rmhmodel.Rd
\name{rmhmodel}
\alias{rmhmodel}
\title{Define Point Process Model for Metropolis-Hastings Simulation.}
\description{
Builds a description of a point process model
for use in simulating the model by the Metropolis-Hastings
algorithm.
}
\usage{
rmhmodel(...)
}
\arguments{
\item{\dots}{Arguments specifying the point process model
in some format.
}
}
\value{
An object of class \code{"rmhmodel"}, which is essentially
a list of parameter values for the model.
There is a \code{print} method for this class, which prints
a sensible description of the model chosen.
}
\details{
Simulated realisations of many point process models
can be generated using the Metropolis-Hastings algorithm
\code{\link{rmh}}. The algorithm requires the model to be specified
in a particular format: an object of class \code{"rmhmodel"}.
The function \code{\link{rmhmodel}} takes a
description of a point process model in some other format, and
converts it into an object of class \code{"rmhmodel"}.
It also checks that the parameters of the model are valid.
The function \code{\link{rmhmodel}} is generic, with methods
for
\describe{
\item{fitted point process models:}{
an object of class \code{"ppm"}, obtained by a call to the
model-fitting function \code{\link{ppm}}.
See \code{\link{rmhmodel.ppm}}.
}
\item{lists:}{
a list of parameter values in a certain format.
See \code{\link{rmhmodel.list}}.
}
\item{default:}{
parameter values specified as separate arguments to \code{\dots}.
See \code{\link{rmhmodel.default}}.
}
}
}
\references{
Diggle, P. J. (2003) \emph{Statistical Analysis of Spatial Point
Patterns} (2nd ed.) Arnold, London.
Diggle, P.J. and Gratton, R.J. (1984)
Monte Carlo methods of inference for implicit statistical models.
\emph{Journal of the Royal Statistical Society, series B}
\bold{46}, 193 -- 212.
Diggle, P.J., Gates, D.J., and Stibbard, A. (1987)
A nonparametric estimator for pairwise-interaction point processes.
Biometrika \bold{74}, 763 -- 770.
\emph{Scandinavian Journal of Statistics} \bold{21}, 359--373.
Geyer, C.J. (1999)
Likelihood Inference for Spatial Point
Processes. Chapter 3 in O.E. Barndorff-Nielsen, W.S. Kendall and
M.N.M. Van Lieshout (eds) \emph{Stochastic Geometry: Likelihood and
Computation}, Chapman and Hall / CRC, Monographs on Statistics and
Applied Probability, number 80. Pages 79--140.
}
\seealso{
\code{\link{rmhmodel.ppm}},
\code{\link{rmhmodel.default}},
\code{\link{rmhmodel.list}},
\code{\link{rmh}},
\code{\link{rmhcontrol}},
\code{\link{rmhstart}},
\code{\link{ppm}},
\code{\link{Strauss}},
\code{\link{Softcore}},
\code{\link{StraussHard}},
\code{\link{MultiStrauss}},
\code{\link{MultiStraussHard}},
\code{\link{DiggleGratton}},
\code{\link{PairPiece}}
}
\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{datagen}