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.

Keywords: models, spatial

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 to list(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").

Examples

data(japanesepines)
  fit <- ppm(japanesepines, ~1, Strauss(0.1))
  simulate(fit, 2)

Documentation reproduced from package spatstat, version 1.16-2, License: GPL (>= 2)

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