Package: IntegratedMRF 1.1.9

IntegratedMRF: Integrated Prediction using Uni-Variate and Multivariate Random Forests

An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.

Authors:Raziur Rahman, Ranadip Pal

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14 exports 0.36 score 34 dependencies 1 mentions 18 scripts 190 downloads

Last updated 6 years agofrom:d049a5140e. Checks:OK: 9. Indexed: yes.

Exports:build_forest_predictbuild_single_treeCombinationCombPredictCombPredictSpecificCrossValidationerror_calculationImputationIntegratedPredictionNode_costpredictingsingle_tree_predictionsplit_nodesplitt

Dependencies:bootstrapclicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclelimSolvelpSolvemagrittrMASSMatrixmgcvMultivariateRandomForestmunsellnlmepillarpkgconfigquadprogR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr