predict.spIntPGOcc | R Documentation |
The function predict
collects posterior predictive samples for a set of new locations given an object of class 'spIntPGOcc'.
## S3 method for class 'spIntPGOcc'
predict(object, X.0, coords.0, n.omp.threads = 1, verbose = TRUE,
n.report = 100, ignore.RE = FALSE, type = 'occupancy', ...)
object |
an object of class |
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 |
coords.0 |
the spatial coordinates corresponding to |
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
|
verbose |
if |
n.report |
the interval to report sampling progress. |
ignore.RE |
logical value that specifies whether or not to remove random occurrence (or detection if |
type |
a quoted keyword indicating what type of prediction to produce. Valid keywords are 'occupancy' to predict latent occupancy probability and latent occupancy values (this is the default), or 'detection' to predict detection probability given new values of detection covariates. Note that prediction of detection probability is not currently supported for integrated models. |
... |
currently no additional arguments |
An object of class predict.spIntPGOcc
that is a list comprised of:
psi.0.samples |
a |
z.0.samples |
a |
The return object will include additional objects used for standard extractor functions.
Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
Hooten, M. B., and Hefley, T. J. (2019). Bringing Bayesian models to life. CRC Press.
set.seed(400)
# Simulate Data -----------------------------------------------------------
# Number of locations in each direction. This is the total region of interest
# where some sites may or may not have a data source.
J.x <- 8
J.y <- 8
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, 0.5)
p.occ <- length(beta)
# Detection covariates
alpha <- list()
alpha[[1]] <- runif(2, 0, 1)
alpha[[2]] <- runif(3, 0, 1)
alpha[[3]] <- runif(2, -1, 1)
alpha[[4]] <- runif(4, -1, 1)
p.det.long <- sapply(alpha, length)
p.det <- sum(p.det.long)
sigma.sq <- 2
phi <- 3 / .5
sp <- TRUE
# 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 = sp,
phi = phi, sigma.sq = sigma.sq, cov.model = 'spherical')
y <- dat$y
X <- dat$X.obs
X.p <- dat$X.p
sites <- dat$sites
X.0 <- dat$X.pred
psi.0 <- dat$psi.pred
coords <- as.matrix(dat$coords.obs)
coords.0 <- as.matrix(dat$coords.pred)
# 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.cov.2.2 = X.p[[2]][, , 3])
det.covs[[3]] <- list(det.cov.3.1 = X.p[[3]][, , 2])
det.covs[[4]] <- list(det.cov.4.1 = X.p[[4]][, , 2],
det.cov.4.2 = X.p[[4]][, , 3],
det.cov.4.3 = X.p[[4]][, , 4])
data.list <- list(y = y,
occ.covs = occ.covs,
det.covs = det.covs,
sites = sites,
coords = coords)
J <- length(dat$z.obs)
# Initial values
inits.list <- list(alpha = list(0, 0, 0, 0),
beta = 0,
phi = 3 / .5,
sigma.sq = 2,
w = rep(0, J),
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)),
phi.unif = c(3/1, 3/.1),
sigma.sq.ig = c(2, 2))
# Tuning
tuning.list <- list(phi = 1)
# Number of batches
n.batch <- 40
# Batch length
batch.length <- 25
# Note that this is just a test case and more iterations/chains may need to
# be run to ensure convergence.
out <- spIntPGOcc(occ.formula = ~ occ.cov,
det.formula = list(f.1 = ~ det.cov.1.1,
f.2 = ~ det.cov.2.1 + det.cov.2.2,
f.3 = ~ det.cov.3.1,
f.4 = ~ det.cov.4.1 + det.cov.4.2 + det.cov.4.3),
data = data.list,
inits = inits.list,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = prior.list,
cov.model = "spherical",
tuning = tuning.list,
n.omp.threads = 1,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
search.type = 'cb',
n.report = 10,
n.burn = 500,
n.thin = 1)
summary(out)
# Predict at new locations ------------------------------------------------
out.pred <- predict(out, X.0, coords.0, verbose = FALSE)
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