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I have a question about predictor importances in randomForest. Once I've run randomForest and got my object, I get their importances: rfresult$importance I also get the "standard errors" of the permutation-based importance measure: rfresult$importanceSD I have 2 questions: 1. Because I am dealing with regressions, I am getting an importance object (rfresult$importance) with two columns, labeled "%IncMSE" (the first column) and "IncNodePurity" (the second column). I assume it's the first one that is the mean decrease in ac...

...n be use to derive the minimum number of variable to be used. Is that a sensible approach? Is there any other suggestion? A measure of the error on "err.rate" would also here really help. Is there any idea how to estimate this? From the variation between runs or with the help of "importanceSD" somehow? Thanks very much in advance for any help. Pierre Dubath

...is also added for regression. Use the new localImp option in randomForest(). * The `importance' component of randomForest object has been changed: The permutation-based measures are not divided by their `standard errors'. Instead, the `standard errors' are stored in the `importanceSD' component. One should use the importance() extractor function rather than something like rf.obj$importance for extracting the importance measures. * The importance() extractor function has been updated: If the permutation-based measures are available, calling importance() with o...

...is also added for regression. Use the new localImp option in randomForest(). * The `importance' component of randomForest object has been changed: The permutation-based measures are not divided by their `standard errors'. Instead, the `standard errors' are stored in the `importanceSD' component. One should use the importance() extractor function rather than something like rf.obj$importance for extracting the importance measures. * The importance() extractor function has been updated: If the permutation-based measures are available, calling importance() with o...

I am trying to use the random forests package for classification in R. The Variable Importance Measures listed are: -mean raw importance score of variable x for class 0 -mean raw importance score of variable x for class 1 -MeanDecreaseAccuracy -MeanDecreaseGini Now I know what these "mean" as in I know their definitions. What I want to know is how to use them. What I am trying to