R: Recognise Stationary and Poisson Point Process Models

is.stationary {spatstat.core} | R Documentation

## Recognise Stationary and Poisson Point Process Models

### Description

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

### Usage

    is.stationary(x)
    ## S3 method for class 'ppm'
    is.stationary(x)
    ## S3 method for class 'kppm'
    is.stationary(x)
    ## S3 method for class 'slrm'
    is.stationary(x)
    ## S3 method for class 'rmhmodel'
    is.stationary(x)
    ## S3 method for class 'dppm'
    is.stationary(x)
    ## S3 method for class 'detpointprocfamily'
    is.stationary(x)

    is.poisson(x)
    ## S3 method for class 'ppm'
    is.poisson(x)
    ## S3 method for class 'kppm'
    is.poisson(x)
    ## S3 method for class 'slrm'
    is.poisson(x)
    ## S3 method for class 'rmhmodel'
    is.poisson(x)
    ## S3 method for class 'interact'
    is.poisson(x)

### Arguments

`x` | A fitted spatial point process model (object of class `"ppm"`, `"kppm"`, `"dppm"` or `"slrm"`) or similar object.

### Details

The argument `x` represents a fitted spatial point process model or a similar object.

`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 the classes `"ppm"` (Gibbs point process models), `"kppm"` (cluster or Cox point process models), `"slrm"` (spatial logistic regression models) and `"rmhmodel"` (model specifications for the Metropolis-Hastings algorithm). Additionally `is.stationary` has a method for classes `"detpointprocfamily"` and `"dppm"` (both determinantal point processes) and `is.poisson` has a method for class `"interact"` (interaction structures for Gibbs models).

`is.poisson.kppm` will return `FALSE`, unless the model `x` is degenerate: either `x` has zero intensity so that its realisations are empty with probability 1, or it is a log-Gaussian Cox process where the log intensity has zero variance.

`is.poisson.slrm` will always return `TRUE`, by convention.

### 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.

`summary.ppm` for detailed information.

Model-fitting functions `ppm`, `dppm`, `kppm`, `slrm`.

### Examples

      fit <- ppm(cells ~ x)
      is.stationary(fit)
      is.poisson(fit)

      fut <- kppm(redwood ~ 1, "MatClust")
      is.stationary(fut)
      is.poisson(fut)

      fot <- slrm(cells ~ x)
      is.stationary(fot)
      is.poisson(fot)

[Package spatstat.core version 2.3-1 Index]