# [R-sig-Geo] Ordinary and Regression Kriging combined to deal with missing values in predictor variables

Edzer Pebesma edzer.pebesma at uni-muenster.de
Mon May 26 13:53:10 CEST 2008

```Paul, calling for gurus is easy, but why don't you try to elaborate on
the problems that you claim this approach has without telling Eelke what
they are?
--
Edzer

Paul Hiemstra wrote:
> Hi Eelke,
>
> I would advise against filling up the RK grid with OK predictions.
> Interpolating the predictors would have my preference, although it has
> it own set of problems. What is the opinion of the r-sig-geo gurus on
> this subject?
>
> cheers,
> Paul
>
> Eelke Folmer wrote:
>> Hello all,
>> I'm using Gstat/R for regression kriging. I don't have values for all
>> locations in the predictor variables for which I want to interpolate
>> a surface. I do however want to make use of the independent
>> predictors. Therfor I combined regression kriging with ordinary kriging:
>> 1. regression kriging:     krige(log(cer+1) ~ pred1 + pred2 ,  data,
>> data.pred.grid, model = vgm.fit1) 2. ordinary kriging:
>> krige(log(cer+1) ~ 1,                     data,  pred.grid,
>> model = vgm.fit0) 3. add the values from the second step to the grid
>> where the first step gives NA:   s0 = surface.krige0 at data\$var1.pred
>>   s1 = surface.krige1 at data\$var1.pred
>>   s1[is.na(s1)] <- 0    # make the NA zero
>>   s0[!is.na(s1)] <- 0   # make everyting that is not NA in s1 zero
>>   s1 = s1 + s0          # now, all locations get a predicted value
>> despite missing predictors
>> surface.krige at data\$var1.pred.inclusive = s1
>>
>> Is this ok, or should I interpolate (in fact, extrapolate) the
>> predictors to get values at all necessary locations instead? Better
>> solutions available?
>> Thank you in advance for time and effort.
>> Best regards,
>> Eelke
>>
>> Eelke Folmer
>> Animal Ecology Group
>> University of Groningen
>> P. O. Box 14
>> 9750 AA Haren
>> The Netherlands
>> +31(0)50 3632091
>>     [[alternative HTML version deleted]]
>>
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>>
>
>

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