GLS fitting of variogram parameters

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

Usage

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

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.

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

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

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

Edzer Pebesma

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

library(sp)
data(meuse)
coordinates(meuse) = ~x+y
if (FALSE) {
fit.variogram.gls(log(zinc)~1, meuse[1:40,], vgm(1, "Sph", 900,1))
}