logratioVariogram {compositions} | R Documentation |

## Empirical variograms for compositions

### Description

Computes the matrix of logratio variograms.

### Usage

```
logratioVariogram(data, ...)
## S3 method for class 'acomp'
logratioVariogram(data,
loc,
maxdist=max(dist(loc))/2,
nbins=20,
dists=seq(0,maxdist,length.out=nbins+1),
bins=cbind(dists[-length(dists)],dists[-1]),
azimuth=0,
azimuth.tol=180,
comp=data,
...
)
```

### Arguments

`data` |
an acomp compositional dataset |

`...` |
arguments for generic functionality |

`loc` |
a matrix or dataframe providing the observation locations of the compositions. Any number of dimension >= 2 is supported. |

`maxdist` |
the maximum distance to compute the variogram for. |

`nbins` |
The number of distance bins to compute the variogram for |

`dists` |
The distances seperating the bins |

`bins` |
a matrix with lower and upper limit for the distances of each bin. A pair is counted if min<h<=max. min and max are provided as columns. bins is computed from maxdist,nbins and dists. If it is provided, it is used directly. |

`azimuth` |
For directional variograms the direction, either as an azimuth angle (i.e. a single real number) for 2D datasets or a unit vector pointing of the same dimension as the locations. The angle is clockwise from North in degree. |

`azimuth.tol` |
The angular tolerance it should be below 90 if a directional variogram is intended. |

`comp` |
do not use, only provided for backwards compatibility. Use |

### Details

The logratio-variogram is the set of variograms of each of the pairwise logratios. It can be proven that it carries the same information as a usual multivariate variogram. The great advantage is that all the funcitions have a direct interpreation and can be estimated even with (MAR) missings in the dataset.

### Value

A list of class `"logratioVariogram"`

.

`vg` |
A nbins x D x D array containing the logratio variograms |

`h` |
A nbins x D x D array containing the mean distance the value is computed on. |

`n` |
A nbins x D x D array containing the number of nonmissing pairs used for the corresponding value. |

### Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

### References

Tolosana, van den Boogaart, Pawlowsky-Glahn (2009) Estimating and modeling variograms of compositional data with occasional missing variables in R, StatGis09

Pawlowsky-Glahn, Vera and Olea, Ricardo A. (2004) Geostatistical Analysis of Compositional Data, Oxford University Press, Studies in Mathematical Geology

### See Also

`vgram2lrvgram`

,
`CompLinModCoReg`

,
`vgmFit`

### Examples

```
## Not run:
data(juraset)
X <- with(juraset,cbind(X,Y))
comp <- acomp(juraset,c("Cd","Cu","Pb","Co","Cr"))
lrv <- logratioVariogram(comp,X,maxdist=1,nbins=10)
plot(lrv)
## End(Not run)
```

*compositions*version 2.0-8 Index]