fit.variogram {sgeostat}R Documentation

Variogram Model Fit

Description

Fit variogram models (exponential, spherical, 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.

Usage

fit.variogram(model="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

model only available for fit.variogram, switches what kind of model should be fitted ("exponential", "wave", "gaussian", "spherical", "linear").
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)

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.html

See Also

est.variogram

Examples


maas.vmod<-fit.gaussian(maas.v,c0=60000,cg=110000,ag=800,plot.it=T,
iterations=30)

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