Man pages for FRESA.CAD
Feature Selection Algorithms for Computer Aided Diagnosis
| backVarElimination.Bin | IDI/NRI-based backwards variable elimination |
| backVarElimination.Res | NeRI-based backwards variable elimination |
| baggedModel | Get the bagged model from a list of models |
| barPlotCiError | Bar plot with error bars |
| benchmarking | Compare performance of different model fitting/filtering... |
| BESS | CV BeSS fit |
| bootstrapValidation.Bin | Bootstrap validation of binary classification models |
| bootstrapValidation.Res | Bootstrap validation of regression models |
| bootstrapVarElimination.Bin | IDI/NRI-based backwards variable elimination with... |
| bootstrapVarElimination.Res | NeRI-based backwards variable elimination with bootstrapping |
| BSWiMS | BSWiMS model selection |
| CalibratedPrediction | Binary Predictions Calibration of Random CV |
| CancerVarNames | Data frame used in several examples of this package |
| ciStats | Estimators and 95CI |
| ClustClass | Hybrid Hierarchical Modeling |
| clusterISODATA | Cluster Clustering using the Isodata Approach |
| CoxRiskCalibration | Baseline hazard and interval time Estimations |
| crossValidationFeatureSelection.Bin | IDI/NRI-based selection of a linear, logistic, or Cox... |
| crossValidationFeatureSelection.Res | NeRI-based selection of a linear, logistic, or Cox... |
| CV_signature | Cross-validated Signature |
| EmpiricalSurvDiff | Estimate the LR value and its associated p-values |
| ensemblePredict | The median prediction from a list of models |
| featureAdjustment | Adjust each listed variable to the provided set of covariates |
| filteredFit | A generic fit method with a filtered step for feature... |
| Filter_mRMR | FRESA.CAD wrapper of mRMRe::mRMR.classic |
| FilterUnivariate | Univariate Filters |
| ForwardModel.Bin | IDI/NRI-based feature selection procedure for linear,... |
| ForwardModel.Res | NeRI-based feature selection procedure for linear, logistic,... |
| FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis... |
| FRESA.Model | Automated model selection |
| FRESAScale | Data frame normalization |
| getDerivedCoefficients | Derived Features of the UPLTM transform |
| getKNNpredictionFromFormula | Predict classification using KNN |
| getSignature | Returns a CV signature template |
| getVar.Bin | Analysis of the effect of each term of a binary... |
| getVar.Res | Analysis of the effect of each term of a linear regression... |
| GMVEBSWiMS | Hybrid Hierarchical Modeling with GMVE and BSWiMS |
| GMVECluster | Set Clustering using the Generalized Minimum Volume Ellipsoid... |
| HCLAS_KNN_CLASS | Latent class based modeling of binary outcomes |
| heatMaps | Plot a heat map of selected variables |
| improvedResiduals | Estimate the significance of the reduction of predicted... |
| IterartiveDecorrelation | Decorrelation of data frames |
| jaccardMatrix | Jaccard Index of two labeled sets |
| KNN_method | KNN Setup for KNN prediction |
| LASSO_MIN | GLMNET fit with feature selection" |
| listTopCorrelatedVariables | List the variables that are highly correlated with each other |
| LM_RIDGE_MIN | Ridge Linear Models |
| modelFitting | Fit a model to the data |
| NAIVE_BAYES | Naive Bayes Modeling |
| nearestCentroid | Class Label Based on the Minimum Mahalanobis Distance |
| nearestneighborimpute | nearest neighbor NA imputation |
| plot.bootstrapValidation.Bin | Plot ROC curves of bootstrap results |
| plot.bootstrapValidation.Res | Plot ROC curves of bootstrap results |
| plot.FRESABenchmark | Plot the results of the model selection benchmark |
| plotModels.ROC | Plot test ROC curves of each cross-validation model |
| poissonProbs | Probability of more than zero events |
| predict.bess | Predicts 'BESS' models |
| predict.CLUSTER_CLASS | Predicts 'ClustClass' outcome |
| predictForFresa | Linear or probabilistic prediction |
| predict.FRESA_BOOST | Predicts BOOST_BSWiMS models |
| predict.FRESA_FILTERFIT | Predicts 'filteredFit' models |
| predict.FRESAKNN | Predicts 'class::knn' models |
| predict.FRESA_LASSO | Predicts GLMNET fitted objects |
| predict.FRESA_NAIVEBAYES | Predicts 'NAIVE_BAYES' models |
| predict.FRESA_RIDGE | Predicts 'LM_RIDGE_MIN' models |
| predict.FRESAsignature | Predicts 'CVsignature' models |
| predict.FRESA_SVM | Predicts 'TUNED_SVM' models |
| predict.GMVE | Predicts 'GMVECluster' clusters |
| predict.GMVE_BSWiMS | Predicts 'GMVEBSWiMS' outcome |
| predictionStats | Prediction Evaluation |
| randomCV | Cross Validation of Prediction Models |
| rankInverseNormalDataFrame | rank-based inverse normal transformation of the data |
| reportEquivalentVariables | Report the set of variables that will perform an equivalent... |
| residualForFRESA | Return residuals from prediction |
| RRPlot | Plot and Analysis of Indices of Risk |
| signatureDistance | Distance to the signature template |
| summary.bootstrapValidation | Generate a report of the results obtained using the... |
| summary.fitFRESA | Returns the summary of the fit |
| summaryReport | Report the univariate analysis, the cross-validation analysis... |
| timeSeriesAnalysis | Fit the listed time series variables to a given model |
| trajectoriesPolyFeatures | Extract the per patient polynomial Coefficients of a feature... |
| TUNED_SVM | Tuned SVM |
| uniRankVar | Univariate analysis of features (additional values returned) |
| univariateRankVariables | Univariate analysis of features |
| updateModel.Bin | Update the IDI/NRI-based model using new data or new... |
| updateModel.Res | Update the NeRI-based model using new data or new threshold... |
| update.uniRankVar | Update the univariate analysis using new data |
