tests/testthat/test-r2_nakagawa.R

In performance: Assessment of Regression Models Performance

skip_on_os("mac")
skip_if_not_installed("lme4")

model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)

test_that("r2_nakagawa", {
  expect_equal(
    r2_nakagawa(model),
    structure(
      list(
        R2_conditional = c(`Conditional R2` = 0.969409477972726),
        R2_marginal = c(`Marginal R2` = 0.65846169440315)
      ),
      class = c("r2_nakagawa", "list")
    ),
    tolerance = 1e-3
  )
})

skip_on_cran()

test_that("r2_nakagawa, ci", {
  set.seed(123)
  out <- r2_nakagawa(model, ci = 0.95)
  expect_equal(
    out$R2_marginal,
    c(`Marginal R2` = 0.65846, CI_low = 0.33708, CI_high = 0.94718),
    tolerance = 1e-3,
    ignore_attr = TRUE
  )
  expect_equal(
    out$R2_conditional,
    c(`Conditional R2` = 0.96941, CI_low = 0.94299, CI_high = 0.98426),
    tolerance = 1e-3,
    ignore_attr = TRUE
  )
})


dat <- structure(list(
  y = structure(
    c(
      1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
      2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
      1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
      2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L
    ),
    .Label = c("0", "1"), class = "factor"
  ),
  x1 = structure(
    c(
      2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
      1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
      1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
      2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L
    ),
    .Label = c("1", "2"), class = "factor"
  ),
  a1 = structure(
    c(
      9L, 1L, 1L, 5L, 9L, 7L, 1L, 1L, 9L, 5L, 5L, 5L, 5L, 6L, 4L, 3L, 5L, 9L, 9L, 6L, 1L, 9L, 9L, 9L, 9L, 5L, 2L, 7L, 7L, 7L, 4L,
      5L, 7L, 4L, 2L, 9L, 2L, 2L, 9L, 9L, 5L, 5L, 7L, 5L, 9L, 8L, 8L, 8L, 8L, 3L, 9L, 5L, 6L, 6L, 3L, 9L, 6L, 9L, 6L, 3L, 5L,
      6L, 6L, 5L, 7L, 4L, 7L, 2L, 5L, 2L, 5L, 5L, 9L, 5L, 9L, 4L, 3L, 5L, 5L, 3L, 9L, 3L, 3L, 3L, 9L, 9L, 3L, 9L, 3L, 3L, 9L,
      8L, 3L, 9L, 7L, 9L, 9L, 6L, 2L, 9L, 9L, 4L, 9L, 7L, 5L, 5L, 5L, 5L, 5L, 5L
    ),
    .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9"), class = "factor"
  ),
  a2 = structure(
    c(
      6L, 6L, 12L, 12L, 11L, 11L, 11L, 14L, 14L, 14L, 14L, 14L, 13L, 1L, 1L, 13L,
      1L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 12L, 12L, 12L, 7L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 5L, 5L, 11L, 7L, 8L, 1L,
      1L, 12L, 4L, 11L, 11L, 11L, 5L, 5L, 5L, 2L, 13L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 7L, 7L, 8L, 8L,
      8L, 9L, 9L, 9L, 7L, 7L, 8L, 8L, 9L, 10L, 10L, 10L, 10L, 1L, 10L, 1L, 10L, 13L, 13L, 6L, 11L, 11L, 14L, 2L, 2L, 2L, 2L,
      6L, 6L, 6L, 3L, 3L, 3L, 13L, 13L, 13L, 6L, 6L, 6L, 4L
    ),
    .Label = c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14"), class = "factor"
  )
), row.names = c(NA, -110L), class = "data.frame")

model <- lme4::glmer(y ~ x1 + (1 | a1) + (1 | a2), family = binomial(link = "logit"), data = dat)

test_that("r2_nakagawa, by_group", {
  out <- r2_nakagawa(model, by_group = TRUE)
  expect_equal(out$R2, c(0, 0.05771, 0.07863), tolerance = 1e-2)
  expect_identical(out$Level, c("Level 1", "a1", "a2"))
})

test_that("icc, by_group", {
  out <- icc(model, by_group = TRUE)
  expect_equal(out$ICC, c(0.3200625, 0.1096797), tolerance = 1e-2)
  expect_identical(out$Group, c("a2", "a1"))
})