predict.intPGOcc {spOccupancy}

Function for prediction at new locations for single-species integrated occupancy models

Description

The function predict collects posterior predictive samples for a set of new locations given an object of class 'intPGOcc'.

Usage

## S3 method for class 'intPGOcc'
predict(object, X.0, ...)

Arguments

object

an object of class intPGOcc

X.0

the design matrix for prediction locations. This should include a column of 1s for the intercept. Covariates should have the same column names as those used when fitting the model with intPGOcc.

...

currently no additional arguments

Value

An object of class predict.intPGOcc that is a list comprised of:

psi.0.samples

a coda object of posterior predictive samples for the latent occurrence probability values.

z.0.samples

a coda object of posterior predictive samples for the latent occurrence values.

The return object will include additional objects used for standard extractor functions.

Author(s)

Jeffrey W. Doser [e-mail],
Andrew O. Finley [e-mail]

Examples

set.seed(1008)

# Simulate Data -----------------------------------------------------------
J.x <- 10
J.y <- 10
J.all <- J.x * J.y
# Number of data sources.
n.data <- 4
# Sites for each data source. 
J.obs <- sample(ceiling(0.2 * J.all):ceiling(0.5 * J.all), n.data, replace = TRUE)
# Replicates for each data source.
n.rep <- list()
for (i in 1:n.data) {
  n.rep[[i]] <- sample(1:4, size = J.obs[i], replace = TRUE)
}
# Occupancy covariates
beta <- c(0.5, 1)
p.occ <- length(beta)
# Detection covariates
alpha <- list()
for (i in 1:n.data) {
  alpha[[i]] <- runif(2, -1, 1)
}
p.det.long <- sapply(alpha, length)
p.det <- sum(p.det.long)

# Simulate occupancy data. 
dat <- simIntOcc(n.data = n.data, J.x = J.x, J.y = J.y, J.obs = J.obs, 
                 n.rep = n.rep, beta = beta, alpha = alpha, sp = FALSE)

y <- dat$y
X <- dat$X.obs
X.p <- dat$X.p
sites <- dat$sites

# Package all data into a list
occ.covs <- X[, 2, drop = FALSE]
colnames(occ.covs) <- c('occ.cov')
det.covs <- list()
# Add covariates one by one
det.covs[[1]] <- list(det.cov.1.1 = X.p[[1]][, , 2]) 
det.covs[[2]] <- list(det.cov.2.1 = X.p[[2]][, , 2]) 
det.covs[[3]] <- list(det.cov.3.1 = X.p[[3]][, , 2]) 
det.covs[[4]] <- list(det.cov.4.1 = X.p[[4]][, , 2]) 
data.list <- list(y = y, 
                  occ.covs = occ.covs,
                  det.covs = det.covs, 
                  sites = sites)

J <- length(dat$z.obs)
# Initial values
inits.list <- list(alpha = list(0, 0, 0, 0), 
                   beta = 0, 
                   z = rep(1, J))
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 2.72), 
                   alpha.normal = list(mean = list(0, 0, 0, 0), 
                                       var = list(2.72, 2.72, 2.72, 2.72)))
n.samples <- 5000
out <- intPGOcc(occ.formula = ~ occ.cov, 
                det.formula = list(f.1 = ~ det.cov.1.1, 
                                   f.2 = ~ det.cov.2.1, 
                                   f.3 = ~ det.cov.3.1, 
                                   f.4 = ~ det.cov.4.1), 
                data = data.list,
                inits = inits.list,
                n.samples = n.samples, 
                priors = prior.list, 
                n.omp.threads = 1, 
                verbose = TRUE, 
                n.report = 1000, 
                n.burn = 4000, 
                n.thin = 1)

summary(out)

# Prediction
X.0 <- dat$X.pred
psi.0 <- dat$psi.pred

out.pred <- predict(out, X.0)
psi.hat.quants <- apply(out.pred$psi.0.samples, 2, quantile, c(0.025, 0.5, 0.975))
plot(psi.0, psi.hat.quants[2, ], pch = 19, xlab = 'True', 
     ylab = 'Fitted', ylim = c(min(psi.hat.quants), max(psi.hat.quants)))
segments(psi.0, psi.hat.quants[1, ], psi.0, psi.hat.quants[3, ])
lines(psi.0, psi.0)