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wincor <- function(x, y = NULL, tr = 0.2, ci = FALSE, nboot = 500, alpha = 0.05, ...){
# Compute the Winsorized correlation between x and y.
#
# tr is the amount of Winsorization
# This function also returns the Winsorized covariance
#
# Pairwise deletion of missing values is performed.
#
cl <- match.call()
if(is.null(y[1])){
y=x[,2]
x=x[,1]
}
sig<-NA
if(length(x)!=length(y))stop("Lengths of vectors are not equal")
m1=cbind(x,y)
m1<-elimna(m1)
nval=nrow(m1)
x<-m1[,1]
y<-m1[,2]
g<-floor(tr*length(x))
xvec<-winval(x,tr)
yvec<-winval(y,tr)
wcor<-cor(xvec,yvec)
wcov<-var(xvec,yvec)
if(sum(x==y)!=length(x)){
test<-wcor*sqrt((length(x)-2)/(1.-wcor^2))
sig<-2*(1-pt(abs(test),length(x)-2*g-2))
} else {
test <- NA
sig <- NA
}
## confidence interval
if (ci) {
data<-matrix(sample(length(y),size=length(y)*nboot,replace=TRUE),nrow=nboot)
bvec <- apply(data, 1, function(i) {
x1 <- x[i]
y1 <- y[i]
g<-floor(tr*length(x1))
xvec<-winval(x1,tr)
yvec<-winval(y1,tr)
wcor<-cor(xvec,yvec)
wcor
})
ihi<-floor((1-alpha/2)*nboot+.5)
ilow<-floor((alpha/2)*nboot+.5)
bsort<-sort(bvec)
corci<-1
corci[1]<-bsort[ilow]
corci[2]<-bsort[ihi]
} else {
corci <- NA
}
## end CI
result <- list(cor = wcor, cov = wcov, test = test, p.value = sig, n = nval, cor_ci = corci, alpha = alpha, call = cl)
class(result) <- "pbcor"
result
}
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