locmincon {spatstat.local}

Locally Fitted Cluster or Cox Point Process Model

Description

Fits a Neyman-Scott cluster process or Cox point process model using local minimum contrast.

Usage

locmincon(..., sigma = NULL, f = 1/4, verbose = TRUE,
                 localstatargs = list(), LocalStats = NULL,
                 tau = NULL)

Arguments

...

Arguments passed to kppm to determine the template homogeneous model.

sigma

Standard deviation of Gaussian kernel for local likelihood.

f

Argument passed to bw.frac to compute a value for sigma if it is missing or NULL.

verbose

Logical. If TRUE, print progress reports.

localstatargs

Optional. List of arguments to be passed to the local statistic localK, localKinhom, localpcf or localpcfinhom.

LocalStats

Optional. Values of the local statistics, if they have already been computed.

tau

Optional. Bandwidth for smoothing the fitted cluster parameters.

Details

The template or homogeneous model is first fitted by kppm. The statistic used to fit the template model is determined (as explained in the help for kppm) by the arguments statistic and trend.

The local version of this statistic is then computed. If statistic="K" and trend=~1 for example, the template model is fitted using the K function Kest, and the local version is the local K function localK. The possibilities are:

statistic	stationary?	template	local
"K"	yes	Kest	localK
"K"	no	Kinhom	localKinhom
"pcf"	yes	pcf	localpcf
"pcf"	no	pcfinhom	localpcfinhom

These local functions, one for each data point, are then spatially averaged, using a Gaussian kernel with standard deviation sigma. Finally the model is fitted to each of the averaged local functions to obtain a local fit at each data point.

Value

Object of class "locmincon".

Author(s)

Adrian Baddeley [e-mail].

References

Baddeley, A. (2017) Local composite likelihood for spatial point patterns. Spatial Statistics 22, 261–295. DOI: 10.1016/j.spasta.2017.03.001

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

See Also

loccit

Examples

   X <- redwood[owin(c(0,1), c(-1,-1/2))]
   fit <- locmincon(X, ~1, "Thomas", sigma=0.07)
   fit