[R] RandomForest question

[Liaw, Andy]

> From: Arne.Muller at sanofi-aventis.com
> 
> Hello,
> 
> I'm trying to find out the optimal number of splits (mtry 
> parameter) for a randomForest classification. The 
> classification is binary and there are 32 explanatory 
> variables (mostly factors with each up to 4 levels but also 
> some numeric variables) and 575 cases.
> 
> I've seen that although there are only 32 explanatory 
> variables the best classification performance is reached when 
> choosing mtry=80. How is it possible that more variables can 
> used than there are in columns the data frame?

It's not.  The code for randomForest.default() has:

    ## Make sure mtry is in reasonable range.
    mtry <- max(1, min(p, round(mtry)))

so it silently sets mtry to number of predictors if it's too large.
As an example:

> library(randomForest)
randomForest 4.5-12 
Type rfNews() to see new features/changes/bug fixes.
> iris.rf = randomForest(Species ~ ., iris, mtry=10)
> iris.rf$mtry
[1] 4

I should probably add a warning in such cases...

Andy

 
> 	thanks for your help
> 	+ kind regards,
> 
> 	Arne
> 
> 
> 
> 
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