https://github.com/cran/spatstat
Revision 4fe059206e698a4b7135d792f3d533b173ecfe77 authored by Adrian Baddeley on 16 May 2012, 12:44:15 UTC, committed by cran-robot on 16 May 2012, 12:44:15 UTC
1 parent df59a11
Tip revision: 4fe059206e698a4b7135d792f3d533b173ecfe77 authored by Adrian Baddeley on 16 May 2012, 12:44:15 UTC
version 1.27-0
version 1.27-0
Tip revision: 4fe0592
rStraussHard.Rd
\name{rStraussHard}
\alias{rStraussHard}
\title{Perfect Simulation of the Strauss-Hardcore Process}
\description{
Generate a random pattern of points, a simulated realisation
of the Strauss-Hardcore process, using a perfect simulation algorithm.
}
\usage{
rStraussHard(beta, gamma = 1, R = 0, H = 0, W = owin())
}
\arguments{
\item{beta}{
intensity parameter (a positive number).
}
\item{gamma}{
interaction parameter (a number between 0 and 1, inclusive).
}
\item{R}{
interaction radius (a non-negative number).
}
\item{H}{
hard core distance (a non-negative number smaller than \code{R}).
}
\item{W}{
window (object of class \code{"owin"}) in which to
generate the random pattern. Currently this must be a rectangular
window.
}
}
\details{
This function generates a realisation of the
Strauss-Hardcore point process in the window \code{W}
using a \sQuote{perfect simulation} algorithm.
The Strauss-Hardcore process is described in \code{\link{StraussHard}}.
The simulation algorithm used to generate the point pattern
is \sQuote{dominated coupling from the past}
as implemented by Berthelsen and Moller (2002, 2003).
This is a \sQuote{perfect simulation} or \sQuote{exact simulation}
algorithm, so called because the output of the algorithm is guaranteed
to have the correct probability distribution exactly (unlike the
Metropolis-Hastings algorithm used in \code{\link{rmh}}, whose output
is only approximately correct).
A limitation of the perfect simulation algorithm
is that the interaction parameter
\eqn{\gamma}{gamma} must be less than or equal to \eqn{1}.
To simulate a Strauss-hardcore process with
\eqn{\gamma > 1}{gamma > 1}, use \code{\link{rmh}}.
There is a tiny chance that the algorithm will
run out of space before it has terminated. If this occurs, an error
message will be generated.
}
\value{
A point pattern (object of class \code{"ppp"}).
}
\references{
Berthelsen, K.K. and Moller, J. (2002)
A primer on perfect simulation for spatial point processes.
\emph{Bulletin of the Brazilian Mathematical Society} 33, 351-367.
Berthelsen, K.K. and Moller, J. (2003)
Likelihood and non-parametric Bayesian MCMC inference
for spatial point processes based on perfect simulation and
path sampling.
\emph{Scandinavian Journal of Statistics} 30, 549-564.
Moller, J. and Waagepetersen, R. (2003).
\emph{Statistical Inference and Simulation for Spatial Point Processes.}
Chapman and Hall/CRC.
}
\author{
Kasper Klitgaard Berthelsen and Adrian Baddeley
\email{Adrian.Baddeley@csiro.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
}
\examples{
Z <- rStraussHard(100,0.7,0.05,0.02)
}
\seealso{
\code{\link{rmh}},
\code{\link{rStrauss}},
\code{\link{StraussHard}}.
}
\keyword{spatial}
\keyword{datagen}
Computing file changes ...