#' Test `poLCAParallel.goodnessfit()`
#'
#' Test the function `poLCAParallel.goodnessfit()`. A list containing random
#' data and random resulting `poLCA()` parameters are generated and then passed
#' to the function, which modifies the list. The modified contents are then
#' tested
#'
#' @param n_data Number of data points
#' @param n_outcomes Vector of integers, number of outcomes for each category
#' @param n_cluster Number of clusters fitted
#' @param prob_na Probability of missing data
test_goodnessfit <- function(n_data, n_outcomes, n_cluster,
                             prob_na) {
  responses <- random_response(n_data, n_outcomes, prob_na)
  probs <- random_unvectorized_probs(n_outcomes, n_cluster)

  prior <- stats::runif(n_cluster)
  prior <- prior / sum(prior)

  lc <- list(
    y = responses,
    P = prior,
    N = n_data,
    probs = probs,
    Nobs = sum(rowSums(responses == 0) == 0)
  )
  lc <- poLCAParallel.goodnessfit(lc)

  test_polca_goodnessfit(lc, n_outcomes)
}

test_that("full-data", {
  set.seed(125826165)
  seeds <- sample.int(.Machine$integer.max, N_REPEAT)
  for (i in seq_len(N_REPEAT)) {
    set.seed(seeds[i])
    expect_no_error(test_goodnessfit(
      100,
      c(2, 3, 5, 2, 2),
      3,
      0
    ))
  }
})

test_that("missing-data", {
  set.seed(680730606)
  seeds <- sample.int(.Machine$integer.max, N_REPEAT)
  for (i in seq_len(N_REPEAT)) {
    set.seed(seeds[i])
    expect_no_error(test_goodnessfit(
      100,
      c(2, 3, 5, 2, 2),
      3,
      0.1
    ))
  }
})