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\name{rSwitzerlpp}
\alias{rSwitzerlpp}
\title{
Switzer-type Point Process on Linear Network
}
\description{
Generate a realisation of the Switzer-type point process
on a linear network.
}
\usage{
rSwitzerlpp(L, lambdacut, rintens = rexp, \dots,
cuts=c("points", "lines"))
}
\arguments{
\item{L}{
Linear network (object of class \code{"linnet"}).
}
\item{lambdacut}{
Intensity of Poisson process of breakpoints.
}
\item{rintens}{
Optional. Random variable generator
used to generate the random intensity in each component.
}
\item{\dots}{
Additional arguments to \code{rintens}.
}
\item{cuts}{
String (partially matched) specifying the type of random cuts to be
generated.
}
}
\details{
This function generates simulated realisations of
the Switzer-type point process on a network,
as described in Baddeley et al (2017).
The linear network is first divided into pieces by a random
mechanism:
\itemize{
\item if \code{cuts="points"},
a Poisson process of breakpoints with intensity \code{lambdacut}
is generated on the network, and these breakpoints separate the
network into connected pieces.
\item if \code{cuts="lines"}, a Poisson line process in the plane
with intensity \code{lambdacut} is generated; these lines divide
space into tiles; the network is divided into subsets associated
with the tiles. Each subset may not be a connected sub-network.
}
In each piece of the network, a random intensity is generated
using the random variable generator \code{rintens} (the default is
a negative exponential random variable with rate 1). Given the
intensity value, a Poisson process is generated with the specified
intensity.
The intensity of the final process is determined by the mean
of the values generated by \code{rintens}. If \code{rintens=rexp} (the
default), then the parameter \code{rate} specifies the inverse of the
intensity.
}
\value{
Point pattern on a linear network (object of class \code{"lpp"})
with an attribute \code{"breaks"} containing the breakpoints (if
\code{cuts="points"}) or the random lines (if \code{cuts="lines"}).
}
\author{
\adrian.
}
\seealso{
\code{\link{rcelllpp}}
}
\references{
Baddeley, A., Nair, G., Rakshit, S. and McSwiggan, G. (2017)
\sQuote{Stationary} point processes are uncommon on
linear networks. \emph{STAT} \bold{6}, {68--78}.
}
\examples{
plot(rSwitzerlpp(domain(spiders), 0.01, rate=100))
plot(rSwitzerlpp(domain(spiders), 0.0005, rate=100, cuts="l"))
}
\keyword{spatial}
\keyword{datagen}
\concept{Linear network}
|
