tests/testthat/test-rope_range.R

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

test_that("rope_range cor", {
  x <- cor.test(ToothGrowth$len, ToothGrowth$dose)
  expect_equal(rope_range(x), c(-0.05, 0.05), tolerance = 1e-3)
})

test_that("rope_range gaussian", {
  data(mtcars)
  mod <- lm(mpg ~ gear + hp, data = mtcars)
  expect_equal(rope_range(mod), c(-0.1 * sd(mtcars$mpg), 0.1 * sd(mtcars$mpg)), tolerance = 1e-3)
})

test_that("rope_range log gaussian", {
  data(iris)
  mod <- lm(log(Sepal.Length) ~ Species, data = iris)
  expect_equal(rope_range(mod), c(-0.01, 0.01), tolerance = 1e-3)
})

test_that("rope_range log gaussian 2", {
  data(mtcars)
  mod <- glm(mpg ~ gear + hp, data = mtcars, family = gaussian("log"))
  expect_equal(rope_range(mod), c(-0.01, 0.01), tolerance = 1e-3)
})

test_that("rope_range logistic", {
  data(mtcars)
  mod <- glm(am ~ gear + hp, data = mtcars, family = binomial())
  expect_equal(rope_range(mod), c(-1 * 0.1 * pi / sqrt(3), 0.1 * pi / sqrt(3)), tolerance = 1e-3)
})



# if ( skip_if_not_or_load_if_installed("brms")) {
#   test_that("rope_range", {
#     model <- brm(mpg ~ wt + gear, data = mtcars, iter = 300)
#
#     expect_equal(
#       rope_range(model),
#       c(-0.6026948, 0.6026948),
#       tolerance = 0.01
#     )
#   })
#
#   test_that("rope_range (multivariate)", {
#     model <- brm(mvbind(mpg, disp) ~ wt + gear, data = mtcars, iter = 300)
#
#     expect_equal(
#       rope_range(model),
#       list(
#         mpg = c(-0.602694, 0.602694),
#         disp = c(-12.393869, 12.393869)
#       ),
#       tolerance = 0.01
#     )
#   })
# }