skip_on_os("mac") skip_on_cran() test_that("icc", { m0 <- lm(Sepal.Length ~ Petal.Length, data = iris) expect_warning(expect_null(icc(m0))) }) test_that("icc", { skip_if_not_installed("lme4") m1 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) expect_equal( icc(m1), data.frame( ICC_adjusted = 0.910433109183341, ICC_conditional = 0.310947768161385, ICC_unadjusted = 0.310947768161385 ), tolerance = 1e-3, ignore_attr = TRUE ) }) # bootstrapped CIs ------------ test_that("icc, CI", { skip_if_not_installed("lme4") data(sleepstudy, package = "lme4") m <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy) set.seed(123) out <- icc(m, ci = 0.95) expect_equal(out$ICC_adjusted, c(0.72166, 0.52239, 0.84024), tolerance = 1e-1) expect_equal(out$ICC_unadjusted, c(0.52057, 0.32429, 0.67123), tolerance = 1e-1) }) test_that("icc", { skip_if_not_installed("curl") skip_if_offline() skip_if_not_installed("httr2") m2 <- insight::download_model("stanreg_lmerMod_1") expect_equal( icc(m2), data.frame( ICC_adjusted = 0.40579, ICC_conditional = 0.21881, ICC_unadjusted = 0.21881 ), tolerance = 1e-2, ignore_attr = TRUE ) }) test_that("icc", { skip_if_not_installed("curl") skip_if_offline() skip_if_not_installed("httr2") m3 <- insight::download_model("brms_mixed_1") set.seed(123) expect_equal( variance_decomposition(m3)$ICC_decomposed, 0.3262006, tolerance = 0.05 ) }) test_that("icc", { skip_if_not_installed("curl") skip_if_offline() skip_if_not_installed("httr2") m3 <- insight::download_model("brms_mixed_1") set.seed(123) expect_equal( icc(m3), data.frame( ICC_adjusted = 0.930217931275196, ICC_conditional = 0.771475122370036, ICC_unadjusted = 0.771475122370036 ), tolerance = 0.05, ignore_attr = TRUE ) }) test_that("icc", { skip_if_not_installed("lme4") data(sleepstudy, package = "lme4") set.seed(12345) sleepstudy$grp <- sample.int(5, size = 180, replace = TRUE) sleepstudy$subgrp <- NA for (i in 1:5) { filter_group <- sleepstudy$grp == i sleepstudy$subgrp[filter_group] <- sample.int(30, size = sum(filter_group), replace = TRUE) } model <- lme4::lmer( Reaction ~ Days + (1 | grp) + (1 | Subject), data = sleepstudy ) expect_equal( icc(model, by_group = TRUE), structure( list( Group = c("Subject", "grp"), ICC = c(0.5896587, 0.0016551) ), class = c("icc_by_group", "data.frame"), row.names = c(NA, -2L) ), tolerance = 0.05 ) }) test_that("icc", { skip_if_not_installed("nlme") skip_if_not_installed("lme4") m <- nlme::lme(Sepal.Length ~ Petal.Length, random = ~ 1 | Species, data = iris) out <- icc(m) expect_equal(out$ICC_adjusted, 0.9104331, tolerance = 0.01) expect_equal(out$ICC_unadjusted, 0.3109478, tolerance = 0.01) }) test_that("icc, glmmTMB 1.1.9+", { skip_if_not_installed("glmmTMB") set.seed(101) dd <- data.frame( z = rnorm(1000), x1 = 1:1000, x2 = runif(1000, 0, 10), re = rep(1:20, each = 50) ) dd <- transform(dd, x3 = as.factor(ifelse( x1 <= 500, "Low", sample(c("Middle", "High"), 1000, replace = TRUE) ))) dd <- transform(dd, x4 = as.factor(ifelse( x1 > 500, "High", sample(c("Absent", "Low"), 1000, replace = TRUE) ))) dd <- transform(dd, z = z + re * 5) expect_message({ mod_TMB <- glmmTMB::glmmTMB( z ~ x1 + x2 + x3 + x4 + (1 | re), data = dd, start = list(theta = 3), control = glmmTMB::glmmTMBControl(rank_check = "adjust") ) }) expect_equal( icc(mod_TMB), data.frame( ICC_adjusted = 0.995480998331767, ICC_conditional = 0.244468078371849, ICC_unadjusted = 0.244468078371849 ), ignore_attr = TRUE, tolerance = 1e-4 ) expect_equal( r2(mod_TMB), list( R2_conditional = c(`Conditional R2` = 0.998890233308478), R2_marginal = c(`Marginal R2` = 0.754422154936629) ), ignore_attr = TRUE, tolerance = 1e-4 ) })