predict.lfJSDM | R Documentation |
The function predict
collects posterior predictive samples for a set of new locations given an object of class 'lfJSDM'.
## S3 method for class 'lfJSDM'
predict(object, X.0, coords.0,
ignore.RE = FALSE, ...)
object |
an object of class lfJSDM |
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 model, the levels of the random effects at the new locations should be included as a column in the design matrix. The ordering of the levels should match the ordering used to fit the data in |
coords.0 |
the spatial coordinates corresponding to |
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 |
A list object of class predict.lfJSDM
that consists 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. |
w.0.samples |
a three-dimensional array of posterior predictive samples for the latent factors. |
The return object will include additional objects used for standard extractor functions.
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(400)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 6
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 0.3)
# Detection
alpha.mean <- c(0.5, 0.2, -0.1)
tau.sq.alpha <- c(0.2, 0.3, 1)
p.det <- length(alpha.mean)
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
n.factors <- 3
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
sp = FALSE, factor.model = TRUE, n.factors = n.factors)
n.samples <- 5000
# Split into fitting and prediction data set
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
# Summarize the multiple replicates into a single value for use in a JSDM
y <- apply(dat$y[, -pred.indx, ], c(1, 2), max, na.rm = TRUE)
# Covariates
X <- dat$X[-pred.indx, ]
# Spatial coordinates
coords <- dat$coords[-pred.indx, ]
# Prediction values
X.0 <- dat$X[pred.indx, ]
psi.0 <- dat$psi[, pred.indx]
coords.0 <- dat$coords[pred.indx, ]
# Package all data into a list
covs <- X[, 2, drop = FALSE]
colnames(covs) <- c('occ.cov')
data.list <- list(y = y,
covs = covs,
coords = coords)
# Occupancy initial values
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1))
# Initial values
lambda.inits <- matrix(0, N, n.factors)
diag(lambda.inits) <- 1
lambda.inits[lower.tri(lambda.inits)] <- rnorm(sum(lower.tri(lambda.inits)))
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
tau.sq.beta = 1,
lambda = lambda.inits)
# Note that this is just a test case and more iterations/chains may need to
# be run to ensure convergence.
out <- lfJSDM(formula = ~ occ.cov,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = TRUE,
n.report = 1000,
n.burn = 4000)
summary(out)
# Predict at new locations ------------------------------------------------
out.pred <- predict(out, X.0, coords.0)
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