Nothing
Cor.test = function(Data, conf.level=0.95)
{
nCol = ncol(Data)
vNum = rep(FALSE, nCol)
for (i in 1:nCol) vNum[i] = is.numeric(Data[,i])
Data = Data[,vNum]
nc0 = ncol(Data)
if (nc0 < 2) stop("Input should have more than 1 column!")
for (i in 1:nc0) if (!is.numeric(Data[,i])) stop("This is only for numeric columns!")
cn0 = colnames(Data)
mC = cor(Data, use="pairwise.complete.obs")
m2 = matrix(as.numeric(!is.na(Data)), ncol=nc0)
colnames(m2) = cn0
x = model.matrix(~ . - 1, data.frame(m2))
n = crossprod(x)
Df = n - 2
Tval = mC*sqrt(Df/(1 - mC^2))
Pval = 2*pt(-abs(Tval), Df)
Zs = atanh(mC)
DL = qnorm(0.5 + conf.level/2)/sqrt(n - 3)
LL = tanh(Zs - DL)
UL = tanh(Zs + DL)
ColNames = c("Estimate", "Lower CL", "Upper CL", "t value", "Df", "Pr(>|t|)")
nr = nc0*(nc0 - 1)/2
nc = length(ColNames)
Res = matrix(nrow=nr, ncol=nc)
colnames(Res) = ColNames
rownames(Res) = 1:nr
cr = 1
for (i in 1:nc0) {
for (j in (i + 1):nc0) {
Res[cr, 1] = mC[i, j]
Res[cr, 2] = LL[i, j]
Res[cr, 3] = UL[i, j]
Res[cr, 4] = Tval[i, j]
Res[cr, 5] = Df[i, j]
Res[cr, 6] = Pval[i, j]
rownames(Res)[cr] = paste0(cn0[i], ":", cn0[j])
cr = cr + 1
}
if (cr > nr) break
}
printCoefmat(Res)
invisible(Res)
}
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