tests/testthat/test_reconstruct_formulas.R

In cvms: Cross-Validation for Model Selection

library(cvms)
context("reconstruct_formulas()")

test_that("formulas without random effects are properly reconstructed from cross_validate results with reconstruct_formulas()", {

  # skip_test_if_old_R_version()

  xpectr::set_test_seed(1)
  data <- participant.scores

  model_formulas <- c(
    "score~age +diagnosis",
    "score~age*diagnosis "
  )


  library(groupdata2)

  data <- fold(data, k = 3, cat_col = "diagnosis", id_col = "participant")
  cv_results <- cross_validate(data, formulas = model_formulas, family = "gaussian")
  cv_results <- cv_results[order(cv_results$RMSE), ]

  expect_equal(reconstruct_formulas(cv_results), c("score ~ age + diagnosis", "score ~ age * diagnosis"))
  expect_equal(reconstruct_formulas(cv_results, topn = 1), c("score ~ age + diagnosis"))
})


test_that("formulas with random effects are properly reconstructed from cross_validate results with reconstruct_formulas()", {

  # skip_test_if_old_R_version()

  xpectr::set_test_seed(1)

  data <- participant.scores

  model_formulas <- c(
    "score~age + (1|participant) + (1|diagnosis)",
    "score~age*diagnosis  +(1|participant)"
  )


  library(groupdata2)

  data <- fold(data, k = 2, cat_col = "diagnosis", id_col = "participant")
  xpectr::suppress_mw({
    cv_results <- cross_validate(data, formulas = model_formulas, family = "gaussian")
  })

  expect_equal(reconstruct_formulas(cv_results), c(
    "score ~ age + (1|participant) + (1|diagnosis)",
    "score ~ age * diagnosis + (1|participant)"
  ))
  expect_equal(reconstruct_formulas(cv_results, topn = 1), "score ~ age + (1|participant) + (1|diagnosis)")
})