forestFeatures {ClassifyR} | R Documentation

## Extract Vectors of Ranked and Selected Features From a Random Forest Object

### Description

Provides a ranking of features based on the total decrease in node impurities from splitting on the variable, averaged over all trees. Also provides the selected features which are those that were used in at least one tree of the forest.

### Usage

      ## S4 method for signature 'randomForest'
    forestFeatures(forest)

### Arguments

`forest` | A trained random forest which was created by `randomForest`.

### Value

An `list` object. The first element is a vector or data frame of features, ranked from best to worst using the Gini index. The second element is a vector or data frame of features used in at least one tree.

### Author(s)

Dario Strbenac

### Examples

          if(require(randomForest))
          {
            genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
            genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample)
                                              c(rnorm(75, 9, 2), rnorm(25, 14, 2))))
            classes <- factor(rep(c("Poor", "Good"), each = 25))
            colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix))
            rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))
            trainingSamples <- c(1:20, 26:45)
            testingSamples <- c(21:25, 46:50)

            trained <- randomForestTrainInterface(genesMatrix[, trainingSamples],
                                                  classes[trainingSamples], ntree = 10)

            forestFeatures(trained)
          }

[Package ClassifyR version 2.4.4 Index]