\name{contourLevels}\alias{contourLevels}\alias{contourLevels.kde}\alias{contourLevels.kda.kde}\title{Contour levels for kde and kda.kde objects}\description{  Contour levels for \code{kde} and \code{kda.kde} objects.} %%\synopsis{%%\method{contourLevels}{kde}(x, prob, cont, nlevels=5, ...)%%\method{contourLevels}{kda.kde}(x, prob, cont, nlevels=5, ...)%%}\usage{contourLevels(x, ...)\method{contourLevels}{kde}(x, prob, cont, nlevels=5, ...)\method{contourLevels}{kda.kde}(x, prob, cont, nlevels=5, ...)}\arguments{  \item{x}{an object of class \code{kde} or \code{kda.kde}}  \item{prob}{vector of probabilities corresponding to highest density regions}  \item{cont}{vector of percentages which correspond to the complement	of \code{prob}}  \item{nlevels}{number of pretty contour levels}   \item{...}{other parameters for \code{\link{contour}}}}  \value{  For \code{kde} objects, returns vector of heights.  For \code{kda.kde}  objects, returns a list of vectors, one for each training group.}\details{  The most straightfoward is to specify \code{prob}. Heights of  the corresponding highest density region with probability \code{prob} are  computed.  The \command{cont} parameter here is consistent with  \command{cont} parameter from \command{plot.kde} and \command{plot.kda.kde}  i.e. \code{cont = (1 - prob)*100}\%.    If both \code{prob} and \code{cont} are missing then a pretty set of  \code{nlevels} contours are computed.} \seealso{\code{\link{contour}}, \code{\link{contourLines}}}\examples{## kde x <- rmvnorm.mixt(n=100, mus=c(0,0), Sigmas=diag(2), props=1)Hx <- Hpi(x)fhatx <- kde(x=x, H=Hx)lev1 <- contourLevels(fhatx, prob=c(0.25, 0.5, 0.75))lev2 <- contourLevels(fhatx, cont=c(75, 50, 25))      ## lev1 == lev2## kda.kdelibrary(MASS)data(iris)ir <- iris[,1]ir.gr <- iris[,5]kda.fhat <- kda.kde(ir, ir.gr, hs=sqrt(c(0.01, 0.04, 0.07)))contourLevels(kda.fhat, prob=c(0.25, 0.5, 0.75))}\keyword{hplot}