\name{cor.bootci}
\alias{cor.bootci}
\title{Bootstrap Confidence Interval for Multivariate Correlation}
\description{
This function calculates Bootstrap confidence interval for multivariate correlation.
The procedure is very similar to those used to calculate Bootstrap CI's for
other parameters, such as mean and correlation. See manuscript for detail.
}
\usage{
cor.bootci(x, y = NULL, m, G, alpha)
}
\arguments{
  \item{x}{data matrix, column represents samples (conditions), and row represents variables (genes), see example below for format information}
  \item{y}{optional, used when x and y are vectors}
  \item{m}{number of replicates}
  \item{G}{number of genes}
  \item{alpha}{significant level}
}
\details{
See manuscript.
}
\value{
  \item{upperCI}{Upper bound of CI}
  \item{lowerCI}{Lower bound of CI}
}
\references{Zhu, D and Li Y. 2007. Multivariate Correlation Estimator for Inferring Functional Relationships
from Replicated 'OMICS' data. Submitted.}
\author{Dongxiao Zhu and Youjuan Li}
\seealso{\code{\link{cor.LRtest}}, \code{\link{cor.LRtest.std}}, \code{\link{cor.test}}, \code{\link{permutest}}}
\examples{
library("CORREP")
d0 <- NULL
## sample size is set to 5 
for(l in 1:5)
d0 <- rbind(d0, rnorm(8))
## data must have row variance of 1 
d0.std <- apply(d0, 2, function(x) x/sd(x))
M <- cor.balance(t(d0.std), m = 2, G= 4)
pv.bootci <- cor.bootci(t(d0.std), m = 2, G= 4, alpha = 0.05)
}
\keyword{multivariate}
\keyword{cluster}
\keyword{models}
\keyword{htest}