is.stationary.ppm: Recognise Stationary and Poisson Point Process Models

In spatstat.core: Core Functionality of the 'spatstat' Family

Recognise Stationary and Poisson Point Process Models

Description

Given a point process model (either a model that has been fitted to data, or a model specified by its parameters), determine whether the model is a stationary point process, and whether it is a Poisson point process.

Usage

## 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 'dppm'
is.stationary(x)
## S3 method for class 'detpointprocfamily'
is.stationary(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 'interact'
is.poisson(x)

Arguments

x	A fitted spatial point process model (object of class "ppm", "kppm", "lppm", "dppm" or "slrm") or a specification of a Gibbs point process model (object of class "rmhmodel") or a 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)

\spatstatAuthors

.

See Also

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

summary.ppm for detailed information about a fitted model.

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)

spatstat.core documentation built on May 18, 2022, 9:05 a.m.