tests/testthat/test-model_parameters.lqmm.R

In easystats/parameters: Processing of Model Parameters

# lqm -----------------------

test_that("model_parameters - lqm", {
  skip_if_not_installed("lqmm")
  # data
  set.seed(123)
  n <- 500
  p <- 1:3 / 4
  set.seed(123)
  x <- runif(n, 0, 1)
  y <- 30 + x + rnorm(n)
  test <<- data.frame(x, y)

  # model
  set.seed(123)
  fit.lqm <-
    lqmm::lqm(
      y ~ x,
      data = test,
      tau = p,
      control = list(verbose = FALSE, loop_tol_ll = 1e-9),
      fit = TRUE
    )

  df_lqm <- as.data.frame(model_parameters(fit.lqm))

  expect_equal(df_lqm$Coefficient,
    c(
      29.3220715172958, 1.1244506550584, 29.9547605920406, 1.1822574944936,
      30.6283792821576, 1.25165747424685
    ),
    tolerance = 0.001
  )
})


# lqmm -----------------------

test_that("model_parameters - lqmm", {
  skip("TODO: fix this test")
  skip_if_not_installed("lqmm")

  # setup
  set.seed(123)

  # data
  M <- 50
  n <- 10
  set.seed(123)
  x <- runif(n * M, 0, 1)
  group <- rep(1:M, each = n)
  y <- 10 * x + rep(rnorm(M, 0, 2), each = n) + rchisq(n * M, 3)
  test <<- data.frame(x, y, group)

  # model
  set.seed(123)
  fit.lqmm <-
    lqmm::lqmm(
      fixed = y ~ x,
      random = ~1,
      group = group,
      data = test,
      tau = 0.5,
      nK = 11,
      type = "normal"
    )

  df_lqmm <- as.data.frame(model_parameters(fit.lqmm))

  expect_equal(df_lqmm,
    structure(
      list(
        Parameter = c("(Intercept)", "x"),
        Coefficient = c(
          3.44347538706013,
          9.25833091219961
        ),
        SE = c(0.491049614414579, 0.458163772053399),
        CI = c(0.95, 0.95),
        CI_low = c(2.47868633791118, 8.35815427623814),
        CI_high = c(4.40826443620908, 10.1585075481611),
        t = c(
          7.01247956617455,
          20.207470509302
        ),
        df_error = c(497L, 497L),
        p = c(
          6.34497395571023e-09,
          2.05172540270515e-25
        )
      ),
      row.names = 1:2,
      pretty_names = c(
        `(Intercept)` = "(Intercept)",
        x = "x"
      ),
      ci = 0.95,
      verbose = TRUE,
      exponentiate = FALSE,
      ordinal_model = FALSE,
      linear_model = TRUE,
      mixed_model = TRUE,
      n_obs = 500L,
      model_class = "lqmm",
      bootstrap = FALSE,
      iterations = 1000,
      ignore_group = TRUE,
      ran_pars = TRUE,
      weighted_nobs = 500,
      model_formula = "y ~ x",
      coefficient_name = "Coefficient",
      zi_coefficient_name = "Log-Odds",
      digits = 2,
      ci_digits = 2,
      p_digits = 3,
      class = "data.frame",
      object_name = "fit.lqmm"
    ),
    tolerance = 0.001
  )
})