skip_on_os(c("mac", "solaris"))
skip_if_not_installed("effects")
skip_if_not_installed("emmeans")
skip_if_not_installed("parsnip")
skip_if_not_installed("sjlabelled")

# lm, linear regression ----
data(efc, package = "ggeffects")
fit <- parsnip::fit(parsnip::linear_reg(), barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

test_that("validate ggpredict parsnip against predict", {
  nd <- data_grid(fit, "c12hour [10, 50, 100]")
  pr <- predict(fit, new_data = nd)
  predicted <- ggpredict(fit, "c12hour [10, 50, 100]")
  expect_equal(predicted$predicted, pr[[".pred"]], tolerance = 1e-3, ignore_attr = TRUE)

  nd <- data_grid(fit, c("c12hour [10, 50, 100]", "c161sex", "c172code"))
  pr <- cbind(predict(fit, new_data = nd), nd)
  pr <- pr[order(pr$c12hour, pr$c161sex, pr$c172code), ]
  predicted <- ggpredict(fit, c("c12hour [10, 50, 100]", "c161sex", "c172code"))
  expect_equal(predicted$predicted, pr[[".pred"]], tolerance = 1e-3, ignore_attr = TRUE)
})

test_that("ggpredict, parsnip print", {
  x <- ggpredict(fit, c("c12hour", "c161sex", "c172code"))
  out <- utils::capture.output(print(x, verbose = FALSE))
  expect_identical(
    out,
    c(
      "# Predicted values of Total score BARTHEL INDEX", "", "c161sex: Male",
      "c172code: [1] low level of education", "", "c12hour | Predicted |       95% CI",
      "----------------------------------", "      0 |     75.00 | 71.40, 78.59",
      "     45 |     63.60 | 60.45, 66.74", "     85 |     53.46 | 50.12, 56.80",
      "    170 |     31.93 | 26.82, 37.05", "", "c161sex: Male", "c172code: [2] intermediate level of education",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     75.71 | 73.31, 78.12", "     45 |     64.32 | 62.41, 66.22",
      "     85 |     54.18 | 51.81, 56.56", "    170 |     32.65 | 27.94, 37.37",
      "", "c161sex: Male", "c172code: [3] high level of education",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     76.43 | 72.88, 79.98", "     45 |     65.03 | 61.67, 68.39",
      "     85 |     54.90 | 51.15, 58.65", "    170 |     33.37 | 27.69, 39.05",
      "", "c161sex: Female", "c172code: [1] low level of education",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     73.95 | 69.35, 78.56", "     45 |     62.56 | 58.22, 66.89",
      "     85 |     52.42 | 47.89, 56.96", "    170 |     30.89 | 24.84, 36.95",
      "", "c161sex: Female", "c172code: [2] intermediate level of education",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     74.67 | 71.05, 78.29", "     45 |     63.27 | 59.88, 66.67",
      "     85 |     53.14 | 49.39, 56.89", "    170 |     31.61 | 25.97, 37.25",
      "", "c161sex: Female", "c172code: [3] high level of education",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     75.39 | 71.03, 79.75", "     45 |     63.99 | 59.72, 68.26",
      "     85 |     53.86 | 49.22, 58.50", "    170 |     32.33 | 25.94, 38.72",
      "", "Adjusted for:", "* neg_c_7 = 11.84"
    )
  )
  x <- ggpredict(fit, c("c12hour", "c161sex", "neg_c_7"), verbose = FALSE)
  out <- utils::capture.output(print(x))
  expect_identical(
    out,
    c(
      "# Predicted values of Total score BARTHEL INDEX", "", "c161sex: Male",
      "neg_c_7: 8", "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     75.78 | 73.38, 78.19", "     45 |     64.38 | 62.48, 66.28",
      "     85 |     54.25 | 51.88, 56.62", "    170 |     32.72 | 28.01, 37.43",
      "", "c161sex: Male", "neg_c_7: 11.8", "", "c12hour | Predicted |       95% CI",
      "----------------------------------", "      0 |     66.82 | 63.70, 69.94",
      "     45 |     55.42 | 52.93, 57.91", "     85 |     45.29 | 42.65, 47.94",
      "    170 |     23.76 | 19.17, 28.34", "", "c161sex: Male", "neg_c_7: 15.7",
      "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     84.51 | 81.74, 87.27", "     45 |     73.11 | 70.51, 75.72",
      "     85 |     62.98 | 59.82, 66.14", "    170 |     41.45 | 36.06, 46.84",
      "", "c161sex: Female", "neg_c_7: 8", "", "c12hour | Predicted |       95% CI",
      "----------------------------------", "      0 |     74.74 | 71.11, 78.36",
      "     45 |     63.34 | 59.94, 66.74", "     85 |     53.21 | 49.46, 56.96",
      "    170 |     31.68 | 26.04, 37.31", "", "c161sex: Female",
      "neg_c_7: 11.8", "", "c12hour | Predicted |       95% CI", "----------------------------------",
      "      0 |     65.78 | 61.53, 70.03", "     45 |     54.38 | 50.49, 58.27",
      "     85 |     44.25 | 40.20, 48.30", "    170 |     22.72 | 17.10, 28.33",
      "", "c161sex: Female", "neg_c_7: 15.7", "", "c12hour | Predicted |       95% CI",
      "----------------------------------", "      0 |     83.47 | 79.72, 87.22",
      "     45 |     72.07 | 68.36, 75.78", "     85 |     61.94 | 57.76, 66.12",
      "    170 |     40.41 | 34.27, 46.55", "", "Adjusted for:", "* c172code = 1.97"
    )
  )
})

test_that("ggemmeans, parsnip", {
  expect_s3_class(ggemmeans(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggemmeans(fit, "c12hour [minmax]"), "data.frame")
})

test_that("test_predictions, parsnip", {
  skip_on_os("linux")
  out <- test_predictions(fit, "c172code")
  expect_equal(out$Slope, 0.71836, tolerance = 0.1)
  expect_equal(out$conf.low, -1.928975, tolerance = 0.1)
})