rmh.ppm {spatstat} | R Documentation |
Given a point process model fitted to data, generate a random simulation of the model, using the Metropolis-Hastings algorithm.
rmh.ppm(model,start,control,...)
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 mpl .
See Details below.
|
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. |
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.
A point pattern (an object of class "ppp"
, see
ppp.object
).
See Warnings in rmh.default
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
rmh
,
rmh.default
,
ppp.object
,
mpl
,
Poisson
,
Strauss
,
StraussHard
,
Softcore
,
Geyer
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")