The R Project SVN R

\name{cmdscale}
\title{Classical (Metric) Multidimensional Scaling}
\usage{
cmdscale(d, k = 2, eig = FALSE)
}
\alias{cmdscale}
\arguments{
\item{d}{a distance structure such as that returned by \code{dist}
or a full symmetric matrix containing the dissimilarities.}
\item{k}{the dimension of the space which the data are to be represented in.}
\item{eig}{indicates whether eigenvalues should be returned.}
}
\description{
  Classical muiltidimensional scaling of a data matrix.
}
\details{
  Multidimensional scaling takes a set of dissimilarities
  and returns a set of points such that the distances between the points
  are approximately equal to the dissimilarities.

  The functions \code{isoMDS} and \code{sammon} in package
  \file{MASS} provide alternative ordination techniques.
}
\value{
  A list containing the following components.
  \item{points}{a matrix with \code{k} columns whose rows give the
    coordinates of the points chosen to represent the dissimilarities.}
  \item{eig}{if requested, the eigenvalues computed during the scaling
    process.}
}
\references{
  Seber, G. A. F. (1984). \emph{Multivariate Analysis}. New York: Wiley.

  Torgerson, W. S. (1958). \emph{Theory and Methods of Scaling}.
  New York: Wiley.
}
\note{
  The S version of this function provides for computing an additional
  ``fiddle'' factor suggested by Torgerson.  \R does not provide this option.
}
\seealso{
\code{\link{dist}}. Also
\code{\link{isoMDS}} and
\code{\link{sammon}} in package \file{MASS}.
}
\examples{
data(eurodist)
loc <- cmdscale(eurodist)
x <- loc[,1]
y <- -loc[,2]
plot(x, y, type="n",
    xlab="", ylab="")
text(x, y, names(eurodist), cex=0.5)
}
\keyword{multivariate}