# psib

0th

Percentile

##### Sibling Probability of Cluster Point Process

Computes the sibling probability of a cluster point process model.

Keywords
models, spatial
##### Usage
psib(object)  # S3 method for kppm
psib(object)
##### Arguments
object

Fitted cluster point process model (object of class "kppm").

##### Details

In a Poisson cluster process, two points are called siblings if they belong to the same cluster, that is, if they had the same parent point. If two points of the process are separated by a distance $r$, the probability that they are siblings is $p(r) = 1 - 1/g(r)$ where $g$ is the pair correlation function of the process.

The value $p(0) = 1 - 1/g(0)$ is the probability that, if two points of the process are situated very close to each other, they came from the same cluster. This probability is an index of the strength of clustering, with high values suggesting strong clustering.

This concept was proposed in Baddeley, Rubak and Turner (2015, page 479) and Baddeley (2016).

A single number.

##### References

Baddeley, A. (2016) Local composite likelihood for spatial point processes. Spatial Statistics, in press.

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

kppm

• psib
• psib.kppm
##### Examples
# NOT RUN {
fit <- kppm(redwood ~1, "Thomas")
psib(fit)
# }

Documentation reproduced from package spatstat, version 1.56-1, License: GPL (>= 2)

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