tests/testthat/test-resid_over_grid.R

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

skip_on_os(c("mac", "solaris"))
skip_if_not_installed("withr")

test_that("residualize over grid", {
  set.seed(1234)
  x <- rnorm(200)
  z <- rnorm(200)
  # quadratic relationship
  y <- 2 * x + x^2 + 4 * z + rnorm(200)

  d <- data.frame(x, y, z)
  model <- lm(y ~ x + z, data = d)

  pr <- ggpredict(model, c("x [all]", "z"))
  out <- residualize_over_grid(pr, model)
  expect_equal(
    head(out$x),
    c(-1.207, 0.277, 1.084, -2.346, 0.429, 0.506),
    tolerance = 1e-3
  )
  expect_equal(
    head(out$predicted),
    c(-1.79724, 4.88871, 3.2322, 4.13356, 1.80159, 5.65953),
    tolerance = 1e-4
  )
  expect_identical(colnames(out), c("x", "group", "predicted"))
})


withr::with_environment(
  new.env(),
  test_that("residualize over grid, scoping", {
    skip_on_cran()
    data(mtcars)
    # inside a list
    models <- list(
      model1 = lm(mpg ~ cyl, data = mtcars)
    )

    x <- ggpredict(
      model = models$model1,
      terms = "cyl"
    )
    model <- ggeffects:::.get_model_object(x)
    residual_data <- residualize_over_grid(grid = x, model = model)
    expect_equal(
      head(round(residual_data$predicted, 5)),
      c(21, 21, 22.8, 21.4, 18.7, 18.1),
      tolerance = 1e-3
    )

    x <- ggpredict(
      model = models[["model1"]],
      terms = "cyl"
    )
    model <- ggeffects:::.get_model_object(x)
    residual_data <- residualize_over_grid(grid = x, model = model)
    expect_equal(
      head(round(residual_data$predicted, 5)),
      c(21, 21, 22.8, 21.4, 18.7, 18.1),
      tolerance = 1e-3
    )

    x <- ggpredict(
      model = models[[1]],
      terms = "cyl"
    )
    model <- ggeffects:::.get_model_object(x)
    residual_data <- residualize_over_grid(grid = x, model = model)
    expect_equal(
      head(round(residual_data$predicted, 5)),
      c(21, 21, 22.8, 21.4, 18.7, 18.1),
      tolerance = 1e-3
    )
  })
)