Are you sure R's ways are not fast enough (there are many layers
underneath lm)? For an example of how you might do this at C/Fortran
level, see the function lqs() in MASS.
On Mon, 8 Sep 2008, Dimitri Liakhovitski wrote:
Dear R-list,
maybe some of you could point me in the right direction:
Are you aware of any FREE Fortran or Java libraries/actual pieces of
code that are VERY efficient (time-wise) in running the regular linear
least-squares multiple regression?
A lot of the effort is in getting the right answer fast, including for
e.g. collinear inputs.
More specifically, I have to run small regression models (between 1
and 15 predictors) on samples of up to N=700 but thousands and
thousands of them.
I am designing a simulation in R and running those regressions and R
itself is way too slow. So, I am thinking of compiling the regression
run itself in Fortran and Java and then calling it from R.
I think Java is unlikely to be fast compared to the Fortran R itself uses.
Have you profiled to find where the time is really being spent (both R and
C/Fortran profiling if necessary).
Thank you very much for any advice!
Dimitri Liakhovitski
MarketTools, Inc.
[EMAIL PROTECTED]
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
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