gstat: fit.variogram.gls – R documentation

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fit.variogram.gls

GLS fitting of variogram parameters

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

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.

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

Edzer Pebesma

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.

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)

Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

v2.0-7

GPL (>= 2.0)

Authors

Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Benedikt Graeler [aut]

Initial release