lohboot(X,
fun=c("pcf", "Kest", "Lest", "pcfinhom", "Kinhom", "Linhom"),
..., nsim=200, confidence=0.95, global=FALSE, type=7)"ppp")."pcf", "Kest", "Lest",
"pcfinhom", "Kinhom" or "Linhom".FALSE (the default), pointwise confidence intervals
are constructed. If TRUE, a global (simultaneous) confidence band is
constructed.quantile
controlling the way the quantiles are calculated."fv")
containing columns giving the estimate of the summary function,
the upper and lower limits of the bootstrap confidence interval,
and the theoretical value of the summary function for a Poisson process.fun using the bootstrap method of Loh (2008). If fun="pcf", for example, the algorithm computes a pointwise
(100 * confidence)% confidence interval for the true value of
the pair correlation function pcf for the point
process. It starts by computing the array of
local pair correlation functions,
localpcf, of the data pattern X.
This array consists of the contributions to pcf from each
data point. Then these contributions are resampled nsim times
with replacement; from each resampled dataset the total contribution
is computed, yielding nsim random pair correlation functions.
The pointwise alpha/2 and 1 - alpha/2 quantiles of
these functions are computed, where alpha = 1 - confidence.
To control the smoothing and estimation algorithm, use the
arguments ..., which are passed to the local version
of the summary function, as shown below:
pcf localpcf
Kest localK
Lest localK
pcfinhom localpcfinhom
Kinhom localKinhom
Linhom localKinhom
}
For fun="Lest", the calculations are first performed
as if fun="Kest", and then the square-root transformation is
applied to obtain the $L$-function.
An alternative to lohboot is varblock.
Kest,
pcf,
Kinhom,
pcfinhom,
localK,
localpcf,
localKinhom,
localpcfinhom. See varblock for an alternative bootstrap technique.
p <- lohboot(simdat, stoyan=0.5)
plot(p)Run the code above in your browser using DataLab