Advice on use of R for Generalised Linear Modelling
Hi,
On Sun, Aug 11, 2013 at 4:47 AM, Alan Sausse <alansausse at 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