R: Switzer-type Point Process on Linear Network

rSwitzerlpp {spatstat} | R Documentation

## Switzer-type Point Process on Linear Network

### Description

Generate a realisation of the Switzer-type point process on a linear network.

### Usage

    rSwitzerlpp(L, lambdacut, rintens = rexp, ...,
             cuts=c("points", "lines"))

### Arguments

`L` | Linear network (object of class `"linnet"`).
`lambdacut` | Intensity of Poisson process of breakpoints.
`rintens` | Optional. Random variable generator used to generate the random intensity in each component.
`...` | Additional arguments to `rintens`.
`cuts` | String (partially matched) specifying the type of random cuts to be generated.

### Details

This function generates simulated realisations of the Switzer-type point process on a network, as described in Baddeley et al (2017).

The linear network is first divided into pieces by a random mechanism:

- if `cuts="points"`, a Poisson process of breakpoints with intensity `lambdacut` is generated on the network, and these breakpoints separate the network into connected pieces.

- if `cuts="lines"`, a Poisson line process in the plane with intensity `lambdacut` is generated; these lines divide space into tiles; the network is divided into subsets associated with the tiles. Each subset may not be a connected sub-network.

In each piece of the network, a random intensity is generated using the random variable generator `rintens` (the default is a negative exponential random variable with rate 1). Given the intensity value, a Poisson process is generated with the specified intensity.

The intensity of the final process is determined by the mean of the values generated by `rintens`. If `rintens=rexp` (the default), then the parameter `rate` specifies the inverse of the intensity.

### Value

Point pattern on a linear network (object of class `"lpp"`) with an attribute `"breaks"` containing the breakpoints (if `cuts="points"`) or the random lines (if `cuts="lines"`).

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au.

### References

Baddeley, A., Nair, G., Rakshit, S. and McSwiggan, G. (2017) ‘Stationary’ point processes are uncommon on linear networks. STAT 6, 68–78.

### See Also

`rcelllpp`

### Examples

       plot(rSwitzerlpp(domain(spiders), 0.01, rate=100))

       plot(rSwitzerlpp(domain(spiders), 0.0005, rate=100, cuts="l"))

[Package spatstat version 1.63-3 Index]