temporary suggestion for testing simulate for design class

tests/testthat/test-Design-methods.R

@@ -43,6 +43,130 @@ test_that("simulate produces consistent results with sentinel patients", {
   expect_snapshot(result)
 })
 
+test_that("simulate for the class design returns correct objects", {
+  design <- h_get_design_data()
+  myTruth <- probFunction(design@model, alpha0 = 7, alpha1 = 8)
+  options <- h_get_mcmc_options()
+
+  mySims <- simulate(
+    design,
+    args = NULL,
+    truth = myTruth,
+    nsim = 1,
+    seed = 819,
+    mcmcOptions = options,
+    parallel = FALSE,
+    derive = list(
+      max_mtd = max,
+      mean_mtd = mean,
+      median_mtd = median
+    )
+  )
+
+  expect_class(mySims, "Simulations") # check for correct class of returned object
+
+  expect_equal(any(sapply(mySims@fit[[1]], is.numeric)), TRUE) # check if all elements in mySims@fit are numeric
+
+  expect_equal(length(mySims@stop_report), 5) # check for length
+
+  expect_logical(mySims@stop_report) # check for stop_report to be logical vector
+
+  expect_list(mySims@data)
+
+  expect_class(mySims@data[[1]], "Data") # check for data object has correct class
+
+  expect_list(mySims@additional_stats)
+
+  expect_list(mySims@additional_stats[[1]])
+
+  expect_length(mySims@additional_stats[[1]], 3)
+
+  expect_equal(mySims@doses, 1)
+})
+
+test_that("simulate for the class design with placebo returns correct objects", {
+  design <- h_get_design_data(TRUE)
+  myTruth <- probFunction(design@model, alpha0 = 7, alpha1 = 8)
+  options <- h_get_mcmc_options()
+
+  mySims <- simulate(
+    design,
+    args = NULL,
+    truth = myTruth,
+    nsim = 1,
+    seed = 819,
+    mcmcOptions = options,
+    parallel = FALSE,
+    derive = list(
+      max_mtd = max,
+      mean_mtd = mean,
+      median_mtd = median
+    )
+  )
+
+  expect_class(mySims, "Simulations") # check for correct class of returned object
+
+  expect_equal(any(sapply(mySims@fit[[1]], is.numeric)), TRUE) # check if all elements in mySims@fit are numeric
+
+  expect_equal(length(mySims@stop_report), 5) # check for length
+
+  expect_logical(mySims@stop_report) # check for stop_report to be logical vector
+
+  expect_list(mySims@data)
+
+  expect_class(mySims@data[[1]], "Data") # check for data object has correct class
+
+  expect_list(mySims@additional_stats)
+
+  expect_list(mySims@additional_stats[[1]])
+
+  expect_length(mySims@additional_stats[[1]], 3)
+
+  expect_equal(mySims@doses, 1)
+})
+
+test_that("simulate for the class design with placebo and sentinel patients returns correct objects", {
+  design <- h_get_design_data(TRUE)
+  myTruth <- probFunction(design@model, alpha0 = 7, alpha1 = 8)
+  options <- h_get_mcmc_options()
+
+  mySims <- simulate(
+    design,
+    args = NULL,
+    truth = myTruth,
+    nsim = 1,
+    seed = 819,
+    mcmcOptions = options,
+    parallel = FALSE,
+    firstSeparate = TRUE,
+    derive = list(
+      max_mtd = max,
+      mean_mtd = mean,
+      median_mtd = median
+    )
+  )
+
+  expect_class(mySims, "Simulations") # check for correct class of returned object
+
+  expect_equal(any(sapply(mySims@fit[[1]], is.numeric)), TRUE) # check if all elements in mySims@fit are numeric
+
+  expect_equal(length(mySims@stop_report), 5) # check for length
+
+  expect_logical(mySims@stop_report) # check for stop_report to be logical vector
+
+  expect_list(mySims@data)
+
+  expect_class(mySims@data[[1]], "Data") # check for data object has correct class
+
+  expect_list(mySims@additional_stats)
+
+  expect_list(mySims@additional_stats[[1]])
+
+  expect_length(mySims@additional_stats[[1]], 3)
+
+  expect_equal(mySims@doses, 1)
+})
+
 ## RuleDesign ----
 
 test_that("simulate-RuleDesign produces consistent results", {
@@ -106,6 +230,115 @@ test_that("simulate-DualDesign produces consistent results", {
   expect_snapshot(result)
 })
 
+test_that("simulate-DualDesign produces consistent results with sentinel patients", {
+  design <- h_get_design_dualdata()
+
+  # define scenarios for the TRUE toxicity and efficacy profiles
+  betaMod <- function(dose, e0, eMax, delta1, delta2, scal) {
+    maxDens <- (delta1^delta1) * (delta2^delta2) / ((delta1 + delta2)^(delta1 + delta2))
+    dose <- dose / scal
+    e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
+  }
+
+  trueBiomarker <- function(dose) {
+    betaMod(dose, e0 = 0.2, eMax = 0.6, delta1 = 5, delta2 = 5 * 0.5 / 0.5, scal = 100)
+  }
+
+  trueTox <- function(dose) {
+    pnorm((dose - 60) / 10)
+  }
+
+  result <- simulate(
+    design,
+    trueTox = trueTox,
+    trueBiomarker = trueBiomarker,
+    sigma2W = 0.01,
+    rho = 0,
+    nsim = 1,
+    parallel = FALSE,
+    seed = 3,
+    startingDose = 6,
+    firstSeparate = TRUE,
+    mcmcOptions = McmcOptions(
+      burnin = 100,
+      step = 1,
+      samples = 300,
+      rng_kind = "Mersenne-Twister",
+      rng_seed = 1234
+    )
+  )
+
+  expect_equal(result@rho_est, 0.07991541, tolerance = 1e-7) # printed result
+
+  expect_equal(result@rho_est, 0.079915412) # actual result
+
+  expect_equal(result@sigma2w_est, 0.03177778, tolerance = 1e-7) # printed result
+
+  expect_equal(result@sigma2w_est, 0.031777778) # actual result
+
+  expect_equal(any(sapply(result@fit_biomarker[[1]], is.numeric)), TRUE) # all elements of fit are numeric
+
+  expect_equal(dim(result@fit_biomarker[[1]])[1], 11)
+
+  expect_equal(dim(result@fit_biomarker[[1]])[2], 3)
+
+  expect_equal(length(result@stop_report), 3) # check for length
+
+  expect_logical(result@stop_report) # check for stop_report to be logical vector
+
+  expect_list(result@data)
+
+  expect_class(result@data[[1]], "Data") # check for data object has correct class
+
+  expect_list(result@additional_stats)
+
+  expect_list(result@additional_stats[[1]])
+
+  expect_length(result@additional_stats[[1]], 0)
+
+  expect_equal(result@doses, 1)
+})
+
+test_that("simulate-DualDesign produces consistent results", {
+  design <- h_get_design_dualdata(TRUE)
+
+  # define scenarios for the TRUE toxicity and efficacy profiles
+  betaMod <- function(dose, e0, eMax, delta1, delta2, scal) {
+    maxDens <- (delta1^delta1) * (delta2^delta2) / ((delta1 + delta2)^(delta1 + delta2))
+    dose <- dose / scal
+    e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
+  }
+
+  trueBiomarker <- function(dose) {
+    betaMod(dose, e0 = 0.2, eMax = 0.6, delta1 = 5, delta2 = 5 * 0.5 / 0.5, scal = 100)
+  }
+
+  trueTox <- function(dose) {
+    pnorm((dose - 60) / 10)
+  }
+
+  result <- simulate(
+    design,
+    trueTox = trueTox,
+    trueBiomarker = trueBiomarker,
+    sigma2W = 0.01,
+    rho = 0,
+    nsim = 1,
+    parallel = FALSE,
+    seed = 3,
+    startingDose = 6,
+    mcmcOptions = McmcOptions(
+      burnin = 100,
+      step = 1,
+      samples = 300,
+      rng_kind = "Mersenne-Twister",
+      rng_seed = 1234
+    )
+  )
+
+  expect_snapshot(result)
+})
+
 
 test_that("simulate-TDSamplesDesign produces consistent results", {
   data <- Data(doseGrid = seq(25, 300, 25))