predict.svcPGBinom | R Documentation |
The function predict
collects posterior predictive samples for a set of new
locations given an object of class 'svcPGBinom'.
## S3 method for class 'svcPGBinom'
predict(object, X.0, coords.0, weights.0, n.omp.threads = 1, verbose = TRUE,
n.report = 100, ignore.RE = FALSE, ...)
object |
an object of class |
X.0 |
the design matrix of covariates at the prediction locations. Note that for spatially-varying coefficients models the order of covariates in |
coords.0 |
the spatial coordinates corresponding to |
weights.0 |
a numeric vector containing the binomial weights (i.e., the total number of
Bernoulli trials) at each site. If |
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 |
ignore.RE |
a logical value indicating whether to include unstructured random effects for prediction. If TRUE, unstructured random effects will be ignored and prediction will only use the fixed effects and the spatial random effects. If FALSE, random effects will be included in the prediction for both observed and unobserved levels of the unstructured random effects. |
n.report |
the interval to report sampling progress. |
... |
currently no additional arguments |
A list object of class predict.svcPGBinom
consisting of:
psi.0.samples |
a |
y.0.samples |
a |
w.0.samples |
a three-dimensional array of posterior predictive samples for the spatial random effects, with dimensions corresponding to MCMC iteration, coefficient, and site. |
run.time |
execution time reported using |
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.
Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
set.seed(1000)
# Sites
J.x <- 10
J.y <- 10
J <- J.x * J.y
# Binomial weights
weights <- sample(10, J, replace = TRUE)
beta <- c(0, 0.5, -0.2, 0.75)
p <- length(beta)
# No unstructured random effects
psi.RE <- list()
# Spatial parameters
sp <- TRUE
# Two spatially-varying covariates.
svc.cols <- c(1, 2)
p.svc <- length(svc.cols)
cov.model <- "exponential"
sigma.sq <- runif(p.svc, 0.4, 1.5)
phi <- runif(p.svc, 3/1, 3/0.2)
# Simulate the data
dat <- simBinom(J.x = J.x, J.y = J.y, weights = weights, beta = beta,
psi.RE = psi.RE, sp = sp, svc.cols = svc.cols,
cov.model = cov.model, sigma.sq = sigma.sq, phi = phi)
# Binomial data
y <- dat$y
# Covariates
X <- dat$X
# Spatial coordinates
coords <- dat$coords
# Subset data for prediction if desired
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y.0 <- y[pred.indx, drop = FALSE]
X.0 <- X[pred.indx, , drop = FALSE]
coords.0 <- coords[pred.indx, ]
y <- y[-pred.indx, drop = FALSE]
X <- X[-pred.indx, , drop = FALSE]
coords <- coords[-pred.indx, ]
weights.0 <- weights[pred.indx]
weights <- weights[-pred.indx]
# Package all data into a list
# Covariates
covs <- cbind(X)
colnames(covs) <- c('int', 'cov.1', 'cov.2', 'cov.3')
# Data list bundle
data.list <- list(y = y,
covs = covs,
coords = coords,
weights = weights)
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 2.72),
sigma.sq.ig = list(a = 2, b = 1),
phi.unif = list(a = 3 / 1, b = 3 / 0.1))
# Starting values
inits.list <- list(beta = 0, alpha = 0,
sigma.sq = 1, phi = phi)
# Tuning
tuning.list <- list(phi = 1)
n.batch <- 10
batch.length <- 25
n.burn <- 100
n.thin <- 1
# Note that this is just a test case and more iterations/chains may need to
# be run to ensure convergence.
out <- svcPGBinom(formula = ~ cov.1 + cov.2 + cov.3,
svc.cols = c(1, 2),
data = data.list,
n.batch = n.batch,
batch.length = batch.length,
inits = inits.list,
priors = prior.list,
accept.rate = 0.43,
cov.model = "exponential",
tuning = tuning.list,
n.omp.threads = 1,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = 2,
n.burn = n.burn,
n.thin = n.thin,
n.chains = 1)
summary(out)
# Predict at new locations ------------------------------------------------
out.pred <- predict(out, X.0, coords.0, weights.0, verbose = FALSE)
str(out.pred)
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