spatstat (version 1.52-1)

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

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

Arguments

x

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

Value

A logical value.

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.

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, lppm, slrm.

Examples

Run this code
# NOT RUN {
  data(cells)
  data(redwood)

  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)
# }

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