On 4/6/07, Wilfred Zegwaard <wilfred.zegwaard@(protected):

> I'm not a programmer, but I have the experience that R is good for
> processing large datasets, especially in combination with specialised
> statistics.

This I find a little surprising, but maybe it's just a sign that I'm
not experienced enough with R yet.

I can't use R for big datasets. At all. Big datasets take forever to
load with read.table, R frequently runs out of memory, and nlm or
gnlm never seem to actually converge to answers. By comparison, I can
point SAS and NLIN at this data without problem. (Of course, SAS is
running on a pretty powerful dedicated machine with a big ram disk, so
that may be part of the problem.)

R's pass-by-value semantics also make it harder than it should be to
deal with where it's crucial that you not make a copy of the data
frame, for fear of running out of memory. Pass-by-reference would
make implementing data transformations so much easier that I don't
really understand how pass-by-value became the standard. (If there's
a trick to doing in-place transformations, I've not found it.)

Right now, I'm considering starting on a project involving some big
Monte Carlo integrations over the complicated posterior parameter
distributions of a nonlinear regression model, and I have the strong
feeling that R will just choke.

R's great for small projects, but as soon as you even a few hundred
megs of data, it seems to break down.

If I'm doing things wrong, please tell me. :-) SAS is a beast to work with.

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