compositions (version 1.10-1)

logratioVariogram: Empirical variograms for compositions

Description

Computes the matrix of logratio variograms.

Usage

logratioVariogram(comp,
                          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  
                          )

Arguments

comp

an acomp compositional dataset

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

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.

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.

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.

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

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)