tests/testthat/test-icc.R

In performance: Assessment of Regression Models Performance

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
  )
})