skip_on_cran()
skip_if_offline()
skip_on_os("mac")
skip_if_not_installed("brms")
skip_if_not_installed("httr2")

# Model fitting -----------------------------------------------------------

m1 <- suppressWarnings(insight::download_model("brms_mixed_6"))
m2 <- insight::download_model("brms_mv_4")
m3 <- insight::download_model("brms_2")
m4 <- insight::download_model("brms_zi_3")
m5 <- insight::download_model("brms_mv_5")
m6 <- insight::download_model("brms_corr_re1")
m7 <- suppressWarnings(insight::download_model("brms_mixed_8"))
m8 <- insight::download_model("brms_ordinal_1")
brms_trunc_1 <- suppressWarnings(download_model("brms_trunc_1"))

all_loaded <- !vapply(list(m1, m2, m3, m4, m5, m6, m7, m8, brms_trunc_1), is.null, TRUE)
skip_if(!all(all_loaded))

# Tests -------------------------------------------------------------------
test_that("get_response.brmsfit: trunc", {
  expect_identical(find_response(brms_trunc_1), "count")
  expect_length(get_response(brms_trunc_1, source = "env"), 236)
  expect_length(get_response(brms_trunc_1, source = "mf"), 236)
})

test_that("get_predicted.brmsfit: ordinal dv", {
  skip_if_not_installed("bayestestR")
  skip_if_not_installed("rstantools")

  pred1 <- get_predicted(m8, ci = 0.95)
  pred2 <- get_predicted(m8, ci_method = "hdi", ci = 0.95)
  expect_s3_class(pred1, "get_predicted")
  expect_s3_class(pred1, "data.frame")
  expect_true(all(c("Row", "Response") %in% colnames(pred1)))

  # ci_method changes intervals but not se or predicted
  pred1 <- data.frame(pred1)
  pred2 <- data.frame(pred2)
  expect_equal(pred1$Row, pred2$Row, ignore_attr = TRUE)
  expect_equal(pred1$Response, pred2$Response, ignore_attr = TRUE)
  expect_equal(pred1$Predicted, pred2$Predicted, ignore_attr = TRUE)
  expect_equal(pred1$SE, pred2$SE, ignore_attr = TRUE)
  expect_false(mean(pred1$CI_low == pred2$CI_low) > 0.1) # most CI bounds are different
  expect_false(mean(pred1$CI_high == pred2$CI_high) > 0.1) # most CI bounds are different

  # compare to manual predictions
  pred3 <- get_predicted(m8, centrality_function = stats::median, ci = 0.95)
  manual <- rstantools::posterior_epred(m8)
  manual <- apply(manual[, , 1], 2, median)
  expect_equal(pred3$Predicted[1:32], manual, ignore_attr = TRUE)
  manual <- rstantools::posterior_epred(m8)
  manual <- apply(manual[, , 1], 2, mean) # nolint
  expect_equal(pred1$Predicted[1:32], manual, ignore_attr = TRUE)
})

test_that("find_statistic", {
  expect_null(find_statistic(m1))
  expect_null(find_statistic(m2))
  expect_null(find_statistic(m3))
  expect_null(find_statistic(m4))
  expect_null(find_statistic(m5))
})

test_that("n_parameters", {
  expect_identical(n_parameters(m1), 65L)
  expect_identical(n_parameters(m1, effects = "fixed"), 5L)
})

test_that("model_info", {
  expect_true(model_info(m3)$is_trial)
  expect_true(model_info(m5)[[1]]$is_zero_inflated)
  expect_true(model_info(m5)[[1]]$is_bayesian)
})

test_that("clean_names", {
  expect_identical(
    clean_names(m1),
    c("count", "Age", "Base", "Trt", "patient")
  )
  expect_identical(
    clean_names(m2),
    c(
      "Sepal.Length",
      "Sepal.Width",
      "Petal.Length",
      "Species"
    )
  )
  expect_identical(clean_names(m3), c("r", "n", "treat", "c2"))
  expect_identical(
    clean_names(m4),
    c("count", "child", "camper", "persons")
  )
  expect_identical(
    clean_names(m5),
    c(
      "count",
      "count2",
      "child",
      "camper",
      "persons",
      "livebait"
    )
  )
})


test_that("find_predictors", {
  expect_identical(find_predictors(m1), list(conditional = c("Age", "Base", "Trt")))
  expect_identical(
    find_predictors(m1, flatten = TRUE),
    c("Age", "Base", "Trt")
  )
  expect_identical(
    find_predictors(m1, effects = "all", component = "all"),
    list(
      conditional = c("Age", "Base", "Trt"),
      random = "patient"
    )
  )
  expect_identical(
    find_predictors(
      m1,
      effects = "all",
      component = "all",
      flatten = TRUE
    ),
    c("Age", "Base", "Trt", "patient")
  )

  expect_identical(
    find_predictors(m2),
    list(
      SepalLength = list(conditional = c(
        "Petal.Length", "Sepal.Width", "Species"
      )),
      SepalWidth = list(conditional = "Species")
    )
  )

  expect_identical(
    find_predictors(m2, flatten = TRUE),
    c("Petal.Length", "Sepal.Width", "Species")
  )
  expect_identical(find_predictors(m3), list(conditional = c("treat", "c2")))
  expect_identical(
    find_predictors(m4),
    list(
      conditional = c("child", "camper"),
      zero_inflated = c("child", "camper")
    )
  )
  expect_identical(
    find_predictors(m4, effects = "random"),
    list(random = "persons", zero_inflated_random = "persons")
  )
  expect_identical(find_predictors(m4, flatten = TRUE), c("child", "camper"))

  expect_identical(
    find_predictors(m5),
    list(
      count = list(
        conditional = c("child", "camper"),
        zero_inflated = "camper"
      ),
      count2 = list(
        conditional = c("child", "livebait"),
        zero_inflated = "child"
      )
    )
  )
})

test_that("find_response", {
  expect_identical(find_response(m1, combine = TRUE), "count")
  expect_identical(
    find_response(m2, combine = TRUE),
    c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
  )
  expect_identical(find_response(m3, combine = TRUE), c("r", "n"))
  expect_identical(find_response(m1, combine = FALSE), "count")
  expect_identical(
    find_response(m2, combine = FALSE),
    c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width")
  )
  expect_identical(find_response(m3, combine = FALSE), c("r", "n"))
  expect_identical(find_response(m4, combine = FALSE), "count")
  expect_identical(
    find_response(m5, combine = TRUE),
    c(count = "count", count2 = "count2")
  )
})

test_that("get_response", {
  expect_length(get_response(m1), 236)
  expect_identical(ncol(get_response(m2)), 2L)
  expect_identical(
    colnames(get_response(m2)),
    c("Sepal.Length", "Sepal.Width")
  )
  expect_identical(ncol(get_response(m3)), 2L)
  expect_identical(colnames(get_response(m3)), c("r", "n"))
  expect_length(get_response(m4), 250)
  expect_identical(colnames(get_response(m5)), c("count", "count2"))
})

test_that("find_variables", {
  expect_identical(
    find_variables(m1),
    list(
      response = "count",
      conditional = c("Age", "Base", "Trt"),
      random = "patient"
    )
  )
  expect_identical(
    find_variables(m6),
    list(
      response = "y",
      conditional = "x",
      random = "id",
      sigma = "x",
      sigma_random = "id"
    )
  )
  expect_identical(
    find_variables(m1, effects = "fixed"),
    list(
      response = "count",
      conditional = c("Age", "Base", "Trt")
    )
  )
  expect_null(find_variables(m1, component = "zi"))

  expect_identical(
    find_variables(m2),
    list(
      response = c(SepalLength = "Sepal.Length", SepalWidth = "Sepal.Width"),
      SepalLength = list(conditional = c(
        "Petal.Length", "Sepal.Width", "Species"
      )),
      SepalWidth = list(conditional = "Species")
    )
  )

  expect_identical(
    find_variables(m2, flatten = TRUE),
    c(
      "Sepal.Length",
      "Sepal.Width",
      "Petal.Length",
      "Species"
    )
  )
  expect_identical(find_variables(m3), list(
    response = c("r", "n"),
    conditional = c("treat", "c2")
  ))

  expect_identical(
    find_variables(m4),
    list(
      response = "count",
      conditional = c("child", "camper"),
      random = "persons",
      zero_inflated = c("child", "camper"),
      zero_inflated_random = "persons"
    )
  )

  expect_identical(
    find_variables(m4, flatten = TRUE),
    c("count", "child", "camper", "persons")
  )
})

test_that("n_obs", {
  expect_identical(n_obs(m1), 236L)
  expect_identical(n_obs(m2), 150L)
  expect_identical(n_obs(m3), 10L)
  expect_identical(n_obs(m4), 250L)
  expect_identical(n_obs(m5), 250L)
})


test_that("find_random", {
  expect_identical(find_random(m5), list(
    count = list(
      random = "persons",
      zero_inflated_random = "persons"
    ),
    count2 = list(
      random = "persons",
      zero_inflated_random = "persons"
    )
  ))
  expect_identical(find_random(m5, flatten = TRUE), "persons")
  expect_identical(find_random(m6, flatten = TRUE), "id")
})


test_that("get_random", {
  zinb <- get_data(m4)
  expect_identical(get_random(m4), zinb[, "persons", drop = FALSE])
})


test_that("get_data", {
  d <- get_data(m6)
  expect_identical(nrow(d), 200L)
  expect_identical(ncol(d), 3L)
})


test_that("find_paramaters", {
  expect_identical(
    find_parameters(m1),
    list(
      conditional = c(
        "b_Intercept",
        "b_Age",
        "b_Base",
        "b_Trt1",
        "b_Base:Trt1"
      ),
      random = c(sprintf("r_patient[%i,Intercept]", 1:59), "sd_patient__Intercept")
    )
  )

  expect_identical(
    find_parameters(m2),
    structure(
      list(
        SepalLength = list(
          conditional = c(
            "b_SepalLength_Intercept",
            "b_SepalLength_Petal.Length",
            "b_SepalLength_Sepal.Width",
            "b_SepalLength_Speciesversicolor",
            "b_SepalLength_Speciesvirginica"
          ),
          sigma = "sigma_SepalLength"
        ),
        SepalWidth = list(
          conditional = c(
            "b_SepalWidth_Intercept",
            "b_SepalWidth_Speciesversicolor",
            "b_SepalWidth_Speciesvirginica"
          ),
          sigma = "sigma_SepalWidth"
        )
      ),
      is_mv = "1"
    )
  )

  expect_identical(
    find_parameters(m4),
    list(
      conditional = c("b_Intercept", "b_child", "b_camper"),
      random = c(sprintf("r_persons[%i,Intercept]", 1:4), "sd_persons__Intercept"),
      zero_inflated = c("b_zi_Intercept", "b_zi_child", "b_zi_camper"),
      zero_inflated_random = c(sprintf("r_persons__zi[%i,Intercept]", 1:4), "sd_persons__zi_Intercept")
    )
  )

  expect_identical(
    find_parameters(m5, effects = "all"),
    structure(
      list(
        count = list(
          conditional = c("b_count_Intercept", "b_count_child", "b_count_camper"),
          random = c(sprintf("r_persons__count[%i,Intercept]", 1:4), "sd_persons__count_Intercept"),
          zero_inflated = c("b_zi_count_Intercept", "b_zi_count_camper"),
          zero_inflated_random = c(sprintf("r_persons__zi_count[%i,Intercept]", 1:4), "sd_persons__zi_count_Intercept")
        ),
        count2 = list(
          conditional = c(
            "b_count2_Intercept",
            "b_count2_child",
            "b_count2_livebait"
          ),
          random = c(sprintf("r_persons__count2[%i,Intercept]", 1:4), "sd_persons__count2_Intercept"),
          zero_inflated = c("b_zi_count2_Intercept", "b_zi_count2_child"),
          zero_inflated_random = c(
            sprintf("r_persons__zi_count2[%i,Intercept]", 1:4),
            "sd_persons__zi_count2_Intercept"
          )
        )
      ),
      is_mv = "1"
    )
  )
})

test_that("find_paramaters", {
  expect_identical(
    colnames(get_parameters(m4)),
    c(
      "b_Intercept",
      "b_child",
      "b_camper",
      "b_zi_Intercept",
      "b_zi_child",
      "b_zi_camper"
    )
  )
  expect_identical(
    colnames(get_parameters(m4, component = "zi")),
    c("b_zi_Intercept", "b_zi_child", "b_zi_camper")
  )
  expect_identical(
    colnames(get_parameters(m4, effects = "all")),
    c(
      "b_Intercept", "b_child", "b_camper", "r_persons[1,Intercept]",
      "r_persons[2,Intercept]", "r_persons[3,Intercept]", "r_persons[4,Intercept]",
      "sd_persons__Intercept", "b_zi_Intercept", "b_zi_child", "b_zi_camper",
      "r_persons__zi[1,Intercept]", "r_persons__zi[2,Intercept]", "r_persons__zi[3,Intercept]",
      "r_persons__zi[4,Intercept]", "sd_persons__zi_Intercept"
    )
  )
  expect_identical(
    colnames(get_parameters(m4, effects = "random", component = "conditional")),
    c(
      "r_persons[1,Intercept]", "r_persons[2,Intercept]", "r_persons[3,Intercept]",
      "r_persons[4,Intercept]", "sd_persons__Intercept"
    )
  )
  expect_identical(
    colnames(get_parameters(m5, effects = "random", component = "conditional")),
    c(
      "r_persons__count[1,Intercept]", "r_persons__count[2,Intercept]",
      "r_persons__count[3,Intercept]", "r_persons__count[4,Intercept]",
      "sd_persons__count_Intercept", "r_persons__count2[1,Intercept]",
      "r_persons__count2[2,Intercept]", "r_persons__count2[3,Intercept]",
      "r_persons__count2[4,Intercept]", "sd_persons__count2_Intercept"
    )
  )

  expect_identical(
    colnames(get_parameters(m5, effects = "all", component = "all")),
    c(
      "b_count_Intercept", "b_count_child", "b_count_camper", "r_persons__count[1,Intercept]",
      "r_persons__count[2,Intercept]", "r_persons__count[3,Intercept]",
      "r_persons__count[4,Intercept]", "sd_persons__count_Intercept",
      "b_zi_count_Intercept", "b_zi_count_camper", "r_persons__zi_count[1,Intercept]",
      "r_persons__zi_count[2,Intercept]", "r_persons__zi_count[3,Intercept]",
      "r_persons__zi_count[4,Intercept]", "sd_persons__zi_count_Intercept",
      "b_count2_Intercept", "b_count2_child", "b_count2_livebait",
      "r_persons__count2[1,Intercept]", "r_persons__count2[2,Intercept]",
      "r_persons__count2[3,Intercept]", "r_persons__count2[4,Intercept]",
      "sd_persons__count2_Intercept", "b_zi_count2_Intercept", "b_zi_count2_child",
      "r_persons__zi_count2[1,Intercept]", "r_persons__zi_count2[2,Intercept]",
      "r_persons__zi_count2[3,Intercept]", "r_persons__zi_count2[4,Intercept]",
      "sd_persons__zi_count2_Intercept"
    )
  )
})

test_that("linkfun", {
  expect_false(is.null(link_function(m1)))
  expect_length(link_function(m2), 2)
  expect_false(is.null(link_function(m3)))
  expect_false(is.null(link_function(m4)))
  expect_length(link_function(m5), 2)
})

test_that("linkinv", {
  expect_false(is.null(link_inverse(m1)))
  expect_length(link_inverse(m2), 2)
  expect_false(is.null(link_inverse(m3)))
  expect_false(is.null(link_inverse(m4)))
  expect_length(link_inverse(m2), 2)
})

test_that("is_multivariate", {
  expect_false(is_multivariate(m1))
  expect_true(is_multivariate(m2))
  expect_false(is_multivariate(m3))
  expect_false(is_multivariate(m4))
  expect_true(is_multivariate(m5))
})

test_that("find_terms", {
  expect_identical(
    find_terms(m2),
    list(
      SepalLength = list(
        response = "Sepal.Length",
        conditional = c("Petal.Length", "Sepal.Width", "Species")
      ),
      SepalWidth = list(
        response = "Sepal.Width",
        conditional = "Species"
      )
    )
  )
})

test_that("find_algorithm", {
  expect_equal(
    find_algorithm(m1),
    list(
      algorithm = "sampling",
      chains = 1,
      iterations = 500,
      warmup = 250
    ),
    ignore_attr = TRUE
  )
})

test_that("get_priors", {
  expect_equal(
    get_priors(m7),
    data.frame(
      Parameter = c(
        "b_Intercept", "b_Age", "b_Base", "b_Trt1", "b_Base:Trt1",
        "sd_patient__Intercept", "sd_patient__Age",
        "cor_patient__Intercept__Age"
      ),
      Distribution = c(
        "student_t", "student_t", "student_t",
        "student_t", "student_t", "cauchy", "cauchy", "lkj"
      ),
      Location = c(1.4, 0, 0, 0, 0, NA, NA, 1),
      Scale = c(2.5, 10, 10, 10, 10, NA, NA, NA),
      df = c(3, 5, 5, 5, 5, NA, NA, NA),
      stringsAsFactors = FALSE
    ),
    ignore_attr = TRUE
  )
  expect_equal(
    get_priors(m3),
    data.frame(
      Parameter = c("b_Intercept", "b_treat1", "b_c2", "b_treat1:c2"),
      Distribution = c("student_t", "uniform", "uniform", "uniform"),
      Location = c(0, NA, NA, NA),
      Scale = c(2.5, NA, NA, NA),
      df = c(3, NA, NA, NA),
      stringsAsFactors = FALSE
    ),
    ignore_attr = TRUE
  )
})

test_that("Issue #645", {
  # apparently BH is required to fit these brms models
  skip_if_not_installed("BH")
  # sink() writing permission fail on some Windows CI machines
  skip_on_os("windows")

  void <- suppressMessages(suppressWarnings(capture.output({
    mod <- brms::brm(
      silent = 2,
      data = mtcars,
      family = brms::cumulative(probit),
      formula = brms::bf(
        cyl ~ 1 + mpg + drat + gearnl,
        gearnl ~ 0 + (1 | gear),
        nl = TRUE
      )
    )
  })))

  p <- find_predictors(mod, flatten = TRUE)
  d <- get_data(mod)
  expect_true("gear" %in% p)
  expect_true("gear" %in% colnames(d))
})

test_that("clean_parameters", {
  expect_equal(
    clean_parameters(m4),
    structure(
      list(
        Parameter = c(
          "b_Intercept",
          "b_child",
          "b_camper",
          "r_persons[1,Intercept]",
          "r_persons[2,Intercept]",
          "r_persons[3,Intercept]",
          "r_persons[4,Intercept]",
          "sd_persons__Intercept",
          "b_zi_Intercept",
          "b_zi_child",
          "b_zi_camper",
          "r_persons__zi[1,Intercept]",
          "r_persons__zi[2,Intercept]",
          "r_persons__zi[3,Intercept]",
          "r_persons__zi[4,Intercept]",
          "sd_persons__zi_Intercept"
        ),
        Effects = c(
          "fixed",
          "fixed",
          "fixed",
          "random",
          "random",
          "random",
          "random",
          "random",
          "fixed",
          "fixed",
          "fixed",
          "random",
          "random",
          "random",
          "random",
          "random"
        ),
        Component = c(
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated"
        ),
        Group = c(
          "",
          "",
          "",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "SD/Cor: persons",
          "",
          "",
          "",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "SD/Cor: persons"
        ),
        Cleaned_Parameter = c(
          "(Intercept)",
          "child",
          "camper",
          "persons.1",
          "persons.2",
          "persons.3",
          "persons.4",
          "(Intercept)",
          "(Intercept)",
          "child",
          "camper",
          "persons.1",
          "persons.2",
          "persons.3",
          "persons.4",
          "(Intercept)"
        )
      ),
      class = c("clean_parameters", "data.frame"),
      row.names = c(
        NA,
        -16L
      )
    ),
    ignore_attr = TRUE
  )

  expect_equal(
    clean_parameters(m5),
    structure(
      list(
        Parameter = c(
          "b_count_Intercept",
          "b_count_child",
          "b_count_camper",
          "b_count2_Intercept",
          "b_count2_child",
          "b_count2_livebait",
          "r_persons__count[1,Intercept]",
          "r_persons__count[2,Intercept]",
          "r_persons__count[3,Intercept]",
          "r_persons__count[4,Intercept]",
          "sd_persons__count_Intercept",
          "r_persons__count2[1,Intercept]",
          "r_persons__count2[2,Intercept]",
          "r_persons__count2[3,Intercept]",
          "r_persons__count2[4,Intercept]",
          "sd_persons__count2_Intercept",
          "b_zi_count_Intercept",
          "b_zi_count_camper",
          "b_zi_count2_Intercept",
          "b_zi_count2_child",
          "r_persons__zi_count[1,Intercept]",
          "r_persons__zi_count[2,Intercept]",
          "r_persons__zi_count[3,Intercept]",
          "r_persons__zi_count[4,Intercept]",
          "sd_persons__zi_count_Intercept",
          "r_persons__zi_count2[1,Intercept]",
          "r_persons__zi_count2[2,Intercept]",
          "r_persons__zi_count2[3,Intercept]",
          "r_persons__zi_count2[4,Intercept]",
          "sd_persons__zi_count2_Intercept"
        ),
        Effects = c(
          "fixed",
          "fixed",
          "fixed",
          "fixed",
          "fixed",
          "fixed",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "fixed",
          "fixed",
          "fixed",
          "fixed",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random",
          "random"
        ),
        Component = c(
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "conditional",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated",
          "zero_inflated"
        ),
        Group = c(
          "",
          "",
          "",
          "",
          "",
          "",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "SD/Cor: persons",
          "Intercept: persons2",
          "Intercept: persons2",
          "Intercept: persons2",
          "Intercept: persons2",
          "SD/Cor: persons",
          "",
          "",
          "",
          "",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "Intercept: persons",
          "SD/Cor: persons",
          "Intercept: persons2",
          "Intercept: persons2",
          "Intercept: persons2",
          "Intercept: persons2",
          "SD/Cor: persons"
        ),
        Response = c(
          "count",
          "count",
          "count",
          "count2",
          "count2",
          "count2",
          "count",
          "count",
          "count",
          "count",
          "count",
          "count2",
          "count2",
          "count2",
          "count2",
          "count2",
          "count",
          "count",
          "count2",
          "count2",
          "count",
          "count",
          "count",
          "count",
          "count",
          "count2",
          "count2",
          "count2",
          "count2",
          "count2"
        ),
        Cleaned_Parameter = c(
          "(Intercept)",
          "child",
          "camper",
          "(Intercept)",
          "child",
          "livebait",
          "persons.1",
          "persons.2",
          "persons.3",
          "persons.4",
          "count_Intercept",
          "persons2.1",
          "persons2.2",
          "persons2.3",
          "persons2.4",
          "count2_Intercept",
          "(Intercept)",
          "camper",
          "(Intercept)",
          "child",
          "persons.1",
          "persons.2",
          "persons.3",
          "persons.4",
          "zi_count_Intercept",
          "persons2.1",
          "persons2.2",
          "persons2.3",
          "persons2.4",
          "zi_count2_Intercept"
        )
      ),
      class = c("clean_parameters", "data.frame"),
      row.names = c(NA, -30L)
    ),
    ignore_attr = TRUE
  )
})

test_that("get_modelmatrix", {
  out <- get_modelmatrix(m1)
  expect_identical(dim(out), c(236L, 4L))
  m9 <- suppressWarnings(insight::download_model("brms_mo2"))
  skip_if(is.null(m9))
  out <- get_modelmatrix(m9)
  expect_identical(dim(out), c(32L, 2L))
})

test_that("get_modelmatrix", {
  m10 <- suppressWarnings(insight::download_model("brms_lf_1"))
  expect_identical(
    find_variables(m10),
    list(
      response = "carb",
      conditional = c("gear", "vs"), disc = c("disc", "cyl")
    )
  )
})

# get variance
test_that("get_variance works", {
  mdl <- suppressWarnings(insight::download_model("brms_mixed_9"))
  out <- get_variance(mdl)
  expect_equal(
    out,
    list(
      var.fixed = 4.91103174480995,
      var.random = 22.4069708072874,
      var.residual = 10.9304525807216,
      var.distribution = 10.9304525807216,
      var.dispersion = 0,
      var.intercept = c(cyl = 22.4069708072874)
    ),
    tolerance = 1e-3,
    ignore_attr = TRUE
  )
  # make sure it's a matrix
  # expect_true(is.matrix(get_modelmatrix(null_model(mdl))))
})


# get variance
test_that("get_variance aligns with get_sigma", {
  skip_if_not_installed("lme4")
  data(mtcars)
  set.seed(123)
  mdl <- suppressWarnings(insight::download_model("brms_mixed_9"))
  VC <- lme4::VarCorr(mdl)
  out1 <- VC$residual__$sd[1, 1]^2 # Residual variance
  out2 <- get_variance(mdl)$var.residual
  out3 <- get_sigma(mdl)^2
  expect_equal(out1, out2, tolerance = 1e-3, ignore_attr = TRUE)
  expect_equal(out1, out3, tolerance = 1e-3, ignore_attr = TRUE)
  expect_equal(out2, out3, tolerance = 1e-3, ignore_attr = TRUE)
})


# get variance
test_that("get_variance works when sigma is modeled", {
  data(mtcars)
  set.seed(123)
  m <- insight::download_model("brms_sigma_1")
  expect_message(
    {
      out <- get_variance(m)
    },
    regex = "modeled directly"
  )
  expect_equal(
    out,
    list(
      var.fixed = 2.712739833628, var.random = 25.6254745619452,
      var.dispersion = 0, var.intercept = c(cyl = 25.6254745619452)
    ),
    ignore_attr = TRUE,
    tolerance = 1e-3
  )
})
