Nothing
R/spPGOcc.R
In spOccupancy: Single-Species, Multi-Species, and Integrated Spatial Occupancy Models
Defines functions
spPGOcc
Documented in
spPGOcc
spPGOcc <- function(occ.formula, det.formula, data, inits, priors,
tuning, cov.model = 'exponential', NNGP = TRUE,
n.neighbors = 15, search.type = 'cb', n.batch,
batch.length, accept.rate = 0.43,
n.omp.threads = 1, verbose = TRUE, n.report = 100,
n.burn = round(.10 * n.batch * batch.length),
n.thin = 1, n.chains = 1,
k.fold, k.fold.threads = 1, k.fold.seed = 100,
k.fold.only = FALSE, ...){
ptm <- proc.time()
# Make it look nice
if (verbose) {
cat("----------------------------------------\n");
cat("\tPreparing to run the model\n");
cat("----------------------------------------\n");
}
# Functions ---------------------------------------------------------------
logit <- function(theta, a = 0, b = 1) {log((theta-a)/(b-theta))}
logit.inv <- function(z, a = 0, b = 1) {b-(b-a)/(1+exp(z))}
rigamma <- function(n, a, b){
1/rgamma(n = n, shape = a, rate = b)
}
# Check for unused arguments ------------------------------------------
formal.args <- names(formals(sys.function(sys.parent())))
elip.args <- names(list(...))
for(i in elip.args){
if(! i %in% formal.args)
warning("'",i, "' is not an argument")
}
# Call ----------------------------------------------------------------
# Returns a call in which all of the specified arguments are
# specified by their full names.
cl <- match.call()
# Some initial checks -------------------------------------------------
if (missing(data)) {
stop("error: data must be specified")
}
if (!is.list(data)) {
stop("error: data must be a list")
}
names(data) <- tolower(names(data))
if (missing(occ.formula)) {
stop("error: occ.formula must be specified")
}
if (missing(det.formula)) {
stop("error: det.formula must be specified")
}
if (!'y' %in% names(data)) {
stop("error: detection-nondetection data y must be specified in data")
}
y <- as.matrix(data$y)
if (!'occ.covs' %in% names(data)) {
if (occ.formula == ~ 1) {
if (verbose) {
message("occupancy covariates (occ.covs) not specified in data.\nAssuming intercept only occupancy model.\n")
}
data$occ.covs <- matrix(1, dim(y)[1], 1)
} else {
stop("error: occ.covs must be specified in data for an occupancy model with covariates")
}
}
if (!'det.covs' %in% names(data)) {
if (det.formula == ~ 1) {
if (verbose) {
message("detection covariates (det.covs) not specified in data.\nAssuming interept only detection model.\n")
}
data$det.covs <- list(int = matrix(1, dim(y)[1], dim(y)[2]))
} else {
stop("error: det.covs must be specified in data for a detection model with covariates")
}
}
if (!is.matrix(data$occ.covs) & !is.data.frame(data$occ.covs)) {
stop("error: occ.covs must be a matrix or data frame")
}
if (!'coords' %in% names(data)) {
stop("error: coords must be specified in data for a spatial occupancy model.")
}
if (!is.matrix(data$coords) & !is.data.frame(data$coords)) {
stop("error: coords must be a matrix or data frame")
}
coords <- as.matrix(data$coords)
if (!is.list(data$det.covs)) {
stop("error: det.covs must be a list of matrices, data frames, and/or vectors")
}
# Check grid index
if (!'grid.index' %in% names(data)) {
if (nrow(data$coords) != nrow(data$y)) {
stop("data$grid.index must be specified if nrow(data$coords) != nrow(data$y)")
}
grid.index <- 1:nrow(coords)
} else {
if (!is.atomic(data$grid.index) | !is.numeric(data$grid.index)) {
stop("data$grid.index must be a numeric vector")
}
if (length(data$grid.index) < nrow(data$coords)) {
stop("length(data$grid.index) must be greater than or equal to nrow(data$coords)")
}
grid.index <- data$grid.index
}
# Check if all spatial coordinates are unique.
unique.coords <- unique(data$coords)
if (nrow(unique.coords) < nrow(data$coords)) {
stop("coordinates provided in coords are not all unique. spOccupancy requires each site to have its own unique pair of spatial coordinates. This may be the result of an error in preparing the data list, or you will need to change what you consider a 'site' in order to meet this requirement. Alternatively, you can use data$grid.index to specify the spatial random effect at a larger spatial level than the individual sites (e.g., a grid). See ?spPGOcc for details.")
}
if (!missing(k.fold)) {
if (!is.numeric(k.fold) | length(k.fold) != 1 | k.fold < 2) {
stop("error: k.fold must be a single integer value >= 2")
}
}
# Neighbors and Ordering ----------------------------------------------
if (NNGP) {
u.search.type <- 2
## Order by x column. Could potentially allow this to be user defined.
ord <- order(coords[,1])
tmp <- lapply(ord, function (a) which(grid.index == a))
tmp.2 <- sapply(tmp, length)
grid.index.c <- unlist(lapply(1:length(tmp.2), function(a) rep(a, tmp.2[a]))) - 1
grid.index.r <- grid.index.c + 1
long.ord <- unlist(lapply(ord, function(a) which(grid.index == a)))
# Reorder everything to align with NN ordering
y <- y[long.ord, , drop = FALSE]
coords <- coords[ord, , drop = FALSE]
# Occupancy covariates
data$occ.covs <- data$occ.covs[long.ord, , drop = FALSE]
for (i in 1:length(data$det.covs)) {
if (!is.null(dim(data$det.covs[[i]]))) {
data$det.covs[[i]] <- data$det.covs[[i]][long.ord, , drop = FALSE]
} else {
data$det.covs[[i]] <- data$det.covs[[i]][long.ord]
}
}
}
# First subset detection covariates to only use those that are included in the analysis.
data$det.covs <- data$det.covs[names(data$det.covs) %in% all.vars(det.formula)]
# Null model support
if (length(data$det.covs) == 0) {
data$det.covs <- list(int = rep(1, dim(y)[1]))
}
# Make both covariates a data frame. Unlist is necessary for when factors
# are supplied.
data$det.covs <- data.frame(lapply(data$det.covs, function(a) unlist(c(a))))
binom <- FALSE
# Check if all detection covariates are at site level, and simplify the data
# if necessary
y.big <- y
if (nrow(data$det.covs) == nrow(y)) {
# Convert data to binomial form
y <- apply(y, 1, sum, na.rm = TRUE)
binom <- TRUE
}
data$occ.covs <- as.data.frame(data$occ.covs)
# Checking missing values ---------------------------------------------
# y -------------------------------
y.na.test <- apply(y.big, 1, function(a) sum(!is.na(a)))
if (sum(y.na.test == 0) > 0) {
stop("error: some sites in y have all missing detection histories. Remove these sites from all objects in the 'data' argument, then use 'predict' to obtain predictions at these locations if desired.")
}
# occ.covs ------------------------
if (sum(is.na(data$occ.covs)) != 0) {
stop("error: missing values in occ.covs. Please remove these sites from all objects in data or somehow replace the NA values with non-missing values (e.g., mean imputation).")
}
# det.covs ------------------------
if (!binom) {
for (i in 1:ncol(data$det.covs)) {
if (sum(is.na(data$det.covs[, i])) > sum(is.na(y.big))) {
stop("error: some elements in det.covs have missing values where there is an observed data value in y. Please either replace the NA values in det.covs with non-missing values (e.g., mean imputation) or set the corresponding values in y to NA where the covariate is missing.")
}
}
# Misalignment between y and det.covs
y.missing <- which(is.na(y))
det.covs.missing <- lapply(data$det.covs, function(a) which(is.na(a)))
for (i in 1:length(det.covs.missing)) {
tmp.indx <- !(y.missing %in% det.covs.missing[[i]])
if (sum(tmp.indx) > 0) {
if (i == 1 & verbose) {
message("There are missing values in data$y with corresponding non-missing values in data$det.covs.\nRemoving these site/replicate combinations for fitting the model.")
}
data$det.covs[y.missing, i] <- NA
}
}
}
# det.covs when binom == TRUE -----
if (binom) {
if (sum(is.na(data$det.covs)) != 0) {
stop("error: missing values in site-level det.covs. Please remove these sites from all objects in data or somehow replace the NA values with non-missing values (e.g., mean imputation).")
}
}
# Check whether random effects are sent in as numeric, and
# return error if they are.
# Occurrence ----------------------
if (!is.null(findbars(occ.formula))) {
occ.re.names <- sapply(findbars(occ.formula), all.vars)
for (i in 1:length(occ.re.names)) {
if (is(data$occ.covs[, occ.re.names[i]], 'factor')) {
stop(paste("error: random effect variable ", occ.re.names[i], " specified as a factor. Random effect variables must be specified as numeric.", sep = ''))
}
if (is(data$occ.covs[, occ.re.names[i]], 'character')) {
stop(paste("error: random effect variable ", occ.re.names[i], " specified as character. Random effect variables must be specified as numeric.", sep = ''))
}
}
}
# Detection -----------------------
if (!is.null(findbars(det.formula))) {
det.re.names <- sapply(findbars(det.formula), all.vars)
for (i in 1:length(det.re.names)) {
if (is(data$det.covs[, det.re.names[i]], 'factor')) {
stop(paste("error: random effect variable ", det.re.names[i], " specified as a factor. Random effect variables must be specified as numeric.", sep = ''))
}
if (is(data$det.covs[, det.re.names[i]], 'character')) {
stop(paste("error: random effect variable ", det.re.names[i], " specified as character. Random effect variables must be specified as numeric.", sep = ''))
}
}
}
# Formula -------------------------------------------------------------
# Occupancy -----------------------
if (is(occ.formula, 'formula')) {
tmp <- parseFormula(occ.formula, data$occ.covs)
X <- as.matrix(tmp[[1]])
X.re <- as.matrix(tmp[[4]])
x.re.names <- colnames(X.re)
x.names <- tmp[[2]]
} else {
stop("error: occ.formula is misspecified")
}
# Get RE level names
re.level.names <- lapply(data$occ.covs[, x.re.names, drop = FALSE],
function (a) sort(unique(a)))
# Detection -----------------------
if (is(det.formula, 'formula')) {
tmp <- parseFormula(det.formula, data$det.covs)
X.p <- as.matrix(tmp[[1]])
x.p.names <- tmp[[2]]
X.p.re <- as.matrix(tmp[[4]])
x.p.re.names <- colnames(X.p.re)
} else {
stop("error: det.formula is misspecified")
}
p.re.level.names <- lapply(data$det.covs[, x.p.re.names, drop = FALSE],
function (a) sort(unique(a)))
# Get basic info from inputs ------------------------------------------
# Number of sites
J <- nrow(y.big)
# Number of coordinates in coordinate grid
J.w <- nrow(coords)
# Number of occupancy parameters
p.occ <- ncol(X)
# Number of detection parameters
p.det <- dim(X.p)[2]
# Number of occurrence random effect parameters
p.occ.re <- ncol(X.re)
# Number of detection random effect parameters
p.det.re <- ncol(X.p.re)
# Number of latent occupancy random effect values
n.occ.re <- length(unlist(apply(X.re, 2, unique)))
n.occ.re.long <- apply(X.re, 2, function(a) length(unique(a)))
# Number of latent detection random effect values
n.det.re <- length(unlist(apply(X.p.re, 2, unique)))
n.det.re.long <- apply(X.p.re, 2, function(a) length(unique(a)))
if (p.det.re == 0) n.det.re.long <- 0
# Number of replicates at each site
n.rep <- apply(y.big, 1, function(a) sum(!is.na(a)))
# Max number of repeat visits
K.max <- dim(y.big)[2]
rep.indx <- list()
for (j in 1:J) {
rep.indx[[j]] <- which(!is.na(y.big[j, ]))
}
# Because I like K better than n.rep
K <- n.rep
if (missing(n.batch)) {
stop("error: must specify number of MCMC batches")
}
if (missing(batch.length)) {
stop("error: must specify length of each MCMC batch")
}
n.samples <- n.batch * batch.length
if (n.burn > n.samples) {
stop("error: n.burn must be less than n.samples")
}
if (n.thin > n.samples) {
stop("error: n.thin must be less than n.samples")
}
# Check if n.burn, n.thin, and n.samples result in an integer and error if otherwise.
if (((n.samples - n.burn) / n.thin) %% 1 != 0) {
stop("the number of posterior samples to save ((n.samples - n.burn) / n.thin) is not a whole number. Please respecify the MCMC criteria such that the number of posterior samples saved is a whole number.")
}
# Get indices to map z to y -------------------------------------------
if (!binom) {
z.long.indx <- rep(1:J, dim(y.big)[2])
z.long.indx <- z.long.indx[!is.na(c(y.big))]
# Subtract 1 for indices in C
z.long.indx <- z.long.indx - 1
} else {
z.long.indx <- 0:(J - 1)
}
# y is stored in the following order: species, site, visit
y <- c(y)
names.long <- which(!is.na(y))
# Remove missing observations when the covariate data are available but
# there are missing detection-nondetection data.
if (nrow(X.p) == length(y)) {
X.p <- X.p[!is.na(y), , drop = FALSE]
}
if (nrow(X.p.re) == length(y) & p.det.re > 0) {
X.p.re <- X.p.re[!is.na(y), , drop = FALSE]
}
y <- y[!is.na(y)]
# Number of pseudoreplicates
n.obs <- nrow(X.p)
# Get random effect matrices all set ----------------------------------
if (p.occ.re > 1) {
for (j in 2:p.occ.re) {
X.re[, j] <- X.re[, j] + max(X.re[, j - 1]) + 1
}
}
if (p.det.re > 1) {
for (j in 2:p.det.re) {
X.p.re[, j] <- X.p.re[, j] + max(X.p.re[, j - 1]) + 1
}
}
# Priors --------------------------------------------------------------
if (missing(priors)) {
priors <- list()
}
names(priors) <- tolower(names(priors))
# Logical vector indicating what parameters are estimated and what
# parameters are fixed. 6 is the total number of parameter types that
# can be estimated here. Note that phi and nu are both fixed if phi.unif = 'fixed'
all.params <- c('beta', 'alpha', 'phi', 'sigma.sq',
'sigma.sq.psi', 'sigma.sq.p')
n.params <- length(all.params)
fixed.params <- rep(FALSE, n.params)
# beta -----------------------
if ("beta.normal" %in% names(priors)) {
if (priors$beta.normal[1] == 'fixed') {
fixed.params[which(all.params == 'beta')] <- TRUE
mu.beta <- rep(0, p.occ)
Sigma.beta <- diag(p.occ)
} else {
if (!is.list(priors$beta.normal) | length(priors$beta.normal) != 2) {
stop("error: beta.normal must be a list of length 2")
}
mu.beta <- priors$beta.normal[[1]]
sigma.beta <- priors$beta.normal[[2]]
if (length(mu.beta) != p.occ & length(mu.beta) != 1) {
if (p.occ == 1) {
stop(paste("error: beta.normal[[1]] must be a vector of length ",
p.occ, " with elements corresponding to betas' mean", sep = ""))
} else {
stop(paste("error: beta.normal[[1]] must be a vector of length ",
p.occ, " or 1 with elements corresponding to betas' mean", sep = ""))
}
}
if (length(sigma.beta) != p.occ & length(sigma.beta) != 1) {
if (p.occ == 1) {
stop(paste("error: beta.normal[[2]] must be a vector of length ",
p.occ, " with elements corresponding to betas' variance", sep = ""))
} else {
stop(paste("error: beta.normal[[2]] must be a vector of length ",
p.occ, " or 1 with elements corresponding to betas' variance", sep = ""))
}
}
if (length(sigma.beta) != p.occ) {
sigma.beta <- rep(sigma.beta, p.occ)
}
if (length(mu.beta) != p.occ) {
mu.beta <- rep(mu.beta, p.occ)
}
Sigma.beta <- sigma.beta * diag(p.occ)
}
} else {
if (verbose) {
message("No prior specified for beta.normal.\nSetting prior mean to 0 and prior variance to 2.72\n")
}
mu.beta <- rep(0, p.occ)
sigma.beta <- rep(2.72, p.occ)
Sigma.beta <- diag(p.occ) * 2.72
}
# alpha -----------------------
if ("alpha.normal" %in% names(priors)) {
if (priors$alpha.normal[1] == 'fixed') {
fixed.params[which(all.params == 'alpha')] <- TRUE
mu.alpha <- rep(0, p.det)
Sigma.alpha <- diag(p.det)
} else {
if (!is.list(priors$alpha.normal) | length(priors$alpha.normal) != 2) {
stop("error: alpha.normal must be a list of length 2")
}
mu.alpha <- priors$alpha.normal[[1]]
sigma.alpha <- priors$alpha.normal[[2]]
if (length(mu.alpha) != p.det & length(mu.alpha) != 1) {
if (p.det == 1) {
stop(paste("error: alpha.normal[[1]] must be a vector of length ",
p.det, " with elements corresponding to alphas' mean", sep = ""))
} else {
stop(paste("error: alpha.normal[[1]] must be a vector of length ",
p.det, " or 1 with elements corresponding to alphas' mean", sep = ""))
}
}
if (length(sigma.alpha) != p.det & length(sigma.alpha) != 1) {
if (p.det == 1) {
stop(paste("error: alpha.normal[[2]] must be a vector of length ",
p.det, " with elements corresponding to alphas' variance", sep = ""))
} else {
stop(paste("error: alpha.normal[[2]] must be a vector of length ",
p.det, " or 1 with elements corresponding to alphas' variance", sep = ""))
}
}
if (length(sigma.alpha) != p.det) {
sigma.alpha <- rep(sigma.alpha, p.det)
}
if (length(mu.alpha) != p.det) {
mu.alpha <- rep(mu.alpha, p.det)
}
Sigma.alpha <- sigma.alpha * diag(p.det)
}
} else {
if (verbose) {
message("No prior specified for alpha.normal.\nSetting prior mean to 0 and prior variance to 2.72\n")
}
mu.alpha <- rep(0, p.det)
sigma.alpha <- rep(2.72, p.det)
Sigma.alpha <- diag(p.det) * 2.72
}
# phi -----------------------------
# Get distance matrix which is used if priors are not specified
if (!NNGP) {
coords.D <- iDist(coords)
}
if ("phi.unif" %in% names(priors)) {
if (priors$phi.unif[1] == 'fixed') {
fixed.params[which(all.params == 'phi')] <- TRUE
phi.a <- 1
phi.b <- 1
if ((cov.model == 'matern') & (priors$nu.unif[1] != 'fixed')) {
message("phi is specified as fixed in priors$phi.unif but nu is not, which is not allowed. Continuing to fit the model with both phi and nu fixed.")
}
} else {
if (!is.vector(priors$phi.unif) | !is.atomic(priors$phi.unif) | length(priors$phi.unif) != 2) {
stop("error: phi.unif must be a vector of length 2 with elements corresponding to phi's lower and upper bounds")
}
phi.a <- priors$phi.unif[1]
phi.b <- priors$phi.unif[2]
}
} else {
if (verbose) {
message("No prior specified for phi.unif.\nSetting uniform bounds based on the range of observed spatial coordinates.\n")
}
if (NNGP) {
coords.D <- iDist(coords)
}
phi.a <- 3 / max(coords.D)
phi.b <- 3 / sort(unique(c(coords.D)))[2]
}
# sigma.sq -----------------------------
# Check if both an ig and uniform prior are specified
if (("sigma.sq.ig" %in% names(priors)) & ("sigma.sq.unif" %in% names(priors))) {
stop("error: cannot specify both an IG and a uniform prior for sigma.sq")
}
if ("sigma.sq.ig" %in% names(priors)) { # inverse-gamma prior.
sigma.sq.ig <- TRUE
if (priors$sigma.sq.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq')] <- TRUE
sigma.sq.a <- 1
sigma.sq.b <- 1
} else {
if (!is.vector(priors$sigma.sq.ig) | !is.atomic(priors$sigma.sq.ig) | length(priors$sigma.sq.ig) != 2) {
stop("error: sigma.sq.ig must be a vector of length 2 with elements corresponding to sigma.sq's shape and scale parameters")
}
sigma.sq.a <- priors$sigma.sq.ig[1]
sigma.sq.b <- priors$sigma.sq.ig[2]
}
} else if ("sigma.sq.unif" %in% names(priors)) { # uniform prior
if (priors$sigma.sq.unif[1] == 'fixed') {
sigma.sq.ig <- TRUE # This just makes the C++ side a bit easier.
fixed.params[which(all.params == 'sigma.sq')] <- TRUE
sigma.sq.a <- 1
sigma.sq.b <- 1
} else {
sigma.sq.ig <- FALSE
if (!is.vector(priors$sigma.sq.unif) | !is.atomic(priors$sigma.sq.unif) | length(priors$sigma.sq.unif) != 2) {
stop("error: sigma.sq.unif must be a vector of length 2 with elements corresponding to sigma.sq's lower and upper bounds")
}
sigma.sq.a <- priors$sigma.sq.unif[1]
sigma.sq.b <- priors$sigma.sq.unif[2]
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.\nUsing an inverse-Gamma prior with the shape parameter set to 2 and scale parameter to 1.\n")
}
sigma.sq.ig <- TRUE
sigma.sq.a <- 2
sigma.sq.b <- 1
}
# nu -----------------------------
if (cov.model == 'matern') {
if (!"nu.unif" %in% names(priors)) {
stop("error: nu.unif must be specified in priors value list")
}
if (priors$nu.unif[1] == 'fixed') {
fixed.params[which(all.params == 'phi')] <- TRUE
nu.a <- 1
nu.b <- 1
if ((cov.model == 'matern') & (priors$phi.unif[1] != 'fixed')) {
message("nu is specified as fixed in priors$nu.unif but phi is not, which is not allowed. Continuing to fit the model with both phi and nu fixed.")
}
} else {
if (!is.vector(priors$nu.unif) | !is.atomic(priors$nu.unif) | length(priors$nu.unif) != 2) {
stop("error: nu.unif must be a vector of length 2 with elements corresponding to nu's lower and upper bounds")
}
nu.a <- priors$nu.unif[1]
nu.b <- priors$nu.unif[2]
}
} else {
nu.a <- 0
nu.b <- 0
}
# sigma.sq.psi --------------------
if (p.occ.re > 0) {
if ("sigma.sq.psi.ig" %in% names(priors)) {
if (priors$sigma.sq.psi.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq.psi')] <- TRUE
sigma.sq.psi.a <- rep(1, p.occ.re)
sigma.sq.psi.b <- rep(1, p.occ.re)
} else {
if (!is.list(priors$sigma.sq.psi.ig) | length(priors$sigma.sq.psi.ig) != 2) {
stop("error: sigma.sq.psi.ig must be a list of length 2")
}
sigma.sq.psi.a <- priors$sigma.sq.psi.ig[[1]]
sigma.sq.psi.b <- priors$sigma.sq.psi.ig[[2]]
if (length(sigma.sq.psi.a) != p.occ.re & length(sigma.sq.psi.a) != 1) {
if (p.occ.re == 1) {
stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ",
p.occ.re, " with elements corresponding to sigma.sq.psis' shape", sep = ""))
} else {
stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ",
p.occ.re, " or 1 with elements corresponding to sigma.sq.psis' shape", sep = ""))
}
}
if (length(sigma.sq.psi.b) != p.occ.re & length(sigma.sq.psi.b) != 1) {
if (p.occ.re == 1) {
stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ",
p.occ.re, " with elements corresponding to sigma.sq.psis' scale", sep = ""))
} else {
stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ",
p.occ.re, " or 1with elements corresponding to sigma.sq.psis' scale", sep = ""))
}
}
if (length(sigma.sq.psi.a) != p.occ.re) {
sigma.sq.psi.a <- rep(sigma.sq.psi.a, p.occ.re)
}
if (length(sigma.sq.psi.b) != p.occ.re) {
sigma.sq.psi.b <- rep(sigma.sq.psi.b, p.occ.re)
}
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.psi.ig.\nSetting prior shape to 0.1 and prior scale to 0.1\n")
}
sigma.sq.psi.a <- rep(0.1, p.occ.re)
sigma.sq.psi.b <- rep(0.1, p.occ.re)
}
} else {
sigma.sq.psi.a <- 0
sigma.sq.psi.b <- 0
}
# sigma.sq.p --------------------
if (p.det.re > 0) {
if ("sigma.sq.p.ig" %in% names(priors)) {
if (priors$sigma.sq.p.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq.p')] <- TRUE
sigma.sq.p.a <- rep(1, p.det.re)
sigma.sq.p.b <- rep(1, p.det.re)
} else {
if (!is.list(priors$sigma.sq.p.ig) | length(priors$sigma.sq.p.ig) != 2) {
stop("error: sigma.sq.p.ig must be a list of length 2")
}
sigma.sq.p.a <- priors$sigma.sq.p.ig[[1]]
sigma.sq.p.b <- priors$sigma.sq.p.ig[[2]]
if (length(sigma.sq.p.a) != p.det.re & length(sigma.sq.p.a) != 1) {
if (p.det.re == 1) {
stop(paste("error: sigma.sq.p.ig[[1]] must be a vector of length ",
p.det.re, " with elements corresponding to sigma.sq.ps' shape", sep = ""))
} else {
stop(paste("error: sigma.sq.p.ig[[1]] must be a vector of length ",
p.det.re, " or 1 with elements corresponding to sigma.sq.ps' shape", sep = ""))
}
}
if (length(sigma.sq.p.b) != p.det.re & length(sigma.sq.p.b) != 1) {
if (p.det.re == 1) {
stop(paste("error: sigma.sq.p.ig[[2]] must be a vector of length ",
p.det.re, " with elements corresponding to sigma.sq.ps' scale", sep = ""))
} else {
stop(paste("error: sigma.sq.p.ig[[2]] must be a vector of length ",
p.det.re, " or 1 with elements corresponding to sigma.sq.ps' scale", sep = ""))
}
}
if (length(sigma.sq.p.a) != p.det.re) {
sigma.sq.p.a <- rep(sigma.sq.p.a, p.det.re)
}
if (length(sigma.sq.p.b) != p.det.re) {
sigma.sq.p.b <- rep(sigma.sq.p.b, p.det.re)
}
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.p.ig.\nSetting prior shape to 0.1 and prior scale to 0.1\n")
}
sigma.sq.p.a <- rep(0.1, p.det.re)
sigma.sq.p.b <- rep(0.1, p.det.re)
}
} else {
sigma.sq.p.a <- 0
sigma.sq.p.b <- 0
}
# Starting values -----------------------------------------------------
if (missing(inits)) {
inits <- list()
}
names(inits) <- tolower(names(inits))
# z -------------------------------
if ("z" %in% names(inits)) {
z.inits <- inits$z
if (!is.vector(z.inits)) {
stop(paste("error: initial values for z must be a vector of length ",
J, sep = ""))
}
if (length(z.inits) != J) {
stop(paste("error: initial values for z must be a vector of length ",
J, sep = ""))
}
# Reorder the user supplied inits values
if (NNGP) {
z.inits <- z.inits[long.ord]
}
z.test <- apply(y.big, 1, max, na.rm = TRUE)
init.test <- sum(z.inits < z.test)
if (init.test > 0) {
stop("error: initial values for latent occurrence (z) are invalid. Please re-specify inits$z so initial values are 1 if the species is observed at that site.")
}
} else {
# In correct order since you reordered y.
z.inits <- apply(y.big, 1, max, na.rm = TRUE)
if (verbose) {
message("z.inits is not specified in initial values.\nSetting initial values based on observed data\n")
}
}
# beta -----------------------
if ("beta" %in% names(inits)) {
beta.inits <- inits[["beta"]]
if (length(beta.inits) != p.occ & length(beta.inits) != 1) {
if (p.occ == 1) {
stop(paste("error: initial values for beta must be of length ", p.occ,
sep = ""))
} else {
stop(paste("error: initial values for beta must be of length ", p.occ, " or 1",
sep = ""))
}
}
if (length(beta.inits) != p.occ) {
beta.inits <- rep(beta.inits, p.occ)
}
} else {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
if (verbose) {
message('beta is not specified in initial values.\nSetting initial values to random values from the prior distribution\n')
}
}
# alpha -----------------------
if ("alpha" %in% names(inits)) {
alpha.inits <- inits[["alpha"]]
if (length(alpha.inits) != p.det & length(alpha.inits) != 1) {
if (p.det == 1) {
stop(paste("error: initial values for alpha must be of length ", p.det,
sep = ""))
} else {
stop(paste("error: initial values for alpha must be of length ", p.det, " or 1",
sep = ""))
}
}
if (length(alpha.inits) != p.det) {
alpha.inits <- rep(alpha.inits, p.det)
}
} else {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
if (verbose) {
message("alpha is not specified in initial values.\nSetting initial values to random values from the prior distribution\n")
}
}
# phi -----------------------------
if ("phi" %in% names(inits)) {
phi.inits <- inits[["phi"]]
if (length(phi.inits) != 1) {
stop("error: initial values for phi must be of length 1")
}
} else {
phi.inits <- runif(1, phi.a, phi.b)
if (verbose) {
message("phi is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
}
# sigma.sq ------------------------
if ("sigma.sq" %in% names(inits)) {
sigma.sq.inits <- inits[["sigma.sq"]]
if (length(sigma.sq.inits) != 1) {
stop("error: initial values for sigma.sq must be of length 1")
}
} else {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
if (verbose) {
message("sigma.sq is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
}
# w -----------------------------
if ("w" %in% names(inits)) {
w.inits <- inits[["w"]]
if (!is.vector(w.inits)) {
stop(paste("error: initial values for w must be a vector of length ",
J.w, sep = ""))
}
if (length(w.inits) != J.w) {
stop(paste("error: initial values for w must be a vector of length ",
J.w, sep = ""))
}
# Reorder the user supplied inits values
if (NNGP) {
w.inits <- w.inits[ord]
}
} else {
w.inits <- rep(0, J.w)
if (verbose) {
message("w is not specified in initial values.\nSetting initial value to 0\n")
}
}
# nu ------------------------
if ("nu" %in% names(inits)) {
nu.inits <- inits[["nu"]]
if (length(nu.inits) != 1) {
stop("error: initial values for nu must be of length 1")
}
} else {
if (cov.model == 'matern') {
if (verbose) {
message("nu is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
nu.inits <- runif(1, nu.a, nu.b)
} else {
nu.inits <- 0
}
}
# sigma.sq.psi -------------------
if (p.occ.re > 0) {
if ("sigma.sq.psi" %in% names(inits)) {
sigma.sq.psi.inits <- inits[["sigma.sq.psi"]]
if (length(sigma.sq.psi.inits) != p.occ.re & length(sigma.sq.psi.inits) != 1) {
if (p.occ.re == 1) {
stop(paste("error: initial values for sigma.sq.psi must be of length ", p.occ.re,
sep = ""))
} else {
stop(paste("error: initial values for sigma.sq.psi must be of length ", p.occ.re,
" or 1", sep = ""))
}
}
if (length(sigma.sq.psi.inits) != p.occ.re) {
sigma.sq.psi.inits <- rep(sigma.sq.psi.inits, p.occ.re)
}
} else {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
if (verbose) {
message("sigma.sq.psi is not specified in initial values.\nSetting initial values to random values between 0.5 and 10\n")
}
}
beta.star.indx <- rep(0:(p.occ.re - 1), n.occ.re.long)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
} else {
sigma.sq.psi.inits <- 0
beta.star.indx <- 0
beta.star.inits <- 0
}
# sigma.sq.p ------------------
if (p.det.re > 0) {
if ("sigma.sq.p" %in% names(inits)) {
sigma.sq.p.inits <- inits[["sigma.sq.p"]]
if (length(sigma.sq.p.inits) != p.det.re & length(sigma.sq.p.inits) != 1) {
if (p.det.re == 1) {
stop(paste("error: initial values for sigma.sq.p must be of length ", p.det.re,
sep = ""))
} else {
stop(paste("error: initial values for sigma.sq.p must be of length ", p.det.re,
" or 1", sep = ""))
}
}
if (length(sigma.sq.p.inits) != p.det.re) {
sigma.sq.p.inits <- rep(sigma.sq.p.inits, p.det.re)
}
} else {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
if (verbose) {
message("sigma.sq.p is not specified in initial values.\nSetting initial values to random values between 0.5 and 10\n")
}
}
alpha.star.indx <- rep(0:(p.det.re - 1), n.det.re.long)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
} else {
sigma.sq.p.inits <- 0
alpha.star.indx <- 0
alpha.star.inits <- 0
}
# Should initial values be fixed --
if ("fix" %in% names(inits)) {
fix.inits <- inits[["fix"]]
if ((fix.inits != TRUE) & (fix.inits != FALSE)) {
stop(paste("error: inits$fix must take value TRUE or FALSE"))
}
} else {
fix.inits <- FALSE
}
if (verbose & fix.inits & (n.chains > 1)) {
message("Fixing initial values across all chains\n")
}
# Covariance Model ----------------------------------------------------
# Order must match util.cpp spCor.
cov.model.names <- c("exponential", "spherical", "matern", "gaussian")
if(! cov.model %in% cov.model.names){
stop("error: specified cov.model '",cov.model,"' is not a valid option; choose from ",
paste(cov.model.names, collapse=", ", sep="") ,".")}
# Obo for cov model lookup on c side
cov.model.indx <- which(cov.model == cov.model.names) - 1
storage.mode(cov.model.indx) <- "integer"
# Get tuning values ---------------------------------------------------
sigma.sq.tuning <- 0
phi.tuning <- 0
nu.tuning <- 0
if (missing(tuning)) {
phi.tuning <- 1
if (cov.model == 'matern') {
nu.tuning <- 1
}
if (!sigma.sq.ig) {
sigma.sq.tuning <- 1
}
} else {
names(tuning) <- tolower(names(tuning))
# phi ---------------------------
if(!"phi" %in% names(tuning)) {
stop("error: phi must be specified in tuning value list")
}
phi.tuning <- tuning$phi
if (length(phi.tuning) != 1) {
stop("error: phi tuning must be a single value")
}
if (cov.model == 'matern') {
# nu --------------------------
if(!"nu" %in% names(tuning)) {
stop("error: nu must be specified in tuning value list")
}
nu.tuning <- tuning$nu
if (length(nu.tuning) != 1) {
stop("error: nu tuning must be a single value")
}
}
if (!sigma.sq.ig) {
# sigma.sq --------------------------
if(!"sigma.sq" %in% names(tuning)) {
stop("error: sigma.sq must be specified in tuning value list")
}
sigma.sq.tuning <- tuning$sigma.sq
if (length(sigma.sq.tuning) != 1) {
stop("error: sigma.sq tuning must be a single value")
}
}
}
# Log the tuning values since they are used in the AMCMC.
tuning.c <- log(c(sigma.sq.tuning, phi.tuning, nu.tuning))
# Set model.deviance to NA for returning when no cross-validation
model.deviance <- NA
curr.chain <- 1
if (!NNGP) {
if (J.w != J) {
stop("data$grid.index is not currently supported when NNGP = FALSE. If specifying the spatial random effect across a grid that is different from the individual site locations, please set NNGP = TRUE.")
}
# Set storage for all variables ---------------------------------------
storage.mode(y) <- "double"
storage.mode(z.inits) <- "double"
storage.mode(X.p) <- "double"
storage.mode(X) <- "double"
consts <- c(J, n.obs, p.occ, p.occ.re, n.occ.re, p.det, p.det.re, n.det.re)
storage.mode(consts) <- "integer"
storage.mode(K) <- "double"
storage.mode(coords.D) <- "double"
storage.mode(beta.inits) <- "double"
storage.mode(alpha.inits) <- "double"
storage.mode(phi.inits) <- "double"
storage.mode(sigma.sq.inits) <- "double"
storage.mode(nu.inits) <- "double"
storage.mode(w.inits) <- "double"
storage.mode(z.long.indx) <- "integer"
storage.mode(mu.beta) <- "double"
storage.mode(Sigma.beta) <- "double"
storage.mode(mu.alpha) <- "double"
storage.mode(Sigma.alpha) <- "double"
storage.mode(phi.a) <- "double"
storage.mode(phi.b) <- "double"
storage.mode(nu.a) <- "double"
storage.mode(nu.b) <- "double"
storage.mode(sigma.sq.a) <- "double"
storage.mode(sigma.sq.b) <- "double"
storage.mode(sigma.sq.ig) <- "integer"
storage.mode(tuning.c) <- "double"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
storage.mode(n.omp.threads) <- "integer"
storage.mode(verbose) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(cov.model.indx) <- "integer"
# chain.info order: current chain, total number of chains
chain.info <- c(curr.chain, n.chains)
storage.mode(chain.info) <- "integer"
fixed.sigma.sq <- fixed.params[which(all.params == 'sigma.sq')]
storage.mode(fixed.sigma.sq) <- "integer"
n.post.samples <- length(seq(from = n.burn + 1,
to = n.samples,
by = as.integer(n.thin)))
# samples.info order: burn-in, thinning rate, number of posterior samples
samples.info <- c(n.burn, n.thin, n.post.samples)
storage.mode(samples.info) <- "integer"
# For detection random effects
storage.mode(X.p.re) <- "integer"
alpha.level.indx <- sort(unique(c(X.p.re)))
storage.mode(alpha.level.indx) <- "integer"
storage.mode(n.det.re.long) <- "integer"
storage.mode(sigma.sq.p.inits) <- "double"
storage.mode(sigma.sq.p.a) <- "double"
storage.mode(sigma.sq.p.b) <- "double"
storage.mode(alpha.star.inits) <- "double"
storage.mode(alpha.star.indx) <- "integer"
# For occurrence random effects
storage.mode(X.re) <- "integer"
beta.level.indx <- sort(unique(c(X.re)))
storage.mode(beta.level.indx) <- "integer"
storage.mode(sigma.sq.psi.inits) <- "double"
storage.mode(sigma.sq.psi.a) <- "double"
storage.mode(sigma.sq.psi.b) <- "double"
storage.mode(n.occ.re.long) <- "integer"
storage.mode(beta.star.inits) <- "double"
storage.mode(beta.star.indx) <- "integer"
# Fit the model -------------------------------------------------------
out.tmp <- list()
out <- list()
if (!k.fold.only) {
for (i in 1:n.chains) {
# Change initial values if i > 1
if ((i > 1) & (!fix.inits)) {
if (!fixed.params[which(all.params == 'beta')]) {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
}
if (!fixed.params[which(all.params == 'alpha')]) {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
}
if (!fixed.params[which(all.params == 'sigma.sq')]) {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
}
if (!fixed.params[which(all.params == 'phi')]) {
phi.inits <- runif(1, phi.a, phi.b)
}
if (cov.model == 'matern') {
if (!fixed.params[which(all.params == 'phi')]) {
nu.inits <- runif(1, nu.a, nu.b)
}
}
if (p.det.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.p')]) {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
}
}
if (p.occ.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.psi')]) {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
}
}
}
storage.mode(chain.info) <- "integer"
# Run the model in C
out.tmp[[i]] <- .Call("spPGOcc", y, X, X.p, coords.D, X.re, X.p.re, consts,
K, n.occ.re.long, n.det.re.long,
beta.inits, alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits,
beta.star.inits, alpha.star.inits, z.inits,
w.inits, phi.inits, sigma.sq.inits, nu.inits, z.long.indx,
beta.star.indx, beta.level.indx, alpha.star.indx,
alpha.level.indx, mu.beta, mu.alpha,
Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b,
sigma.sq.psi.a, sigma.sq.psi.b, sigma.sq.p.a, sigma.sq.p.b,
tuning.c, cov.model.indx,
n.batch, batch.length,
accept.rate, n.omp.threads, verbose, n.report,
samples.info, chain.info, fixed.sigma.sq, sigma.sq.ig)
chain.info[1] <- chain.info[1] + 1
}
# Calculate R-Hat ---------------
out <- list()
out$rhat <- list()
if (n.chains > 1) {
# as.vector removes the "Upper CI" when there is only 1 variable.
out$rhat$beta <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$beta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$alpha <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$alpha.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$theta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$theta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.p.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.psi.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
} else {
out$rhat$beta <- rep(NA, p.occ)
out$rhat$alpha <- rep(NA, p.det)
out$rhat$theta <- rep(NA, ifelse(cov.model == 'matern', 3, 2))
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- rep(NA, p.det.re)
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- rep(NA, p.occ.re)
}
}
out$beta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$beta.samples))))
colnames(out$beta.samples) <- x.names
out$alpha.samples <- mcmc(do.call(rbind,
lapply(out.tmp, function(a) t(a$alpha.samples))))
colnames(out$alpha.samples) <- x.p.names
out$theta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$theta.samples))))
if (cov.model != 'matern') {
colnames(out$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out$z.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$z.samples))))
out$psi.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$psi.samples))))
out$like.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$like.samples))))
out$w.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$w.samples))))
if (p.occ.re > 0) {
out$sigma.sq.psi.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.psi.samples))))
colnames(out$sigma.sq.psi.samples) <- x.re.names
out$beta.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$beta.star.samples))))
tmp.names <- unlist(re.level.names)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long), tmp.names, sep = '-')
colnames(out$beta.star.samples) <- beta.star.names
out$re.level.names <- re.level.names
}
if (p.det.re > 0) {
out$sigma.sq.p.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.p.samples))))
colnames(out$sigma.sq.p.samples) <- x.p.re.names
out$alpha.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$alpha.star.samples))))
tmp.names <- unlist(p.re.level.names)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long), tmp.names, sep = '-')
colnames(out$alpha.star.samples) <- alpha.star.names
out$p.re.level.names <- p.re.level.names
}
# Calculate effective sample sizes
out$ESS <- list()
out$ESS$beta <- effectiveSize(out$beta.samples)
out$ESS$alpha <- effectiveSize(out$alpha.samples)
out$ESS$theta <- effectiveSize(out$theta.samples)
if (p.det.re > 0) {
out$ESS$sigma.sq.p <- effectiveSize(out$sigma.sq.p.samples)
}
if (p.occ.re > 0) {
out$ESS$sigma.sq.psi <- effectiveSize(out$sigma.sq.psi.samples)
}
out$X <- X
out$X.p <- X.p
out$X.p.re <- X.p.re
out$X.re <- X.re
out$y <- y.big
out$call <- cl
out$n.samples <- batch.length * n.batch
out$cov.model.indx <- cov.model.indx
out$type <- "GP"
out$coords <- coords
out$n.post <- n.post.samples
out$n.thin <- n.thin
out$n.burn <- n.burn
out$n.chains <- n.chains
if (p.det.re > 0) {
out$pRE <- TRUE
} else {
out$pRE <- FALSE
}
if (p.occ.re > 0) {
out$psiRE <- TRUE
} else {
out$psiRE <- FALSE
}
}
# K-fold cross-validation ---------
if (!missing(k.fold)) {
if (verbose) {
cat("----------------------------------------\n");
cat("\tCross-validation\n");
cat("----------------------------------------\n");
message(paste("Performing ", k.fold, "-fold cross-validation using ", k.fold.threads,
" thread(s).", sep = ''))
}
set.seed(k.fold.seed)
# Number of sites in each hold out data set.
sites.random <- sample(1:J)
sites.k.fold <- split(sites.random, sites.random %% k.fold)
registerDoParallel(k.fold.threads)
model.deviance <- foreach (i = 1:k.fold, .combine = sum) %dopar% {
curr.set <- sort(sites.random[sites.k.fold[[i]]])
if (binom) {
y.indx <- !(1:J %in% curr.set)
} else {
y.indx <- !((z.long.indx + 1) %in% curr.set)
}
y.fit <- y[y.indx]
y.0 <- y[!y.indx]
y.big.fit <- y.big[-curr.set, , drop = FALSE]
y.big.0 <- y.big[curr.set, , drop = FALSE]
z.inits.fit <- z.inits[-curr.set]
X.p.fit <- X.p[y.indx, , drop = FALSE]
X.p.0 <- X.p[!y.indx, , drop = FALSE]
X.fit <- X[-curr.set, , drop = FALSE]
X.0 <- X[curr.set, , drop = FALSE]
J.fit <- nrow(X.fit)
J.0 <- nrow(X.0)
coords.fit <- coords[-curr.set, , drop = FALSE]
coords.0 <- coords[curr.set, , drop = FALSE]
coords.D.fit <- coords.D[-curr.set, -curr.set, drop = FALSE]
coords.D.0 <- coords.D[curr.set, curr.set, drop = FALSE]
K.fit <- K[-curr.set]
K.0 <- K[curr.set]
rep.indx.fit <- rep.indx[-curr.set]
rep.indx.0 <- rep.indx[curr.set]
n.obs.fit <- nrow(X.p.fit)
n.obs.0 <- nrow(X.p.0)
# Random Detection Effects
X.p.re.fit <- X.p.re[y.indx, , drop = FALSE]
X.p.re.0 <- X.p.re[!y.indx, , drop = FALSE]
n.det.re.fit <- length(unique(c(X.p.re.fit)))
n.det.re.long.fit <- apply(X.p.re.fit, 2, function(a) length(unique(a)))
if (p.det.re > 0) {
alpha.star.indx.fit <- rep(0:(p.det.re - 1), n.det.re.long.fit)
alpha.level.indx.fit <- sort(unique(c(X.p.re.fit)))
alpha.star.inits.fit <- rnorm(n.det.re.fit, 0,
sqrt(sigma.sq.p.inits[alpha.star.indx.fit + 1]))
p.re.level.names.fit <- list()
for (t in 1:p.det.re) {
tmp.indx <- alpha.level.indx.fit[alpha.star.indx.fit == t - 1]
p.re.level.names.fit[[t]] <- unlist(p.re.level.names)[tmp.indx + 1]
}
} else {
alpha.star.indx.fit <- alpha.star.indx
alpha.level.indx.fit <- alpha.level.indx
alpha.star.inits.fit <- alpha.star.inits
}
# Random Occurrence Effects
X.re.fit <- X.re[-curr.set, , drop = FALSE]
X.re.0 <- X.re[curr.set, , drop = FALSE]
n.occ.re.fit <- length(unique(c(X.re.fit)))
n.occ.re.long.fit <- apply(X.re.fit, 2, function(a) length(unique(a)))
if (p.occ.re > 0) {
beta.star.indx.fit <- rep(0:(p.occ.re - 1), n.occ.re.long.fit)
beta.level.indx.fit <- sort(unique(c(X.re.fit)))
beta.star.inits.fit <- rnorm(n.occ.re.fit, 0,
sqrt(sigma.sq.psi.inits[beta.star.indx.fit + 1]))
re.level.names.fit <- list()
for (t in 1:p.occ.re) {
tmp.indx <- beta.level.indx.fit[beta.star.indx.fit == t - 1]
re.level.names.fit[[t]] <- unlist(re.level.names)[tmp.indx + 1]
}
} else {
beta.star.indx.fit <- beta.star.indx
beta.level.indx.fit <- beta.level.indx
beta.star.inits.fit <- beta.star.inits
re.level.names.fit <- re.level.names
}
if (!binom) {
z.long.indx.fit <- rep(1:J.fit, dim(y.big.fit)[2])
z.long.indx.fit <- z.long.indx.fit[!is.na(c(y.big.fit))]
# Subtract 1 for indices in C
z.long.indx.fit <- z.long.indx.fit - 1
z.0.long.indx <- rep(1:J.0, dim(y.big.0)[2])
z.0.long.indx <- z.0.long.indx[!is.na(c(y.big.0))]
# Don't subtract 1 for z.0.long.indx since its used in R only
} else {
z.long.indx.fit <- 0:(J.fit - 1)
z.0.long.indx <- 1:J.0
}
verbose.fit <- FALSE
n.omp.threads.fit <- 1
storage.mode(y.fit) <- "double"
storage.mode(z.inits.fit) <- "double"
storage.mode(X.p.fit) <- "double"
storage.mode(X.fit) <- "double"
storage.mode(coords.D.fit) <- "double"
storage.mode(K.fit) <- "double"
consts.fit <- c(J.fit, n.obs.fit, p.occ, p.occ.re, n.occ.re.fit,
p.det, p.det.re, n.det.re.fit)
storage.mode(consts.fit) <- "integer"
storage.mode(z.long.indx.fit) <- "integer"
storage.mode(n.samples) <- "integer"
storage.mode(n.omp.threads.fit) <- "integer"
storage.mode(verbose.fit) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(X.p.re.fit) <- "integer"
storage.mode(n.det.re.long.fit) <- "integer"
storage.mode(alpha.star.inits.fit) <- "double"
storage.mode(alpha.star.indx.fit) <- "integer"
storage.mode(alpha.level.indx.fit) <- "integer"
storage.mode(X.re.fit) <- "integer"
storage.mode(n.occ.re.long.fit) <- "integer"
storage.mode(beta.star.inits.fit) <- "double"
storage.mode(beta.star.indx.fit) <- "integer"
storage.mode(beta.level.indx.fit) <- "integer"
chain.info[1] <- 1
storage.mode(chain.info) <- "integer"
# Run the model in C
out.fit <- .Call("spPGOcc", y.fit, X.fit, X.p.fit, coords.D.fit, X.re.fit, X.p.re.fit,
consts.fit, K.fit, n.occ.re.long.fit, n.det.re.long.fit, beta.inits,
alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits, beta.star.inits.fit,
alpha.star.inits.fit, z.inits.fit, w.inits, phi.inits, sigma.sq.inits,
nu.inits, z.long.indx.fit, beta.star.indx.fit,
beta.level.indx.fit, alpha.star.indx.fit, alpha.level.indx.fit, mu.beta,
mu.alpha, Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b, sigma.sq.psi.a, sigma.sq.psi.b,
sigma.sq.p.a, sigma.sq.p.b, tuning.c, cov.model.indx,
n.batch, batch.length, accept.rate, n.omp.threads.fit, verbose.fit,
n.report, samples.info, chain.info, fixed.sigma.sq, sigma.sq.ig)
out.fit$beta.samples <- mcmc(t(out.fit$beta.samples))
colnames(out.fit$beta.samples) <- x.names
out.fit$alpha.samples <- mcmc(t(out.fit$alpha.samples))
colnames(out.fit$alpha.samples) <- x.p.names
out.fit$theta.samples <- mcmc(t(out.fit$theta.samples))
if (cov.model != 'matern') {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out.fit$w.samples <- mcmc(t(out.fit$w.samples))
out.fit$X <- X.fit
out.fit$y <- y.big.fit
out.fit$X.p <- X.p.fit
out.fit$call <- cl
out.fit$type <- "GP"
out.fit$n.samples <- n.samples
out.fit$coords <- coords.fit
out.fit$cov.model.indx <- cov.model.indx
out.fit$n.post <- n.post.samples
out.fit$n.thin <- n.thin
out.fit$n.burn <- n.burn
out.fit$n.chains <- 1
if (p.det.re > 0) {
out.fit$pRE <- TRUE
} else {
out.fit$pRE <- FALSE
}
if (p.occ.re > 0) {
out.fit$sigma.sq.psi.samples <- mcmc(t(out.fit$sigma.sq.psi.samples))
colnames(out.fit$sigma.sq.psi.samples) <- x.re.names
out.fit$beta.star.samples <- mcmc(t(out.fit$beta.star.samples))
tmp.names <- unlist(re.level.names.fit)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$beta.star.samples) <- beta.star.names
out.fit$re.level.names <- re.level.names.fit
out.fit$X.re <- X.re.fit
}
if (p.det.re > 0) {
out.fit$sigma.sq.p.samples <- mcmc(t(out.fit$sigma.sq.p.samples))
colnames(out.fit$sigma.sq.p.samples) <- x.p.re.names
out.fit$alpha.star.samples <- mcmc(t(out.fit$alpha.star.samples))
tmp.names <- unlist(p.re.level.names.fit)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$alpha.star.samples) <- alpha.star.names
out.fit$p.re.level.names <- p.re.level.names.fit
out.fit$X.p.re <- X.p.re.fit
}
if (p.occ.re > 0) {
out.fit$psiRE <- TRUE
} else {
out.fit$psiRE <- FALSE
}
class(out.fit) <- "spPGOcc"
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.occ.re > 0) {
tmp <- unlist(re.level.names)
X.re.0 <- matrix(tmp[c(X.re.0 + 1)], nrow(X.re.0), ncol(X.re.0))
colnames(X.re.0) <- x.re.names
}
# Predict occurrence at new sites
if (p.occ.re > 0) {X.0 <- cbind(X.0, X.re.0)}
out.pred <- predict.spPGOcc(out.fit, X.0, coords.0, verbose = FALSE)
# Detection
# Generate detection values
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.det.re > 0) {
tmp <- unlist(p.re.level.names)
X.p.re.0 <- matrix(tmp[c(X.p.re.0 + 1)], nrow(X.p.re.0), ncol(X.p.re.0))
colnames(X.p.re.0) <- x.p.re.names
}
if (p.det.re > 0) {X.p.0 <- cbind(X.p.0, X.p.re.0)}
out.p.pred <- predict.spPGOcc(out.fit, X.p.0, type = 'detection')
if (binom) {
like.samples <- matrix(NA, nrow(y.big.0), ncol(y.big.0))
for (j in 1:nrow(X.p.0)) {
for (k in rep.indx.0[[j]]) {
like.samples[j, k] <- mean(dbinom(y.big.0[j, k], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
} else {
like.samples <- rep(NA, nrow(X.p.0))
for (j in 1:nrow(X.p.0)) {
like.samples[j] <- mean(dbinom(y.0[j], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
sum(log(like.samples), na.rm = TRUE)
}
model.deviance <- -2 * model.deviance
# Return objects from cross-validation
out$k.fold.deviance <- model.deviance
stopImplicitCluster()
} # cross-validation
} else {
# Nearest Neighbor Search ---------------------------------------------
if(verbose){
cat("----------------------------------------\n");
cat("\tBuilding the neighbor list\n");
cat("----------------------------------------\n");
}
search.type.names <- c("brute", "cb")
if(!search.type %in% search.type.names){
stop("error: specified search.type '",search.type,
"' is not a valid option; choose from ",
paste(search.type.names, collapse=", ", sep="") ,".")
}
storage.mode(n.neighbors) <- "integer"
storage.mode(n.omp.threads) <- "integer"
## Indexes
if(search.type == "brute"){
indx <- mkNNIndx(coords, n.neighbors, n.omp.threads)
} else{
indx <- mkNNIndxCB(coords, n.neighbors, n.omp.threads)
}
nn.indx <- indx$nnIndx
nn.indx.lu <- indx$nnIndxLU
nn.indx.run.time <- indx$run.time
storage.mode(nn.indx) <- "integer"
storage.mode(nn.indx.lu) <- "integer"
storage.mode(u.search.type) <- "integer"
storage.mode(J) <- "integer"
storage.mode(J.w) <- "integer"
if(verbose){
cat("----------------------------------------\n");
cat("Building the neighbors of neighbors list\n");
cat("----------------------------------------\n");
}
indx <- mkUIndx(J.w, n.neighbors, nn.indx, nn.indx.lu, u.search.type)
u.indx <- indx$u.indx
u.indx.lu <- indx$u.indx.lu
ui.indx <- indx$ui.indx
u.indx.run.time <- indx$run.time
# Set storage for all variables ---------------------------------------
storage.mode(y) <- "double"
storage.mode(z.inits) <- "double"
storage.mode(X.p) <- "double"
storage.mode(X) <- "double"
consts <- c(J, n.obs, p.occ, p.occ.re, n.occ.re, p.det, p.det.re, n.det.re, J.w)
storage.mode(consts) <- "integer"
storage.mode(grid.index.c) <- "integer"
storage.mode(K) <- "double"
storage.mode(coords) <- "double"
storage.mode(beta.inits) <- "double"
storage.mode(alpha.inits) <- "double"
storage.mode(phi.inits) <- "double"
storage.mode(sigma.sq.inits) <- "double"
storage.mode(nu.inits) <- "double"
storage.mode(w.inits) <- "double"
storage.mode(z.long.indx) <- "integer"
storage.mode(mu.beta) <- "double"
storage.mode(Sigma.beta) <- "double"
storage.mode(mu.alpha) <- "double"
storage.mode(Sigma.alpha) <- "double"
storage.mode(phi.a) <- "double"
storage.mode(phi.b) <- "double"
storage.mode(nu.a) <- "double"
storage.mode(nu.b) <- "double"
storage.mode(sigma.sq.a) <- "double"
storage.mode(sigma.sq.b) <- "double"
storage.mode(sigma.sq.ig) <- "integer"
storage.mode(tuning.c) <- "double"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
storage.mode(n.omp.threads) <- "integer"
storage.mode(verbose) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(nn.indx) <- "integer"
storage.mode(nn.indx.lu) <- "integer"
storage.mode(u.indx) <- "integer"
storage.mode(u.indx.lu) <- "integer"
storage.mode(ui.indx) <- "integer"
storage.mode(n.neighbors) <- "integer"
storage.mode(cov.model.indx) <- "integer"
chain.info <- c(curr.chain, n.chains)
storage.mode(chain.info) <- "integer"
storage.mode(fixed.params) <- "integer"
n.post.samples <- length(seq(from = n.burn + 1,
to = n.samples,
by = as.integer(n.thin)))
storage.mode(n.post.samples) <- "integer"
samples.info <- c(n.burn, n.thin, n.post.samples)
storage.mode(samples.info) <- "integer"
# For detection random effects
storage.mode(X.p.re) <- "integer"
alpha.level.indx <- sort(unique(c(X.p.re)))
storage.mode(alpha.level.indx) <- "integer"
storage.mode(n.det.re.long) <- "integer"
storage.mode(sigma.sq.p.inits) <- "double"
storage.mode(sigma.sq.p.a) <- "double"
storage.mode(sigma.sq.p.b) <- "double"
storage.mode(alpha.star.inits) <- "double"
storage.mode(alpha.star.indx) <- "integer"
# For occurrence random effects
storage.mode(X.re) <- "integer"
beta.level.indx <- sort(unique(c(X.re)))
storage.mode(beta.level.indx) <- "integer"
storage.mode(sigma.sq.psi.inits) <- "double"
storage.mode(sigma.sq.psi.a) <- "double"
storage.mode(sigma.sq.psi.b) <- "double"
storage.mode(beta.star.inits) <- "double"
storage.mode(beta.star.indx) <- "integer"
# Fit the model -------------------------------------------------------
out.tmp <- list()
out <- list()
if (!k.fold.only) {
for (i in 1:n.chains) {
# Change initial values if i > 1
if ((i > 1) & (!fix.inits)) {
if (!fixed.params[which(all.params == 'beta')]) {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
}
if (!fixed.params[which(all.params == 'alpha')]) {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
}
if (!fixed.params[which(all.params == 'sigma.sq')]) {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
}
if (!fixed.params[which(all.params == 'phi')]) {
phi.inits <- runif(1, phi.a, phi.b)
}
if (cov.model == 'matern') {
if (!fixed.params[which(all.params == 'phi')]) {
nu.inits <- runif(1, nu.a, nu.b)
}
}
if (p.det.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.p')]) {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
}
}
if (p.occ.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.psi')]) {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
}
}
}
storage.mode(chain.info) <- "integer"
# Run the model in C
out.tmp[[i]] <- .Call("spPGOccNNGP", y, X, X.p, coords, X.re, X.p.re, consts,
K, n.occ.re.long, n.det.re.long,
n.neighbors, nn.indx, nn.indx.lu, u.indx, u.indx.lu, ui.indx,
beta.inits, alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits,
beta.star.inits, alpha.star.inits, z.inits,
w.inits, phi.inits, sigma.sq.inits, nu.inits, z.long.indx,
beta.star.indx, beta.level.indx, alpha.star.indx,
alpha.level.indx, mu.beta, mu.alpha,
Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b,
sigma.sq.psi.a, sigma.sq.psi.b, sigma.sq.p.a, sigma.sq.p.b,
tuning.c, cov.model.indx,
n.batch, batch.length,
accept.rate, n.omp.threads, verbose, n.report,
samples.info, chain.info, fixed.params, sigma.sq.ig, grid.index.c)
chain.info[1] <- chain.info[1] + 1
}
# Calculate R-Hat ---------------
out <- list()
out$rhat <- list()
if (n.chains > 1) {
# as.vector removes the "Upper CI" when there is only 1 variable.
out$rhat$beta <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$beta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$alpha <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$alpha.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$theta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$theta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.p.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.psi.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
} else {
out$rhat$beta <- rep(NA, p.occ)
out$rhat$alpha <- rep(NA, p.det)
out$rhat$theta <- rep(NA, ifelse(cov.model == 'matern', 3, 2))
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- rep(NA, p.det.re)
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- rep(NA, p.occ.re)
}
}
out$beta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$beta.samples))))
colnames(out$beta.samples) <- x.names
out$alpha.samples <- mcmc(do.call(rbind,
lapply(out.tmp, function(a) t(a$alpha.samples))))
colnames(out$alpha.samples) <- x.p.names
out$theta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$theta.samples))))
if (cov.model != 'matern') {
colnames(out$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
# Get everything back in the original order
out$coords <- coords[order(ord), ]
out$z.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$z.samples))))
out$z.samples <- mcmc(out$z.samples[, order(long.ord), drop = FALSE])
out$X <- X[order(long.ord), , drop = FALSE]
out$X.re <- X.re[order(long.ord), , drop = FALSE]
out$w.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$w.samples))))
out$w.samples <- mcmc(out$w.samples[, order(ord), drop = FALSE])
out$psi.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$psi.samples))))
out$psi.samples <- mcmc(out$psi.samples[, order(long.ord), drop = FALSE])
out$like.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$like.samples))))
out$like.samples <- mcmc(out$like.samples[, order(long.ord), drop = FALSE])
# Get detection covariate stuff in right order. Method of doing this
# depends on if there are observation level covariates or not.
if (!binom) {
tmp <- matrix(NA, J * K.max, p.det)
tmp[names.long, ] <- X.p
tmp <- array(tmp, dim = c(J, K.max, p.det))
tmp <- tmp[order(long.ord), , ]
out$X.p <- matrix(tmp, J * K.max, p.det)
out$X.p <- out$X.p[apply(out$X.p, 1, function(a) sum(is.na(a))) == 0, , drop = FALSE]
colnames(out$X.p) <- x.p.names
tmp <- matrix(NA, J * K.max, p.det.re)
tmp[names.long, ] <- X.p.re
tmp <- array(tmp, dim = c(J, K.max, p.det.re))
tmp <- tmp[order(long.ord), , ]
out$X.p.re <- matrix(tmp, J * K.max, p.det.re)
out$X.p.re <- out$X.p.re[apply(out$X.p.re, 1, function(a) sum(is.na(a))) == 0, , drop = FALSE]
colnames(out$X.p.re) <- x.p.re.names
} else {
out$X.p <- X.p[order(long.ord), , drop = FALSE]
out$X.p.re <- X.p.re[order(long.ord), , drop = FALSE]
}
out$y <- y.big[order(long.ord), , drop = FALSE]
if (p.occ.re > 0) {
out$sigma.sq.psi.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.psi.samples))))
colnames(out$sigma.sq.psi.samples) <- x.re.names
out$beta.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$beta.star.samples))))
tmp.names <- unlist(re.level.names)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long), tmp.names, sep = '-')
colnames(out$beta.star.samples) <- beta.star.names
out$re.level.names <- re.level.names
}
if (p.det.re > 0) {
out$sigma.sq.p.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.p.samples))))
colnames(out$sigma.sq.p.samples) <- x.p.re.names
out$alpha.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$alpha.star.samples))))
tmp.names <- unlist(p.re.level.names)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long), tmp.names, sep = '-')
colnames(out$alpha.star.samples) <- alpha.star.names
out$p.re.level.names <- p.re.level.names
}
# Calculate effective sample sizes
out$ESS <- list()
out$ESS$beta <- effectiveSize(out$beta.samples)
out$ESS$alpha <- effectiveSize(out$alpha.samples)
out$ESS$theta <- effectiveSize(out$theta.samples)
if (p.det.re > 0) {
out$ESS$sigma.sq.p <- effectiveSize(out$sigma.sq.p.samples)
}
if (p.occ.re > 0) {
out$ESS$sigma.sq.psi <- effectiveSize(out$sigma.sq.psi.samples)
}
out$call <- cl
out$n.samples <- batch.length * n.batch
out$n.neighbors <- n.neighbors
out$cov.model.indx <- cov.model.indx
out$type <- "NNGP"
out$n.post <- n.post.samples
out$n.thin <- n.thin
out$n.burn <- n.burn
out$n.chains <- n.chains
if (p.det.re > 0) {
out$pRE <- TRUE
} else {
out$pRE <- FALSE
}
if (p.occ.re > 0) {
out$psiRE <- TRUE
} else {
out$psiRE <- FALSE
}
}
# K-fold cross-validation ---------
if (!missing(k.fold)) {
if (verbose) {
cat("----------------------------------------\n");
cat("\tCross-validation\n");
cat("----------------------------------------\n");
message(paste("Performing ", k.fold, "-fold cross-validation using ", k.fold.threads,
" thread(s).", sep = ''))
}
set.seed(k.fold.seed)
# Number of sites in each hold out data set.
sites.random <- sample(1:J.w)
sites.k.fold <- split(sites.random, sites.random %% k.fold)
registerDoParallel(k.fold.threads)
model.deviance <- foreach (i = 1:k.fold, .combine = sum) %dopar% {
curr.set.small <- sort(sites.random[sites.k.fold[[i]]])
curr.set <- which(grid.index.r %in% curr.set.small)
if (binom) {
y.indx <- !(1:J %in% curr.set)
} else {
y.indx <- !((z.long.indx + 1) %in% curr.set)
}
y.fit <- y[y.indx]
y.0 <- y[!y.indx]
y.big.fit <- y.big[-curr.set, , drop = FALSE]
y.big.0 <- y.big[curr.set, , drop = FALSE]
z.inits.fit <- z.inits[-curr.set]
X.p.fit <- X.p[y.indx, , drop = FALSE]
X.p.0 <- X.p[!y.indx, , drop = FALSE]
X.fit <- X[-curr.set, , drop = FALSE]
X.0 <- X[curr.set, , drop = FALSE]
J.fit <- nrow(X.fit)
J.0 <- nrow(X.0)
coords.fit <- coords[-curr.set.small, , drop = FALSE]
coords.0 <- coords[curr.set.small, , drop = FALSE]
J.w.fit <- nrow(coords.fit)
tmp.grid.fit <- grid.index.r[-curr.set]
tmp.grid.0 <- grid.index.r[curr.set]
# Reorder the grids to get their indices to work
grid.fit <- vector('numeric', length = length(tmp.grid.fit))
for (j in 1:nrow(coords.fit)) {
grid.fit[which(tmp.grid.fit == (1:J.w)[-curr.set.small][j])] <- j
}
grid.0 <- vector('numeric', length = length(tmp.grid.0))
for (j in 1:nrow(coords.0)) {
grid.0[which(tmp.grid.0 == curr.set.small[j])] <- j
}
grid.fit.c <- grid.fit - 1
K.fit <- K[-curr.set]
K.0 <- K[curr.set]
rep.indx.fit <- rep.indx[-curr.set]
rep.indx.0 <- rep.indx[curr.set]
n.obs.fit <- nrow(X.p.fit)
n.obs.0 <- nrow(X.p.0)
# Random Detection Effects
X.p.re.fit <- X.p.re[y.indx, , drop = FALSE]
X.p.re.0 <- X.p.re[!y.indx, , drop = FALSE]
n.det.re.fit <- length(unique(c(X.p.re.fit)))
n.det.re.long.fit <- apply(X.p.re.fit, 2, function(a) length(unique(a)))
if (p.det.re > 0) {
alpha.star.indx.fit <- rep(0:(p.det.re - 1), n.det.re.long.fit)
alpha.level.indx.fit <- sort(unique(c(X.p.re.fit)))
alpha.star.inits.fit <- rnorm(n.det.re.fit, 0,
sqrt(sigma.sq.p.inits[alpha.star.indx.fit + 1]))
p.re.level.names.fit <- list()
for (t in 1:p.det.re) {
tmp.indx <- alpha.level.indx.fit[alpha.star.indx.fit == t - 1]
p.re.level.names.fit[[t]] <- unlist(p.re.level.names)[tmp.indx + 1]
}
} else {
alpha.star.indx.fit <- alpha.star.indx
alpha.level.indx.fit <- alpha.level.indx
alpha.star.inits.fit <- alpha.star.inits
}
# Random Occurrence Effects
X.re.fit <- X.re[-curr.set, , drop = FALSE]
X.re.0 <- X.re[curr.set, , drop = FALSE]
n.occ.re.fit <- length(unique(c(X.re.fit)))
n.occ.re.long.fit <- apply(X.re.fit, 2, function(a) length(unique(a)))
if (p.occ.re > 0) {
beta.star.indx.fit <- rep(0:(p.occ.re - 1), n.occ.re.long.fit)
beta.level.indx.fit <- sort(unique(c(X.re.fit)))
beta.star.inits.fit <- rnorm(n.occ.re.fit, 0,
sqrt(sigma.sq.psi.inits[beta.star.indx.fit + 1]))
re.level.names.fit <- list()
for (t in 1:p.occ.re) {
tmp.indx <- beta.level.indx.fit[beta.star.indx.fit == t - 1]
re.level.names.fit[[t]] <- unlist(re.level.names)[tmp.indx + 1]
}
} else {
beta.star.indx.fit <- beta.star.indx
beta.level.indx.fit <- beta.level.indx
beta.star.inits.fit <- beta.star.inits
re.level.names.fit <- re.level.names
}
if (!binom) {
z.long.indx.fit <- rep(1:J.fit, dim(y.big.fit)[2])
z.long.indx.fit <- z.long.indx.fit[!is.na(c(y.big.fit))]
# Subtract 1 for indices in C
z.long.indx.fit <- z.long.indx.fit - 1
z.0.long.indx <- rep(1:J.0, dim(y.big.0)[2])
z.0.long.indx <- z.0.long.indx[!is.na(c(y.big.0))]
# Don't subtract 1 for z.0.long.indx since its used in R only
} else {
z.long.indx.fit <- 0:(J.fit - 1)
z.0.long.indx <- 1:J.0
}
# Nearest Neighbor Search ---
verbose.fit <- FALSE
n.omp.threads.fit <- 1
## Indexes
if(search.type == "brute"){
indx <- mkNNIndx(coords.fit, n.neighbors, n.omp.threads.fit)
} else{
indx <- mkNNIndxCB(coords.fit, n.neighbors, n.omp.threads.fit)
}
nn.indx.fit <- indx$nnIndx
nn.indx.lu.fit <- indx$nnIndxLU
indx <- mkUIndx(J.w.fit, n.neighbors, nn.indx.fit,
nn.indx.lu.fit, u.search.type)
u.indx.fit <- indx$u.indx
u.indx.lu.fit <- indx$u.indx.lu
ui.indx.fit <- indx$ui.indx
storage.mode(y.fit) <- "double"
storage.mode(z.inits.fit) <- "double"
storage.mode(X.p.fit) <- "double"
storage.mode(X.fit) <- "double"
storage.mode(coords.fit) <- "double"
storage.mode(K.fit) <- "double"
consts.fit <- c(J.fit, n.obs.fit, p.occ, p.occ.re, n.occ.re.fit,
p.det, p.det.re, n.det.re.fit, J.w.fit)
storage.mode(consts.fit) <- "integer"
storage.mode(z.long.indx.fit) <- "integer"
storage.mode(nn.indx.fit) <- "integer"
storage.mode(nn.indx.lu.fit) <- "integer"
storage.mode(u.indx.fit) <- "integer"
storage.mode(u.indx.lu.fit) <- "integer"
storage.mode(ui.indx.fit) <- "integer"
storage.mode(n.samples) <- "integer"
storage.mode(n.omp.threads.fit) <- "integer"
storage.mode(verbose.fit) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(X.p.re.fit) <- "integer"
storage.mode(n.det.re.long.fit) <- "integer"
storage.mode(alpha.star.inits.fit) <- "double"
storage.mode(alpha.star.indx.fit) <- "integer"
storage.mode(alpha.level.indx.fit) <- "integer"
storage.mode(X.re.fit) <- "integer"
storage.mode(n.occ.re.long.fit) <- "integer"
storage.mode(beta.star.inits.fit) <- "double"
storage.mode(beta.star.indx.fit) <- "integer"
storage.mode(beta.level.indx.fit) <- "integer"
chain.info[1] <- 1
storage.mode(chain.info) <- "integer"
storage.mode(grid.fit.c) <- 'integer'
# Run the model in C
out.fit <- .Call("spPGOccNNGP", y.fit, X.fit, X.p.fit, coords.fit, X.re.fit, X.p.re.fit,
consts.fit, K.fit, n.occ.re.long.fit, n.det.re.long.fit,
n.neighbors, nn.indx.fit, nn.indx.lu.fit, u.indx.fit,
u.indx.lu.fit, ui.indx.fit, beta.inits,
alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits, beta.star.inits.fit,
alpha.star.inits.fit, z.inits.fit, w.inits, phi.inits, sigma.sq.inits,
nu.inits, z.long.indx.fit, beta.star.indx.fit,
beta.level.indx.fit, alpha.star.indx.fit, alpha.level.indx.fit, mu.beta,
mu.alpha, Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b, sigma.sq.psi.a, sigma.sq.psi.b,
sigma.sq.p.a, sigma.sq.p.b, tuning.c, cov.model.indx,
n.batch, batch.length, accept.rate, n.omp.threads.fit, verbose.fit,
n.report, samples.info, chain.info, fixed.params, sigma.sq.ig, grid.fit.c)
out.fit$beta.samples <- mcmc(t(out.fit$beta.samples))
colnames(out.fit$beta.samples) <- x.names
out.fit$alpha.samples <- mcmc(t(out.fit$alpha.samples))
colnames(out.fit$alpha.samples) <- x.p.names
out.fit$theta.samples <- mcmc(t(out.fit$theta.samples))
if (cov.model != 'matern') {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out.fit$w.samples <- mcmc(t(out.fit$w.samples))
out.fit$X <- X.fit
out.fit$y <- y.big.fit
out.fit$X.p <- X.p.fit
out.fit$call <- cl
out.fit$type <- "NNGP"
out.fit$n.neighbors <- n.neighbors
out.fit$n.samples <- n.samples
out.fit$coords <- coords.fit
out.fit$cov.model.indx <- cov.model.indx
out.fit$n.post <- n.post.samples
out.fit$n.thin <- n.thin
out.fit$n.burn <- n.burn
out.fit$n.chains <- 1
if (p.det.re > 0) {
out.fit$pRE <- TRUE
} else {
out.fit$pRE <- FALSE
}
if (p.occ.re > 0) {
out.fit$sigma.sq.psi.samples <- mcmc(t(out.fit$sigma.sq.psi.samples))
colnames(out.fit$sigma.sq.psi.samples) <- x.re.names
out.fit$beta.star.samples <- mcmc(t(out.fit$beta.star.samples))
tmp.names <- unlist(re.level.names.fit)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$beta.star.samples) <- beta.star.names
out.fit$re.level.names <- re.level.names.fit
out.fit$X.re <- X.re.fit
}
if (p.det.re > 0) {
out.fit$sigma.sq.p.samples <- mcmc(t(out.fit$sigma.sq.p.samples))
colnames(out.fit$sigma.sq.p.samples) <- x.p.re.names
out.fit$alpha.star.samples <- mcmc(t(out.fit$alpha.star.samples))
tmp.names <- unlist(p.re.level.names.fit)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$alpha.star.samples) <- alpha.star.names
out.fit$p.re.level.names <- p.re.level.names.fit
out.fit$X.p.re <- X.p.re.fit
}
if (p.occ.re > 0) {
out.fit$psiRE <- TRUE
} else {
out.fit$psiRE <- FALSE
}
class(out.fit) <- "spPGOcc"
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.occ.re > 0) {
tmp <- unlist(re.level.names)
X.re.0 <- matrix(tmp[c(X.re.0 + 1)], nrow(X.re.0), ncol(X.re.0))
colnames(X.re.0) <- x.re.names
}
# Predict occurrence at new sites
if (p.occ.re > 0) {X.0 <- cbind(X.0, X.re.0)}
out.pred <- predict.spPGOcc(out.fit, X.0, coords.0, verbose = FALSE, grid.index.0 = grid.0)
# Detection
# Generate detection values
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.det.re > 0) {
tmp <- unlist(p.re.level.names)
X.p.re.0 <- matrix(tmp[c(X.p.re.0 + 1)], nrow(X.p.re.0), ncol(X.p.re.0))
colnames(X.p.re.0) <- x.p.re.names
}
if (p.det.re > 0) {X.p.0 <- cbind(X.p.0, X.p.re.0)}
out.p.pred <- predict.spPGOcc(out.fit, X.p.0, type = 'detection')
if (binom) {
like.samples <- matrix(NA, nrow(y.big.0), ncol(y.big.0))
for (j in 1:nrow(X.p.0)) {
for (k in rep.indx.0[[j]]) {
like.samples[j, k] <- mean(dbinom(y.big.0[j, k], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
} else {
like.samples <- rep(NA, nrow(X.p.0))
for (j in 1:nrow(X.p.0)) {
like.samples[j] <- mean(dbinom(y.0[j], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
sum(log(like.samples), na.rm = TRUE)
}
model.deviance <- -2 * model.deviance
# Return objects from cross-validation
out$k.fold.deviance <- model.deviance
} # cross-validation
} # NNGP or GP
class(out) <- "spPGOcc"
out$run.time <- proc.time() - ptm
return(out)
}
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spOccupancy documentation built on April 3, 2025, 10:03 p.m.
R Package Documentation
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Defines functions spPGOcc
Documented in spPGOcc
spPGOcc <- function(occ.formula, det.formula, data, inits, priors,
tuning, cov.model = 'exponential', NNGP = TRUE,
n.neighbors = 15, search.type = 'cb', n.batch,
batch.length, accept.rate = 0.43,
n.omp.threads = 1, verbose = TRUE, n.report = 100,
n.burn = round(.10 * n.batch * batch.length),
n.thin = 1, n.chains = 1,
k.fold, k.fold.threads = 1, k.fold.seed = 100,
k.fold.only = FALSE, ...){
ptm <- proc.time()
# Make it look nice
if (verbose) {
cat("----------------------------------------\n");
cat("\tPreparing to run the model\n");
cat("----------------------------------------\n");
}
# Functions ---------------------------------------------------------------
logit <- function(theta, a = 0, b = 1) {log((theta-a)/(b-theta))}
logit.inv <- function(z, a = 0, b = 1) {b-(b-a)/(1+exp(z))}
rigamma <- function(n, a, b){
1/rgamma(n = n, shape = a, rate = b)
}
# Check for unused arguments ------------------------------------------
formal.args <- names(formals(sys.function(sys.parent())))
elip.args <- names(list(...))
for(i in elip.args){
if(! i %in% formal.args)
warning("'",i, "' is not an argument")
}
# Call ----------------------------------------------------------------
# Returns a call in which all of the specified arguments are
# specified by their full names.
cl <- match.call()
# Some initial checks -------------------------------------------------
if (missing(data)) {
stop("error: data must be specified")
}
if (!is.list(data)) {
stop("error: data must be a list")
}
names(data) <- tolower(names(data))
if (missing(occ.formula)) {
stop("error: occ.formula must be specified")
}
if (missing(det.formula)) {
stop("error: det.formula must be specified")
}
if (!'y' %in% names(data)) {
stop("error: detection-nondetection data y must be specified in data")
}
y <- as.matrix(data$y)
if (!'occ.covs' %in% names(data)) {
if (occ.formula == ~ 1) {
if (verbose) {
message("occupancy covariates (occ.covs) not specified in data.\nAssuming intercept only occupancy model.\n")
}
data$occ.covs <- matrix(1, dim(y)[1], 1)
} else {
stop("error: occ.covs must be specified in data for an occupancy model with covariates")
}
}
if (!'det.covs' %in% names(data)) {
if (det.formula == ~ 1) {
if (verbose) {
message("detection covariates (det.covs) not specified in data.\nAssuming interept only detection model.\n")
}
data$det.covs <- list(int = matrix(1, dim(y)[1], dim(y)[2]))
} else {
stop("error: det.covs must be specified in data for a detection model with covariates")
}
}
if (!is.matrix(data$occ.covs) & !is.data.frame(data$occ.covs)) {
stop("error: occ.covs must be a matrix or data frame")
}
if (!'coords' %in% names(data)) {
stop("error: coords must be specified in data for a spatial occupancy model.")
}
if (!is.matrix(data$coords) & !is.data.frame(data$coords)) {
stop("error: coords must be a matrix or data frame")
}
coords <- as.matrix(data$coords)
if (!is.list(data$det.covs)) {
stop("error: det.covs must be a list of matrices, data frames, and/or vectors")
}
# Check grid index
if (!'grid.index' %in% names(data)) {
if (nrow(data$coords) != nrow(data$y)) {
stop("data$grid.index must be specified if nrow(data$coords) != nrow(data$y)")
}
grid.index <- 1:nrow(coords)
} else {
if (!is.atomic(data$grid.index) | !is.numeric(data$grid.index)) {
stop("data$grid.index must be a numeric vector")
}
if (length(data$grid.index) < nrow(data$coords)) {
stop("length(data$grid.index) must be greater than or equal to nrow(data$coords)")
}
grid.index <- data$grid.index
}
# Check if all spatial coordinates are unique.
unique.coords <- unique(data$coords)
if (nrow(unique.coords) < nrow(data$coords)) {
stop("coordinates provided in coords are not all unique. spOccupancy requires each site to have its own unique pair of spatial coordinates. This may be the result of an error in preparing the data list, or you will need to change what you consider a 'site' in order to meet this requirement. Alternatively, you can use data$grid.index to specify the spatial random effect at a larger spatial level than the individual sites (e.g., a grid). See ?spPGOcc for details.")
}
if (!missing(k.fold)) {
if (!is.numeric(k.fold) | length(k.fold) != 1 | k.fold < 2) {
stop("error: k.fold must be a single integer value >= 2")
}
}
# Neighbors and Ordering ----------------------------------------------
if (NNGP) {
u.search.type <- 2
## Order by x column. Could potentially allow this to be user defined.
ord <- order(coords[,1])
tmp <- lapply(ord, function (a) which(grid.index == a))
tmp.2 <- sapply(tmp, length)
grid.index.c <- unlist(lapply(1:length(tmp.2), function(a) rep(a, tmp.2[a]))) - 1
grid.index.r <- grid.index.c + 1
long.ord <- unlist(lapply(ord, function(a) which(grid.index == a)))
# Reorder everything to align with NN ordering
y <- y[long.ord, , drop = FALSE]
coords <- coords[ord, , drop = FALSE]
# Occupancy covariates
data$occ.covs <- data$occ.covs[long.ord, , drop = FALSE]
for (i in 1:length(data$det.covs)) {
if (!is.null(dim(data$det.covs[[i]]))) {
data$det.covs[[i]] <- data$det.covs[[i]][long.ord, , drop = FALSE]
} else {
data$det.covs[[i]] <- data$det.covs[[i]][long.ord]
}
}
}
# First subset detection covariates to only use those that are included in the analysis.
data$det.covs <- data$det.covs[names(data$det.covs) %in% all.vars(det.formula)]
# Null model support
if (length(data$det.covs) == 0) {
data$det.covs <- list(int = rep(1, dim(y)[1]))
}
# Make both covariates a data frame. Unlist is necessary for when factors
# are supplied.
data$det.covs <- data.frame(lapply(data$det.covs, function(a) unlist(c(a))))
binom <- FALSE
# Check if all detection covariates are at site level, and simplify the data
# if necessary
y.big <- y
if (nrow(data$det.covs) == nrow(y)) {
# Convert data to binomial form
y <- apply(y, 1, sum, na.rm = TRUE)
binom <- TRUE
}
data$occ.covs <- as.data.frame(data$occ.covs)
# Checking missing values ---------------------------------------------
# y -------------------------------
y.na.test <- apply(y.big, 1, function(a) sum(!is.na(a)))
if (sum(y.na.test == 0) > 0) {
stop("error: some sites in y have all missing detection histories. Remove these sites from all objects in the 'data' argument, then use 'predict' to obtain predictions at these locations if desired.")
}
# occ.covs ------------------------
if (sum(is.na(data$occ.covs)) != 0) {
stop("error: missing values in occ.covs. Please remove these sites from all objects in data or somehow replace the NA values with non-missing values (e.g., mean imputation).")
}
# det.covs ------------------------
if (!binom) {
for (i in 1:ncol(data$det.covs)) {
if (sum(is.na(data$det.covs[, i])) > sum(is.na(y.big))) {
stop("error: some elements in det.covs have missing values where there is an observed data value in y. Please either replace the NA values in det.covs with non-missing values (e.g., mean imputation) or set the corresponding values in y to NA where the covariate is missing.")
}
}
# Misalignment between y and det.covs
y.missing <- which(is.na(y))
det.covs.missing <- lapply(data$det.covs, function(a) which(is.na(a)))
for (i in 1:length(det.covs.missing)) {
tmp.indx <- !(y.missing %in% det.covs.missing[[i]])
if (sum(tmp.indx) > 0) {
if (i == 1 & verbose) {
message("There are missing values in data$y with corresponding non-missing values in data$det.covs.\nRemoving these site/replicate combinations for fitting the model.")
}
data$det.covs[y.missing, i] <- NA
}
}
}
# det.covs when binom == TRUE -----
if (binom) {
if (sum(is.na(data$det.covs)) != 0) {
stop("error: missing values in site-level det.covs. Please remove these sites from all objects in data or somehow replace the NA values with non-missing values (e.g., mean imputation).")
}
}
# Check whether random effects are sent in as numeric, and
# return error if they are.
# Occurrence ----------------------
if (!is.null(findbars(occ.formula))) {
occ.re.names <- sapply(findbars(occ.formula), all.vars)
for (i in 1:length(occ.re.names)) {
if (is(data$occ.covs[, occ.re.names[i]], 'factor')) {
stop(paste("error: random effect variable ", occ.re.names[i], " specified as a factor. Random effect variables must be specified as numeric.", sep = ''))
}
if (is(data$occ.covs[, occ.re.names[i]], 'character')) {
stop(paste("error: random effect variable ", occ.re.names[i], " specified as character. Random effect variables must be specified as numeric.", sep = ''))
}
}
}
# Detection -----------------------
if (!is.null(findbars(det.formula))) {
det.re.names <- sapply(findbars(det.formula), all.vars)
for (i in 1:length(det.re.names)) {
if (is(data$det.covs[, det.re.names[i]], 'factor')) {
stop(paste("error: random effect variable ", det.re.names[i], " specified as a factor. Random effect variables must be specified as numeric.", sep = ''))
}
if (is(data$det.covs[, det.re.names[i]], 'character')) {
stop(paste("error: random effect variable ", det.re.names[i], " specified as character. Random effect variables must be specified as numeric.", sep = ''))
}
}
}
# Formula -------------------------------------------------------------
# Occupancy -----------------------
if (is(occ.formula, 'formula')) {
tmp <- parseFormula(occ.formula, data$occ.covs)
X <- as.matrix(tmp[[1]])
X.re <- as.matrix(tmp[[4]])
x.re.names <- colnames(X.re)
x.names <- tmp[[2]]
} else {
stop("error: occ.formula is misspecified")
}
# Get RE level names
re.level.names <- lapply(data$occ.covs[, x.re.names, drop = FALSE],
function (a) sort(unique(a)))
# Detection -----------------------
if (is(det.formula, 'formula')) {
tmp <- parseFormula(det.formula, data$det.covs)
X.p <- as.matrix(tmp[[1]])
x.p.names <- tmp[[2]]
X.p.re <- as.matrix(tmp[[4]])
x.p.re.names <- colnames(X.p.re)
} else {
stop("error: det.formula is misspecified")
}
p.re.level.names <- lapply(data$det.covs[, x.p.re.names, drop = FALSE],
function (a) sort(unique(a)))
# Get basic info from inputs ------------------------------------------
# Number of sites
J <- nrow(y.big)
# Number of coordinates in coordinate grid
J.w <- nrow(coords)
# Number of occupancy parameters
p.occ <- ncol(X)
# Number of detection parameters
p.det <- dim(X.p)[2]
# Number of occurrence random effect parameters
p.occ.re <- ncol(X.re)
# Number of detection random effect parameters
p.det.re <- ncol(X.p.re)
# Number of latent occupancy random effect values
n.occ.re <- length(unlist(apply(X.re, 2, unique)))
n.occ.re.long <- apply(X.re, 2, function(a) length(unique(a)))
# Number of latent detection random effect values
n.det.re <- length(unlist(apply(X.p.re, 2, unique)))
n.det.re.long <- apply(X.p.re, 2, function(a) length(unique(a)))
if (p.det.re == 0) n.det.re.long <- 0
# Number of replicates at each site
n.rep <- apply(y.big, 1, function(a) sum(!is.na(a)))
# Max number of repeat visits
K.max <- dim(y.big)[2]
rep.indx <- list()
for (j in 1:J) {
rep.indx[[j]] <- which(!is.na(y.big[j, ]))
}
# Because I like K better than n.rep
K <- n.rep
if (missing(n.batch)) {
stop("error: must specify number of MCMC batches")
}
if (missing(batch.length)) {
stop("error: must specify length of each MCMC batch")
}
n.samples <- n.batch * batch.length
if (n.burn > n.samples) {
stop("error: n.burn must be less than n.samples")
}
if (n.thin > n.samples) {
stop("error: n.thin must be less than n.samples")
}
# Check if n.burn, n.thin, and n.samples result in an integer and error if otherwise.
if (((n.samples - n.burn) / n.thin) %% 1 != 0) {
stop("the number of posterior samples to save ((n.samples - n.burn) / n.thin) is not a whole number. Please respecify the MCMC criteria such that the number of posterior samples saved is a whole number.")
}
# Get indices to map z to y -------------------------------------------
if (!binom) {
z.long.indx <- rep(1:J, dim(y.big)[2])
z.long.indx <- z.long.indx[!is.na(c(y.big))]
# Subtract 1 for indices in C
z.long.indx <- z.long.indx - 1
} else {
z.long.indx <- 0:(J - 1)
}
# y is stored in the following order: species, site, visit
y <- c(y)
names.long <- which(!is.na(y))
# Remove missing observations when the covariate data are available but
# there are missing detection-nondetection data.
if (nrow(X.p) == length(y)) {
X.p <- X.p[!is.na(y), , drop = FALSE]
}
if (nrow(X.p.re) == length(y) & p.det.re > 0) {
X.p.re <- X.p.re[!is.na(y), , drop = FALSE]
}
y <- y[!is.na(y)]
# Number of pseudoreplicates
n.obs <- nrow(X.p)
# Get random effect matrices all set ----------------------------------
if (p.occ.re > 1) {
for (j in 2:p.occ.re) {
X.re[, j] <- X.re[, j] + max(X.re[, j - 1]) + 1
}
}
if (p.det.re > 1) {
for (j in 2:p.det.re) {
X.p.re[, j] <- X.p.re[, j] + max(X.p.re[, j - 1]) + 1
}
}
# Priors --------------------------------------------------------------
if (missing(priors)) {
priors <- list()
}
names(priors) <- tolower(names(priors))
# Logical vector indicating what parameters are estimated and what
# parameters are fixed. 6 is the total number of parameter types that
# can be estimated here. Note that phi and nu are both fixed if phi.unif = 'fixed'
all.params <- c('beta', 'alpha', 'phi', 'sigma.sq',
'sigma.sq.psi', 'sigma.sq.p')
n.params <- length(all.params)
fixed.params <- rep(FALSE, n.params)
# beta -----------------------
if ("beta.normal" %in% names(priors)) {
if (priors$beta.normal[1] == 'fixed') {
fixed.params[which(all.params == 'beta')] <- TRUE
mu.beta <- rep(0, p.occ)
Sigma.beta <- diag(p.occ)
} else {
if (!is.list(priors$beta.normal) | length(priors$beta.normal) != 2) {
stop("error: beta.normal must be a list of length 2")
}
mu.beta <- priors$beta.normal[[1]]
sigma.beta <- priors$beta.normal[[2]]
if (length(mu.beta) != p.occ & length(mu.beta) != 1) {
if (p.occ == 1) {
stop(paste("error: beta.normal[[1]] must be a vector of length ",
p.occ, " with elements corresponding to betas' mean", sep = ""))
} else {
stop(paste("error: beta.normal[[1]] must be a vector of length ",
p.occ, " or 1 with elements corresponding to betas' mean", sep = ""))
}
}
if (length(sigma.beta) != p.occ & length(sigma.beta) != 1) {
if (p.occ == 1) {
stop(paste("error: beta.normal[[2]] must be a vector of length ",
p.occ, " with elements corresponding to betas' variance", sep = ""))
} else {
stop(paste("error: beta.normal[[2]] must be a vector of length ",
p.occ, " or 1 with elements corresponding to betas' variance", sep = ""))
}
}
if (length(sigma.beta) != p.occ) {
sigma.beta <- rep(sigma.beta, p.occ)
}
if (length(mu.beta) != p.occ) {
mu.beta <- rep(mu.beta, p.occ)
}
Sigma.beta <- sigma.beta * diag(p.occ)
}
} else {
if (verbose) {
message("No prior specified for beta.normal.\nSetting prior mean to 0 and prior variance to 2.72\n")
}
mu.beta <- rep(0, p.occ)
sigma.beta <- rep(2.72, p.occ)
Sigma.beta <- diag(p.occ) * 2.72
}
# alpha -----------------------
if ("alpha.normal" %in% names(priors)) {
if (priors$alpha.normal[1] == 'fixed') {
fixed.params[which(all.params == 'alpha')] <- TRUE
mu.alpha <- rep(0, p.det)
Sigma.alpha <- diag(p.det)
} else {
if (!is.list(priors$alpha.normal) | length(priors$alpha.normal) != 2) {
stop("error: alpha.normal must be a list of length 2")
}
mu.alpha <- priors$alpha.normal[[1]]
sigma.alpha <- priors$alpha.normal[[2]]
if (length(mu.alpha) != p.det & length(mu.alpha) != 1) {
if (p.det == 1) {
stop(paste("error: alpha.normal[[1]] must be a vector of length ",
p.det, " with elements corresponding to alphas' mean", sep = ""))
} else {
stop(paste("error: alpha.normal[[1]] must be a vector of length ",
p.det, " or 1 with elements corresponding to alphas' mean", sep = ""))
}
}
if (length(sigma.alpha) != p.det & length(sigma.alpha) != 1) {
if (p.det == 1) {
stop(paste("error: alpha.normal[[2]] must be a vector of length ",
p.det, " with elements corresponding to alphas' variance", sep = ""))
} else {
stop(paste("error: alpha.normal[[2]] must be a vector of length ",
p.det, " or 1 with elements corresponding to alphas' variance", sep = ""))
}
}
if (length(sigma.alpha) != p.det) {
sigma.alpha <- rep(sigma.alpha, p.det)
}
if (length(mu.alpha) != p.det) {
mu.alpha <- rep(mu.alpha, p.det)
}
Sigma.alpha <- sigma.alpha * diag(p.det)
}
} else {
if (verbose) {
message("No prior specified for alpha.normal.\nSetting prior mean to 0 and prior variance to 2.72\n")
}
mu.alpha <- rep(0, p.det)
sigma.alpha <- rep(2.72, p.det)
Sigma.alpha <- diag(p.det) * 2.72
}
# phi -----------------------------
# Get distance matrix which is used if priors are not specified
if (!NNGP) {
coords.D <- iDist(coords)
}
if ("phi.unif" %in% names(priors)) {
if (priors$phi.unif[1] == 'fixed') {
fixed.params[which(all.params == 'phi')] <- TRUE
phi.a <- 1
phi.b <- 1
if ((cov.model == 'matern') & (priors$nu.unif[1] != 'fixed')) {
message("phi is specified as fixed in priors$phi.unif but nu is not, which is not allowed. Continuing to fit the model with both phi and nu fixed.")
}
} else {
if (!is.vector(priors$phi.unif) | !is.atomic(priors$phi.unif) | length(priors$phi.unif) != 2) {
stop("error: phi.unif must be a vector of length 2 with elements corresponding to phi's lower and upper bounds")
}
phi.a <- priors$phi.unif[1]
phi.b <- priors$phi.unif[2]
}
} else {
if (verbose) {
message("No prior specified for phi.unif.\nSetting uniform bounds based on the range of observed spatial coordinates.\n")
}
if (NNGP) {
coords.D <- iDist(coords)
}
phi.a <- 3 / max(coords.D)
phi.b <- 3 / sort(unique(c(coords.D)))[2]
}
# sigma.sq -----------------------------
# Check if both an ig and uniform prior are specified
if (("sigma.sq.ig" %in% names(priors)) & ("sigma.sq.unif" %in% names(priors))) {
stop("error: cannot specify both an IG and a uniform prior for sigma.sq")
}
if ("sigma.sq.ig" %in% names(priors)) { # inverse-gamma prior.
sigma.sq.ig <- TRUE
if (priors$sigma.sq.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq')] <- TRUE
sigma.sq.a <- 1
sigma.sq.b <- 1
} else {
if (!is.vector(priors$sigma.sq.ig) | !is.atomic(priors$sigma.sq.ig) | length(priors$sigma.sq.ig) != 2) {
stop("error: sigma.sq.ig must be a vector of length 2 with elements corresponding to sigma.sq's shape and scale parameters")
}
sigma.sq.a <- priors$sigma.sq.ig[1]
sigma.sq.b <- priors$sigma.sq.ig[2]
}
} else if ("sigma.sq.unif" %in% names(priors)) { # uniform prior
if (priors$sigma.sq.unif[1] == 'fixed') {
sigma.sq.ig <- TRUE # This just makes the C++ side a bit easier.
fixed.params[which(all.params == 'sigma.sq')] <- TRUE
sigma.sq.a <- 1
sigma.sq.b <- 1
} else {
sigma.sq.ig <- FALSE
if (!is.vector(priors$sigma.sq.unif) | !is.atomic(priors$sigma.sq.unif) | length(priors$sigma.sq.unif) != 2) {
stop("error: sigma.sq.unif must be a vector of length 2 with elements corresponding to sigma.sq's lower and upper bounds")
}
sigma.sq.a <- priors$sigma.sq.unif[1]
sigma.sq.b <- priors$sigma.sq.unif[2]
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.\nUsing an inverse-Gamma prior with the shape parameter set to 2 and scale parameter to 1.\n")
}
sigma.sq.ig <- TRUE
sigma.sq.a <- 2
sigma.sq.b <- 1
}
# nu -----------------------------
if (cov.model == 'matern') {
if (!"nu.unif" %in% names(priors)) {
stop("error: nu.unif must be specified in priors value list")
}
if (priors$nu.unif[1] == 'fixed') {
fixed.params[which(all.params == 'phi')] <- TRUE
nu.a <- 1
nu.b <- 1
if ((cov.model == 'matern') & (priors$phi.unif[1] != 'fixed')) {
message("nu is specified as fixed in priors$nu.unif but phi is not, which is not allowed. Continuing to fit the model with both phi and nu fixed.")
}
} else {
if (!is.vector(priors$nu.unif) | !is.atomic(priors$nu.unif) | length(priors$nu.unif) != 2) {
stop("error: nu.unif must be a vector of length 2 with elements corresponding to nu's lower and upper bounds")
}
nu.a <- priors$nu.unif[1]
nu.b <- priors$nu.unif[2]
}
} else {
nu.a <- 0
nu.b <- 0
}
# sigma.sq.psi --------------------
if (p.occ.re > 0) {
if ("sigma.sq.psi.ig" %in% names(priors)) {
if (priors$sigma.sq.psi.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq.psi')] <- TRUE
sigma.sq.psi.a <- rep(1, p.occ.re)
sigma.sq.psi.b <- rep(1, p.occ.re)
} else {
if (!is.list(priors$sigma.sq.psi.ig) | length(priors$sigma.sq.psi.ig) != 2) {
stop("error: sigma.sq.psi.ig must be a list of length 2")
}
sigma.sq.psi.a <- priors$sigma.sq.psi.ig[[1]]
sigma.sq.psi.b <- priors$sigma.sq.psi.ig[[2]]
if (length(sigma.sq.psi.a) != p.occ.re & length(sigma.sq.psi.a) != 1) {
if (p.occ.re == 1) {
stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ",
p.occ.re, " with elements corresponding to sigma.sq.psis' shape", sep = ""))
} else {
stop(paste("error: sigma.sq.psi.ig[[1]] must be a vector of length ",
p.occ.re, " or 1 with elements corresponding to sigma.sq.psis' shape", sep = ""))
}
}
if (length(sigma.sq.psi.b) != p.occ.re & length(sigma.sq.psi.b) != 1) {
if (p.occ.re == 1) {
stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ",
p.occ.re, " with elements corresponding to sigma.sq.psis' scale", sep = ""))
} else {
stop(paste("error: sigma.sq.psi.ig[[2]] must be a vector of length ",
p.occ.re, " or 1with elements corresponding to sigma.sq.psis' scale", sep = ""))
}
}
if (length(sigma.sq.psi.a) != p.occ.re) {
sigma.sq.psi.a <- rep(sigma.sq.psi.a, p.occ.re)
}
if (length(sigma.sq.psi.b) != p.occ.re) {
sigma.sq.psi.b <- rep(sigma.sq.psi.b, p.occ.re)
}
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.psi.ig.\nSetting prior shape to 0.1 and prior scale to 0.1\n")
}
sigma.sq.psi.a <- rep(0.1, p.occ.re)
sigma.sq.psi.b <- rep(0.1, p.occ.re)
}
} else {
sigma.sq.psi.a <- 0
sigma.sq.psi.b <- 0
}
# sigma.sq.p --------------------
if (p.det.re > 0) {
if ("sigma.sq.p.ig" %in% names(priors)) {
if (priors$sigma.sq.p.ig[1] == 'fixed') {
fixed.params[which(all.params == 'sigma.sq.p')] <- TRUE
sigma.sq.p.a <- rep(1, p.det.re)
sigma.sq.p.b <- rep(1, p.det.re)
} else {
if (!is.list(priors$sigma.sq.p.ig) | length(priors$sigma.sq.p.ig) != 2) {
stop("error: sigma.sq.p.ig must be a list of length 2")
}
sigma.sq.p.a <- priors$sigma.sq.p.ig[[1]]
sigma.sq.p.b <- priors$sigma.sq.p.ig[[2]]
if (length(sigma.sq.p.a) != p.det.re & length(sigma.sq.p.a) != 1) {
if (p.det.re == 1) {
stop(paste("error: sigma.sq.p.ig[[1]] must be a vector of length ",
p.det.re, " with elements corresponding to sigma.sq.ps' shape", sep = ""))
} else {
stop(paste("error: sigma.sq.p.ig[[1]] must be a vector of length ",
p.det.re, " or 1 with elements corresponding to sigma.sq.ps' shape", sep = ""))
}
}
if (length(sigma.sq.p.b) != p.det.re & length(sigma.sq.p.b) != 1) {
if (p.det.re == 1) {
stop(paste("error: sigma.sq.p.ig[[2]] must be a vector of length ",
p.det.re, " with elements corresponding to sigma.sq.ps' scale", sep = ""))
} else {
stop(paste("error: sigma.sq.p.ig[[2]] must be a vector of length ",
p.det.re, " or 1 with elements corresponding to sigma.sq.ps' scale", sep = ""))
}
}
if (length(sigma.sq.p.a) != p.det.re) {
sigma.sq.p.a <- rep(sigma.sq.p.a, p.det.re)
}
if (length(sigma.sq.p.b) != p.det.re) {
sigma.sq.p.b <- rep(sigma.sq.p.b, p.det.re)
}
}
} else {
if (verbose) {
message("No prior specified for sigma.sq.p.ig.\nSetting prior shape to 0.1 and prior scale to 0.1\n")
}
sigma.sq.p.a <- rep(0.1, p.det.re)
sigma.sq.p.b <- rep(0.1, p.det.re)
}
} else {
sigma.sq.p.a <- 0
sigma.sq.p.b <- 0
}
# Starting values -----------------------------------------------------
if (missing(inits)) {
inits <- list()
}
names(inits) <- tolower(names(inits))
# z -------------------------------
if ("z" %in% names(inits)) {
z.inits <- inits$z
if (!is.vector(z.inits)) {
stop(paste("error: initial values for z must be a vector of length ",
J, sep = ""))
}
if (length(z.inits) != J) {
stop(paste("error: initial values for z must be a vector of length ",
J, sep = ""))
}
# Reorder the user supplied inits values
if (NNGP) {
z.inits <- z.inits[long.ord]
}
z.test <- apply(y.big, 1, max, na.rm = TRUE)
init.test <- sum(z.inits < z.test)
if (init.test > 0) {
stop("error: initial values for latent occurrence (z) are invalid. Please re-specify inits$z so initial values are 1 if the species is observed at that site.")
}
} else {
# In correct order since you reordered y.
z.inits <- apply(y.big, 1, max, na.rm = TRUE)
if (verbose) {
message("z.inits is not specified in initial values.\nSetting initial values based on observed data\n")
}
}
# beta -----------------------
if ("beta" %in% names(inits)) {
beta.inits <- inits[["beta"]]
if (length(beta.inits) != p.occ & length(beta.inits) != 1) {
if (p.occ == 1) {
stop(paste("error: initial values for beta must be of length ", p.occ,
sep = ""))
} else {
stop(paste("error: initial values for beta must be of length ", p.occ, " or 1",
sep = ""))
}
}
if (length(beta.inits) != p.occ) {
beta.inits <- rep(beta.inits, p.occ)
}
} else {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
if (verbose) {
message('beta is not specified in initial values.\nSetting initial values to random values from the prior distribution\n')
}
}
# alpha -----------------------
if ("alpha" %in% names(inits)) {
alpha.inits <- inits[["alpha"]]
if (length(alpha.inits) != p.det & length(alpha.inits) != 1) {
if (p.det == 1) {
stop(paste("error: initial values for alpha must be of length ", p.det,
sep = ""))
} else {
stop(paste("error: initial values for alpha must be of length ", p.det, " or 1",
sep = ""))
}
}
if (length(alpha.inits) != p.det) {
alpha.inits <- rep(alpha.inits, p.det)
}
} else {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
if (verbose) {
message("alpha is not specified in initial values.\nSetting initial values to random values from the prior distribution\n")
}
}
# phi -----------------------------
if ("phi" %in% names(inits)) {
phi.inits <- inits[["phi"]]
if (length(phi.inits) != 1) {
stop("error: initial values for phi must be of length 1")
}
} else {
phi.inits <- runif(1, phi.a, phi.b)
if (verbose) {
message("phi is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
}
# sigma.sq ------------------------
if ("sigma.sq" %in% names(inits)) {
sigma.sq.inits <- inits[["sigma.sq"]]
if (length(sigma.sq.inits) != 1) {
stop("error: initial values for sigma.sq must be of length 1")
}
} else {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
if (verbose) {
message("sigma.sq is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
}
# w -----------------------------
if ("w" %in% names(inits)) {
w.inits <- inits[["w"]]
if (!is.vector(w.inits)) {
stop(paste("error: initial values for w must be a vector of length ",
J.w, sep = ""))
}
if (length(w.inits) != J.w) {
stop(paste("error: initial values for w must be a vector of length ",
J.w, sep = ""))
}
# Reorder the user supplied inits values
if (NNGP) {
w.inits <- w.inits[ord]
}
} else {
w.inits <- rep(0, J.w)
if (verbose) {
message("w is not specified in initial values.\nSetting initial value to 0\n")
}
}
# nu ------------------------
if ("nu" %in% names(inits)) {
nu.inits <- inits[["nu"]]
if (length(nu.inits) != 1) {
stop("error: initial values for nu must be of length 1")
}
} else {
if (cov.model == 'matern') {
if (verbose) {
message("nu is not specified in initial values.\nSetting initial value to random value from the prior distribution\n")
}
nu.inits <- runif(1, nu.a, nu.b)
} else {
nu.inits <- 0
}
}
# sigma.sq.psi -------------------
if (p.occ.re > 0) {
if ("sigma.sq.psi" %in% names(inits)) {
sigma.sq.psi.inits <- inits[["sigma.sq.psi"]]
if (length(sigma.sq.psi.inits) != p.occ.re & length(sigma.sq.psi.inits) != 1) {
if (p.occ.re == 1) {
stop(paste("error: initial values for sigma.sq.psi must be of length ", p.occ.re,
sep = ""))
} else {
stop(paste("error: initial values for sigma.sq.psi must be of length ", p.occ.re,
" or 1", sep = ""))
}
}
if (length(sigma.sq.psi.inits) != p.occ.re) {
sigma.sq.psi.inits <- rep(sigma.sq.psi.inits, p.occ.re)
}
} else {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
if (verbose) {
message("sigma.sq.psi is not specified in initial values.\nSetting initial values to random values between 0.5 and 10\n")
}
}
beta.star.indx <- rep(0:(p.occ.re - 1), n.occ.re.long)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
} else {
sigma.sq.psi.inits <- 0
beta.star.indx <- 0
beta.star.inits <- 0
}
# sigma.sq.p ------------------
if (p.det.re > 0) {
if ("sigma.sq.p" %in% names(inits)) {
sigma.sq.p.inits <- inits[["sigma.sq.p"]]
if (length(sigma.sq.p.inits) != p.det.re & length(sigma.sq.p.inits) != 1) {
if (p.det.re == 1) {
stop(paste("error: initial values for sigma.sq.p must be of length ", p.det.re,
sep = ""))
} else {
stop(paste("error: initial values for sigma.sq.p must be of length ", p.det.re,
" or 1", sep = ""))
}
}
if (length(sigma.sq.p.inits) != p.det.re) {
sigma.sq.p.inits <- rep(sigma.sq.p.inits, p.det.re)
}
} else {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
if (verbose) {
message("sigma.sq.p is not specified in initial values.\nSetting initial values to random values between 0.5 and 10\n")
}
}
alpha.star.indx <- rep(0:(p.det.re - 1), n.det.re.long)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
} else {
sigma.sq.p.inits <- 0
alpha.star.indx <- 0
alpha.star.inits <- 0
}
# Should initial values be fixed --
if ("fix" %in% names(inits)) {
fix.inits <- inits[["fix"]]
if ((fix.inits != TRUE) & (fix.inits != FALSE)) {
stop(paste("error: inits$fix must take value TRUE or FALSE"))
}
} else {
fix.inits <- FALSE
}
if (verbose & fix.inits & (n.chains > 1)) {
message("Fixing initial values across all chains\n")
}
# Covariance Model ----------------------------------------------------
# Order must match util.cpp spCor.
cov.model.names <- c("exponential", "spherical", "matern", "gaussian")
if(! cov.model %in% cov.model.names){
stop("error: specified cov.model '",cov.model,"' is not a valid option; choose from ",
paste(cov.model.names, collapse=", ", sep="") ,".")}
# Obo for cov model lookup on c side
cov.model.indx <- which(cov.model == cov.model.names) - 1
storage.mode(cov.model.indx) <- "integer"
# Get tuning values ---------------------------------------------------
sigma.sq.tuning <- 0
phi.tuning <- 0
nu.tuning <- 0
if (missing(tuning)) {
phi.tuning <- 1
if (cov.model == 'matern') {
nu.tuning <- 1
}
if (!sigma.sq.ig) {
sigma.sq.tuning <- 1
}
} else {
names(tuning) <- tolower(names(tuning))
# phi ---------------------------
if(!"phi" %in% names(tuning)) {
stop("error: phi must be specified in tuning value list")
}
phi.tuning <- tuning$phi
if (length(phi.tuning) != 1) {
stop("error: phi tuning must be a single value")
}
if (cov.model == 'matern') {
# nu --------------------------
if(!"nu" %in% names(tuning)) {
stop("error: nu must be specified in tuning value list")
}
nu.tuning <- tuning$nu
if (length(nu.tuning) != 1) {
stop("error: nu tuning must be a single value")
}
}
if (!sigma.sq.ig) {
# sigma.sq --------------------------
if(!"sigma.sq" %in% names(tuning)) {
stop("error: sigma.sq must be specified in tuning value list")
}
sigma.sq.tuning <- tuning$sigma.sq
if (length(sigma.sq.tuning) != 1) {
stop("error: sigma.sq tuning must be a single value")
}
}
}
# Log the tuning values since they are used in the AMCMC.
tuning.c <- log(c(sigma.sq.tuning, phi.tuning, nu.tuning))
# Set model.deviance to NA for returning when no cross-validation
model.deviance <- NA
curr.chain <- 1
if (!NNGP) {
if (J.w != J) {
stop("data$grid.index is not currently supported when NNGP = FALSE. If specifying the spatial random effect across a grid that is different from the individual site locations, please set NNGP = TRUE.")
}
# Set storage for all variables ---------------------------------------
storage.mode(y) <- "double"
storage.mode(z.inits) <- "double"
storage.mode(X.p) <- "double"
storage.mode(X) <- "double"
consts <- c(J, n.obs, p.occ, p.occ.re, n.occ.re, p.det, p.det.re, n.det.re)
storage.mode(consts) <- "integer"
storage.mode(K) <- "double"
storage.mode(coords.D) <- "double"
storage.mode(beta.inits) <- "double"
storage.mode(alpha.inits) <- "double"
storage.mode(phi.inits) <- "double"
storage.mode(sigma.sq.inits) <- "double"
storage.mode(nu.inits) <- "double"
storage.mode(w.inits) <- "double"
storage.mode(z.long.indx) <- "integer"
storage.mode(mu.beta) <- "double"
storage.mode(Sigma.beta) <- "double"
storage.mode(mu.alpha) <- "double"
storage.mode(Sigma.alpha) <- "double"
storage.mode(phi.a) <- "double"
storage.mode(phi.b) <- "double"
storage.mode(nu.a) <- "double"
storage.mode(nu.b) <- "double"
storage.mode(sigma.sq.a) <- "double"
storage.mode(sigma.sq.b) <- "double"
storage.mode(sigma.sq.ig) <- "integer"
storage.mode(tuning.c) <- "double"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
storage.mode(n.omp.threads) <- "integer"
storage.mode(verbose) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(cov.model.indx) <- "integer"
# chain.info order: current chain, total number of chains
chain.info <- c(curr.chain, n.chains)
storage.mode(chain.info) <- "integer"
fixed.sigma.sq <- fixed.params[which(all.params == 'sigma.sq')]
storage.mode(fixed.sigma.sq) <- "integer"
n.post.samples <- length(seq(from = n.burn + 1,
to = n.samples,
by = as.integer(n.thin)))
# samples.info order: burn-in, thinning rate, number of posterior samples
samples.info <- c(n.burn, n.thin, n.post.samples)
storage.mode(samples.info) <- "integer"
# For detection random effects
storage.mode(X.p.re) <- "integer"
alpha.level.indx <- sort(unique(c(X.p.re)))
storage.mode(alpha.level.indx) <- "integer"
storage.mode(n.det.re.long) <- "integer"
storage.mode(sigma.sq.p.inits) <- "double"
storage.mode(sigma.sq.p.a) <- "double"
storage.mode(sigma.sq.p.b) <- "double"
storage.mode(alpha.star.inits) <- "double"
storage.mode(alpha.star.indx) <- "integer"
# For occurrence random effects
storage.mode(X.re) <- "integer"
beta.level.indx <- sort(unique(c(X.re)))
storage.mode(beta.level.indx) <- "integer"
storage.mode(sigma.sq.psi.inits) <- "double"
storage.mode(sigma.sq.psi.a) <- "double"
storage.mode(sigma.sq.psi.b) <- "double"
storage.mode(n.occ.re.long) <- "integer"
storage.mode(beta.star.inits) <- "double"
storage.mode(beta.star.indx) <- "integer"
# Fit the model -------------------------------------------------------
out.tmp <- list()
out <- list()
if (!k.fold.only) {
for (i in 1:n.chains) {
# Change initial values if i > 1
if ((i > 1) & (!fix.inits)) {
if (!fixed.params[which(all.params == 'beta')]) {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
}
if (!fixed.params[which(all.params == 'alpha')]) {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
}
if (!fixed.params[which(all.params == 'sigma.sq')]) {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
}
if (!fixed.params[which(all.params == 'phi')]) {
phi.inits <- runif(1, phi.a, phi.b)
}
if (cov.model == 'matern') {
if (!fixed.params[which(all.params == 'phi')]) {
nu.inits <- runif(1, nu.a, nu.b)
}
}
if (p.det.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.p')]) {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
}
}
if (p.occ.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.psi')]) {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
}
}
}
storage.mode(chain.info) <- "integer"
# Run the model in C
out.tmp[[i]] <- .Call("spPGOcc", y, X, X.p, coords.D, X.re, X.p.re, consts,
K, n.occ.re.long, n.det.re.long,
beta.inits, alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits,
beta.star.inits, alpha.star.inits, z.inits,
w.inits, phi.inits, sigma.sq.inits, nu.inits, z.long.indx,
beta.star.indx, beta.level.indx, alpha.star.indx,
alpha.level.indx, mu.beta, mu.alpha,
Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b,
sigma.sq.psi.a, sigma.sq.psi.b, sigma.sq.p.a, sigma.sq.p.b,
tuning.c, cov.model.indx,
n.batch, batch.length,
accept.rate, n.omp.threads, verbose, n.report,
samples.info, chain.info, fixed.sigma.sq, sigma.sq.ig)
chain.info[1] <- chain.info[1] + 1
}
# Calculate R-Hat ---------------
out <- list()
out$rhat <- list()
if (n.chains > 1) {
# as.vector removes the "Upper CI" when there is only 1 variable.
out$rhat$beta <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$beta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$alpha <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$alpha.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$theta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$theta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.p.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.psi.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
} else {
out$rhat$beta <- rep(NA, p.occ)
out$rhat$alpha <- rep(NA, p.det)
out$rhat$theta <- rep(NA, ifelse(cov.model == 'matern', 3, 2))
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- rep(NA, p.det.re)
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- rep(NA, p.occ.re)
}
}
out$beta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$beta.samples))))
colnames(out$beta.samples) <- x.names
out$alpha.samples <- mcmc(do.call(rbind,
lapply(out.tmp, function(a) t(a$alpha.samples))))
colnames(out$alpha.samples) <- x.p.names
out$theta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$theta.samples))))
if (cov.model != 'matern') {
colnames(out$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out$z.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$z.samples))))
out$psi.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$psi.samples))))
out$like.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$like.samples))))
out$w.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$w.samples))))
if (p.occ.re > 0) {
out$sigma.sq.psi.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.psi.samples))))
colnames(out$sigma.sq.psi.samples) <- x.re.names
out$beta.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$beta.star.samples))))
tmp.names <- unlist(re.level.names)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long), tmp.names, sep = '-')
colnames(out$beta.star.samples) <- beta.star.names
out$re.level.names <- re.level.names
}
if (p.det.re > 0) {
out$sigma.sq.p.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.p.samples))))
colnames(out$sigma.sq.p.samples) <- x.p.re.names
out$alpha.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$alpha.star.samples))))
tmp.names <- unlist(p.re.level.names)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long), tmp.names, sep = '-')
colnames(out$alpha.star.samples) <- alpha.star.names
out$p.re.level.names <- p.re.level.names
}
# Calculate effective sample sizes
out$ESS <- list()
out$ESS$beta <- effectiveSize(out$beta.samples)
out$ESS$alpha <- effectiveSize(out$alpha.samples)
out$ESS$theta <- effectiveSize(out$theta.samples)
if (p.det.re > 0) {
out$ESS$sigma.sq.p <- effectiveSize(out$sigma.sq.p.samples)
}
if (p.occ.re > 0) {
out$ESS$sigma.sq.psi <- effectiveSize(out$sigma.sq.psi.samples)
}
out$X <- X
out$X.p <- X.p
out$X.p.re <- X.p.re
out$X.re <- X.re
out$y <- y.big
out$call <- cl
out$n.samples <- batch.length * n.batch
out$cov.model.indx <- cov.model.indx
out$type <- "GP"
out$coords <- coords
out$n.post <- n.post.samples
out$n.thin <- n.thin
out$n.burn <- n.burn
out$n.chains <- n.chains
if (p.det.re > 0) {
out$pRE <- TRUE
} else {
out$pRE <- FALSE
}
if (p.occ.re > 0) {
out$psiRE <- TRUE
} else {
out$psiRE <- FALSE
}
}
# K-fold cross-validation ---------
if (!missing(k.fold)) {
if (verbose) {
cat("----------------------------------------\n");
cat("\tCross-validation\n");
cat("----------------------------------------\n");
message(paste("Performing ", k.fold, "-fold cross-validation using ", k.fold.threads,
" thread(s).", sep = ''))
}
set.seed(k.fold.seed)
# Number of sites in each hold out data set.
sites.random <- sample(1:J)
sites.k.fold <- split(sites.random, sites.random %% k.fold)
registerDoParallel(k.fold.threads)
model.deviance <- foreach (i = 1:k.fold, .combine = sum) %dopar% {
curr.set <- sort(sites.random[sites.k.fold[[i]]])
if (binom) {
y.indx <- !(1:J %in% curr.set)
} else {
y.indx <- !((z.long.indx + 1) %in% curr.set)
}
y.fit <- y[y.indx]
y.0 <- y[!y.indx]
y.big.fit <- y.big[-curr.set, , drop = FALSE]
y.big.0 <- y.big[curr.set, , drop = FALSE]
z.inits.fit <- z.inits[-curr.set]
X.p.fit <- X.p[y.indx, , drop = FALSE]
X.p.0 <- X.p[!y.indx, , drop = FALSE]
X.fit <- X[-curr.set, , drop = FALSE]
X.0 <- X[curr.set, , drop = FALSE]
J.fit <- nrow(X.fit)
J.0 <- nrow(X.0)
coords.fit <- coords[-curr.set, , drop = FALSE]
coords.0 <- coords[curr.set, , drop = FALSE]
coords.D.fit <- coords.D[-curr.set, -curr.set, drop = FALSE]
coords.D.0 <- coords.D[curr.set, curr.set, drop = FALSE]
K.fit <- K[-curr.set]
K.0 <- K[curr.set]
rep.indx.fit <- rep.indx[-curr.set]
rep.indx.0 <- rep.indx[curr.set]
n.obs.fit <- nrow(X.p.fit)
n.obs.0 <- nrow(X.p.0)
# Random Detection Effects
X.p.re.fit <- X.p.re[y.indx, , drop = FALSE]
X.p.re.0 <- X.p.re[!y.indx, , drop = FALSE]
n.det.re.fit <- length(unique(c(X.p.re.fit)))
n.det.re.long.fit <- apply(X.p.re.fit, 2, function(a) length(unique(a)))
if (p.det.re > 0) {
alpha.star.indx.fit <- rep(0:(p.det.re - 1), n.det.re.long.fit)
alpha.level.indx.fit <- sort(unique(c(X.p.re.fit)))
alpha.star.inits.fit <- rnorm(n.det.re.fit, 0,
sqrt(sigma.sq.p.inits[alpha.star.indx.fit + 1]))
p.re.level.names.fit <- list()
for (t in 1:p.det.re) {
tmp.indx <- alpha.level.indx.fit[alpha.star.indx.fit == t - 1]
p.re.level.names.fit[[t]] <- unlist(p.re.level.names)[tmp.indx + 1]
}
} else {
alpha.star.indx.fit <- alpha.star.indx
alpha.level.indx.fit <- alpha.level.indx
alpha.star.inits.fit <- alpha.star.inits
}
# Random Occurrence Effects
X.re.fit <- X.re[-curr.set, , drop = FALSE]
X.re.0 <- X.re[curr.set, , drop = FALSE]
n.occ.re.fit <- length(unique(c(X.re.fit)))
n.occ.re.long.fit <- apply(X.re.fit, 2, function(a) length(unique(a)))
if (p.occ.re > 0) {
beta.star.indx.fit <- rep(0:(p.occ.re - 1), n.occ.re.long.fit)
beta.level.indx.fit <- sort(unique(c(X.re.fit)))
beta.star.inits.fit <- rnorm(n.occ.re.fit, 0,
sqrt(sigma.sq.psi.inits[beta.star.indx.fit + 1]))
re.level.names.fit <- list()
for (t in 1:p.occ.re) {
tmp.indx <- beta.level.indx.fit[beta.star.indx.fit == t - 1]
re.level.names.fit[[t]] <- unlist(re.level.names)[tmp.indx + 1]
}
} else {
beta.star.indx.fit <- beta.star.indx
beta.level.indx.fit <- beta.level.indx
beta.star.inits.fit <- beta.star.inits
re.level.names.fit <- re.level.names
}
if (!binom) {
z.long.indx.fit <- rep(1:J.fit, dim(y.big.fit)[2])
z.long.indx.fit <- z.long.indx.fit[!is.na(c(y.big.fit))]
# Subtract 1 for indices in C
z.long.indx.fit <- z.long.indx.fit - 1
z.0.long.indx <- rep(1:J.0, dim(y.big.0)[2])
z.0.long.indx <- z.0.long.indx[!is.na(c(y.big.0))]
# Don't subtract 1 for z.0.long.indx since its used in R only
} else {
z.long.indx.fit <- 0:(J.fit - 1)
z.0.long.indx <- 1:J.0
}
verbose.fit <- FALSE
n.omp.threads.fit <- 1
storage.mode(y.fit) <- "double"
storage.mode(z.inits.fit) <- "double"
storage.mode(X.p.fit) <- "double"
storage.mode(X.fit) <- "double"
storage.mode(coords.D.fit) <- "double"
storage.mode(K.fit) <- "double"
consts.fit <- c(J.fit, n.obs.fit, p.occ, p.occ.re, n.occ.re.fit,
p.det, p.det.re, n.det.re.fit)
storage.mode(consts.fit) <- "integer"
storage.mode(z.long.indx.fit) <- "integer"
storage.mode(n.samples) <- "integer"
storage.mode(n.omp.threads.fit) <- "integer"
storage.mode(verbose.fit) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(X.p.re.fit) <- "integer"
storage.mode(n.det.re.long.fit) <- "integer"
storage.mode(alpha.star.inits.fit) <- "double"
storage.mode(alpha.star.indx.fit) <- "integer"
storage.mode(alpha.level.indx.fit) <- "integer"
storage.mode(X.re.fit) <- "integer"
storage.mode(n.occ.re.long.fit) <- "integer"
storage.mode(beta.star.inits.fit) <- "double"
storage.mode(beta.star.indx.fit) <- "integer"
storage.mode(beta.level.indx.fit) <- "integer"
chain.info[1] <- 1
storage.mode(chain.info) <- "integer"
# Run the model in C
out.fit <- .Call("spPGOcc", y.fit, X.fit, X.p.fit, coords.D.fit, X.re.fit, X.p.re.fit,
consts.fit, K.fit, n.occ.re.long.fit, n.det.re.long.fit, beta.inits,
alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits, beta.star.inits.fit,
alpha.star.inits.fit, z.inits.fit, w.inits, phi.inits, sigma.sq.inits,
nu.inits, z.long.indx.fit, beta.star.indx.fit,
beta.level.indx.fit, alpha.star.indx.fit, alpha.level.indx.fit, mu.beta,
mu.alpha, Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b, sigma.sq.psi.a, sigma.sq.psi.b,
sigma.sq.p.a, sigma.sq.p.b, tuning.c, cov.model.indx,
n.batch, batch.length, accept.rate, n.omp.threads.fit, verbose.fit,
n.report, samples.info, chain.info, fixed.sigma.sq, sigma.sq.ig)
out.fit$beta.samples <- mcmc(t(out.fit$beta.samples))
colnames(out.fit$beta.samples) <- x.names
out.fit$alpha.samples <- mcmc(t(out.fit$alpha.samples))
colnames(out.fit$alpha.samples) <- x.p.names
out.fit$theta.samples <- mcmc(t(out.fit$theta.samples))
if (cov.model != 'matern') {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out.fit$w.samples <- mcmc(t(out.fit$w.samples))
out.fit$X <- X.fit
out.fit$y <- y.big.fit
out.fit$X.p <- X.p.fit
out.fit$call <- cl
out.fit$type <- "GP"
out.fit$n.samples <- n.samples
out.fit$coords <- coords.fit
out.fit$cov.model.indx <- cov.model.indx
out.fit$n.post <- n.post.samples
out.fit$n.thin <- n.thin
out.fit$n.burn <- n.burn
out.fit$n.chains <- 1
if (p.det.re > 0) {
out.fit$pRE <- TRUE
} else {
out.fit$pRE <- FALSE
}
if (p.occ.re > 0) {
out.fit$sigma.sq.psi.samples <- mcmc(t(out.fit$sigma.sq.psi.samples))
colnames(out.fit$sigma.sq.psi.samples) <- x.re.names
out.fit$beta.star.samples <- mcmc(t(out.fit$beta.star.samples))
tmp.names <- unlist(re.level.names.fit)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$beta.star.samples) <- beta.star.names
out.fit$re.level.names <- re.level.names.fit
out.fit$X.re <- X.re.fit
}
if (p.det.re > 0) {
out.fit$sigma.sq.p.samples <- mcmc(t(out.fit$sigma.sq.p.samples))
colnames(out.fit$sigma.sq.p.samples) <- x.p.re.names
out.fit$alpha.star.samples <- mcmc(t(out.fit$alpha.star.samples))
tmp.names <- unlist(p.re.level.names.fit)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$alpha.star.samples) <- alpha.star.names
out.fit$p.re.level.names <- p.re.level.names.fit
out.fit$X.p.re <- X.p.re.fit
}
if (p.occ.re > 0) {
out.fit$psiRE <- TRUE
} else {
out.fit$psiRE <- FALSE
}
class(out.fit) <- "spPGOcc"
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.occ.re > 0) {
tmp <- unlist(re.level.names)
X.re.0 <- matrix(tmp[c(X.re.0 + 1)], nrow(X.re.0), ncol(X.re.0))
colnames(X.re.0) <- x.re.names
}
# Predict occurrence at new sites
if (p.occ.re > 0) {X.0 <- cbind(X.0, X.re.0)}
out.pred <- predict.spPGOcc(out.fit, X.0, coords.0, verbose = FALSE)
# Detection
# Generate detection values
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.det.re > 0) {
tmp <- unlist(p.re.level.names)
X.p.re.0 <- matrix(tmp[c(X.p.re.0 + 1)], nrow(X.p.re.0), ncol(X.p.re.0))
colnames(X.p.re.0) <- x.p.re.names
}
if (p.det.re > 0) {X.p.0 <- cbind(X.p.0, X.p.re.0)}
out.p.pred <- predict.spPGOcc(out.fit, X.p.0, type = 'detection')
if (binom) {
like.samples <- matrix(NA, nrow(y.big.0), ncol(y.big.0))
for (j in 1:nrow(X.p.0)) {
for (k in rep.indx.0[[j]]) {
like.samples[j, k] <- mean(dbinom(y.big.0[j, k], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
} else {
like.samples <- rep(NA, nrow(X.p.0))
for (j in 1:nrow(X.p.0)) {
like.samples[j] <- mean(dbinom(y.0[j], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
sum(log(like.samples), na.rm = TRUE)
}
model.deviance <- -2 * model.deviance
# Return objects from cross-validation
out$k.fold.deviance <- model.deviance
stopImplicitCluster()
} # cross-validation
} else {
# Nearest Neighbor Search ---------------------------------------------
if(verbose){
cat("----------------------------------------\n");
cat("\tBuilding the neighbor list\n");
cat("----------------------------------------\n");
}
search.type.names <- c("brute", "cb")
if(!search.type %in% search.type.names){
stop("error: specified search.type '",search.type,
"' is not a valid option; choose from ",
paste(search.type.names, collapse=", ", sep="") ,".")
}
storage.mode(n.neighbors) <- "integer"
storage.mode(n.omp.threads) <- "integer"
## Indexes
if(search.type == "brute"){
indx <- mkNNIndx(coords, n.neighbors, n.omp.threads)
} else{
indx <- mkNNIndxCB(coords, n.neighbors, n.omp.threads)
}
nn.indx <- indx$nnIndx
nn.indx.lu <- indx$nnIndxLU
nn.indx.run.time <- indx$run.time
storage.mode(nn.indx) <- "integer"
storage.mode(nn.indx.lu) <- "integer"
storage.mode(u.search.type) <- "integer"
storage.mode(J) <- "integer"
storage.mode(J.w) <- "integer"
if(verbose){
cat("----------------------------------------\n");
cat("Building the neighbors of neighbors list\n");
cat("----------------------------------------\n");
}
indx <- mkUIndx(J.w, n.neighbors, nn.indx, nn.indx.lu, u.search.type)
u.indx <- indx$u.indx
u.indx.lu <- indx$u.indx.lu
ui.indx <- indx$ui.indx
u.indx.run.time <- indx$run.time
# Set storage for all variables ---------------------------------------
storage.mode(y) <- "double"
storage.mode(z.inits) <- "double"
storage.mode(X.p) <- "double"
storage.mode(X) <- "double"
consts <- c(J, n.obs, p.occ, p.occ.re, n.occ.re, p.det, p.det.re, n.det.re, J.w)
storage.mode(consts) <- "integer"
storage.mode(grid.index.c) <- "integer"
storage.mode(K) <- "double"
storage.mode(coords) <- "double"
storage.mode(beta.inits) <- "double"
storage.mode(alpha.inits) <- "double"
storage.mode(phi.inits) <- "double"
storage.mode(sigma.sq.inits) <- "double"
storage.mode(nu.inits) <- "double"
storage.mode(w.inits) <- "double"
storage.mode(z.long.indx) <- "integer"
storage.mode(mu.beta) <- "double"
storage.mode(Sigma.beta) <- "double"
storage.mode(mu.alpha) <- "double"
storage.mode(Sigma.alpha) <- "double"
storage.mode(phi.a) <- "double"
storage.mode(phi.b) <- "double"
storage.mode(nu.a) <- "double"
storage.mode(nu.b) <- "double"
storage.mode(sigma.sq.a) <- "double"
storage.mode(sigma.sq.b) <- "double"
storage.mode(sigma.sq.ig) <- "integer"
storage.mode(tuning.c) <- "double"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
storage.mode(n.omp.threads) <- "integer"
storage.mode(verbose) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(nn.indx) <- "integer"
storage.mode(nn.indx.lu) <- "integer"
storage.mode(u.indx) <- "integer"
storage.mode(u.indx.lu) <- "integer"
storage.mode(ui.indx) <- "integer"
storage.mode(n.neighbors) <- "integer"
storage.mode(cov.model.indx) <- "integer"
chain.info <- c(curr.chain, n.chains)
storage.mode(chain.info) <- "integer"
storage.mode(fixed.params) <- "integer"
n.post.samples <- length(seq(from = n.burn + 1,
to = n.samples,
by = as.integer(n.thin)))
storage.mode(n.post.samples) <- "integer"
samples.info <- c(n.burn, n.thin, n.post.samples)
storage.mode(samples.info) <- "integer"
# For detection random effects
storage.mode(X.p.re) <- "integer"
alpha.level.indx <- sort(unique(c(X.p.re)))
storage.mode(alpha.level.indx) <- "integer"
storage.mode(n.det.re.long) <- "integer"
storage.mode(sigma.sq.p.inits) <- "double"
storage.mode(sigma.sq.p.a) <- "double"
storage.mode(sigma.sq.p.b) <- "double"
storage.mode(alpha.star.inits) <- "double"
storage.mode(alpha.star.indx) <- "integer"
# For occurrence random effects
storage.mode(X.re) <- "integer"
beta.level.indx <- sort(unique(c(X.re)))
storage.mode(beta.level.indx) <- "integer"
storage.mode(sigma.sq.psi.inits) <- "double"
storage.mode(sigma.sq.psi.a) <- "double"
storage.mode(sigma.sq.psi.b) <- "double"
storage.mode(beta.star.inits) <- "double"
storage.mode(beta.star.indx) <- "integer"
# Fit the model -------------------------------------------------------
out.tmp <- list()
out <- list()
if (!k.fold.only) {
for (i in 1:n.chains) {
# Change initial values if i > 1
if ((i > 1) & (!fix.inits)) {
if (!fixed.params[which(all.params == 'beta')]) {
beta.inits <- rnorm(p.occ, mu.beta, sqrt(sigma.beta))
}
if (!fixed.params[which(all.params == 'alpha')]) {
alpha.inits <- rnorm(p.det, mu.alpha, sqrt(sigma.alpha))
}
if (!fixed.params[which(all.params == 'sigma.sq')]) {
if (sigma.sq.ig) {
sigma.sq.inits <- rigamma(1, sigma.sq.a, sigma.sq.b)
} else {
sigma.sq.inits <- runif(1, sigma.sq.a, sigma.sq.b)
}
}
if (!fixed.params[which(all.params == 'phi')]) {
phi.inits <- runif(1, phi.a, phi.b)
}
if (cov.model == 'matern') {
if (!fixed.params[which(all.params == 'phi')]) {
nu.inits <- runif(1, nu.a, nu.b)
}
}
if (p.det.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.p')]) {
sigma.sq.p.inits <- runif(p.det.re, 0.5, 10)
alpha.star.inits <- rnorm(n.det.re, 0, sqrt(sigma.sq.p.inits[alpha.star.indx + 1]))
}
}
if (p.occ.re > 0) {
if (!fixed.params[which(all.params == 'sigma.sq.psi')]) {
sigma.sq.psi.inits <- runif(p.occ.re, 0.5, 10)
beta.star.inits <- rnorm(n.occ.re, 0, sqrt(sigma.sq.psi.inits[beta.star.indx + 1]))
}
}
}
storage.mode(chain.info) <- "integer"
# Run the model in C
out.tmp[[i]] <- .Call("spPGOccNNGP", y, X, X.p, coords, X.re, X.p.re, consts,
K, n.occ.re.long, n.det.re.long,
n.neighbors, nn.indx, nn.indx.lu, u.indx, u.indx.lu, ui.indx,
beta.inits, alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits,
beta.star.inits, alpha.star.inits, z.inits,
w.inits, phi.inits, sigma.sq.inits, nu.inits, z.long.indx,
beta.star.indx, beta.level.indx, alpha.star.indx,
alpha.level.indx, mu.beta, mu.alpha,
Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b,
sigma.sq.psi.a, sigma.sq.psi.b, sigma.sq.p.a, sigma.sq.p.b,
tuning.c, cov.model.indx,
n.batch, batch.length,
accept.rate, n.omp.threads, verbose, n.report,
samples.info, chain.info, fixed.params, sigma.sq.ig, grid.index.c)
chain.info[1] <- chain.info[1] + 1
}
# Calculate R-Hat ---------------
out <- list()
out$rhat <- list()
if (n.chains > 1) {
# as.vector removes the "Upper CI" when there is only 1 variable.
out$rhat$beta <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$beta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$alpha <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$alpha.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
out$rhat$theta <- gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$theta.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2]
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.p.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- as.vector(gelman.diag(mcmc.list(lapply(out.tmp, function(a)
mcmc(t(a$sigma.sq.psi.samples)))),
autoburnin = FALSE, multivariate = FALSE)$psrf[, 2])
}
} else {
out$rhat$beta <- rep(NA, p.occ)
out$rhat$alpha <- rep(NA, p.det)
out$rhat$theta <- rep(NA, ifelse(cov.model == 'matern', 3, 2))
if (p.det.re > 0) {
out$rhat$sigma.sq.p <- rep(NA, p.det.re)
}
if (p.occ.re > 0) {
out$rhat$sigma.sq.psi <- rep(NA, p.occ.re)
}
}
out$beta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$beta.samples))))
colnames(out$beta.samples) <- x.names
out$alpha.samples <- mcmc(do.call(rbind,
lapply(out.tmp, function(a) t(a$alpha.samples))))
colnames(out$alpha.samples) <- x.p.names
out$theta.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$theta.samples))))
if (cov.model != 'matern') {
colnames(out$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
# Get everything back in the original order
out$coords <- coords[order(ord), ]
out$z.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$z.samples))))
out$z.samples <- mcmc(out$z.samples[, order(long.ord), drop = FALSE])
out$X <- X[order(long.ord), , drop = FALSE]
out$X.re <- X.re[order(long.ord), , drop = FALSE]
out$w.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$w.samples))))
out$w.samples <- mcmc(out$w.samples[, order(ord), drop = FALSE])
out$psi.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$psi.samples))))
out$psi.samples <- mcmc(out$psi.samples[, order(long.ord), drop = FALSE])
out$like.samples <- mcmc(do.call(rbind, lapply(out.tmp, function(a) t(a$like.samples))))
out$like.samples <- mcmc(out$like.samples[, order(long.ord), drop = FALSE])
# Get detection covariate stuff in right order. Method of doing this
# depends on if there are observation level covariates or not.
if (!binom) {
tmp <- matrix(NA, J * K.max, p.det)
tmp[names.long, ] <- X.p
tmp <- array(tmp, dim = c(J, K.max, p.det))
tmp <- tmp[order(long.ord), , ]
out$X.p <- matrix(tmp, J * K.max, p.det)
out$X.p <- out$X.p[apply(out$X.p, 1, function(a) sum(is.na(a))) == 0, , drop = FALSE]
colnames(out$X.p) <- x.p.names
tmp <- matrix(NA, J * K.max, p.det.re)
tmp[names.long, ] <- X.p.re
tmp <- array(tmp, dim = c(J, K.max, p.det.re))
tmp <- tmp[order(long.ord), , ]
out$X.p.re <- matrix(tmp, J * K.max, p.det.re)
out$X.p.re <- out$X.p.re[apply(out$X.p.re, 1, function(a) sum(is.na(a))) == 0, , drop = FALSE]
colnames(out$X.p.re) <- x.p.re.names
} else {
out$X.p <- X.p[order(long.ord), , drop = FALSE]
out$X.p.re <- X.p.re[order(long.ord), , drop = FALSE]
}
out$y <- y.big[order(long.ord), , drop = FALSE]
if (p.occ.re > 0) {
out$sigma.sq.psi.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.psi.samples))))
colnames(out$sigma.sq.psi.samples) <- x.re.names
out$beta.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$beta.star.samples))))
tmp.names <- unlist(re.level.names)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long), tmp.names, sep = '-')
colnames(out$beta.star.samples) <- beta.star.names
out$re.level.names <- re.level.names
}
if (p.det.re > 0) {
out$sigma.sq.p.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$sigma.sq.p.samples))))
colnames(out$sigma.sq.p.samples) <- x.p.re.names
out$alpha.star.samples <- mcmc(
do.call(rbind, lapply(out.tmp, function(a) t(a$alpha.star.samples))))
tmp.names <- unlist(p.re.level.names)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long), tmp.names, sep = '-')
colnames(out$alpha.star.samples) <- alpha.star.names
out$p.re.level.names <- p.re.level.names
}
# Calculate effective sample sizes
out$ESS <- list()
out$ESS$beta <- effectiveSize(out$beta.samples)
out$ESS$alpha <- effectiveSize(out$alpha.samples)
out$ESS$theta <- effectiveSize(out$theta.samples)
if (p.det.re > 0) {
out$ESS$sigma.sq.p <- effectiveSize(out$sigma.sq.p.samples)
}
if (p.occ.re > 0) {
out$ESS$sigma.sq.psi <- effectiveSize(out$sigma.sq.psi.samples)
}
out$call <- cl
out$n.samples <- batch.length * n.batch
out$n.neighbors <- n.neighbors
out$cov.model.indx <- cov.model.indx
out$type <- "NNGP"
out$n.post <- n.post.samples
out$n.thin <- n.thin
out$n.burn <- n.burn
out$n.chains <- n.chains
if (p.det.re > 0) {
out$pRE <- TRUE
} else {
out$pRE <- FALSE
}
if (p.occ.re > 0) {
out$psiRE <- TRUE
} else {
out$psiRE <- FALSE
}
}
# K-fold cross-validation ---------
if (!missing(k.fold)) {
if (verbose) {
cat("----------------------------------------\n");
cat("\tCross-validation\n");
cat("----------------------------------------\n");
message(paste("Performing ", k.fold, "-fold cross-validation using ", k.fold.threads,
" thread(s).", sep = ''))
}
set.seed(k.fold.seed)
# Number of sites in each hold out data set.
sites.random <- sample(1:J.w)
sites.k.fold <- split(sites.random, sites.random %% k.fold)
registerDoParallel(k.fold.threads)
model.deviance <- foreach (i = 1:k.fold, .combine = sum) %dopar% {
curr.set.small <- sort(sites.random[sites.k.fold[[i]]])
curr.set <- which(grid.index.r %in% curr.set.small)
if (binom) {
y.indx <- !(1:J %in% curr.set)
} else {
y.indx <- !((z.long.indx + 1) %in% curr.set)
}
y.fit <- y[y.indx]
y.0 <- y[!y.indx]
y.big.fit <- y.big[-curr.set, , drop = FALSE]
y.big.0 <- y.big[curr.set, , drop = FALSE]
z.inits.fit <- z.inits[-curr.set]
X.p.fit <- X.p[y.indx, , drop = FALSE]
X.p.0 <- X.p[!y.indx, , drop = FALSE]
X.fit <- X[-curr.set, , drop = FALSE]
X.0 <- X[curr.set, , drop = FALSE]
J.fit <- nrow(X.fit)
J.0 <- nrow(X.0)
coords.fit <- coords[-curr.set.small, , drop = FALSE]
coords.0 <- coords[curr.set.small, , drop = FALSE]
J.w.fit <- nrow(coords.fit)
tmp.grid.fit <- grid.index.r[-curr.set]
tmp.grid.0 <- grid.index.r[curr.set]
# Reorder the grids to get their indices to work
grid.fit <- vector('numeric', length = length(tmp.grid.fit))
for (j in 1:nrow(coords.fit)) {
grid.fit[which(tmp.grid.fit == (1:J.w)[-curr.set.small][j])] <- j
}
grid.0 <- vector('numeric', length = length(tmp.grid.0))
for (j in 1:nrow(coords.0)) {
grid.0[which(tmp.grid.0 == curr.set.small[j])] <- j
}
grid.fit.c <- grid.fit - 1
K.fit <- K[-curr.set]
K.0 <- K[curr.set]
rep.indx.fit <- rep.indx[-curr.set]
rep.indx.0 <- rep.indx[curr.set]
n.obs.fit <- nrow(X.p.fit)
n.obs.0 <- nrow(X.p.0)
# Random Detection Effects
X.p.re.fit <- X.p.re[y.indx, , drop = FALSE]
X.p.re.0 <- X.p.re[!y.indx, , drop = FALSE]
n.det.re.fit <- length(unique(c(X.p.re.fit)))
n.det.re.long.fit <- apply(X.p.re.fit, 2, function(a) length(unique(a)))
if (p.det.re > 0) {
alpha.star.indx.fit <- rep(0:(p.det.re - 1), n.det.re.long.fit)
alpha.level.indx.fit <- sort(unique(c(X.p.re.fit)))
alpha.star.inits.fit <- rnorm(n.det.re.fit, 0,
sqrt(sigma.sq.p.inits[alpha.star.indx.fit + 1]))
p.re.level.names.fit <- list()
for (t in 1:p.det.re) {
tmp.indx <- alpha.level.indx.fit[alpha.star.indx.fit == t - 1]
p.re.level.names.fit[[t]] <- unlist(p.re.level.names)[tmp.indx + 1]
}
} else {
alpha.star.indx.fit <- alpha.star.indx
alpha.level.indx.fit <- alpha.level.indx
alpha.star.inits.fit <- alpha.star.inits
}
# Random Occurrence Effects
X.re.fit <- X.re[-curr.set, , drop = FALSE]
X.re.0 <- X.re[curr.set, , drop = FALSE]
n.occ.re.fit <- length(unique(c(X.re.fit)))
n.occ.re.long.fit <- apply(X.re.fit, 2, function(a) length(unique(a)))
if (p.occ.re > 0) {
beta.star.indx.fit <- rep(0:(p.occ.re - 1), n.occ.re.long.fit)
beta.level.indx.fit <- sort(unique(c(X.re.fit)))
beta.star.inits.fit <- rnorm(n.occ.re.fit, 0,
sqrt(sigma.sq.psi.inits[beta.star.indx.fit + 1]))
re.level.names.fit <- list()
for (t in 1:p.occ.re) {
tmp.indx <- beta.level.indx.fit[beta.star.indx.fit == t - 1]
re.level.names.fit[[t]] <- unlist(re.level.names)[tmp.indx + 1]
}
} else {
beta.star.indx.fit <- beta.star.indx
beta.level.indx.fit <- beta.level.indx
beta.star.inits.fit <- beta.star.inits
re.level.names.fit <- re.level.names
}
if (!binom) {
z.long.indx.fit <- rep(1:J.fit, dim(y.big.fit)[2])
z.long.indx.fit <- z.long.indx.fit[!is.na(c(y.big.fit))]
# Subtract 1 for indices in C
z.long.indx.fit <- z.long.indx.fit - 1
z.0.long.indx <- rep(1:J.0, dim(y.big.0)[2])
z.0.long.indx <- z.0.long.indx[!is.na(c(y.big.0))]
# Don't subtract 1 for z.0.long.indx since its used in R only
} else {
z.long.indx.fit <- 0:(J.fit - 1)
z.0.long.indx <- 1:J.0
}
# Nearest Neighbor Search ---
verbose.fit <- FALSE
n.omp.threads.fit <- 1
## Indexes
if(search.type == "brute"){
indx <- mkNNIndx(coords.fit, n.neighbors, n.omp.threads.fit)
} else{
indx <- mkNNIndxCB(coords.fit, n.neighbors, n.omp.threads.fit)
}
nn.indx.fit <- indx$nnIndx
nn.indx.lu.fit <- indx$nnIndxLU
indx <- mkUIndx(J.w.fit, n.neighbors, nn.indx.fit,
nn.indx.lu.fit, u.search.type)
u.indx.fit <- indx$u.indx
u.indx.lu.fit <- indx$u.indx.lu
ui.indx.fit <- indx$ui.indx
storage.mode(y.fit) <- "double"
storage.mode(z.inits.fit) <- "double"
storage.mode(X.p.fit) <- "double"
storage.mode(X.fit) <- "double"
storage.mode(coords.fit) <- "double"
storage.mode(K.fit) <- "double"
consts.fit <- c(J.fit, n.obs.fit, p.occ, p.occ.re, n.occ.re.fit,
p.det, p.det.re, n.det.re.fit, J.w.fit)
storage.mode(consts.fit) <- "integer"
storage.mode(z.long.indx.fit) <- "integer"
storage.mode(nn.indx.fit) <- "integer"
storage.mode(nn.indx.lu.fit) <- "integer"
storage.mode(u.indx.fit) <- "integer"
storage.mode(u.indx.lu.fit) <- "integer"
storage.mode(ui.indx.fit) <- "integer"
storage.mode(n.samples) <- "integer"
storage.mode(n.omp.threads.fit) <- "integer"
storage.mode(verbose.fit) <- "integer"
storage.mode(n.report) <- "integer"
storage.mode(X.p.re.fit) <- "integer"
storage.mode(n.det.re.long.fit) <- "integer"
storage.mode(alpha.star.inits.fit) <- "double"
storage.mode(alpha.star.indx.fit) <- "integer"
storage.mode(alpha.level.indx.fit) <- "integer"
storage.mode(X.re.fit) <- "integer"
storage.mode(n.occ.re.long.fit) <- "integer"
storage.mode(beta.star.inits.fit) <- "double"
storage.mode(beta.star.indx.fit) <- "integer"
storage.mode(beta.level.indx.fit) <- "integer"
chain.info[1] <- 1
storage.mode(chain.info) <- "integer"
storage.mode(grid.fit.c) <- 'integer'
# Run the model in C
out.fit <- .Call("spPGOccNNGP", y.fit, X.fit, X.p.fit, coords.fit, X.re.fit, X.p.re.fit,
consts.fit, K.fit, n.occ.re.long.fit, n.det.re.long.fit,
n.neighbors, nn.indx.fit, nn.indx.lu.fit, u.indx.fit,
u.indx.lu.fit, ui.indx.fit, beta.inits,
alpha.inits, sigma.sq.psi.inits, sigma.sq.p.inits, beta.star.inits.fit,
alpha.star.inits.fit, z.inits.fit, w.inits, phi.inits, sigma.sq.inits,
nu.inits, z.long.indx.fit, beta.star.indx.fit,
beta.level.indx.fit, alpha.star.indx.fit, alpha.level.indx.fit, mu.beta,
mu.alpha, Sigma.beta, Sigma.alpha, phi.a, phi.b,
sigma.sq.a, sigma.sq.b, nu.a, nu.b, sigma.sq.psi.a, sigma.sq.psi.b,
sigma.sq.p.a, sigma.sq.p.b, tuning.c, cov.model.indx,
n.batch, batch.length, accept.rate, n.omp.threads.fit, verbose.fit,
n.report, samples.info, chain.info, fixed.params, sigma.sq.ig, grid.fit.c)
out.fit$beta.samples <- mcmc(t(out.fit$beta.samples))
colnames(out.fit$beta.samples) <- x.names
out.fit$alpha.samples <- mcmc(t(out.fit$alpha.samples))
colnames(out.fit$alpha.samples) <- x.p.names
out.fit$theta.samples <- mcmc(t(out.fit$theta.samples))
if (cov.model != 'matern') {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi')
} else {
colnames(out.fit$theta.samples) <- c('sigma.sq', 'phi', 'nu')
}
out.fit$w.samples <- mcmc(t(out.fit$w.samples))
out.fit$X <- X.fit
out.fit$y <- y.big.fit
out.fit$X.p <- X.p.fit
out.fit$call <- cl
out.fit$type <- "NNGP"
out.fit$n.neighbors <- n.neighbors
out.fit$n.samples <- n.samples
out.fit$coords <- coords.fit
out.fit$cov.model.indx <- cov.model.indx
out.fit$n.post <- n.post.samples
out.fit$n.thin <- n.thin
out.fit$n.burn <- n.burn
out.fit$n.chains <- 1
if (p.det.re > 0) {
out.fit$pRE <- TRUE
} else {
out.fit$pRE <- FALSE
}
if (p.occ.re > 0) {
out.fit$sigma.sq.psi.samples <- mcmc(t(out.fit$sigma.sq.psi.samples))
colnames(out.fit$sigma.sq.psi.samples) <- x.re.names
out.fit$beta.star.samples <- mcmc(t(out.fit$beta.star.samples))
tmp.names <- unlist(re.level.names.fit)
beta.star.names <- paste(rep(x.re.names, n.occ.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$beta.star.samples) <- beta.star.names
out.fit$re.level.names <- re.level.names.fit
out.fit$X.re <- X.re.fit
}
if (p.det.re > 0) {
out.fit$sigma.sq.p.samples <- mcmc(t(out.fit$sigma.sq.p.samples))
colnames(out.fit$sigma.sq.p.samples) <- x.p.re.names
out.fit$alpha.star.samples <- mcmc(t(out.fit$alpha.star.samples))
tmp.names <- unlist(p.re.level.names.fit)
alpha.star.names <- paste(rep(x.p.re.names, n.det.re.long.fit), tmp.names, sep = '-')
colnames(out.fit$alpha.star.samples) <- alpha.star.names
out.fit$p.re.level.names <- p.re.level.names.fit
out.fit$X.p.re <- X.p.re.fit
}
if (p.occ.re > 0) {
out.fit$psiRE <- TRUE
} else {
out.fit$psiRE <- FALSE
}
class(out.fit) <- "spPGOcc"
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.occ.re > 0) {
tmp <- unlist(re.level.names)
X.re.0 <- matrix(tmp[c(X.re.0 + 1)], nrow(X.re.0), ncol(X.re.0))
colnames(X.re.0) <- x.re.names
}
# Predict occurrence at new sites
if (p.occ.re > 0) {X.0 <- cbind(X.0, X.re.0)}
out.pred <- predict.spPGOcc(out.fit, X.0, coords.0, verbose = FALSE, grid.index.0 = grid.0)
# Detection
# Generate detection values
# Get RE levels correct for when they aren't supplied at values starting at 1.
if (p.det.re > 0) {
tmp <- unlist(p.re.level.names)
X.p.re.0 <- matrix(tmp[c(X.p.re.0 + 1)], nrow(X.p.re.0), ncol(X.p.re.0))
colnames(X.p.re.0) <- x.p.re.names
}
if (p.det.re > 0) {X.p.0 <- cbind(X.p.0, X.p.re.0)}
out.p.pred <- predict.spPGOcc(out.fit, X.p.0, type = 'detection')
if (binom) {
like.samples <- matrix(NA, nrow(y.big.0), ncol(y.big.0))
for (j in 1:nrow(X.p.0)) {
for (k in rep.indx.0[[j]]) {
like.samples[j, k] <- mean(dbinom(y.big.0[j, k], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
} else {
like.samples <- rep(NA, nrow(X.p.0))
for (j in 1:nrow(X.p.0)) {
like.samples[j] <- mean(dbinom(y.0[j], 1,
out.p.pred$p.0.samples[, j] * out.pred$z.0.samples[, z.0.long.indx[j]]))
}
}
sum(log(like.samples), na.rm = TRUE)
}
model.deviance <- -2 * model.deviance
# Return objects from cross-validation
out$k.fold.deviance <- model.deviance
} # cross-validation
} # NNGP or GP
class(out) <- "spPGOcc"
out$run.time <- proc.time() - ptm
return(out)
}
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