Gaussian Weighted Sum of Pairwise Vectors — Kest

Estimate K function -type summary for second order reweighted ("inhomogeneous") pattern using a Gaussian kernel sitting in the origin of the Fry-plot.

Usage

Kest_gaussian(
  x,
  u,
  kappa,
  r,
  lambda = NULL,
  lambda_h,
  renormalise = TRUE,
  border = 1,
  ...
)

Arguments

x: pp, list with $x~coordinates $bbox~bounding box
u: unit vector(s) of direction, as row vectors. Default: x and y axes, viz. c(1,0) and c(0,1). This gives the major axis of the ellipsoid (direction of largest variance in the Gaussian density).
kappa: The ratio of the secondary axis to the main axis going along 'u'.
r: radius vector at which to evaluate. Corresponds to the radii of the 95% quantile in x-axis, before rotation in directions u.
lambda: optional vector of intensity estimates at points
lambda_h: if lambda missing, use this bandwidth in a kernel estimate of lambda(x)
renormalise: See details.
border: Use border correction? Default=1, yes. At the moment no other version available!
...: passed on to e.g. intensity_at_points

Value

Returns a dataframe.

Details

TODO Gaussian kernel sitting at the fry-space, sum over fry-points.