skip_on_cran()


test_that("model_parameters - cgam", {
  skip_if_not(getRversion() >= "4.0.0")
  skip_if_not_installed("cgam")

  # cgam -----------------------

  data(cubic, package = "cgam")

  # model
  m_cgam <- cgam::cgam(formula = y ~ cgam::incr.conv(x), data = cubic)

  df_cgam <- parameters::model_parameters(m_cgam)

  expect_equal(
    df_cgam,
    structure(
      list(
        Parameter = "(Intercept)",
        Coefficient = 1.187,
        SE = 0.3054,
        CI = 0.95,
        CI_low = 0.569520101908619,
        CI_high = 1.80447989809138,
        t = 3.8868,
        df_error = 39.5,
        p = 4e-04
      ),
      row.names = c(NA, -1L),
      sigma = 2.15946395506817,
      residual_df = 39.5,
      pretty_names = c(`(Intercept)` = "(Intercept)"),
      ci = 0.95,
      verbose = TRUE,
      exponentiate = FALSE,
      ordinal_model = FALSE,
      linear_model = TRUE,
      mixed_model = FALSE,
      n_obs = 50L,
      model_class = "cgam",
      bootstrap = FALSE,
      iterations = 1000,
      robust_vcov = FALSE,
      ignore_group = TRUE,
      ran_pars = TRUE,
      show_summary = FALSE,
      log_link = FALSE,
      logit_link = FALSE,
      pretty_labels = c(`(Intercept)` = "(Intercept)"),
      test_statistic = "t-statistic",
      log_response = FALSE,
      log_predictors = FALSE,
      weighted_nobs = 50,
      model_formula = "y ~ cgam::incr.conv(x)",
      coefficient_name = "Coefficient",
      zi_coefficient_name = "Log-Odds",
      digits = 2,
      p_digits = 3,
      footer_digits = 3,
      class = c("parameters_model", "see_parameters_model", "data.frame"),
      object_name = "m_cgam"
    ),
    tolerance = 0.01
  )
})

# cgamm -----------------------

test_that("model_parameters - cgamm", {
  skip_if_not(getRversion() >= "4.0.0")
  skip_if_not_installed("cgam")

  # setup
  set.seed(123)

  # simulate a balanced data set with 30 clusters
  # each cluster has 30 data points
  n <- 30
  m <- 30

  # the standard deviation of between cluster error terms is 1
  # the standard deviation of within cluster error terms is 2
  sige <- 1
  siga <- 2

  # generate a continuous predictor
  x <- 1:(m * n)
  for (i in 1:m) {
    x[(n * (i - 1) + 1):(n * i)] <- round(runif(n), 3)
  }
  # generate a group factor
  group <- trunc(0:((m * n) - 1) / n) + 1

  # generate the fixed-effect term
  mu <- 10 * exp(10 * x - 5) / (1 + exp(10 * x - 5))

  # generate the random-intercept term asscosiated with each group
  avals <- rnorm(m, 0, siga)

  # generate the response
  y <- 1:(m * n)
  for (i in 1:m) {
    y[group == i] <- mu[group == i] + avals[i] + rnorm(n, 0, sige)
  }

  # use REML method to fit the model
  ans <- cgam::cgamm(formula = y ~ cgam::s.incr(x) + (1 | group), reml = TRUE)

  df <- suppressWarnings(parameters::model_parameters(ans))

  expect_equal(
    df,
    structure(
      list(
        Parameter = c("(Intercept)", "cgam::s.incr(x)"),
        Coefficient = c(5.5174, NA),
        SE = c(0.3631, NA),
        CI = c(0.95, NA),
        CI_low = c(4.80476838465533, NA),
        CI_high = c(6.23003161534467, NA),
        `t / F` = c(15.1954, NA),
        df = c(NA, 8.4),
        df_error = c(890.4, NA),
        p = c(0, 0),
        Component = c("conditional", "smooth_terms")
      ),
      row.names = c(NA, -2L),
      ci = 0.95,
      verbose = TRUE,
      exponentiate = FALSE,
      ordinal_model = FALSE,
      linear_model = TRUE,
      mixed_model = TRUE,
      model_class = c("cgamm", "cgam", "lmer"),
      bootstrap = FALSE,
      iterations = 1000,
      robust_vcov = FALSE,
      ignore_group = TRUE,
      ran_pars = TRUE,
      show_summary = FALSE,
      log_link = FALSE,
      logit_link = FALSE,
      pretty_labels = c(
        `(Intercept)` = "(Intercept)",
        `cgam::s.incr(x)` = "cgam::s.incr(x)"
      ),
      test_statistic = "t-statistic",
      log_response = FALSE,
      log_predictors = FALSE,
      model_formula = "y ~ cgam::s.incr(x)",
      coefficient_name = "Coefficient",
      zi_coefficient_name = "Log-Odds",
      digits = 2,
      p_digits = 3,
      footer_digits = 3,
      class = c("parameters_model", "see_parameters_model", "data.frame"), object_name = "ans"
    ),
    tolerance = 0.01
  )
})