some first workings

R/plotOc.R

@@ -1,19 +1,36 @@
 #' Display the operating characteristics using an oc object
 #'
-#' Reads results from \code{\link{ocPredprob}}, \code{\link{ocPostprob}}
+#' Reads results from [ocPredprob()]
 #' etc. and displays a bar plot of the operating characteristics
 #'
-#' @param z returned oc value
+#' @typed oc : list
+#' returned oc parameters
 #' @return nothing, only plots as side effect
 #'
 #' @importFrom graphics barplot title
 #'
 #' @example examples/plotOc.R
 #' @export
 #' @keywords graphics
-plotOc <- function(z) {
+plotOc <- function(oc) {
+
+  if (wiggle == FALSE) {
+    data <-  table(oc$Decision, oc$SampleSize) / oc$params$sim
+  } else {
+
+  }
+
+ggplot(oc, aes(x = name, y = value)) +
+  geom_bar(stat = "identity") +
+  ggtitle("Percentage of trials that Go and Stop per look") +
+  ylabs("Percentage %") +
+  xlabs("Looks and sample size")
+
+
+
   ## plot function for oc.predprob or oc.postprob, or the dist versions of them
-  graphics::barplot(table(z$Decision, z$SampleSize) / z$params$sim, beside = TRUE)
+  graphics::barplot(table(oc$Decision, oc$SampleSize) / oc$params$sim, beside = TRUE)
+
 
   ## get the parameter
   parDat <- lapply(z$params, deparse)

examples/plotOc.R

@@ -1,9 +1,46 @@
 # get operating character result from oc.postprob
 
-res1 <- ocPostprob(
+oc <- 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 = 50000
+  p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 100, wiggle = FALSE
 )
-res1$oc
+oc$oc
+# plotOc(oc)
 
-plotOc(res1)
+
+oc <- 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 = 100, wiggle = TRUE
+)
+oc$oc
+
+data <- data.frame(Decision = as.factor(oc$Decision),
+           Looks = oc$SampleSize)
+
+table(data$Decision, data$Looks)
+
+data %>% group_by(Decision, Looks) %>% summarise(n = n())
+
+data %>% group_by(SampleSize) %>% summarise(n = n())
+
+ggplot(dat, aes(x = SampleSize, y = Decision)) +
+  geom_bar(stat = "identity")
+
+data %>% group_by(Looks, Decision) %>% summarise(tot = n())
+
+
+table(oc$Decision, oc$SampleSize) / oc$params$sim
+table(oc$Decision, oc$SampleSize)
+
+oc$Decision <- ifelse(oc$Decision == TRUE, "GO",
+                      ifelse(oc$Decision == FALSE, "Stop", "Grey"))
+
+oc$Decision
+
+data.frame(looks = c(res1$wiggled_nnrE))
+
+table(oc$Decision, oc$SampleSize)
+
+tt = data.frame(Decision = res$Decision,
+                look = res$SampleSize)
+tt %>% group_by(look) %>% summarise(success = per())