Description Usage Arguments Value Author(s) References Examples

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

1 | ```
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. |

Stephane Dray

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

1 2 3 4 5 6 | ```
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.

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