% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multivariance-functions.R
\name{rejection.level}
\alias{rejection.level}
\title{rejection level for the test statistic}
\usage{
rejection.level(alpha)
}
\arguments{
\item{alpha}{level of significance}
}
\description{
Under independence the probability for the normalized and Nscaled (squared) multivariance to be above this level is less than \code{alpha}. The same holds for the normalized, Nscaled and Escaled (squared) total multivariance and m-multivariance.
}
\details{
This is based on a distribution-free approach. The value might be very conservative. This is the counterpart to \code{\link{multivariance.pvalue}}. For a less conservative approach see \code{\link{resample.rejection.level}}.

The estimate is only valid for \code{alpha} smaller than 0.215.
}
\examples{
rejection.level(0.05) #the rejection level, for comparison with the following values
total.multivariance(matrix(rnorm(100*3),ncol = 3)) #independent sample
total.multivariance(coins(100)) #dependent sample which is 2-independent

# and the p values are (to compare with alpha)
multivariance.pvalue(total.multivariance(matrix(rnorm(100*3),ncol = 3))) #independent sample
multivariance.pvalue(total.multivariance(coins(100))) #dependent sample which is 2-independent

\dontrun{
# visualization of the rejection level
curve(rejection.level(x),xlim = c(0.001,0.215),xlab = "alpha")
}

}