#' @srrstats {G5.0, G5.4, G5.4a, G5.4b, G5.4c} The parameter estimates of
#'   model fitted to a classic Grunfeld data match with brms and plm.
run_extended_tests <- identical(Sys.getenv("DYNAMITE_EXTENDED_TESTS"), "true")

data.table::setDTthreads(1) # For CRAN

test_that("parameters of the Grunfield model are recovered", {
  skip_if_not(run_extended_tests)

  # data from plm package
  Grunfeld <- readRDS("grunfeld.rds")

  set.seed(1)
  # dynamite defines prior for the intercept based on the mean at the first time
  # point, which differs from the brms, so use dummy intercept instead in both
  Grunfeld$intercept <- 1
  f <- obs(inv ~ -1 + intercept + value + capital + random(~-1 + intercept),
           family = "gaussian") + random_spec(noncentered = FALSE)
  p <- get_priors(f, Grunfeld, time = "year", group = "firm")
  # set very vague priors
  p$prior[] <- rep("normal(0, 1000)", nrow(p))
  fit <- dynamite(
    dformula = f,
    data = Grunfeld,
    time = "year",
    group = "firm",
    priors = p,
    refresh = 0,
    seed = 1,
    chains = 2,
    cores = 2,
    iter = 20000,
    warmup = 1000
  )

  # Not run, values are stored
  # fit_plm <- plm(inv ~ value + capital,
  #   data = Grunfeld,
  #   index = c("firm", "year"), effect = "individual", model = "within"
  # )
  #plm_est <- dput(coef(fit_plm))
  plm_est <- c(value = 0.110123804120719, capital = 0.310065341300139)
  expect_equal(
    plm_est,
    coef(fit)$mean[2:3],
    tolerance = 0.01,
    ignore_attr = TRUE
  )

  # Not run, values are stored
  # library(brms)
  # fit_brm <- brm(inv ~ 0 + Intercept + value + capital + (1 | firm),
  #   Grunfeld,
  #   prior = prior(normal(0, 1000), class = "b") +
  #     prior(normal(0, 1000), class = "sd") +
  #     prior(normal(0, 1000), class = "sigma"),
  #     refresh = 0, seed = 1,
  #   chains = 2, cores = 2, iter = 150000, warmup = 5000,
  #   control = list(adapt_delta = 0.9))
  # brms_est <- round(unname(posterior_summary(fit_brm)[, "Estimate"]), 6)[1:15]
  brms_est <- c(
    -57.917705, 0.109846, 0.308349, 100.941716, 53.086506, -9.896139,
    158.194407, -173.491487, 29.99692, -54.875308, 34.459838, -7.931004,
    0.646235, -28.227079, 50.50187
  )
  # reorder parameters to match dynamite
  brms_est <- brms_est[c(3, 1, 2, 6:15, 5, 4)]
  sumr <- as_draws(fit) |> posterior::summarise_draws(
    posterior::default_mcse_measures(),
    posterior::default_summary_measures()
  )
  for (i in 1:15) {
    expect_equal(
      sumr$mean[i],
      brms_est[i],
      tolerance = 100 * sumr$mcse_mean[i],
      label = sumr$variable[i],
      ignore_attr = TRUE
    )
  }
})