## S3 method for class 'ppm':
rmh(model,start=NULL,
control=rmhcontrol(),
..., verbose=TRUE, project=TRUE)"ppm", see ppm.object) which it is desired
to simulate. This fitted model is usually the result of a call
to rmhstart for description of these arguments.
Defaults to list(x.start=data.ppm(model))rmhcontrol
for description of these arguments.rmh.default.project=TRUE the closest valid model will be simulated;
if "ppp"; see
ppp.object).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
DiggleGratton,
Geyer,
MultiStrauss,
MultiStraussHard,
PairPiece,
Poisson,
Strauss,
StraussHard
and Softcore,
including nonstationary models. See 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.
They are passed directly to rmhstart and
rmhcontrol respectively.
See rmhstart and
rmhcontrol for details of these parameters.
Note that if you specify control$expand > 1 (so that the
model will be simulated on a window larger than the original data
window) then the model must be capable of extrapolation to this
larger window. This excludes models which depend on external covariates.
After extracting the relevant information from the fitted model
object model, rmh.ppm simply invokes the default
rmh algorithm rmh.default, unless the model
is Poisson.
If the model is Poisson then the Metropolis-Hastings
algorithm is not needed, and the model is simulated directly, using
one of rpoispp, rmpoispp,
rpoint or rmpoint.
See rmh.default for further information about the
implementation, or about the Metropolis-Hastings algorithm.
simulate.ppm,
rmh,
rmhmodel,
rmhcontrol,
rmhstart,
rmh.default,
ppp.object,
ppm,
PairPiece,
Poisson,
Strauss,
StraussHard,
Softcore,
Geyer,
AreaInter,
DiggleGrattondata(swedishpines)
X <- swedishpines
plot(X, main="Swedish Pines data")
# Poisson process
fit <- ppm(X, ~1, Poisson())
Xsim <- rmh(fit)
plot(Xsim, main="simulation from fitted Poisson model")
# Strauss process
fit <- ppm(X, ~1, Strauss(r=7))
Xsim <- rmh(fit, control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted Strauss model")
# Strauss process simulated on a larger window
# then clipped to original window
Xsim <- rmh(fit, control=list(nrep=1e3, expand=2, periodic=TRUE))
# Strauss - hard core process
fit <- ppm(X, ~1, StraussHard(r=7,hc=2))
Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted Strauss hard core model")
# Geyer saturation process
fit <- ppm(X, ~1, Geyer(r=7,sat=2))
Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted Geyer model")
# Area-interaction process
fit <- ppm(X, ~1, AreaInter(r=7))
Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted area-interaction model")
# soft core interaction process
Q <- quadscheme(X, nd=50)
fit <- ppm(Q, ~1, Softcore(kappa=0.1), correction="isotropic")
Xsim <- rmh(fit, start=list(n.start=X$n), control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted Soft Core model")
data(cells)
plot(cells)
# Diggle-Gratton pairwise interaction model
fit <- ppm(cells, ~1, DiggleGratton(0.05, 0.1))
Xsim <- rmh(fit, start=list(n.start=cells$n), control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted Diggle-Gratton model")
X <- rSSI(0.05, 100)
plot(X, main="new data")
# piecewise-constant pairwise interaction function
fit <- ppm(X, ~1, PairPiece(seq(0.02, 0.1, by=0.01)))
Xsim <- rmh(fit, control=list(nrep=1e3))
plot(Xsim, main="simulation from fitted pairwise model")
# marked point pattern
data(amacrine)
Y <- amacrine
plot(Y, main="Amacrine data")
# marked Poisson models
fit <- ppm(Y)
Ysim <- rmh(fit)
plot(Ysim, main="simulation from ppm(Y)")
fit <- ppm(Y,~marks)
Ysim <- rmh(fit)
plot(Ysim, main="simulation from ppm(Y, ~marks)")
fit <- ppm(Y,~polynom(x,y,2))
Ysim <- rmh(fit)
plot(Ysim, main="simulation from ppm(Y, ~polynom(x,y,2))")
fit <- ppm(Y,~marks+polynom(x,y,2))
Ysim <- rmh(fit)
plot(Ysim, main="simulation from ppm(Y, ~marks+polynom(x,y,2))")
fit <- ppm(Y,~marks*polynom(x,y,2))
Ysim <- rmh(fit)
plot(Ysim, main="simulation from ppm(Y, ~marks*polynom(x,y,2))")
# multitype Strauss models
MS <- MultiStrauss(types = levels(Y$marks),
radii=matrix(0.07, ncol=2, nrow=2))
fit <- ppm(Y, ~marks, MS)
Ysim <- rmh(fit, control=list(nrep=1e3))
plot(Ysim, main="simulation from fitted Multitype Strauss")
fit <- ppm(Y,~marks*polynom(x,y,2), MS)
Ysim <- rmh(fit, control=list(nrep=1e3))
plot(Ysim, main="simulation from fitted inhomogeneous Multitype Strauss")Run the code above in your browser using DataLab