tests/testthat/test-modelling.R

In BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

# Test data
test_data <- data.frame(
  simulation = rep(1, 6),
  dose = c(0, 1, 2, 3, 4, 5),
  response = c(0, 1, 2, 3, 4, 5)
)

# Mock getPosterior function
getPosterior <- function(data, prior_list, mu_hat, se_hat) {
  list(
    means = c(0, 1, 2, 3, 4, 5),
    vars = c(1, 1, 1, 1, 1, 1),
    weights = c(1, 1, 1, 1, 1, 1)
  )
}

# Test predictModelFit function
test_that("predictModelFit works correctly", {
  model_fit <- list(
    model = "emax",
    coeffs = c(e0 = 0, eMax = 1, ed50 = 2),
    dose_levels = c(0, 1, 2, 3, 4, 5)
  )
  
  pred_vals <- predictModelFit(model_fit)
  expect_type(pred_vals, "double")
})

# Test for addModelWeights
test_that("addModelWeights works correctly in a simple case", {
  # Create a test case
  test_model_fits1 <- list(
    model1 = list(gAIC = 1),
    model2 = list(gAIC = 1),
    model3 = list(gAIC = 1)
  )
  expected_output1 <- list(
    list(gAIC = 1, model_weight = 1/3),
    list(gAIC = 1, model_weight = 1/3),
    list(gAIC = 1, model_weight = 1/3)
  )
  
  # Call the function with the test case
  result <- addModelWeights(model_fits = test_model_fits1)
  # Assert the expected behavior
  expect_true(is.list(result))
  expect_equal(length(result), length(test_model_fits1))
  expect_true(all(sapply(result, function(x) "model_weight" %in% names(x))))
  expect_equal(result, expected_output1)
  
  test_model_fits2 <- list(
    model1 = list(gAIC = 100),
    model2 = list(gAIC = 0),
    model3 = list(gAIC = 100)
  )
  expected_output2 <- list(
    list(gAIC = 100, model_weight = 0),
    list(gAIC = 0, model_weight = 1),
    list(gAIC = 100, model_weight = 0)
  )
  result <- addModelWeights(model_fits = test_model_fits2)
  expect_equal(result, expected_output2, tolerance = 1e-10)
})

# Test for getGenAIC
test_that("getGenAIC calculates AIC correctly using snapshot test and a simple example", {
  # Create a test case
  test_model_fit <- list(
    pred_values = rep(1, 3),
    coeffs = rep(1, 3)
  )
  test_post_combs <- list(
    means = matrix(rep(1, 6), nrow = 2),
    vars = matrix(rep(1, 6), nrow = 2),
    weights = c(1, 1)
  )
  
  result <- getGenAIC(model_fit = test_model_fit, post_combs = test_post_combs)
  # expected result determined with snapshot of behavior prior to first release on CRAN
  expected_result <- 6 
  # Assert the expected behavior
  expect_equal(result, expected_result)
})