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\name{is.multitype.lppm}
\alias{is.multitype.lppm}
\title{Test Whether A Point Process Model is Multitype}
\description{
Tests whether a fitted point process model on a network involves ``marks''
attached to the points that classify the points into several types.
}
\usage{
\method{is.multitype}{lppm}(X, \dots)
}
\arguments{
\item{X}{
Fitted point process model on a linear network
(object of class \code{"lppm"})
usually obtained from \code{\link{lppm}}.
}
\item{\dots}{
Ignored.
}
}
\value{
Logical value, equal to \code{TRUE} if
\code{X} is a model that was fitted to a multitype point pattern dataset.
}
\details{
``Marks'' are observations attached to each point of a point pattern.
For example the \code{\link[spatstat.data]{chicago}} dataset contains
the locations of crimes, each crime location
being marked by the type of crime.
The argument \code{X} is a fitted point process model
on a network (an object of class \code{"lppm"}) typically obtained
by fitting a model to point pattern data on a network
using \code{\link{lppm}}.
This function returns \code{TRUE} if the \emph{original data}
(to which the model \code{X} was fitted) were a multitype point pattern.
Note that this is not the same as testing whether the
model involves terms that depend on the marks (i.e. whether the
fitted model ignores the marks in the data).
See the Examples for a trick for doing this.
If this function returns \code{TRUE}, the implications are
(for example) that
any simulation of this model will require simulation of random marks
as well as random point locations.
}
\seealso{
\code{\link[spatstat.geom]{is.multitype}},
\code{\link{is.multitype.lpp}}
}
\examples{
fit <- lppm(chicago ~ x)
is.multitype(fit)
# TRUE because chicago data are multitype
## To check whether the model involves marks:
"marks" \%in\% spatstat.utils::variablesinformula(formula(fit))
}
\author{
\adrian
and \rolf
}
\keyword{spatial}
\keyword{manip}
\keyword{models}
\concept{Linear network}
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