Recognise Stationary and Poisson Point Process Models on a Network
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.
## S3 method for class 'lppm' is.stationary(x) ## S3 method for class 'lppm' is.poisson(x)
x |
A fitted spatial point process model on a linear network
(object of class |
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.
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.
is.stationary,
is.poisson for generics.
summary.lppm for detailed information.
Model-fitting function
lppm.
fit <- lppm(spiders ~ x) is.stationary(fit) is.poisson(fit)
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