spatstat.linnet (version 3.2-2)

simulate.lppm: Simulate a Fitted Point Process Model on a Linear Network

Description

Generates simulated realisations from a fitted Poisson point process model on a linear network.

Usage

# S3 method for lppm
simulate(object, nsim=1, ...,
                         new.coef=NULL,
                         progress=(nsim > 1),
                         drop=FALSE)

Value

A list of length nsim containing simulated point patterns (objects of class "lpp") on the same linear network as the original data used to fit the model. The result also belongs to the class "solist", so that it can be plotted, and the class "timed", so that the total computation time is recorded.

Arguments

object

Fitted point process model on a linear network. An object of class "lppm".

nsim

Number of simulated realisations.

progress

Logical flag indicating whether to print progress reports for the sequence of simulations.

new.coef

New values for the canonical parameters of the model. A numeric vector of the same length as coef(object).

...

Arguments passed to predict.lppm to determine the spatial resolution of the image of the fitted intensity used in the simulation.

drop

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

Author

Adrian Baddeley

, Rolf Turner

and Ege Rubak

Details

This function is a method for the generic function simulate for the class "lppm" of fitted point process models on a linear network.

Only Poisson process models are supported so far.

Simulations are performed by rpoislpp.

See Also

lppm, rpoislpp, simulate

Examples

  fit <- lppm(unmark(chicago) ~ y)
  simulate(fit)[[1]]