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\name{cluster.overplot}
\alias{cluster.overplot}
\title{Shift overlying points into clusters}
\usage{
cluster.overplot(x,y,away=NULL,tol=NULL,...)
}
\arguments{
\item{x,y}{Numeric data vectors or the first two columns of a matrix
or data frame. Typically the x/y coordinates of points to be plotted.}
\item{away}{How far to move overlying points in user units. Defaults to
the width of a lower case "o" in the x direction and 5/8 of the
height of a lower case "o" in the y direction.}
\item{tol}{The largest distance between points that will be considered
to be overlying. Defaults to 1/2 of the width of a lower case "o" in
the x direction and 1/2 of the height of a lower case "o" in the y
direction.}
\item{...}{additional arguments returned as they are passed.}
}
\description{
\samp{cluster.overplot} checks for overlying points in the x and y
coordinates passed. Those points that are overlying are moved to form
a small cluster of up to nine points. For large numbers of overlying
points, see \link{count.overplot} or \link{sizeplot}.
If you are unsure of the number of overplots in your data, run
\samp{count.overplot} first to see if there are any potential clusters
larger than nine.
}
\value{
A list with two components. For unique x-y pairs the elements will be
the same as in the original. For overlying points up to eight additional
points will be generated that will create a cluster of points instead of one.
}
\keyword{misc}
\author{Jim Lemon}
\seealso{\link{count.overplot},\link{sizeplot}}
\examples{
xy.mat<-cbind(sample(1:10,200,TRUE),sample(1:10,200,TRUE))
clusteredpoints<-
cluster.overplot(xy.mat,col=rep(c("red","green"),each=100))
plot(clusteredpoints,col=clusteredpoints$col,
main="Cluster overplot test")
}
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