R/md_precision.R

In pwr2ppl: Power Analyses for Common Designs (Power to the People)

#'Compute Precision Analyses for Mean Differences
#'@param m1 Mean of first group
#'@param m2 Mean of second group
#'@param s1 Standard deviation of first group
#'@param s2 Standard deviation of second group
#'@param nlow starting sample size
#'@param nhigh ending sample size
#'@param by Incremental increase in sample (e.g. nlow = 10, nhigh = 24, by = 2, produces estimates of 10, 12, and 14)
#'@param propn1 Proportion in First Group
#'@param ci Type of Confidence Interval (e.g., .95)
#'@examples
#'md_prec(m1=2,m2 =0, s1=5, s2=5,nlow=100, nhigh =1600, propn1=.5, ci=.95, by=100)
#'md_prec(m1=0,m2 =0, s1=5, s2=5,nlow=100, nhigh =40000, propn1=.5, ci=.95, by=1000)
#'@return Precision Analyses for Mean Differences
#'@export
#'

md_prec<-function(m1,m2,s1,s2,nlow, nhigh, propn1= .5, ci=.95, by=1)

  {
  result <- data.frame(matrix(ncol = 6))
  colnames(result) <- c("n1", "n2","d","LL","UL","Precision")
  for(n in seq(nlow,nhigh, by)){
    n1<-n * propn1
    n2<-n * (1-propn1)
    var1 <- s1*s1
    var2 <- s2*s2
    nxs1 <- (n1-1)*(var1)
    nxs2 <- (n2-1)*(var2)
    s2p<-(nxs1+nxs2)/(n1+n2-2)
    sp <- sqrt(s2p)
    d<-(m1-m2)/sp
    a<-MBESS::ci.smd(smd=d, n.1=n1,n.2=n2, conf.level = .95)
    ll<-a[1]
    ul<-a[3]
    ll<-as.numeric(ll)
    ul<-as.numeric(ul)
    ll_m<-ll*sp
    ul_m<-ul*sp
    precision<-round((ul_m-ll_m),4)
    ll_m<-round((ll_m),4)
    ul_m<-round((ul_m),4)
    result[n, 1]<-n1
    result[n, 2]<-n2
    result[n, 3]<-d
    result[n, 4]<-ll_m
    result[n, 5]<-ul_m
    result[n, 6]<-precision}
    output<-na.omit(result)
    rownames(output)<- c()
    output}