R/corr.is.R

In cwendorf/DGB: DGB

# DGB
## Correlation Difference from Two (Independent) Samples

ci.corr.is <- function(alpha, corr1, corr2, n1, n2) {
 # Computes a confidence interval for a difference in population 
 # Pearson correlations from two independent samples
 # Arguments: 
 #   alpha: alpha value for 1-alpha confidence
 #   corr1: sample correlation between y and x in group 1
 #   corr2: sample correlation between y and x in group 2
 #   n1:    sample size of group 1
 #   n2:    sample size of group 2
 # Values:
 #   lower limit, upper limit
 z <- qnorm(1 - alpha/2)
 se1 <- sqrt(1/((n1 - 3)))
 zr1 <- log((1 + corr1)/(1 - corr1))/2
 LL0 <- zr1 - z*se1
 UL0 <- zr1 + z*se1
 LL1 <- (exp(2*LL0) - 1)/(exp(2*LL0) + 1)
 UL1 <- (exp(2*UL0) - 1)/(exp(2*UL0) + 1)
 se2 <- sqrt(1/((n2 - 3)))
 zr2 <- log((1 + corr2)/(1 - corr2))/2
 LL0 <- zr2 - z*se2
 UL0 <- zr2 + z*se2
 LL2 <- (exp(2*LL0) - 1)/(exp(2*LL0) + 1)
 UL2 <- (exp(2*UL0) - 1)/(exp(2*UL0) + 1)
 LL <- corr1 - corr2 - sqrt((corr1 - LL1)^2 + (UL2 - corr2)^2)
 UL <- corr1 - corr2 + sqrt((UL1 - corr1)^2 + (corr2 - LL2)^2)
 CI <- c(LL, UL)
 return(CI)
}