\name{rppm}
\alias{rppm}
\title{
Recursively Partitioned Point Process Model
}
\description{
Fits a recursive partition model to point pattern data.
}
\usage{
rppm(\dots, rpargs=list())
}
\arguments{
\item{\dots}{
Arguments passed to \code{\link{ppm}}
specifying the point pattern data and the explanatory covariates.
}
\item{rpargs}{
Optional list of arguments passed to \code{\link[rpart]{rpart}}
controlling the recursive partitioning procedure.
}
}
\details{
This function attempts to find a simple rule for predicting
low and high intensity regions of points in a point pattern,
using explanatory covariates.
The arguments \code{\dots} specify the point pattern data
and explanatory covariates in the same way as they would be
in the function \code{\link{ppm}}.
The recursive partitioning algorithm \code{\link[rpart]{rpart}}
is then used to find a partitioning rule.
}
\value{
An object of class \code{"rppm"}. There are methods
for \code{print}, \code{plot}, \code{fitted}, \code{predict} and
\code{prune} for this class.
}
\references{
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984)
\emph{Classification and Regression Trees}. Wadsworth.
}
\author{
\spatstatAuthors.
}
\seealso{
\code{\link{plot.rppm}},
\code{\link{predict.rppm}},
\code{\link{prune.rppm}}.
}
\examples{
# New Zealand trees data: trees planted along border
# Use covariates 'x', 'y'
nzfit <- rppm(nztrees ~ x + y)
nzfit
prune(nzfit, cp=0.035)
# Murchison gold data: numeric and logical covariates
mur <- solapply(murchison, rescale, s=1000, unitname="km")
mur$dfault <- distfun(mur$faults)
#
mfit <- rppm(gold ~ dfault + greenstone, data=mur)
mfit
# Gorillas data: factor covariates
# (symbol '.' indicates 'all variables')
gfit <- rppm(unmark(gorillas) ~ . , data=gorillas.extra)
gfit
}
\keyword{spatial}
\keyword{models}