spatstat.local (version 5.1-0)

bw.loccit: Cross Validated Bandwidth Selection for Locally Fitted Point Process Model

Description

Uses cross-validation to select a smoothing bandwidth for locally fitting a Cox or cluster point process model.

Usage

bw.loccit(..., use.fft=TRUE, 
               srange = NULL, ns = 9, sigma = NULL,
               fftopt=list(), 
               verbose = TRUE)

Value

A numerical value giving the selected bandwidth. The result also belongs to the class "bw.optim"

which can be plotted.

Arguments

...

Arguments passed to kppm to fit the homogeneous version of the model.

use.fft

Logical value indicating whether to use a quick-and-dirty approximation based on a first order Taylor expansion.

srange

Range of values of the smoothing parameter sigma to be searched. A numeric vector of length 2 giving the minimum and maximum values of sigma.

ns

Number of values of the smoothing parameter sigma in the range srange to be searched. A positive integer.

sigma

Vector of values of the smoothing parameter to be searched.

fftopt

Developer use only.

verbose

Logical value indicating whether to display progress reports.

Author

Adrian Baddeley .

Details

This function determines the optimal value of the smoothing parameter sigma to be used in a call to loccit.

The function loccit fits a Cox or cluster point process model to point pattern data by local composite likelihood. The degree of local smoothing is controlled by a smoothing parameter sigma which is an argument to loccit.

For each value of sigma in a search interval, the function bw.loccit fits the model locally and evaluates a cross-validation criterion. The optimal value of sigma is returned.

References

Baddeley, A. (2017) Local composite likelihood

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))]
  Ns <- if(interactive()) 16 else 2
  b <- bw.loccit(X, ~1, "Thomas", srange=c(0.07, 0.14), ns=Ns)
  b
  plot(b)