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context("stan_colext function and methods")
on_mac <- tolower(Sys.info()[["sysname"]]) == "darwin"
on_cran <- !identical(Sys.getenv("NOT_CRAN"), "true")
skip_if(on_mac & on_cran, "On CRAN mac")
#Simulate dataset
set.seed(123)
M <- 150; T <- 3; J <- 3
z <- matrix(NA, M, T)
z[,1] <- rbinom(M, 1, 0.3)
for (i in 1:M){
for (t in 1:(T-1)){
if(z[i, t] == 1) z[i,(t+1)] <- rbinom(1, 1, 0.75)
if(z[i, t] == 0) z[i,(t+1)] <- rbinom(1, 1, 0.6)
}
}
x3 <- matrix(rnorm(M*T*J), M, T*J)
pest <- plogis(0 + x3*0.7)
zrep <- z[,rep(1:T, each=J)]
y <- matrix(NA, M, T*J)
for (i in 1:M){
idx <- 1
for (t in 1:T){
for (j in 1:J){
y[i,idx] <- rbinom(1, 1, pest[i,idx]*zrep[i,idx])
idx <- idx + 1
}
}
}
sc <- data.frame(x1=factor(sample(letters[1:10], M, replace=TRUE)),
x2=rnorm(M))
oc <- list(x3=x3)
ysc <- data.frame(x4=rnorm(M*T))
umf <- unmarkedMultFrame(y, numPrimary=T, siteCovs=sc, obsCovs=oc,
yearlySiteCovs=ysc)
umf2 <- umf
umf2@y[1,] <- NA
umf2@y[2,1] <- NA
good_fit <- TRUE
tryCatch({
fit <- suppressWarnings(stan_colext(~x2,~x4,~1,~1, umf[1:10,], chains=2, iter=100,
refresh=0))
fit_na <- suppressWarnings(stan_colext(~x2,~x4,~1,~1, umf2[1:10,], chains=2,
iter=100, refresh=0))
}, error=function(e){
good_fit <<- FALSE
})
skip_if(!good_fit, "Test setup failed")
test_that("stan_pcount output structure is correct",{
expect_is(fit, "ubmsFitColext")
expect_is(fit, "ubmsFitOccu")
expect_equal(nsamples(fit), 100)
})
test_that("stan_colext produces accurate results",{
skip_on_cran()
skip_on_ci()
skip_on_covr()
set.seed(123)
fit_long <- suppressWarnings(stan_colext(~x2,~x4,~1,~1, umf,
chains=2, iter=300, refresh=0))
fit_unm <- colext(~x2,~x4,~1,~1, umf)
#similar to truth
beta <- c(log(0.3/0.7), 0, log(0.6/0.4), 0, log(0.25/0.75), 0)
expect_RMSE(coef(fit_long), beta, 0.15)
#similar to unmarked
expect_RMSE(coef(fit_long), coef(fit_unm), 0.03)
#similar to previous known values
known <- c(-0.86420,-0.0755,0.58441,0.13419,-1.04717,0.04023)
expect_RMSE(coef(fit_long), known, 0.02)
})
test_that("stan_colext handles NA values",{
expect_RMSE(coef(fit_na), coef(fit), 1.5)
})
test_that("ubmsFitColext gof method works",{
set.seed(123)
g <- gof(fit, draws=5, quiet=TRUE)
expect_between(g@estimate, 10, 50)
gof_plot_method <- methods::getMethod("plot", "ubmsGOF")
pdf(NULL)
pg <- gof_plot_method(g)
dev.off()
expect_is(pg, "gg")
})
test_that("ubmsFitColext gof method works with missing values",{
set.seed(123)
g <- gof(fit_na, draws=5, quiet=TRUE)
expect_is(g, "ubmsGOF")
})
test_that("ubmsFitColext predict method works",{
pr <- predict(fit_na, "state")
expect_is(pr, "data.frame")
expect_equal(dim(pr), c(10, 4))
expect_between(pr[1,1], 0, 1)
pr <- predict(fit_na, "det")
expect_equal(dim(pr), c(90,4))
expect_between(pr[1,1], 0, 1)
#with newdata
nd <- data.frame(x2=c(0,1))
pr <- predict(fit_na, "state", newdata=nd)
expect_equal(dim(pr), c(2,4))
expect_between(pr[1,1], 0, 1)
})
test_that("ubmsFitColext sim_z method works",{
set.seed(123)
samples <- get_samples(fit, 5)
zz <- sim_z(fit, samples, re.form=NULL)
expect_is(zz, "matrix")
expect_equal(dim(zz), c(length(samples), 10*umf@numPrimary))
expect_between(mean(zz), 0.5, 1 )
expect_equal(max(zz), 1)
set.seed(123)
pz <- posterior_predict(fit, "z", draws=5)
expect_equivalent(zz, pz)
})
test_that("ubmsFitColext sim_y method works",{
set.seed(123)
samples <- get_samples(fit, 5)
yy <- sim_y(fit, samples, re.form=NULL)
expect_is(yy, "matrix")
expect_equal(dim(yy), c(length(samples), 10*obsNum(umf)))
expect_equal(max(yy), 1)
set.seed(123)
py <- posterior_predict(fit, "y", draws=5)
expect_equivalent(yy, py)
})
test_that("Posterior sim methods for ubmsFitColext work with NAs",{
zna <- posterior_predict(fit_na, "z", draws=3)
expect_equal(dim(zna), c(3,10*umf2@numPrimary))
expect_true(all(!is.na(zna[,1])))
yna <- posterior_predict(fit_na, "y", draws=3)
expect_equal(dim(yna), c(3, 10*obsNum(umf2)))
expect_equal(sum(is.na(yna[1,])), 10)
expect_equal(sum(is.na(yna[2,])), 10)
})
test_that("Posterior linear pred methods work for ubmsFitColext",{
set.seed(123)
samples <- get_samples(fit, 3)
lp1 <- sim_lp(fit, "state", transform=TRUE, samples=samples,
newdata=NULL, re.form=NULL)
expect_equal(dim(lp1), c(length(samples), 10))
set.seed(123)
pl <- posterior_linpred(fit, draws=3, submodel="state")
})
test_that("Fitted/residual methods work with ubmsFitColext",{
ubms_fitted <- methods::getMethod("fitted", "ubmsFit")
ubms_residuals <- methods::getMethod("residuals", "ubmsFit")
ubms_plot <- methods::getMethod("plot", "ubmsFit")
ft <- ubms_fitted(fit, "state", draws=5)
ft2 <- ubms_fitted(fit, "det", draws=5)
expect_equal(dim(ft), c(5,10*umf@numPrimary))
expect_equal(dim(ft2), c(5,10*obsNum(umf)))
res <- ubms_residuals(fit, "state", draws=5)
res2 <- ubms_residuals(fit, "det", draws=5)
expect_equal(dim(res), c(5,10*umf@numPrimary))
expect_equal(dim(res2), c(5,10*obsNum(umf)))
pdf(NULL)
rp <- plot_residuals(fit, "state")
mp <- plot_marginal(fit, "state")
dev.off()
expect_is(rp, "gg")
expect_is(mp, "gtable")
})
test_that("projected function and sim_state works with ubmsFitColext",{
set.seed(123)
samples <- get_samples(fit, 3)
pro <- sim_projected(fit, samples, NULL)
expect_equal(dim(pro), c(3, 10*umf@numPrimary))
expect_between(mean(pro), 0.5, 1)
set.seed(123)
expect_equivalent(projected(fit, 3), pro)
pro_na <- sim_projected(fit_na, samples, NULL)
expect_equal(dim(pro), dim(pro_na))
set.seed(123)
expect_equivalent(sim_state(fit, samples), pro)
set.seed(123)
expect_equal(dim(sim_state(fit_na, samples)), dim(pro))
})
test_that("turnover function works with ubmsFitColext",{
set.seed(123)
samples <- get_samples(fit, 3)
turn <- sim_turnover(fit, samples, NULL)
expect_equal(dim(turn), c(3, 10*(umf@numPrimary-1)))
expect_between(mean(turn), 0, 0.5)
set.seed(123)
expect_equivalent(turnover(fit, 3), turn)
turn_na <- sim_turnover(fit_na, samples, NULL)
expect_equal(dim(turn), dim(turn_na))
})
test_that("attempt to fit spatial model fails", {
expect_error(stan_colext(~x2+RSR(x,y,1),~x4,~1,~1, umf[1:10,], chains=2,
iter=100, refresh=0))
})
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