rForest {statTarget} | R Documentation

## Random Forest classfication in statTarget

### Description

rForest provides the Breiman's random forest algorithm for classification and permutation-based variable importance measures (PIMP-algorithm).

### Usage

    rForest(file,ntree = 100,times = 100, gDist = TRUE,
    seed = 123,...)

### Arguments

`file` | An data frame or 'Stat File' from statTarget software.
`ntree` | Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
`times` | The number of permutations for permutation-based variable importance measures.
`gDist` | If gDist is TRUE the null importance distributions are approximated with Gaussian distributions else with empirical cumulative distributions.
`seed` | For the same set of random variables and reproducible results.
`...` | A generic function in randomForest package

### Value

Objects Two objects from statTarget_rForest (1. randomForest,rfModel; 2. PIMPresult, pimpModel)

VarImp The original Gini importance

PerVarImp A matrix, where the permuted VarImp measures for the predictor variable.

p-value The probability of observing the original VarImp or a larger value, given the fitted null importance distribution.

p.ks.test The p-values of the Kolmogorov-Smirnov Tests for each row PerVarImp.

### Author(s)

Hemi Luan, hemi.luan@gmail.com

### References

Altmann A.,Tolosi L.,Sander O. and Lengauer T. (2010) Permutation importance: a corrected feature importance measure, Bioinformatics 26 (10), 1340-1347.

Ender Celik. (2015) vita: Variable Importance Testing Approaches. R package version 1.0.0 https://CRAN.R-project.org/package=vita

### Examples

    datpath <- system.file('extdata',package = 'statTarget')
    statFile <- paste(datpath,'data_example.csv', sep='/')
    getFile <- read.csv(statFile,header=TRUE)
    rFtest <- rForest(getFile,ntree = 10,times = 5)

[Package statTarget version 1.17.4 Index]