Recognise Stationary and Poisson Point Process Models
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.
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)
x |
A fitted spatial point process model
(object of class |
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.
A logical value.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
is.marked to determine whether a model is a marked
point process.
summary.ppm for detailed information.
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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