test_that("Define a iglm object and check all the results information", {
  n_actor <- 20
  block <- matrix(nrow = 5, ncol = 5, data = 1)
  neighborhood <- as.matrix(Matrix::bdiag(replicate(n_actor / 5, block, simplify = FALSE)))

  overlapping_degree <- 0.5
  neighborhood <- matrix(nrow = n_actor, ncol = n_actor, data = 0)
  block <- matrix(nrow = 5, ncol = 5, data = 0)
  size_neighborhood <- 5
  size_overlap <- ceiling(size_neighborhood * overlapping_degree)

  end <- floor((n_actor - size_neighborhood) / size_overlap)
  for (i in 0:end) {
    neighborhood[(1 + size_overlap * i):(size_neighborhood + size_overlap * i), (1 + size_overlap * i):(size_neighborhood + size_overlap * i)] <- 1
  }
  neighborhood[(n_actor - size_neighborhood + 1):(n_actor), (n_actor - size_neighborhood + 1):(n_actor)] <- 1
  type_x <- "binomial"
  type_y <- "binomial"

  xyz_obj_new <- iglm.data(neighborhood = neighborhood, directed = FALSE, type_x = type_x, type_y = type_y)
  xyz_obj_new$overlap
  gt_coef <- c(3, -1, -1)
  gt_coef_pop <- c(rnorm(n = n_actor, -2, 1))

  sampler_new <- sampler.iglm(
    n_burn_in = 10, n_simulation = 1,
    sampler_x = sampler.net.attr(n_proposals = n_actor * 10, seed = 13),
    sampler_y = sampler.net.attr(n_proposals = n_actor * 10, seed = 32),
    sampler_z = sampler.net.attr(n_proposals = sum(neighborhood > 0) * 10, seed = 134),
    init_empty = F
  )
  # xyz_obj_new$neighborhood
  # xyz_obj_new$z_network <- xyz_obj_new$neighborhood

  model_tmp_new <- iglm(
    formula = xyz_obj_new ~ edges(mode = "local") + attribute_y + attribute_x + degrees,
    coef = gt_coef, coef_degrees = gt_coef_pop, sampler = sampler_new,
    control = control.iglm(
      accelerated = FALSE, max_it = 200,
      display_progress = TRUE
    )
  )

  tmp_name <- paste(tempfile(), ".RDS")
  model_tmp_new$results$save(file = tmp_name)
  loaded_results <- results(file = tmp_name)
  model_tmp_new$simulate()
  expect_equal(length(loaded_results$samples), expected = 0)
  expect_equal(length(model_tmp_new$results$samples), expected = 1)
  model_tmp_new$results$save(file = tmp_name)
  loaded_results <- results(file = tmp_name)
  expect_equal(length(loaded_results$samples), expected = 1)
  file.remove(tmp_name)
})