https://github.com/cran/spatstat
Tip revision: cdb30ea65c419f6a98076c49a5d7b5fb7fea0f1a authored by Adrian Baddeley on 27 February 2015, 07:32:22 UTC
version 1.41-1
version 1.41-1
Tip revision: cdb30ea
predict.lppm.Rd
\name{predict.lppm}
\alias{predict.lppm}
\title{
Predict Point Process Model on Linear Network
}
\description{
Given a fitted point process model on a linear network,
compute the fitted intensity or conditional intensity of the model.
}
\usage{
\method{predict}{lppm}(object, ...,
type = "trend", locations = NULL, new.coef=NULL)
}
\arguments{
\item{object}{
The fitted model. An object of class \code{"lppm"},
see \code{\link{lppm}}.
}
\item{type}{
Type of values to be computed. Either \code{"trend"},
\code{"cif"} or \code{"se"}.
}
\item{locations}{
Optional. Locations at which predictions should be computed.
Either a data frame with two columns of coordinates,
or a binary image mask.
}
\item{new.coef}{
Optional. Numeric vector of model coefficients,
to be used instead of the fitted coefficients
\code{coef(object)} when calculating
the prediction.
}
\item{\dots}{
Optional arguments passed to \code{\link[spatstat]{as.mask}}
to determine the
pixel resolution (if \code{locations} is missing).
}
}
\details{
This function computes the fitted poin process intensity,
fitted conditional intensity, or standard error of the fitted
intensity, for a point process model on a linear network.
It is a method for the generic \code{\link[stats]{predict}}
for the class \code{"lppm"}.
The argument \code{object} should be an object of class \code{"lppm"}
(produced by \code{\link{lppm}}) representing a point process model
on a linear network.
Predicted values are computed at the locations given by the
argument \code{locations}. If this argument is missing,
then predicted values are computed at a fine grid of points
on the linear network.
\itemize{
\item
If \code{locations} is missing or \code{NULL} (the default),
the return value is a pixel image (object of class \code{"linim"}
which inherits class \code{"im"})
corresponding to a discretisation
of the linear network, with numeric pixel values giving the
predicted values at each location on the linear network.
\item
If \code{locations} is a data frame, the result is a
numeric vector of predicted values at the locations specified by
the data frame.
\item
If \code{locations} is a binary mask, the result is a pixel image
with predicted values computed at the pixels of the mask.
}
}
\value{
A pixel image (object of class \code{"linim"} which inherits
class \code{"im"}) or
a numeric vector, depending on the argument \code{locations}.
See Details.
}
\author{
Adrian Baddeley
\email{Adrian.Baddeley@uwa.edu.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
}
\seealso{
\code{\link{lpp}},
\code{\link{linim}}
}
\examples{
example(lpp)
fit <- lppm(X, ~x)
v <- predict(fit, type="trend")
plot(v)
}
\references{
Ang, Q.W. (2010)
\emph{Statistical methodology for events on a network}.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
\emph{Scandinavian Journal of Statistics} \bold{39}, 591--617.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012)
Fitting Poisson point process models to events
on a linear network. Manuscript in preparation.
}
\keyword{spatial}
\keyword{models}