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

# lm, linear regression ----

data(efc, package = "ggeffects")
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

test_that("validate ggpredict lm against predict", {
  nd <- data_grid(fit, "c12hour [10, 50, 100]")
  pr <- predict(fit, newdata = nd, se.fit = TRUE)
  expected <- pr$fit + stats::qt(0.975, df.residual(fit)) * pr$se.fit
  predicted <- ggpredict(fit, "c12hour [10, 50, 100]")
  expect_equal(predicted$conf.high, expected, tolerance = 1e-3, ignore_attr = TRUE)
  expect_equal(predicted$predicted, pr$fit, tolerance = 1e-3, ignore_attr = TRUE)
})

test_that("ggpredict, lm", {
  expect_s3_class(ggpredict(fit, "c12hour"), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex")), "data.frame")
})

test_that("ggpredict, lm print", {
  x <- ggpredict(fit, c("c12hour", "c161sex", "c172code"))
  out <- utils::capture.output(print(x))
  expect_identical(
    out,
    c(
      "# Predicted values of Total score BARTHEL INDEX", "", "#  c161sex = Male",
      "# 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 = [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 |     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 = [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 |     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]", "", "#  c161sex = Female",
      "# 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]", "", "Adjusted for:",
      "* neg_c_7 = 11.84"
    )
  )
  x <- ggpredict(fit, c("c12hour", "c161sex", "neg_c_7"))
  out <- utils::capture.output(print(x))
  expect_identical(
    out,
    c(
      "# Predicted values of Total score BARTHEL INDEX", "", "# c161sex = Male",
      "# neg_c_7 = 11.8", "", "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 |     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 = 15.7", "", "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 |     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 = 8", "", "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]", "", "# c161sex = Female",
      "# neg_c_7 = 8", "", "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]", "", "Adjusted for:",
      "* c172code = 1.97"
    )
  )
})

test_that("ggpredict, lm by", {
  expect_identical(nrow(ggpredict(fit, "c12hour [10:20]")), 11L)
  expect_identical(nrow(ggpredict(fit, "c12hour [10:20 by=.2]")), 51L)
  expect_identical(nrow(ggpredict(fit, "c12hour [10:20 by = .2]")), 51L)
  expect_identical(nrow(ggpredict(fit, "c12hour [10:20by=.2]")), 51L)
})

test_that("ggpredict, lm-vcov", {
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), vcov_fun = "vcovHC", vcov_type = "HC1"), "data.frame")
})

test_that("ggpredict, lm-prediction-interval", {
  pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "predict")
  expect_equal(pr$conf.low[1], 27.36046, tolerance = 1e-4)
  pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "conf")
  expect_equal(pr$conf.low[1], 71.0235294, tolerance = 1e-4)
  pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "predict", vcov_fun = "vcovHC", vcov_type = "HC1")
  expect_equal(pr$conf.low[1], 27.37019, tolerance = 1e-4)

  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), interval = "predict", ci_level = NA), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), interval = "conf", ci_level = NA), "data.frame")
})

test_that("ggpredict, lm-noci", {
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = NA), "data.frame")
})

test_that("ggpredict, lm, ci_level", {
  expect_s3_class(ggpredict(fit, "c12hour", ci_level = 0.8), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = 0.8), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8), "data.frame")
})

test_that("ggpredict, lm, typical", {
  expect_s3_class(ggpredict(fit, "c12hour", ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(
    ggpredict(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8, typical = "median"),
    "data.frame"
  )
})

test_that("ggpredict, lm, condition", {
  expect_s3_class(
    ggpredict(fit, "c172code", condition = c(c12hour = 40), ci_level = 0.8, typical = "median"),
    "data.frame"
  )
  expect_s3_class(
    ggpredict(
      fit,
      c("c172code", "c161sex"),
      condition = c(c12hour = 40),
      ci_level = 0.8,
      typical = "median"
    ),
    "data.frame"
  )
})

test_that("ggpredict, lm, pretty", {
  expect_s3_class(
    ggpredict(fit, "c12hour", full.data = TRUE, ci_level = 0.8, typical = "median"),
    "data.frame"
  )
  expect_s3_class(
    ggpredict(fit, c("c12hour", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"),
    "data.frame"
  )
})

test_that("ggpredict, lm, full.data", {
  expect_s3_class(ggpredict(fit, "c172code", full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(
    ggpredict(fit, c("c172code", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"),
    "data.frame"
  )
})

test_that("ggeffect, lm", {
  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")
})

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

test_that("ggemmeans, lm, ci_level", {
  expect_s3_class(ggemmeans(fit, "c12hour", ci_level = 0.8), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), ci_level = 0.8), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8), "data.frame")
})

test_that("ggemmeans, lm, typical", {
  expect_s3_class(ggemmeans(fit, "c12hour", ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8, typical = "median"), "data.frame")
})

test_that("ggemmeans, lm, condition", {
  expect_s3_class(ggemmeans(fit, "c172code", condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c172code", "c161sex"), condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame")
})

test_that("ggemmeans, lm, pretty", {
  expect_s3_class(ggemmeans(fit, "c12hour", full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame")
})


data(efc, package = "ggeffects")
efc$c172code <- sjlabelled::to_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

test_that("ggpredict, lm", {
  expect_s3_class(ggpredict(fit, "c12hour [20,30,40]"), "data.frame")
  expect_s3_class(ggpredict(fit, "c12hour [30:60]"), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour  [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})

test_that("ggpredict, lm", {
  expect_s3_class(ggpredict(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggpredict(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})


test_that("ggeffect, lm", {
  expect_s3_class(ggeffect(fit, "c12hour [20,30,40]"), "data.frame")
  expect_s3_class(ggeffect(fit, "c12hour [30:60]"), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour  [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})

test_that("ggeffect, lm", {
  expect_s3_class(ggeffect(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggeffect(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})


test_that("ggemmeans, lm", {
  expect_s3_class(ggemmeans(fit, "c12hour [20,30,40]"), "data.frame")
  expect_s3_class(ggemmeans(fit, "c12hour [30:60]"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour  [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})

test_that("ggemmeans, lm", {
  expect_s3_class(ggemmeans(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggemmeans(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame")
})


data(efc, package = "ggeffects")
efc$c172code <- sjlabelled::to_label(efc$c172code)
fit <- lm(barthtot ~ log(c12hour) + c161sex + c172code, data = efc)

test_that("ggpredict, lm, log", {
  expect_warning(ggpredict(fit, "c12hour [meansd]"))
  expect_s3_class(ggpredict(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggpredict(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]")), "data.frame")
})


test_that("ggeffect, lm, log", {
  expect_s3_class(ggeffect(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggeffect(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(suppressWarnings(ggeffect(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]"))), "data.frame")
})

test_that("ggeffect, lm, no_space", {
  expect_s3_class(ggeffect(fit, "c12hour[meansd]"), "data.frame")
  expect_s3_class(ggeffect(fit, "c12hour[minmax]"), "data.frame")
  expect_s3_class(ggeffect(fit, c("c12hour", "c172code[high level of education,low level of education]")), "data.frame")
  expect_s3_class(suppressWarnings(ggeffect(fit, c("c12hour[exp]", "c172code[high level of education,low level of education]"))), "data.frame")
})


test_that("ggemmeans, lm, log", {
  expect_s3_class(ggemmeans(fit, "c12hour [meansd]"), "data.frame")
  expect_s3_class(ggemmeans(fit, "c12hour [minmax]"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]")), "data.frame")
})

test_that("ggemmeans, lm, no_space", {
  expect_s3_class(ggemmeans(fit, "c12hour[meansd]"), "data.frame")
  expect_s3_class(ggemmeans(fit, "c12hour[minmax]"), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour", "c172code[high level of education,low level of education]")), "data.frame")
  expect_s3_class(ggemmeans(fit, c("c12hour[exp]", "c172code[high level of education,low level of education]")), "data.frame")
})


test_that("ggpredict, lm formula", {
  expect_s3_class(ggpredict(fit, ~ c12hour), "data.frame")
  expect_s3_class(ggpredict(fit, ~ c12hour + c161sex), "data.frame")
  expect_s3_class(ggpredict(fit, ~ c12hour + c161sex + c172code), "data.frame")
})


d <- subset(efc, select = c(barthtot, c12hour, neg_c_7, c172code))
d <- na.omit(d)
m1 <- lm(barthtot ~ c12hour + poly(neg_c_7, 2) + c172code, data = d)
m2 <- lm(barthtot ~ c12hour + poly(neg_c_7, 3, raw = TRUE) + c172code, data = d)
m3 <- lm(barthtot ~ scale(c12hour) + poly(neg_c_7, 2) + c172code, data = d)

test_that("ggpredict, lm", {
  expect_s3_class(ggpredict(m1, "neg_c_7"), "data.frame")
  expect_s3_class(ggpredict(m2, "neg_c_7"), "data.frame")
  expect_s3_class(ggpredict(m3, "neg_c_7"), "data.frame")
  expect_s3_class(ggpredict(m3, "c12hour"), "data.frame")
})

test_that("ggemmeans, lm", {
  expect_s3_class(ggemmeans(m1, "neg_c_7"), "data.frame")
  expect_s3_class(ggemmeans(m2, "neg_c_7"), "data.frame")
  expect_s3_class(ggemmeans(m3, "neg_c_7"), "data.frame")
  expect_s3_class(ggemmeans(m3, "c12hour"), "data.frame")
})

data(efc, package = "ggeffects")
fit <- lm(barthtot ~ c12hour + neg_c_7, data = efc)

test_that("ggemmeans, lm", {
  p1 <- ggemmeans(fit, "neg_c_7")
  p2 <- ggeffect(fit, "neg_c_7")
  p3 <- ggpredict(fit, "neg_c_7")
  expect_equal(p1$predicted[1], 78.2641, tolerance = 1e-3)
  expect_equal(p2$predicted[1], 78.2641, tolerance = 1e-3)
  expect_equal(p3$predicted[1], 78.2641, tolerance = 1e-3)
})

test_that("ggemmeans, lm", {
  p1 <- ggemmeans(fit, "neg_c_7 [5,10]")
  p2 <- ggeffect(fit, "neg_c_7 [5,10]")
  p3 <- ggpredict(fit, "neg_c_7 [5,10]")
  expect_equal(p1$predicted[1], 80.58504, tolerance = 1e-3)
  expect_equal(p2$predicted[1], 80.58504, tolerance = 1e-3)
  expect_equal(p3$predicted[1], 80.58504, tolerance = 1e-3)
})