simulate.ppm
From spatstat v1.16-2
by Adrian Baddeley
Simulate a Fitted Gibbs Point Process Model
Generates simulated realisations from a fitted Gibbs or Poisson point process model.
Usage
## S3 method for class 'ppm':
simulate(object, nsim=1, ...,
start = NULL,
control = rmhcontrol(),
project=TRUE,
verbose=FALSE, progress=TRUE)
Arguments
- object
- Fitted point process model.
An object of class
"ppm"
. - nsim
- Number of simulated realisations.
- start
- Data determining the initial state
of the Metropolis-Hastings algorithm. See
rmhstart
for description of these arguments. Defaults tolist(x.start=data.ppm(model))
- control
- Data controlling the running of
the Metropolis-Hastings algorithm. See
rmhcontrol
for description of these arguments. - ...
- Ignored.
- verbose
- Logical flag indicating whether to print progress reports
from
rmh.ppm
during the simulation of each point pattern. - progress
- Logical flag indicating whether to print progress reports for the sequence of simulations.
- project
- Logical flag indicating what to do if the fitted model is
invalid (in the sense that the values of the fitted coefficients do not
specify a valid point process).
If
project=TRUE
the closest valid model will be simulated; if
Details
This function is a method for the generic function
simulate
for the class "ppm"
of fitted
point process models.
Simulations are performed by rmh.ppm
.
Value
- A list of length
nsim
containing simulated point patterns (objects of class"ppp"
).
See Also
Examples
data(japanesepines)
fit <- ppm(japanesepines, ~1, Strauss(0.1))
simulate(fit, 2)
Community examples
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