kpqfun {ads}

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)

[e-mail]

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)