skip_if_not_installed("bayestestR")

test_that("equivalence_test", {
  data(mtcars)
  m <- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
  x <- equivalence_test(m)
  expect_identical(c(nrow(x), ncol(x)), c(5L, 9L))

  expect_type(capture.output(equivalence_test(m)), "character")
  expect_snapshot(print(x))
})

test_that("equivalence_test, robust", {
  skip_if_not_installed("sandwich")
  data(mtcars)
  m <- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
  x <- equivalence_test(m, vcov = "HC3")
  expect_snapshot(print(x))
})

test_that("equivalence_test, unequal rope-range", {
  data(iris)
  m <- lm(Sepal.Length ~ Species, data = iris)
  rez <- equivalence_test(m, range = c(-Inf, 0.1))
  expect_identical(rez$ROPE_Equivalence, c("Rejected", "Rejected", "Rejected"))
  expect_identical(rez$ROPE_low, c(-Inf, -Inf, -Inf))

  rez <- equivalence_test(m, range = c(-99, 0.1))
  expect_identical(rez$ROPE_Equivalence, c("Rejected", "Rejected", "Rejected"))
  expect_identical(rez$ROPE_low, c(-99, -99, -99))

  data(mtcars)
  mtcars[c("gear", "cyl")] <- lapply(mtcars[c("gear", "cyl")], as.factor)
  m <- lm(mpg ~ hp + gear + cyl, data = mtcars)

  rez <- equivalence_test(m, range = c(-Inf, 0.5))
  expect_identical(
    rez$ROPE_Equivalence,
    c("Rejected", "Accepted", "Undecided", "Rejected", "Accepted", "Undecided")
  )

  # validate that range of CI equals approximated normal distribution
  diff_ci <- abs(diff(c(rez$CI_low[3], rez$CI_high[3])))
  set.seed(123)
  out <- bayestestR::distribution_normal(
    n = 1000,
    mean = rez$CI_high[3] - (diff_ci / 2),
    sd = (diff_ci / 2) / 3.290525
  )
  expect_equal(range(out)[1], rez$CI_low[3], tolerance = 1e-4)
  expect_equal(range(out)[2], rez$CI_high[3], tolerance = 1e-4)
  # need procedure for SGP here...
  diff_ci <- abs(diff(c(rez$CI_low[3], rez$CI_high[3])))
  z_value <- stats::qnorm((1 + 0.95) / 2)
  sd_dist <- diff_ci / diff(c(-1 * z_value, z_value))
  set.seed(123)
  out <- bayestestR::distribution_normal(
    n = 10000,
    mean = rez$CI_high[3] - (diff_ci / 2),
    sd = sd_dist
  )
  expect_equal(
    rez$SGPV[3],
    bayestestR::rope(out, range = c(-Inf, 0.5), ci = 1)$ROPE_Percentage,
    tolerance = 1e-4
  )

  rez <- equivalence_test(m, range = c(-0.5, 0.5))
  expect_identical(
    rez$ROPE_Equivalence,
    c("Rejected", "Accepted", "Undecided", "Rejected", "Rejected", "Undecided")
  )

  rez <- equivalence_test(m, range = c(-2, 2))
  expect_identical(
    rez$ROPE_Equivalence,
    c("Rejected", "Accepted", "Undecided", "Rejected", "Rejected", "Undecided")
  )
})

test_that("equivalence_test, unequal rope-range, plots", {
  skip_on_cran()
  skip_if_not_installed("vdiffr")
  data(iris)
  m <- lm(Sepal.Length ~ Species, data = iris)
  rez <- equivalence_test(m, range = c(-Inf, 0.1))
  vdiffr::expect_doppelganger(
    "Equivalence-Test 1",
    plot(rez)
  )

  rez <- equivalence_test(m, range = c(-99, 0.1))
  vdiffr::expect_doppelganger(
    "Equivalence-Test 2",
    plot(rez)
  )

  data(mtcars)
  mtcars[c("gear", "cyl")] <- lapply(mtcars[c("gear", "cyl")], as.factor)
  m <- lm(mpg ~ hp + gear + cyl, data = mtcars)

  rez <- equivalence_test(m, range = c(-Inf, 0.5))
  vdiffr::expect_doppelganger(
    "Equivalence-Test 3",
    plot(rez)
  )

  rez <- equivalence_test(m, range = c(-0.5, 0.5))
  vdiffr::expect_doppelganger(
    "Equivalence-Test 4",
    plot(rez)
  )

  rez <- equivalence_test(m, range = c(-2, 2))
  vdiffr::expect_doppelganger(
    "Equivalence-Test 5",
    plot(rez)
  )
})