kval {ads} | R Documentation |
Computes local second-order neighbour density estimates for an univariate spatial point pattern, i.e. the number of neighbours per unit area within sample circles of regularly increasing radii r, centred at each point of the pattern (see Details).
kval(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 kval
returns individual values of K(r) and associated functions (see kfun
)
estimated for each point of the pattern. For a given distance r, these values can be mapped within the sampling window
(Getis & Franklin 1987, P?Pelissier & Goreaud 2001).
A list of class c("vads","kval")
with essentially the following components:
r |
a vector of regularly spaced out distances ( |
xy |
a data frame with 2 components giving (x,y) coordinates of points of the pattern. |
gval |
a matrix of size (length(xy),length(r)) giving individual values of the pair density function g(r). |
nval |
a matrix of size (length(xy),length(r)) giving individual values of the neighbour density function n(r). |
kval |
a matrix of size (length(xy),length(r)) giving individual values of Ripley's function K(r). |
lval |
a matrix of size (length(xy),length(r)) giving individual values the modified Ripley's function L(r). |
Function kval
ignores the marks of multivariate and marked point patterns (they are all considered to be univariate patterns).
There are printing, summary and plotting methods for "vads"
objects.
Getis, A. and Franklin, J. 1987. Second-order neighborhood analysis of mapped point patterns. Ecology, 68:473-477.
P?Pelissier, R. and Goreaud, F. 2001. A practical approach to the study of spatial structure in simple cases of heterogeneous vegetation. Journal of Vegetation Science, 12:99-108.
plot.vads
,
kfun
,
dval
,
k12val
.
data(BPoirier) BP <- BPoirier ## Not run: spatial point pattern in a rectangle sampling window of size [0,110] x [0,90] swr <- spp(BP$trees, win=BP$rect) kvswr <- kval(swr, 25, 1) summary(kvswr) plot(kvswr) ## Not run: spatial point pattern in a circle with radius 50 centred on (55,45) swc <- spp(BP$trees, win=c(55,45,45)) kvswc <- kval(swc, 25, 1) summary(kvswc) plot(kvswc) ## Not run: spatial point pattern in a complex sampling window swrt <- spp(BP$trees, win=BP$rect, tri=BP$tri1) kvswrt <- kval(swrt, 25, 1) summary(kvswrt) plot(kvswrt)