spAbundance (version 0.2.1)

predict.svcMsAbund: Function for prediction at new locations for multivariate spatially-varying coefficient GLMMs

Description

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

Usage

# S3 method for svcMsAbund
predict(object, X.0, coords.0, n.omp.threads = 1, 
        verbose = TRUE, n.report = 100, ignore.RE = FALSE, 
        z.0.samples, ...)

Value

A list object of class predict.svcMsAbund. 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.

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.

w.0.samples

a four-dimensional array of posterior predictive samples for the spatial factors for each spatially-varying coefficient. Dimensions correspond to MCMC sample, spatial factor, site, and spatially varying coefficient.

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

Arguments

object

an object of class svcMsAbund

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 svcMsAbund. The covariates should be organized in the same order as they were specified in the corresponding formula argument of svcMsAbund. 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 svcMsAbund. See example below.

coords.0

the spatial coordinates corresponding to X.0. Note that spAbundance assumes coordinates are specified in a projected coordinate system.

n.omp.threads

a positive integer indicating the number of threads to use for SMP parallel processing. The package must be compiled for OpenMP support. For most Intel-based machines, we recommend setting n.omp.threads up to the number of hyperthreaded cores. Note, n.omp.threads > 1 might not work on some systems.

verbose

if TRUE, model specification and progress of the sampler is printed to the screen. Otherwise, nothing is printed to the screen.

n.report

the interval to report sampling progress.

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 zero-inflated Gaussian 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 zero-inflated 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 zero-inflated Gaussian.

...

currently no additional arguments

Author

Jeffrey W. Doser ,
Andrew O. Finley

Examples

set.seed(408)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- rep(1, J)
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 <- TRUE 
factor.model <- TRUE
n.factors <- 2
svc.cols <- c(1, 2)
cov.model <- 'spherical'
tau.sq <- runif(n.sp, 0.1, 2)
phi <- runif(n.factors * length(svc.cols), 3 / 1, 3 / .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, family = 'Gaussian', tau.sq = tau.sq,
                  factor.model = factor.model, n.factors = n.factors, 
                  phi = phi, cov.model = cov.model, svc.cols = svc.cols)

# 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]
# Coordinates
coords <- dat$coords[-pred.indx, ]
# Prediction values
y.0 <- dat$y[, pred.indx, drop = FALSE]
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- dat$coords[pred.indx, ]

# Package all data into a list
covs <- data.frame(abund.cov.1 = X[, 2])
data.list <- list(y = y, covs = covs, coords = coords)
prior.list <- list(beta.comm.normal = list(mean = 0, var = 100),
                   tau.sq.ig = list(a = .01, b = .01),
                   phi.unif = list(a = 3 / 1, b = 3 / .1),
                   tau.sq.beta.ig = list(a = .1, b = .1))
inits.list <- list(beta.comm = 0,
                   beta = 0,
                   kappa = 0.5,
                   tau.sq = 1, 
                   phi = 3 / .5,
                   tau.sq.beta = 1)
tuning.list <- list(kappa = 0.3, beta = 0.1, lambda = 0.5, w = 0.5, 
                    phi = 1)

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

out <- svcMsAbund(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,
                  svc.cols = c(1, 2),
                  n.factors = n.factors,
                  cov.model = 'exponential',
                  family = 'Gaussian',
                  verbose = TRUE,
                  n.neighbors = 5, 
                  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, coords.0)
str(out.pred)