Computes the matrix of logratio variograms.
logratioVariogram(data, ...)
# S3 method for 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,
...
)
A list of class "logratioVariogram"
.
A nbins x D x D array containing the logratio variograms
A nbins x D x D array containing the mean distance the value is computed on.
A nbins x D x D array containing the number of nonmissing pairs used for the corresponding value.
an acomp compositional dataset
arguments for generic functionality
a matrix or dataframe providing the observation locations of the compositions. Any number of dimension >= 2 is supported.
the maximum distance to compute the variogram for.
The number of distance bins to compute the variogram for
The distances seperating the 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.
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.
The angular tolerance it should be below 90 if a directional variogram is intended.
do not use, only provided for backwards compatibility. Use data
instead
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
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.
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
vgram2lrvgram
,
CompLinModCoReg
,
vgmFit
if (FALSE) {
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)
}
Run the code above in your browser using DataLab