R: Simulate a Fitted Point Process Model on a Linear Network

simulate.lppm {spatstat.linnet} | R Documentation

## 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 class 'lppm'
    simulate(object, nsim=1, ...,
                             new.coef=NULL,
                             progress=(nsim > 1),
                             drop=FALSE)

### 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.

### 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`.

### 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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

, Rolf Turner r.turner@auckland.ac.nz

and Ege Rubak rubak@math.aau.dk

### See Also

`lppm`, `rpoislpp`, `simulate`

### Examples

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

[Package spatstat.linnet version 2.0-0 Index]