https://github.com/cran/bayestestR
Tip revision: 092b63c552bdf3196413c25583520dc23033769b authored by Dominique Makowski on 30 October 2021, 13:00:02 UTC
version 0.11.5
version 0.11.5
Tip revision: 092b63c
test-BFBayesFactor.R
if (require("testthat") && require("BayesFactor") && suppressPackageStartupMessages(require("bayestestR", quietly = TRUE))) {
set.seed(333)
x <- BayesFactor::correlationBF(y = iris$Sepal.Length, x = iris$Sepal.Width)
test_that("p_direction", {
expect_equal(as.numeric(p_direction(x)), 0.9225, tolerance = 1)
})
# BF t.test one sample ---------------------------
data(sleep)
diffScores <- sleep$extra[1:10] - sleep$extra[11:20]
x <- BayesFactor::ttestBF(x = diffScores)
test_that("p_direction", {
expect_equal(as.numeric(p_direction(x)), 0.99675, tolerance = 1)
})
# BF t.test two samples ---------------------------
data(chickwts)
chickwts <- chickwts[chickwts$feed %in% c("horsebean", "linseed"), ]
chickwts$feed <- factor(chickwts$feed)
x <- BayesFactor::ttestBF(formula = weight ~ feed, data = chickwts)
test_that("p_direction", {
expect_equal(as.numeric(p_direction(x)), 1, tolerance = 1)
})
# BF t.test meta-analytic ---------------------------
t <- c(-.15, 2.39, 2.42, 2.43)
N <- c(100, 150, 97, 99)
x <- BayesFactor::meta.ttestBF(t = t, n1 = N, rscale = 1)
test_that("p_direction", {
expect_equal(as.numeric(p_direction(x)), 0.99975, tolerance = 1)
})
# # ---------------------------
# # "BF ANOVA"
# data(ToothGrowth)
# ToothGrowth$dose <- factor(ToothGrowth$dose)
# levels(ToothGrowth$dose) <- c("Low", "Medium", "High")
# x <- BayesFactor::anovaBF(len ~ supp*dose, data=ToothGrowth)
# test_that("p_direction", {
# expect_equal(as.numeric(p_direction(x)), 91.9, tol=0.1)
# })
#
# # ---------------------------
# # "BF ANOVA Random"
# data(puzzles)
# x <- BayesFactor::anovaBF(RT ~ shape*color + ID, data = puzzles, whichRandom="ID")
# test_that("p_direction", {
# expect_equal(as.numeric(p_direction(x)), 91.9, tol=0.1)
# })
#
#
# # ---------------------------
# # "BF lm"
# x <- BayesFactor::lmBF(len ~ supp + dose, data = ToothGrowth)
# test_that("p_direction", {
# expect_equal(as.numeric(p_direction(x)), 91.9, tol=0.1)
# })
#
#
# x2 <- BayesFactor::lmBF(len ~ supp + dose + supp:dose, data = ToothGrowth)
# x <- x / x2
# test_that("p_direction", {
# expect_equal(as.numeric(p_direction(x)), 91.9, tol=0.1)
# })
test_that("rope_range", {
x <- BayesFactor::lmBF(len ~ supp + dose, data = ToothGrowth)
expect_equal(rope_range(x)[2], sd(ToothGrowth$len) / 10)
x <- BayesFactor::ttestBF(
ToothGrowth$len[ToothGrowth$supp == "OJ"],
ToothGrowth$len[ToothGrowth$supp == "VC"]
)
expect_equal(rope_range(x)[2], sd(ToothGrowth$len) / 10)
x <- BayesFactor::ttestBF(formula = len ~ supp, data = ToothGrowth)
expect_equal(rope_range(x)[2], sd(ToothGrowth$len) / 10)
# else
x <- BayesFactor::correlationBF(ToothGrowth$len, ToothGrowth$dose)
expect_equal(rope_range(x, verbose = FALSE), c(-0.05, 0.05))
})
}