Function for prediction at new locations for single-species spatial occupancy models
predict.spPGOcc.RdThe function predict collects posterior predictive samples for a set of new
locations given an object of class `spPGOcc`. Prediction is possible for both the
latent occupancy state as well as detection.
Usage
# S3 method for spPGOcc
predict(object, X.0, coords.0, n.omp.threads = 1, verbose = TRUE,
n.report = 100, ignore.RE = FALSE, type = 'occupancy', grid.index.0, ...)Arguments
- object
an object of class
spPGOcc- 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
type = 'detection') portion of 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 inspPGOcc. Columns should correspond to the order of how covariates were specified in the corresponding formula argument ofspPGOcc. Column names of the random effects must match the name of the random effects, if specified in the corresponding formula argument ofspPGOcc.- coords.0
the spatial coordinates corresponding to
X.0. Note thatspOccupancyassumes 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.threadsup 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.- 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.
- 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.
- grid.index.0
an indexing vector used to specify how each row in
X.0corresponds to the coordinates specified incoords.0. Only relevant if the spatial random effect was estimated at a higher spatial resolution (e.g., grid cells) than point locations.- ...
currently no additional arguments
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
Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
Value
A list object of class predict.spPGOcc. When type = 'occupancy', the list consists of:
- psi.0.samples
a
codaobject of posterior predictive samples for the latent occurrence probability values.- z.0.samples
a
codaobject of posterior predictive samples for the latent occurrence values.- w.0.samples
a
codaobject of posterior predictive samples for the latent spatial random effects.- run.time
execution time reported using
proc.time().
When type = 'detection', the list consists of:
- p.0.samples
a
codaobject of posterior predictive samples for the detection probability values.- run.time
execution time reported using
proc.time().
The return object will include additional objects used for standard extractor functions.
Examples
set.seed(400)
# Simulate Data -----------------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- sample(2:4, J, replace = TRUE)
beta <- c(0.5, 2)
p.occ <- length(beta)
alpha <- c(0, 1)
p.det <- length(alpha)
phi <- 3 / .6
sigma.sq <- 2
dat <- simOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, beta = beta, alpha = alpha,
sigma.sq = sigma.sq, phi = phi, sp = TRUE, cov.model = 'exponential')
# Split into fitting and prediction data set
pred.indx <- sample(1:J, round(J * .5), replace = FALSE)
y <- dat$y[-pred.indx, ]
# Occupancy covariates
X <- dat$X[-pred.indx, ]
# Prediction covariates
X.0 <- dat$X[pred.indx, ]
# Detection covariates
X.p <- dat$X.p[-pred.indx, , ]
coords <- as.matrix(dat$coords[-pred.indx, ])
coords.0 <- as.matrix(dat$coords[pred.indx, ])
psi.0 <- dat$psi[pred.indx]
w.0 <- dat$w[pred.indx]
# Package all data into a list
occ.covs <- X[, -1, drop = FALSE]
colnames(occ.covs) <- c('occ.cov')
det.covs <- list(det.cov.1 = X.p[, , 2])
data.list <- list(y = y,
occ.covs = occ.covs,
det.covs = det.covs,
coords = coords)
# Number of batches
n.batch <- 10
# Batch length
batch.length <- 25
n.iter <- n.batch * batch.length
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 2.72),
alpha.normal = list(mean = 0, var = 2.72),
sigma.sq.ig = c(2, 2),
phi.unif = c(3/1, 3/.1))
# Initial values
inits.list <- list(alpha = 0, beta = 0,
phi = 3 / .5,
sigma.sq = 2,
w = rep(0, nrow(X)),
z = apply(y, 1, max, na.rm = TRUE))
# Tuning
tuning.list <- list(phi = 1)
out <- spPGOcc(occ.formula = ~ occ.cov,
det.formula = ~ det.cov.1,
data = data.list,
inits = inits.list,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = prior.list,
cov.model = 'exponential',
tuning = tuning.list,
n.omp.threads = 1,
verbose = TRUE,
NNGP = FALSE,
n.neighbors = 15,
search.type = 'cb',
n.report = 10,
n.burn = 50,
n.thin = 1)
#> ----------------------------------------
#> Preparing to run the model
#> ----------------------------------------
#> ----------------------------------------
#> Model description
#> ----------------------------------------
#> Spatial Occupancy Model with Polya-Gamma latent
#> variable fit with 32 sites.
#>
#> Samples per chain: 250 (10 batches of length 25)
#> Burn-in: 50
#> Thinning Rate: 1
#> Number of Chains: 1
#> Total Posterior Samples: 200
#>
#> Using the exponential spatial correlation model.
#>
#> Source compiled with OpenMP support and model fit using 1 thread(s).
#>
#> Adaptive Metropolis with target acceptance rate: 43.0
#> ----------------------------------------
#> Chain 1
#> ----------------------------------------
#> Sampling ...
#> Batch: 10 of 10, 100.00%
summary(out)
#>
#> Call:
#> spPGOcc(occ.formula = ~occ.cov, det.formula = ~det.cov.1, data = data.list,
#> inits = inits.list, priors = prior.list, tuning = tuning.list,
#> cov.model = "exponential", NNGP = FALSE, n.neighbors = 15,
#> search.type = "cb", n.batch = n.batch, batch.length = batch.length,
#> accept.rate = 0.43, n.omp.threads = 1, verbose = TRUE, n.report = 10,
#> n.burn = 50, n.thin = 1)
#>
#> Samples per Chain: 250
#> Burn-in: 50
#> Thinning Rate: 1
#> Number of Chains: 1
#> Total Posterior Samples: 200
#> Run Time (min): 6e-04
#>
#> Occurrence (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) 0.2255 0.6817 -1.0214 0.2097 1.4761 NA 74
#> occ.cov 1.1047 0.6466 -0.0081 1.0144 2.4427 NA 41
#>
#> Detection (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) -0.3683 0.3907 -1.0475 -0.3752 0.4917 NA 63
#> det.cov.1 0.3928 0.2839 -0.1736 0.3922 0.9233 NA 200
#>
#> Spatial Covariance:
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> sigma.sq 1.2104 1.4396 0.3354 0.7656 6.1260 NA 21
#> phi 14.0706 7.2719 4.0212 12.5428 26.6998 NA 19
# Predict at new locations ------------------------------------------------
out.pred <- predict(out, X.0, coords.0, verbose = FALSE)