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 Adrian.Baddeley@curtin.edu.au.

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 for spatial point patterns. Spatial Statistics, In press. 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.

Examples

Run this code

  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)

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