> From: [EMAIL PROTECTED]
>
> 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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