[R] Running randomForests on large datasets

[Nagu]

Thank you Andy.

It is throwing memory allocation error for me for numerous
combinations of ntree and nodesize values. I tried with memory.limit()
and memory.size to use the maximum memory but the error was
consistent. But one thing I noticed was that I had tough time even
just loading the dataset previously. I, then, used Rcmdr library to
load the same data, and it was faster than just loading with the R
console and it didn't throw any memory errors like it used to throw
previously, now and then. I thought that may be this was a fluke with
Rcmdr, I, then, opened it a few more times and every time Rcmdr was
consistent in loading the large dataset without any allocation errors.
I also tried with opening a few other programs on the desktop,
repeated the process, it loaded just fine.

Any ideas on how Rcmdr is loading the file as opposed to R console (I
am using read.table())?

Anyway, I thought I'd share this observation with the others. Thank
you Andy for your ideas. I'll keep tinkering with the parameters.

Thank you,
Nagu


On Wed, Feb 27, 2008 at 5:24 AM, Liaw, Andy <andy_liaw at merck.com> wrote:
> There are a couple of things you may want to try, if you can load the
>  data into R and still have enough to spare:
>
>  - Run randomForest() with fewer trees, say 10 to start with.
>
>  - Run randomForest() with nodesize set to something larger than the
>  default (5 for classification).  This puts a limit on the size of the
>  trees being grown.  Try something like 21 and see if that runs, and
>  adjust accordingly.
>
>  HTH,
>  Andy
>
>
>  From: Nagu
>
>
>
>  > Hi,
>  >
>  > I am trying to run randomForests on a datasets of size 500000X650 and
>  > R pops up memory allocation error. Are there any better ways to deal
>  > with large datasets in R, for example, Splus had something like
>  > bigData library.
>  >
>  > Thank you,
>  > Nagu
>  >
>
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