[R-sig-Geo] UK, KED or OCK? [SEC=UNCLASSIFIED]

[Jin.Li at ga.gov.au]

Hi Edzer,

It is true that demo(cokriging) shows that it is using ordinary cokriging,
but if we examine its formula, it kriges multiple primary variables without
secondary information. The ordinary co-kriging (OCK) I mentioned (such as
those defined in: Goovaerts, 1997. Geostatistics for Natural Resources
Evaluation.) interpolates one primary variable using one or more secondary
variables to improve the estimations. I guess that is the key difference.

In demo(examples), the multivariate kriging as shown in ex11.cmd claims it is
using ordinary cokriging, but it actually has no difference with that in
demo(cokriging), i.e. it kriges multiple primary variables without secondary
information.

I thought multiple kriging in ex10.cmd in demo(examples) was the one I was
after. After a further check, it is the ordinary kriging.  I hope this
explanation is clear enough. 

Thanks a lot for any further suggestions.

Best wishes,
Jin

-----Original Message-----
From: Edzer Pebesma [mailto:edzer.pebesma at uni-muenster.de] 
Sent: Monday, 18 August 2008 6:32
To: Li Jin
Cc: r-sig-geo at stat.math.ethz.ch
Subject: Re: [R-sig-Geo] UK, KED or OCK? [SEC=UNCLASSIFIED]

Hi Jin,

Which differences do you exactly refer to? I'd say that what happens in 
demo(cokriging) is ordinary cokriging. As it says:
...
[using ordinary cokriging]

Best regards,
--
Edzer

Jin.Li at ga.gov.au wrote:
> Hi All,
>
>  
>
> I am going to compare a few spatial interpolation techniques including
> kriging with an external drift (KED) and ordinary co-kriging (OCK) (such as
> those defined in: Goovaerts, 1997. Geostatistics for Natural Resources
> Evaluation.) to interpolate marine sediment data (mud content in this case)
> using bathymetry as a secondary variable. However, it seems that the
ordinary
> cokriging in gstat as shown in demo(cokriging) is different from the OCK we
> planned to use. Is it possible to do such OCK in gstat? Any comments and
> example? Thanks.
>
>  
>
> As to KED, I tried 
>
>   
>> vgm1 <- variogram(sqrt(mud)~bathy, data.file.dev)
>>     
>
>   
>> model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1))
>>     
>
>   
>> # plot(vgm1, model.1)
>>     
>
>   
>> coordinates(data.file.pred) = ~LON+LAT
>>     
>
>   
>> mud.ok <- krige(sqrt(mud)~bathy, data.file.dev, data.file.pred, model =
>>     
> model.1)
>
> [using universal kriging]
>
>  
>
>   
>> vgm1 <- variogram(sqrt(mud)~LON+LAT, data.file.dev)
>>     
>
>   
>> model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1))
>>     
>
>   
>> # plot(vgm1, model.1)
>>     
>
>   
>> coordinates(data.file.pred) = ~LON+LAT
>>     
>
>   
>> mud.ok <- krige(sqrt(mud)~LON+LAT, data.file.dev, data.file.pred, model =
>>     
> model.1)
>
> [using universal kriging]
>
>  
>
> Both of them are UK. But the first one seems regression kriging. Is it
> identical to KED in this case? If not, any comments and examples of KED are
> appreciated.
>
>  
>
> Cheers,
>
>  
>
> Jin
>
> --------------------------------------------
>
> Jin Li, PhD
>
> Spatial Modeller/
>
> Computational Statistician
>
> Marine & Coastal Environment
>
> Geoscience Australia
>
>
>
> Ph: 61 (02) 6249 9899
>
> Fax: 61 (02) 6249 9956
>
> email: jin.li at ga.gov.au <mailto:jin.li at ga.gov.au> 
>
> --------------------------------------------
>
>
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>
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>   

-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster,
Weseler Straße 253, 48151 Münster, Germany.  Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de/