Hi 

I have a set of two dimensional data( measured on a grid).  Initial
analysis strongly suggests that there is directional anisotropy, so I've
been trying to deal with this.

One way I've found is to fit the variogram model by maximum likelihood
(or REML) using geoR.  geoR seems to account for anisotropy by deforming
the locations, according to the anisotropy parameters (direction of
maximum range, and the anisotropy ratio).  Cross--validation suggests
that this performs well, relative to fitting various models to
isotropic sample variogram.

The thing is, I want to use gstat for kriging.  The reason for this is
that I want to do block kriging.  However, gstat seems to account for
anisotropy by adjusting the variogram range according to the anisotropy
parameters.  

So it seems that geoR deforms the coordinates, whereas gstat
"deforms" the variogram....

The upshot of this is that, when I use gstat to do punctual kriging
using the parameters estimated in geoR, I get different results to what
I get when I use geoR for kriging.  A possible alternative, is to use
geoR to predict the deformed locations, and then use gstat to do the
kriging WITHOUT specifying the anisotropy parameter.  If I do this then
gstat and geoR give the same results for kriging.  This suggests that
there is no difference in the algorithms used for kriging between the
two packages.  This approach seems to be OK for doing punctual
kriging...  However, I want to do block kriging (square blocks).  The
approach I've adopted for punctual kriging seems to fall over
here.  This is because I would specify the block size (in gstat) in the
deformed coordinate space, whereas I actually want the blocks to be
specified in the original geographic coordinate space.  Am I making
sense?

What a mess!!!  Can any one suggest an alternative, or is this all a bit
silly?

cheers

Nick



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