tests/testthat/test-s3methods.R

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

test_that("print.BayesianMCPMod works as intented", {
  expect_error(print.BayesianMCPMod())

})


test_that("print.BayesianMCP works as intented", {
  expect_error(print.BayesianMCP())

  data <- simulateData(
    n_patients  = n_patients,
    dose_levels = dose_levels,
    sd          = sd,
    mods        = mods,
    n_sim       = n_sim
  )

  posterior_list <- getPosterior(
    data = getModelData(data, names(mods)[1]),
    prior_list = prior_list
  )

  contr_mat = getContr(
    mods = mods,
    dose_levels = dose_levels,
    dose_weights = n_patients,
    prior_list = prior_list
  )

  crit_pval = getCritProb(
    mods = mods,
    dose_levels = dose_levels,
    dose_weights = n_patients,
    alpha_crit_val = alpha_crit_val
  )

  b_mcp <- performBayesianMCP(
    posterior_list = posterior_list,
    contr = contr_mat,
    crit_prob_adj = crit_pval
  )

  expect_s3_class(b_mcp, "BayesianMCP")
  expect_no_error(print(b_mcp))
  expect_type(print(b_mcp), "double")

  mods <- DoseFinding::Mods(
  linear = NULL,
  emax = c(0.5, 1.2),
  exponential = 2,
  doses = c(0, 0.5, 2, 4, 8)
)
dose_levels <- c(0, 0.5, 2, 4, 8)
sd_posterior <- c(2.8, 3, 2.5, 3.5, 4)
contr_mat <- getContr(
  mods          = mods,
  dose_levels   = dose_levels,
  cov_posterior = diag(sd_posterior)
)
critVal <- getCritProb(
  mods           = mods,
  dose_weights   = c(50, 50, 50, 50, 50), # reflecting the planned sample size
  dose_levels    = dose_levels,
  alpha_crit_val = 0.6
)
prior_list <- list(
  Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 5), sigma = 2),
  DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
  DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2),
  DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2),
  DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2)
)
mu <- c(0, 1, 1.5, 2, 2.5)
S_hat <- diag(c(5, 4, 6, 7, 8))
posterior_list <- getPosterior(
  prior_list = prior_list,
  mu_hat = mu,
  S_hat = S_hat
)

x <- performBayesianMCPMod(
  posterior_list = posterior_list,
  contr = contr_mat,
  crit_prob_adj = critVal,
  simple = FALSE,
  delta = 1
)

expect_s3_class(x, "BayesianMCPMod")
expect_no_error(print(x))
expect_type(print(x), "list")


})

test_that("predict.ModelFits works as intented", {
  expect_error(predict.ModelFits())

})

test_that("s3 postList functions work as intented", {

  dose_levels <- c(0, 2.5, 5, 10)

  prior_list <- list(
    Ctrl = RBesT::mixnorm(comp1 = c(w = 0.4, m = 0, s = 5), comp2 = c(w = 0.6, m = 2, s = 3), sigma = 2),
    DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
    DG_2 = RBesT::mixnorm(comp1 = c(w = 0.3, m = 1.2, s = 11), comp2 = c(w = 0.7, m = 1, s = 4), sigma = 2),
    DG_3 = RBesT::mixnorm(comp1 = c(w = 0.2, m = 1.3, s = 11), comp2 = c(w = 0.5, m = 1, s = 4), comp3 = c(w = 0.3, m = 7, s = 1), sigma = 2),
    DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2)
  )

  mu_hat <- c(0, 1, 1.5, 2, 2.5)
  S_hat <- diag(c(5, 4, 6, 7, 8)^2)

  post_test_list <- getPosterior(
    prior_list = prior_list,
    mu_hat     = mu_hat,
    S_hat      = S_hat
  )
  expect_error(summary.postList())
  expect_type(summary.postList(post_test_list), "double")
  expect_error(print.postList())
  expect_no_error(print(post_test_list))
  expect_type(post_test_list, "list")
})

test_that("test modelFits s3 methods", {

  model_shapes <- colnames(contr_mat$contMat)
  dose_levels  <- as.numeric(rownames(contr_mat$contMat))

  model_fits  <- getModelFits(
    models      = model_shapes,
    dose_levels = dose_levels,
    posterior   = posterior_list,
    simple      = simple)

  pred <- predict(model_fits)
  pred_dosage <- predict(model_fits, doses = dose_levels)

  expect_type(pred, "list")
  expect_true(is.null(attr(pred, "doses")))
  expect_identical(attr(pred_dosage, "doses"), dose_levels)
  expect_type(print(model_fits), "list")
})