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kp.fun: Multiscale second-order neigbourhood 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).

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).

encoding

latin1

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).

See Also

plot.fads, spp, kfun, k12fun, kpqfun.

Examples

data(BPoirier)
  BP <- BPoirier
  # 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)
  
 # 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)
  
  # 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)