compositions (version 2.0-0)

variograms: Variogram functions

Description

Valid scalar variogram model functions.

Usage

vgram.sph( h , nugget = 0, sill = 1, range= 1,... )
vgram.exp( h , nugget = 0, sill = 1, range= 1,... )
vgram.gauss( h , nugget = 0, sill = 1, range= 1,... )
vgram.cardsin( h , nugget = 0, sill = 1, range= 1,... )
vgram.lin( h , nugget = 0, sill = 1, range= 1,... )
vgram.pow( h , nugget = 0, sill = 1, range= 1,... )
vgram.nugget( h , nugget = 1,...,tol=1E-8 )

Arguments

h

a vector providing distances, a matrix of distance vectors in its rows or a data.frame of distance vectors.

nugget

The size of the nugget effect (i.e. the limit to 0). At zero itself the value is always 0.

sill

The sill (i.e. the limit to infinity)

range

The range parameter. I.e. the distance in which sill is reached or if this does not exist, where the value is in some sense nearly the sill.

…

not used

tol

The distance that is considered as nonzero.

Value

A vector of size NROW(h), giving the variogram values.

Details

The univariate variograms are used in the CompLinCoReg as building blocks of multivariate variogram models.

• sphSpherical variogram

• expExponential variogram

• gaussThe Gaussian variogram.

• gaussThe cardinal sine variogram.

• linLinear Variogram. Increases over the sill, which is reached at range.

• powThe power variogram. Increases over the sill, which is reached at range.

• nuggetThe pure nugget effect variogram.

References

Cressie, N.C. (1993) Spatial statistics

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

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)
# }