simulate.kppm
Simulate a Fitted Cluster Point Process Model
Generates simulated realisations from a fitted cluster point process model.
Usage
# S3 method for kppm
simulate(object, nsim = 1, seed=NULL, ...,
window=NULL, covariates=NULL, verbose=TRUE, retry=10,
drop=FALSE)
Arguments
- object
Fitted cluster point process model. An object of class
"kppm"
.- nsim
Number of simulated realisations.
- seed
an object specifying whether and how to initialise the random number generator. Either
NULL
or an integer that will be used in a call toset.seed
before simulating the point patterns.- …
Additional arguments passed to the relevant random generator. See Details.
- window
Optional. Window (object of class
"owin"
) in which the model should be simulated.- covariates
Optional. A named list containing new values for the covariates in the model.
- verbose
Logical. Whether to print progress reports (when
nsim > 1
).- retry
Number of times to repeat the simulation if it fails (e.g. because of insufficient memory).
- drop
Logical. If
nsim=1
anddrop=TRUE
, the result will be a point pattern, rather than a list containing a point pattern.
Details
This function is a method for the generic function
simulate
for the class "kppm"
of fitted
cluster point process models.
Simulations are performed by
rThomas
,
rMatClust
,
rCauchy
,
rVarGamma
or rLGCP
depending on the model.
Additional arguments …
are passed to the relevant function
performing the simulation.
For example the argument saveLambda
is recognised by all of the
simulation functions.
The return value is a list of point patterns.
It also carries an attribute "seed"
that
captures the initial state of the random number generator.
This follows the convention used in
simulate.lm
(see simulate
).
It can be used to force a sequence of simulations to be
repeated exactly, as shown in the examples for simulate
.
Value
A list of length nsim
containing simulated point patterns
(objects of class "ppp"
).
The return value also carries an attribute "seed"
that
captures the initial state of the random number generator.
See Details.
See Also
kppm
,
rThomas
,
rMatClust
,
rCauchy
,
rVarGamma
,
rLGCP
,
simulate.ppm
,
simulate
Examples
# NOT RUN {
fit <- kppm(redwood ~1, "Thomas")
simulate(fit, 2)
fitx <- kppm(redwood ~x, "Thomas")
simulate(fitx, 2)
# }