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FRESA.CAD (version 3.4.3)

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

289

Version

3.4.3

License

LGPL (>= 2)

Maintainer

Jose Tamez-Pena

Last Published

October 9th, 2022

Functions in FRESA.CAD (3.4.3)

BSWiMS model selection

FRESA.CAD-package

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

Hybrid Hierarchical Modeling

EmpiricalSurvDiff

Estimate the LR value and its associated p-values

Hybrid Hierarchical Modeling with GMVE and BSWiMS

KNN Setup for KNN prediction

Naive Bayes Modeling

Ridge Linear Models

FilterUnivariate

Univariate Filters

Automated model selection

ForwardSelection.Model.Res

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

GLMNET fit with feature selection"

Data frame normalization

Get the bagged model from a list of models

Cross-validated Signature

backVarElimination_Res

NeRI-based backwards variable elimination

bootstrapVarElimination_Bin

IDI/NRI-based backwards variable elimination with bootstrapping

bootstrapVarElimination_Res

NeRI-based backwards variable elimination with bootstrapping

backVarElimination_Bin

IDI/NRI-based backwards variable elimination

getKNNpredictionFromFormula

Predict classification using KNN

bootstrapValidation_Res

Bootstrap validation of regression models

bootstrapValidation_Bin

Bootstrap validation of binary classification models

Data frame used in several examples of this package

getDerivedCoefficients

Derived Features of the GDSTM transform

crossValidationFeatureSelection_Res

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

Cluster Clustering using the Isodata Approach

Jaccard Index of two labeled sets

improvedResiduals

Estimate the significance of the reduction of predicted residuals

ensemblePredict

The median prediction from a list of models

listTopCorrelatedVariables

List the variables that are highly correlated with each other

Fit a model to the data

featureAdjustment

Adjust each listed variable to the provided set of covariates

plot.bootstrapValidation_Res

Plot ROC curves of bootstrap results

Plot test ROC curves of each cross-validation model

A generic fit method with a filtered step for feature selection

predict.FRESA_HLCM

Predicts BOOST_BSWiMS models

predict.FRESA_FILTERFIT

Predicts filteredFit models

crossValidationFeatureSelection_Bin

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

ForwardSelection.Model.Bin

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

Plot a heat map of selected variables

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

predict.CLUSTER_CLASS

Predicts ClustClass outcome

predict.GMVE_BSWiMS

Predicts GMVEBSWiMS outcome

predict.FRESAKNN

Predicts class::knn models

mRMR.classic_FRESA

FRESA.CAD wrapper of mRMRe::mRMR.classic

Cross Validation of Prediction Models

rankInverseNormalDataFrame

rank-based inverse normal transformation of the data

predict.FRESA_BESS

Predicts BESS models

summary.bootstrapValidation_Bin

Generate a report of the results obtained using the bootstrapValidation_Bin function

signatureDistance

Distance to the signature template

Univariate analysis of features (additional values returned)

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

trajectoriesPolyFeatures

Extract the per patient polynomial Coefficients of a feature trayectory

summary.fitFRESA

Returns the summary of the fit

Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)

plot.FRESA_benchmark

Plot the results of the model selection benchmark

predict.FRESA_SVM

Predicts TUNED_SVM models

Latent class based modeling of binary outcomes

predict.FRESA_RIDGE

Predicts LM_RIDGE_MIN models

predict.FRESAsignature

Predicts CVsignature models

Predicts GMVECluster clusters

plot.bootstrapValidation_Bin

Plot ROC curves of bootstrap results

Bar plot with error bars

predictionStats

Prediction Evaluation

predict.fitFRESA

Linear or probabilistic prediction

nearestCentroid

Class Label Based on the Minimum Mahalanobis Distance

predict.FRESA_GLMNET

Predicts GLMNET fitted objects

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

update.uniRankVar

Update the univariate analysis using new data

nearestNeighborImpute

nearest neighbor NA imputation

Returns a CV signature template

Compare performance of different model fitting/filtering algorithms

univariateRankVariables

Univariate analysis of features

updateModel.Bin

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

predict.FRESA_NAIVEBAYES

Predicts NAIVE_BAYES models

updateModel.Res

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

residualForFRESA

Return residuals from prediction

timeSerieAnalysis

Fit the listed time series variables to a given model

GDSTMDecorrelation

Decorrelation of data frames

reportEquivalentVariables

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