R package for random forests model selection, class balance and validation
Random Forests Model Selection, inference, fit and performance evaluation
rfUtilities 2.2-0 (GitHub development release)
- added ranger random forests implementation support
| Code | Description |
|---|---|
accuracy |
Calculates suite of accuracy statistics for classification or regression models (called by rf.crossValidation) |
bivariate.partialDependence |
Bivariate partial-dependency plot |
collinear |
Evaluation of pair-wise linear or nonlinear correlations in data |
ensembleTest |
(experimental) test for degree of correlation across the ensemble, over correlation can indicate overfit |
logLoss |
Calculates Logarithmic or Likelihood loss function |
multi.collinear |
Multi-collinearity test with matrix permutation. |
occurrence.threshold |
A statistical sensitivity test for occurrence probability thresholds |
probability.calibration |
Isotonic probability calibration |
ranger.proximity |
Derives a proximity matrix for a ranger object |
rf.class.sensitivity |
Random Forests class-level sensitivity analysis |
rf.classBalance |
Random Forests Class Balance (Zero Inflation Correction) Model with covariance convergence |
rf.combine |
Combine Random Forests Ensembles |
rf.crossValidation |
Random Forests classification or regression cross-validation, added simplified arguments and ranger support |
rf.effectSize |
Random Forests class-level parameter effect size |
rf.imp.freq |
Random Forests variable selection frequency |
rf.modelSel |
Random Forests Model Selection, simplified arguments and added ranger support |
rf.partial.ci |
Random Forests regression partial dependency plot with confidence intervals |
rf.partial.prob |
Random Forest probability scaled partial dependency plots |
rf.regression.fit |
Evaluates fit and overfit of random forests regression models |
rf.significance |
Significance test for classification or regression random forests models, simplified arguments and added ranger support |
rf.unsupervised |
Unsupervised Random Forests with cluster support |
spatial.uncertainty |
(experimental) creates spatial estimate of uncertainty using an Infinitesimal Jackknife to calculate standard errors |
Bugs: Users are encouraged to report bugs here. Go to issues in the menu above, and press new issue to start a new bug report, documentation correction or feature request. You can direct questions to [email protected].
To install rfUtilities in R use install.packages() to download current stable release from CRAN
or, for the development version, run the following (requires the remotes package):
remotes::install_github("jeffreyevans/rfUtilities")
Tutorial: See (http://evansmurphy.wixsite.com/evansspatial/random-forest-sdm).
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