skip_on_cran()
skip_if_not_installed("emmeans")
skip_if_not_installed("marginaleffects", minimum_version = "0.29.0")

test_that("estimate_means() - lm", {
  data(mtcars)
  data(iris)

  dat <- mtcars
  dat$gear <- as.factor(dat$gear)
  dat$cyl <- as.factor(dat$cyl)
  dat <<- dat

  # Simple
  model <- lm(vs ~ cyl, data = dat)
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  # Interaction (factor * continuous)
  model <- lm(mpg ~ wt * gear, data = dat)
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_named(estim1, c("gear", "Mean", "SE", "CI_low", "CI_high"))
  expect_named(estim2, c("gear", "Mean", "SE", "CI_low", "CI_high", "t", "df"))
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  # At specific levels
  model <- lm(Sepal.Width ~ Species, data = iris)
  estim1 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'virginica')", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 5L))
  expect_identical(dim(estim2), c(2L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_named(estim1, c("Species", "Mean", "SE", "CI_low", "CI_high"))
  expect_named(estim2, c("Species", "Mean", "SE", "CI_low", "CI_high", "t", "df"))
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  # Interactions between factors
  dat <- iris
  dat$Petal.Length_factor <- ifelse(dat$Petal.Length < 4.2, "A", "B")
  dat <<- dat

  model <- lm(Sepal.Width ~ Species * Petal.Length_factor, data = dat)
  estim1 <- suppressMessages(estimate_means(model, by = "all", backend = "emmeans"))
  estim2 <- suppressWarnings(suppressMessages(estimate_means(model, by = "all", backend = "marginaleffects")))
  expect_identical(dim(estim1), c(6L, 6L))
  expect_identical(dim(estim2), c(6L, 8L))
  expect_equal(estim2$Mean, c(3.428, 3.79557, 2.54211, 2.90968, 2.60643, 2.974), tolerance = 1e-4)
  expect_named(estim1, c("Species", "Petal.Length_factor", "Mean", "SE", "CI_low", "CI_high"))
  expect_named(estim2, c("Species", "Petal.Length_factor", "Mean", "SE", "CI_low", "CI_high", "t", "df"))

  # No interaction (two factors)
  model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris)
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)
  expect_named(estim2, c("Species", "Mean", "SE", "CI_low", "CI_high", "t", "df"))

  # At specific levels of continuous
  estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(10L, 5L))
  expect_identical(dim(estim2), c(10L, 7L))
  # Note that the absolute values are different here... for unclear reasons
  expect_lt(max(diff(estim1$Mean) - diff(estim2$Mean)), 1e-10)
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)

  model <- lm(Petal.Length ~ Sepal.Width + Species, data = iris)
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = "all", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "all", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(30L, 6L))
  expect_identical(dim(estim2), c(30L, 8L))
  expect_equal(estim1$Mean[order(estim1$Sepal.Width)], estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low[order(estim1$Sepal.Width)], estim2$CI_low, tolerance = 1e-3)

  # In formula modification
  model <- lm(mpg ~ wt * as.factor(gear), data = mtcars)
  estim <- suppressMessages(estimate_means(model))
  expect_equal(dim(estim), c(3L, 7L))
  estim <- suppressMessages(estimate_means(model, backend = "emmeans"))
  expect_equal(dim(estim), c(3L, 5L))
  model <- lm(mpg ~ wt * as.factor(gear), data = mtcars)
  estim <- estimate_means(model, by = c("wt", "gear"), backend = "marginaleffects")
  expect_identical(dim(estim), c(30L, 8L))

  # One continuous and one factor
  model <- lm(Petal.Length ~ Species * Sepal.Width, data = iris)

  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width"), length = 2, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width"), length = 2, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(6L, 6L))
  expect_identical(dim(estim2), c(6L, 8L))
  expect_equal(estim1$Mean[order(estim1$Species)], estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low[order(estim1$Species)], estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'setosa')", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Species=c('versicolor', 'setosa')", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 5L))
  expect_identical(dim(estim2), c(2L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 5L))
  expect_identical(dim(estim2), c(2L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = list(Sepal.Width = c(2, 4), Species = c("versicolor", "setosa")), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = list(Sepal.Width = c(2, 4), Species = c("versicolor", "setosa")), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(4L, 6L))
  expect_identical(dim(estim2), c(4L, 8L))
  expect_equal(estim1$Mean, estim2$Mean[order(estim2$Species)], tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low[order(estim2$Species)], tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width=0"), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("Species", "Sepal.Width=0"), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 6L))
  expect_identical(dim(estim2), c(3L, 8L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width", length = 5, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width", length = 5, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(5L, 5L))
  expect_identical(dim(estim2), c(5L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Sepal.Width=c(2, 4)", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 5L))
  expect_identical(dim(estim2), c(2L, 7L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-3)

  estim1 <- suppressMessages(estimate_means(model, by = c("Species=c('versicolor', 'setosa')", "Sepal.Width=c(2, 4)"), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("Species=c('versicolor', 'setosa')", "Sepal.Width=c(2, 4)"), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(4L, 6L))
  expect_identical(dim(estim2), c(4L, 8L))
  expect_equal(estim1$Mean, estim2$Mean[order(estim2$Sepal.Width)], tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low[order(estim2$Sepal.Width)], tolerance = 1e-3)

  # Two factors
  dat <- iris
  dat$Petal.Length_factor <- ifelse(dat$Petal.Length < 4.2, "A", "B")
  dat <<- dat
  model <- lm(Petal.Length ~ Species * Petal.Length_factor, data = dat)

  estim1 <- suppressMessages(estimate_means(model, by = "all", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "all", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(6L, 6L))
  expect_identical(dim(estim2), c(6L, 8L))
  expect_equal(estim2$Mean, c(1.462, 2.24638, 3.77368, 4.55806, 4.76762, 5.552), tolerance = 1e-4)

  estim1 <- suppressMessages(estimate_means(model, by = "Petal.Length_factor", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = "Petal.Length_factor", backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 5L))
  expect_identical(dim(estim2), c(2L, 7L))
  expect_equal(estim2$Mean, c(3.33443, 4.11881), tolerance = 1e-4)

  estim <- suppressMessages(estimate_means(model, by = "Petal.Length_factor='B'", backend = "marginaleffects"))
  expect_equal(estim$Mean, 4.11882, tolerance = 1e-4)

  # Three factors
  dat <- mtcars
  dat[c("gear", "vs", "am")] <- lapply(dat[c("gear", "vs", "am")], as.factor)
  dat <<- dat
  model <- lm(mpg ~ gear * vs * am, data = dat)

  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(12L, 7L))
  expect_identical(dim(estim2), c(12L, 9L))
  expect_equal(
    estim2$Mean,
    c(
      15.05, 22.03333, 20.33333, 27.31667, 14.01667, 21, 21.05, 28.03333,
      12.14167, 19.125, 23.41667, 30.4
    ),
    tolerance = 1e-4
  )

  estim1 <- suppressMessages(estimate_means(model, by = c("gear", "vs", "am='1'"), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("gear", "vs", "am='1'"), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(6L, 7L))
  expect_identical(dim(estim2), c(6L, 9L))
  expect_equal(
    estim2$Mean,
    c(22.03333, 27.31667, 21, 28.03333, 19.125, 30.4),
    tolerance = 1e-4
  )

  estim1 <- suppressMessages(estimate_means(model, by = c("gear='5'", "vs"), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("gear='5'", "vs"), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(2L, 7L))
  expect_identical(dim(estim2), c(2L, 8L))
  expect_equal(estim2$Mean, c(15.63333, 26.90833), tolerance = 1e-4)

  dat <- iris
  dat$factor1 <- ifelse(dat$Sepal.Width > 3, "A", "B")
  dat$factor2 <- ifelse(dat$Petal.Length > 3.5, "C", "D")
  dat$factor3 <- ifelse(dat$Sepal.Length > 5, "E", "F")
  dat <<- dat

  model <- lm(Petal.Width ~ factor1 * factor2 * factor3, data = dat)
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(8L, 7L))
  expect_identical(dim(estim2), c(8L, 9L))
  expect_equal(
    estim2$Mean,
    c(2.004, 2.6842, 0.27727, 0.23, 1.60435, 1.7, 1.05, 0.41818),
    tolerance = 1e-4
  )

  estim1 <- suppressMessages(estimate_means(model, by = c("factor1", "factor2", "factor3='E'"), backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, by = c("factor1", "factor2", "factor3='E'"), backend = "marginaleffects"))
  expect_identical(dim(estim1), c(4L, 7L))
  expect_identical(dim(estim2), c(4L, 9L))
  expect_equal(estim1$Mean, estim2$Mean[order(estim2$factor2, decreasing = TRUE)], tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low[order(estim2$factor2, decreasing = TRUE)], tolerance = 1e-3)
})


test_that("estimate_means() - lm, protect integers", {
  skip_if(packageVersion("insight") <= "1.1.0")
  data(mtcars)
  model <- lm(vs ~ cyl, data = mtcars)
  out1 <- estimate_means(model, "cyl", protect_integers = TRUE)
  out2 <- estimate_means(model, "cyl", protect_integers = FALSE)
  expect_identical(dim(out1), c(3L, 7L))
  expect_identical(dim(out2), c(10L, 7L))
})


test_that("estimate_expectation() - at specific values", {
  data(iris)
  m <- lm(Sepal.Width ~ Petal.Length + Species * Petal.Width, data = iris)
  expect_silent(estimate_expectation(m, by = c("Species", "Petal.Width = [fivenum]"), preserve_range = FALSE))
  estim1 <- estimate_expectation(m, by = c("Species", "Petal.Width = [fivenum]"), preserve_range = FALSE)
  estim2 <- estimate_expectation(m, by = c("Species", "Petal.Width = [fivenum]"), preserve_range = FALSE)
  expect_equal(estim1$Predicted, estim2$Predicted, tolerance = 1e-4)
  expect_equal(
    estim1$Petal.Width,
    c(0.1, 0.3, 1.3, 1.8, 2.5, 0.1, 0.3, 1.3, 1.8, 2.5, 0.1, 0.3, 1.3, 1.8, 2.5),
    tolerance = 1e-4
  )
  expect_equal(
    estim2$Petal.Width,
    c(0.1, 0.3, 1.3, 1.8, 2.5, 0.1, 0.3, 1.3, 1.8, 2.5, 0.1, 0.3, 1.3, 1.8, 2.5),
    tolerance = 1e-4
  )
})


test_that("estimate_means() - glm", {
  data(iris)
  # GLM
  dat <- iris
  dat$y <- as.numeric(as.factor(ifelse(dat$Sepal.Width > 3, "A", "B"))) - 1
  dat <<- dat
  model <- glm(y ~ Species, family = "binomial", data = dat)

  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 4L))
  expect_true(all(estim1$Probability >= 0) & all(estim1$Probability <= 1))
  expect_true(all(estim2$Probability >= 0) & all(estim2$Probability <= 1))
  expect_equal(estim1$Probability, estim2$Probability, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-2)
  expect_named(estim2, c("Species", "Probability", "CI_low", "CI_high"))

  dat <- iris
  dat$Petal.Length_factor <- as.factor(ifelse(dat$Petal.Length < 4.2, "A", "B"))
  dat <<- dat
  model <- glm(Petal.Length_factor ~ Species, data = dat, family = "binomial")

  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects", verbose = FALSE))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 4L))
  expect_equal(estim1$Probability, estim2$Probability, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-2)

  estim1 <- suppressMessages(estimate_means(model, predict = "link", backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, predict = "link", verbose = FALSE, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 6L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-2)

  model <- glm(Petal.Length ~ Species, data = iris, family = "Gamma")
  estim1 <- suppressMessages(estimate_means(model, backend = "emmeans"))
  estim2 <- suppressMessages(estimate_means(model, backend = "marginaleffects"))
  expect_identical(dim(estim1), c(3L, 5L))
  expect_identical(dim(estim2), c(3L, 5L))
  expect_equal(estim1$Mean, estim2$Mean, tolerance = 1e-4)
  expect_equal(estim1$CI_low, estim2$CI_low, tolerance = 1e-2)
})


test_that("get_marginaleffects, overall mean", {
  skip_if_not_installed("marginaleffects", minimum_version = "0.29.0")
  skip_if_not_installed("emmeans")

  model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
  out1 <- as.data.frame(get_emmeans(model, by = NULL))
  out2 <- as.data.frame(get_marginalmeans(model, by = NULL))
  expect_equal(out1$emmean, out2$estimate, tolerance = 0.2)
})


test_that("get_marginaleffects, value definition in `by`", {
  set.seed(123)
  n <- 200
  d <- data.frame(
    score = rnorm(n),
    grp = as.factor(sample(c("treatment", "control"), n, TRUE)),
    time = as.factor(sample.int(3, n, TRUE))
  )
  model2 <- lm(score ~ grp * time, data = d)

  predictions <- estimate_means(model2, c("time = 2", "grp"), backend = "marginaleffects")
  expect_equal(predictions$Mean, c(0.23165, 0.17628), tolerance = 1e-4)
  expect_identical(predictions$time, structure(c(2L, 2L), levels = c("1", "2", "3"), class = "factor"))

  predictions <- estimate_means(model2, c("time = factor(2)", "grp"), backend = "marginaleffects")
  expect_equal(predictions$Mean, c(0.23165, 0.17628), tolerance = 1e-4)
  expect_identical(predictions$time, structure(c(1L, 1L), levels = "2", class = "factor"))

  difference <- estimate_contrasts(model2, c("time = factor(2)", "grp"), backend = "marginaleffects")
  expect_equal(difference$Difference, -0.05536674, tolerance = 1e-4)
  expect_identical(as.character(difference$Level1), "treatment")
  expect_identical(as.character(difference$Level2), "control")

  difference <- estimate_contrasts(model2, c("time = 2", "grp"), backend = "marginaleffects")
  expect_equal(difference$Difference, -0.05536674, tolerance = 1e-4)
  expect_identical(as.character(difference$Level1), "treatment")
  expect_identical(as.character(difference$Level2), "control")

  ## FIXME: this currently errors when calling avg_predictions() in get_marginalmeans()
  # expect_error(
  #   estimate_contrasts(model2, "time = factor(2)", by = "grp", backend = "marginaleffects"),
  #   regex = "No contrasts"
  # )


  set.seed(123)
  n <- 200
  d <- data.frame(
    score = rnorm(n),
    grp = as.factor(sample(c("treatment", "control"), n, TRUE)),
    time = as.numeric(sample.int(3, n, TRUE))
  )
  model2 <- lm(score ~ grp * as.factor(time), data = d)

  predictions <- estimate_means(model2, c("time = 2", "grp"), backend = "marginaleffects")
  expect_equal(predictions$Mean, c(0.23165, 0.17628), tolerance = 1e-4)
  expect_identical(predictions$time, c(2, 2))

  difference <- estimate_contrasts(model2, c("time = 2", "grp"), backend = "marginaleffects")
  expect_equal(difference$Difference, -0.05536674, tolerance = 1e-4)
  expect_identical(difference$Parameter, "treatment - control")
})


test_that("estimate_means, values inside correct bounds", {
  # see https://strengejacke.github.io/ggeffects/articles/technical_stata.html
  set.seed(5)

  data <- data.frame(
    outcome = rbinom(100, 1, 0.5),
    var1 = as.factor(rbinom(100, 1, 0.1)),
    var2 = rnorm(100, 10, 7)
  )

  m <- glm(
    outcome ~ var1 * var2,
    data = data,
    family = binomial(link = "logit")
  )

  out1 <- estimate_means(m, c("var2 = [sd]", "var1"), backend = "marginaleffects")
  out2 <- estimate_means(m, c("var2 = [sd]", "var1"), backend = "emmeans")
  expect_true(all(out1$Probability >= 0 & out1$Probability <= 1))
  expect_true(all(out2$Probability >= 0 & out2$Probability <= 1))
  expect_true(all(out1$CI_low >= 0 & out1$CI_low <= 1))
  expect_true(all(out1$CI_high >= 0 & out1$CI_high <= 1))
  expect_true(all(out2$CI_low >= 0 & out2$CI_low <= 1))
  expect_true(all(out2$CI_high >= 0 & out2$CI_high <= 1))
})


test_that("estimate_means, full averaging", {
  data(efc, package = "modelbased")
  efc <- datawizard::to_factor(efc, c("c161sex", "c172code", "e16sex", "e42dep"))
  levels(efc$c172code) <- c("low", "mid", "high")
  m <- lm(neg_c_7 ~ c12hour + barthtot + e42dep + c161sex * c172code, data = efc)

  estim1 <- marginaleffects::avg_predictions(m, by = c("c161sex", "c172code"))
  estim2 <- estimate_means(m, by = c("c161sex", "c172code"), estimate = "average")
  expect_equal(estim1$estimate, estim2$Mean, tolerance = 1e-4)

  estim1 <- marginaleffects::avg_predictions(m, variables = c("c161sex", "c172code"))
  estim2 <- estimate_means(m, by = c("c161sex", "c172code"), estimate = "population")
  expect_equal(estim1$estimate, estim2$Mean, tolerance = 1e-4)

  # See https://github.com/vincentarelbundock/marginaleffects/issues/1575
  # marginaleffects has a new behavior for calculating averages for numerics
  # in the data grid, this, `newdata = "balanced"` gives slightly different results
  # we therefor calculate the data grid manually here
  d <- insight::get_datagrid(m, by = c("c161sex", "c172code"), factors = "all")
  estim1 <- marginaleffects::avg_predictions(m, newdata = d, by = c("c161sex", "c172code"))
  estim2 <- estimate_means(m, by = c("c161sex", "c172code"), estimate = "typical")
  expect_equal(estim1$estimate, estim2$Mean, tolerance = 1e-4)

  estim2 <- estimate_means(m, by = c("c161sex", "c172code='mid'"), estimate = "average")
  expect_identical(dim(estim2), c(2L, 8L))
})


test_that("estimate_means, df", {
  data(efc, package = "modelbased")
  efc <- datawizard::to_factor(efc, c("c161sex", "c172code", "e16sex", "e42dep"))
  levels(efc$c172code) <- c("low", "mid", "high")
  m <- lm(neg_c_7 ~ c12hour + barthtot + e42dep + c161sex * c172code, data = efc)

  out1 <- estimate_contrasts(m, "c161sex", by = "c172code", df = Inf)
  out2 <- estimate_contrasts(m, "c161sex", by = "c172code")
  expect_true(all(out1$p != out2$p))
})


test_that("estimate_means, error on invalid type", {
  data(iris)
  m <- lm(Sepal.Length ~ Species, data = iris)
  expect_error(
    estimate_contrasts(m, "Species", type = "abc"),
    regex = "The option provided"
  )
  expect_error(
    estimate_means(m, "Species", predict = "abc"),
    regex = "The option provided"
  )
})


test_that("estimate_means, coxph-survival", {
  skip_if_not_installed("survival")
  model <- survival::coxph(
    survival::Surv(dtime, death) ~ hormon + age + I(age^2),
    data = survival::rotterdam
  )
  emm <- estimate_means(
    model,
    c("dtime=c(1000, 2000, 3000, 4000)", "hormon"),
    predict = "survival"
  )
  expect_named(
    emm,
    c("dtime", "hormon", "Probability", "SE", "CI_low", "CI_high", "z")
  )
  expect_equal(
    emm$Probability,
    c(0.8982, 0.86121, 0.7775, 0.70451, 0.68016, 0.58485, 0.58174, 0.47051),
    tolerance = 1e-4
  )
  emm <- estimate_means(
    model,
    "hormon",
    predict = "risk"
  )
  expect_named(
    emm,
    c("hormon", "Mean", "SE", "CI_low", "CI_high", "z")
  )
  expect_equal(
    emm$Mean,
    c(0.82661, 1.15039),
    tolerance = 1e-4
  )
})
