anisLinearKinhom: Inhomogeneous K-function accounting for directions

In Moradii/anisotropylpp: methods for (an)isotropic point patterns on linear networks

Description

Inhomogeneous K-function accounting for directions

Usage

anisLinearKinhom(
  X,
  lambda = lambda,
  normalize = TRUE,
  r = NULL,
  phi = NULL,
  maxphi = 360,
  edge = TRUE,
  nxy = 10,
  parallel = FALSE,
  verbose = FALSE,
  ...
)

Arguments

X	an object of class lpp
lambda	estimated intensity at data points. If not given, it will be automatically obtained from densityQuick.lpp
normalize	logical. whether to normalize the estimate. See linearKinhom
r	optional. distance vector wherein the K-funtion is evaluated
phi	optional. angle vector wherein the K-funtion is evaluated
maxphi	max angle. this will be used only when phi is not given
edge	optional. whether to use edge correction or not
nxy	dimension. this will be considered as the length of r and phi when they are not given
parallel	logical. if TRUE, it uses mclapply.
verbose	logical. if TRUE, it prints the progress of the function.
...	arguments passed to mclapply for configuration of the cores.

Details

This function calculates the inhomogeneous K-function over a grid of distances and angles.

Value

an object of class sumlpp

Note

this function can be quite slow, we suggest to use nnangle.lpp

Author(s)

Mehdi Moradi [e-mail]

References

Moradi, M., Mateu, J,. and Comas, C. (2020) Directional analysis for point patterns on linear networks. Stat.

See Also

linearKinhom

Examples

# generate random relaisations
X <- rpoislpp(0.001,branchnet)
K <- anisLinearKinhom(X)

Moradii/anisotropylpp documentation built on Oct. 22, 2020, 7:05 a.m.