Super Learner Prediction
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Documentation for package ‘SuperLearner’ version 2.0-29
Help Pages
-- A --
| All | screening algorithms for SuperLearner |
-- C --
| coef.CV.SuperLearner | Function to get V-fold cross-validated risk estimate for super learner |
| coef.SuperLearner | Super Learner Prediction Function |
| create.Learner | Factory for learner wrappers |
| create.SL.xgboost | Factory for XGBoost SL wrappers |
| CV.SuperLearner | Function to get V-fold cross-validated risk estimate for super learner |
| CVFolds | Generate list of row numbers for each fold in the cross-validation |
-- L --
| listWrappers | list all wrapper functions in SuperLearner |
-- M --
| mcSuperLearner | Super Learner Prediction Function |
| method.AUC | Method to estimate the coefficients for the super learner |
| method.CC_LS | Method to estimate the coefficients for the super learner |
| method.CC_nloglik | Method to estimate the coefficients for the super learner |
| method.NNloglik | Method to estimate the coefficients for the super learner |
| method.NNLS | Method to estimate the coefficients for the super learner |
| method.NNLS2 | Method to estimate the coefficients for the super learner |
| method.template | Method to estimate the coefficients for the super learner |
-- P --
| plot.CV.SuperLearner | Graphical display of the V-fold CV risk estimates |
| predict.SL.bartMachine | bartMachine prediction |
| predict.SL.bayesglm | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.biglasso | Prediction wrapper for SL.biglasso |
| predict.SL.caret | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.cforest | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.earth | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.gam | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.gbm | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.glm | Prediction for SL.glm |
| predict.SL.glmnet | Prediction for an SL.glmnet object |
| predict.SL.ipredbagg | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.kernelKnn | Prediction for SL.kernelKnn |
| predict.SL.knn | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.ksvm | Prediction for SL.ksvm |
| predict.SL.lda | Prediction wrapper for SL.lda |
| predict.SL.leekasso | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.lm | Prediction for SL.lm |
| predict.SL.loess | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.logreg | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.mean | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.nnet | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.nnls | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.polymars | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.qda | Prediction wrapper for SL.qda |
| predict.SL.randomForest | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.ranger | Prediction wrapper for ranger random forests |
| predict.SL.ridge | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.rpart | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.speedglm | Prediction for SL.speedglm |
| predict.SL.speedlm | Prediction for SL.speedlm |
| predict.SL.step | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.stepAIC | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.svm | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.template | Wrapper functions for prediction algorithms in SuperLearner |
| predict.SL.xgboost | XGBoost prediction on new data |
| predict.SuperLearner | Predict method for SuperLearner object |
| print.CV.SuperLearner | Function to get V-fold cross-validated risk estimate for super learner |
| print.summary.CV.SuperLearner | Summary Function for Cross-Validated Super Learner |
| print.SuperLearner | Super Learner Prediction Function |
-- R --
| recombineCVSL | Recombine a CV.SuperLearner fit using a new metalearning method |
| recombineSL | Recombine a SuperLearner fit using a new metalearning method |
-- S --
| SampleSplitSuperLearner | Super Learner Prediction Function |
| screen.corP | screening algorithms for SuperLearner |
| screen.corRank | screening algorithms for SuperLearner |
| screen.glmnet | screening algorithms for SuperLearner |
| screen.randomForest | screening algorithms for SuperLearner |
| screen.SIS | screening algorithms for SuperLearner |
| screen.template | screening algorithms for SuperLearner |
| screen.ttest | screening algorithms for SuperLearner |
| SL.bartMachine | Wrapper for bartMachine learner |
| SL.bayesglm | Wrapper functions for prediction algorithms in SuperLearner |
| SL.biglasso | SL wrapper for biglasso |
| SL.caret | Wrapper functions for prediction algorithms in SuperLearner |
| SL.caret.rpart | Wrapper functions for prediction algorithms in SuperLearner |
| SL.cforest | cforest (party) |
| SL.earth | Wrapper functions for prediction algorithms in SuperLearner |
| SL.gam | Wrapper functions for prediction algorithms in SuperLearner |
| SL.gbm | Wrapper functions for prediction algorithms in SuperLearner |
| SL.glm | Wrapper for glm |
| SL.glm.interaction | Wrapper functions for prediction algorithms in SuperLearner |
| SL.glmnet | Elastic net regression, including lasso and ridge |
| SL.ipredbagg | Wrapper functions for prediction algorithms in SuperLearner |
| SL.kernelKnn | SL wrapper for KernelKNN |
| SL.knn | Wrapper functions for prediction algorithms in SuperLearner |
| SL.ksvm | Wrapper for Kernlab's SVM algorithm |
| SL.lda | SL wrapper for MASS:lda |
| SL.leekasso | Wrapper functions for prediction algorithms in SuperLearner |
| SL.lm | Wrapper for lm |
| SL.loess | Wrapper functions for prediction algorithms in SuperLearner |
| SL.logreg | Wrapper functions for prediction algorithms in SuperLearner |
| SL.mean | Wrapper functions for prediction algorithms in SuperLearner |
| SL.nnet | Wrapper functions for prediction algorithms in SuperLearner |
| SL.nnls | Wrapper functions for prediction algorithms in SuperLearner |
| SL.polymars | Wrapper functions for prediction algorithms in SuperLearner |
| SL.qda | SL wrapper for MASS:qda |
| SL.randomForest | Wrapper functions for prediction algorithms in SuperLearner |
| SL.ranger | SL wrapper for ranger |
| SL.ridge | Wrapper functions for prediction algorithms in SuperLearner |
| SL.rpart | Wrapper functions for prediction algorithms in SuperLearner |
| SL.rpartPrune | Wrapper functions for prediction algorithms in SuperLearner |
| SL.speedglm | Wrapper for speedglm |
| SL.speedlm | Wrapper for speedlm |
| SL.step | Wrapper functions for prediction algorithms in SuperLearner |
| SL.step.forward | Wrapper functions for prediction algorithms in SuperLearner |
| SL.step.interaction | Wrapper functions for prediction algorithms in SuperLearner |
| SL.stepAIC | Wrapper functions for prediction algorithms in SuperLearner |
| SL.svm | Wrapper functions for prediction algorithms in SuperLearner |
| SL.template | Wrapper functions for prediction algorithms in SuperLearner |
| SL.xgboost | XGBoost SuperLearner wrapper |
| snowSuperLearner | Super Learner Prediction Function |
| summary.CV.SuperLearner | Summary Function for Cross-Validated Super Learner |
| SuperLearner | Super Learner Prediction Function |
| SuperLearner.control | Control parameters for the SuperLearner |
| SuperLearner.CV.control | Control parameters for the cross validation steps in 'SuperLearner' |
| SuperLearnerDocs | Show the NEWS file for the SuperLearner package |
| SuperLearnerNews | Show the NEWS file for the SuperLearner package |
-- T --
| trimLogit | truncated-probabilities logit transformation |
-- W --
| write.method.template | Method to estimate the coefficients for the super learner |
| write.screen.template | screening algorithms for SuperLearner |
| write.SL.template | Wrapper functions for prediction algorithms in SuperLearner |