Feature Selection Algorithms for Computer Aided Diagnosis

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Documentation for package ‘FRESA.CAD’ version 2.2.0

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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 forward models
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
cancerVarNames Data frame used in several examples of this package
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
featureAdjustment Adjust each listed variable to the provided set of covariates
ForwardSelection.Model.Bin IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regresion 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
getKNNpredictionFromFormula Predict classification using KNN
getVar.Bin Analysis of the effect of each term of a binary classification model by analyzing its reclassification performance
getVar.Res Analysis of the effect of each term of a linear regression model by analyzing its residuals
heatMaps Plot a heat map of selected variables
improvedResiduals Estimate the significance of the reduction of predicted residuals
listTopCorrelatedVariables List the variables that are highly correlated with each other
medianPredict The median prediction from a list of models
modelFitting Fit a model to the data
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
plotModels.ROC Plot test ROC curves of each cross-validation model
predictForFresa Linear or probabilistic prediction
rankInverseNormalDataFrame Perform a z-transformation of the data using the rank-based inverse normal transformation
reportEquivalentVariables Report the set of variables that will perform an equivalent IDI discriminant function
residualForFRESA Return residuals from prediction
summary Generate a report of the results obtained using the bootstrapValidation_Bin function
summary.bootstrapValidation_Bin Generate a report of the results obtained using the bootstrapValidation_Bin function
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
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