Uses likelihood cross-validation to select a smoothing bandwidth for the kernel estimation of point process intensity.
bw.ppl(X, …, srange=NULL, ns=16, sigma=NULL, weights=NULL)A point pattern (object of class "ppp").
Ignored.
Optional numeric vector of length 2 giving the range of values of bandwidth to be searched.
Optional integer giving the number of values of bandwidth to search.
Optional. Vector of values of the bandwidth to be searched.
Overrides the values of ns and srange.
Optional. Numeric vector of weights for the points of X.
Argument passed to density.ppp.
A numerical value giving the selected bandwidth.
The result also belongs to the class "bw.optim"
which can be plotted.
This function selects an appropriate bandwidth sigma
for the kernel estimator of point process intensity
computed by density.ppp.
The bandwidth \(\sigma\) is chosen to maximise the point process likelihood cross-validation criterion $$ \mbox{LCV}(\sigma) = \sum_i \log\hat\lambda_{-i}(x_i) - \int_W \hat\lambda(u) \, {\rm d}u $$ where the sum is taken over all the data points \(x_i\), where \(\hat\lambda_{-i}(x_i)\) is the leave-one-out kernel-smoothing estimate of the intensity at \(x_i\) with smoothing bandwidth \(\sigma\), and \(\hat\lambda(u)\) is the kernel-smoothing estimate of the intensity at a spatial location \(u\) with smoothing bandwidth \(\sigma\). See Loader(1999, Section 5.3).
The value of \(\mbox{LCV}(\sigma)\) is computed
directly, using density.ppp,
for ns different values of \(\sigma\)
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 (rescaled) mean-square error
as a function of sigma.
Loader, C. (1999) Local Regression and Likelihood. Springer, New York.
# NOT RUN {
if(interactive()) {
b <- bw.ppl(redwood)
plot(b, main="Likelihood cross validation for redwoods")
plot(density(redwood, b))
}
# }
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