variogmultiv: Function to compute multivariate empirical variogram.

Description Usage Arguments Value Author(s) References Examples

Description

Compute a multivariate empirical variogram. It is strictly equivalent to summing univariate variograms.

Usage

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variogmultiv(Y,xy, dmin=0,dmax=max(dist(xy)),nclass=20)

Arguments

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.

Value

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.

Author(s)

Stephane Dray

References

Wagner H. H. (2003) Spatial covariance in plant communities: integrating ordination, geostatistics, and variance testing. Ecology 84, 1045–1057.

Examples

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data(oribatid)
fau <- sqrt(oribatid$fau/outer(apply(oribatid$fau,1,sum),rep(1,ncol(oribatid$fau)),"*")) # Hellinger transformation
faudt <- resid(lm(as.matrix(fau)~as.matrix(oribatid$xy))) # Removing linear effect
mvspec<-variogmultiv(faudt,oribatid$xy,nclass=20)
mvspec
plot(mvspec$d,mvspec$var,ty='b',pch=20,xlab="Distance", ylab="C(distance)")

spacemakeR documentation built on May 31, 2017, 2:24 a.m.