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

[Eelke Folmer]

Thank you Paul,
I did indeed get more advice from Tomislav Hengl, the same as yours. And 
given the reliability of the (through kriging) predictions of the auxiliary 
variable it indeed makes most sense as you advice also.
Cheers,
Eelke

Tomislav Hengl writes:
I would rather try to extrapolate the predictors (pred1 etc.) and then run
only one model. At least the transition of the values in the final map
will be continuous.

In any case, RK predictions where you do not have values of predictors
will be poor. Only if the regression model is not really significant
(R-square<0.3), then you might rely on the kriging part.

----- Original Message ----- 
From: "Paul Hiemstra" <p.hiemstra at geo.uu.nl>
To: "Eelke Folmer" <E.O.Folmer at rug.nl>
Cc: <r-sig-geo at stat.math.ethz.ch>
Sent: Monday, May 26, 2008 10:47 AM
Subject: Re: [R-sig-Geo] Ordinary and Regression Kriging combined to deal 
with missing values in predictor variables


> 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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>
>
> -- 
> Drs. Paul Hiemstra
> Department of Physical Geography
> Faculty of Geosciences
> University of Utrecht
> Heidelberglaan 2
> P.O. Box 80.115
> 3508 TC Utrecht
> Phone: +31302535773
> Fax: +31302531145
> http://intamap.geo.uu.nl/~paul
>
>