### updated proportions code

parent 84f5582f
 ... ... @@ -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[, ... ...
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