| predict.intMsPGOcc {spOccupancy} | R Documentation |
Function for prediction at new locations for integrated multi-species occupancy models
Description
The function predict collects posterior predictive samples for a set of new locations given an object of class 'intMsPGOcc'. Prediction is currently possible only for the latent occupancy state.
Usage
## S3 method for class 'intMsPGOcc'
predict(object, X.0, ignore.RE = FALSE, ...)
Arguments
object |
an object of class intMsPGOcc |
X.0 |
the design matrix of covariates at the prediction locations. This should include a column of 1s for the intercept if an intercept is included in the model. If random effects are included in the occupancy (or detection if |
ignore.RE |
a logical value indicating whether to include unstructured random effects for prediction. If TRUE, random effects will be ignored and prediction will only use the fixed effects. If FALSE, random effects will be included in the prediction for both observed and unobserved levels of the random effect. |
... |
currently no additional arguments |
Value
A list object of class predict.intMsPGOcc consisting of:
psi.0.samples |
a three-dimensional array of posterior predictive samples for the latent occurrence probability values. |
z.0.samples |
a three-dimensional array of posterior predictive samples for the latent occurrence values. |
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 intercept 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 doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
Examples
set.seed(91)
J.x <- 10
J.y <- 10
# Total number of data sources across the study region
J.all <- J.x * J.y
# Number of data sources.
n.data <- 2
# Sites for each data source.
J.obs <- sample(ceiling(0.2 * J.all):ceiling(0.5 * J.all), n.data, replace = TRUE)
n.rep <- list()
n.rep[[1]] <- rep(3, J.obs[1])
n.rep[[2]] <- rep(4, J.obs[2])
# Number of species observed in each data source
N <- c(8, 3)
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.4, 0.3)
# Detection
# Detection covariates
alpha.mean <- list()
tau.sq.alpha <- list()
# Number of detection parameters in each data source
p.det.long <- c(4, 3)
for (i in 1:n.data) {
alpha.mean[[i]] <- runif(p.det.long[i], -1, 1)
tau.sq.alpha[[i]] <- runif(p.det.long[i], 0.1, 1)
}
# Random effects
psi.RE <- list()
p.RE <- list()
beta <- matrix(NA, nrow = max(N), ncol = p.occ)
for (i in 1:p.occ) {
beta[, i] <- rnorm(max(N), beta.mean[i], sqrt(tau.sq.beta[i]))
}
alpha <- list()
for (i in 1:n.data) {
alpha[[i]] <- matrix(NA, nrow = N[i], ncol = p.det.long[i])
for (t in 1:p.det.long[i]) {
alpha[[i]][, t] <- rnorm(N[i], alpha.mean[[i]][t], sqrt(tau.sq.alpha[[i]])[t])
}
}
sp <- FALSE
factor.model <- FALSE
# Simulate occupancy data
dat <- simIntMsOcc(n.data = n.data, J.x = J.x, J.y = J.y,
J.obs = J.obs, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = sp, factor.model = factor.model,
n.factors = n.factors)
J <- nrow(dat$coords.obs)
y <- dat$y
X <- dat$X.obs
X.p <- dat$X.p
X.re <- dat$X.re.obs
X.p.re <- dat$X.p.re
sites <- dat$sites
species <- dat$species
# Package all data into a list
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int', 'occ.cov.1')
#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.cov.1.2 = X.p[[1]][, , 3],
det.cov.1.3 = X.p[[1]][, , 4])
det.covs[[2]] <- list(det.cov.2.1 = X.p[[2]][, , 2],
det.cov.2.2 = X.p[[2]][, , 3])
data.list <- list(y = y,
occ.covs = occ.covs,
det.covs = det.covs,
sites = sites,
species = species)
# Take a look at the data.list structure for integrated multi-species
# occupancy models.
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0,
var = 2.73),
alpha.comm.normal = list(mean = list(0, 0),
var = list(2.72, 2.72)),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = list(0.1, 0.1),
b = list(0.1, 0.1)))
inits.list <- list(alpha.comm = list(0, 0),
beta.comm = 0,
tau.sq.beta = 1,
tau.sq.alpha = list(1, 1),
alpha = list(a = matrix(rnorm(p.det.long[1] * N[1]), N[1], p.det.long[1]),
b = matrix(rnorm(p.det.long[2] * N[2]), N[2], p.det.long[2])),
beta = 0)
# Fit the model.
out <- intMsPGOcc(occ.formula = ~ occ.cov.1,
det.formula = list(f.1 = ~ det.cov.1.1 + det.cov.1.2 + det.cov.1.3,
f.2 = ~ det.cov.2.1 + det.cov.2.2),
inits = inits.list,
priors = prior.list,
data = data.list,
n.samples = 100,
n.omp.threads = 1,
verbose = TRUE,
n.report = 10,
n.burn = 50,
n.thin = 1,
n.chains = 1)
#Predict at new locations.
X.0 <- dat$X.pred
psi.0 <- dat$psi.pred
out.pred <- predict(out, X.0, ignore.RE = TRUE)
# Create prediction for one species.
curr.sp <- 2
psi.hat.quants <- apply(out.pred$psi.0.samples[,curr.sp, ],
2, quantile, c(0.025, 0.5, 0.975))
plot(psi.0[curr.sp, ], psi.hat.quants[2, ], pch = 19, xlab = 'True',
ylab = 'Predicted', ylim = c(min(psi.hat.quants), max(psi.hat.quants)),
main = paste("Species ", curr.sp, sep = ''))
segments(psi.0[curr.sp, ], psi.hat.quants[1, ], psi.0[curr.sp, ], psi.hat.quants[3, ])
lines(psi.0[curr.sp, ], psi.0[curr.sp, ])