Fit cluster point process model
Fit a homogeneous or inhomogeneous cluster point process model to a point pattern.
kppm(X, trend = ~1, clusters = "Thomas", covariates = NULL, ...)
- Point pattern (object of class
"ppp") to which the model should be fitted.
- An Rformula, with no left hand side, specifying the form of the log intensity.
- Character string determining the cluster model.
- The values of any spatial covariates (other than the Cartesian coordinates) required by the model. A named list of pixel images.
- Arguments passed to
matclust.estKcontrolling the minimum contrast fitting algorithm.
This function fits a cluster point process model to the
point pattern dataset
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
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
clusters="Thomas" for the Thomas process
clusters="MatClust" for the Matern cluster process.
- An object of class
"kppm"representing the fitted model. There are methods for printing, plotting, predicting, simulating and updating objects of this class.
Waagepetersen, R. (2006). An estimation function approach to inference for inhomogeneous Neyman-Scott processes. Submitted.
data(redwood) kppm(redwood, ~1, "Thomas") kppm(redwood, ~x, "MatClust")