tests/testthat/test-p_map.R

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

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

  expect_equal(as.numeric(p_map(distribution_normal(1000))), 1, tolerance = 0.1)
  expect_equal(as.numeric(p_map(distribution_normal(1000, 1, 1))), 0.62, tolerance = 0.1)
  expect_equal(as.numeric(p_map(distribution_normal(1000, 2, 1))), 0.15, tolerance = 0.1)
  expect_equal(as.numeric(p_map(distribution_normal(1000, 3, 0.01))), 0, tolerance = 0.1)
})


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

  m <- insight::download_model("stanreg_merMod_5")
  p <- insight::get_parameters(m, effects = "all")

  expect_equal(
    p_map(m, effects = "all")$p_MAP,
    p_map(p)$p_MAP,
    tolerance = 0.1
  )
})


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

  m <- insight::download_model("brms_zi_3")
  p <- insight::get_parameters(m, effects = "all", component = "all")

  expect_equal(
    p_map(m, effects = "all", component = "all")$p_MAP,
    p_map(p)$p_MAP,
    tolerance = 0.1
  )
})



test_that("p_map | null", {
  x <- distribution_normal(4000, mean = 1)
  expect_equal(p_map(x), 0.6194317, ignore_attr = TRUE, tolerance = 0.01)
  expect_equal(p_map(x, null = 1), 1, ignore_attr = TRUE, tolerance = 0.01)
})