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skip_on_cran()
skip_if_not_installed("bayestestR", minimum_version = "0.15.0")
skip_if_not_installed("distributional")
skip_if_not_installed("withr")
withr::with_environment(
new.env(),
test_that("p_significance", {
data(mtcars)
m <<- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
set.seed(123)
x <- p_significance(m)
expect_identical(c(nrow(x), ncol(x)), c(5L, 5L))
expect_named(x, c("Parameter", "CI", "CI_low", "CI_high", "ps"))
expect_snapshot(print(x))
mp <- model_parameters(m)
set.seed(123)
x2 <- p_significance(mp)
expect_equal(x$ps, x2$ps, tolerance = 1e-4)
set.seed(123)
x <- p_significance(m, ci = 0.8)
expect_equal(x$ps, c(1, 0.3983, 0.9959, 0.6188, 0), tolerance = 1e-3)
set.seed(123)
x <- p_significance(m, threshold = 0.5)
expect_equal(x$ps, c(1, 0.4393, 0.9969, 0.6803, 0), tolerance = 1e-4)
set.seed(123)
# Test p_significance with custom thresholds for specific parameters
x <- p_significance(m, threshold = list(cyl = 0.5, wt = 0.7))
expect_equal(x$ps, c(1, 0.5982, 0.9955, 0.6803, 1e-04), tolerance = 1e-4)
})
)
test_that("p_significance, glmmTMB", {
skip_if_not_installed("glmmTMB")
data(Salamanders, package = "glmmTMB")
m1 <- glmmTMB::glmmTMB(count ~ mined + cover + (1 | site),
zi = ~mined,
family = poisson,
data = Salamanders
)
out <- p_significance(m1)
expect_identical(c(nrow(out), ncol(out)), c(5L, 6L))
expect_named(out, c("Parameter", "CI", "CI_low", "CI_high", "ps", "Component"))
expect_equal(out$ps, c(0.6451, 1, 0.9015, 1, 1), tolerance = 1e-4)
expect_identical(
out$Parameter,
c(
"(Intercept)_cond", "minedno_cond", "cover_cond", "(Intercept)_zi",
"minedno_zi"
)
)
})
test_that("p_significance, robust", {
skip_if_not_installed("sandwich")
data(mtcars)
m <- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
set.seed(123)
x <- p_significance(m, vcov = "HC3")
expect_snapshot(print(x))
})
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