# Randomization test on correlation
# The program will first read the data and calculate r.
# Then it will calculate parametric estimates of 95% confidence limits
# This assumes that the first column of data is ID
# and columns 2 and 3 are X and Y
data <- read.table(file.choose(), header = T)
x <- data[,2]
y <- data[,3]
N <- length(x)
r <- cor(x,y)
###################
# Function to compute Fisher's rprime
Fisher <- function(r) {
  rp <- 0.5*log(abs(1+r)/(1-r))
  rp
  }
##################

# Function to go from rprime to r
rback <- function(rp)  {
  rback <- (exp(rp/.5)-1)/(exp(rp/.5)+1)
  }
##################

# CI on r
CI <- function(rp,N)  {
  CIlp <- rp - 1.96*sqrt(1/(N-3))
  CIupp <- rp + 1.96*sqrt(1/(N-3))
  CIlow <- rback(CIlp)
  CIup <- rback(CIupp)
  CInt <- c(CIlow, CIup)
  CInt
# The following line works fine uless you source the file.
#return(list(Lower.Conf.Limit.R = CIlow, Upper.Conf.Limit.R=CIup))
  }

rp <- Fisher(r)
# Calculate and print the confidence interval
ConfInt <- CI(rp,N)
cat("The correlation is ",r, " on ", N-1, " df.", "\n")
cat("The 95% confidence limits are ", "\n")
print(ConfInt)