# rSwitzerlpp: Switzer-type Point Process on Linear Network In spatstat.linnet: Linear Networks Functionality of the 'spatstat' Family

## Description

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

## Usage

 ```1 2``` ```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"`).

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## References

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

`rcelllpp`
 ```1 2 3``` ``` plot(rSwitzerlpp(domain(spiders), 0.01, rate=100)) plot(rSwitzerlpp(domain(spiders), 0.0005, rate=100, cuts="l")) ```