tests/testthat/test-lmer.R

In ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"

if (.runThisTest && suppressWarnings(
  requiet("testthat") &&
  requiet("ggeffects") &&
  requiet("sjlabelled") &&
  requiet("lme4") &&
  requiet("marginaleffects") &&
  requiet("sjmisc")
)) {
  # lmer ----

  data(efc, package = "ggeffects")
  efc$grp <- sjlabelled::to_label(efc$e15relat)
  fit <- lme4::lmer(neg_c_7 ~ c12hour + e42dep + c161sex + c172code + (1 | grp), data = efc)

  test_that("validate ggpredict lmer against predict", {
    nd <- data_grid(fit, "e42dep")
    pr <- predict(fit, newdata = nd, re.form = NA)
    predicted <- ggpredict(fit, "e42dep")
    expect_equal(predicted$predicted, pr, tolerance = 1e-3, ignore_attr = TRUE)
  })

  test_that("validate ggpredict lmer against marginaleffects", {
    out1 <- marginaleffects::predictions(
      fit,
      variables = "e42dep",
      newdata = marginaleffects::datagrid(fit)
    )
    out1 <- out1[order(out1$e42dep), ]
    out2 <- ggpredict(
      fit,
      "e42dep",
      condition = c(grp = "child"),
      type = "random",
      interval = "confidence"
    )
    expect_equal(
      out1$estimate,
      out2$predicted,
      tolerance = 1e-4,
      ignore_attr = TRUE
    )
    expect_equal(
      out1$estimate - stats::qt(0.975, df = 826) * out1$std.error,
      out2$conf.low,
      tolerance = 1e-4,
      ignore_attr = TRUE
    )
  })

  test_that("ggpredict, lmer", {
    expect_s3_class(ggpredict(fit, "c12hour"), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex")), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code")), "data.frame")
    expect_s3_class(ggpredict(fit, "c12hour", type = "re"), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), type = "re"), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "re"), "data.frame")
  })

  test_that("ggpredict, lmer", {
    pr <- ggpredict(fit, "c12hour")
    expect_equal(pr$std.error[1:5], c(0.2911, 0.2852, 0.2799, 0.2752, 0.2713), tolerance = 1e-3)
    pr <- ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "re")
    expect_equal(pr$std.error[1:5], c(3.5882, 3.58185, 3.58652, 3.58162, 3.57608), tolerance = 1e-3)
  })

  test_that("ggpredict, lmer-simulate", {
    expect_s3_class(ggpredict(fit, "c12hour", type = "sim"), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), type = "sim"), "data.frame")
    expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), type = "sim"), "data.frame")
  })

  test_that("ggeffect, lmer", {
    expect_s3_class(ggeffect(fit, "c12hour"), "data.frame")
    expect_s3_class(ggeffect(fit, c("c12hour", "c161sex")), "data.frame")
    expect_s3_class(ggeffect(fit, c("c12hour", "c161sex", "c172code")), "data.frame")
  })

  data(efc, package = "ggeffects")

  efc$cluster <- as.factor(efc$e15relat)
  efc <- sjmisc::std(efc, c160age, e42dep)

  m <- lme4::lmer(
    neg_c_7 ~ c160age_z * e42dep_z + c161sex + (1 | cluster),
    data = efc
  )

  test_that("ggeffect, lmer", {
    p1 <- ggpredict(m, terms = c("c160age_z", "e42dep_z [-1.17,2.03]"))
    p2 <- ggemmeans(m, terms = c("c160age_z", "e42dep_z [-1.17,2.03]"))
    expect_equal(p1$predicted[1], p2$predicted[1], tolerance = 1e-3)
  })


  data(efc, package = "ggeffects")
  efc$cluster <- as.factor(efc$e15relat)
  efc <- sjlabelled::as_label(efc, e42dep, c172code, c161sex)
  efc$c172code[efc$c172code == "intermediate level of education"] <- NA

  m <- lme4::lmer(
    neg_c_7 ~ c172code + e42dep + c161sex + (1 | cluster),
    data = efc
  )

  test_that("ggeffect, lmer", {
    expect_s3_class(ggpredict(m, terms = "e42dep"), "data.frame")
    expect_s3_class(ggemmeans(m, terms = "e42dep"), "data.frame")
  })

  test_that("ggeffect, lmer", {
    p1 <- ggpredict(m, terms = "e42dep")
    p2 <- ggemmeans(m, terms = "e42dep")
    p3 <- ggemmeans(m, terms = "e42dep", condition = c(c161sex = "Male", c172code = "low level of education"))
    expect_equal(p1$predicted[1], 8.902934, tolerance = 1e-3)
    expect_equal(p2$predicted[1], 9.742945, tolerance = 1e-3)
    expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
  })

  m <- suppressWarnings(lme4::lmer(
    log(Reaction) ~ Days + I(Days^2) + (1 + Days + exp(Days) | Subject),
    data = sleepstudy
  ))

  test_that("ggeffect, lmer", {
    p1 <- ggpredict(m, terms = "Days")
    p2 <- ggemmeans(m, terms = "Days")
    p3 <- ggeffect(m, terms = "Days")
    expect_equal(p1$predicted[1], 253.5178, tolerance = 1e-3)
    expect_equal(p2$predicted[1], 253.5178, tolerance = 1e-3)
    expect_equal(p3$predicted[1], 5.535434, tolerance = 1e-3)
  })

  test_that("ggeffect, lmer", {
    expect_s3_class(ggpredict(m, terms = c("Days", "Subject [sample=5]"), type = "re"), "data.frame")
  })
}