updated proportions code

R/power.R

@@ -65,8 +65,8 @@ estimateMeanEffectSizes <- function(mean,sd,samples,power){
 #'
 #' Returns a data.table of effect sizes (%) for a given sample size. Calculates these for p = 0.01, 0.05, 0.1 & 0.2. Pick out the ones you want.
 #'
-#' @param mean the estimated mean value to use
-#' @param sd the estimated stadnard deviation to use
+#' @param p1 the estimated proportion in sample 1
+#' @param sd the estimated proportion in sample 2
 #' @param samples a list of sample sizes to iterate over
 #' @param power power value to use
 #'
@@ -77,27 +77,23 @@ estimateMeanEffectSizes <- function(mean,sd,samples,power){
 #' @import pwr
 #' @family Power functions
 
-estimateProportionEffectSizes <- function(samples,power){
+estimateProportionSampleSizes <- function(p1, p2, samples ,power){
   # obtain effect sizes using supplied mean & sd
   sigs <- c(0.01,0.05,0.1,0.2) # force these, can always remove later
   nSigs <- length(sigs)
-  nSamps <- length(samples)
   # initialise power results array
   resultsArray <- array(numeric(nSamps*nSigs),
                         dim=c(nSamps,nSigs)
   )
   # loop over significance values
   for (p in 1:nSigs){
-    for (s in 1:nSamps){ # loop over the sample sizes
-      # pwr.t.test?
       result <- pwr::pwr.2p.test(
-        n = samples[s],
-        h = NULL,
+        h = ES.h(p1 = p1, p2 = p2),
+        n = NULL,
         sig.level = sigs[p],
         power = power
       )
-      resultsArray[s,p] <- result$h # report effect size against sample size
-    }
+      resultsArray[s,p] <- result$n # report effect size against sample size
   }
   dt <- data.table::as.data.table(resultsArray) # convert to dt for tidying
   dt <- dt[,