 1.9290 1.0935 627
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 49.0794 488.8173 0.0743 3.3564 232.9159 1.5231 569
## Cogon_Patch_Size 518.6803 8400.6073 0.0988 8.3957 2324.9326 1.1902 2178
## Veg_shannon_index 56.1864 814.7953 0.0656 1.4025 239.0029 1.2225 2442
## total_shrub_cover 17.5487 130.9668 0.0634 1.1601 105.4061 1.4472 517
## Avg_Cogongrass_Cover 50.2384 361.0669 0.0727 3.1105 260.3137 1.2061 1723
## Tree_Density 241.7987 1972.7028 0.0947 9.2096 1374.3397 1.0409 1869
## Avg_Canopy_Cover 4621.3406 137258.2747 1.5903 74.0161 5836.6175 1.2836 3000
## avg_veg_height 18.3396 123.3919 0.0612 0.9599 113.9842 1.7923 225
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 84.8134 416.178 0.0636 1.3066 912.5337 3.4332 61
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5305 2.0405 -5.1714 -2.8701 1.8462 1.0048 809
## week -0.0261 1.6460 -3.2811 0.0077 3.1987 1.0036 1639
## I(week^2) -0.0406 1.7149 -3.3698 -0.0579 3.2988 1.0099 1306
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.702220e+01 2.909822e+02 0.0624 5.0542 3.239514e+02 1.1006 3000
## week 4.974067e+16 1.946576e+18 0.0968 87.2817 2.721039e+14 1.3392 2093
## I(week^2) 3.883175e+14 1.284084e+16 0.0858 281.1609 1.996326e+12 1.3775 1077
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.9829 2.7317 -1.6522 1.3929
## (Intercept)-Sylvilagus_floridanus -1.3997 3.0730 -9.7114 -0.9428
## Cogon_Patch_Size-Canis_latrans 3.6231 6.2567 -1.3134 1.7380
## Cogon_Patch_Size-Sylvilagus_floridanus -4.6625 9.3985 -32.4511 -2.0455
## Veg_shannon_index-Canis_latrans 2.4491 2.0246 -0.1380 1.9948
## Veg_shannon_index-Sylvilagus_floridanus 2.5195 2.9752 -0.4529 1.9499
## total_shrub_cover-Canis_latrans 1.4044 1.9510 -1.3277 1.0497
## total_shrub_cover-Sylvilagus_floridanus 0.5916 1.9017 -3.3709 0.6008
## Avg_Cogongrass_Cover-Canis_latrans 3.2734 2.6982 -0.2654 2.6495
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5112 2.7183 -5.0613 0.7509
## Tree_Density-Canis_latrans -5.2529 4.7411 -18.8348 -3.7102
## Tree_Density-Sylvilagus_floridanus -4.8949 4.4373 -17.1223 -3.5604
## Avg_Canopy_Cover-Canis_latrans -1.2788 2.6469 -9.7713 -0.5529
## Avg_Canopy_Cover-Sylvilagus_floridanus 15.2875 14.9142 2.0912 10.2201
## avg_veg_height-Canis_latrans -1.1029 2.0125 -7.2783 -0.7321
## avg_veg_height-Sylvilagus_floridanus -0.7942 2.7852 -6.2500 -0.5903
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 9.5054 1.4439 72
## (Intercept)-Sylvilagus_floridanus 3.1393 1.2131 87
## Cogon_Patch_Size-Canis_latrans 22.8355 2.8234 27
## Cogon_Patch_Size-Sylvilagus_floridanus 1.9189 2.8755 30
## Veg_shannon_index-Canis_latrans 8.2784 1.3167 119
## Veg_shannon_index-Sylvilagus_floridanus 9.9000 1.4549 65
## total_shrub_cover-Canis_latrans 7.1270 1.3134 95
## total_shrub_cover-Sylvilagus_floridanus 4.2701 1.0543 320
## Avg_Cogongrass_Cover-Canis_latrans 10.5971 1.1496 163
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 4.7178 1.1410 286
## Tree_Density-Canis_latrans -0.6110 1.2148 93
## Tree_Density-Sylvilagus_floridanus 0.3168 1.1364 99
## Avg_Canopy_Cover-Canis_latrans 1.1897 3.1774 14
## Avg_Canopy_Cover-Sylvilagus_floridanus 65.8849 3.1517 13
## avg_veg_height-Canis_latrans 1.5458 2.1964 45
## avg_veg_height-Sylvilagus_floridanus 2.8301 1.4844 188
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7036 1.539000e-01 -5.0082
## (Intercept)-Sylvilagus_floridanus -5.0498 2.436000e-01 -5.5615
## week-Canis_latrans -1459822.0260 2.162867e+08 -328527.9566
## week-Sylvilagus_floridanus 1731002.0393 1.302989e+08 -447520.8440
## I(week^2)-Canis_latrans 128896.2661 8.989845e+06 -105428.5352
## I(week^2)-Sylvilagus_floridanus -17939.0684 1.843886e+07 -74196.3844
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6940 -4.4145 1.0242 229
## (Intercept)-Sylvilagus_floridanus -5.0433 -4.5858 1.0038 145
## week-Canis_latrans -0.1580 511332.9217 1.2532 3490
## week-Sylvilagus_floridanus -0.0740 453534.4572 1.2620 6721
## I(week^2)-Canis_latrans 0.0636 44164.9950 1.3130 4200
## I(week^2)-Sylvilagus_floridanus 0.0467 92079.2950 1.2896 103013
#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): 1.544
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2240 0.9928 -1.7788 0.1876 2.3074 1.0314 794
## Avg_Cogongrass_Cover 0.0904 0.9467 -1.8227 0.0972 1.9869 1.0153 1403
## total_shrub_cover 0.1563 0.9143 -1.8578 0.1887 1.9797 1.0087 1603
## avg_veg_height -0.1292 0.8515 -1.8353 -0.1199 1.6158 1.0010 1506
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.2424 219.1391 0.0629 0.9185 38.9094 1.2639 1465
## Avg_Cogongrass_Cover 6.6500 33.9146 0.0683 1.1155 39.8731 1.0180 3000
## total_shrub_cover 9.5405 121.5513 0.0558 0.8859 42.5891 1.2623 3000
## avg_veg_height 4.6056 25.5631 0.0513 0.5241 31.2481 1.0334 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.5734 4.6084 0.0677 0.9292 15.4906 1.0873 378
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6665 1.9761 -5.2181 -3.0405 1.6559 1.0009 903
## week -0.0416 1.6737 -3.3146 -0.0158 3.2168 1.0012 1613
## I(week^2) -0.0052 1.6588 -3.2718 0.0037 3.2779 1.0032 1542
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.670960e+01 3.600888e+02 0.0617 4.4950 2.738688e+02 1.0030 3000
## week 1.792593e+18 7.030233e+19 0.1072 474.2323 1.277410e+15 1.3459 1725
## I(week^2) 1.829933e+20 6.918206e+21 0.1028 306.7243 5.042662e+17 1.3584 1508
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7525 0.8567 -0.7277 0.6625
## (Intercept)-Sylvilagus_floridanus -0.1684 0.9808 -2.0388 -0.2077
## Avg_Cogongrass_Cover-Canis_latrans 0.7429 0.8724 -0.7404 0.6622
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4775 0.7880 -2.2008 -0.4212
## total_shrub_cover-Canis_latrans 0.7565 0.7513 -0.4362 0.6703
## total_shrub_cover-Sylvilagus_floridanus -0.2894 0.8751 -2.3816 -0.2041
## avg_veg_height-Canis_latrans -0.1188 0.7109 -1.4868 -0.1207
## avg_veg_height-Sylvilagus_floridanus -0.1992 0.7135 -1.6388 -0.1837
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.8095 1.0492 330
## (Intercept)-Sylvilagus_floridanus 1.9315 1.0828 284
## Avg_Cogongrass_Cover-Canis_latrans 2.6955 1.0080 797
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9455 1.0066 774
## total_shrub_cover-Canis_latrans 2.4992 1.0148 522
## total_shrub_cover-Sylvilagus_floridanus 1.1926 1.0725 298
## avg_veg_height-Canis_latrans 1.3190 1.0085 786
## avg_veg_height-Sylvilagus_floridanus 1.2066 1.0141 895
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.714300e+00 1.772000e-01 -5.070000e+00
## (Intercept)-Sylvilagus_floridanus -5.027600e+00 2.929000e-01 -5.614800e+00
## week-Canis_latrans -2.164753e+07 1.215044e+09 -1.303227e+06
## week-Sylvilagus_floridanus 1.992992e+07 1.250198e+09 -6.937196e+05
## I(week^2)-Canis_latrans 1.609784e+08 2.374635e+10 -1.030668e+07
## I(week^2)-Sylvilagus_floridanus -8.428844e+06 6.118453e+09 -1.536735e+07
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7110 -4.3898 1.0964 174
## (Intercept)-Sylvilagus_floridanus -5.0127 -4.4921 1.2182 104
## week-Canis_latrans 0.0014 1144168.5248 1.2904 2866
## week-Sylvilagus_floridanus -0.0062 2028891.5579 1.3034 4021
## I(week^2)-Canis_latrans -0.0300 31690699.4460 1.2949 13165
## I(week^2)-Sylvilagus_floridanus -0.1558 29085912.5015 1.2904 9925
#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): 1.4558
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0686 0.9703 -1.9302 -0.0865 1.9648 1.0009 1292
## Tree_Density -1.1369 1.0432 -3.0975 -1.1563 1.2187 1.0055 1119
## Avg_Canopy_Cover 0.6125 1.2460 -1.9842 0.6346 3.0845 1.0058 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.3130 69.5334 0.0621 0.9755 45.3559 1.0464 2783
## Tree_Density 6.6168 46.2102 0.0560 0.7105 42.1015 1.2203 2539
## Avg_Canopy_Cover 36.9718 327.0319 0.2354 5.3550 208.3385 1.2395 2495
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3338 3.1963 0.0513 0.4381 7.9568 1.0605 332
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7090 1.9520 -5.1715 -3.1549 1.6132 1.0053 889
## week 0.0256 1.6229 -3.1993 0.0541 3.1693 1.0001 1603
## I(week^2) -0.0982 1.7113 -3.4828 -0.0792 3.2586 1.0028 1263
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.772400e+01 7.950432e+02 0.0586 3.6523 2.319666e+02 1.3322 3000
## week 2.005884e+21 6.106793e+22 0.0945 144.2429 1.836749e+16 1.3939 965
## I(week^2) 2.480950e+15 6.110112e+16 0.0987 274.2516 1.542010e+14 1.4455 816
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4438 0.9938 -1.0632 0.3472 2.4320
## (Intercept)-Sylvilagus_floridanus -0.6312 0.8648 -2.2907 -0.6569 1.1212
## Tree_Density-Canis_latrans -1.3441 0.8665 -3.3607 -1.2363 -0.0133
## Tree_Density-Sylvilagus_floridanus -1.6990 1.1331 -4.3526 -1.5331 0.0706
## Avg_Canopy_Cover-Canis_latrans -0.3770 0.6177 -1.6830 -0.3538 0.7019
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1772 2.0158 0.6373 2.7732 8.2084
## Rhat ESS
## (Intercept)-Canis_latrans 1.1025 172
## (Intercept)-Sylvilagus_floridanus 1.0482 324
## Tree_Density-Canis_latrans 1.0097 572
## Tree_Density-Sylvilagus_floridanus 1.0265 420
## Avg_Canopy_Cover-Canis_latrans 1.0214 575
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1165 124
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.688900e+00 1.756000e-01 -5.0537
## (Intercept)-Sylvilagus_floridanus -4.923400e+00 2.665000e-01 -5.4629
## week-Canis_latrans -2.861258e+08 5.514950e+10 -2251734.5064
## week-Sylvilagus_floridanus 3.352186e+08 2.876394e+10 -6200160.4952
## I(week^2)-Canis_latrans 1.403263e+06 6.396504e+07 -1205000.4769
## I(week^2)-Sylvilagus_floridanus -3.491598e+05 2.555156e+07 -1108863.8524
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6761 -4.3646 1.0164 186
## (Intercept)-Sylvilagus_floridanus -4.9052 -4.4344 1.0600 140
## week-Canis_latrans 0.1638 3216078.9410 1.2931 32724
## week-Sylvilagus_floridanus 0.1539 1603837.9406 1.3038 6943
## I(week^2)-Canis_latrans -0.2313 788672.4824 1.3369 2994
## I(week^2)-Sylvilagus_floridanus -0.2543 699197.1060 1.3095 3621
#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): 1.4907
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2162 1.1206 -2.1268 0.2092 2.5111 1.0033 1123
## Cogon_Patch_Size 0.0759 1.2821 -2.4777 0.0706 2.6340 1.0058 2378
## Avg_Cogongrass_Cover 0.0062 0.8344 -1.6604 0.0109 1.7066 1.0149 1055
## total_shrub_cover 0.2697 0.9312 -1.7581 0.2633 2.1263 1.0043 807
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.0859 176.3183 0.0767 1.7196 87.2523 1.1647 1828
## Cogon_Patch_Size 51.3401 295.7211 0.1568 6.3751 300.2183 1.0657 2658
## Avg_Cogongrass_Cover 2.9821 11.6459 0.0463 0.5391 21.2539 1.0091 2834
## total_shrub_cover 11.4472 284.2914 0.0557 0.7560 37.6733 1.3407 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2614 4.3228 0.0657 0.9395 12.2885 1.0088 418
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5412 2.0276 -5.2092 -2.8447 1.8245 1.0224 860
## week 0.0642 1.6485 -3.1672 0.0847 3.2227 1.0007 1384
## I(week^2) 0.0212 1.6561 -3.1257 0.0078 3.2751 1.0016 1282
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.052630e+01 8.618515e+02 0.0628 4.9751 3.577316e+02 1.0892 3000
## week 1.472020e+20 3.270068e+21 0.1335 4152.5761 1.371277e+19 1.4464 586
## I(week^2) 2.269693e+23 4.354868e+24 0.0883 46.7229 3.672588e+17 1.5380 228
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.1868 1.2978 -0.7081 1.0044
## (Intercept)-Sylvilagus_floridanus -0.5617 1.2101 -3.0580 -0.5388
## Cogon_Patch_Size-Canis_latrans 2.2863 2.2765 -0.2257 1.6842
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0025 2.1214 -7.5489 -1.5324
## Avg_Cogongrass_Cover-Canis_latrans 0.2207 0.6241 -0.8624 0.1860
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2000 0.7286 -1.7690 -0.1793
## total_shrub_cover-Canis_latrans 0.7317 0.7961 -0.4641 0.6232
## total_shrub_cover-Sylvilagus_floridanus -0.1013 1.0744 -2.7135 -0.0328
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 4.3505 1.0762 232
## (Intercept)-Sylvilagus_floridanus 1.7831 1.0198 192
## Cogon_Patch_Size-Canis_latrans 8.0866 1.1923 246
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6502 1.0463 241
## Avg_Cogongrass_Cover-Canis_latrans 1.5470 1.0373 629
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2061 1.0087 967
## total_shrub_cover-Canis_latrans 2.4325 1.0320 492
## total_shrub_cover-Sylvilagus_floridanus 1.7966 1.0158 185
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.670200e+00 1.637000e-01 -5.009700e+00
## (Intercept)-Sylvilagus_floridanus -4.999400e+00 3.242000e-01 -5.712500e+00
## week-Canis_latrans 2.761808e+07 6.007753e+09 -7.434944e+08
## week-Sylvilagus_floridanus 2.851366e+06 1.393719e+10 -5.943706e+08
## I(week^2)-Canis_latrans -5.692046e+09 2.926767e+11 -4.903508e+05
## I(week^2)-Sylvilagus_floridanus 1.178990e+10 4.810267e+11 -5.609283e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6639 -4.365500e+00 1.0222 209
## (Intercept)-Sylvilagus_floridanus -4.9707 -4.440500e+00 1.0792 83
## week-Canis_latrans 0.0642 5.787876e+08 1.1781 8045
## week-Sylvilagus_floridanus 0.3413 6.601311e+08 1.2766 209203
## I(week^2)-Canis_latrans 0.1228 3.906411e+05 1.3276 1353
## I(week^2)-Sylvilagus_floridanus 0.1558 4.053712e+05 1.3490 1511
#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): 1.4042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0132 0.8786 -1.7841 0.0083 1.9150 1.0053 1109
## Veg_shannon_index 0.7638 0.8187 -1.0758 0.8020 2.3362 1.0008 1313
## Avg_Cogongrass_Cover 0.2004 0.9060 -1.7610 0.2119 2.0855 1.0031 2188
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.1971 97.1359 0.0630 0.7962 26.1883 1.0387 3000
## Veg_shannon_index 4.0439 25.5746 0.0501 0.5589 22.8519 1.0110 3000
## Avg_Cogongrass_Cover 8.4146 97.2001 0.0713 1.0833 38.5411 1.2324 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.321 2.4518 0.0551 0.5563 7.0841 1.0927 671
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6854 1.9649 -5.1040 -3.1005 1.6121 1.0055 884
## week 0.0266 1.6888 -3.2276 -0.0211 3.3978 1.0043 1288
## I(week^2) 0.0034 1.6345 -3.1256 -0.0362 3.2919 1.0157 1627
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.025992e+02 2.134812e+03 0.0582 3.6482 2.788953e+02 1.1241 3000
## week 2.281862e+15 4.536469e+16 0.0992 73.4371 3.833188e+12 1.5134 437
## I(week^2) 4.828603e+14 1.393369e+16 0.1152 597.1356 1.036978e+13 1.4027 849
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4814 0.7384 -0.9205 0.4424
## (Intercept)-Sylvilagus_floridanus -0.3540 0.7783 -1.8089 -0.3877
## Veg_shannon_index-Canis_latrans 1.1923 0.6125 0.2006 1.1170
## Veg_shannon_index-Sylvilagus_floridanus 0.7157 0.6478 -0.4576 0.6827
## Avg_Cogongrass_Cover-Canis_latrans 0.9534 0.7155 -0.1731 0.8699
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3561 0.6149 -1.6158 -0.3250
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0249 1.0144 609
## (Intercept)-Sylvilagus_floridanus 1.3399 1.0237 317
## Veg_shannon_index-Canis_latrans 2.5554 1.0076 888
## Veg_shannon_index-Sylvilagus_floridanus 2.1351 1.0063 629
## Avg_Cogongrass_Cover-Canis_latrans 2.6324 1.0073 800
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8145 0.9997 1038
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6509 1.504000e-01 -4.9596
## (Intercept)-Sylvilagus_floridanus -4.9273 2.678000e-01 -5.4704
## week-Canis_latrans -832867.7740 5.776458e+07 -53654.1032
## week-Sylvilagus_floridanus 28164.5142 3.224005e+07 -79132.0335
## I(week^2)-Canis_latrans -585554.4174 2.438480e+07 -422825.3384
## I(week^2)-Sylvilagus_floridanus -661796.3306 1.711546e+07 -415663.0716
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6434 -4.3744 1.0083 248
## (Intercept)-Sylvilagus_floridanus -4.9134 -4.4559 1.0126 130
## week-Canis_latrans 0.0117 72314.4244 1.3038 6430
## week-Sylvilagus_floridanus 0.1359 45686.5648 1.2789 8553
## I(week^2)-Canis_latrans -0.1024 331598.7594 1.3363 1978
## I(week^2)-Sylvilagus_floridanus -0.0515 282398.2831 1.4003 801
#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): 1.4132
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0275 0.8493 -1.7103 0.0265 1.6914 1.0024 1728
## Avg_Cogongrass_Cover -0.0240 0.8018 -1.6950 -0.0195 1.6170 1.0010 2276
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5146 29.6596 0.0569 0.7879 25.1966 1.0277 3000
## Avg_Cogongrass_Cover 4.5833 22.1885 0.0596 0.7766 31.2848 1.1649 2698
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4897 2.8993 0.0549 0.5899 8.4669 1.0055 711
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7222 1.9226 -5.1616 -3.1295 1.4824 1.0032 891
## week 0.0239 1.6060 -3.1637 0.0170 3.1254 1.0036 1633
## I(week^2) -0.0420 1.6682 -3.3889 -0.0184 3.2777 1.0076 1542
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.034110e+01 3.760503e+02 0.0614 3.5524 1.957185e+02 1.0900 3000
## week 1.356238e+17 4.566009e+18 0.1018 373.3252 1.300878e+14 1.3756 1301
## I(week^2) 2.125384e+11 5.662001e+12 0.1001 164.5610 4.649836e+10 1.3182 1810
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4644 0.6927 -0.8666 0.4435
## (Intercept)-Sylvilagus_floridanus -0.3828 0.6987 -1.7379 -0.3857
## Avg_Cogongrass_Cover-Canis_latrans 0.4457 0.5384 -0.4432 0.3831
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4840 0.5461 -1.6941 -0.4482
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9191 1.0039 633
## (Intercept)-Sylvilagus_floridanus 0.9932 1.0243 649
## Avg_Cogongrass_Cover-Canis_latrans 1.7114 1.0043 1382
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4474 1.0159 1493
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.674600e+00 1.614000e-01 -5.0265
## (Intercept)-Sylvilagus_floridanus -4.904100e+00 2.742000e-01 -5.5371
## week-Canis_latrans -1.044014e+07 5.015682e+08 -771507.6518
## week-Sylvilagus_floridanus 1.496936e+05 2.749724e+08 -787400.4125
## I(week^2)-Canis_latrans -7.983558e+02 2.418220e+05 -22545.1708
## I(week^2)-Sylvilagus_floridanus 2.175768e+03 5.331160e+05 -21638.7136
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6665 -4.3701 1.0354 224
## (Intercept)-Sylvilagus_floridanus -4.8845 -4.4518 1.0063 116
## week-Canis_latrans 0.1488 908277.8915 1.3338 2243
## week-Sylvilagus_floridanus 0.2979 1345518.5689 1.2903 193484
## I(week^2)-Canis_latrans -0.0550 29448.1106 1.1583 3404
## I(week^2)-Sylvilagus_floridanus -0.0830 22972.6225 1.2072 12064
# 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): 1.4922
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5968 1.0077 -2.5735 -0.6166 1.4857 1.0009 634
## Avg_Cogongrass_Cover -0.5438 1.1051 -2.5918 -0.5760 1.8129 1.0088 1482
## I(Avg_Cogongrass_Cover^2) 1.0398 1.1101 -1.3653 1.0578 3.1288 1.0045 1019
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.8695 41.7902 0.0544 0.7594 34.6530 1.0566 1606
## Avg_Cogongrass_Cover 61.4427 2398.6454 0.0643 1.6106 83.9112 1.3035 3000
## I(Avg_Cogongrass_Cover^2) 15.7765 111.1428 0.0593 1.2307 93.6457 1.0560 2661
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0647 4.455 0.0562 0.6484 13.8656 1.0167 280
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6607 1.9818 -5.1575 -3.0859 1.6691 1.0041 897
## week -0.0498 1.6119 -3.2509 -0.0383 3.1331 1.0063 1876
## I(week^2) 0.0653 1.6759 -3.1961 0.0890 3.4045 1.0099 1200
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.609460e+01 2.113551e+02 0.0634 4.5849 2.210773e+02 1.1112 3000
## week 6.222732e+29 1.961904e+31 0.1347 8968.0447 4.960393e+23 1.3871 1286
## I(week^2) 2.460979e+15 5.434173e+16 0.1251 292.9923 4.789619e+13 1.4791 664
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4657 0.9792 -2.3961 -0.4606
## (Intercept)-Sylvilagus_floridanus -1.1246 1.0356 -3.3547 -1.0910
## Avg_Cogongrass_Cover-Canis_latrans -0.0946 0.9202 -1.8018 -0.1253
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.7604 1.4312 -5.0100 -1.5638
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1842 1.5679 0.0674 1.8674
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1399 1.0765 -0.3070 0.9591
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4432 1.0138 277
## (Intercept)-Sylvilagus_floridanus 0.8597 1.0079 510
## Avg_Cogongrass_Cover-Canis_latrans 1.8240 1.0029 974
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.3414 1.0122 210
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.2315 1.0286 185
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.9110 1.0347 143
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.731400e+00 1.737000e-01 -5.111900e+00
## (Intercept)-Sylvilagus_floridanus -5.012900e+00 3.247000e-01 -5.685600e+00
## week-Canis_latrans -7.542275e+12 3.293195e+14 -2.271457e+09
## week-Sylvilagus_floridanus -8.656515e+12 8.086748e+14 -1.478693e+09
## I(week^2)-Canis_latrans -3.082028e+05 4.571820e+07 -7.961888e+05
## I(week^2)-Sylvilagus_floridanus -6.896842e+05 3.902850e+07 -4.927966e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7171 -4.426700e+00 1.0920 168
## (Intercept)-Sylvilagus_floridanus -4.9827 -4.459600e+00 1.0948 77
## week-Canis_latrans -0.3689 4.045491e+09 1.3417 1950
## week-Sylvilagus_floridanus -0.0960 1.292995e+10 1.3017 8767
## I(week^2)-Canis_latrans 0.1618 6.034838e+05 1.2925 6148
## I(week^2)-Sylvilagus_floridanus 0.0764 5.955700e+05 1.3168 2399
# 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): 1.542
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5157 1.5926 -3.4704 -0.5751 2.7889 1.0017 460
## Cogon_Patch_Size 0.3279 1.5793 -2.8564 0.3545 3.4412 1.0074 1043
## Veg_shannon_index 1.4374 1.4106 -1.5351 1.5141 4.0909 1.0028 1180
## total_shrub_cover 0.5078 1.3281 -2.2570 0.5239 3.0956 1.0179 428
## Avg_Cogongrass_Cover -0.4413 1.5118 -3.4198 -0.3987 2.6192 1.0035 441
## Tree_Density -1.1401 1.7383 -4.3250 -1.2403 2.5330 1.0109 331
## Avg_Canopy_Cover 0.4585 1.5724 -2.7103 0.4987 3.4807 1.0003 2604
## I(Avg_Cogongrass_Cover^2) 1.1693 1.5056 -2.1265 1.2733 3.8483 1.0067 781
## avg_veg_height -0.6154 1.2800 -3.1931 -0.6146 1.9270 1.0154 725
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 60.3348 384.0356 0.0692 3.0129 390.8296 1.0130
## Cogon_Patch_Size 603.8930 6707.1566 0.1981 40.4418 3624.5983 1.4453
## Veg_shannon_index 97.4513 985.8325 0.0665 2.4815 413.5957 1.0591
## total_shrub_cover 20.6472 164.7996 0.0641 1.3594 118.3974 1.0528
## Avg_Cogongrass_Cover 92.1138 1783.8756 0.0729 2.1789 258.1041 1.2301
## Tree_Density 625.2691 10090.5257 0.0994 25.7589 3022.0853 1.2087
## Avg_Canopy_Cover 1887.4982 32939.4550 1.4211 120.0801 7188.6232 1.3410
## I(Avg_Cogongrass_Cover^2) 165.0561 1592.0134 0.0927 7.5775 780.3114 1.0866
## avg_veg_height 21.8006 365.0637 0.0614 1.0985 88.2637 1.1101
## ESS
## (Intercept) 1399
## Cogon_Patch_Size 2436
## Veg_shannon_index 1442
## total_shrub_cover 1607
## Avg_Cogongrass_Cover 3000
## Tree_Density 2275
## Avg_Canopy_Cover 1194
## I(Avg_Cogongrass_Cover^2) 1012
## avg_veg_height 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 12.356 37.7156 0.0651 1.6135 114.8673 1.2477 111
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6486 1.9878 -5.1770 -3.0265 1.5923 1.0067 823
## week -0.0408 1.6367 -3.2846 -0.0686 3.1664 1.0008 1288
## I(week^2) 0.0178 1.5979 -3.1017 0.0208 3.1487 1.0107 1534
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.828810e+01 5.147736e+02 0.0665 4.4880 2.544643e+02 1.0994 3000
## week 6.291504e+16 1.519236e+18 0.1198 946.2591 2.321119e+15 1.4348 724
## I(week^2) 6.770434e+11 1.750003e+13 0.0903 57.1104 1.339035e+11 1.2157 1398
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -0.0109 2.7942 -4.9434
## (Intercept)-Sylvilagus_floridanus -2.7999 4.1207 -13.9663
## Cogon_Patch_Size-Canis_latrans 9.1462 10.1217 -0.3250
## Cogon_Patch_Size-Sylvilagus_floridanus -3.9275 6.1654 -20.5913
## Veg_shannon_index-Canis_latrans 3.6934 3.4220 0.3529
## Veg_shannon_index-Sylvilagus_floridanus 3.0208 2.9149 -0.4967
## total_shrub_cover-Canis_latrans 1.2229 1.8523 -1.9957
## total_shrub_cover-Sylvilagus_floridanus 0.2489 2.3123 -4.6317
## Avg_Cogongrass_Cover-Canis_latrans -0.2896 4.2446 -8.4796
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2238 3.1348 -8.7338
## Tree_Density-Canis_latrans -8.0692 7.7540 -29.4831
## Tree_Density-Sylvilagus_floridanus -6.5263 7.3115 -26.2107
## Avg_Canopy_Cover-Canis_latrans -1.0997 1.8367 -5.9580
## Avg_Canopy_Cover-Sylvilagus_floridanus 18.4683 16.6331 1.8255
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.7635 5.0234 0.9003
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9194 2.3050 -2.1470
## avg_veg_height-Canis_latrans -1.2998 1.7843 -6.0627
## avg_veg_height-Sylvilagus_floridanus -0.7895 2.0115 -5.0794
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -0.3144 6.9982 1.0697 161
## (Intercept)-Sylvilagus_floridanus -1.8931 3.3061 1.2067 60
## Cogon_Patch_Size-Canis_latrans 5.4421 39.5240 1.8332 42
## Cogon_Patch_Size-Sylvilagus_floridanus -2.4628 4.8388 1.0181 56
## Veg_shannon_index-Canis_latrans 2.7439 13.5188 1.1706 82
## Veg_shannon_index-Sylvilagus_floridanus 2.3907 11.7358 1.1309 75
## total_shrub_cover-Canis_latrans 1.0880 5.1562 1.0639 169
## total_shrub_cover-Sylvilagus_floridanus 0.4254 4.2139 1.0528 192
## Avg_Cogongrass_Cover-Canis_latrans -0.5417 8.9892 1.1024 119
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9209 4.3406 1.0219 446
## Tree_Density-Canis_latrans -5.4135 -0.7022 1.2503 56
## Tree_Density-Sylvilagus_floridanus -4.1598 0.7081 1.0528 68
## Avg_Canopy_Cover-Canis_latrans -0.7429 1.5108 1.1181 127
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.4017 70.0903 1.2034 32
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.0152 20.0249 1.5866 51
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6326 7.5531 1.1281 209
## avg_veg_height-Canis_latrans -1.0326 1.5410 1.1281 124
## avg_veg_height-Sylvilagus_floridanus -0.6662 2.6957 1.0297 309
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.6973 1.609000e-01 -5.0355
## (Intercept)-Sylvilagus_floridanus -5.0887 2.582000e-01 -5.6028
## week-Canis_latrans -3314236.0618 2.616919e+08 -5978136.4023
## week-Sylvilagus_floridanus 1204091.1948 1.204446e+08 -6267180.6253
## I(week^2)-Canis_latrans -1689.9378 6.901852e+05 -24696.9288
## I(week^2)-Sylvilagus_floridanus 8380.8327 8.156787e+05 -18411.9027
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.6898 -4.4061 1.1063 208
## (Intercept)-Sylvilagus_floridanus -5.0870 -4.6069 1.2609 126
## week-Canis_latrans -0.0810 5910976.7230 1.2765 10152
## week-Sylvilagus_floridanus -0.2597 8211429.2607 1.2119 7417
## I(week^2)-Canis_latrans 0.0336 24404.9786 1.1709 3897
## I(week^2)-Sylvilagus_floridanus 0.0377 27363.0099 1.2179 3854
#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): 1.5532
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0891 0.7699 -1.5149 0.0883 1.6458 1.0002 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.301 72.2194 0.0559 0.623 21.6289 1.1271 2753
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6031 2.0175 -5.2046 -2.9336 1.8337 1.0143 852
## shrub_cover -0.0023 0.7345 -1.5337 -0.0253 1.5060 1.0054 2027
## veg_height -0.1727 0.7154 -1.6358 -0.1986 1.4285 1.0025 2164
## week 0.0099 1.6545 -3.2207 -0.0164 3.2815 0.9999 1288
## I(week^2) 0.0490 1.6725 -3.1760 0.0279 3.3387 1.0016 1496
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.592340e+01 6.847889e+02 0.0644 4.7806 2.311339e+02 1.2829 3000
## shrub_cover 4.582000e+00 4.789830e+01 0.0573 0.5689 1.957840e+01 1.3762 2764
## veg_height 3.255000e+00 1.571850e+01 0.0712 0.6495 2.385270e+01 1.0878 3000
## week 4.906995e+19 9.235417e+20 0.0896 219.3042 7.689095e+16 1.5464 397
## I(week^2) 4.160700e+18 8.182484e+19 0.0979 298.1929 7.713798e+16 1.1411 786
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.4408 0.4060 -0.2907 0.4217 1.2744 1.0271
## (Intercept)-Sylvilagus_floridanus -0.3247 0.4376 -1.1746 -0.3219 0.5457 1.0025
## ESS
## (Intercept)-Canis_latrans 1601
## (Intercept)-Sylvilagus_floridanus 1089
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.767300e+00 1.948000e-01 -5.178300e+00
## (Intercept)-Sylvilagus_floridanus -4.928400e+00 2.423000e-01 -5.429300e+00
## shrub_cover-Canis_latrans -3.355000e-01 2.062000e-01 -7.160000e-01
## shrub_cover-Sylvilagus_floridanus 3.851000e-01 4.190000e-01 -3.512000e-01
## veg_height-Canis_latrans -6.128000e-01 1.883000e-01 -1.010700e+00
## veg_height-Sylvilagus_floridanus 1.634000e-01 2.686000e-01 -3.669000e-01
## week-Canis_latrans 3.032858e+08 1.203378e+10 -5.290524e+06
## week-Sylvilagus_floridanus -7.242773e+07 8.349429e+09 -2.261268e+07
## I(week^2)-Canis_latrans 5.806371e+07 1.711542e+09 -3.633255e+06
## I(week^2)-Sylvilagus_floridanus -1.759401e+05 1.209553e+09 -4.214696e+06
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7606 -4.4121 1.1531 153
## (Intercept)-Sylvilagus_floridanus -4.9258 -4.4878 1.0126 180
## shrub_cover-Canis_latrans -0.3419 0.0834 1.0269 181
## shrub_cover-Sylvilagus_floridanus 0.3591 1.2203 1.0243 122
## veg_height-Canis_latrans -0.6045 -0.2834 1.0523 130
## veg_height-Sylvilagus_floridanus 0.1605 0.6788 1.0316 158
## week-Canis_latrans 0.2102 36979481.3035 1.3476 1879
## week-Sylvilagus_floridanus 0.0374 18674147.9119 1.2977 8639
## I(week^2)-Canis_latrans 0.1078 10165407.4677 1.1515 1770
## I(week^2)-Sylvilagus_floridanus -0.0635 8642791.3708 1.1093 26571
#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): 1.7217
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4784 1.3249 -2.1686 0.4851 3.0688 1.0931 341
## Cogon_Patch_Size 0.0174 1.3814 -2.7391 0.0150 2.8230 1.0081 1144
## Veg_shannon_index 1.2403 1.1926 -1.2869 1.2728 3.3709 1.0252 766
## total_shrub_cover 0.1102 1.3902 -2.8234 0.1494 2.8277 1.0259 632
## Avg_Cogongrass_Cover 0.6722 1.4055 -2.2864 0.7253 3.3080 1.0438 712
## Tree_Density -1.2871 1.5471 -4.1576 -1.3735 2.0308 1.0032 736
## Avg_Canopy_Cover 0.4668 1.5367 -2.7344 0.4885 3.4428 1.0028 1962
## avg_veg_height -0.1927 1.1687 -2.4332 -0.2162 2.0754 1.0301 591
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.3935 475.8390 0.0720 2.1126 156.1870 1.2950 3000
## Cogon_Patch_Size 109.8700 917.6563 0.0968 6.9618 694.5238 1.1054 1785
## Veg_shannon_index 12.9947 68.8076 0.0606 0.8873 104.7991 1.0584 771
## total_shrub_cover 196.8068 3323.1538 0.1112 8.4480 713.5336 1.3566 2049
## Avg_Cogongrass_Cover 29.7747 169.4066 0.0840 3.0952 203.7481 1.1592 1373
## Tree_Density 147.0441 2286.2063 0.0767 7.1109 691.3995 1.2511 3000
## Avg_Canopy_Cover 545.0199 6445.7073 0.4535 50.2520 2179.2555 1.2443 551
## avg_veg_height 11.2706 107.4232 0.0548 0.7438 70.0489 1.2623 2413
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.4142 24.7956 0.0618 1.28 48.9523 1.0732 201
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6439 2.0202 -5.2627 -2.9878 1.6387 1.0061 860
## shrub_cover 0.1104 0.8358 -1.6085 0.1068 1.7892 1.0128 1509
## veg_height -0.1623 0.7047 -1.4984 -0.1982 1.4227 1.0167 1541
## week -0.0416 1.6510 -3.2910 -0.0560 3.0962 1.0020 1501
## I(week^2) 0.0985 1.6183 -3.1162 0.1054 3.2605 1.0046 1110
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.313680e+01 4.754428e+02 0.0641 4.7118 2.719727e+02 1.0777 3000
## shrub_cover 6.375600e+00 4.643900e+01 0.1013 1.1850 3.272460e+01 1.1195 3000
## veg_height 4.644300e+00 6.866500e+01 0.0723 0.5994 2.045680e+01 1.2045 1939
## week 1.973001e+27 5.033296e+28 0.1097 556.6642 8.583485e+21 1.4356 682
## I(week^2) 1.347411e+29 3.431205e+30 0.0940 427.5054 2.375259e+25 1.4361 697
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.6776 1.7504 -1.0017 1.4047
## (Intercept)-Sylvilagus_floridanus -0.3472 2.4112 -6.1495 -0.0723
## Cogon_Patch_Size-Canis_latrans 2.4128 3.3593 -0.9981 1.5670
## Cogon_Patch_Size-Sylvilagus_floridanus -3.4438 5.4043 -19.8514 -1.8124
## Veg_shannon_index-Canis_latrans 1.8385 1.2507 -0.4122 1.7610
## Veg_shannon_index-Sylvilagus_floridanus 1.6816 1.5547 -0.9715 1.6287
## total_shrub_cover-Canis_latrans 2.2483 1.9367 -0.4076 1.8599
## total_shrub_cover-Sylvilagus_floridanus -3.4027 5.2183 -18.5567 -1.9737
## Avg_Cogongrass_Cover-Canis_latrans 2.5760 2.2120 -0.6869 2.1977
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1127 2.3919 -5.7268 0.2353
## Tree_Density-Canis_latrans -4.7013 3.7012 -14.0785 -3.6267
## Tree_Density-Sylvilagus_floridanus -3.3378 3.4373 -12.2595 -2.6297
## Avg_Canopy_Cover-Canis_latrans -0.4575 0.9322 -2.5555 -0.3878
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.6467 8.0201 0.6241 9.0353
## avg_veg_height-Canis_latrans -0.2207 1.2481 -2.7474 -0.2045
## avg_veg_height-Sylvilagus_floridanus -0.3965 1.9791 -4.1170 -0.2997
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.1281 1.1515 244
## (Intercept)-Sylvilagus_floridanus 3.8722 1.1248 115
## Cogon_Patch_Size-Canis_latrans 11.8287 1.1839 116
## Cogon_Patch_Size-Sylvilagus_floridanus 2.0193 1.0630 66
## Veg_shannon_index-Canis_latrans 4.5359 1.0308 256
## Veg_shannon_index-Sylvilagus_floridanus 4.8248 1.1328 195
## total_shrub_cover-Canis_latrans 7.0482 1.1221 147
## total_shrub_cover-Sylvilagus_floridanus 2.2988 2.0345 48
## Avg_Cogongrass_Cover-Canis_latrans 8.2529 1.1288 194
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.6929 1.0934 302
## Tree_Density-Canis_latrans -0.6012 1.1810 95
## Tree_Density-Sylvilagus_floridanus 1.9441 1.1001 120
## Avg_Canopy_Cover-Canis_latrans 1.1975 1.0349 345
## Avg_Canopy_Cover-Sylvilagus_floridanus 34.4215 1.1146 42
## avg_veg_height-Canis_latrans 2.2474 1.1323 242
## avg_veg_height-Sylvilagus_floridanus 2.8108 1.0640 175
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.791300e+00 1.661000e-01 -5.125300e+00
## (Intercept)-Sylvilagus_floridanus -5.060600e+00 2.331000e-01 -5.527600e+00
## shrub_cover-Canis_latrans -4.587000e-01 2.084000e-01 -8.571000e-01
## shrub_cover-Sylvilagus_floridanus 7.926000e-01 4.435000e-01 -7.510000e-02
## veg_height-Canis_latrans -6.068000e-01 1.742000e-01 -9.445000e-01
## veg_height-Sylvilagus_floridanus 1.641000e-01 2.574000e-01 -3.534000e-01
## week-Canis_latrans -2.008214e+11 3.837383e+13 -8.615708e+09
## week-Sylvilagus_floridanus 1.473558e+11 3.701869e+13 -4.724344e+09
## I(week^2)-Canis_latrans -2.592102e+12 2.985720e+14 -1.001371e+11
## I(week^2)-Sylvilagus_floridanus -1.107313e+13 3.153879e+14 -1.892742e+11
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7893 -4.475500e+00 1.0227 208
## (Intercept)-Sylvilagus_floridanus -5.0564 -4.609600e+00 1.0486 161
## shrub_cover-Canis_latrans -0.4629 -5.110000e-02 1.0190 160
## shrub_cover-Sylvilagus_floridanus 0.7911 1.609800e+00 1.3144 67
## veg_height-Canis_latrans -0.6082 -2.688000e-01 1.1633 153
## veg_height-Sylvilagus_floridanus 0.1699 6.492000e-01 1.0729 129
## week-Canis_latrans -0.1768 1.649296e+09 1.2931 30949
## week-Sylvilagus_floridanus 0.0124 2.467479e+09 1.2919 21694
## I(week^2)-Canis_latrans 0.1131 6.850079e+10 1.2978 11860
## I(week^2)-Sylvilagus_floridanus 0.0478 2.578533e+10 1.4081 832
#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): 1.9222
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8957 1.1945 -1.5976 0.9573 3.1588 1.0194 383
## Avg_Cogongrass_Cover -0.0929 1.1529 -2.3985 -0.1009 2.2130 1.0161 1049
## total_shrub_cover -0.1601 1.3974 -2.9594 -0.1566 2.5839 1.0410 593
## avg_veg_height 0.4365 1.0223 -1.5411 0.4067 2.4433 1.0538 355
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.9648 256.4563 0.0570 0.8759 85.0352 1.3792 3000
## Avg_Cogongrass_Cover 21.7535 215.8870 0.0875 2.4780 116.0670 1.3013 2187
## total_shrub_cover 115.7861 946.2804 0.2423 11.4895 696.2377 1.1179 988
## avg_veg_height 5.0867 21.1926 0.0481 0.6640 42.8072 1.0851 1100
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.8497 29.3305 0.0802 1.6293 56.4314 2.203 57
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4715 2.0676 -5.3118 -2.5801 1.7677 1.0035 800
## shrub_cover 0.1842 0.9024 -1.7084 0.1919 2.0199 1.0098 855
## veg_height -0.2612 0.7067 -1.7641 -0.2779 1.2892 1.0021 2188
## week -0.0341 1.6328 -3.1607 -0.0195 3.2177 1.0044 1240
## I(week^2) -0.0354 1.6359 -3.0856 -0.0811 3.2903 1.0035 1403
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.575160e+01 2.786128e+02 0.0734 7.2797 2.918982e+02 1.1264 3000
## shrub_cover 6.639800e+00 3.470280e+01 0.1217 1.4382 3.971800e+01 1.0651 3000
## veg_height 3.255200e+00 2.203080e+01 0.0669 0.6059 1.880910e+01 1.1149 3000
## week 6.216794e+22 1.091084e+24 0.0960 57.6171 7.923916e+20 1.5820 326
## I(week^2) 2.262381e+19 8.198834e+20 0.1083 95.5833 3.186461e+14 1.3642 1498
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.5991 1.7017 -0.9723 1.3474
## (Intercept)-Sylvilagus_floridanus 1.3995 1.8682 -1.2678 1.1241
## Avg_Cogongrass_Cover-Canis_latrans 0.9955 1.5617 -1.0612 0.7379
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3662 1.5118 -4.9636 -1.1614
## total_shrub_cover-Canis_latrans 1.8560 1.3994 -0.1789 1.6347
## total_shrub_cover-Sylvilagus_floridanus -4.2438 4.4503 -17.0025 -3.1449
## avg_veg_height-Canis_latrans 0.4894 1.0191 -1.2461 0.3980
## avg_veg_height-Sylvilagus_floridanus 0.6924 1.3192 -1.4583 0.5721
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.0220 1.1521 102
## (Intercept)-Sylvilagus_floridanus 7.1246 1.0919 87
## Avg_Cogongrass_Cover-Canis_latrans 5.0910 1.1824 213
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9836 1.0361 305
## total_shrub_cover-Canis_latrans 5.2464 1.0385 159
## total_shrub_cover-Sylvilagus_floridanus 1.7867 1.3028 34
## avg_veg_height-Canis_latrans 2.5814 1.0647 507
## avg_veg_height-Sylvilagus_floridanus 3.4799 1.0750 123
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.866000e+00 2.008000e-01 -5.272000e+00
## (Intercept)-Sylvilagus_floridanus -5.190000e+00 2.427000e-01 -5.628500e+00
## shrub_cover-Canis_latrans -4.674000e-01 2.164000e-01 -8.798000e-01
## shrub_cover-Sylvilagus_floridanus 1.026900e+00 5.339000e-01 -4.176000e-01
## veg_height-Canis_latrans -6.593000e-01 1.815000e-01 -1.022000e+00
## veg_height-Sylvilagus_floridanus 3.900000e-02 2.662000e-01 -4.353000e-01
## week-Canis_latrans 1.285505e+09 2.950237e+11 -2.700124e+07
## week-Sylvilagus_floridanus 2.076738e+09 2.088061e+11 -6.497101e+06
## I(week^2)-Canis_latrans -1.941787e+06 4.566268e+09 -1.488197e+05
## I(week^2)-Sylvilagus_floridanus -3.505179e+07 2.270490e+09 -1.700404e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8709 -4.477400e+00 1.0155 134
## (Intercept)-Sylvilagus_floridanus -5.2012 -4.650500e+00 1.0239 154
## shrub_cover-Canis_latrans -0.4736 -2.940000e-02 1.0138 150
## shrub_cover-Sylvilagus_floridanus 1.0856 1.901200e+00 1.6561 38
## veg_height-Canis_latrans -0.6571 -3.053000e-01 1.0198 168
## veg_height-Sylvilagus_floridanus 0.0228 6.219000e-01 1.1975 104
## week-Canis_latrans -0.0105 1.350798e+08 1.2922 33730
## week-Sylvilagus_floridanus -0.1076 1.764373e+08 1.3002 13840
## I(week^2)-Canis_latrans 0.0263 2.311232e+05 1.2904 515019
## I(week^2)-Sylvilagus_floridanus -0.0279 1.636052e+05 1.3139 3907
#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): 1.6255
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0371 0.9592 -1.9086 0.0389 2.0168 1.0118 922
## Tree_Density -1.1284 1.0326 -3.0994 -1.1533 1.0888 1.0048 1447
## Avg_Canopy_Cover 0.5244 1.3265 -2.2689 0.5776 3.0582 1.0010 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.1301 52.5279 0.0593 0.8700 38.7872 1.2199 2267
## Tree_Density 10.2692 96.8090 0.0531 0.7625 61.1844 1.1654 3000
## Avg_Canopy_Cover 182.0981 6007.2301 0.4884 9.2519 387.8045 1.3253 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.9042 28.6799 0.0482 0.4725 38.3546 2.3649 89
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6508 1.9937 -5.2135 -3.0285 1.5989 1.0100 790
## shrub_cover 0.0892 0.7546 -1.4401 0.0689 1.7055 1.0103 2185
## veg_height -0.2110 0.7141 -1.6937 -0.2278 1.3373 1.0001 2312
## week 0.0226 1.6741 -3.2449 0.0275 3.1890 1.0017 1330
## I(week^2) 0.0459 1.6497 -3.2330 0.0544 3.0866 1.0069 1501
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 3.804760e+01 3.074311e+02 0.0625 4.5599 2.362061e+02 1.2007
## shrub_cover 3.985700e+00 1.836450e+01 0.0712 0.6800 2.915120e+01 1.0516
## veg_height 5.441700e+00 7.081020e+01 0.0748 0.6606 2.079360e+01 1.1158
## week 9.435798e+14 2.990400e+16 0.0996 93.0658 7.868540e+11 1.3861
## I(week^2) 7.056801e+22 2.611968e+24 0.1268 10581.4051 1.606950e+19 1.3613
## ESS
## (Intercept) 3000
## shrub_cover 2733
## veg_height 3000
## week 1085
## I(week^2) 1235
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4894 0.7897 -0.9488 0.4338 2.3416
## (Intercept)-Sylvilagus_floridanus -0.4327 0.9961 -2.4466 -0.3998 1.5101
## Tree_Density-Canis_latrans -1.3839 0.8163 -3.2006 -1.2928 -0.0230
## Tree_Density-Sylvilagus_floridanus -1.7176 1.4961 -5.5331 -1.5032 0.3345
## Avg_Canopy_Cover-Canis_latrans -0.5945 0.7769 -2.4433 -0.4903 0.4489
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1894 2.4713 0.9428 3.6836 10.2872
## Rhat ESS
## (Intercept)-Canis_latrans 1.0254 323
## (Intercept)-Sylvilagus_floridanus 1.0317 310
## Tree_Density-Canis_latrans 1.0031 720
## Tree_Density-Sylvilagus_floridanus 1.1039 291
## Avg_Canopy_Cover-Canis_latrans 1.3235 161
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0978 164
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.796100e+00 1.914000e-01 -5.178600e+00
## (Intercept)-Sylvilagus_floridanus -4.930900e+00 2.339000e-01 -5.408900e+00
## shrub_cover-Canis_latrans -3.221000e-01 2.079000e-01 -7.365000e-01
## shrub_cover-Sylvilagus_floridanus 5.540000e-01 3.788000e-01 -1.562000e-01
## veg_height-Canis_latrans -6.356000e-01 1.796000e-01 -1.001700e+00
## veg_height-Sylvilagus_floridanus 1.667000e-01 2.400000e-01 -3.304000e-01
## week-Canis_latrans -1.297068e+05 2.120490e+07 -6.288706e+04
## week-Sylvilagus_floridanus 6.159549e+05 2.735195e+07 -4.628621e+04
## I(week^2)-Canis_latrans 3.599796e+09 1.576392e+11 -3.956474e+08
## I(week^2)-Sylvilagus_floridanus -1.057047e+09 1.466076e+11 -3.308128e+08
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7989 -4.418400e+00 1.3188 162
## (Intercept)-Sylvilagus_floridanus -4.9196 -4.501000e+00 1.0064 194
## shrub_cover-Canis_latrans -0.3164 8.450000e-02 1.0689 175
## shrub_cover-Sylvilagus_floridanus 0.5459 1.314300e+00 1.1288 139
## veg_height-Canis_latrans -0.6308 -2.966000e-01 1.1916 173
## veg_height-Sylvilagus_floridanus 0.1671 6.189000e-01 1.0518 213
## week-Canis_latrans -0.0070 4.604808e+04 1.2940 11799
## week-Sylvilagus_floridanus 0.2010 6.793406e+04 1.3405 1990
## I(week^2)-Canis_latrans -0.0339 3.263752e+08 1.3414 2049
## I(week^2)-Sylvilagus_floridanus 0.0208 2.814160e+08 1.2955 12648
#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): 1.8407
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6817 1.2428 -1.8999 0.6918 3.1262 1.0209 352
## Cogon_Patch_Size 0.0500 1.3314 -2.6361 0.0692 2.6119 1.0026 2252
## Avg_Cogongrass_Cover 0.1101 0.9999 -1.9263 0.1231 2.1812 1.0035 1123
## total_shrub_cover -0.1052 1.3461 -2.8407 -0.1210 2.5423 1.0583 286
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.1646 255.0904 0.0700 1.5386 118.7129 1.1934 3000
## Cogon_Patch_Size 141.2808 2440.7261 0.1177 8.3199 510.6876 1.1571 3000
## Avg_Cogongrass_Cover 6.6540 66.8130 0.0587 0.9084 36.6484 1.2492 2756
## total_shrub_cover 48.1328 219.7959 0.1348 7.0996 332.4499 1.0442 1954
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 6.2108 14.9685 0.0815 1.6735 45.3496 1.0784 163
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5098 2.0161 -5.2435 -2.6971 1.6168 1.0149 747
## shrub_cover 0.1721 0.8998 -1.7063 0.1796 2.0440 1.0248 897
## veg_height -0.2389 0.6872 -1.6653 -0.2574 1.2623 1.0039 2085
## week 0.0162 1.6579 -3.2111 -0.0053 3.3299 1.0028 1677
## I(week^2) 0.0177 1.6945 -3.3627 0.0007 3.2994 1.0118 1299
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.583880e+01 8.557627e+02 0.0692 6.4398 3.519625e+02 1.2817 3000
## shrub_cover 7.950900e+00 9.445620e+01 0.1103 1.2632 3.892630e+01 1.2677 3000
## veg_height 5.877300e+00 1.509870e+02 0.0638 0.5713 1.880980e+01 1.3233 3000
## week 1.046103e+25 4.251638e+26 0.1027 388.9764 3.032605e+22 1.3494 1971
## I(week^2) 3.177611e+14 9.627280e+15 0.1041 190.0161 4.043420e+12 1.3931 936
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 1.7582 1.7184 -0.8494 1.4911
## (Intercept)-Sylvilagus_floridanus 0.3687 1.9285 -3.5715 0.4772
## Cogon_Patch_Size-Canis_latrans 2.6490 2.7090 -0.4012 1.8822
## Cogon_Patch_Size-Sylvilagus_floridanus -2.8682 3.6559 -12.4953 -1.8301
## Avg_Cogongrass_Cover-Canis_latrans 0.5508 0.8350 -0.7208 0.4349
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3332 1.2991 -3.0794 -0.2770
## total_shrub_cover-Canis_latrans 1.6675 1.5008 -0.3822 1.3797
## total_shrub_cover-Sylvilagus_floridanus -2.9181 2.8450 -9.9071 -2.3601
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 5.8771 1.0860 156
## (Intercept)-Sylvilagus_floridanus 3.6699 1.1662 134
## Cogon_Patch_Size-Canis_latrans 10.0808 1.0645 153
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9688 1.1256 124
## Avg_Cogongrass_Cover-Canis_latrans 2.6026 1.0363 477
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.1281 1.0576 312
## total_shrub_cover-Canis_latrans 5.2994 1.0210 155
## total_shrub_cover-Sylvilagus_floridanus 1.5011 1.1754 64
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.800800e+00 1.875000e-01 -5.158300e+00
## (Intercept)-Sylvilagus_floridanus -5.148400e+00 2.724000e-01 -5.683100e+00
## shrub_cover-Canis_latrans -4.669000e-01 2.152000e-01 -8.776000e-01
## shrub_cover-Sylvilagus_floridanus 9.000000e-01 5.091000e-01 -3.593000e-01
## veg_height-Canis_latrans -6.097000e-01 1.745000e-01 -9.797000e-01
## veg_height-Sylvilagus_floridanus 4.960000e-02 2.610000e-01 -4.388000e-01
## week-Canis_latrans -6.612637e+09 2.728902e+12 -1.551242e+10
## week-Sylvilagus_floridanus -2.013576e+10 1.265580e+12 -1.602878e+10
## I(week^2)-Canis_latrans 2.438256e+05 2.622632e+07 -2.533616e+05
## I(week^2)-Sylvilagus_floridanus -1.918075e+05 8.128383e+06 -1.575992e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8031 -4.422500e+00 1.0665 128
## (Intercept)-Sylvilagus_floridanus -5.1528 -4.603400e+00 1.1343 84
## shrub_cover-Canis_latrans -0.4781 -1.260000e-02 1.0048 153
## shrub_cover-Sylvilagus_floridanus 0.9483 1.741200e+00 1.6454 36
## veg_height-Canis_latrans -0.5981 -2.919000e-01 1.0224 186
## veg_height-Sylvilagus_floridanus 0.0384 5.822000e-01 1.0665 109
## week-Canis_latrans -0.0444 1.623352e+10 1.2909 17058
## week-Sylvilagus_floridanus -0.0570 1.072983e+10 1.3154 5365
## I(week^2)-Canis_latrans 0.0759 2.066181e+05 1.2990 10864
## I(week^2)-Sylvilagus_floridanus 0.0470 2.840154e+05 1.3724 1048
#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): 1.586
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1335 0.9995 -1.9530 0.1513 2.1756 1.0054 962
## Veg_shannon_index 0.7216 0.8788 -1.2539 0.7643 2.4313 1.0132 1973
## Avg_Cogongrass_Cover 0.3229 1.0063 -1.8040 0.3200 2.3307 1.0018 1943
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.5956 70.2988 0.0602 0.9179 44.4853 1.2976 2234
## Veg_shannon_index 5.9616 50.9858 0.0574 0.6607 33.9990 1.1624 2308
## Avg_Cogongrass_Cover 11.6222 80.2538 0.0862 1.8137 62.1571 1.0741 2381
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4413 2.9123 0.0542 0.5164 8.7155 1.0271 445
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7147 1.9876 -5.2332 -3.0782 1.5088 1.0077 810
## shrub_cover 0.0360 0.7269 -1.4775 0.0103 1.5708 1.0141 1405
## veg_height -0.1922 0.7134 -1.6848 -0.2050 1.3535 1.0020 1843
## week 0.0304 1.6427 -3.2029 0.0280 3.4530 1.0048 1970
## I(week^2) -0.0383 1.7133 -3.4243 -0.0552 3.1905 1.0061 1208
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.168013e+02 3.829653e+03 0.0636 3.9593 2.354162e+02 1.3213 3000
## shrub_cover 3.563300e+00 2.243560e+01 0.0535 0.5401 2.670920e+01 1.1400 2572
## veg_height 8.331500e+00 2.203814e+02 0.0730 0.6469 2.397640e+01 1.3092 3000
## week 2.190957e+24 6.531960e+25 0.1087 5172.3289 1.860628e+21 1.3978 864
## I(week^2) 2.572728e+15 5.711769e+16 0.0896 76.2871 9.841678e+12 1.4792 992
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7381 0.8181 -0.7160 0.6768
## (Intercept)-Sylvilagus_floridanus -0.3298 0.9035 -2.0160 -0.3625
## Veg_shannon_index-Canis_latrans 1.2397 0.6804 0.0502 1.1887
## Veg_shannon_index-Sylvilagus_floridanus 0.6155 0.6897 -0.5551 0.5614
## Avg_Cogongrass_Cover-Canis_latrans 1.3780 0.9477 0.0025 1.2255
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4535 0.6992 -1.9196 -0.4095
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5528 1.0222 527
## (Intercept)-Sylvilagus_floridanus 1.5045 1.0526 230
## Veg_shannon_index-Canis_latrans 2.7311 1.0068 650
## Veg_shannon_index-Sylvilagus_floridanus 2.0367 1.1013 400
## Avg_Cogongrass_Cover-Canis_latrans 3.5946 1.0067 522
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8639 1.0041 661
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.785100e+00 1.755000e-01 -5.152000e+00
## (Intercept)-Sylvilagus_floridanus -4.956000e+00 2.962000e-01 -5.628800e+00
## shrub_cover-Canis_latrans -2.664000e-01 2.029000e-01 -6.716000e-01
## shrub_cover-Sylvilagus_floridanus 3.633000e-01 4.425000e-01 -4.560000e-01
## veg_height-Canis_latrans -6.577000e-01 1.687000e-01 -9.912000e-01
## veg_height-Sylvilagus_floridanus 1.718000e-01 2.666000e-01 -3.751000e-01
## week-Canis_latrans 3.874652e+10 1.884549e+12 -1.008154e+09
## week-Sylvilagus_floridanus -1.106015e+10 1.024413e+12 -1.316553e+09
## I(week^2)-Canis_latrans -7.317128e+05 4.594721e+07 -2.052894e+05
## I(week^2)-Sylvilagus_floridanus -7.549833e+05 3.544660e+07 -1.012278e+05
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7754 -4.464000e+00 1.0927 188
## (Intercept)-Sylvilagus_floridanus -4.9159 -4.451200e+00 1.0138 107
## shrub_cover-Canis_latrans -0.2666 1.239000e-01 1.0765 190
## shrub_cover-Sylvilagus_floridanus 0.3525 1.313100e+00 1.1789 102
## veg_height-Canis_latrans -0.6515 -3.400000e-01 1.0218 190
## veg_height-Sylvilagus_floridanus 0.1827 6.815000e-01 1.0050 160
## week-Canis_latrans 0.1396 2.088910e+09 1.3307 2168
## week-Sylvilagus_floridanus -0.1227 1.335822e+09 1.2961 5072
## I(week^2)-Canis_latrans -0.0725 1.127465e+05 1.3153 2607
## I(week^2)-Sylvilagus_floridanus -0.0302 1.745387e+05 1.3349 2946
#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): 1.5917
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0943 0.9340 -1.8855 0.1177 1.9773 1.0071 1460
## Avg_Cogongrass_Cover 0.0461 0.8558 -1.7591 0.0437 1.8001 1.0019 1963
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9206 199.0583 0.0593 0.8951 40.2583 1.2817 3000
## Avg_Cogongrass_Cover 6.9917 70.2231 0.0657 0.8998 32.1934 1.2640 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8978 4.5792 0.0527 0.5885 12.1793 1.1097 328
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6238 2.0000 -5.1528 -2.9532 1.5961 1.0003 753
## shrub_cover 0.0539 0.7407 -1.4706 0.0431 1.5902 1.0165 1206
## veg_height -0.1950 0.7548 -1.6936 -0.2241 1.5164 1.0071 1999
## week -0.0198 1.6774 -3.3133 -0.0428 3.3270 1.0012 1550
## I(week^2) 0.0401 1.6110 -3.1200 0.0762 3.1966 1.0022 1555
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.396990e+01 3.478774e+02 0.0614 4.5634 2.520723e+02 1.1272 3000
## shrub_cover 5.403800e+00 1.170224e+02 0.0642 0.6223 1.853380e+01 1.2478 3000
## veg_height 4.009600e+00 3.142190e+01 0.0714 0.6704 2.369410e+01 1.1853 3000
## week 7.762139e+17 2.886580e+19 0.1120 240.5095 9.761102e+14 1.3467 1600
## I(week^2) 1.980540e+13 5.356226e+14 0.1009 216.4479 6.939771e+11 1.3530 1166
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6455 0.7690 -0.6825 0.5821
## (Intercept)-Sylvilagus_floridanus -0.3089 0.7244 -1.7059 -0.3269
## Avg_Cogongrass_Cover-Canis_latrans 0.6492 0.6596 -0.3413 0.5629
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4981 0.5733 -1.7886 -0.4503
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3468 1.0198 512
## (Intercept)-Sylvilagus_floridanus 1.2187 1.0016 572
## Avg_Cogongrass_Cover-Canis_latrans 2.1102 1.0099 792
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5305 1.0018 902
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.8098 1.706000e-01 -5.1445
## (Intercept)-Sylvilagus_floridanus -4.9033 2.340000e-01 -5.3870
## shrub_cover-Canis_latrans -0.2776 1.926000e-01 -0.6504
## shrub_cover-Sylvilagus_floridanus 0.4538 4.549000e-01 -0.4101
## veg_height-Canis_latrans -0.6513 1.705000e-01 -0.9774
## veg_height-Sylvilagus_floridanus 0.1509 2.588000e-01 -0.3986
## week-Canis_latrans 5647466.3275 2.736437e+08 -2239943.5142
## week-Sylvilagus_floridanus 7146593.9441 8.244130e+08 -1769720.0387
## I(week^2)-Canis_latrans 9411.8721 3.298840e+06 -78519.6431
## I(week^2)-Sylvilagus_floridanus -119796.0488 4.521692e+06 -114716.5159
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.8090 -4.4800 1.0164 202
## (Intercept)-Sylvilagus_floridanus -4.8988 -4.4682 1.0539 187
## shrub_cover-Canis_latrans -0.2717 0.0968 1.0200 181
## shrub_cover-Sylvilagus_floridanus 0.4592 1.3556 1.0472 105
## veg_height-Canis_latrans -0.6542 -0.3079 1.0526 210
## veg_height-Sylvilagus_floridanus 0.1665 0.6196 1.0157 137
## week-Canis_latrans -0.0280 2041820.3935 1.2549 3714
## week-Sylvilagus_floridanus -0.1110 1672806.0255 1.2893 12740
## I(week^2)-Canis_latrans 0.0534 52962.5845 1.1095 6154
## I(week^2)-Sylvilagus_floridanus 0.2411 43743.1054 1.2928 3019
# 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): 1.6337
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4555 0.9606 -2.2490 -0.4936 1.5533 1.0138 1171
## Avg_Cogongrass_Cover -0.4560 1.1355 -2.6532 -0.4911 1.9311 1.0029 1696
## I(Avg_Cogongrass_Cover^2) 0.9491 1.0740 -1.3724 0.9783 2.9869 1.0062 1092
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.6084 42.3652 0.0615 0.8557 38.8517 1.1728 1965
## Avg_Cogongrass_Cover 27.3399 367.2317 0.0822 2.4741 109.5252 1.0686 3000
## I(Avg_Cogongrass_Cover^2) 13.9713 207.7883 0.0581 0.9499 73.5384 1.3418 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9465 4.5578 0.0489 0.6063 12.9797 1.0589 272
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5717 2.0357 -5.1769 -2.8789 1.8756 1.0074 834
## shrub_cover -0.0081 0.7207 -1.5315 -0.0234 1.5769 1.0079 1230
## veg_height -0.1302 0.7760 -1.7234 -0.1643 1.6769 1.0024 2693
## week -0.0361 1.7234 -3.3302 -0.0446 3.3919 1.0021 1247
## I(week^2) 0.0451 1.6008 -3.1877 0.0551 3.2067 1.0003 1549
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.12526e+01 1.860405e+02 0.0588 5.2270 2.906455e+02 1.0170 2321
## shrub_cover 4.87500e+00 9.813660e+01 0.0521 0.5004 1.808620e+01 1.2963 3000
## veg_height 5.63030e+00 4.840870e+01 0.0898 0.7990 2.840890e+01 1.0908 3000
## week 5.45774e+21 1.558641e+23 0.1102 224.6188 1.118099e+17 1.4075 816
## I(week^2) 7.14005e+16 1.421713e+18 0.1001 54.0187 1.500724e+15 1.5215 804
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2034 0.9283 -1.8318 -0.2317
## (Intercept)-Sylvilagus_floridanus -1.0514 0.9736 -3.1359 -1.0168
## Avg_Cogongrass_Cover-Canis_latrans 0.3271 1.0098 -1.4182 0.2333
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.9461 1.4928 -5.8007 -1.6873
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9029 1.3528 -0.1133 1.6845
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9708 0.7743 -0.2632 0.8769
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7938 1.0260 502
## (Intercept)-Sylvilagus_floridanus 0.7401 1.0237 626
## Avg_Cogongrass_Cover-Canis_latrans 2.6327 1.0042 750
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1302 1.0076 151
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1100 1.1104 248
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8567 1.0042 291
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.786600e+00 1.696000e-01 -5.1149
## (Intercept)-Sylvilagus_floridanus -4.975800e+00 2.575000e-01 -5.5351
## shrub_cover-Canis_latrans -2.870000e-01 1.936000e-01 -0.6694
## shrub_cover-Sylvilagus_floridanus 3.076000e-01 4.309000e-01 -0.4100
## veg_height-Canis_latrans -6.438000e-01 1.788000e-01 -0.9905
## veg_height-Sylvilagus_floridanus 2.969000e-01 2.732000e-01 -0.2280
## week-Canis_latrans -1.222449e+09 4.879188e+10 -9309912.9898
## week-Sylvilagus_floridanus 4.970614e+07 5.138057e+10 -4966720.1283
## I(week^2)-Canis_latrans -8.727636e+05 2.174231e+08 -1123384.2847
## I(week^2)-Sylvilagus_floridanus -3.267720e+06 1.823726e+08 -1397125.6076
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7892 -4.4527 1.0603 159
## (Intercept)-Sylvilagus_floridanus -4.9603 -4.5135 1.0478 143
## shrub_cover-Canis_latrans -0.2839 0.0860 1.0178 209
## shrub_cover-Sylvilagus_floridanus 0.2623 1.2444 1.0584 98
## veg_height-Canis_latrans -0.6446 -0.2881 1.0296 165
## veg_height-Sylvilagus_floridanus 0.2917 0.8409 1.0319 128
## week-Canis_latrans 0.0017 8357682.2735 1.3520 1531
## week-Sylvilagus_floridanus -0.1249 11672978.3449 1.2904 1877690
## I(week^2)-Canis_latrans -0.0568 1778465.2717 1.2924 4663
## I(week^2)-Sylvilagus_floridanus 0.0777 1565358.7460 1.3209 6575
# 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): 1.6813
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2757 1.4343 -3.0975 -0.2545 2.5686 1.0255 468
## Cogon_Patch_Size 0.2045 1.5110 -2.8728 0.2322 3.1426 1.0083 1143
## Veg_shannon_index 1.3784 1.2995 -1.5108 1.4249 3.7150 1.0026 1022
## total_shrub_cover 0.1593 1.4630 -2.8215 0.2130 2.9795 1.0078 394
## Avg_Cogongrass_Cover -0.2084 1.4502 -3.0227 -0.2045 2.5857 1.0045 650
## Tree_Density -1.1525 1.5817 -4.0587 -1.2015 2.1882 1.0196 703
## Avg_Canopy_Cover 0.4329 1.5944 -2.8076 0.5051 3.5268 1.0014 2722
## I(Avg_Cogongrass_Cover^2) 1.1866 1.3599 -1.7229 1.2205 3.7002 1.0166 465
## avg_veg_height -0.1442 1.2095 -2.5003 -0.1768 2.3135 1.0007 615
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 55.0049 875.9609 0.0691 2.3414 281.8421 1.3387
## Cogon_Patch_Size 422.3127 8631.2625 0.1525 21.9150 1776.3090 1.2681
## Veg_shannon_index 21.9779 137.3598 0.0597 1.3732 141.4834 1.1893
## total_shrub_cover 269.3401 7662.5297 0.1000 11.9048 930.9410 1.3125
## Avg_Cogongrass_Cover 88.4494 2913.9184 0.0630 2.2921 262.4284 1.3240
## Tree_Density 257.8304 3042.8795 0.0862 11.6445 1486.3050 1.4135
## Avg_Canopy_Cover 626.7833 5407.0886 0.7122 66.5510 3337.8943 1.1475
## I(Avg_Cogongrass_Cover^2) 44.6869 346.9891 0.0734 2.5675 263.1771 1.1625
## avg_veg_height 33.7497 541.7417 0.0573 0.9141 92.1358 1.3927
## ESS
## (Intercept) 2028
## Cogon_Patch_Size 3000
## Veg_shannon_index 642
## total_shrub_cover 3000
## Avg_Cogongrass_Cover 3000
## Tree_Density 2421
## Avg_Canopy_Cover 3000
## I(Avg_Cogongrass_Cover^2) 1291
## avg_veg_height 549
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.3515 21.9767 0.058 1.0952 55.7987 1.8927 85
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5120 2.0337 -5.2128 -2.7352 1.6980 1.0006 776
## shrub_cover 0.1246 0.8227 -1.5851 0.1191 1.8710 1.0052 2108
## veg_height -0.2082 0.7063 -1.6552 -0.2442 1.3879 1.0006 2281
## week -0.0118 1.6103 -3.2053 -0.0283 3.0513 1.0246 1641
## I(week^2) -0.0042 1.6519 -3.2054 0.0088 3.2092 1.0008 1791
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.35349e+01 1.540645e+03 0.0739 6.0077 3.236329e+02 1.2826 3000
## shrub_cover 6.22550e+00 7.238700e+01 0.0844 1.0946 3.104650e+01 1.2264 3000
## veg_height 5.81840e+00 1.047880e+02 0.0644 0.5837 2.296290e+01 1.2682 3000
## week 1.01809e+16 3.005047e+17 0.1082 201.4026 3.896509e+12 1.3828 965
## I(week^2) 3.80086e+17 7.116255e+18 0.1282 314.3921 1.494115e+16 1.2568 550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4708 2.5147 -3.2725 0.1596
## (Intercept)-Sylvilagus_floridanus -1.9543 3.6692 -12.0701 -1.3275
## Cogon_Patch_Size-Canis_latrans 5.2981 5.3030 -0.3189 3.9804
## Cogon_Patch_Size-Sylvilagus_floridanus -4.6727 7.1286 -26.0710 -2.6751
## Veg_shannon_index-Canis_latrans 2.3988 1.7265 -0.2347 2.1931
## Veg_shannon_index-Sylvilagus_floridanus 2.1327 1.6773 -0.6516 1.9440
## total_shrub_cover-Canis_latrans 2.4497 2.3554 -0.5639 1.9268
## total_shrub_cover-Sylvilagus_floridanus -4.0649 6.0695 -19.2305 -2.4035
## Avg_Cogongrass_Cover-Canis_latrans 0.5240 2.9437 -4.0587 0.1951
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4080 2.8623 -8.5460 -0.9751
## Tree_Density-Canis_latrans -6.0425 5.7506 -21.6767 -4.1376
## Tree_Density-Sylvilagus_floridanus -4.2293 5.0884 -18.9361 -2.8201
## Avg_Canopy_Cover-Canis_latrans -0.6155 1.2558 -3.7983 -0.4915
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.0218 9.2024 1.0537 10.1019
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3274 2.5431 0.1951 2.6580
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3930 1.7511 -1.9039 1.3240
## avg_veg_height-Canis_latrans -0.2887 1.3008 -2.9320 -0.2888
## avg_veg_height-Sylvilagus_floridanus -0.3640 2.8618 -5.5666 -0.1168
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.3981 1.0139 185
## (Intercept)-Sylvilagus_floridanus 2.9683 1.2520 74
## Cogon_Patch_Size-Canis_latrans 18.2833 1.1499 148
## Cogon_Patch_Size-Sylvilagus_floridanus 2.5137 1.2808 63
## Veg_shannon_index-Canis_latrans 6.3599 1.1459 180
## Veg_shannon_index-Sylvilagus_floridanus 5.9732 1.0763 244
## total_shrub_cover-Canis_latrans 8.9952 1.0468 104
## total_shrub_cover-Sylvilagus_floridanus 3.2406 1.0433 34
## Avg_Cogongrass_Cover-Canis_latrans 7.7545 1.0698 238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1184 1.0262 345
## Tree_Density-Canis_latrans -0.3837 1.3719 68
## Tree_Density-Sylvilagus_floridanus 2.1681 1.0313 70
## Avg_Canopy_Cover-Canis_latrans 1.2793 1.1906 175
## Avg_Canopy_Cover-Sylvilagus_floridanus 41.9126 1.4679 38
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 11.0828 1.0232 149
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.9008 1.0631 188
## avg_veg_height-Canis_latrans 2.2425 1.0260 431
## avg_veg_height-Sylvilagus_floridanus 3.7378 1.3717 51
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7782 1.765000e-01 -5.1397
## (Intercept)-Sylvilagus_floridanus -5.0603 2.445000e-01 -5.5844
## shrub_cover-Canis_latrans -0.3940 2.069000e-01 -0.7900
## shrub_cover-Sylvilagus_floridanus 0.8111 4.741000e-01 -0.0555
## veg_height-Canis_latrans -0.6019 1.788000e-01 -0.9669
## veg_height-Sylvilagus_floridanus 0.1269 2.674000e-01 -0.3879
## week-Canis_latrans -1115898.3640 4.270215e+07 -138094.3926
## week-Sylvilagus_floridanus -479300.2262 6.063057e+07 -108309.6561
## I(week^2)-Canis_latrans -7378376.8292 4.278146e+08 -2690538.9092
## I(week^2)-Sylvilagus_floridanus 4993456.6176 4.067873e+08 -1766164.2569
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7791 -4.4413 1.0117 168
## (Intercept)-Sylvilagus_floridanus -5.0524 -4.6131 1.0417 134
## shrub_cover-Canis_latrans -0.3990 0.0234 1.0090 158
## shrub_cover-Sylvilagus_floridanus 0.8044 1.7471 1.0143 52
## veg_height-Canis_latrans -0.5952 -0.2596 1.0180 183
## veg_height-Sylvilagus_floridanus 0.1241 0.6715 1.0773 149
## week-Canis_latrans 0.0048 58506.3700 1.2931 2280
## week-Sylvilagus_floridanus -0.1829 62817.2759 1.2685 33616
## I(week^2)-Canis_latrans -0.1174 2992227.9792 1.1869 5585
## I(week^2)-Sylvilagus_floridanus -0.1159 5475851.0857 1.1430 3450
waicOcc(ms_full_full_T25, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -408.98078 20.09993 858.16142
waicOcc(ms_full_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -420.16290 21.03742 882.40065
waicOcc(ms_full_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -417.62354 14.10207 863.45122
waicOcc(ms_full_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -419.01950 17.84718 873.73337
waicOcc(ms_full_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.37380 16.45843 877.66447
waicOcc(ms_full_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -424.78048 14.05739 877.67573
waicOcc(ms_full_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -429.131245 8.948903 876.160296
waicOcc(ms_full_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.29132 16.67901 877.94066
waicOcc(ms_full_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -406.61803 19.16964 851.57533
waicOcc(ms_null_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -437.349011 5.357997 885.414016
waicOcc(ms_null_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -418.97085 16.37236 870.68642
waicOcc(ms_null_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.79695 16.18077 895.95543
waicOcc(ms_null_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -427.75664 11.25237 878.01803
waicOcc(ms_null_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -430.01866 15.24969 890.53669
waicOcc(ms_null_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -430.99420 13.20479 888.39798
waicOcc(ms_null_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -433.39673 10.93579 888.66505
waicOcc(ms_null_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.48810 12.73134 888.43887
waicOcc(ms_null_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -416.46693 16.09721 865.12830
waicOcc(ms_week_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -419.56042 15.67682 870.47449
waicOcc(ms_week_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.92936 16.79968 897.45808
waicOcc(ms_week_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -437.390021 5.037262 884.854566
waicOcc(ms_week_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -430.87885 12.87607 887.50985
waicOcc(ms_week_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -429.79957 15.48331 890.56575
waicOcc(ms_week_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -427.6792 10.9643 877.2870
waicOcc(ms_week_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -433.46345 10.63344 888.19378
waicOcc(ms_week_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.60737 11.94477 887.10428
waicOcc(ms_week_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -416.35774 16.35689 865.42926
waicOcc(ms_cover_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -409.14677 18.12574 854.54501
waicOcc(ms_cover_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -420.32600 17.31784 875.28769
waicOcc(ms_cover_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -429.092464 8.205003 874.594935
waicOcc(ms_cover_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.37155 16.47478 877.69265
waicOcc(ms_cover_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -418.3029 19.4326 875.4710
waicOcc(ms_cover_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -417.60403 14.24396 863.69599
waicOcc(ms_cover_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -424.70541 14.30664 878.02412
waicOcc(ms_cover_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.59475 15.63654 876.46259
waicOcc(ms_cover_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -407.20049 18.90634 852.21366
waicOcc(ms_weekQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -418.56342 16.57191 870.27067
waicOcc(ms_weekQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.99092 16.02315 896.02814
waicOcc(ms_weekQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -437.352617 5.649536 886.004305
waicOcc(ms_weekQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.09130 12.32254 886.82768
waicOcc(ms_weekQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -430.0954 15.0664 890.3236
waicOcc(ms_weekQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -427.70793 11.31747 878.05081
waicOcc(ms_weekQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -433.53642 10.60663 888.28611
waicOcc(ms_weekQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -431.58574 13.09076 889.35301
waicOcc(ms_weekQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -415.96167 15.57923 863.08178
waicOcc(ms_fullQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -409.42753 18.10066 855.05637
waicOcc(ms_fullQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -419.9160 18.7826 877.3972
waicOcc(ms_fullQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -429.134956 8.996626 876.263165
waicOcc(ms_fullQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.56465 17.28795 879.70519
waicOcc(ms_fullQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -418.88068 17.98766 873.73668
waicOcc(ms_fullQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -417.92147 13.85071 863.54436
waicOcc(ms_fullQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -424.69665 14.05363 877.50056
waicOcc(ms_fullQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -422.54543 15.56485 876.22056
waicOcc(ms_fullQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -407.53509 19.33914 853.74845
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: 0.4533
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.643
## Sylvilagus_floridanus Bayesian p-value: 0.2637
## 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): 1.6813
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2757 1.4343 -3.0975 -0.2545 2.5686 1.0255 468
## Cogon_Patch_Size 0.2045 1.5110 -2.8728 0.2322 3.1426 1.0083 1143
## Veg_shannon_index 1.3784 1.2995 -1.5108 1.4249 3.7150 1.0026 1022
## total_shrub_cover 0.1593 1.4630 -2.8215 0.2130 2.9795 1.0078 394
## Avg_Cogongrass_Cover -0.2084 1.4502 -3.0227 -0.2045 2.5857 1.0045 650
## Tree_Density -1.1525 1.5817 -4.0587 -1.2015 2.1882 1.0196 703
## Avg_Canopy_Cover 0.4329 1.5944 -2.8076 0.5051 3.5268 1.0014 2722
## I(Avg_Cogongrass_Cover^2) 1.1866 1.3599 -1.7229 1.2205 3.7002 1.0166 465
## avg_veg_height -0.1442 1.2095 -2.5003 -0.1768 2.3135 1.0007 615
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 55.0049 875.9609 0.0691 2.3414 281.8421 1.3387
## Cogon_Patch_Size 422.3127 8631.2625 0.1525 21.9150 1776.3090 1.2681
## Veg_shannon_index 21.9779 137.3598 0.0597 1.3732 141.4834 1.1893
## total_shrub_cover 269.3401 7662.5297 0.1000 11.9048 930.9410 1.3125
## Avg_Cogongrass_Cover 88.4494 2913.9184 0.0630 2.2921 262.4284 1.3240
## Tree_Density 257.8304 3042.8795 0.0862 11.6445 1486.3050 1.4135
## Avg_Canopy_Cover 626.7833 5407.0886 0.7122 66.5510 3337.8943 1.1475
## I(Avg_Cogongrass_Cover^2) 44.6869 346.9891 0.0734 2.5675 263.1771 1.1625
## avg_veg_height 33.7497 541.7417 0.0573 0.9141 92.1358 1.3927
## ESS
## (Intercept) 2028
## Cogon_Patch_Size 3000
## Veg_shannon_index 642
## total_shrub_cover 3000
## Avg_Cogongrass_Cover 3000
## Tree_Density 2421
## Avg_Canopy_Cover 3000
## I(Avg_Cogongrass_Cover^2) 1291
## avg_veg_height 549
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.3515 21.9767 0.058 1.0952 55.7987 1.8927 85
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5120 2.0337 -5.2128 -2.7352 1.6980 1.0006 776
## shrub_cover 0.1246 0.8227 -1.5851 0.1191 1.8710 1.0052 2108
## veg_height -0.2082 0.7063 -1.6552 -0.2442 1.3879 1.0006 2281
## week -0.0118 1.6103 -3.2053 -0.0283 3.0513 1.0246 1641
## I(week^2) -0.0042 1.6519 -3.2054 0.0088 3.2092 1.0008 1791
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.35349e+01 1.540645e+03 0.0739 6.0077 3.236329e+02 1.2826 3000
## shrub_cover 6.22550e+00 7.238700e+01 0.0844 1.0946 3.104650e+01 1.2264 3000
## veg_height 5.81840e+00 1.047880e+02 0.0644 0.5837 2.296290e+01 1.2682 3000
## week 1.01809e+16 3.005047e+17 0.1082 201.4026 3.896509e+12 1.3828 965
## I(week^2) 3.80086e+17 7.116255e+18 0.1282 314.3921 1.494115e+16 1.2568 550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4708 2.5147 -3.2725 0.1596
## (Intercept)-Sylvilagus_floridanus -1.9543 3.6692 -12.0701 -1.3275
## Cogon_Patch_Size-Canis_latrans 5.2981 5.3030 -0.3189 3.9804
## Cogon_Patch_Size-Sylvilagus_floridanus -4.6727 7.1286 -26.0710 -2.6751
## Veg_shannon_index-Canis_latrans 2.3988 1.7265 -0.2347 2.1931
## Veg_shannon_index-Sylvilagus_floridanus 2.1327 1.6773 -0.6516 1.9440
## total_shrub_cover-Canis_latrans 2.4497 2.3554 -0.5639 1.9268
## total_shrub_cover-Sylvilagus_floridanus -4.0649 6.0695 -19.2305 -2.4035
## Avg_Cogongrass_Cover-Canis_latrans 0.5240 2.9437 -4.0587 0.1951
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4080 2.8623 -8.5460 -0.9751
## Tree_Density-Canis_latrans -6.0425 5.7506 -21.6767 -4.1376
## Tree_Density-Sylvilagus_floridanus -4.2293 5.0884 -18.9361 -2.8201
## Avg_Canopy_Cover-Canis_latrans -0.6155 1.2558 -3.7983 -0.4915
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.0218 9.2024 1.0537 10.1019
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3274 2.5431 0.1951 2.6580
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3930 1.7511 -1.9039 1.3240
## avg_veg_height-Canis_latrans -0.2887 1.3008 -2.9320 -0.2888
## avg_veg_height-Sylvilagus_floridanus -0.3640 2.8618 -5.5666 -0.1168
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.3981 1.0139 185
## (Intercept)-Sylvilagus_floridanus 2.9683 1.2520 74
## Cogon_Patch_Size-Canis_latrans 18.2833 1.1499 148
## Cogon_Patch_Size-Sylvilagus_floridanus 2.5137 1.2808 63
## Veg_shannon_index-Canis_latrans 6.3599 1.1459 180
## Veg_shannon_index-Sylvilagus_floridanus 5.9732 1.0763 244
## total_shrub_cover-Canis_latrans 8.9952 1.0468 104
## total_shrub_cover-Sylvilagus_floridanus 3.2406 1.0433 34
## Avg_Cogongrass_Cover-Canis_latrans 7.7545 1.0698 238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1184 1.0262 345
## Tree_Density-Canis_latrans -0.3837 1.3719 68
## Tree_Density-Sylvilagus_floridanus 2.1681 1.0313 70
## Avg_Canopy_Cover-Canis_latrans 1.2793 1.1906 175
## Avg_Canopy_Cover-Sylvilagus_floridanus 41.9126 1.4679 38
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 11.0828 1.0232 149
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.9008 1.0631 188
## avg_veg_height-Canis_latrans 2.2425 1.0260 431
## avg_veg_height-Sylvilagus_floridanus 3.7378 1.3717 51
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7782 1.765000e-01 -5.1397
## (Intercept)-Sylvilagus_floridanus -5.0603 2.445000e-01 -5.5844
## shrub_cover-Canis_latrans -0.3940 2.069000e-01 -0.7900
## shrub_cover-Sylvilagus_floridanus 0.8111 4.741000e-01 -0.0555
## veg_height-Canis_latrans -0.6019 1.788000e-01 -0.9669
## veg_height-Sylvilagus_floridanus 0.1269 2.674000e-01 -0.3879
## week-Canis_latrans -1115898.3640 4.270215e+07 -138094.3926
## week-Sylvilagus_floridanus -479300.2262 6.063057e+07 -108309.6561
## I(week^2)-Canis_latrans -7378376.8292 4.278146e+08 -2690538.9092
## I(week^2)-Sylvilagus_floridanus 4993456.6176 4.067873e+08 -1766164.2569
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7791 -4.4413 1.0117 168
## (Intercept)-Sylvilagus_floridanus -5.0524 -4.6131 1.0417 134
## shrub_cover-Canis_latrans -0.3990 0.0234 1.0090 158
## shrub_cover-Sylvilagus_floridanus 0.8044 1.7471 1.0143 52
## veg_height-Canis_latrans -0.5952 -0.2596 1.0180 183
## veg_height-Sylvilagus_floridanus 0.1241 0.6715 1.0773 149
## week-Canis_latrans 0.0048 58506.3700 1.2931 2280
## week-Sylvilagus_floridanus -0.1829 62817.2759 1.2685 33616
## I(week^2)-Canis_latrans -0.1174 2992227.9792 1.1869 5585
## I(week^2)-Sylvilagus_floridanus -0.1159 5475851.0857 1.1430 3450
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$beta.samples)
## 'mcmc' num [1:3000, 1:18] 0.1756 0.2269 -0.0609 0.8702 0.6846 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : 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$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.965
MCMCplot(ms_fullQ_fullQ_T25$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] 7.80e-05 4.01e-05 6.48e-02 2.36e-02 2.89e-05 ...
## $ z.0.samples : int [1:3000, 1:2, 1:100] 0 0 0 0 0 0 0 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T25, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:2, 1:100] 7.80e-05 4.01e-05 6.48e-02 2.36e-02 2.89e-05 ...
psi.0.quants <- apply(out.pred$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] 2.36e-05 2.08e-01 1.00 3.16e-05 2.21e-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 0.208 0.221 0.219 0.225 0.238 ...
## $ psi.low : num 2.36e-05 3.16e-05 4.52e-05 6.81e-05 8.88e-05 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T25) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.6813
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2757 1.4343 -3.0975 -0.2545 2.5686 1.0255 468
## Cogon_Patch_Size 0.2045 1.5110 -2.8728 0.2322 3.1426 1.0083 1143
## Veg_shannon_index 1.3784 1.2995 -1.5108 1.4249 3.7150 1.0026 1022
## total_shrub_cover 0.1593 1.4630 -2.8215 0.2130 2.9795 1.0078 394
## Avg_Cogongrass_Cover -0.2084 1.4502 -3.0227 -0.2045 2.5857 1.0045 650
## Tree_Density -1.1525 1.5817 -4.0587 -1.2015 2.1882 1.0196 703
## Avg_Canopy_Cover 0.4329 1.5944 -2.8076 0.5051 3.5268 1.0014 2722
## I(Avg_Cogongrass_Cover^2) 1.1866 1.3599 -1.7229 1.2205 3.7002 1.0166 465
## avg_veg_height -0.1442 1.2095 -2.5003 -0.1768 2.3135 1.0007 615
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 55.0049 875.9609 0.0691 2.3414 281.8421 1.3387
## Cogon_Patch_Size 422.3127 8631.2625 0.1525 21.9150 1776.3090 1.2681
## Veg_shannon_index 21.9779 137.3598 0.0597 1.3732 141.4834 1.1893
## total_shrub_cover 269.3401 7662.5297 0.1000 11.9048 930.9410 1.3125
## Avg_Cogongrass_Cover 88.4494 2913.9184 0.0630 2.2921 262.4284 1.3240
## Tree_Density 257.8304 3042.8795 0.0862 11.6445 1486.3050 1.4135
## Avg_Canopy_Cover 626.7833 5407.0886 0.7122 66.5510 3337.8943 1.1475
## I(Avg_Cogongrass_Cover^2) 44.6869 346.9891 0.0734 2.5675 263.1771 1.1625
## avg_veg_height 33.7497 541.7417 0.0573 0.9141 92.1358 1.3927
## ESS
## (Intercept) 2028
## Cogon_Patch_Size 3000
## Veg_shannon_index 642
## total_shrub_cover 3000
## Avg_Cogongrass_Cover 3000
## Tree_Density 2421
## Avg_Canopy_Cover 3000
## I(Avg_Cogongrass_Cover^2) 1291
## avg_veg_height 549
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 7.3515 21.9767 0.058 1.0952 55.7987 1.8927 85
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5120 2.0337 -5.2128 -2.7352 1.6980 1.0006 776
## shrub_cover 0.1246 0.8227 -1.5851 0.1191 1.8710 1.0052 2108
## veg_height -0.2082 0.7063 -1.6552 -0.2442 1.3879 1.0006 2281
## week -0.0118 1.6103 -3.2053 -0.0283 3.0513 1.0246 1641
## I(week^2) -0.0042 1.6519 -3.2054 0.0088 3.2092 1.0008 1791
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.35349e+01 1.540645e+03 0.0739 6.0077 3.236329e+02 1.2826 3000
## shrub_cover 6.22550e+00 7.238700e+01 0.0844 1.0946 3.104650e+01 1.2264 3000
## veg_height 5.81840e+00 1.047880e+02 0.0644 0.5837 2.296290e+01 1.2682 3000
## week 1.01809e+16 3.005047e+17 0.1082 201.4026 3.896509e+12 1.3828 965
## I(week^2) 3.80086e+17 7.116255e+18 0.1282 314.3921 1.494115e+16 1.2568 550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4708 2.5147 -3.2725 0.1596
## (Intercept)-Sylvilagus_floridanus -1.9543 3.6692 -12.0701 -1.3275
## Cogon_Patch_Size-Canis_latrans 5.2981 5.3030 -0.3189 3.9804
## Cogon_Patch_Size-Sylvilagus_floridanus -4.6727 7.1286 -26.0710 -2.6751
## Veg_shannon_index-Canis_latrans 2.3988 1.7265 -0.2347 2.1931
## Veg_shannon_index-Sylvilagus_floridanus 2.1327 1.6773 -0.6516 1.9440
## total_shrub_cover-Canis_latrans 2.4497 2.3554 -0.5639 1.9268
## total_shrub_cover-Sylvilagus_floridanus -4.0649 6.0695 -19.2305 -2.4035
## Avg_Cogongrass_Cover-Canis_latrans 0.5240 2.9437 -4.0587 0.1951
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.4080 2.8623 -8.5460 -0.9751
## Tree_Density-Canis_latrans -6.0425 5.7506 -21.6767 -4.1376
## Tree_Density-Sylvilagus_floridanus -4.2293 5.0884 -18.9361 -2.8201
## Avg_Canopy_Cover-Canis_latrans -0.6155 1.2558 -3.7983 -0.4915
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.0218 9.2024 1.0537 10.1019
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3274 2.5431 0.1951 2.6580
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3930 1.7511 -1.9039 1.3240
## avg_veg_height-Canis_latrans -0.2887 1.3008 -2.9320 -0.2888
## avg_veg_height-Sylvilagus_floridanus -0.3640 2.8618 -5.5666 -0.1168
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 6.3981 1.0139 185
## (Intercept)-Sylvilagus_floridanus 2.9683 1.2520 74
## Cogon_Patch_Size-Canis_latrans 18.2833 1.1499 148
## Cogon_Patch_Size-Sylvilagus_floridanus 2.5137 1.2808 63
## Veg_shannon_index-Canis_latrans 6.3599 1.1459 180
## Veg_shannon_index-Sylvilagus_floridanus 5.9732 1.0763 244
## total_shrub_cover-Canis_latrans 8.9952 1.0468 104
## total_shrub_cover-Sylvilagus_floridanus 3.2406 1.0433 34
## Avg_Cogongrass_Cover-Canis_latrans 7.7545 1.0698 238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1184 1.0262 345
## Tree_Density-Canis_latrans -0.3837 1.3719 68
## Tree_Density-Sylvilagus_floridanus 2.1681 1.0313 70
## Avg_Canopy_Cover-Canis_latrans 1.2793 1.1906 175
## Avg_Canopy_Cover-Sylvilagus_floridanus 41.9126 1.4679 38
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 11.0828 1.0232 149
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 4.9008 1.0631 188
## avg_veg_height-Canis_latrans 2.2425 1.0260 431
## avg_veg_height-Sylvilagus_floridanus 3.7378 1.3717 51
##
## Detection (logit scale):
## Mean SD 2.5%
## (Intercept)-Canis_latrans -4.7782 1.765000e-01 -5.1397
## (Intercept)-Sylvilagus_floridanus -5.0603 2.445000e-01 -5.5844
## shrub_cover-Canis_latrans -0.3940 2.069000e-01 -0.7900
## shrub_cover-Sylvilagus_floridanus 0.8111 4.741000e-01 -0.0555
## veg_height-Canis_latrans -0.6019 1.788000e-01 -0.9669
## veg_height-Sylvilagus_floridanus 0.1269 2.674000e-01 -0.3879
## week-Canis_latrans -1115898.3640 4.270215e+07 -138094.3926
## week-Sylvilagus_floridanus -479300.2262 6.063057e+07 -108309.6561
## I(week^2)-Canis_latrans -7378376.8292 4.278146e+08 -2690538.9092
## I(week^2)-Sylvilagus_floridanus 4993456.6176 4.067873e+08 -1766164.2569
## 50% 97.5% Rhat ESS
## (Intercept)-Canis_latrans -4.7791 -4.4413 1.0117 168
## (Intercept)-Sylvilagus_floridanus -5.0524 -4.6131 1.0417 134
## shrub_cover-Canis_latrans -0.3990 0.0234 1.0090 158
## shrub_cover-Sylvilagus_floridanus 0.8044 1.7471 1.0143 52
## veg_height-Canis_latrans -0.5952 -0.2596 1.0180 183
## veg_height-Sylvilagus_floridanus 0.1241 0.6715 1.0773 149
## week-Canis_latrans 0.0048 58506.3700 1.2931 2280
## week-Sylvilagus_floridanus -0.1829 62817.2759 1.2685 33616
## I(week^2)-Canis_latrans -0.1174 2992227.9792 1.1869 5585
## I(week^2)-Sylvilagus_floridanus -0.1159 5475851.0857 1.1430 3450
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$beta.samples)
## 'mcmc' num [1:3000, 1:18] 0.1756 0.2269 -0.0609 0.8702 0.6846 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : 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$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.965
MCMCplot(ms_fullQ_fullQ_T25$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()
#}