.runThisTest <- Sys.getenv("RunAllbayestestRTests") == "yes"
if (.runThisTest) {
if (require("rstanarm") && require("bayestestR") && require("httr") && require("insight")) {
test_that("rstanarm", {
skip_on_cran()
set.seed(333)
model <- insight::download_model("stanreg_lm_1")
expect_equal(rope_range(model)[1], -0.602, tol = 0.1)
model <- insight::download_model("stanreg_meanfield_lm_1")
expect_equal(rope_range(model)[1], -0.602, tol = 0.1)
model <- insight::download_model("stanreg_fullrank_lm_1")
expect_equal(rope_range(model)[1], -0.602, tol = 0.1)
model <- insight::download_model("stanreg_lmerMod_1")
expect_equal(rope_range(model)[1], -0.097, tol = 0.1)
model <- insight::download_model("stanreg_glm_1")
expect_equal(rope_range(model)[1], -0.18, tol = 0.1)
model <- insight::download_model("stanreg_merMod_1")
expect_equal(rope_range(model)[1], -0.18, tol = 0.1)
model <- insight::download_model("stanreg_gamm4_1")
expect_equal(rope_range(model)[1], -0.043, tol = 0.1)
model <- insight::download_model("stanreg_gam_1")
params <- describe_posterior(model, centrality = "all", test = "all", dispersion = TRUE)
expect_equal(c(nrow(params), ncol(params)), c(4, 22))
expect_is(hdi(model), "data.frame")
expect_is(ci(model), "data.frame")
expect_is(rope(model), "data.frame")
# expect_true("equivalence_test" %in% class(equivalence_test(model)))
expect_is(map_estimate(model), "data.frame")
expect_is(p_map(model), "data.frame")
expect_is(mhdior(model), "data.frame")
expect_is(p_direction(model), "data.frame")
# expect_error(equivalence_test(model, range = c(.1, .3, .5)))
# print(equivalence_test(model, ci = c(.1, .3, .5)))
})
test_that("rstanarm", {
skip_on_cran()
set.seed(333)
model <- insight::download_model("stanreg_glm_3")
out <- describe_posterior(model, effects = "all", components = "all", centrality = "mean")
s <- summary(model)
expect_identical(colnames(out), c(
"Parameter", "Mean", "CI", "CI_low", "CI_high", "pd", "ROPE_CI",
"ROPE_low", "ROPE_high", "ROPE_Percentage", "Rhat", "ESS"
))
expect_equal(s[1:4, 1, drop = TRUE], out$Mean, check.attributes = FALSE, tolerance = 1e-3)
expect_equal(s[1:4, 8, drop = TRUE], out$Rhat, check.attributes = FALSE, tolerance = 1e-1)
})
test_that("rstanarm", {
skip_on_cran()
set.seed(333)
model <- insight::download_model("stanreg_merMod_3")
out <- describe_posterior(model, effects = "all", components = "all", centrality = "mean")
s <- summary(model)
expect_identical(colnames(out), c(
"Parameter", "Effects", "Mean", "CI", "CI_low", "CI_high",
"pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage",
"Rhat", "ESS"
))
expect_equal(s[1:8, 1, drop = TRUE], out$Mean, check.attributes = FALSE, tolerance = 1e-3)
expect_equal(s[1:8, 8, drop = TRUE], out$Rhat, check.attributes = FALSE, tolerance = 1e-1)
})
test_that("rstanarm", {
skip_on_cran()
set.seed(333)
model <- insight::download_model("stanmvreg_1")
out <- describe_posterior(model, effects = "fixed", components = "all", centrality = "mean", test = NULL)
s <- summary(model)
expect_identical(colnames(out), c(
"Parameter", "Response", "Mean", "CI", "CI_low", "CI_high",
"Rhat", "ESS"
))
expect_equal(s[c(1:2, 5:7), 1, drop = TRUE], out$Mean, check.attributes = FALSE, tolerance = 1e-3)
expect_equal(s[c(1:2, 5:7), 10, drop = TRUE], out$Rhat, check.attributes = FALSE, tolerance = 1e-1)
})
test_that("rstanarm", {
skip_on_cran()
set.seed(333)
model <- insight::download_model("stanmvreg_1")
out <- describe_posterior(model, effects = "fixed", components = "all", centrality = "mean", test = NULL, priors = TRUE)
expect_identical(colnames(out), c(
"Parameter", "Response", "Mean", "CI", "CI_low", "CI_high",
"Rhat", "ESS", "Prior_Distribution", "Prior_Location",
"Prior_Scale"
))
expect_equal(nrow(out), 5)
})
}
}