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FRESA.CAD (version 2.2.0)

Feature Selection Algorithms for Computer Aided Diagnosis

Description

Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

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Version

Install

install.packages('FRESA.CAD')

Monthly Downloads

395

Version

2.2.0

License

LGPL (>= 2)

Maintainer

Jose Gerardo TamezPena

Last Published

March 11th, 2016

Functions in FRESA.CAD (2.2.0)

bootstrapValidation_Res

Bootstrap validation of regression models
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
plotModels.ROC

Plot test ROC curves of each cross-validation model
uniRankVar

Univariate analysis of features (additional values returned)
cancerVarNames

Data frame used in several examples of this package
medianPredict

The median prediction from a list of models
summary.bootstrapValidation_Bin

Generate a report of the results obtained using the bootstrapValidation_Bin function
plot.bootstrapValidation_Res

Plot ROC curves of bootstrap results
improvedResiduals

Estimate the significance of the reduction of predicted residuals
getKNNpredictionFromFormula

Predict classification using KNN
plot.bootstrapValidation_Bin

Plot ROC curves of bootstrap results
modelFitting

Fit a model to the data
residualForFRESA

Return residuals from prediction
summaryReport

Report the univariate analysis, the cross-validation analysis and the correlation analysis
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
timeSerieAnalysis

Fit the listed time series variables to a given model
listTopCorrelatedVariables

List the variables that are highly correlated with each other
heatMaps

Plot a heat map of selected variables
univariateRankVariables

Univariate analysis of features
rankInverseNormalDataFrame

Perform a z-transformation of the data using the rank-based inverse normal transformation
bootstrapValidation_Bin

Bootstrap validation of binary classification models
update.uniRankVar

Update the univariate analysis using new data
ForwardSelection.Model.Bin

IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regresion models
updateModel.Bin

Update the IDI/NRI-based model using new data or new threshold values
baggedModel

Get the bagged model from a list of forward models
updateModel.Res

Update the NeRI-based model using new data or new threshold values
ForwardSelection.Model.Res

NeRI-based feature selection procedure for linear, logistic, or Cox proportional hazards regression models
backVarElimination_Res

NeRI-based backwards variable elimination
bootstrapVarElimination_Res

NeRI-based backwards variable elimination with bootstrapping
bootstrapVarElimination_Bin

IDI/NRI-based backwards variable elimination with bootstrapping
backVarElimination_Bin

IDI/NRI-based backwards variable elimination
crossValidationFeatureSelection_Bin

IDI/NRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables
FRESA.Model

Automated model selection
reportEquivalentVariables

Report the set of variables that will perform an equivalent IDI discriminant function
predictForFresa

Linear or probabilistic prediction
FRESA.CAD-package

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