fit.variogram.gls: GLS fitting of variogram parameters

In gstat: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

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.

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 and Probability Letters, 43, 93-98.

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

See Also

fit.variogram,

Examples

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)

gstat documentation built on Sept. 11, 2024, 7:46 p.m.