library(lgpr)
source("helpers/SW.R")
# -------------------------------------------------------------------------
N_ITER <- 22 # should be even
N_CHAINS <- 1
context("Covariate selection")
test_that("selection can be done when sample_f = FALSE", {
SW({
fit <- example_fit(iter = N_ITER, chains = N_CHAINS)
})
sel_before <- select(fit)
expect_equal(length(sel_before$Component), 5)
# After clearing postproc info
fit <- clear_postproc(fit)
expect_output(
{
expect_message(
{
select(fit, verbose = TRUE)
},
"No existing postprocessing information stored"
)
},
"Computing analytic function posteriors"
)
sel_after <- select(fit, verbose = FALSE)
expect_equal(sel_before, sel_after)
})
test_that("selection can be done when sample_f = TRUE", {
SW({
fit <- example_fit(
iter = N_ITER,
chains = N_CHAINS,
likelihood = "binomial"
)
})
sel_before <- select(fit, threshold = 0.3)
expect_equal(length(sel_before$Component), 5)
# After clearing postproc info
fit <- clear_postproc(fit)
sel_after <- select(fit, verbose = FALSE, threshold = 0.3)
expect_equal(sel_before, sel_after)
})
test_that("other select() functions can be used", {
SW({
fit <- example_fit(iter = N_ITER, chains = N_CHAINS)
})
sel <- select_freq(fit)
expect_equal(length(sel$Component), 5)
sel <- select.integrate(fit, verbose = FALSE)
expect_equal(dim(sel$selected), c(101, 5))
sel <- select_freq.integrate(fit, verbose = FALSE)
expect_equal(dim(sel$freq), c(101, 5))
})
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