# Knet: Geometrically-Corrected K Function on Network In spatstat.Knet: Extension to 'spatstat' for Large Datasets on a Linear Network

## Description

Compute the geometrically-corrected K function for a point pattern on a linear network.

## Usage

 1 Knet(X, r = NULL, freq, ..., verbose=FALSE)

## Arguments

 X Point pattern on a linear network (object of class "lpp"). r Optional. Numeric vector of values of the function argument r. There is a sensible default. freq Vector of frequencies corresponding to the point events on the network. The length of this vector should be equal to the number of points on the network. The default frequency is one for every point on the network. ... Ignored. verbose A logical for printing iteration number corresponding to each point event on the network.

## Details

This command computes the geometrically-corrected K function, proposed by Ang et al (2012), from point pattern data on a linear network. The algorithm used in this computation is discussed in Rakshit et al (2019).

The spatstat function linearK is usable (and slightly faster) for the same purpose for small datasets, but requires substantial amounts of memory. For larger datasets, the function Knet is much more efficient.

## Value

Function value table (object of class "fv").

## Author(s)

Suman Rakshit (modified by Adrian Baddeley)

## References

Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591–617.

Rakshit, S., Baddeley, A. and Nair, G. (2019) Efficient code for second order analysis of events on a linear network. Journal of Statistical Software 90 (1) 1–37. DOI: 10.18637/jss.v090.i01

## Examples

 1 2 3 UC <- unmark(chicago) r <- seq(0, 1000, length = 41) K <- Knet(UC, r = r)

spatstat.Knet documentation built on Aug. 9, 2019, 5:07 p.m.