spatstat (version 1.17-0)

kppm: Fit cluster point process model

Description

Fit a homogeneous or inhomogeneous cluster point process model to a point pattern.

Usage

kppm(X, trend = ~1, clusters = "Thomas", covariates = NULL, ...)

Arguments

X

Point pattern (object of class "ppp") to which the model should be fitted.

trend

An Rformula, with no left hand side, specifying the form of the log intensity.

clusters

Character string determining the cluster model. Partially matched. Options are "Thomas" and "MatClust".

covariates

The values of any spatial covariates (other than the Cartesian coordinates) required by the model. A named list of pixel images.

...

Arguments passed to thomas.estK or matclust.estK controlling the minimum contrast fitting algorithm.

Value

• An object of class "kppm" representing the fitted model. There are methods for printing, plotting, predicting, simulating and updating objects of this class.

Details

This function fits a cluster point process model to the point pattern dataset X.

The algorithm first estimates the intensity function of the point process, by fitting a Poisson process with log intensity of the form specified by the formula trend. Then the inhomogeneous $K$ function is estimated using the fitted intensity. Finally the parameters of the cluster model are estimated by the method of minimum contrast using the inhomogeneous $K$ function.

Currently the only options for the cluster mechanism are clusters="Thomas" for the Thomas process and clusters="MatClust" for the Matern cluster process.

References

Waagepetersen, R. (2006). An estimation function approach to inference for inhomogeneous Neyman-Scott processes. Submitted.

See Also

plot.kppm, predict.kppm, simulate.kppm, update.kppm, thomas.estK, matclust.estK, mincontrast, Kinhom, ppm

Examples

data(redwood)
  kppm(redwood, ~1, "Thomas")
  kppm(redwood, ~x, "MatClust")