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\name{pltree}
\alias{pltree}
\alias{pltree.twins}
\title{Plot Clustering Tree of a Hierarchical Clustering}
\description{
\code{pltree()} Draws a clustering tree (\dQuote{dendrogram}) on the
current graphics device. We provide the \code{twins} method draws the
tree of a \code{twins} object, i.e., hierarchical clustering,
typically resulting from \code{\link{agnes}()} or \code{\link{diana}()}.
}
\usage{
pltree(x, \dots)
\method{pltree}{twins}(x, main = paste("Dendrogram of ", deparse1(x$call)),
labels = NULL, ylab = "Height", \dots)
}
\arguments{
\item{x}{in general, an \R object for which a \code{pltree} method is
defined; specifically, an object of class \code{"twins"}, typically
created by either \code{\link{agnes}()} or \code{\link{diana}()}.}
\item{main}{main title with a sensible default.}
\item{labels}{labels to use; the default is constructed from \code{x}.}
\item{ylab}{label for y-axis.}
\item{\dots}{graphical parameters (see \code{\link{par}}) may also
be supplied as arguments to this function.}
}
\value{
a NULL value is returned.
}
\details{
Creates a plot of a clustering tree given a \code{twins} object. The
leaves of the tree are the original observations. In case of an
agglomerative clustering, two branches come together at the distance
between the two clusters being merged. For a divisive clustering, a
branch splits up at the diameter of the cluster being splitted.
Note that currently the method function simply calls
\code{plot(\link[stats]{as.hclust}(x), ...)}, which dispatches to
\code{\link{plot.hclust}(..)}. If more flexible plots are needed,
consider \code{xx <- \link{as.dendrogram}(\link{as.hclust}(x))} and plotting
\code{xx}, see \code{\link{plot.dendrogram}}.
}
\seealso{
\code{\link{agnes}}, \code{\link{agnes.object}}, \code{\link{diana}},
\code{\link{diana.object}}, \code{\link{hclust}}, \code{\link{par}},
\code{\link{plot.agnes}}, \code{\link{plot.diana}}.
}
\examples{
data(votes.repub)
agn <- agnes(votes.repub)
pltree(agn)
dagn <- as.dendrogram(as.hclust(agn))
dagn2 <- as.dendrogram(as.hclust(agn), hang = 0.2)
op <- par(mar = par("mar") + c(0,0,0, 2)) # more space to the right
plot(dagn2, horiz = TRUE)
plot(dagn, horiz = TRUE, center = TRUE,
nodePar = list(lab.cex = 0.6, lab.col = "forest green", pch = NA),
main = deparse(agn$call))
par(op)
}
\keyword{cluster}
\keyword{hplot}
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