ads (version 1.5-5)

kp.fun: 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).

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).

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

# NOT RUN {
  data(BPoirier)
  BP <- BPoirier
  
# }
# NOT RUN {
multivariate spatial point pattern in a rectangle sampling window
# }
# NOT RUN {
  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)
# }
# NOT RUN {
  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
# }
# NOT RUN {
  swrtm <- spp(BP$trees, win=BP$rect, tri=BP$tri2, marks=BP$species)
  kp.swrtm <- kp.fun(swrtm, 25, 1)
  plot(kp.swrtm)
# }