spatstat (version 1.64-1)

simulate.kppm: Simulate a Fitted Cluster Point Process Model


Generates simulated realisations from a fitted cluster point process model.


# S3 method for kppm
simulate(object, nsim = 1, seed=NULL, ...,
         window=NULL, covariates=NULL, verbose=TRUE, retry=10,



Fitted cluster point process model. An object of class "kppm".


Number of simulated realisations.


an object specifying whether and how to initialise the random number generator. Either NULL or an integer that will be used in a call to set.seed before simulating the point patterns.

Additional arguments passed to the relevant random generator. See Details.


Optional. Window (object of class "owin") in which the model should be simulated.


Optional. A named list containing new values for the covariates in the model.


Logical. Whether to print progress reports (when nsim > 1).


Number of times to repeat the simulation if it fails (e.g. because of insufficient memory).


Logical. If nsim=1 and drop=TRUE, the result will be a point pattern, rather than a list containing a point pattern.


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.


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.

See Also

kppm, rThomas, rMatClust, rCauchy, rVarGamma, rLGCP, simulate.ppm, simulate


Run this code
  fit <- kppm(redwood ~1, "Thomas")
  simulate(fit, 2)
  fitx <- kppm(redwood ~x, "Thomas")
  simulate(fitx, 2)
# }

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