res-> result, removed randomdist in vignette too, clean

R/ocPostprob.R

@@ -209,9 +209,6 @@ h_get_oc <- function(all_sizes, nnr, decision, nnrE, nnrF) {
 #' @typed wiggle : flag
 #'  generate random look locations (not default).
 #'  if `TRUE`, optional to specify `dist` (see @details).
-#' @typed randomdist : flag
-#'  Random distance added to looks. if `TRUE`, and `wiggle = TRUE`, function will
-#'  generate and add a random distance within range of the closest looks.
 #'
 #' @return A list with the following elements:
 #'
@@ -231,18 +228,17 @@ h_get_oc <- function(all_sizes, nnr, decision, nnrE, nnrF) {
 #' @example examples/ocPostprob.R
 #' @export
 ocPostprob <- function(nnE, truep, p0, p1, tL, tU, parE = c(1, 1),
-                       sim = 50000, wiggle = FALSE, randomdist = FALSE, nnF = nnE) {
+                       sim = 50000, wiggle = FALSE, nnF = nnE) {
   nn <- sort(unique(c(nnF, nnE)))
   assert_number(sim, lower = 1, finite = TRUE)
   assert_flag(wiggle)
-  assert_flag(randomdist)
   if (sim < 50000) {
     warning("Advise to use sim >= 50000 to achieve convergence")
   }
   decision <- vector(length = sim)
   all_sizes <- vector(length = sim)
   for (k in seq_len(sim)) {
-    if (length(nn) != 1 && wiggle && randomdist) {
+    if (length(nn) != 1 && wiggle) {
       dist <- h_get_distance(nn = nn)
       nnr <- h_get_looks(dist = dist, nnE = nnE, nnF = nnF)
       nnrE <- nnr$nnrE

examples/ocPostprob.R

@@ -6,39 +6,39 @@
 # Prior of treatment arm parE= Beta(1,1).
 res <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-  sim = 50000, wiggle = TRUE, randomdist = TRUE, nnF = c(10, 20, 30)
+  sim = 50000, wiggle = TRUE, nnF = c(10, 20, 30)
 )
 
 res$oc
 
 # We specify the distance in this example.
 res <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-  sim = 50000, wiggle = TRUE, randomdist = TRUE, nnF = c(10, 20, 30)
+  sim = 50000, wiggle = TRUE, nnF = c(10, 20, 30)
 )
 
 res$oc
 
 # Here, nnE = nnF, and no wiggle room is allowed. Random distance also not supplied.
 res <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-  sim = 50000, wiggle = FALSE, randomdist = TRUE, nnF = c(10, 20, 30)
+  sim = 50000, wiggle = FALSE, nnF = c(10, 20, 30)
 )
 
 res$oc
 
 # Here, we only have one Futility and Efficacy look or stop.
 res <- ocPostprob(
   nnE = c(10), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-  sim = 50000, wiggle = FALSE, randomdist = TRUE, nnF = 10
+  sim = 50000, wiggle = FALSE, nnF = 10
 )
 
 res$oc
 
 # Here, we only have one Futility but many Efficacy looks or stop.
 res <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-  sim = 50000, wiggle = FALSE, randomdist = TRUE, nnF = 10
+  sim = 50000, wiggle = FALSE, nnF = 10
 )
 
 res$oc

tests/testthat/test-ocPostprob.R

@@ -97,62 +97,62 @@ test_that("the ExpectedN is within range based on vector of looks", {
 # ocPostprob ----
 test_that("the sum of Eff, Fut, Gray zone probabiliy is 1", {
   set.seed(1989)
-  expect_warning(res1 <- ocPostprob(
+  expect_warning(result <- ocPostprob(
     nnE = 40, truep = 0.5, p0 = 0.45, p1 = 0.45, tL = 0.9, tU = 0.7,
     parE = c(1, 1), sim = 10000
   ), "Advise to use sim >= 50000 to achieve convergence")
-  results <- sum(res1$oc[5:7])
-  expect_equal(results, 1)
+  result_sum <- sum(result$oc[5:7])
+  expect_equal(result_sum, 1)
 })
 
 test_that("the PrFutility increases with increase futility looks", {
   set.seed(1989)
-  expect_warning(res_fut <- ocPostprob(
+  expect_warning(result_fut <- ocPostprob(
     nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-    sim = 10000, wiggle = FALSE, randomdist = TRUE, nnF = c(10, 20, 30)
+    sim = 10000, wiggle = FALSE, nnF = c(10, 20, 30)
   ), "Advise to use sim >= 50000 to achieve convergence")
 
-  res_fut$oc$PrFutility
-  expect_warning(res_one_fut <- ocPostprob(
+  result_fut$oc$PrFutility
+  expect_warning(result_one_fut <- ocPostprob(
     nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-    sim = 10000, wiggle = FALSE, randomdist = TRUE, nnF = 10
+    sim = 10000, wiggle = FALSE, nnF = 10
   ), "Advise to use sim >= 50000 to achieve convergence")
-  res_one_fut$oc$PrFutility
-  expect_true(res_fut$oc$PrFutility > res_one_fut$oc$PrFutility)
+  result_one_fut$oc$PrFutility
+  expect_true(result_fut$oc$PrFutility > result_one_fut$oc$PrFutility)
 })
 
 test_that("the PrEfficacy increases with increase Efficacy looks", {
   set.seed(1989)
-  expect_warning(res_eff <- ocPostprob(
+  expect_warning(result_eff <- ocPostprob(
     nnE = c(30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-    sim = 10000, wiggle = FALSE, randomdist = TRUE, nnF = 30
+    sim = 10000, wiggle = FALSE, nnF = 30
   ), "Advise to use sim >= 50000 to achieve convergence")
 
-  res_eff$oc$PrEfficacy
-  expect_warning(res_more_eff <- ocPostprob(
+  result_eff$oc$PrEfficacy
+  expect_warning(result_more_eff <- ocPostprob(
     nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),
-    sim = 10000, wiggle = FALSE, randomdist = TRUE, nnF = c(10, 20, 30)
+    sim = 10000, wiggle = FALSE, nnF = c(10, 20, 30)
   ), "Advise to use sim >= 50000 to achieve convergence")
-  res_more_eff$oc$PrEfficacy
-  expect_true(res_more_eff$oc$PrEfficacy > res_eff$oc$PrEfficacy)
+  result_more_eff$oc$PrEfficacy
+  expect_true(result_more_eff$oc$PrEfficacy > result_eff$oc$PrEfficacy)
 })
 
 # ocPostprob  ---
 test_that("ocPostprob gives results that are within range to stats::pbinom", {
   set.seed(1989)
   # Go criteria is P_E(truep >= 0.45) > 0.70
   # Stop criteria is P_E(truep <= 0.45) > 0.90
-  res1 <- ocPostprob(
+  result <- ocPostprob(
     nnE = 40, truep = 0.5, p0 = 0.45, p1 = 0.45, tL = 0.9, tU = 0.7,
     parE = c(1, 1), sim = 50000
   )
   # Pre-calculation indicate that :
   # Go criteria: 20 out of 40, means >= 50% response rate
-  expect_equal(res1$oc$PrEfficacy, 0.56226)
+  expect_equal(result$oc$PrEfficacy, 0.56226)
   p.go <- 1 - pbinom(q = 20 - 1, size = 40, prob = 0.5)
-  expect_true(abs(p.go - res1$oc$PrEfficacy) < 1e-3)
+  expect_true(abs(p.go - result$oc$PrEfficacy) < 1e-3)
   # Stop criteria: 13 out of 40, means <= 32.5% response rate.
-  expect_equal(res1$oc$PrFutility, 0.01998)
+  expect_equal(result$oc$PrFutility, 0.01998)
   p.stop <- pbinom(q = 13, size = 40, prob = 0.5)
-  expect_true(abs(p.stop - res1$oc$PrFutility) < 1e-2)
+  expect_true(abs(p.stop - result$oc$PrFutility) < 1e-2)
 })

vignettes/introduction.Rmd

@@ -696,7 +696,7 @@ we issue the following command:
 set.seed(4)
 results <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.4, p0 = 0.2, p1 = 0.3, tL = 0.6,
-  tU = 0.8, parE = c(1, 1), sim = 10000, wiggle = FALSE, randomdist = FALSE, nnF = c(10, 20, 30)
+  tU = 0.8, parE = c(1, 1), sim = 10000, wiggle = FALSE, nnF = c(10, 20, 30)
 )
 results$oc
 ```