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.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.
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")