https://github.com/cran/bayestestR
Tip revision: 428249f43a9c6fd0c425b28deb5fee51a9525d69 authored by Dominique Makowski on 18 September 2022, 01:46:03 UTC
version 0.13.0
version 0.13.0
Tip revision: 428249f
test-rope.R
if (requiet("bayestestR") && requiet("testthat") && requiet("rstanarm") && requiet("brms")) {
test_that("rope", {
expect_equal(as.numeric(rope(distribution_normal(1000, 0, 1), verbose = FALSE)), 0.084, tolerance = 0.01)
expect_equal(equivalence_test(distribution_normal(1000, 0, 1))$ROPE_Equivalence, "Undecided")
expect_equal(length(capture.output(print(equivalence_test(distribution_normal(1000))))), 9)
expect_equal(length(capture.output(print(equivalence_test(distribution_normal(1000),
ci = c(0.8, 0.9)
)))), 14)
expect_equal(as.numeric(rope(distribution_normal(1000, 2, 0.01), verbose = FALSE)), 0, tolerance = 0.01)
expect_equal(equivalence_test(distribution_normal(1000, 2, 0.01))$ROPE_Equivalence, "Rejected")
expect_equal(as.numeric(rope(distribution_normal(1000, 0, 0.001), verbose = FALSE)), 1, tolerance = 0.01)
expect_equal(equivalence_test(distribution_normal(1000, 0, 0.001))$ROPE_Equivalence, "Accepted")
expect_equal(equivalence_test(distribution_normal(1000, 0, 0.001), ci = 1)$ROPE_Equivalence, "Accepted")
# print(rope(rnorm(1000, mean = 0, sd = 3), ci = .5))
expect_equal(rope(rnorm(1000, mean = 0, sd = 3), ci = c(.1, .5, .9), verbose = FALSE)$CI, c(.1, .5, .9))
x <- equivalence_test(distribution_normal(1000, 1, 1), ci = c(.50, .99))
expect_equal(x$ROPE_Percentage[2], 0.0484, tolerance = 0.01)
expect_equal(x$ROPE_Equivalence[2], "Undecided")
expect_error(rope(distribution_normal(1000, 0, 1), range = c(0.0, 0.1, 0.2)))
set.seed(333)
expect_s3_class(rope(distribution_normal(1000, 0, 1), verbose = FALSE), "rope")
expect_error(rope(distribution_normal(1000, 0, 1), range = c("A", 0.1)))
expect_equal(
as.numeric(rope(distribution_normal(1000, 0, 1),
range = c(-0.1, 0.1)
)), 0.084,
tolerance = 0.01
)
})
.runThisTest <- Sys.getenv("RunAllbayestestRTests") == "yes"
if (.runThisTest) {
if (requiet("insight")) {
m <- insight::download_model("stanreg_merMod_5")
p <- insight::get_parameters(m, effects = "all")
test_that("rope", {
expect_equal(
# fix range to -.1/.1, to compare to data frame method
rope(m, range = c(-.1, .1), effects = "all", verbose = FALSE)$ROPE_Percentage,
rope(p, verbose = FALSE)$ROPE_Percentage,
tolerance = 1e-3
)
})
m <- insight::download_model("brms_zi_3")
p <- insight::get_parameters(m, effects = "all", component = "all")
test_that("rope", {
expect_equal(
rope(m, effects = "all", component = "all", verbose = FALSE)$ROPE_Percentage,
rope(p, verbose = FALSE)$ROPE_Percentage,
tolerance = 1e-3
)
})
}
}
}
.runThisTest <- Sys.getenv("RunAllbayestestRTests") == "yes"
# if (.runThisTest && require("brms", quietly = TRUE)) {
# set.seed(123)
# model <- brm(mpg ~ wt + gear, data = mtcars, iter = 500)
# rope <- rope(model, verbose = FALSE)
#
# test_that("rope (brms)", {
# expect_equal(rope$ROPE_high, -rope$ROPE_low, tolerance = 0.01)
# expect_equal(rope$ROPE_high[1], 0.6026948)
# expect_equal(rope$ROPE_Percentage, c(0.00, 0.00, 0.50), tolerance = 0.1)
# })
#
# model <- brm(mvbind(mpg, disp) ~ wt + gear, data = mtcars, iter = 500)
# rope <- rope(model, verbose = FALSE)
#
# test_that("rope (brms, multivariate)", {
# expect_equal(rope$ROPE_high, -rope$ROPE_low, tolerance = 0.01)
# expect_equal(rope$ROPE_high[1], 0.6026948, tolerance = 0.01)
# expect_equal(rope$ROPE_high[4], 12.3938694, tolerance = 0.01)
# expect_equal(
# rope$ROPE_Percentage,
# c(0, 0, 0.493457, 0.072897, 0, 0.508411),
# tolerance = 0.1
# )
# })
# }
if (require("BayesFactor", quietly = TRUE)) {
mods <- regressionBF(mpg ~ am + cyl, mtcars, progress = FALSE)
rx <- suppressMessages(rope(mods, verbose = FALSE))
expect_equal(rx$ROPE_high, -rx$ROPE_low, tolerance = 0.01)
expect_equal(rx$ROPE_high[1], 0.6026948, tolerance = 0.01)
}