"rmh"(model, start=NULL, control=default.rmhcontrol(model, w=w), ..., w = NULL, project=TRUE, nsim=1, drop=TRUE, saveinfo=TRUE, verbose=TRUE, new.coef=NULL)"ppm", see ppm.object) which it is desired
to simulate. This fitted model is usually the result of a call
to ppm. See Details below.
rmhstart for description of these arguments.
Defaults to list(x.start=data.ppm(model))
rmhcontrol
for description of these arguments.
rmhcontrol,
or to rmh.default, or to covariate functions in the model.
project=TRUE the closest valid model will be simulated;
if project=FALSE an error will occur.
nsim=1 and drop=TRUE (the default), the
result will be a point pattern, rather than a list
containing a single point pattern.
coef(model).
"ppp"; see
ppp.object) or a list of point patterns.
rmh.default.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 ppm.
The current implementation enables simulation from any fitted model
involving the interactions
AreaInter,
DiggleGratton,
DiggleGatesStibbard,
Geyer,
Hardcore,
MultiStrauss,
MultiStraussHard,
PairPiece,
Poisson,
Strauss,
StraussHard
and Softcore,
including nonstationary models. See the examples.
It is also possible to simulate hybrids of several such models.
See Hybrid and the examples.
It is possible that the fitted coefficients of a point process model
may be ``illegal'', i.e. that there may not exist a
mathematically well-defined point process with the given parameter
values. For example, a Strauss process with interaction
parameter $gamma > 1$ does not exist,
but the model-fitting procedure used in ppm will sometimes
produce values of $gamma$ greater than 1.
In such cases, if project=FALSE then an error will occur,
while if project=TRUE then rmh.ppm will find
the nearest legal model and simulate
this model instead. (The nearest legal model is obtained by
projecting the vector of coefficients onto the set of
valid coefficient vectors. The result is usually the Poisson process
with the same fitted intensity.)
The arguments start and control are lists of
parameters determining the initial state and the iterative
behaviour, respectively, of the Metropolis-Hastings algorithm.
The argument start is passed directly to rmhstart.
See rmhstart for details of the parameters of the
initial state, and their default values.
The argument control is first passed to
rmhcontrol. Then if any additional arguments ...
are given, update.rmhcontrol is called to update the
parameter values. See rmhcontrol for details of
the iterative behaviour parameters, and default.rmhcontrol
for their default values.
Note that if you specify expansion of the simulation window
using the parameter expand (so that the
model will be simulated on a window larger than the original data
window) then the model must be capable of extrapolation to this
larger window. This is usually not possible for models which
depend on external covariates, because the domain of a covariate image
is usually the same as the domain of the fitted model.
After extracting the relevant information from the fitted model
object model, rmh.ppm invokes the default
rmh algorithm rmh.default, unless the model
is Poisson. If the model is Poisson then the Metropolis-Hastings
algorithm is not needed, and the model is simulated directly, using
one of rpoispp, rmpoispp,
rpoint or rmpoint.
See rmh.default for further information about the
implementation, or about the Metropolis-Hastings algorithm.
simulate.ppm,
rmh,
rmhmodel,
rmhcontrol,
default.rmhcontrol,
update.rmhcontrol,
rmhstart,
rmh.default,
ppp.object,
ppm, Interactions:
AreaInter,
DiggleGratton,
DiggleGatesStibbard,
Geyer,
Hardcore,
Hybrid,
MultiStrauss,
MultiStraussHard,
PairPiece,
Poisson,
Strauss,
StraussHard,
Softcore
live <- interactive()
op <- spatstat.options()
spatstat.options(rmh.nrep=1e5)
Nrep <- 1e5
X <- swedishpines
if(live) plot(X, main="Swedish Pines data")
# Poisson process
fit <- ppm(X, ~1, Poisson())
Xsim <- rmh(fit)
if(live) plot(Xsim, main="simulation from fitted Poisson model")
# Strauss process
fit <- ppm(X, ~1, Strauss(r=7))
Xsim <- rmh(fit)
if(live) plot(Xsim, main="simulation from fitted Strauss model")
## Not run:
# # Strauss process simulated on a larger window
# # then clipped to original window
# Xsim <- rmh(fit, control=list(nrep=Nrep, expand=1.1, periodic=TRUE))
# Xsim <- rmh(fit, nrep=Nrep, expand=2, periodic=TRUE)
# ## End(Not run)
## Not run:
# X <- rSSI(0.05, 100)
# # piecewise-constant pairwise interaction function
# fit <- ppm(X, ~1, PairPiece(seq(0.02, 0.1, by=0.01)))
# Xsim <- rmh(fit)
# ## End(Not run)
# marked point pattern
Y <- amacrine
## Not run:
# # marked Poisson models
# fit <- ppm(Y)
# fit <- ppm(Y,~marks)
# fit <- ppm(Y,~polynom(x,2))
# fit <- ppm(Y,~marks+polynom(x,2))
# fit <- ppm(Y,~marks*polynom(x,y,2))
# Ysim <- rmh(fit)
# ## End(Not run)
# multitype Strauss models
MS <- MultiStrauss(radii=matrix(0.07, ncol=2, nrow=2),
types = levels(Y$marks))
## Not run:
# fit <- ppm(Y ~marks, MS)
# Ysim <- rmh(fit)
# ## End(Not run)
fit <- ppm(Y ~ marks*polynom(x,y,2), MS)
Ysim <- rmh(fit)
if(live) plot(Ysim, main="simulation from fitted inhomogeneous Multitype Strauss")
spatstat.options(op)
## Not run:
# # Hybrid model
# fit <- ppm(redwood, ~1, Hybrid(A=Strauss(0.02), B=Geyer(0.1, 2)))
# Y <- rmh(fit)
# ## End(Not run)
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