Leonidas,
I see that you are not happy with the output, but it is not so clear what you
actually expect to see.
If you use stackApply directly, the indices are used in the names. Layer 1 and
8 belong to the group with index 4. It is the first group in the list of
indexes, so the first layer o
Hi Frederico,
gstat does not have different behaviour, because autofitVariogram is using
fit.variogram, after some preprocessing. You get the convergency problems with
fit.variogram because you don't supply start values for the variogram, which is
done in the preprocessing of autofitVariogram.
Frederico,
I don't think you need to worry about this warning in this case.
autofitVariogram tests a lot of variogram models (and different kappa-values)
in the search for the best one, and then fit.variogram tries to optimize the
parameters based on these. It seems the warnings were generate
Stefano,
Fixing the nugget at zero for the Gaussian varigoram can often cause numerical
problems in kriging. If you have two observations that are close in space,
their modelled semivariance will be so small that you get numerical issues with
the covariance matrix. In some cases it will be sing
