markstat (version 0.1.5)

lmcorr.ppp: Local mark correlation functions for homogeneous point patterns on Euclidean spaces.

Description

Local mark correlation functions for homogeneous point patterns on Euclidean spaces.

Usage

# S3 method for ppp
lmcorr(X,
ftype = c("variogram", "stoyan", "rcorr", "shimatani", "beisbart",
 "isham", "stoyancov", "schlather"),
r = NULL,
method = c("density","loess"),
normalise = TRUE,
f = NULL,
tol = 0.01,
...)

Value

A data.frame where the first column contains the values of the argument \(r\) at which the mark correlation function is evaluated, and the remaining columns contain the estimated values of the mark correlation function for each data point; column names correspond to the IDs of the data points. If there are multiple numeric marks, the result will instead be a list, with each element corresponding to one of the marks.

Arguments

X

An object of class ppp.

ftype

Type of the test function \(t_f\). Currently any selection of "variogram", "stoyan", "rcorr", "shimatani", "beisbart", "isham", "stoyancov", "schlather".

r

Optional. The values of the argument \(r\) at which the mark correlation function should be evaluated.

method

Type of smoothing, either density or loess. See details.

normalise

If normalise=FALSE, only the numerator of the expression for the mark correlation function will be computed.

f

Optional. Test function \(t_f\) used in the definition of the mark correlation function. If ftype is given, \(t_f\) should be NULL.

tol

Tolerance used in the calculation of the conditional mean of the marks. This is used only if ftype is schlather.

...

Arguments passed to unnormdensity or loess.

Author

Mehdi Moradi and Matthias Eckardt

Details

This function computes local mark correlation functions for a stationary point pattern in \(\mathbb{R}^2\). See the details of test functions used in mcorr.ppp. Technical details are given in Eckardt and Moradi (2025).

References

Eckardt, M., & Moradi, M. (2025). Local indicators of mark association for marked spatial point processes.

See Also

mcorr.ppp.

Examples

 library(spatstat.geom)
 library(spatstat.random)
 X <- rpoispp(100)
 marks(X) <- runif(npoints(X),10,11)
 lmcorr.ppp(X, ftype = "stoyan", method = "density")