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\name{outlier}
\alias{outlier}
\alias{outlier.randomForest}
\alias{outlier.default}
\title{Compute outlying measures}
\description{Compute outlying measures based on a proximity matrix.}
\usage{
\method{outlier}{default}(x, cls=NULL, ...)
\method{outlier}{randomForest}(x, ...)
}
\arguments{
\item{x}{a proximity matrix (a square matrix with 1 on the diagonal
and values between 0 and 1 in the off-diagonal positions); or an object of
class \code{\link{randomForest}}, whose \code{type} is not
\code{regression}.}
\item{cls}{the classes the rows in the proximity matrix belong to. If
not given, all data are assumed to come from the same class.}
\item{...}{arguments for other methods.}
}
\value{
A numeric vector containing the outlying measures. The outlying
measure of a case is computed as n / sum(squared proximity), normalized by
subtracting the median and divided by the MAD, within each class.
}
\seealso{
\code{\link{randomForest}}
}
\examples{
set.seed(1)
iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)
plot(outlier(iris.rf), type="h",
col=c("red", "green", "blue")[as.numeric(iris$Species)])
}
\keyword{classif}
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