spOccupancy fits single-species, multi-species, and integrated spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Póly-Gamma data augmentation. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. The package provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. For multi-species models, spOccupancy provides functions to account for residual species correlations in a joint species distribution model framework while accounting for imperfect detection. As of v0.4.0, spOccupancy provides functions for multi-season (i.e., spatio-temporal) single-species occupancy models. Below we provide a very brief introduction to some of the package’s functionality, and illustrate just one of the model fitting funcitons. For more information, see the resources referenced at the bottom of this page.

## Installation

You can install the released version of spOccupancy from CRAN with:

install.packages("spOccupancy")

## Functionality

spOccupancy Function Description
PGOcc() Single-species occupancy model
spPGOcc() Single-species spatial occupancy model
intPGOcc() Single-species occupancy model with multiple data sources
spIntPGOcc() Single-species spatial occupancy model with multiple data sources
msPGOcc() Multi-species occupancy model
spMsPGOcc() Multi-species spatial occupancy model
lfJSDM() Joint species distribution model without imperfect detection
sfJSDM() Spatial joint species distribution model without imperfect detection
lfMsPGOcc() Multi-species occupancy model with species correlations
sfMsPGOcc() Multi-species spatial occupancy model with species correlations
tPGOcc() Single-species multi-season occupancy model
stPGOcc() Single-species multi-season spatio-temporal occupancy model
ppcOcc() Posterior predictive check using Bayesian p-values
waicOcc() Compute Widely Applicable Information Criterion (WAIC)
simOcc() Simulate single-species occupancy data
simMsOcc() Simulate multi-species occupancy data
simIntOcc() Simulate single-species occupancy data from multiple data sources

## Example usage

### Load package and data

To get started with spOccupancy we load the package and an example data set. We use data on twelve foliage-gleaning birds from the Hubbard Brook Experimental Forest, which is available in the spOccupancy package as the hbef2015 object. Here we will only work with one bird species, the Black-throated Blue Warbler (BTBW), and so we subset the hbef2015 object to only include this species.

library(spOccupancy)
data(hbef2015)
sp.names <- dimnames(hbef2015$y)[[1]] btbwHBEF <- hbef2015 btbwHBEF$y <- btbwHBEF$y[sp.names == "BTBW", , ] ### Fit a spatial occupancy model using spPGOcc() Below we fit a single-species spatial occupancy model to the BTBW data using a Nearest Neighbor Gaussian Process. We use the default priors and initial values for the occurrence (beta) and regression (alpha) coefficients, the spatial variance (sigma.sq), the spatial range parameter (phi), the spatial random effects (w), and the latent occurrence values (z). We assume occurrence is a function of linear and quadratic elevation along with a spatial random intercept. We model detection as a function of linear and quadratic day of survey and linear time of day the survey occurred. # Specify model formulas btbw.occ.formula <- ~ scale(Elevation) + I(scale(Elevation)^2) btbw.det.formula <- ~ scale(day) + scale(tod) + I(scale(day)^2) We run the model using an Adaptive MCMC sampler with a target acceptance rate of 0.43. We run 3 chains of the model each for 10,000 iterations split into 400 batches each of length 25. For each chain, we discard the first 6000 iterations as burn-in and use a thinning rate of 4 for a resulting 3000 samples from the joint posterior. We fit the model using 5 nearest neighbors and an exponential correlation function. We also specify the k.fold argument to perform 2-fold cross-validation after fitting the full model. Run ?spPGOcc for more detailed information on all function arguments. # Run the model out <- spPGOcc(occ.formula = btbw.occ.formula, det.formula = btbw.det.formula, data = btbwHBEF, n.batch = 400, batch.length = 25, accept.rate = 0.43, cov.model = "exponential", NNGP = TRUE, n.neighbors = 5, n.burn = 2000, n.thin = 4, n.chains = 3, verbose = FALSE, k.fold = 2) This will produce a large output object, and you can use str(out) to get an overview of what’s in there. Here we use the summary() function to print a concise but informative summary of the model fit. summary(out) #> #> Call: #> spPGOcc(occ.formula = btbw.occ.formula, det.formula = btbw.det.formula, #> data = btbwHBEF, cov.model = "exponential", NNGP = TRUE, #> n.neighbors = 5, n.batch = 400, batch.length = 25, accept.rate = 0.43, #> verbose = FALSE, n.burn = 2000, n.thin = 4, n.chains = 3, #> k.fold = 2) #> #> Samples per Chain: 10000 #> Burn-in: 2000 #> Thinning Rate: 4 #> Number of Chains: 3 #> Total Posterior Samples: 6000 #> Run Time (min): 1.4284 #> #> Occurrence (logit scale): #> Mean SD 2.5% 50% 97.5% Rhat ESS #> (Intercept) 3.9306 0.5843 2.9649 3.8718 5.3272 1.0703 278 #> scale(Elevation) -0.5134 0.2112 -0.9457 -0.5051 -0.1178 1.0070 1110 #> I(scale(Elevation)^2) -1.1388 0.2044 -1.6050 -1.1181 -0.7881 1.0705 466 #> #> Detection (logit scale): #> Mean SD 2.5% 50% 97.5% Rhat ESS #> (Intercept) 0.6663 0.1136 0.4486 0.6656 0.8900 0.9999 5769 #> scale(day) 0.2933 0.0704 0.1531 0.2929 0.4308 1.0000 6000 #> scale(tod) -0.0290 0.0702 -0.1638 -0.0293 0.1070 1.0024 6000 #> I(scale(day)^2) -0.0763 0.0863 -0.2447 -0.0768 0.0910 1.0011 6000 #> #> Spatial Covariance: #> Mean SD 2.5% 50% 97.5% Rhat ESS #> sigma.sq 1.0481 0.9929 0.2094 0.7385 3.8056 1.2883 86 #> phi 0.0085 0.0078 0.0005 0.0053 0.0274 1.1606 53 ### Posterior predictive check The function ppcOcc performs a posterior predictive check on the resulting list from the call to spPGOcc. For binary data, we need to perform Goodness of Fit assessments on some binned form of the data rather than the raw binary data. Below we perform a posterior predictive check on the data grouped by site with a Freeman-Tukey fit statistic, and then use the summary function to summarize the check with a Bayesian p-value. ppc.out <- ppcOcc(out, fit.stat = 'freeman-tukey', group = 1) summary(ppc.out) #> #> Call: #> ppcOcc(object = out, fit.stat = "freeman-tukey", group = 1) #> #> Samples per Chain: 10000 #> Burn-in: 2000 #> Thinning Rate: 4 #> Number of Chains: 3 #> Total Posterior Samples: 6000 #> #> Bayesian p-value: 0.4873 #> Fit statistic: freeman-tukey ### Model selection using WAIC and k-fold cross-validation The waicOcc function computes the Widely Applicable Information Criterion (WAIC) for use in model selection and assessment (note that due to Monte Carlo error your results will differ slightly). waicOcc(out) #> elpd pD WAIC #> -682.35028 20.86682 1406.43420 Alternatively, we can perform k-fold cross-validation (CV) directly in our call to spPGOcc using the k.fold argument and compare models using a deviance scoring rule. We fit the model with k.fold = 2 and so below we access the deviance scoring rule from the 2-fold cross-validation. If we have additional candidate models to compare this model with, then we might select for inference the one with the lowest value of this CV score. out$k.fold.deviance
#> [1] 1496.56

### Prediction

Prediction is possible using the predict function, a set of occurrence covariates at the new locations, and the spatial coordinates of the new locations. The object hbefElev contains elevation data across the entire Hubbard Brook Experimental Forest. Below we predict BTBW occurrence across the forest, which are stored in the out.pred object.

# First standardize elevation using mean and sd from fitted model
elev.pred <- (hbefElev$val - mean(btbwHBEF$occ.covs[, 1])) / sd(btbwHBEF\$occ.covs[, 1])
coords.0 <- as.matrix(hbefElev[, c('Easting', 'Northing')])
X.0 <- cbind(1, elev.pred, elev.pred^2)
out.pred <- predict(out, X.0, coords.0, verbose = FALSE)

The vignette("modelFitting") provides a more detailed description and tutorial of the core functions in spOccupancy. For full statistical details on the MCMC samplers for core functions in spOccupancy, see vignette("mcmcSamplers"). In addition, see our recent paper that describes the package in more detail (Doser et al. 2022a). For a detailed description and tutorial of joint species distribution models in spOccupancy that account for residual species correlations, see vignette("factorModels"), as well as vignette("mcmcFactorModels") for full statistical details. For a description and tutoral of multi-season (spatio-temporal) occupancy models in spOccupancy, see vignette("spaceTimeModels").