Function for prediction at new locations for spatial factor joint species distribution model
predict.sfJSDM.RdThe function predict collects posterior predictive samples for a set of new locations given an object of class `sfJSDM`.
Usage
# S3 method for sfJSDM
predict(object, X.0, coords.0, n.omp.threads = 1, verbose = TRUE,
n.report = 100, ignore.RE = FALSE, ...)Arguments
- object
an object of class sfJSDM
- 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
sfJSDM. Columns should correspond to the order of how covariates were specified in the formula argument ofsfJSDM. Column names of the random effects must match the name of the random effects, if specified in the formula argument ofsfJSDM.- 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.- n.report
the interval to report sampling progress.
- 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.
- ...
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
An list object of class predict.sfJSDM 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 spatial factors.
- 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 <- 7
J.y <- 7
J <- J.x * J.y
n.rep <- sample(2:4, size = J, replace = TRUE)
N <- 5
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, -0.15)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 0.3)
# Detection
alpha.mean <- c(0.5, 0.2, -.2)
tau.sq.alpha <- c(0.2, 0.3, 0.8)
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
phi <- runif(n.factors, 3/1, 3/.4)
sp <- TRUE
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
phi = phi, sigma.sq = sigma.sq, sp = TRUE, cov.model = 'exponential',
factor.model = TRUE, n.factors = n.factors)
#> sigma.sq is specified but will be set to 1 for spatial latent factor model
# Number of batches
n.batch <- 10
# Batch length
batch.length <- 25
n.samples <- n.batch * batch.length
# 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$y[, -pred.indx, ], c(1, 2), max, na.rm = TRUE)
# Occupancy covariates
X <- dat$X[-pred.indx, ]
# Coordinates
coords <- as.matrix(dat$coords[-pred.indx, ])
# Prediction values
X.0 <- dat$X[pred.indx, ]
coords.0 <- as.matrix(dat$coords[pred.indx, ])
psi.0 <- dat$psi[, 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)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
phi.unif = list(a = 3/1, b = 3/.1))
# Starting 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(beta.comm = 0,
beta = 0,
tau.sq.beta = 1,
phi = 3 / .5,
sigma.sq = 2,
lambda = lambda.inits)
# Tuning
tuning.list <- list(phi = 1)
out <- sfJSDM(formula = ~ occ.cov,
data = data.list,
inits = inits.list,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
n.factors = 3,
priors = prior.list,
cov.model = "exponential",
tuning = tuning.list,
n.omp.threads = 1,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
search.type = 'cb',
n.report = 10,
n.burn = 100,
n.thin = 1)
#> ----------------------------------------
#> Preparing to run the model
#> ----------------------------------------
#> w is not specified in initial values.
#> Setting initial value to 0
#> ----------------------------------------
#> Building the neighbor list
#> ----------------------------------------
#> ----------------------------------------
#> Building the neighbors of neighbors list
#> ----------------------------------------
#> ----------------------------------------
#> Model description
#> ----------------------------------------
#> Spatial Factor NNGP JSDM with Polya-Gamma latent
#> variable fit with 37 sites and 5 species.
#>
#> Samples per chain: 250 (10 batches of length 25)
#> Burn-in: 100
#> Thinning Rate: 1
#> Number of Chains: 1
#> Total Posterior Samples: 150
#>
#> Using the exponential spatial correlation model.
#>
#> Using 3 latent spatial factors.
#> Using 5 nearest neighbors.
#>
#> 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, level = 'both')
#>
#> Call:
#> sfJSDM(formula = ~occ.cov, data = data.list, inits = inits.list,
#> priors = prior.list, tuning = tuning.list, cov.model = "exponential",
#> NNGP = TRUE, n.neighbors = 5, search.type = "cb", n.factors = 3,
#> n.batch = n.batch, batch.length = batch.length, accept.rate = 0.43,
#> n.omp.threads = 1, verbose = TRUE, n.report = 10, n.burn = 100,
#> n.thin = 1)
#>
#> Samples per Chain: 250
#> Burn-in: 100
#> Thinning Rate: 1
#> Number of Chains: 1
#> Total Posterior Samples: 150
#> Run Time (min): 0.0034
#>
#> ----------------------------------------
#> Community Level
#> ----------------------------------------
#> Means (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) 0.1007 0.3342 -0.4977 0.1030 0.7540 NA 42
#> occ.cov 0.2026 0.3276 -0.4323 0.2034 0.7658 NA 64
#>
#> Variances (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) 0.5499 0.8968 0.0521 0.2857 2.7510 NA 53
#> occ.cov 0.3043 0.6094 0.0377 0.1507 1.0189 NA 45
#>
#> ----------------------------------------
#> Species Level
#> ----------------------------------------
#> Estimates (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept)-sp1 0.5571 0.4246 -0.1451 0.5193 1.6090 NA 39
#> (Intercept)-sp2 0.3407 0.3506 -0.3803 0.3308 1.0841 NA 75
#> (Intercept)-sp3 0.0657 0.4983 -0.8131 0.0648 1.2181 NA 47
#> (Intercept)-sp4 -0.2450 0.4318 -1.0940 -0.2119 0.5054 NA 45
#> (Intercept)-sp5 -0.3054 0.4211 -1.1914 -0.2870 0.4447 NA 65
#> occ.cov-sp1 0.0387 0.3622 -0.7727 0.0739 0.6604 NA 69
#> occ.cov-sp2 0.2206 0.3287 -0.4796 0.2140 0.8650 NA 77
#> occ.cov-sp3 0.2236 0.3007 -0.3038 0.2575 0.7830 NA 82
#> occ.cov-sp4 0.4315 0.3682 -0.3728 0.4270 1.1493 NA 48
#> occ.cov-sp5 0.1062 0.3345 -0.5790 0.1472 0.7096 NA 81
#>
#> ----------------------------------------
#> Spatial Covariance
#> ----------------------------------------
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> phi-1 14.1144 7.9703 3.7721 13.0015 28.4244 NA 8
#> phi-2 14.6929 7.4450 3.8134 13.0415 27.8781 NA 15
#> phi-3 15.3113 6.8360 5.2080 14.4169 27.5695 NA 19
# Predict at new locations ------------------------------------------------
out.pred <- predict(out, X.0, coords.0, verbose = FALSE)