bw.ppl(X, ..., srange=NULL, ns=16, sigma=NULL, weights=NULL)"ppp").
ns and srange.
X.
Argument passed to density.ppp.
"bw.optim"
which can be plotted.
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 $\lambda[-i](x_i)$ is the leave-one-out kernel-smoothing estimate of the intensity at $x[i]$ with smoothing bandwidth $\sigma$, and $\lambda(u)$ 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 $LCV(\sigma)$ 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.
density.ppp,
bw.diggle,
bw.scott
b <- bw.ppl(redwood)
plot(b, main="Likelihood cross validation for redwoods")
plot(density(redwood, b))
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