

   VVaarriiooggrraamm MMooddeell FFiitt

        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)

   AArrgguummeennttss::

   variogram.obj: a variogram object generated by `est.vari
             ogram()'

   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 proce
             dure to execute.

   tolerance: the tolerance used to determine if model conver
             gence 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)

   DDeessccrriippttiioonn::

        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, `param
        eters', and a semi-variogram model function, `model'.

   VVaalluuee::

        A variogram.model object:

   parameters: vector of fitted model parameters

      model: function implementing a valid variogram model

   NNoottee::

        `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 regres
        sion is weighted by n(h)/gamma(h)^2 where the numerator
        is the number of pairs of points in a given lag.

   RReeffeerreenncceess::

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

   SSeeee AAllssoo::

        `est.variogram'

   EExxaammpplleess::

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

