Thank you for reminding me, Gabor. I forgot to mention: So far, I have run one test set of regressions using lm. It took R 270 sec. I need to run 1,800,000 of those, which would imply 15.4 years of computing time :)
I have not done the same for lm.fit because I am not sure how to get model R squared from lm.fit. Dimitri On Mon, Sep 8, 2008 at 12:17 PM, Gabor Grothendieck <[EMAIL PROTECTED]> wrote: > I would test the speed before making such as assumption. Note that > lm.fit is faster than lm and if they have the same x matrix then > you can do many in one call by having y be a matrix. > > On Mon, Sep 8, 2008 at 12:05 PM, Dimitri Liakhovitski <[EMAIL PROTECTED]> > 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? >> 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. >> >> Thank you very much for any advice! >> >> Dimitri Liakhovitski >> MarketTools, Inc. >> [EMAIL PROTECTED] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > -- Dimitri Liakhovitski MarketTools, Inc. [EMAIL PROTECTED] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
