temporary suggestion for testing simulate for design class

tests/testthat/test-helpers_design.R

@@ -205,3 +205,203 @@ test_that("h_determine_dlts returns correctly updated data object for placebo =
   expect_s4_class(result, "Data")
   expect_equal(result, expected_result)
 })
+
+test_that("simulate for the class design works end to end", {
+  emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))
+
+  model <- LogisticLogNormal(
+    mean = c(-0.85, 1),
+    cov =
+      matrix(c(1, -0.5, -0.5, 1),
+        nrow = 2
+      ),
+    ref_dose = 56
+  )
+
+  myNextBest <- NextBestNCRM(
+    target = c(0.2, 0.35),
+    overdose = c(0.35, 1),
+    max_overdose_prob = 0.25
+  )
+
+  mySize1 <- CohortSizeRange(
+    intervals = c(0, 30),
+    cohort_size = c(1, 3)
+  )
+  mySize2 <- CohortSizeDLT(
+    intervals = c(0, 1),
+    cohort_size = c(1, 3)
+  )
+  mySize <- maxSize(mySize1, mySize2)
+
+  myStopping1 <- StoppingMinCohorts(nCohorts = 3)
+  myStopping2 <- StoppingTargetProb(
+    target = c(0.2, 0.35),
+    prob = 0.5
+  )
+  myStopping3 <- StoppingMinPatients(nPatients = 20)
+  myStopping <- (myStopping1 & myStopping2) | myStopping3
+
+  myIncrements <- IncrementsRelative(
+    intervals = c(0, 20),
+    increments = c(1, 0.33)
+  )
+
+  design <- Design(
+    model = model,
+    nextBest = myNextBest,
+    stopping = myStopping,
+    increments = myIncrements,
+    cohort_size = mySize,
+    data = emptydata,
+    startingDose = 3
+  ) # check if default design constructor is enough to test simulate method
+
+  myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)
+
+  options <- McmcOptions(
+    burnin = 100,
+    step = 2,
+    samples = 1000,
+    rng_kind = "Mersenne-Twister",
+    rng_seed = 1234
+  )
+  time <- system.time(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
+    ),
+  ))[3]
+
+  expected <-
+    new(
+      "Simulations",
+      fit = list(structure(
+        list(
+          middle = c(
+            0.0278840989442296,
+            0.0585187075243448,
+            0.0861763463009416,
+            0.152046506082125,
+            0.215028653301285,
+            0.27436876755974,
+            0.329137865843746,
+            0.463222935947319,
+            0.529835624620552,
+            0.658215004013923,
+            0.709356365120325
+          ),
+          lower = c(
+            0.000124727518807684,
+            0.00169457982279485,
+            0.00571810889381897,
+            0.0262840977019272,
+            0.0631561484944847,
+            0.108053895434153,
+            0.14935814438918,
+            0.254733947607829,
+            0.299313351022304,
+            0.377782206208248,
+            0.41692555950274
+          ),
+          upper = c(
+            0.173968745159563,
+            0.236079098676,
+            0.285833540365002,
+            0.35945722660184,
+            0.41825266097964,
+            0.461892665717615,
+            0.517432747132171,
+            0.666615508629213,
+            0.750262264008957,
+            0.886377462207185,
+            0.928390287149569
+          )
+        ),
+        row.names = c(NA, 11L),
+        class = "data.frame"
+      )),
+      stop_report = structure(
+        c(
+          TRUE, TRUE,
+          TRUE, TRUE, FALSE
+        ),
+        dim = c(1L, 5L),
+        dimnames = list(
+          NULL,
+          c(
+            NA_character_,
+            NA_character_,
+            NA_character_,
+            NA_character_,
+            NA_character_
+          )
+        )
+      ),
+      stop_reasons = list(
+        list(
+          list(
+            "Number of cohorts is 9 and thus reached the prespecified minimum number 3",
+            "Probability for target toxicity is 53 % for dose 20 and thus above the required 50 %"
+          ),
+          "Number of patients is 19 and thus below the prespecified minimum number 20"
+        )
+      ),
+      additional_stats = list(
+        list(
+          max_mtd = 70.6519400150433,
+          mean_mtd = 21.3728034044661,
+          median_mtd = 20.2435513052115
+        )
+      ),
+      data = list(
+        new(
+          "Data",
+          x = c(
+            3, 5, 10, 20, 20, 20, 20, 25,
+            25, 25, 25, 25, 25, 25, 25, 25, 15, 15, 15
+          ),
+          y = c(
+            0L, 0L,
+            0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
+            0L, 0L
+          ),
+          doseGrid = c(
+            1, 3, 5, 10, 15, 20, 25, 40, 50, 80,
+            100
+          ),
+          nGrid = 11L,
+          xLevel = c(
+            2L, 3L, 4L, 6L, 6L, 6L, 6L,
+            7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 5L
+          ),
+          placebo = FALSE,
+          ID = 1:19,
+          cohort = c(
+            1L, 2L, 3L, 4L, 5L, 5L, 5L, 6L,
+            6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L
+          ),
+          nObs = 19L
+        )
+      ),
+      doses = 20,
+      seed = 819L
+    )
+
+  expect_equal(mySims, expected)
+
+  expect_class(mySims, "Simulations")
+
+  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
+})