skip_if_not_installed("nlme")
skip_if_not_installed("lme4")

data(sleepstudy, package = "lme4")
data(Orthodont, package = "nlme")
data(Ovary, package = "nlme")

m1 <- nlme::lme(Reaction ~ Days,
  random = ~ 1 + Days | Subject,
  data = sleepstudy
)

m2 <- nlme::lme(distance ~ age + Sex, data = Orthodont, random = ~1)

set.seed(123)
sleepstudy$mygrp <- sample.int(5, size = 180, replace = TRUE)
sleepstudy$mysubgrp <- NA
for (i in 1:5) {
  filter_group <- sleepstudy$mygrp == i
  sleepstudy$mysubgrp[filter_group] <-
    sample.int(30, size = sum(filter_group), replace = TRUE)
}

m3 <- nlme::lme(Reaction ~ Days,
  random = ~ 1 | mygrp / mysubgrp,
  data = sleepstudy
)

# from easystats/insight/482
cr <<- nlme::corAR1(form = ~ 1 | Mare)
m4 <- nlme::lme(follicles ~ Time, Ovary, correlation = cr)

test_that("nested_varCorr", {
  skip_on_cran()

  expect_equal(
    insight:::.get_nested_lme_varcorr(m3)$mysubgrp[1, 1],
    7.508310765,
    tolerance = 1e-3
  )
  expect_equal(
    insight:::.get_nested_lme_varcorr(m3)$mygrp[1, 1],
    0.004897827,
    tolerance = 1e-2
  )
})


test_that("model_info", {
  expect_true(model_info(m1)$is_linear)
})

test_that("find_predictors", {
  expect_identical(find_predictors(m1), list(conditional = "Days"))
  expect_identical(find_predictors(m2), list(conditional = c("age", "Sex")))
  expect_identical(
    find_predictors(m1, effects = "all"),
    list(conditional = "Days", random = "Subject")
  )
  expect_identical(
    find_predictors(m2, effects = "all"),
    list(conditional = c("age", "Sex"), random = "Subject")
  )
  expect_identical(find_predictors(m1, flatten = TRUE), "Days")
  expect_identical(
    find_predictors(m1, effects = "random"),
    list(random = "Subject")
  )
  expect_identical(
    find_predictors(m2, effects = "random"),
    list(random = "Subject")
  )
})

test_that("find_response", {
  expect_identical(find_response(m1), "Reaction")
  expect_identical(find_response(m2), "distance")
})

test_that("get_response", {
  expect_equal(get_response(m1), sleepstudy$Reaction, ignore_attr = TRUE)
})

test_that("find_random", {
  expect_identical(find_random(m1), list(random = "Subject"))
  expect_identical(find_random(m2), list(random = "Subject"))
})

test_that("get_random", {
  expect_equal(get_random(m1), data.frame(Subject = sleepstudy$Subject), ignore_attr = TRUE)
  expect_equal(get_random(m2), data.frame(Subject = Orthodont$Subject), ignore_attr = TRUE)
})

test_that("link_inverse", {
  expect_equal(link_inverse(m1)(0.2), 0.2, tolerance = 1e-5)
})

test_that("get_data", {
  expect_equal(nrow(get_data(m1)), 180, ignore_attr = TRUE)
  expect_identical(colnames(get_data(m1)), c("Reaction", "Days", "Subject"))
  expect_identical(colnames(get_data(m2)), c("distance", "age", "Sex", "Subject"))
})

test_that("get_df", {
  expect_equal(get_df(m1, type = "residual"), c(161, 161), ignore_attr = TRUE)
  expect_equal(get_df(m1, type = "normal"), Inf, ignore_attr = TRUE)
  expect_equal(get_df(m1, type = "wald"), c(161, 161), ignore_attr = TRUE)
  expect_equal(get_df(m2, type = "residual"), c(80, 80, 25), ignore_attr = TRUE)
  expect_equal(get_df(m2, type = "normal"), Inf, ignore_attr = TRUE)
  expect_equal(get_df(m3, type = "residual"), c(98, 76), ignore_attr = TRUE)
  expect_equal(get_df(m3, type = "normal"), Inf, ignore_attr = TRUE)
})

test_that("find_formula", {
  expect_length(find_formula(m1), 2)
  expect_equal(
    find_formula(m1),
    list(
      conditional = as.formula("Reaction ~ Days"),
      random = as.formula("~1 + Days | Subject")
    ),
    ignore_attr = TRUE
  )
  expect_length(find_formula(m2), 2)
  expect_equal(
    find_formula(m2),
    list(
      conditional = as.formula("distance ~ age + Sex"),
      random = as.formula("~1 | Subject")
    ),
    ignore_attr = TRUE
  )
  expect_length(find_formula(m4), 2)
  expect_equal(
    find_formula(m4),
    list(
      conditional = as.formula("follicles ~ Time"),
      correlation = as.formula("~1 | Mare")
    ),
    ignore_attr = TRUE
  )
})

test_that("find_variables", {
  expect_identical(
    find_variables(m1),
    list(
      response = "Reaction",
      conditional = "Days",
      random = "Subject"
    )
  )
  expect_identical(
    find_variables(m1, flatten = TRUE),
    c("Reaction", "Days", "Subject")
  )
  expect_identical(
    find_variables(m2),
    list(
      response = "distance",
      conditional = c("age", "Sex"),
      random = "Subject"
    )
  )
  expect_identical(
    find_variables(m4),
    list(
      response = "follicles",
      conditional = "Time",
      correlation = "Mare"
    )
  )
})

test_that("n_obs", {
  expect_equal(n_obs(m1), 180, ignore_attr = TRUE)
})

test_that("linkfun", {
  expect_false(is.null(link_function(m1)))
})

test_that("find_parameters", {
  expect_identical(
    find_parameters(m1),
    list(
      conditional = c("(Intercept)", "Days"),
      random = c("(Intercept)", "Days")
    )
  )
  expect_equal(nrow(get_parameters(m1)), 2) # nolint
  expect_identical(get_parameters(m1)$Parameter, c("(Intercept)", "Days"))
  expect_identical(
    find_parameters(m2),
    list(
      conditional = c("(Intercept)", "age", "SexFemale"),
      random = "(Intercept)"
    )
  )
})

test_that("find_algorithm", {
  expect_identical(
    find_algorithm(m1),
    list(algorithm = "REML", optimizer = "nlminb")
  )
})

test_that("get_variance", {
  skip_on_cran()

  expect_equal(
    get_variance(m1),
    list(
      var.fixed = 908.95336262308865116211,
      var.random = 1698.06593646939654718153,
      var.residual = 654.94240352794997761521,
      var.distribution = 654.94240352794997761521,
      var.dispersion = 0,
      var.intercept = c(Subject = 612.07951112963326067984),
      var.slope = c(Subject.Days = 35.07130179308116169068),
      cor.slope_intercept = c(Subject = 0.06600000000000000311)
    ),
    tolerance = 1e-3
  )
})

test_that("find_statistic", {
  expect_identical(find_statistic(m1), "t-statistic")
  expect_identical(find_statistic(m2), "t-statistic")
  expect_identical(find_statistic(m3), "t-statistic")
})


test_that("Issue #658", {
  skip_if_not_installed("nlme")
  models <- lapply(
    c("", " + Sex"),
    function(x) {
      nlme::lme(as.formula(paste0("distance  ~ age", x)),
        random = ~1,
        data = Orthodont
      )
    }
  )
  dat <- lapply(models, get_data)
  form <- lapply(models, find_formula)
  expect_s3_class(form[[1]], "insight_formula")
  expect_s3_class(form[[2]], "insight_formula")
  expect_s3_class(dat[[1]], "data.frame")
  expect_s3_class(dat[[2]], "data.frame")
})