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readRAST6() in {spgrass6}

On Mon, 16 May 2011, Dylan Beaudette wrote:

            
Correct, the upper limit on other OS is higher in practice.
Right, for some res=, you will get a systematic sample that will let you 
decimate your data. Randomly shifting the window while resampling will 
help you see whether your classification model boundaries change.

As both Dylan and I have said, there is no good reason for calibrating the 
classification model with more data than necessary, even if it were 
possible. The only reason to use very much data would be if discrimination 
between very rare classes is your target, but even then you could stratify 
your sample to get better representation of critical areas.

You seem to be looking for classification signatures, from which you are 
going to predict. Sampling will give you distributions of these 
signatures, which could be used for similation by tile. If you are very 
concerned about the statistical quality of your classification signatures, 
you could go fuzzy, but your main goal is to get to the distributions of 
these signatures, IMO. Once you have them, you can predict. You do not 
need to have GBs of dat in memory to do this adequately.

So one could agree that software (OS, R, whatever) is your limitation, but 
it is only a limitation if you are not able to consider more statistical 
approaches to your apparent problem, which is finding out how to classify 
your input, and then to predict to output.

Roger