Probably the easiest way to think
about it is that most of the extra
time is the overhead of calling a
function.  So counting the number
of calls to R functions is going to
tell you how much overhead there is.
(Remember that functions call other
functions.)

On 26/06/2011 08:21, Jeff Newmiller wrote:
For the same reason the Cray XMP was fast at numerical computations... a loop 
written in a low level language can be optimized to work faster than one 
written in a higher level language. The XMP optimized loops into hardware, but 
R just optimizes them in C code, exposed to the R programmer as vector 
operations.
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Alexander Engelhardt<a...@chaotic-neutral.de>  wrote:

Hey,
I just read another post about calling R from C. Someone on
stackoverflow (DWin makes me suspect its David W.?) referenced this:
http://www.math.univ-montp2.fr/~pudlo/R_files/call_R.pdf
Which made me think: Why is a loop in R bad, but in C not?

And where exactly does looping cost the most? I wrote a piece of code
for my bachelor's thesis where I loop from 1 to 500, and estimate a
boosted model in every iteration. The procedure takes 2-6 minutes. In
this example the loop (instead of some kind of apply()) shouldn't cost
too much time, right?
I suspect it's way worse if someone would loop from 1 to 10000 and
perform only a small task (a mean(), for example) in each loop. Can
someone confirm this?

Regards,
Alex

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