indicator kriging with R

Hi Dafne and Edzer,

I too have been looking into gstat indicator kriging and simulation. My 
interest is because indicator simulations can (at least in GSLib) 
account for systems where extreme values are more/less spatially 
correlated than median values; that is the upper and lower threshold 
variograms can have a very different range to the mean values.

However, following 
http://rstudio-pubs-static.s3.amazonaws.com/10216_57cb0ffbb2e94586b29dfb1849dd49f0.html, 
I've just realised that gstat indicator kriging requires all indicator 
variograms to have an intrinsic correlation or be a linear model of 
coregionalisation, and hence requiring each variogram to have at least 
an equal range (FYI, to test this change line 'meuse.fit = fit.lmc(x, 
meuse.i)' to 'meuse.fit = fit.lmc(x, meuse.i,fit.ranges=TRUE)'). This is 
most likely because, unlike GSLib, gstat uses cokriging but this 
limitation defeats one of the advantages of indicator simulation over 
Gaussian simulation.

Anyway, can anyone propose a work around to this limitation? The only 
solution I can see is to krige each threshold independently but this 
does eliminate the option of accounting for cross-correlations between 
the thresholds or more advanced indicator feature; such as including 
spatially varying constraints eg 
http://www.mssanz.org.au/modsim2011/I9/peterson.pdf

Cheers

Tim


On 22/10/15 20:12, Dafne Berg wrote:

Dear Edzer,
yes it is exactly what I meant. Thanks for the confirmation that it is not
yet implemented in gstat. I will have a look to gslib and the book.

Thanks a lot

Dafne

On 22 October 2015 at 09:38, Edzer Pebesma <edzer.pebesma at uni-muenster.de>
wrote:

Yes, this is what indicator kriging does.

What you want is probably going back from the [0..1] estimated
probabilities to the original Z values. Gstat doesn't help here, but it
shouldn't be too hard to write a function for this. You need to make
assumptions about the distribution between different cutoff values, in
particular about both extreme (open) classes. I think the GSLIB and
Goovaerts books would be the first ones to look into.

On 22/10/15 10:19, Dafne Berg wrote:

Dear list,
I am trying to do a indicator kriging of a continuous variable. I am
especially interested in performing the "order relation correction". In

the

documentation of "predict" in gstat it is mentioned as debug level 64
(order relation violations (indicator kriging values before and after

order

relation correction).

However, when running predict I can only get the values in the range 0 to
1, as expected for indicator kriging.

I am probably missing something very basic here, but I will be grateful

for

any pointers in the right direction

Thank you very much in advance

Best wishes

Dafne

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--
Edzer Pebesma
Institute for Geoinformatics (ifgi),  University of M?nster,
Heisenbergstra?e 2, 48149 M?nster, Germany; +49 251 83 33081
Journal of Statistical Software:   http://www.jstatsoft.org/
Computers & Geosciences:   http://elsevier.com/locate/cageo/
Spatial Statistics Society http://www.spatialstatistics.info

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