View source: R/pcf_anin_conical.R
pcf_anin | R Documentation |
Estimate a sector/cone pcf function for second order reweighted ("inhomogeneous") pattern.
pcf_anin( x, u, epsilon, r, lambda = NULL, lambda_h, r_h, stoyan = 0.15, renormalise = TRUE, border = 1, divisor = "d", ... ) pcf_anin_conical( x, u, epsilon, r, lambda = NULL, lambda_h, r_h, stoyan = 0.15, renormalise = TRUE, border = 1, divisor = "d", ... )
x |
pp, list with $x~coordinates $bbox~bounding box |
u |
unit vector(s) of direction, as row vectors. Default: x and y axis. |
epsilon |
Central half angle for the directed sector/cone (total angle of the rotation cone is 2*epsilon) |
r |
radius vector at which to evaluate the function |
lambda |
optional vector of intensity estimates at points |
lambda_h |
if lambda missing, use this bandwidth in a kernel estimate of lambda(x) |
r_h |
smoothing for range dimension, epanechnikov kernel |
stoyan |
If r_h not given, use r_h=stoyan/lambda^(1/dim). Same as 'stoyan' in spatstat's pcf. |
renormalise |
See details. |
border |
Use translation correction? Default=1, yes. Only for cuboidal windows. |
divisor |
See spatstat's pcf.ppp for this. |
... |
passed on to e.g. intensity_at_points |
Computes a second order reweighted version of the sector-pcf. The sector pcf differs from the true anisotropic pcf by assuming that the anisotropic pcf is constant over the small arc/cap of the sector, thus averaging over that data-area and providing more stable estimates.
Lambda(x) at points can be given, or else it will be estimated using kernel smoothing. See intensity_at_points.
If 'renormalise=TRUE', we normalise the lambda estimate so that sum(1/lambda(x))=|W|. This corresponds in spatstat
's Kinhom
to setting 'normpower=2'.
Returns a dataframe.
pcf_anin_cylinder
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