[R-sig-Geo] cokriging question

[Edzer Pebesma]

Good morning Dave (late afternoon here),

Chris Taylor wrote:
> Good morning Edzer and Dave,
> Thanks for bringing up this point.  I had a similar issue recently 
> using krige().  Observations at 5800 locations, attempting to krige() 
> predictions at 112,000 locations resulted in the same "memory.c" error 
> message.  Reducing predicted locations to <<50,000 and reducing 
> max.dist seemed to help, but the predictions still took a very long 
> time (>2 hours).  (Running winxp with 4GB memory.)
> Can you clarify your suspicion regarding the "lack of standardization 
> of coordinates"?
In this message, a trend was modelled based on x and y coordinates, as 
follows:
       x                y                DN4          indicator4
 Min.   :670462   Min.   :4215236   Min.   :18.00   Min.   :0.0000
 1st Qu.:670683   1st Qu.:4215456   1st Qu.:24.00   1st Qu.:0.0000
 Median :670904   Median :4215677   Median :32.50   Median :0.0000
 Mean   :670904   Mean   :4215677   Mean   :43.26   Mean   :0.4795
 3rd Qu.:671125   3rd Qu.:4215898   3rd Qu.:64.00   3rd Qu.:1.0000
 Max.   :671346   Max.   :4216119   Max.   :87.00   Max.   :1.0000
 > 
g<-gstat(id="indicator6",formula=indicator6~x+y+x*y+sqrt(x)+sqrt(y),location=~x+y,data=band6.data,...

computing the x*y will give numbers many orders of magnitude larger than 
sqrt(x),or the intercept. The advice is usually to (somewhat) 
standardize coordinates before using them as a trend. But I doubt this 
helps you very much.

I find it hard to consider > 2 hours as a very long time before I know 
all the details (e.g. how many points were there within your maxdist?), 
the reason why you want an instant answer, and preferably have heard a 
comparison with other software. If you then tell me what your budget is, 
I might come up with possible solutions (starting very cheap, e.g. look 
at demo(snow) in package gstat, or use an OS that can assign this 4Gb to 
a single process).
--
Edzer
>
> Chris
>
> Edzer Pebesma wrote:
>> Dave,
>>
>> 12000 observations fit, in the c representation, in less than 1 Mb 
>> (64 bytes per observation).
>>
>> The issue is that you think that passing maxdist to predict.gstat has 
>> an effect. It doesn't; you need to pass it to function gstat().
>>
>> The same thing happened in this
>> https://stat.ethz.ch/pipermail/r-sig-geo/2008-September/004182.html
>> message, where nmax was passed to predict.gstat, and simulation took 
>> forever. The other issue in that question was, I suspect, lack of 
>> standardization of coordinates, used in a trend surface.
>> -- 
>> Edzer
>>
>> Dave Depew wrote:
>>> Is there a limit to the # of observations or size of file that can 
>>> be co-kriged in gstat?
>>> I have a ~12000 observation data set (2 variables), the variograms, 
>>> cross variogram and lmc are fit well, and co-kriging starts ok
>>>
>>> Linear Model of Coregionalization found. Good.
>>> [using ordinary cokriging]
>>>
>>> then immediately outputs
>>>
>>> "memory.c", line 57: can't allocate memory in function m_get()
>>> Error in predict.gstat(fit.ck, newdata = EcoSAV.grid, maxdist = 100) :
>>>  m_get
>>>
>>> Iv tried different maxdist from 10 to 1000, with exactly the same 
>>> result.
>>> I recently upgraded my RAM to 4Gb and flipped the windows XP /3GB 
>>> switch.
>>>
>>>
>>
>

-- 
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/
http://www.springer.com/978-0-387-78170-9