# Feature Selection Algorithms for Computer Aided Diagnosis

## Documentation for package ‘FRESA.CAD’ version 3.1.0

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

## Help Pages

FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD)
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
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
cancerVarNames | Data frame used in several examples of this package
correlated_Remove | Univariate Filters
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
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
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
getKNNpredictionFromFormula | Predict classification using KNN
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
heatMaps | Plot a heat map of selected variables
improvedResiduals | Estimate the significance of the reduction of predicted residuals
KNN_method | KNN Setup for KNN prediction
LASSO | CV LASSO fit with s="lambda.min" or s="lambda.1se"
LASSO_1SE | CV LASSO fit with s="lambda.min" or s="lambda.1se"
LASSO_MIN | CV LASSO fit with s="lambda.min" or s="lambda.1se"
listTopCorrelatedVariables | List the variables that are highly correlated with each other
LM_RIDGE_MIN | Ridge Linear Models
modelFitting | Fit a model to the data
mRMR.classic_FRESA | FRESA.CAD wrapper of mRMRe::mRMR.classic
NAIVE_BAYES | Naive Bayes Modeling
nearestNeighborImpute | nearest neighbor NA imputation
OrdinalBenchmark | Compare performance of different model fitting/filtering algorithms
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
predict | Linear or probabilistic prediction
predict.fitFRESA | Linear or probabilistic prediction
predict.FRESAKNN | Predicts 'class::knn' models
predict.FRESAsignature | Predicts 'CVsignature' models
predict.FRESA_LASSO | Predicts LASSO fitted objects
predict.FRESA_NAIVEBAYES | Predicts 'NAIVE_BAYES' models
predict.FRESA_RIDGE | Predicts 'LM_RIDGE_MIN' models
predictionStats_binary | Prediction Evaluation
predictionStats_ordinal | Prediction Evaluation
predictionStats_regression | Prediction Evaluation
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
signatureDistance | Distance to the signature template
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
timeSerieAnalysis | Fit the listed time series variables to a given model
uniRankVar | Univariate analysis of features (additional values returned)
univariateRankVariables | Univariate analysis of features
univariate_correlation | Univariate Filters
univariate_Logit | Univariate Filters
univariate_residual | 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