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FRESA.CAD (version 3.3.1)

Feature Selection Algorithms for Computer Aided Diagnosis

Description

Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal 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

290

Version

3.3.1

License

LGPL (>= 2)

Maintainer

Jose Tamez-Pena

Last Published

August 17th, 2021

Functions in FRESA.CAD (3.3.1)

Hybrid Hierarchical Modeling

Ridge Linear Models

Naive Bayes Modeling

bootstrapValidation_Bin

Bootstrap validation of binary classification models

KNN Setup for KNN prediction

GLMNET fit with feature selection"

bootstrapValidation_Res

Bootstrap validation of regression models

bootstrapVarElimination_Bin

IDI/NRI-based backwards variable elimination with bootstrapping

Plot a heat map of selected variables

A generic fit method with a filtered step for feature selection

featureAdjustment

Adjust each listed variable to the provided set of covariates

Data frame normalization

bootstrapVarElimination_Res

NeRI-based backwards variable elimination with bootstrapping

FilterUnivariate

Univariate Filters

getKNNpredictionFromFormula

Predict classification using KNN

Returns a CV signature template

Latent class based modeling of binary outcomes

Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)

timeSerieAnalysis

Fit the listed time series variables to a given model

predict.FRESA_SVM

Predicts TUNED_SVM models

plot.bootstrapValidation_Bin

Plot ROC curves of bootstrap results

predict.FRESAsignature

Predicts CVsignature models

plot.bootstrapValidation_Res

Plot ROC curves of bootstrap results

improvedResiduals

Estimate the significance of the reduction of predicted residuals

rankInverseNormalDataFrame

rank-based inverse normal transformation of the data

trajectoriesPolyFeatures

Extract the per patient polynomial Coefficients of a feature trayectory

Cross-validated Signature

reportEquivalentVariables

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

ForwardSelection.Model.Bin

IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regression models

mRMR.classic_FRESA

FRESA.CAD wrapper of mRMRe::mRMR.classic

Automated model selection

FRESA.CAD-package

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

Plot test ROC curves of each cross-validation model

Data frame used in several examples of this package

Univariate analysis of features (additional values returned)

univariateRankVariables

Univariate analysis of features

predict.CLUSTER_CLASS

Predicts ClustClass outcome

nearestCentroid

Class Label Based on the Minimum Mahalanobis Distance

Get the bagged model from a list of models

ensemblePredict

The median prediction from a list of models

crossValidationFeatureSelection_Res

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

Fit a model to the data

backVarElimination_Res

NeRI-based backwards variable elimination

predict.FRESAKNN

Predicts class::knn models

Predicts GMVECluster clusters

ForwardSelection.Model.Res

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

predict.FRESA_HLCM

Predicts BOOST_BSWiMS models

updateModel.Bin

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

update.uniRankVar

Update the univariate analysis using new data

predict.GMVE_BSWiMS

Predicts GMVEBSWiMS outcome

predictionStats

Prediction Evaluation

Cross Validation of Prediction Models

Hybrid Hierarchical Modeling with GMVE and BSWiMS

Bar plot with error bars

Cluster Clustering using the Isodata Approach

crossValidationFeatureSelection_Bin

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

plot.FRESA_benchmark

Plot the results of the model selection benchmark

nearestNeighborImpute

nearest neighbor NA imputation

predict.FRESA_NAIVEBAYES

Predicts NAIVE_BAYES models

Compare performance of different model fitting/filtering algorithms

predict.FRESA_RIDGE

Predicts LM_RIDGE_MIN models

predict.FRESA_BESS

Predicts BESS models

BSWiMS model selection

backVarElimination_Bin

IDI/NRI-based backwards variable elimination

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

Jaccard Index of two labeled sets

listTopCorrelatedVariables

List the variables that are highly correlated with each other

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

signatureDistance

Distance to the signature template

predict.fitFRESA

Linear or probabilistic prediction

predict.FRESA_FILTERFIT

Predicts filteredFit models

summary.bootstrapValidation_Bin

Generate a report of the results obtained using the bootstrapValidation_Bin function

summary.fitFRESA

Returns the summary of the fit

predict.FRESA_GLMNET

Predicts GLMNET fitted objects

updateModel.Res

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

residualForFRESA

Return residuals from prediction

featureDecorrelation

Supervised decorrelation of dataframe features

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

EmpiricalSurvDiff

Estimate the LR value and its associated p-values