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FRESA.CAD (version 2.1.3)

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

309

Version

2.1.3

License

LGPL (>= 2)

Maintainer

Jose Gerardo TamezPena

Last Published

September 4th, 2015

Functions in FRESA.CAD (2.1.3)

listTopCorrelatedVariables

List the variables that are highly correlated with each other
cancerVarNames

Data frame used in several examples of this package
improvedResiduals

Estimate the significance of the reduction of predicted residuals
FRESA.CAD-package

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

Bootstrap validation of regression models
predictForFresa

Linear or probabilistic prediction
uniRankVar

Univariate analysis of features (additional values returned)
getKNNpredictionFromFormula

Predict classification using KNN
crossValidationFeatureSelection_Res

NeRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables
medianPredict

The median prediction from a list of models
ForwardSelection.Model.Res

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

NeRI-based backwards variable elimination with bootstrapping
ForwardSelection.Model.Bin

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

Automated model selection
crossValidationFeatureSelection_Bin

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

Plot ROC curves of bootstrap results
rankInverseNormalDataFrame

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

IDI/NRI-based backwards variable elimination
reportEquivalentVariables

Report the set of variables that will perform an equivalent IDI discriminant function
updateModel.Res

Update the NeRI-based model using new data or new threshold values
updateModel.Bin

Update the IDI/NRI-based model using new data or new threshold values
plotModels.ROC

Plot test ROC curves of each cross-validation model
featureAdjustment

Adjust each listed variable to the provided set of covariates
timeSerieAnalysis

Fit the listed time series variables to a given model
summary.bootstrapValidation_Bin

Generate a report of the results obtained using the bootstrapValidation_Bin function
backVarElimination_Res

NeRI-based backwards variable elimination
getVar.Bin

Analysis of the effect of each term of a binary classification model by analyzing its reclassification performance
bootstrapVarElimination_Bin

IDI/NRI-based backwards variable elimination with bootstrapping
residualForFRESA

Return residuals from prediction
getVar.Res

Analysis of the effect of each term of a linear regression model by analyzing its residuals
univariateRankVariables

Univariate analysis of features
plot.bootstrapValidation_Bin

Plot ROC curves of bootstrap results
heatMaps

Plot a heat map of selected variables
update.uniRankVar

Update the univariate analysis using new data
modelFitting

Fit a model to the data
bootstrapValidation_Bin

Bootstrap validation of binary classification models
summaryReport

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