tests/testthat/test-bayesfactor_parameters.R

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

test_that("bayesfactor_parameters data frame", {
  skip_if_offline()
  skip_if_not_or_load_if_installed("rstanarm")
  skip_if_not_or_load_if_installed("BayesFactor")
  skip_if_not_or_load_if_installed("httr")
  skip_if_not_or_load_if_installed("brms")

  Xprior <- data.frame(
    x = distribution_normal(1e4),
    y = distribution_normal(1e4)
  )
  Xposterior <- data.frame(
    x = distribution_normal(1e4, mean = 0.5),
    y = distribution_normal(1e4, mean = -0.5)
  )

  # point
  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = 0, verbose = FALSE)
  expect_equal(bfsd$log_BF, c(0.12, 0.12), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = 1, verbose = FALSE)
  expect_equal(bfsd$log_BF, c(0.44, -0.35), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = -1, verbose = FALSE)
  expect_equal(bfsd$log_BF, c(-0.35, 0.44), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0.5, direction = 0, verbose = FALSE)
  expect_equal(bfsd$log_BF, c(-0.12, 0.37), tolerance = 0.1)

  expect_warning(bayesfactor_parameters(Xposterior, Xprior))

  w <- capture_warnings(bfsd <- bayesfactor_parameters(Xposterior))
  expect_match(w, "Prior", all = FALSE)
  expect_match(w, "40", all = FALSE)
  expect_equal(bfsd$log_BF, c(0, 0), tolerance = 0.1)

  # interval
  expect_warning(
    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 0),
    regexp = NA
  )
  expect_equal(bfsd$log_BF, c(0.13, 0.13), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 1)
  expect_equal(bfsd$log_BF, c(0.47, -0.39), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = -1)
  expect_equal(bfsd$log_BF, c(-0.39, 0.47), tolerance = 0.1)

  # interval with inf
  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, Inf))
  expect_equal(bfsd$log_BF, c(-0.81, 0.80), tolerance = 0.1)

  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-Inf, .1))
  expect_equal(bfsd$log_BF, c(0.80, -0.81), tolerance = 0.1)
})


test_that("bayesfactor_parameters RSTANARM", {
  skip_on_cran()
  skip_if_offline()
  skip_if_not_or_load_if_installed("rstanarm")
  skip_if_not_or_load_if_installed("BayesFactor")
  skip_if_not_or_load_if_installed("httr")
  skip_if_not_or_load_if_installed("brms")

  fit <- suppressMessages(stan_glm(mpg ~ ., data = mtcars, refresh = 0))

  set.seed(333)
  fit_p <- unupdate(fit, verbose = FALSE)
  expect_warning(BF2 <- bayesfactor_parameters(fit, fit_p))

  set.seed(333)
  BF1 <- bayesfactor_parameters(fit, verbose = FALSE)

  expect_equal(BF1, BF2)

  model_flat <- suppressMessages(
    stan_glm(extra ~ group, data = sleep, prior = NULL, refresh = 0)
  )
  suppressMessages(
    expect_error(bayesfactor_parameters(model_flat))
  )

  skip_on_ci()
  fit10 <- update(fit, chains = 10, iter = 5100, warmup = 100)
  suppressMessages(
    expect_warning(bayesfactor_parameters(fit10), regexp = NA)
  )
})


# bayesfactor_parameters BRMS ---------------------------------------------
#
#   test_that("bayesfactor_parameters BRMS", {
#     skip_if_offline()
#     skip_if_not_or_load_if_installed("rstanarm")
#     skip_if_not_or_load_if_installed("BayesFactor")
#     skip_if_not_or_load_if_installed("httr")
#     skip_if_not_or_load_if_installed("brms")
#
#     brms_mixed_6 <- insight::download_model("brms_mixed_6")
#
#     set.seed(222)
#     brms_mixed_6_p <- unupdate(brms_mixed_6)
#     bfsd1 <- bayesfactor_parameters(brms_mixed_6, brms_mixed_6_p, effects = "fixed")
#
#     set.seed(222)
#     bfsd2 <- bayesfactor_parameters(brms_mixed_6, effects = "fixed")
#
#     expect_equal(log(bfsd1$BF), log(bfsd2$BF), tolerance = .11)
#
#
#     brms_mixed_1 <- insight::download_model("brms_mixed_1")
#     expect_error(bayesfactor_parameters(brms_mixed_1))
#   })