On Windows, I find that having as much memory as I can possibly afford makes a real difference with R. Since I always end up having larger datasets/problems then I thought I'd have. My general strategy is to maximize the amount of memory first -- if that doesn't work, then think about getting a faster processor.

-roger

Michael Dewey wrote:

If I am buying a PC where the most compute intensive task will be running R and I do not have unlimited resources what trade-offs should I make?
Specifically should I go for
1 - more memory, or
2 - faster processor, or
3 - something else?
If it makes a difference I shall be running Windows on it and I am thinking about getting a portable which I understand makes upgrading more difficult.


Extra background: the tasks I notice going slowly at the moment are fitting models with lme which have complex random effects and bootstrapping. By the standards of r-help posters I have small datasets (few thousand cases, few hundred variables). In order to facilitate working with colleagues I need to stick with windows even if linux would be more efficient


Michael Dewey [EMAIL PROTECTED] http://www.aghmed.fsnet.co.uk/home.html

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