spatstat (version 1.3-2)

rmh.ppm: Simulate from a Fitted Point Process Model

Description

Given a point process model fitted to data, generate a random simulation of the model, using the Metropolis-Hastings algorithm.

Usage

rmh.ppm(model,start,control,...)

Arguments

model

A fitted point process model (object of class "ppm", see ppm.object) which it is desired to simulate. This fitted model is usually the result of a call to

start

A list of arguments determining the initial state of the Metropolis-Hastings algorithm. See rmh.default for description of these arguments.

control

A list of arguments controlling the running of the Metropolis-Hastings algorithm. See rmh.default for description of these arguments.

...

Further arguments are ignored.

Value

• A point pattern (an object of class "ppp", see ppp.object).

Warnings

See Warnings in rmh.default

Details

This function generates simulated realisations from a point process model that has been fitted to point pattern data. It is a method for the generic function rmh for the class "ppm" of fitted point process models. To simulate other kinds of point process models, see rmh or rmh.default. The argument model describes the fitted model. It must be an object of class "ppm" (see ppm.object) and will typically be the result of a call to the point process model fitting function mpl.

The current implementation is experimental, and only a few processes can be simulated. At present the fitted model must not have any spatial trend, and the only models possible are the Poisson, Strauss, Strauss/Hard Core, Soft Core, and Geyer interactions. These are fitted by mpl using Poisson, Strauss, StraussHard, Softcore and Geyer respectively. See the examples.

The arguments start and control are lists of parameters determining the initial state and the iterative behaviour, respectively, of the Metropolis-Hastings algorithm. They are passed directly to rmh.default. See rmh.default for details of these parameters.

After extracting the relevant information from the fitted model object model, rmh.ppm simply invokes the default rmh algorithm rmh.default.

See rmh.default for further information about the implementation, or about the Metropolis-Hastings algorithm.

See Also

rmh, rmh.default, ppp.object, mpl, Poisson, Strauss, StraussHard, Softcore, Geyer

Examples

require(spatstat)
   data(swedishpines)
   X <- swedishpines
   plot(X, main="Swedish Pines data")

   fit <- mpl(X, ~1, Strauss(r=7), rbord=7)
   Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
   plot(Xsim, main="simulation from fitted Strauss model")

   fit <- mpl(X, ~1, StraussHard(r=7,hc=2), rbord=7)
   Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
   plot(Xsim, main="simulation from fitted Strauss hard core model")

   fit <- mpl(X, ~1, Geyer(r=7,sat=2), rbord=7)
   Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
   plot(Xsim, main="simulation from fitted Geyer model")

   fit <- mpl(X, ~1, Softcore(kappa=0.1))
   Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
   plot(Xsim, main="simulation from fitted Soft Core model")