R: Variogram Model Fit

Variogram Model Fit

Usage

fit.variogram(type="exponential", ...)
fit.exponential(variogram.obj, c0, ce, ae, type='c', iterations=10, tolerance=1e-06, plot.it=F, weighted=T)
fit.gaussian(variogram.obj, c0, cg, ag, type='c', iterations=10, tolerance=1e-06, plot.it=F, weighted=T)
fit.spherical(variogram.obj, c0, cs, as, type='c', delta=0.1, iterations=10, tolerance=1e-06, plot.it=F, weighted=T)
fit.wave(variogram.obj, c0, cw, aw, type='c', iterations=10, tolerance=1e-06, plot.it=F, weighted=T)
fit.linear(variogram.obj, plot.it=F)

Arguments

`variogram.obj`	a variogram object generated by `est.variogram()`
`c0, ce, ae`	initial estimates for the exponential variogram model
`c0, cg, ag`	initial estimates for the gaussian variogram model
`c0, cs, as`	initial estimates for the sperical variogram model
`c0, cw, aw`	initial estimates for the periodical variogram model
`type`	one of `'c'` (classic), `'r'` (robust), `'m'` (median). Indicates to which type of empirical variogram estimate the model is to be fit.
`iterations`	the number of iterations of the fitting procedure to execute.
`tolerance`	the tolerance used to determine if model convergence has been achieved.
`delta`	initial stepsize (relative) for pseudo Newton approximation, applies only to `fit.spherical`
`plot.it`	if T, the variogram estimate will be plotted each iteration.
`weighted`	if T, the fit will be done using weighted least squares, where the weightes are given in Cressie (1991, p. 99)

Description

Fit variogram models (exponential, gaussian, linear) to empirical variogram estimates.

An object of class variogram.model represents a fitted variogram model generated by fitting a function to a variogram object. A variogram.model object is composed of a list consisting of a vector of parameters, parameters, and a semi-variogram model function, model.

Value

A variogram.model object:

`parameters`	vector of fitted model parameters
`model`	function implementing a valid variogram model

Note

fit.exponential, fit.gaussian and fit.wave use an iterative, Gauss-Newton fitting algorithm to fit to an exponential or gaussian variogram model to empirical variogram estimates. fit.spherical uses the same algorithm but with differential quotients in place of first derivatives. When weighted is T, the regression is weighted by n(h)/gamma(h)^2 where the numerator is the number of pairs of points in a given lag.

References

http://www.gis.iastate.edu/SGeoStat/homepage.htm

Examples

maas.vmod<-fit.gaussian(maas.v,c0=60000,cg=110000,ag=800,plot.it=T)

[Package Contents]