tests/testthat/test-p_significance.R

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

test_that("p_significance", {
  skip_if_offline()
  skip_if_not_or_load_if_installed("rstanarm")

  # numeric
  set.seed(333)
  x <- distribution_normal(10000, 1, 1)
  ps <- p_significance(x)
  expect_equal(as.numeric(ps), 0.816, tolerance = 0.1)
  expect_equal(nrow(p_significance(data.frame(replicate(4, rnorm(100))))), 4)
  expect_s3_class(ps, "p_significance")
  expect_equal(tail(capture.output(print(ps)), 1), "Practical Significance (threshold: 0.10): 0.82")
})

test_that("stanreg", {
  skip_if_offline()
  skip_if_not_or_load_if_installed("rstanarm")

  m <- insight::download_model("stanreg_merMod_5")

  expect_equal(
    p_significance(m, effects = "all")$ps[1],
    0.99,
    tolerance = 1e-2
  )
})

test_that("brms", {
  skip_if_offline()
  skip_if_not_or_load_if_installed("rstanarm")

  m2 <- insight::download_model("brms_1")

  expect_equal(
    p_significance(m2, effects = "all")$ps,
    c(1.0000, 0.9985, 0.9785),
    tolerance = 0.01
  )
})