R/corr.R In pwr2ppl: Power Analyses for Common Designs (Power to the People)

Documented in corr

```#'Compute power for Pearson's Correlation
#'Takes correlation and range of values
#'@param r Correlation
#'@param nlow Starting sample size
#'@param nhigh Ending sample size
#'@param by Incremental increase in sample size from low to high
#'@param tails one or two-tailed tests (default is 2)
#'@param alpha Type I error (default is .05)
#'@examples
#'corr(r=.30, nlow=60, nhigh=100,by=2)
#'@return Power for Pearson's Correlation
#'@export
#'
#'
corr<-function(r,nlow, nhigh, alpha=.05, tails=2, by=1)
{
result <- data.frame(matrix(ncol = 2))
colnames(result) <- c( "n","Power")
d<-abs(2*abs(r))/(1-r^2)^.5
for(n in seq(nlow,nhigh, by)){
delta<-(d*(n-2)^.5)/2
alphatails<-alpha/tails
tabled<-stats::qt(1-alphatails, df=n-2)
t<-1-stats::pt(alphatails, 1, n-2)
Power<-round(1-stats::pt(tabled, n-2,delta),4)
message("Power for n of ", n, " = ", Power)
result[n, 1]<-n
result[n, 2]<-Power}
output<-na.omit(result)
rownames(output)<- c()
output
}
```

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pwr2ppl documentation built on Sept. 6, 2022, 5:06 p.m.