https://github.com/cran/spatstat
Tip revision: 198d8db539783cb2d4f1347b81b82519926116b2 authored by Adrian Baddeley on 01 April 2009, 11:43:18 UTC
version 1.15-1
version 1.15-1
Tip revision: 198d8db
is.marked.ppm.Rd
\name{is.marked.ppm}
\alias{is.marked.ppm}
\title{Test Whether A Point Process Model is Marked}
\description{
Tests whether a fitted point process model involves ``marks''
attached to the points.
}
\usage{
\method{is.marked}{ppm}(X, \dots)
}
\arguments{
\item{X}{
Fitted point process model (object of class \code{"ppm"})
usually obtained from \code{\link{ppm}}.
}
\item{\dots}{
Ignored.
}
}
\value{
Logical value, equal to \code{TRUE} if
\code{X} is a model that was fitted to a marked point pattern dataset.
}
\details{
``Marks'' are observations attached to each point of a point pattern.
For example the \code{\link{longleaf}} dataset contains the locations
of trees, each tree being marked by its diameter;
the \code{\link{amacrine}} dataset gives the locations of cells
of two types (on/off) and the type of cell may be regarded as a mark attached
to the location of the cell.
The argument \code{X} is a fitted point process model
(an object of class \code{"ppm"}) typically obtained
by fitting a model to point pattern data using \code{\link{ppm}}.
This function returns \code{TRUE} if the \emph{original data}
(to which the model \code{X} was fitted) were a marked 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).
Currently we have not implemented a test for 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{is.marked}},
\code{\link{is.marked.ppp}}
}
\examples{
data(lansing)
# Multitype point pattern --- trees marked by species
\testonly{
# Smaller dataset
data(betacells)
lansing <- betacells[seq(2, 135, by=3), ]
}
fit1 <- ppm(lansing, ~ marks, Poisson())
is.marked(fit1)
# TRUE
fit2 <- ppm(lansing, ~ 1, Poisson())
is.marked(fit2)
# TRUE
data(cells)
# Unmarked point pattern
fit3 <- ppm(cells, ~ 1, Poisson())
is.marked(fit3)
# FALSE
}
\author{Adrian Baddeley
\email{adrian@maths.uwa.edu.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{r.turner@auckland.ac.nz}
}
\keyword{spatial}
\keyword{manip}
\keyword{models}