getSVCSamples {spOccupancy}

Extract spatially-varying coefficient MCMC samples

Description

Function for extracting the full spatially-varying coefficient MCMC samples from an spOccupancy model object.

Usage

getSVCSamples(object, pred.object, ...)

Arguments

object

an object of class svcPGOcc, svcPGBinom, svcTPGOcc, svcTPGBinom, svcMsPGOcc, svcTMsPGOcc.

pred.object

a prediction object from a spatially-varying coefficient model fit using spOccupancy. Should be of class predict.svcPGOcc, predict.svcPGBinom, predict.svcTPGOcc, predict.svcTPGBinom, predict.svcMsPGOcc, or predict.svcTMsPGOcc. If specified, SVC samples are extracted at the prediction locations.

...

currently no additional arguments

Value

A list of coda::mcmc objects of the spatially-varying coefficient MCMC samples for all spatially-varying coefficients estimated in the model (including the intercept if specified). Note these values correspond to the sum of the estimated spatial and non-spatial effect to give the overall effect of the covariate at each location. Each element of the list is a two-dimensional matrix where dimensions correspond to MCMC sample and site. If pred.object is specified, values are returned for the prediction locations instead of the sampled locations.

Note

For multi-species models, the value of the SVC will be returned at all spatial locations for each species even when range.ind is specified in the data list when fitting the model. This may not be desirable for complete summaries of the SVC for each species, so if specifying range.ind in the data list, you may want to subsequently process the SVC samples for each species to be restricted to each species range.

Author(s)

Jeffrey W. Doser [e-mail],

Examples

set.seed(400)
# Simulate Data -----------------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- sample(2:4, J, replace = TRUE)
beta <- c(0.5, 2)
p.occ <- length(beta)
alpha <- c(0, 1)
p.det <- length(alpha)
phi <- c(3 / .6, 3 / .8)
sigma.sq <- c(1.2, 0.7)
svc.cols <- c(1, 2)
dat <- simOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, beta = beta, alpha = alpha, 
              sigma.sq = sigma.sq, phi = phi, sp = TRUE, cov.model = 'exponential', 
              svc.cols = svc.cols)
# Detection-nondetection data
y <- dat$y
# Occupancy covariates
X <- dat$X
# Detection covarites
X.p <- dat$X.p
# Spatial coordinates
coords <- dat$coords

# Package all data into a list
occ.covs <- X[, -1, drop = FALSE]
colnames(occ.covs) <- c('occ.cov')
det.covs <- list(det.cov.1 = X.p[, , 2])
data.list <- list(y = y, 
                  occ.covs = occ.covs, 
                  det.covs = det.covs, 
                  coords = coords)

# Number of batches
n.batch <- 10
# Batch length
batch.length <- 25
n.iter <- n.batch * batch.length
# Priors 
prior.list <- list(beta.normal = list(mean = 0, var = 2.72), 
                   alpha.normal = list(mean = 0, var = 2.72),
                   sigma.sq.ig = list(a = 2, b = 1), 
                   phi.unif = list(a = 3/1, b = 3/.1)) 
# Initial values
inits.list <- list(alpha = 0, beta = 0,
                   phi = 3 / .5, 
                   sigma.sq = 2,
                   w = matrix(0, nrow = length(svc.cols), ncol = nrow(X)),
                   z = apply(y, 1, max, na.rm = TRUE))
# Tuning
tuning.list <- list(phi = 1) 

out <- svcPGOcc(occ.formula = ~ occ.cov, 
                det.formula = ~ det.cov.1, 
                data = data.list, 
                inits = inits.list, 
                n.batch = n.batch, 
                batch.length = batch.length, 
                accept.rate = 0.43, 
                priors = prior.list,
                cov.model = 'exponential', 
                svc.cols = c(1, 2),
                tuning = tuning.list, 
                n.omp.threads = 1, 
                verbose = TRUE, 
                NNGP = TRUE, 
                n.neighbors = 5, 
                search.type = 'cb', 
                n.report = 10, 
                n.burn = 50, 
                n.thin = 1)

svc.samples <- getSVCSamples(out)
str(svc.samples)