GLS fitting of variogram parameters

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Description

Fits variogram parameters (nugget, sill, range) to variogram cloud, using GLS (generalized least squares) fitting. Only for direct variograms.

Usage

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fit.variogram.gls(formula, data, model, maxiter = 30, 
		eps = .01, trace = TRUE, ignoreInitial = TRUE, cutoff = Inf,
		plot = FALSE)

Arguments

formula

formula defining the response vector and (possible) regressors; in case of absence of regressors, use e.g. z~1

data

object of class Spatial

model

variogram model to be fitted, output of vgm

maxiter

maximum number of iterations

eps

convergence criterium

trace

logical; if TRUE, prints parameter trace

ignoreInitial

logical; if FALSE, initial parameter are taken from model; if TRUE, initial values of model are ignored and taken from variogram cloud: nugget: mean(y)/2, sill: mean(y)/2, range median(h0)/4 with y the semivariance cloud value and h0 the distances

cutoff

maximum distance up to which point pairs are taken into consideration

plot

logical; if TRUE, a plot is returned with variogram cloud and fitted model; else, the fitted model is returned.

Value

an object of class "variogramModel"; see fit.variogram; if plot is TRUE, a plot is returned instead.

Note

Inspired by the code of Mihael Drinovac, which was again inspired by code from Ernst Glatzer, author of package vardiag.

Author(s)

Edzer Pebesma

References

Mueller, W.G., 1999: Least-squares fitting from the variogram cloud. Statistics \& Probability Letters, 43, 93-98.

Mueller, W.G., 2007: Collecting Spatial Data. Springer, Heidelberg.

See Also

fit.variogram,

Examples

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library(sp)
data(meuse)
coordinates(meuse) = ~x+y
## Not run: 
fit.variogram.gls(log(zinc)~1, meuse[1:40,], vgm(1, "Sph", 900,1))

## End(Not run)

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