Estimate Permutation p-Values for Random Forest Importance Metrics



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rfPermute estimates the significance of importance metrics for a Random Forest model by permuting the response variable. It will produce null distributions of importance metrics for each predictor variable and p-value of observed. The package also includes several summary and visualization functions for randomForest and rfPermute results.


To install the stable version from CRAN:


To install the latest version from GitHub:

# make sure you have Rtools installed
if (!require('devtools')) install.packages('devtools')

# install from GitHub


Current Functions

classConfInt Classification Confidence Intervals
cleanRFdata Clean Random Forest Input Data
confusionMatrix Confusion Matrix
exptdErrRate Expected Error Rate
impHeatmap Importance Heatmap
pctCorrect Percent Correctly Classified
plotConfMat Heatmap representation of Confusion Matrix
plotImpVarDist Distribution of Important Variables
plotInbag Distribution of sample inbag rates
plotNull Plot Random Forest Importance Null Distributions
plotOOBtimes Distribution of sample OOB rates
plotPredictedProbs Distribution of prediction assignment probabilities
plotRFtrace Trace of cumulative error rates in forest
plotVotes Vote Distribution
plot.rp.importance Plot Random Forest Importance Distributions
proximityPlot Plot Random Forest Proximity Scores
rfPermute Estimate Permutation p-values for Random Forest Importance Metrics
rp.combine Combine rfPermute Objects
rp.importance Extract rfPermute Importance Scores and p-values

version 2.1.6

  • Added plotConfMat, plotOOBtimes, plotRFtrace, and plotInbag, and plotImpVarDist visualizations.
  • Changed confusionMatrix so it will work when randomForest model doesn't have a $confusion element, like when model is result of combine-ing multiple models.
  • Improved efficiency and stability of parallel processing code. Changed default value of num.cores to NULL.

version 2.1.5

  • Added type argument to plotVotes to choose between area and bar charts.
  • Changed plot.rfPermute to plotNull to avoid clashes and maintain functionality of randomForest::plot.randomForest.
  • Changed name of proximity.plot to proximityPlot, exptd.err.rate to exptdErrRate, and to cleanRFdata to make camelCase naming scheme more consistent in package.
  • Changed plotNull from base graphics to ggplot2.
  • Added symb.metab data set.

version 2.1.1

  • Added n argument to impHeatmap.
  • Added functions: classConfInt, confusionMatrix, plotVotes, pctCorrect.

version 2.0.1

  • Fixed bug in plot.rfPermute that was reporting the p-value incorrectly at the top of the figure.
  • Fixed multi-threading in rfPermute so it works on Windows too.
  • Added impHeatmap function.
  • Switched proximity.plot to use ggplot2 graphics.

version 2.0

  • Fixed bug with calculation of p-values not respecting importance measure scaling (division by standard deviations). New format of output of rfPemute has separate $null.dist and $pval elements, each with results for unscaled and scaled importance mesures. See ?rfPermute for more information.
  • rp.importance and plot.rfPermute now take a scale argument to specify whether or not importance values should be scaled by standard deviations.
  • If nrep = 0 for rfPermute, a randomForest object is returned.

version 1.9.3

  • Fixed import declarations to avoid grid name clashes.
  • Fixed logic error in where fixed predictors were not removed.
  • Fixed error in use of main argument in plot.rp.importance.

version 1.9.2

  • Added this
  • Added
  • Added num.cores argument to rfPermute to take advantage of multi-threading

version 1.9.1

  • Added internal keyword to calc.imp.pval to keep it from indexing
  • Updated imports to match new CRAN policies