gstat (version 2.0-3)

fit.variogram.gls: GLS fitting of variogram parameters

Description

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)

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.

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

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