Data from: Efficient Bayesian analysis of occupancy models with logit link functions

Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the
probability that a species under investigation occupies a site. Bayesian
analysis of these models can be undertaken using statistical packages such
as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these
packages were not developed specifically to fit occupancy models, one
often experiences long run times when undertaking an analysis. Bayesian
spatial single‐season occupancy models can also be fit using the R package
stocc. The approach assumes that the detection and occupancy regression
effects are modeled using probit link functions. The use of the logistic
link function, however, is algebraically more tractable and allows one to
easily interpret the coefficient effects of an estimated model by using
odds ratios, which is not easily done for a probit link function for
models that do not include spatial random effects. We develop a Gibbs
sampler to obtain posterior samples from the posterior distribution of the
parameters of various occupancy models (nonspatial and spatial) when logit
link functions are used to model the regression effects of the detection
and occupancy processes. We apply our methods to data extracted from the
2nd Southern African Bird Atlas Project to produce a species distribution
map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida
meleagris) for South Africa. We found that the Gibbs sampling algorithm
developed produces posterior samples that are identical to those obtained
when using JAGS and Stan and that in certain cases the posterior chains
mix much faster than those obtained when using JAGS, stocc, and Stan. Our
algorithms are implemented in the R package, Rcppocc. The software is
freely available and stored on GitHub
(https://github.com/AllanClark/Rcppocc).