spatstat (version 1.64-1)

bw.voronoi: Cross Validated Bandwidth Selection for Voronoi Estimator of Intensity on a Network

Description

Uses cross-validation to select a smoothing bandwidth for the Voronoi estimate of point process intensity on a linear network.

Usage

bw.voronoi(X, …, probrange = c(0.2, 0.8), nprob = 10,
           prob = NULL, nrep = 100, verbose = TRUE, warn=TRUE)

Arguments

X

Point pattern on a linear network (object of class "lpp").

…

Ignored.

probrange

Numeric vector of length 2 giving the range of bandwidths (retention probabilities) to be assessed.

nprob

Integer. Number of bandwidths to be assessed.

prob

Optional. A numeric vector of bandwidths (retention probabilities) to be assessed. Entries must be probabilities between 0 and 1. Overrides nprob and probrange.

nrep

Number of simulated realisations to be used for the computation.

verbose

Logical value indicating whether to print progress reports.

warn

Logical. If TRUE, issue a warning if the maximum of the cross-validation criterion occurs at one of the ends of the search interval.

Value

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

Details

This function uses likelihood cross-validation to choose the optimal value of the thinning fraction f (the retention probability) to be used in the smoothed Voronoi estimator of point process intensity densityVoronoi.lpp.

References

Moradi, M., Cronie, 0., Rubak, E., Lachieze-Rey, R., Mateu, J. and Baddeley, A. (2019) Resample-smoothing of Voronoi intensity estimators. Statistics and Computing, in press.

See Also

densityVoronoi.lpp

Examples

# NOT RUN {
   np <- if(interactive()) 10 else 3
   nr <- if(interactive()) 100 else 2
   b <- bw.voronoi(spiders, nprob=np, nrep=nr)
   b
   plot(b)
# }