R: Cross Validated Bandwidth Selection for Voronoi Estimator of...

bw.voronoi {spatstat.linnet} | R Documentation

## 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.

### 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`.

### Value

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

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk and Mehdi Moradi.

### 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

       np <- if(interactive()) 10 else 3
       nr <- if(interactive()) 100 else 2
       b <- bw.voronoi(spiders, nprob=np, nrep=nr)
       b
       plot(b)

[Package spatstat.linnet version 2.3-1 Index]