predict.rppm.Rd
\name{predict.rppm}
\alias{fitted.rppm}
\alias{predict.rppm}
\title{
Make Predictions From a Recursively Partitioned Point Process Model
}
\description{
Given a model which has been fitted to point pattern data
by recursive partitioning, compute the predicted intensity
of the model.
}
\usage{
\method{predict}{rppm}(object, \dots)
\method{fitted}{rppm}(object, \dots)
}
\arguments{
\item{object}{
Fitted point process model of class \code{"rppm"}
produced by the function \code{\link{rppm}}.
}
\item{\dots}{
Optional arguments passed to \code{\link{predict.ppm}}
to specify the locations where prediction is required.
(Ignored by \code{fitted.rppm})
}
}
\details{
These functions are methods for the generic functions
\code{\link[stats]{fitted}} and \code{\link[stats]{predict}}.
They compute the fitted intensity of a point process model.
The argument \code{object} should be a fitted point process model
of class \code{"rppm"} produced by the function \code{\link{rppm}}.
The \code{fitted} method computes the fitted intensity at the original data
points, yielding a numeric vector with one entry for each data point.
The \code{predict} method computes the fitted intensity at
any locations. By default, predictions are
calculated at a regular grid of spatial locations, and the result
is a pixel image giving the predicted intensity values at these
locations.
Alternatively, predictions can be performed at other
locations, or a finer grid of locations, or only at certain specified
locations, using additional arguments \code{\dots}
which will be interpreted by \code{\link{predict.ppm}}.
Common arguments are \code{ngrid} to increase the grid resolution,
\code{window} to specify the prediction region, and \code{locations}
to specify the exact locations of predictions.
See \code{\link{predict.ppm}} for details of these arguments.
Predictions are computed by evaluating the explanatory covariates at
each desired location, and applying the recursive partitioning rule
to each set of covariate values.
}
\value{
The result of \code{fitted.rppm} is a numeric vector.
The result of \code{predict.rppm} is a pixel image, a list of pixel images,
or a numeric vector.
}
\author{
\spatstatAuthors.
}
\seealso{
\code{\link{rppm}},
\code{\link{plot.rppm}}
}
\examples{
fit <- rppm(unmark(gorillas) ~ vegetation, data=gorillas.extra)
plot(predict(fit))
lambdaX <- fitted(fit)
lambdaX[1:5]
# Mondriaan pictures
plot(predict(rppm(redwoodfull ~ x + y)))
points(redwoodfull)
}
\keyword{spatial}
\keyword{models}