tests/testthat/test-lfMsPGOcc.R
In doserjef/spOccupancy: Single-Species, Multi-Species, and Integrated Spatial Occupancy Models
# Test lfMsPGOcc.R -------------------------------------------------------
skip_on_cran()
# Intercept only ----------------------------------------------------------
set.seed(20)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, factor.model = TRUE, n.factors = 3)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
data.list <- list(y = y, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
fix = TRUE,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
out.k.fold <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1, k.fold.only = TRUE)
expect_equal(length(out.k.fold$k.fold.deviance), N)
expect_type(out.k.fold$k.fold.deviance, "double")
expect_equal(sum(out.k.fold$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.factors = 3,
n.samples = n.samples,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
# Check non-integer n.post -------------
test_that("non-integer n.post", {
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.thin = 13,
n.factors = 3,
n.samples = n.samples,
n.omp.threads = 1,
verbose = FALSE))
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.omp.threads = 1,
n.factors = 3,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence covariate only -----------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5, 0.3)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.3, 2.8)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, factor.model = TRUE, n.factor = 3)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.cov.2')
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + occ.cov.2
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.cov.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Detection covariate only -----------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Covariates on both ------------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Interactions on both ----------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5, -0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3, 1.2)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.cov.2')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 * occ.cov.2
det.formula <- ~ det.cov.1 * det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- dat$X
X.0 <- cbind(X.0, X.0[, 2] * X.0[, 3])
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.cov.2', 'occ.cov.1:occ.cov.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, J))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, J))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
X.p.0 <- cbind(X.p.0, X.p.0[, 2] * X.p.0[, 3])
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Site covariate on detection ---------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3],
occ.cov.1 = X[, 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ occ.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 1,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(1, data.list$det.covs$occ.cov.1)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, nrow(X.p.0)))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercept on occurrence ------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45),
sigma.sq.psi = c(1.3))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X, X.re)
colnames(X.0) <- c('int', 'occ.factor.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on occurrence --------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1', 'occ.factor.2')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence REs + covariates ---------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.str <- cbind(X.0, X.re.0)
colnames(X.str) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.str, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence REs + covariates in all --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
det.covs <- list(det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ det.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercepts on detection ------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30),
sigma.sq.p = c(2))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ (1 | det.factor.1)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0)
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, 1], dat$X.p.re[, 1, 1])
colnames(X.p.0) <- c('intercept', 'det.factor.1')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on detection ---------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, 1], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts with covariate -------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ det.cov.1 + (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts with covariate on both -----------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.5)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0)
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on both --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(20),
sigma.sq.psi = c(0.5))
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1)
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.factor.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercepts on both with covariates -------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.2)
# Detection
alpha.mean <- c(0, -0.5)
tau.sq.alpha <- c(1, 1.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(20, 15),
sigma.sq.psi = c(0.5, 2.4))
p.RE <- list(levels = c(30, 45, 30),
sigma.sq.p = c(2, 1.5, 0.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2],
det.factor.3 = X.p.re[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2) + (1 | det.factor.3) + det.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:3])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2',
'det.factor.3')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Third dimension of y != max(n.rep) --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
n.rep.max <- max(n.rep)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, n.rep.max = n.rep.max)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, n.rep.max))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, n.rep.max))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, n.rep.max))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, n.rep.max))
})
doserjef/spOccupancy documentation built on Feb. 28, 2025, 1:19 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Test lfMsPGOcc.R -------------------------------------------------------
skip_on_cran()
# Intercept only ----------------------------------------------------------
set.seed(20)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, factor.model = TRUE, n.factors = 3)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
data.list <- list(y = y, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
fix = TRUE,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
out.k.fold <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1, k.fold.only = TRUE)
expect_equal(length(out.k.fold$k.fold.deviance), N)
expect_type(out.k.fold$k.fold.deviance, "double")
expect_equal(sum(out.k.fold$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.factors = 3,
n.samples = n.samples,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
# Check non-integer n.post -------------
test_that("non-integer n.post", {
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.thin = 13,
n.factors = 3,
n.samples = n.samples,
n.omp.threads = 1,
verbose = FALSE))
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.omp.threads = 1,
n.factors = 3,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence covariate only -----------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5, 0.3)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.3, 2.8)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, factor.model = TRUE, n.factor = 3)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.cov.2')
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + occ.cov.2
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.cov.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Detection covariate only -----------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
J.fit <- nrow(X)
ppc.out <- ppcOcc(out, 'chi-square', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Covariates on both ------------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Interactions on both ----------------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5, -0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3, 1.2)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.cov.2')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 * occ.cov.2
det.formula <- ~ det.cov.1 * det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- dat$X
X.0 <- cbind(X.0, X.0[, 2] * X.0[, 3])
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.cov.2', 'occ.cov.1:occ.cov.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, J))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, J))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
X.p.0 <- cbind(X.p.0, X.p.0[, 2] * X.p.0[, 3])
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Site covariate on detection ---------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3],
occ.cov.1 = X[, 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ occ.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 1,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(1, data.list$det.covs$occ.cov.1)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, nrow(X.p.0)))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercept on occurrence ------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45),
sigma.sq.psi = c(1.3))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X, X.re)
colnames(X.0) <- c('int', 'occ.factor.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on occurrence --------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1', 'occ.factor.2')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence REs + covariates ---------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
# det.covs <- list(det.cov.1 = X.p[, , 2],
# det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ 1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.str <- cbind(X.0, X.re.0)
colnames(X.str) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.str, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
J.str <- 100
X.p.0 <- matrix(1, nrow = J.str, ncol = p.det)
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J.str))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Occurrence REs + covariates in all --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(45, 15),
sigma.sq.psi = c(1.3, 0.5))
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
det.covs <- list(det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ det.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
# data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
# data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercepts on detection ------------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30),
sigma.sq.p = c(2))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ (1 | det.factor.1)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0)
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, 1], dat$X.p.re[, 1, 1])
colnames(X.p.0) <- c('intercept', 'det.factor.1')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on detection ---------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, 1], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts with covariate -------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ 1
det.formula <- ~ det.cov.1 + (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts with covariate on both -----------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.5)
# Detection
alpha.mean <- c(0, 1)
tau.sq.alpha <- c(1, 2.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
# X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
# X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X)
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, FALSE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
# data.list$occ.covs <- as.data.frame(data.list$occ.covs)
# data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
# data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0)
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Multiple random intercepts on both --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(20),
sigma.sq.psi = c(0.5))
p.RE <- list(levels = c(30, 45),
sigma.sq.p = c(2, 1.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.factor.1')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ (1 | occ.factor.1)
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2)
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.factor.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:2])
colnames(X.p.0) <- c('intercept', 'det.factor.1', 'det.factor.2')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Random intercepts on both with covariates -------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 0.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.2)
# Detection
alpha.mean <- c(0, -0.5)
tau.sq.alpha <- c(1, 1.5)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list(levels = c(20, 15),
sigma.sq.psi = c(0.5, 2.4))
p.RE <- list(levels = c(30, 45, 30),
sigma.sq.p = c(2, 1.5, 0.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
X.re <- dat$X.re[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
X.re.0 <- dat$X.re[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
X.p.re <- dat$X.p.re[-pred.indx, , , drop = FALSE]
occ.covs <- cbind(X, X.re)
colnames(occ.covs) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
det.covs <- list(det.factor.1 = X.p.re[, , 1],
det.factor.2 = X.p.re[, , 2],
det.cov.1 = X.p[, , 2],
det.factor.3 = X.p.re[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1 + (1 | occ.factor.1) + (1 | occ.factor.2)
det.formula <- ~ (1 | det.factor.1) + (1 | det.factor.2) + (1 | det.factor.3) + det.cov.1
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, TRUE)
expect_equal(out$psiRE, TRUE)
})
# Check RE error ----------------------
test_that("random effect gives error when non-numeric", {
data.list$occ.covs <- as.data.frame(data.list$occ.covs)
data.list$occ.covs$occ.factor.1 <- factor(data.list$occ.covs$occ.factor.1)
data.list$det.covs$det.factor.1 <- factor(data.list$det.covs$det.factor.1)
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
data.list$occ.covs$occ.factor.1 <- as.character(factor(data.list$occ.covs$occ.factor.1))
data.list$det.covs$det.factor.1 <- as.character(factor(data.list$det.covs$det.factor.1))
expect_error(out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 6,
n.chains = 1))
})
# Check RE levels ---------------------
test_that("random effect levels are correct", {
expect_equal(sort(unique(unlist(out$re.level.names))),
sort(unique(c(X.re))))
expect_equal(sort(unique(unlist(out$p.re.level.names))),
sort(unique(c(X.p.re))))
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
X.0 <- cbind(X.0, X.re.0)
colnames(X.0) <- c('int', 'occ.cov.1', 'occ.factor.1', 'occ.factor.2')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- cbind(dat$X.p[, 1, ], dat$X.p.re[, 1, 1:3])
colnames(X.p.0) <- c('intercept', 'det.cov.1', 'det.factor.1', 'det.factor.2',
'det.factor.3')
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, max(n.rep)))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, max(n.rep)))
})
# Third dimension of y != max(n.rep) --------------------------------------
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
n.rep.max <- max(n.rep)
N <- 8
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, 1.5)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 2.3)
# Detection
alpha.mean <- c(0, 0.5, 1.2)
tau.sq.alpha <- c(1, 2, 3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
alpha.true <- alpha
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = FALSE, n.rep.max = n.rep.max)
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
occ.covs <- X
colnames(occ.covs) <- c('int', 'occ.cov.1')
det.covs <- list(det.cov.1 = X.p[, , 2],
det.cov.2 = X.p[, , 3])
data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
# Starting values
inits.list <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(y, c(1, 2), max, na.rm = TRUE))
n.samples <- 1000
n.report <- 100
occ.formula <- ~ occ.cov.1
det.formula <- ~ det.cov.1 + det.cov.2
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
inits = inits.list,
n.samples = n.samples,
n.factors = 3,
priors = prior.list,
n.omp.threads = 1,
verbose = FALSE,
n.report = n.report,
n.burn = 400,
n.thin = 2,
n.chains = 2,
k.fold = 2,
k.fold.threads = 1)
# To make sure it worked --------------
test_that("out is of class lfMsPGOcc", {
expect_s3_class(out, "lfMsPGOcc")
})
# Check cross-validation --------------
test_that("cross-validation works", {
expect_equal(length(out$k.fold.deviance), N)
expect_type(out$k.fold.deviance, "double")
expect_equal(sum(out$k.fold.deviance < 0), 0)
})
# Check random effects ----------------
test_that("random effects are empty", {
expect_equal(out$pRE, FALSE)
expect_equal(out$psiRE, FALSE)
})
# Check output data output is correct -
test_that("out$y == y", {
expect_equal(out$y, y)
})
# Check default priors ----------------
test_that("default priors, inits, burn, thin work", {
out <- lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = n.samples,
n.factors = 3,
n.omp.threads = 1,
verbose = FALSE)
expect_s3_class(out, "lfMsPGOcc")
})
test_that("verbose prints to the screen", {
expect_output(lfMsPGOcc(occ.formula = occ.formula,
det.formula = det.formula,
data = data.list,
n.samples = 100,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 1,
n.thin = 1))
})
# Check waicOcc -----------------------
test_that("waicOCC works for lfMsPGOcc", {
# as.vector gets rid of names
waic.out <- as.vector(waicOcc(out))
expect_equal(length(waic.out), 3)
expect_equal(waic.out[3], -2 * (waic.out[1] - waic.out[2]))
})
test_that("fitted works for lfMsPGOcc", {
fitted.out <- fitted(out)
expect_equal(length(fitted.out), 2)
expect_equal(class(fitted.out$y.rep.samples), "array")
expect_equal(class(fitted.out$p.samples), "array")
expect_equal(dim(fitted.out$y.rep.samples), dim(fitted.out$p.samples))
})
test_that("predict works for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
colnames(X.0) <- c('int', 'occ.cov.1')
pred.out <- predict(out, X.0, coords.0)
expect_type(pred.out, "list")
expect_equal(dim(pred.out$psi.0.samples), c(n.post.samples, N, nrow(X.0)))
expect_equal(dim(pred.out$z.0.samples), c(n.post.samples, N, nrow(X.0)))
})
test_that("detection prediction works", {
X.p.0 <- dat$X.p[, 1, ]
pred.out <- predict(out, X.p.0, type = 'detection')
expect_type(pred.out, 'list')
expect_equal(dim(pred.out$p.0.samples), c(out$n.post * out$n.chains, N, J))
})
test_that("posterior predictive checks work for lfMsPGOcc", {
n.post.samples <- out$n.post * out$n.chains
ppc.out <- ppcOcc(out, 'chi-square', 2)
J.fit <- nrow(X)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, n.rep.max))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, n.rep.max))
ppc.out <- ppcOcc(out, 'chi-square', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 1)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, J.fit))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, J.fit))
ppc.out <- ppcOcc(out, 'freeman-tukey', 2)
expect_type(ppc.out, "list")
expect_equal(dim(ppc.out$fit.y), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.rep), c(n.post.samples, N))
expect_equal(dim(ppc.out$fit.y.group.quants), c(5, N, n.rep.max))
expect_equal(dim(ppc.out$fit.y.rep.group.quants), c(5, N, n.rep.max))
})
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