https://github.com/cran/bayestestR
Revision 9985109256c08d654edb46adb9cb20c913fb1888 authored by Dominique Makowski on 29 May 2019, 14:10:02 UTC, committed by cran-robot on 29 May 2019, 14:10:02 UTC
1 parent fe07bfa
Tip revision: 9985109256c08d654edb46adb9cb20c913fb1888 authored by Dominique Makowski on 29 May 2019, 14:10:02 UTC
version 0.2.0
version 0.2.0
Tip revision: 9985109
test-bayesfactor.R
context("bayesfactor_*")
# bayesfactor_savagedickey ------------------------------------------------
test_that("bayesfactor_savagedickey", {
testthat::skip_on_cran()
set.seed(444)
Xprior <- rnorm(1000)
Xposterior <- rnorm(1000, 0.7, 0.2)
bfsd <- bayestestR::bayesfactor_savagedickey(Xposterior, prior = Xprior, hypothesis = 0, direction = 0)
testthat::expect_equal(log(bfsd$BF), 3.7, tolerance = 0.1)
bfsd <- bayestestR::bayesfactor_savagedickey(Xposterior, prior = Xprior, hypothesis = 0, direction = 1)
testthat::expect_equal(log(bfsd$BF), 4.3, tolerance = 0.1)
bfsd <- bayestestR::bayesfactor_savagedickey(Xposterior, prior = Xprior, hypothesis = 0, direction = -1)
testthat::expect_equal(log(bfsd$BF), -2.5, tolerance = 0.1)
bfsd <- bayestestR::bayesfactor_savagedickey(Xposterior, prior = Xprior, hypothesis = 1, direction = 0)
testthat::expect_equal(log(bfsd$BF), -0.8, tolerance = 0.1)
testthat::expect_warning(bfsd <- bayestestR::bayesfactor_savagedickey(Xposterior))
testthat::expect_equal(log(bfsd$BF), 0, tolerance = 0.1)
library(rstanarm)
set.seed(333)
junk <- capture.output(model <- stan_glm(extra ~ group, data = sleep))
bfsd <- bayestestR::bayesfactor_savagedickey(model)
testthat::expect_equal(log(bfsd$BF), c(-2.69, -0.14), tolerance = 0.2)
# SKIP FOR TRAVIS
# library(brms)
# brms_mixed_6 <- insight::download_model("brms_mixed_6")
# set.seed(222)
# bfsd <- bayesfactor_savagedickey(brms_mixed_6, effects = "fixed")
# testthat::expect_equal(log(bfsd$BF), c(-6.0, -5.8, 0.7, -2.7, -7.4), tolerance = 0.2)
#
# brms_mixed_1 <- insight::download_model("brms_mixed_1")
# testthat::expect_error(bayesfactor_savagedickey(brms_mixed_1))
})
# bayesfactor_models ------------------------------------------------------
set.seed(444)
mo1 <- lme4::lmer(Sepal.Length ~ (1 | Species), data = iris)
mo2 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
mo3 <- lme4::lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
mo4 <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris)
mo4_e <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris[-1, ])
# both uses of denominator
BFM1 <- bayestestR::bayesfactor_models(mo2, mo3, mo4, mo1, denominator = 4)
BFM2 <- bayestestR::bayesfactor_models(mo2, mo3, mo4, denominator = mo1)
BFM3 <- bayestestR::bayesfactor_models(mo2, mo3, mo4, mo1, denominator = mo1)
brms_4bf_1 <- insight::download_model("brms_4bf_1")
brms_4bf_2 <- insight::download_model("brms_4bf_2")
brms_4bf_3 <- insight::download_model("brms_4bf_3")
brms_4bf_4 <- insight::download_model("brms_4bf_4")
brms_4bf_5 <- insight::download_model("brms_4bf_5")
# set.seed(444)
# library(brms)
# brms_models <- suppressWarnings(bayestestR::bayesfactor_models(brms_4bf_1,brms_4bf_2,brms_4bf_3, brms_4bf_4, brms_4bf_5))
#
# library(rstanarm)
# junk <- capture.output(stan_bf_0 <- stan_glm(Sepal.Length ~ 1, data = iris,
# diagnostic_file = file.path(tempdir(), "df0.csv")))
# junk <- capture.output(stan_bf_1 <- stan_glm(Sepal.Length ~ Species, data = iris,
# diagnostic_file = file.path(tempdir(), "df1.csv")))
# test_that("bayesfactor_models", {
# # brms
# testthat::expect_warning(bayestestR::bayesfactor_models(brms_4bf_1,brms_4bf_2))
# testthat::expect_is(brms_models,"bayesfactor_models")
# testthat::expect_equal(log(brms_models$BF),c(0, 68.5, 102.5, 128.6, 128.8), tolerance = 0.1)
#
# # rstanarm
# testthat::expect_warning(bayestestR::bayesfactor_models(stan_bf_0,stan_bf_1))
# stan_models <- suppressWarnings(bayestestR::bayesfactor_models(stan_bf_0,stan_bf_1))
# testthat::expect_is(stan_models,"bayesfactor_models")
# testthat::expect_equal(log(stan_models$BF),c(0, 65.19), tolerance = 0.1)
#
# ## BIC
# testthat::expect_equal(BFM1,BFM2)
# testthat::expect_equal(BFM1,BFM3)
#
# # only on same data!
# testthat::expect_error(bayestestR::bayesfactor_models(mo1, mo2, mo4_e))
#
#
# # update models
# testthat::expect_equal(log(update(BFM2,subset = c(1,2))$BF),c(0,57.3,54.52), tolerance = 0.1)
#
# # update reference
# testthat::expect_equal(log(update(BFM2,reference = 1)$BF),c(0,-2.8,-6.2,-57.4),tolerance = 0.1)
# })
# bayesfactor_inclusion ---------------------------------------------------
test_that("bayesfactor_inclusion", {
# BayesFactor
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
BF_ToothGrowth <- BayesFactor::anovaBF(len ~ dose * supp, ToothGrowth)
testthat::expect_equal(
bayestestR::bayesfactor_inclusion(BF_ToothGrowth),
bayestestR::bayesfactor_inclusion(bayestestR::bayesfactor_models(BF_ToothGrowth))
)
# with random effects in all models:
testthat::expect_true(is.nan(bayestestR::bayesfactor_inclusion(BFM1)[1, "BF.Inc"]))
# + match_models
bfinc_matched <- bayestestR::bayesfactor_inclusion(BFM1, match_models = TRUE)
testthat::expect_equal(bfinc_matched$P.Inc.prior, c(1, 0.25, 0.5, 0.25), tolerance = 0.1)
testthat::expect_equal(bfinc_matched$P.Inc.posterior, c(1, 0.94, 0.06, 0), tolerance = 0.1)
testthat::expect_equal(log(bfinc_matched$BF.Inc), c(NaN, 57.37, -2.82, -5.25), tolerance = 0.1)
})
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