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. |

Stéphane Dray [email protected]

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 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.

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