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kpqfun: Multiscale second-order neighbourhood analysis of a multivariate spatial point pattern

Description

(Formerly kijfun) Computes a set of K- and K12-functions for all possible pairs of marks (p,q) in a multivariate spatial point pattern defined in a simple (rectangular or circular) or complex sampling window (see Details).

Usage

kpqfun(p, upto, by)

Value

A list of class "fads" with essentially the following components:

r

a vector of regularly spaced distances (seq(by,upto,by)).

labpq

a vector containing the (p,q) paired levels of p$marks.

gpq

a data frame containing values of the pair density functions g(r) and g12(r).

npq

a data frame containing values of the local neighbour density functions n(r) and n12(r).

kpq

a data frame containing values of the K(r) and K12(r) functions.

lpq

a data frame containing values of the modified L(r) and L12(r) functions.

Each component except r is a data frame with the following variables:

obs

a vector of estimated values for the observed point pattern.

theo

a vector of theoretical values expected under the null hypotheses of spatial randomness (see kfun) and population independence (see k12fun).

Arguments

p

a "spp" object defining a multivariate spatial point pattern in a given sampling window (see spp).

upto

maximum radius of the sample circles (see Details).

by

interval length between successive sample circles radii (see Details).

Details

Function kpqfun is simply a wrapper to kfun and k12fun, which computes either K(r) for points of mark p when p=q or K12(r) between the marks p and q otherwise.

See Also

plot.fads, spp, kfun, k12fun, kp.fun.