The advice given is sensible. For a timing study see http://rwiki.sciviews.org/doku.php?id=tips:rqcasestudy
We found that for optimization calculations, putting the objective function calculation or parts thereof in Fortran was helpful. But we kept those routines pretty small -- less than a page -- and we just called them to evaluate things, avoiding passing around information back and forth to R. JN On 13-10-22 06:00 AM, r-help-requ...@r-project.org wrote: > Message: 58 > Date: Tue, 22 Oct 2013 05:47:15 -0400 > From: Jim Holtman <jholt...@gmail.com> > To: Alexandre Khelifa <akhel...@logitech.com> > Cc: "r-help@r-project.org" <r-help@r-project.org> > Subject: Re: [R] R - How to "physically" Increase Speed > Message-ID: <73d989da-b6b3-421d-838c-903da3435...@gmail.com> > Content-Type: text/plain; charset=us-ascii > > I would start with taking a subset of the data (definitely some that would > run in less than 10 minutes) and use the profiler "Rprof" to see where time > is being spent. you can use the the task monitor (if on windows) to see how > much memory you are using; it sounds like you did not need the extra memory. > > You might see if you can partition your data so you can run multiple versions > of R and then merge the results. > > Anything that takes more than a half hour, for me, is looked into to see > where the problems are. For example dataframes arevexpensive to access and > conversion to matrices is one way to speed it up. the is where the profiler > helps. > ______________________________________________ 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.
