[R] Can't seem to finish a randomForest.... Just goes and goe s!

[David L. Van Brunt, Ph.D.]

Thanks for the pointer!! Can't believe you got back to me so quickly on a
Sunday evening. I'll give that a shot and let you know how it goes.

On 4/4/04 19:07, "Liaw, Andy" <andy_liaw at merck.com> wrote:

> When you have fairly large data, _do not use the formula interface_, as a
> couple of copies of the data would be made.  Try simply:
> 
> Myforest.rf <- randomForest(Mydata[, -46], Mydata[,46],
>                           ntrees=100, mtry=7)
> 
> [Note that you don't need to set proximity (not proximities) or importance
> to FALSE, as that's the default already.]
> 
> You might also want to use do.trace=1 to see if trees are actually being
> grown (assuming there's no output buffering as in Rgui on Windows, otherwise
> you'll probably want to turn that off).
> 
> I had run randomForest on data set much larger than that, without problem,
> so I don't imagine your data would be `difficult'.  (I have not used the
> Mac, though.)
> 
> Andy
> 
>> From: David L. Van Brunt, Ph.D.
>> 
>> Playing with randomForest, samples run fine. But on real data, no go.
>> 
>> Here's the setup: OS X, same behavior whether I'm using
>> R-Aqua 1.8.1 or the
>> Fink compile-of-my-own with X-11, R version 1.8.1.
>> 
>> This is on OS X 10.3 (aka "Panther"), G4 800Mhz with 512M
>> physical RAM.
>> 
>> I have not altered the Startup options of R.
>> 
>> Data set is read in from a text file with "read.table", and
>> has 46 variables
>> and 1,855 cases. Trying the following:
>> 
>> The DV is categorical, 0 or 1. Most of the IV's are either
>> continuous, or
>> correctly read in as factors. The largest factor has 30
>> levels.... Only the
>> DV seems to need identifying as a factor to force class trees over
>> regresssion:
>> 
>>> Mydata$V46<-as.factor(Mydata$V46)
>>> Myforest.rf<-randomForest(V46~.,data=Mydata,ntrees=100,mtry=7
> ,proximities=FALSE
>> , importance=FALSE)
>> 
>> 5 hours later, R.bin was still taking up 75% of my processor.
>>  When I've
>> tried this with larger data, I get errors referring to the
>> buffer (sorry,
>> not in front of me right now).
>> 
>> Any ideas on this? The data don't seem horrifically large.
>> Seems like there
>> are a few options for setting memory size, but I'm  not sure
>> which of them
>> to try tweaking, or if that's even the issue.
>> 
>> ______________________________________________
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>> 
>> 
> 
> 
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-- 
David L. Van Brunt, Ph.D.
Outlier Consulting & Development
mailto: <ocd at well-wired.com>