kpqfun {ads}  R Documentation 
(Formerly kijfun
) Computes a set of K and K12functions 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).
kpqfun(p, upto, by)
p 
a 
upto 
maximum radius of the sample circles (see Details). 
by 
interval length between successive sample circles radii (see 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.
A list of class "fads"
with essentially the following components:
r 
a vector of regularly spaced distances ( 
labpq 
a vector containing the (p,q) paired levels of 
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 
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 
There are printing and plotting methods for "fads"
objects.
plot.fads
,
spp
,
kfun
,
k12fun
,
kp.fun
.
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)