\name{qpHist}
\alias{qpHist}

\title{
Histograms of non-rejection rates
}
\description{
Plots the distribution of non-rejection rates.
}
\usage{
qpHist(nrrMatrix, K=NULL,
       titlehist = "all estimated\nnon-rejection rates", freq=TRUE)
}
\arguments{
  \item{nrrMatrix}{matrix of non-rejection rates.}
  \item{K}{concentration matrix of the generative distribution (whenever
       available).}
  \item{titlehist}{main title of the histogram(s).}
  \item{freq}{logical; if TRUE, the histograms show frequencies (counts) of
       occurrence of the different non-rejection rate values; if FALSE, then
       probability densities are plotted}
}
\details{
This function plots histograms using the R-function \code{\link{hist}} and therefore
the way they are displayed follows that of this R-function.
}
\value{
None
}
\references{
Castelo, R. and Roverato, A. A robust procedure for
Gaussian graphical model search from microarray data with p larger than n,
\emph{J. Mach. Learn. Res.}, 7:2621-2650, 2006.
}
\author{R. Castelo and A. Roverato}
\seealso{
  \code{\link{qpNrr}}
  \code{\link{qpAvgNrr}}
  \code{\link{qpEdgeNrr}}
  \code{\link{qpGraphDensity}}
  \code{\link{qpClique}}
}
\examples{
nVar <- 50 # number of variables
maxCon <- 5  # maximum connectivity per variable
nObs <- 30 # number of observations to simulate

I <- qpRndGraph(n.vtx=nVar, n.bd=maxCon)
K <- qpI2K(I)

X <- qpSampleMvnorm(K, nObs)

nrr.estimates <- qpNrr(X, q=5, verbose=FALSE)

qpHist(nrr.estimates, K)
}
\keyword{models}
\keyword{multivariate}