Hi, On Sun, Aug 11, 2013 at 4:47 AM, Alan Sausse <alansau...@gmail.com> wrote: > Hi, > > Not an expert R user, something of a novice - please be gentle with me! > > I have a particular interest in generalised linear models (GLMs) and I'm > experienced in fitting them using other bits of software. > > R can fit GLMs of course, using the glm() command. I have some large > multivariate data sets I'd like to fit GLMs to, ideally using R. Two > concerns though: > > Firstly, I'm told that R isn't especially fast at fitting GLMs, especially > if the data files are too large to fit into RAM. Can anyone advise if > there are alternatives to glm() around which might cope better. For > example, I've heard that RevolutionR is available, and claims to fit GLMs > faster in these cases. Might it be possible, alternatively, to write some > very quick code using C (for example) and to get R to invoke this instead? > Has anyone tried to do this?
Likely not -- you'll need to have RevolutionR around for that, and if you've have RevoR, then just use RevoR -- not sure what the point would be call RevoR-specific functionality from R. Perhaps the biglm package can help you from R, though, as it provides a bigglm function that can do GLMs with out-of-memory data -- no idea how well/fast it works, though. You should also consider that your data may not require that, though -- glmnet, for instance, works incredibly fast on large data. If your data can actually be loaded (perhaps via a sparse matrix), then you can try that. HTH, -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech ______________________________________________ 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.
