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\name{tolEllipsePlot}
\alias{tolEllipsePlot}
\title{Tolerance Ellipse Plot}
\description{
Plots the 0.975 tolerance ellipse of the bivariate data set \code{x}.
The ellipse is defined by those data points whose distance
is equal to the squareroot of the 0.975 chisquare quantile
with 2 degrees of freedom.
}
\usage{
tolEllipsePlot(x, m.cov = covMcd(x), cutoff = NULL, id.n = NULL,
classic = FALSE, tol = 1e-07,
xlab = "", ylab = "",
main = "Tolerance ellipse (97.5\%)",
txt.leg = c("robust", "classical"),
col.leg = c("red", "blue"),
lty.leg = c("solid","dashed"))
}
\arguments{
\item{x}{a two dimensional matrix or data frame. }
\item{m.cov}{an object similar to those of class \code{"mcd"}; however
only its components \code{center} and \code{cov} will be used. If
missing, the MCD will be computed (via \code{\link{covMcd}()}).}
\item{cutoff}{numeric distance needed to flag data points outside the
ellipse.}
\item{id.n}{number of observations to be identified by a label. If
not supplied, the number of observations with distance larger than
\code{cutoff} is used.}
\item{classic}{whether to plot the classical distances as well,
\code{FALSE} by default.}
\item{tol}{tolerance to be used for computing the inverse, see
\code{\link{solve}}. Defaults to \code{1e-7}.}
\item{xlab, ylab, main}{passed to \code{\link{plot.default}}.}
\item{txt.leg, col.leg, lty.leg}{character vectors of length 2 for the
legend, only used if \code{classic = TRUE}.}
}
\author{Peter Filzmoser, Valentin Todorov and Martin Maechler}
%\details{}
%\value{}
%\references{ }
\seealso{
\code{\link{covPlot}} which calls \code{tolEllipsePlot()} when
desired.
\code{\link[cluster]{ellipsoidhull}} and
\code{\link[cluster]{predict.ellipsoid}} from package \CRANpkg{cluster}.
}
\examples{
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
mcd <- covMcd(hbk.x) # compute mcd in advance
## must be a 2-dimensional data set: take the first two columns :
tolEllipsePlot(hbk.x[,1:2])
## an "impressive" example:
data(telef)
tolEllipsePlot(telef, classic=TRUE)
}
\keyword{hplot}
\keyword{robust}
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