bw.CvLHeat: Bandwidth Selection for Diffusion Smoother by Cronie-van...

In spatstat.core: Core Functionality of the 'spatstat' Family

Bandwidth Selection for Diffusion Smoother by Cronie-van Lieshout Rule

Description

Selects an optimal bandwidth for diffusion smoothing using the Cronie-van Lieshout rule.

Usage

bw.CvLHeat(X, ..., srange=NULL, ns=16, sigma=NULL,
         leaveoneout=TRUE, verbose = TRUE)

Arguments

X	Point pattern (object of class "ppp").
...	Arguments passed to densityHeat.ppp.
srange	Numeric vector of length 2 specifying a range of bandwidths to be considered.
ns	Integer. Number of candidate bandwidths to be considered.
sigma	Maximum smoothing bandwidth. A numeric value, or a pixel image, or a function(x,y). Alternatively a numeric vector containing a sequence of candidate bandwidths.
leaveoneout	Logical value specifying whether intensity values at data points should be estimated using the leave-one-out rule.
verbose	Logical value specifying whether to print progress reports.

Details

This algorithm selects the optimal global bandwidth for kernel estimation of intensity for the dataset X using diffusion smoothing densityHeat.ppp.

If sigma is a numeric value, the algorithm finds the optimal bandwidth tau <= sigma.

If sigma is a pixel image or function, the algorithm finds the optimal fraction 0 < f <= 1 such that smoothing with f * sigma would be optimal.

Value

A numerical value giving the selected bandwidth (if sigma was a numeric value) or the selected fraction of the maximum bandwidth (if sigma was a pixel image or function). The result also belongs to the class "bw.optim" which can be plotted.

Author(s)

Adrian Baddeley.

See Also

bw.pplHeat for an alternative method.

densityHeat.ppp

Examples

  online <- interactive()
  if(!online) op <- spatstat.options(npixel=32)
  f <- function(x,y) { dnorm(x, 2.3, 0.1) * dnorm(y, 2.0, 0.2) }
  X <- rpoint(15, f, win=letterR)
  plot(X)
  b <- bw.CvLHeat(X, sigma=0.25)
  b
  plot(b)
  if(!online) spatstat.options(op)

spatstat.core documentation built on May 18, 2022, 9:05 a.m.