Dear all,
I am aiming to calculate variograms using variogram() from gstat.
The problem is that some of my data-sets are very large (> 400000 points).
Running the command takes some hours of time and does not give any 
error-message.
Nevertheless the result seem not to be appropriate - the first few bins 
are ok (up to a distance of about 300) but then it gains lags which are 
much larger than the spatial extent of the data and the bins are not 
continuous any more. Running the code on smaller areas gives correct 
results.
That's why I think that the problem is the memory.

I am running the code with R 2.10.1 on a linux grid (Intel(R)Core(TM) 
i7-2600CPU@3.40GHz; 32 bit).

So my questions:
- is there a better way to calculate variograms with such large data 
sets or do I have to reduce the data?
- Could parallel computation (on multiple cores) be a solution? And if 
yes, how could that be done?

Here is the code I am using:
"scans" is a 3 column vector containing x, y, and z values resulting 
from a high resolution (1 m) digital elevation model. The extent of the 
data is about 600*600 m, the

#define 50 bins log-scaled and with a maximum of 600
x = seq(1,50,1);
a = exp(log(600)/50);
logwidth = a^x;

#variogram
coordinates(scans) = ~V1+V2;
v = variogram(V3~1, scans,  boundaries = logwidth);

Thank you very much,
Tom

-- 
Thomas Grünewald
WSL Institute for Snow and Avalanche Research SLF
Research Unit Snow and Permafrost
Team Snow Cover and Micrometeorology
Flüelastr. 11
CH-7260 Davos Dorf
Tel. +41/81/417 0365
Fax. +41/81/417 0110
gruenewald@slf.ch
http://www.slf.ch


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