# TODO: decide how to rearrange the tests skip_on_ci() skip_if_not_or_load_if_installed("rstanarm") skip_if_not_or_load_if_installed("emmeans") set.seed(300) model <- stan_glm(extra ~ group, data = sleep, refresh = 0, chains = 6, iter = 7000, warmup = 200 ) em_ <- emmeans(model, ~group) c_ <- pairs(em_) emc_ <- emmeans(model, pairwise ~ group) all_ <- rbind(em_, c_) all_summ <- summary(all_) set.seed(4) model_p <- unupdate(model, verbose = FALSE) set.seed(300) # estimate + hdi ---------------------------------------------------------- test_that("emmGrid hdi", { xhdi <- hdi(all_, ci = 0.95) expect_equal(xhdi$CI_low, all_summ$lower.HPD, tolerance = 0.1) expect_equal(xhdi$CI_high, all_summ$upper.HPD, tolerance = 0.1) xhdi2 <- hdi(emc_, ci = 0.95) expect_equal(xhdi$CI_low, xhdi2$CI_low) }) test_that("emmGrid point_estimate", { xpest <- point_estimate(all_, centrality = "all", dispersion = TRUE) expect_equal(xpest$Median, all_summ$emmean, tolerance = 0.1) xpest2 <- point_estimate(emc_, centrality = "all", dispersion = TRUE) expect_equal(xpest$Median, xpest2$Median) }) # Basics ------------------------------------------------------------------ test_that("emmGrid ci", { xci <- ci(all_, ci = 0.9) expect_equal(length(xci$CI_low), 3) expect_equal(length(xci$CI_high), 3) }) test_that("emmGrid eti", { xeti <- eti(all_, ci = 0.9) expect_equal(length(xeti$CI_low), 3) expect_equal(length(xeti$CI_high), 3) }) test_that("emmGrid equivalence_test", { xeqtest <- equivalence_test(all_, ci = 0.9, range = c(-0.1, 0.1)) expect_equal(length(xeqtest$ROPE_Percentage), 3) expect_equal(length(xeqtest$ROPE_Equivalence), 3) }) test_that("emmGrid estimate_density", { xestden <- estimate_density(c_, method = "logspline", precision = 5) expect_equal(length(xestden$x), 5) }) test_that("emmGrid map_estimate", { xmapest <- map_estimate(all_, method = "kernel") expect_equal(length(xmapest$MAP_Estimate), 3) }) test_that("emmGrid p_direction", { xpd <- p_direction(all_, method = "direct") expect_equal(length(xpd$pd), 3) }) test_that("emmGrid p_map", { xpmap <- p_map(all_, precision = 2^9) expect_equal(length(xpmap$p_MAP), 3) }) test_that("emmGrid p_rope", { xprope <- p_rope(all_, range = c(-0.1, 0.1)) expect_equal(length(xprope$p_ROPE), 3) }) test_that("emmGrid p_significance", { xsig <- p_significance(all_, threshold = c(-0.1, 0.1)) expect_equal(length(xsig$ps), 3) }) test_that("emmGrid rope", { xrope <- rope(all_, range = "default", ci = .9) expect_equal(length(xrope$ROPE_Percentage), 3) }) # describe_posterior ------------------------------------------------------ test_that("emmGrid describe_posterior", { expect_equal( describe_posterior(all_)$median, describe_posterior(emc_)$median ) skip_on_cran() expect_equal( describe_posterior(all_, bf_prior = model_p, test = "bf")$log_BF, describe_posterior(emc_, bf_prior = model_p, test = "bf")$log_BF ) }) # BFs --------------------------------------------------------------------- test_that("emmGrid bayesfactor_parameters", { skip_on_cran() set.seed(4) expect_equal( bayesfactor_parameters(all_, prior = model, verbose = FALSE), bayesfactor_parameters(all_, prior = model_p, verbose = FALSE), tolerance = 0.001 ) emc_p <- emmeans(model_p, pairwise ~ group) xbfp <- bayesfactor_parameters(all_, prior = model_p, verbose = FALSE) xbfp2 <- bayesfactor_parameters(emc_, prior = model_p, verbose = FALSE) xbfp3 <- bayesfactor_parameters(emc_, prior = emc_p, verbose = FALSE) expect_equal(xbfp$log_BF, xbfp2$log_BF, tolerance = 0.1) expect_equal(xbfp$log_BF, xbfp3$log_BF, tolerance = 0.1) expect_warning( suppressMessages( bayesfactor_parameters(all_) ), regexp = "Prior not specified" ) # error - cannot deal with regrid / transform e <- capture_error(suppressMessages(bayesfactor_parameters(regrid(all_), prior = model))) expect_match(as.character(e), "Unable to reconstruct prior estimates") }) test_that("emmGrid bayesfactor_restricted", { skip_on_cran() set.seed(4) hyps <- c("`1` < `2`", "`1` < 0") xrbf <- bayesfactor_restricted(em_, prior = model_p, hypothesis = hyps) expect_equal(length(xrbf$log_BF), 2) expect_equal(length(xrbf$p_prior), 2) expect_equal(length(xrbf$p_posterior), 2) expect_warning(bayesfactor_restricted(em_, hypothesis = hyps)) xrbf2 <- bayesfactor_restricted(emc_, prior = model_p, hypothesis = hyps) expect_equal(xrbf, xrbf2, tolerance = 0.1) }) test_that("emmGrid si", { skip_on_cran() set.seed(4) xrsi <- si(all_, prior = model_p, verbose = FALSE) expect_equal(length(xrsi$CI_low), 3) expect_equal(length(xrsi$CI_high), 3) xrsi2 <- si(emc_, prior = model_p, verbose = FALSE) expect_equal(xrsi$CI_low, xrsi2$CI_low) expect_equal(xrsi$CI_high, xrsi2$CI_high) }) # For non linear models --------------------------------------------------- set.seed(333) df <- data.frame( G = rep(letters[1:3], each = 2), Y = rexp(6) ) fit_bayes <- stan_glm(Y ~ G, data = df, family = Gamma(link = "identity"), refresh = 0 ) fit_bayes_prior <- unupdate(fit_bayes, verbose = FALSE) bayes_sum <- emmeans(fit_bayes, ~G) bayes_sum_prior <- emmeans(fit_bayes_prior, ~G) test_that("emmGrid bayesfactor_parameters", { set.seed(333) skip_on_cran() xsdbf1 <- bayesfactor_parameters(bayes_sum, prior = fit_bayes, verbose = FALSE) xsdbf2 <- bayesfactor_parameters(bayes_sum, prior = bayes_sum_prior, verbose = FALSE) expect_equal(xsdbf1$log_BF, xsdbf2$log_BF, tolerance = 0.1) }) # link vs response test_that("emmGrid bayesfactor_parameters / describe w/ nonlinear models", { skip_on_cran() model <- stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0 ) probs <- emmeans(model, "mpg", type = "resp") link <- emmeans(model, "mpg") probs_summ <- summary(probs) link_summ <- summary(link) xhdi <- hdi(probs, ci = 0.95) xpest <- point_estimate(probs, centrality = "median", dispersion = TRUE) expect_equal(xhdi$CI_low, probs_summ$lower.HPD, tolerance = 0.1) expect_equal(xhdi$CI_high, probs_summ$upper.HPD, tolerance = 0.1) expect_equal(xpest$Median, probs_summ$prob, tolerance = 0.1) xhdi <- hdi(link, ci = 0.95) xpest <- point_estimate(link, centrality = "median", dispersion = TRUE) expect_equal(xhdi$CI_low, link_summ$lower.HPD, tolerance = 0.1) expect_equal(xhdi$CI_high, link_summ$upper.HPD, tolerance = 0.1) expect_equal(xpest$Median, link_summ$emmean, tolerance = 0.1) })