variogmultiv {adespatial} | R Documentation |
Compute a multivariate empirical variogram. It is strictly equivalent to summing univariate variograms
variogmultiv(Y, xy, dmin = 0, dmax = max(dist(xy)), nclass = 20)
Y |
A matrix with numeric data |
xy |
A matrix with coordinates of samples |
dmin |
The minimum distance value at which the variogram is computed (i.e. lower bound of the first class) |
dmax |
The maximum distance value at which the variogram is computed (i.e. higher bound of the last class) |
nclass |
Number of classes of distances |
A list:
d |
Distances (i.e. centers of distance classes). |
var |
Empirical semi-variances. |
n.w |
Number of connections between samples for a given distance. |
n.c |
Number of samples used for the computation of the variogram. |
dclass |
Character vector with the names of the distance classes. |
Stéphane Dray stephane.dray@univ-lyon1.fr
Wagner H. H. (2003) Spatial covariance in plant communities: integrating ordination, geostatistics, and variance testing. Ecology, 84, 1045–1057
if(require(ade4)){ data(oribatid) # Hellinger transformation fau <- sqrt(oribatid$fau / outer(apply(oribatid$fau, 1, sum), rep(1, ncol(oribatid$fau)), "*")) # Removing linear effect faudt <- resid(lm(as.matrix(fau) ~ as.matrix(oribatid$xy))) mvspec <- variogmultiv(faudt, oribatid$xy, nclass = 20) mvspec plot(mvspec$d, mvspec$var,type = 'b', pch = 20, xlab = "Distance", ylab = "C(distance)") }