kp.fun: Multiscale second-order neighbourhood analysis of a...

In ads: Spatial Point Patterns Analysis

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

Description

(Formerly ki.fun) Computes a set of K12-functions between all possible marks p and the other marks in a multivariate spatial point pattern defined in a simple (rectangular or circular) or complex sampling window (see Details).

Usage

kp.fun(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 kp.fun is simply a wrapper to k12fun, which computes K12(r) between each mark p of the pattern and all other marks grouped together (the j points).

Value

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

r	a vector of regularly spaced distances (seq(by,upto,by)).
labp	a vector containing the levels i of p$marks.
gp.	a data frame containing values of the pair density function g12(r).
np.	a data frame containing values of the local neighbour density function n12(r).
kp.	a data frame containing values of the K12(r) function.
lp.	a data frame containing values of the modified L12(r) function.

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 hypothesis of 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, kpqfun.

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)
  kp.swrm <- kp.fun(swrm, 25, 1)
  plot(kp.swrm)
  
 ## 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)
  kp.swcm <- kp.fun(swcm, 25, 1)
  plot(kp.swcm)
  
  ## Not run: multivariate spatial point pattern in a complex sampling window
  swrtm <- spp(BP$trees, win=BP$rect, tri=BP$tri2, marks=BP$species)
  kp.swrtm <- kp.fun(swrtm, 25, 1)
  plot(kp.swrtm)

ads documentation built on May 29, 2024, 4:13 a.m.