Kest.fft(X, sigma, r=NULL, ..., breaks=NULL)"ppp", or data
in any format acceptable to as.ppp().
as.mask determining the
spatial resolution for the FFT calculation.
"fv" (see fv.object).Essentially a data frame containing columnsKest
for estimating the $K$ function. It may be useful for
very large patterns of points. Whereas Kest computes the distance between
each pair of points analytically, this function discretises the
point pattern onto a rectangular pixel raster and applies
Fast Fourier Transform techniques to estimate $K(t)$.
The hard work is done by the function Kmeasure.
The result is an approximation whose accuracy depends on the
resolution of the pixel raster. The resolution is controlled
by the arguments ..., or by setting the parameter npixel in
spatstat.options.
Diggle, P.J. Statistical analysis of spatial point patterns. Academic Press, 1983.
Ohser, J. (1983) On estimators for the reduced second moment measure of point processes. Mathematische Operationsforschung und Statistik, series Statistics, 14, 63 -- 71. Ripley, B.D. Statistical inference for spatial processes. Cambridge University Press, 1988.
Stoyan, D, Kendall, W.S. and Mecke, J. (1995) Stochastic geometry and its applications. 2nd edition. Springer Verlag.
Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
Kest,
Kmeasure,
spatstat.options
pp <- runifpoint(10000)
Kpp <- Kest.fft(pp, 0.01)
plot(Kpp)
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