# bayesfactor_models BIC --------------------------------------------------
test_that("bayesfactor_models BIC", {
  skip_if_not_or_load_if_installed("lme4")
  set.seed(444)
  void <- suppressMessages(capture.output({
    mo1 <- lme4::lmer(Sepal.Length ~ (1 | Species), data = iris)
    mo2 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
    mo3 <- lme4::lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
    mo4 <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris)
    mo5 <- lme4::lmer(Sepal.Length ~ Petal.Length * Petal.Width + (Petal.Length | Species), data = iris)
    mo4_e <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris[-1, ])
  }))

  # both uses of denominator
  BFM1 <<- bayesfactor_models(mo2, mo3, mo4, mo1, denominator = 4)
  BFM2 <- bayesfactor_models(mo2, mo3, mo4, denominator = mo1)
  BFM3 <- bayesfactor_models(mo2, mo3, mo4, mo1, denominator = mo1)
  BFM4 <<- bayesfactor_models(mo2, mo3, mo4, mo5, mo1, denominator = mo1)

  expect_equal(BFM1, BFM2, tolerance = 1e-4, ignore_attr = TRUE)
  expect_equal(BFM1, BFM3, tolerance = 1e-4, ignore_attr = TRUE)
  expect_equal(
    BFM1,
    bayesfactor_models(list(mo2 = mo2, mo3 = mo3, mo4 = mo4, mo1 = mo1), denominator = 4),
    tolerance = 1e-4, ignore_attr = TRUE
  )

  # only on same data!
  expect_warning(bayesfactor_models(mo1, mo2, mo4_e))

  # update models
  expect_equal(update(BFM2, subset = c(1, 2))$log_BF, c(1, 57.3, 54.52), tolerance = 0.1)

  # update reference
  expect_equal(update(BFM2, reference = 1)$log_BF,
    c(0, -2.8, -6.2, -57.4),
    tolerance = 0.1
  )
})


test_that("bayesfactor_models BIC, transformed responses", {
  skip_if_not_or_load_if_installed("lme4")

  m1 <- lm(mpg ~ 1, mtcars)
  m2 <- lm(sqrt(mpg) ~ 1, mtcars)

  BF1 <- bayesfactor_models(m1, m2, check_response = TRUE)
  expect_equal(BF1$log_BF[2], 2.4404 / 2, tolerance = 0.01)

  BF2 <- bayesfactor_models(m1, m2, check_response = FALSE)
  expect_false(isTRUE(all.equal(BF1, BF2)))
})

test_that("bayesfactor_models BIC (unsupported / diff nobs)", {
  skip_if_not_or_load_if_installed("lme4")

  skip_on_cran()
  set.seed(444)

  fit1 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length, iris)
  fit2a <- lm(Sepal.Length ~ Sepal.Width, iris[-1, ]) # different number of objects
  fit2b <- lm(Sepal.Length ~ Sepal.Width, iris) # not supported
  class(fit2b) <- "NOTLM"
  logLik.NOTLM <<- function(...) {
    stats:::logLik.lm(...)
  }

  # Should warm
  expect_warning(bayesfactor_models(fit1, fit2a))

  # Should fail
  suppressWarnings(expect_message(bayesfactor_models(fit1, fit2b), "Unable"))
})


# bayesfactor_models STAN ---------------------------------------------

test_that("bayesfactor_models STAN", {
  skip_if_not_or_load_if_installed("lme4")
  skip_if_not_or_load_if_installed("rstanarm")
  skip_if_not_or_load_if_installed("bridgesampling")
  skip_if_not_or_load_if_installed("brms")

  skip_on_cran()
  set.seed(333)
  stan_bf_0 <- rstanarm::stan_glm(
    Sepal.Length ~ 1,
    data = iris,
    refresh = 0,
    iter = 500,
    diagnostic_file = file.path(tempdir(), "df0.csv")
  )
  stan_bf_1 <- suppressWarnings(rstanarm::stan_glm(
    Sepal.Length ~ Species,
    data = iris,
    refresh = 0,
    iter = 500,
    diagnostic_file = file.path(tempdir(), "df1.csv")
  ))


  set.seed(333) # compare against bridgesampling
  bridge_BF <- bridgesampling::bayes_factor(
    bridgesampling::bridge_sampler(stan_bf_1, silent = TRUE),
    bridgesampling::bridge_sampler(stan_bf_0, silent = TRUE)
  )

  set.seed(333)
  suppressMessages({
    expect_warning({
      stan_models <- bayesfactor_models(stan_bf_0, stan_bf_1)
    })
  })
  expect_s3_class(stan_models, "bayesfactor_models")
  expect_length(stan_models$log_BF, 2)
  expect_equal(stan_models$log_BF[2], log(bridge_BF$bf), tolerance = 0.1)
})

test_that("bayesfactor_models BRMS", {
  # Checks for brms models
  skip_on_cran()
  # skip_on_ci()

  skip_if_not_or_load_if_installed("bridgesampling")
  skip_if_not_or_load_if_installed("brms")

  set.seed(333)
  stan_brms_model_0 <- suppressWarnings(brms::brm(
    Sepal.Length ~ 1,
    data = iris,
    iter = 500,
    refresh = 0,
    save_pars = brms::save_pars(all = TRUE),
    silent = 2
  ))

  stan_brms_model_1 <- suppressWarnings(brms::brm(
    Sepal.Length ~ Petal.Length,
    data = iris,
    iter = 500,
    refresh = 0,
    save_pars = brms::save_pars(all = TRUE),
    silent = 2
  ))

  set.seed(444)
  suppressWarnings(suppressMessages(
    expect_message(
      {
        bfm <- bayesfactor_models(stan_brms_model_0, stan_brms_model_1)
      },
      regexp = "marginal"
    )
  ))

  set.seed(444)
  stan_brms_model_0wc <- brms::add_criterion(
    stan_brms_model_0,
    criterion = "marglik",
    repetitions = 5,
    silent = 2
  )

  stan_brms_model_1wc <- brms::add_criterion(
    stan_brms_model_1,
    criterion = "marglik",
    repetitions = 5,
    silent = 2
  )

  suppressWarnings(expect_message(
    {
      bfmwc <- bayesfactor_models(stan_brms_model_0wc, stan_brms_model_1wc)
    },
    regexp = NA
  ))
  expect_equal(bfmwc$log_BF, bfm$log_BF, tolerance = 0.01)
})


# bayesfactor_inclusion ---------------------------------------------------
test_that("bayesfactor_inclusion | BayesFactor", {
  skip_if_not_or_load_if_installed("lme4")
  skip_if_not_or_load_if_installed("BayesFactor")

  set.seed(444)
  # BayesFactor
  ToothGrowth$dose <- as.factor(ToothGrowth$dose)
  BF_ToothGrowth <- BayesFactor::anovaBF(len ~ dose * supp, ToothGrowth)
  expect_equal(
    bayesfactor_inclusion(BF_ToothGrowth),
    bayesfactor_inclusion(bayesfactor_models(BF_ToothGrowth)),
    tolerance = 1e-4, ignore_attr = TRUE
  )
})

test_that("bayesfactor_inclusion | LMM", {
  skip_if_not_or_load_if_installed("lme4")
  skip_if_not_or_load_if_installed("BayesFactor")

  # with random effects in all models:
  expect_true(is.nan(bayesfactor_inclusion(BFM1)["1:Species", "log_BF"]))

  bfinc_all <- bayesfactor_inclusion(BFM4, match_models = FALSE)
  expect_equal(bfinc_all$p_prior, c(1, 0.8, 0.6, 0.4, 0.2), tolerance = 0.1)
  expect_equal(bfinc_all$p_posterior, c(1, 1, 0.12, 0.01, 0), tolerance = 0.1)
  expect_equal(bfinc_all$log_BF, c(NaN, 57.651, -2.352, -4.064, -4.788), tolerance = 0.1)

  # plus match_models
  bfinc_matched <- bayesfactor_inclusion(BFM4, match_models = TRUE)
  expect_equal(bfinc_matched$p_prior, c(1, 0.2, 0.6, 0.2, 0.2), tolerance = 0.1)
  expect_equal(bfinc_matched$p_posterior, c(1, 0.875, 0.125, 0.009, 0.002), tolerance = 0.1)
  expect_equal(bfinc_matched$log_BF, c(NaN, 58.904, -3.045, -3.573, -1.493), tolerance = 0.1)
})
