kpqfun {ads}R Documentation

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)

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.

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).

Note

There are printing and plotting methods for "fads" objects.

Author(s)

Raphael.Pelissier@ird.fr

See Also

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

Examples

  data(BPoirier)
  BP <- BPoirier
 ## Not run: multivariate spatial point pattern in a rectangle sampling window
  swrm <- spp(BP$trees, win=BP$rect, marks=BP$species)
  kpqswrm <- kpqfun(swrm, 25, 1)
  plot(kpqswrm)
  
 ## Not run: multivariate spatial point pattern in a circle with radius 50 centred on (55,45)
  swcm <- spp(BP$trees, win=c(55,45,45), marks=BP$species)
  kpqswcm <- kpqfun(swcm, 25, 1)
  plot(kpqswcm)
  
  ## Not run: multivariate spatial point pattern in a complex sampling window
  swrtm <- spp(BP$trees, win=BP$rect, tri=BP$tri2, marks=BP$species)
  kpqswrtm <- kpqfun(swrtm, 25, 1)
  plot(kpqswrtm)


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