# variogmultiv: Function to compute multivariate empirical variogram In adespatial: Multivariate Multiscale Spatial Analysis

## Description

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

## Usage

 `1` ```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)

Stéphane Dray [email protected]

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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)") } ```

adespatial documentation built on Sept. 27, 2018, 5:04 p.m.