Simulate a Fitted Point Process Model on a Linear Network
Generates simulated realisations from a fitted Poisson point process model on a linear network.
## S3 method for class 'lppm': simulate(object, nsim=1, ..., new.coef=NULL, progress=(nsim > 1), drop=FALSE)
- Fitted point process model on a linear network.
An object of class
- Number of simulated realisations.
- Logical flag indicating whether to print progress reports for the sequence of simulations.
- New values for the canonical parameters of the model.
A numeric vector of the same length as
- Arguments passed to
predict.lppmto determine the spatial resolution of the image of the fitted intensity used in the simulation.
- Logical. If
drop=TRUE, the result will be a point pattern, rather than a list containing a point pattern.
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
- A list of length
nsimcontaining 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.
fit <- lppm(unmark(chicago) ~ y) simulate(fit)[]