predict.msAbund {spAbundance}

Function for prediction at new locations for multivariate GLMMs

Description

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

Usage

## S3 method for class 'msAbund'
predict(object, X.0, ignore.RE = FALSE, z.0.samples, ...)

Arguments

object

an object of class msAbund

X.0

the design matrix of covariates at the prediction locations. This can be either a two-dimensional matrix with rows corresponding to sites and columns corresponding to covariates, or can be a three-dimensional array, with dimensions corresponding to site, replicate, and covariate, respectively. Note that the first covariate should consist of all 1s for the intercept if an intercept is included in the model. If random effects are included in the the model, the levels of the random effects at the new locations/time periods should be included as an element of the three-dimensional array. The ordering of the levels should match the ordering used to fit the data in msAbund. The covariates should be organized in the same order as they were specified in the corresponding formula argument of msAbund. Names of the third dimension (covariates) of any random effects in X.0 must match the name of the random effects used to fit the model, if specified in the corresponding formula argument of msAbund. See example below.

ignore.RE

logical value that specifies whether or not to remove unstructured random effects from the subsequent predictions. If TRUE, unstructured random effects will be included. If FALSE, unstructured random effects will be set to 0 and predictions will only be generated from the fixed effects.

z.0.samples

a three-dimensional array with dimensions corresponding to MCMC samples, species, and prediction locations. The array contains the full posterior samples of the predicted binary portion of a Gaussian zero-inflated model. In the context of abundance models, this typically corresponds to estimates of the presence or absence of each species at the location. When using spOccupancy to generate the first stage samples of the zi-Gaussian model, this is the object contained in the z.0.samples object of the predition function for the spOccupancy object. Ignored for all model types other than zi-Gaussian.

...

currently no additional arguments

Value

A list object of class predict.msAbund. The list consists of:

mu.0.samples

a three or four-dimensional object of posterior predictive samples for the expected abundance values with dimensions corresponding to posterior predictive sample, species, site, and replicate. Note if an offset was used when fitting the model with msAbund, the abundance values are reported per unit of the offset.

y.0.samples

a three or four-dimensional object of posterior predictive samples for the abundance values with dimensions corresponding to posterior predictive sample, species, site, and replicate. These will be in the same units as mu.0.samples.

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

Note

When ignore.RE = FALSE, both sampled levels and non-sampled levels of random effects are supported for prediction. For sampled levels, the posterior distribution for the random effect corresponding to that level of the random effect will be used in the prediction. For non-sampled levels, random values are drawn from a normal distribution using the posterior samples of the random effect variance, which results in fully propagated uncertainty in predictions with models that incorporate random effects.

Author(s)

Jeffrey W. Doser [e-mail],

Examples

set.seed(408)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- sample(3, size = J, replace = TRUE)
n.sp <- 6
# Community-level covariate effects
beta.mean <- c(-2, 0.5)
p.abund <- length(beta.mean)
tau.sq.beta <- c(0.2, 1.2)
mu.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = n.sp, ncol = p.abund)
for (i in 1:p.abund) {
  beta[, i] <- rnorm(n.sp, beta.mean[i], sqrt(tau.sq.beta[i]))
}
sp <- FALSE
kappa <- runif(n.sp, 0.1, 1)

dat <- simMsAbund(J.x = J.x, J.y = J.y, n.rep = n.rep, n.sp = n.sp, beta = beta,
                  mu.RE = mu.RE, sp = sp, kappa = kappa, family = 'NB')

# Split into fitting and prediction data set
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , , drop = FALSE]
# Prediction values
y.0 <- dat$y[, pred.indx, , drop = FALSE]
X.0 <- dat$X[pred.indx, , , drop = FALSE]

# Package all data into a list
covs <- list(int = X[, , 1], abund.cov.1 = X[, , 2])
data.list <- list(y = y, covs = covs)
prior.list <- list(beta.comm.normal = list(mean = 0, var = 100),
                   kappa.unif = list(a = 0, b = 10),
                   tau.sq.beta.ig = list(a = .1, b = .1))
inits.list <- list(beta.comm = 0,
                   beta = 0,
                   kappa = 0.5,
                   tau.sq.beta = 1)
tuning.list <- list(kappa = 0.3, beta = 0.1)

# Small
n.batch <- 2
batch.length <- 25
n.burn <- 20
n.thin <- 1
n.chains <- 1

out <- msAbund(formula = ~ abund.cov.1,
               data = data.list,
               n.batch = n.batch,
               inits = inits.list,
               priors = prior.list,
               tuning = tuning.list,
               batch.length = batch.length,
               n.omp.threads = 1,
               verbose = TRUE,
               n.report = 1,
               n.burn = n.burn,
               n.thin = n.thin,
               n.chains = n.chains)

# Predict at new locations
out.pred <- predict(out, X.0)
str(out.pred)