Feature Selection Algorithms for Computer Aided Diagnosis

Documentation for package ‘FRESA.CAD’ version 3.4.7

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

-- A --

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 --

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 --

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 --

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