Add tests and example of pred for binomProbs()

R/confint.R

@@ -175,7 +175,6 @@ confint.rxSolve <- function(object, parm = NULL, level = 0.95, ...) {
   } else {
     .ret <- .ret[, list(p1 = .p,
                         eff = stats::quantile(.SD$value, probs = .p, na.rm = TRUE)), by = c("id", "time", "trt", .by)]
-
   }
   .ret <- .ret[, setNames(as.list(stats::quantile(.SD$eff, probs = .p2, na.rm = TRUE)),
                           sprintf("p%s", .p2 * 100)),

R/utils.R

@@ -963,9 +963,14 @@ meanProbs.default <- function(x, probs=seq(0, 1, 0.25), na.rm=FALSE,
 #'   https://doi.org/10.1016/j.jspi.2019.07.001.
 #' @examples
 #'
-#' quantile(x<- rbinom(7001, p=0.375, size=1))
+#' x<- rbinom(7001, p=0.375, size=1)
 #' binomProbs(x)
 #'
+#' # you can also use the prediction interval
+#' \donttest{
+#' binomProbs(x, pred=TRUE)
+#' }
+#'
 #' # Can get some extra statistics if you request onlyProbs=FALSE
 #' binomProbs(x, onlyProbs=FALSE)
 #'

tests/testthat/test-binomProb.R

@@ -149,25 +149,6 @@ test_that("test binomProb()", {
   expect_equal(binomProbs(x, method="ac"), t1)
 
 
-  f <- function(theta, w, n, Y, U) {
-    w * pbeta(1 - theta, n - Y + 1, Y) +
-      (1 - w) * pbeta(1 - theta, n - Y, Y + 1) - U
-  }
-
-  m <- 7
-  n <- 7
-  Y <- 3
-  #M <- 1000000
-  m <- 1000
-  c <- vector()
-  for (i in 1:M) {
-    w <- runif(1, 0, 1)
-    U <- runif(1, 0, 1)
-    V <- runif(1, 0, 1)
-    root1 <- uniroot(f, c(0, 1), w=w, n=n, Y=Y, U=U)$root
-    d <- qbinom(V, m, root1)
-    c <- c(c, d)
-  }
-  LIM <- quantile(c, c(0.05, 0.95))
+  expect_equal(names(binomProbs(x, pred=TRUE)), names(binomProbs(x)))
 
 })