Description Usage Arguments Details Value Author(s)
This function fits a spatial occupancy model where the true occupancy is a function of a spatial process. An efficient Gibbs sampling algorithm is used by formulating the detection and occupancy process models with a probit model instead of the traditional logit based model.
1 2  spatial.occupancy(detection.model, occupancy.model, spatial.model, so.data,
prior, control, initial.values = NULL)

detection.model 
A formula object describing the detection portion of
the occupancy model. The variables described by the detection model are
located in the 
occupancy.model 
A formula object describing the fixed effects portion
of the spatial occupancy process. The variables described by the occupancy
model are located in the 
spatial.model 
A named list object describing the spatial component of
the occupancy process. Currently the only possible models are ICAR, restricted spatial regression,
process convolution models, and no spatial model (i.e., eta = 0). Thus, 
so.data 
An 
prior 
A named list that provides the parameter values for the prior
distributions. At the current time the elements of the list must contain

control 
A named list with the control parameters for the MCMC. The
elements of the list must include: (1) 
initial.values 
A named list that can include any or all of the following vectors or scalers
(1) 
A Gibbs sampler is run to draw an MCMC sample of the spatial occupancy
parameters beta
(detection parameters), gamma
(the occupancy
parameters), psi
(the model occupancy generating process), and the
realized occupancy.
A list with the following elements:
beta 
An object of class

gamma 
An object of
class 
psi 
An object of
class 
real.occ 
An
object of class 
tau 
An object of
class 
occupancy.df 
A data frame with the spatial coordinates, site id, and posterior mean and variance of psi, eta, and real.occ 
D.m 
The posterior predictive loss criterion of Gelfand and Ghosh (1998; Biometrika 85:111) for model selection. The criterion is a combination of a goodnessoffit measure, G.m, and a complexity measure, P.m, similar information criteria such as AIC and BIC. D.m = G.m + P.m. Lower values of D.m imply lower expected loss in predicting new data with the posterior model parameters. 
G.m 
The goodnessoffit portion of D.m 
P.m 
The model complexity component of D.m 
detection.model 
The detection model call. 
occupancy.model 
The occupancy model call. 
model 
A character version of the joint occupancy and detection model call. This is useful for saving results. 
Devin S. Johnson <devin.johnson@noaa.gov>
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