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. > ______________________________________________ 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.
