Feature Selection Algorithms for Computer Aided Diagnosis

Documentation for package ‘FRESA.CAD’ version 3.4.4

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

-- 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 --

cancerVarNames	Data frame used in several examples of this package
ClustClass	Hybrid Hierarchical Modeling
clusterISODATA	Cluster Clustering using the Isodata Approach
correlated_Remove	Univariate Filters
CoxBenchmark	Compare performance of different model fitting/filtering algorithms
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

-- 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 UPSTM 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
improvedResiduals	Estimate the significance of the reduction of predicted residuals

-- J --

Jaccard Index of two labeled sets

-- K --

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 --

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 --

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
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

-- S --

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

-- 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