R: Recognise Stationary and Poisson Point Process Models on a...

is.stationary.lppm {spatstat.linnet} | R Documentation

## Recognise Stationary and Poisson Point Process Models on a Network

### Description

Given a point process model that has been fitted to data on a network, determine whether the model is a stationary point process, and whether it is a Poisson point process.

### Usage

    ## S3 method for class 'lppm'
    is.stationary(x)

    ## S3 method for class 'lppm'
    is.poisson(x)

### Arguments

`x` | A fitted spatial point process model on a linear network (object of class `"lppm"`).

### Details

The argument `x` represents a fitted spatial point process model on a linear network.

`is.stationary(x)` returns `TRUE` if `x` represents a stationary point process, and `FALSE` if not.

`is.poisson(x)` returns `TRUE` if `x` represents a Poisson point process, and `FALSE` if not.

The functions `is.stationary` and `is.poisson` are generic, with methods for many classes of models.

### Value

A logical value.

### 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

`is.marked` to determine whether a model is a marked point process.

`is.stationary`, `is.poisson` for generics.

`summary.lppm` for detailed information.

Model-fitting function `lppm`.

### Examples

      fit <- lppm(spiders ~ x)
      is.stationary(fit)
      is.poisson(fit)

[Package spatstat.linnet version 2.0-0 Index]