# Feature Selection Algorithms for Computer Aided Diagnosis

## Documentation for package ‘FRESA.CAD’ version 3.4.7

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C E F G H I J K L M N O P R S T U

FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD)

## -- A --

adjustProb | Probability of more than zero events

## -- B --

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
BESS | CV BeSS fit
BESS_EBIC | CV BeSS fit
BESS_GSECTION | CV BeSS fit
BinaryBenchmark | Compare performance of different model fitting/filtering algorithms
bootstrapValidation_Bin | Bootstrap validation of binary classification models
bootstrapValidation_Res | Bootstrap validation of regression models
bootstrapVarElimination_Bin | IDI/NRI-based backwards variable elimination with bootstrapping
bootstrapVarElimination_Res | NeRI-based backwards variable elimination with bootstrapping
BSWiMS.model | BSWiMS model selection

## -- C --

CalibrationProbPoissonRisk | Baseline hazard and interval time Estimations
cancerVarNames | Data frame used in several examples of this package
ClassMetric95ci | Estimators and 95CI
ClustClass | Hybrid Hierarchical Modeling
clusterISODATA | Cluster Clustering using the Isodata Approach
concordance95ci | Estimators and 95CI
correlated_Remove | Univariate Filters
CoxBenchmark | Compare performance of different model fitting/filtering algorithms
CoxRiskCalibration | Baseline hazard and interval time Estimations
crossValidationFeatureSelection_Bin | IDI/NRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables
crossValidationFeatureSelection_Res | NeRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables
CVsignature | Cross-validated Signature

## -- E --

EmpiricalSurvDiff | Estimate the LR value and its associated p-values
ensemblePredict | The median prediction from a list of models
expectedEventsPerInterval | Probability of more than zero events

## -- F --

featureAdjustment | Adjust each listed variable to the provided set of covariates
filteredFit | A generic fit method with a filtered step for feature selection
FilterUnivariate | Univariate Filters
ForwardSelection.Model.Bin | IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regression models
ForwardSelection.Model.Res | NeRI-based feature selection procedure for linear, logistic, or Cox proportional hazards regression models
FRESA.CAD | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD)
FRESA.Model | Automated model selection
FRESAScale | Data frame normalization

## -- G --

getKNNpredictionFromFormula | Predict classification using KNN
getLatentCoefficients | Derived Features of the UPLTM transform
getMedianLogisticCalibratedPrediction | Binary Predictions Calibration of Random CV
getMedianSurvCalibratedPrediction | Binary Predictions Calibration of Random CV
getObservedCoef | Derived Features of the UPLTM transform
getSignature | Returns a CV signature template
getVar.Bin | Analysis of the effect of each term of a binary classification model by analysing its reclassification performance
getVar.Res | Analysis of the effect of each term of a linear regression model by analysing its residuals
GLMNET | GLMNET fit with feature selection"
GLMNET_ELASTICNET_1SE | GLMNET fit with feature selection"
GLMNET_ELASTICNET_MIN | GLMNET fit with feature selection"
GLMNET_RIDGE_1SE | GLMNET fit with feature selection"
GLMNET_RIDGE_MIN | GLMNET fit with feature selection"
GMVEBSWiMS | Hybrid Hierarchical Modeling with GMVE and BSWiMS
GMVECluster | Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)

## -- H --

heatMaps | Plot a heat map of selected variables
HLCM | Latent class based modeling of binary outcomes
HLCM_EM | Latent class based modeling of binary outcomes

## -- I --

IDeA | Decorrelation of data frames
ILAA | Decorrelation of data frames
improvedResiduals | Estimate the significance of the reduction of predicted residuals

## -- J --

jaccardMatrix | Jaccard Index of two labeled sets

## -- K --

KNN_method | KNN Setup for KNN prediction

## -- L --

LASSO_1SE | GLMNET fit with feature selection"
LASSO_MIN | GLMNET fit with feature selection"
listTopCorrelatedVariables | List the variables that are highly correlated with each other
LM_RIDGE_MIN | Ridge Linear Models

## -- M --

MAE95ci | Estimators and 95CI
meanTimeToEvent | Probability of more than zero events
metric95ci | Estimators and 95CI
modelFitting | Fit a model to the data
mRMR.classic_FRESA | FRESA.CAD wrapper of mRMRe::mRMR.classic

## -- N --

NAIVE_BAYES | Naive Bayes Modeling
nearestCentroid | Class Label Based on the Minimum Mahalanobis Distance
nearestNeighborImpute | nearest neighbor NA imputation

## -- O --

OrdinalBenchmark | Compare performance of different model fitting/filtering algorithms

## -- P --

plot | Plot ROC curves of bootstrap results
plot.bootstrapValidation_Bin | Plot ROC curves of bootstrap results
plot.bootstrapValidation_Res | Plot ROC curves of bootstrap results
plot.FRESA_benchmark | Plot the results of the model selection benchmark
plotModels.ROC | Plot test ROC curves of each cross-validation model
ppoisGzero | Probability of more than zero events
predict | Linear or probabilistic prediction
predict.CLUSTER_CLASS | Predicts 'ClustClass' outcome
predict.fitFRESA | Linear or probabilistic prediction
predict.FRESAKNN | Predicts 'class::knn' models
predict.FRESAsignature | Predicts 'CVsignature' models
predict.FRESA_BESS | Predicts 'BESS' models
predict.FRESA_FILTERFIT | Predicts 'filteredFit' models
predict.FRESA_GLMNET | Predicts GLMNET fitted objects
predict.FRESA_HLCM | Predicts BOOST_BSWiMS models
predict.FRESA_NAIVEBAYES | Predicts 'NAIVE_BAYES' models
predict.FRESA_RIDGE | Predicts 'LM_RIDGE_MIN' models
predict.FRESA_SVM | Predicts 'TUNED_SVM' models
predict.GMVE | Predicts 'GMVECluster' clusters
predict.GMVE_BSWiMS | Predicts 'GMVEBSWiMS' outcome
predictDecorrelate | Decorrelation of data frames
predictionStats_binary | Prediction Evaluation
predictionStats_ordinal | Prediction Evaluation
predictionStats_regression | Prediction Evaluation
predictionStats_survival | Prediction Evaluation

## -- R --

randomCV | Cross Validation of Prediction Models
rankInverseNormalDataFrame | rank-based inverse normal transformation of the data
RegresionBenchmark | Compare performance of different model fitting/filtering algorithms
reportEquivalentVariables | Report the set of variables that will perform an equivalent IDI discriminant function
residualForFRESA | Return residuals from prediction
RRPlot | Plot and Analysis of Indices of Risk

## -- S --

signatureDistance | Distance to the signature template
sperman95ci | Estimators and 95CI
summary | Returns the summary of the fit
summary.bootstrapValidation_Bin | Generate a report of the results obtained using the bootstrapValidation_Bin function
summary.fitFRESA | Returns the summary of the fit
summaryReport | Report the univariate analysis, the cross-validation analysis and the correlation analysis

## -- T --

timeSerieAnalysis | Fit the listed time series variables to a given model
trajectoriesPolyFeatures | Extract the per patient polynomial Coefficients of a feature trayectory
TUNED_SVM | Tuned SVM

## -- U --

uniRankVar | Univariate analysis of features (additional values returned)
univariateRankVariables | Univariate analysis of features
univariate_BinEnsemble | Univariate Filters
univariate_correlation | Univariate Filters
univariate_cox | Univariate Filters
univariate_DTS | Univariate Filters
univariate_KS | Univariate Filters
univariate_Logit | Univariate Filters
univariate_residual | Univariate Filters
univariate_Strata | Univariate Filters
univariate_tstudent | Univariate Filters
univariate_Wilcoxon | Univariate Filters
update | Update the univariate analysis using new data
update.uniRankVar | Update the univariate analysis using new data
updateModel.Bin | Update the IDI/NRI-based model using new data or new threshold values
updateModel.Res | Update the NeRI-based model using new data or new threshold values