Description Maximum likelihood Least squares See Also Examples
Here, some details of RFfit
are given
concerning the fitting of models for Gaussian random fields.
This documentation is far from being complete.
The application of the usual maximum likelihood method and reporting the result is the default.
The weighted least squares methods minimize
∑_{i} w_i (\hat γ(h_i)  γ(h_i))^2
over all parametrized models of γ. Here, i runs over all N bins of the binned variogram \hat γ and h_i is the centre of bin i.
The following variants of the least squares methods,
passed as sub.methods
in RFfit
are implemented:
'self'
w_i = (γ(h_i))^{2}
'plain'
w_i = 1 for all i.
'sqrt.nr'
w_i^2 equals the number of points n_i in bin i.
'sd.inv'
1 / w_i equals the standard deviation of the variogram cloud within bin i.
'internal'
Three subvariants are implemented:
'internal1'
w_i^2 = (Ni+1) n_i
'internal2'
w_i = Ni+1
'internal3'
w_i^2 = Ni+1
1 2 3  RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
## See 'RFfit'.

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