https://github.com/cran/bayestestR
Tip revision: 2565fc870cd7f0a64d857ff89e682dc9344dc7c1 authored by Dominique Makowski on 12 February 2020, 04:10:16 UTC
version 0.5.2
version 0.5.2
Tip revision: 2565fc8
test-describe_posterior.R
if (require("rstanarm") && require("brms")) {
context("describe_posterior")
test_that("describe_posterior", {
set.seed(333)
# Numeric
x <- distribution_normal(1000)
rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all"))
testthat::expect_equal(dim(rez), c(1, 19))
testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
"CI_high", "p_map", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
"ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF"))
rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9)))
testthat::expect_equal(dim(rez), c(2, 19))
rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile")
testthat::expect_equal(dim(rez), c(1, 4))
# Dataframes
x <- data.frame(replicate(4, rnorm(100)))
rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all"))
testthat::expect_equal(dim(rez), c(4, 19))
rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9)))
testthat::expect_equal(dim(rez), c(8, 19))
rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile")
testthat::expect_equal(dim(rez), c(4, 4))
# Rstanarm
library(rstanarm)
x <- insight::download_model("stanreg_lm_1")
rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
testthat::expect_equal(dim(rez), c(2, 21))
testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
"CI_high", "p_MAP", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
"ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF", "Rhat",
"ESS"))
rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9))
testthat::expect_equal(dim(rez), c(4, 21))
rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile", diagnostic = NULL, priors = FALSE)
testthat::expect_equal(dim(rez), c(2, 4))
# Brms
library(brms)
x <- insight::download_model("brms_mixed_1")
rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, ci = c(0.8, 0.9))
testthat::expect_equal(dim(rez), c(4, 16))
testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
"CI_high", "pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage",
"ESS", "Rhat"))
rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile", diagnostic = NULL)
testthat::expect_equal(dim(rez), c(2, 4))
# BayesFactor
# library(BayesFactor)
# x <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
# rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
# testthat::expect_equal(dim(rez), c(4, 16))
# rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9))
# testthat::expect_equal(dim(rez), c(8, 16))
# rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method="quantile")
# testthat::expect_equal(dim(rez), c(4, 4))
})
if (require("insight")) {
m <- insight::download_model("stanreg_merMod_5")
p <- insight::get_parameters(m, effects = "all")
test_that("describe_posterior", {
testthat::expect_equal(
describe_posterior(m, effects = "all")$Median,
describe_posterior(p)$Median,
tolerance = 1e-3
)
})
m <- insight::download_model("brms_zi_3")
p <- insight::get_parameters(m, effects = "all", component = "all")
test_that("describe_posterior", {
testthat::expect_equal(
describe_posterior(m, effects = "all", component = "all")$Median,
describe_posterior(p)$Median,
tolerance = 1e-3
)
})
}
test_that("describe_posterior w/ BF+SI", {
testthat::skip_on_cran()
testthat::skip_on_travis()
x <- insight::download_model("stanreg_lm_1")
set.seed(555)
rez <- describe_posterior(x, ci_method = "SI", test = "bf")
# test si
set.seed(555)
rez_si <- si(x)
testthat::expect_equal(rez$CI_low, rez_si$CI_low, tolerance = 0.1)
testthat::expect_equal(rez$CI_high, rez_si$CI_high, tolerance = 0.1)
# test BF
set.seed(555)
rez_bf <- bayesfactor_parameters(x)
testthat::expect_equal(rez$BF, rez_bf$BF, tolerance = 0.1)
})
}