rmh.ppm
Simulate from a Fitted Point Process Model
Given a point process model fitted to data, generate a random simulation of the model, using the Metropolis-Hastings algorithm.
- Keywords
- spatial
Usage
rmh.ppm(model,start,control,...)Arguments
- model
- A fitted point process model
(object of class
"ppm", seeppm.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.defaultfor description of these arguments. - control
- A list of arguments controlling the
running of the Metropolis-Hastings algorithm.
See
rmh.defaultfor description of these arguments. - ...
- Further arguments are ignored.
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.
Value
- A point pattern (an object of class
"ppp", seeppp.object).
Warnings
See Warnings in rmh.default
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")