bw.CvL | R Documentation |
Uses Cronie and van Lieshout's criterion based on Cambell's formula to select a smoothing bandwidth for the kernel estimation of point process intensity.
bw.CvL(X, ..., srange = NULL, ns = 16, sigma = NULL, warn=TRUE)
X |
A point pattern (object of class |
... |
Ignored. |
srange |
Optional numeric vector of length 2 giving the range of values of bandwidth to be searched. |
ns |
Optional integer giving the number of values of bandwidth to search. |
sigma |
Optional. Vector of values of the bandwidth to be searched.
Overrides the values of |
warn |
Logical. If |
This function selects an appropriate bandwidth sigma
for the kernel estimator of point process intensity
computed by density.ppp
.
The bandwidth σ is chosen to minimise the discrepancy between the area of the observation window and the sum of reciprocal estimated intensity values at the points of the point process
CvL(σ) = (|W| - sum[i] 1/λ(x[i]))^2
where the sum is taken over all the data points x[i], and where λ(x[i]) is the kernel-smoothing estimate of the intensity at x[i] with smoothing bandwidth σ.
The value of CvL(σ) is computed
directly, using density.ppp
,
for ns
different values of σ
between srange[1]
and srange[2]
.
The result is a numerical value giving the selected bandwidth.
The result also belongs to the class "bw.optim"
which can be plotted to show the bandwidth selection criterion
as a function of sigma
.
A numerical value giving the selected bandwidth.
The result also belongs to the class "bw.optim"
which can be plotted.
Ottmar Cronie ottmar.cronie@umu.se and Marie-Colette van Lieshout Marie-Colette.van.Lieshout@cwi.nl adapted for spatstat by \spatstatAuthors.
Cronie, O and Van Lieshout, M N M (2018) A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions, Biometrika, 105, 455-462.
density.ppp
,
bw.diggle
,
bw.scott
,
bw.ppl
,
bw.frac
.
if(interactive()) { b <- bw.CvL(redwood) b plot(b, main="Cronie and van Lieshout bandwidth criterion for redwoods") plot(density(redwood, b)) plot(density(redwood, bw.CvL)) }
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