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FRESA.CAD (version 3.4.7)

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

414

Version

3.4.7

License

LGPL (>= 2)

Maintainer

Jose Tamez-Pena

Last Published

November 24th, 2023

Functions in FRESA.CAD (3.4.7)

Data frame normalization

mRMR.classic_FRESA

FRESA.CAD wrapper of mRMRe::mRMR.classic

Latent class based modeling of binary outcomes

Hybrid Hierarchical Modeling with GMVE and BSWiMS

ForwardSelection.Model.Res

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

ForwardSelection.Model.Bin

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

KNN Setup for KNN prediction

Set Clustering using the Generalized Minimum Volume Ellipsoid (GMVE)

FilterUnivariate

Univariate Filters

Decorrelation of data frames

Plot and Analysis of Indices of Risk

backVarElimination_Res

NeRI-based backwards variable elimination

Ridge Linear Models

Compare performance of different model fitting/filtering algorithms

Get the bagged model from a list of models

backVarElimination_Bin

IDI/NRI-based backwards variable elimination

Bar plot with error bars

GLMNET fit with feature selection"

Naive Bayes Modeling

Estimators and 95CI

bootstrapValidation_Bin

Bootstrap validation of binary classification models

featureAdjustment

Adjust each listed variable to the provided set of covariates

bootstrapValidation_Res

Bootstrap validation of regression models

ensemblePredict

The median prediction from a list of models

bootstrapVarElimination_Res

NeRI-based backwards variable elimination with bootstrapping

bootstrapVarElimination_Bin

IDI/NRI-based backwards variable elimination with bootstrapping

Plot a heat map of selected variables

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

improvedResiduals

Estimate the significance of the reduction of predicted residuals

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

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

plot.bootstrapValidation_Bin

Plot ROC curves of bootstrap results

getLatentCoefficients

Derived Features of the UPLTM transform

A generic fit method with a filtered step for feature selection

nearestNeighborImpute

nearest neighbor NA imputation

plot.bootstrapValidation_Res

Plot ROC curves of bootstrap results

plot.FRESA_benchmark

Plot the results of the model selection benchmark

predict.GMVE_BSWiMS

Predicts GMVEBSWiMS outcome

predict.FRESA_BESS

Predicts BESS models

predict.FRESA_FILTERFIT

Predicts filteredFit models

predict.FRESA_HLCM

Predicts BOOST_BSWiMS models

nearestCentroid

Class Label Based on the Minimum Mahalanobis Distance

predict.FRESA_GLMNET

Predicts GLMNET fitted objects

Fit a model to the data

predict.FRESA_NAIVEBAYES

Predicts NAIVE_BAYES models

getKNNpredictionFromFormula

Predict classification using KNN

predict.FRESAKNN

Predicts class::knn models

predict.CLUSTER_CLASS

Predicts ClustClass outcome

Returns a CV signature template

Jaccard Index of two labeled sets

Plot test ROC curves of each cross-validation model

predict.FRESA_RIDGE

Predicts LM_RIDGE_MIN models

Probability of more than zero events

listTopCorrelatedVariables

List the variables that are highly correlated with each other

predict.fitFRESA

Linear or probabilistic prediction

predict.FRESA_SVM

Predicts TUNED_SVM models

Cluster Clustering using the Isodata Approach

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

Cross Validation of Prediction Models

rankInverseNormalDataFrame

rank-based inverse normal transformation of the data

summary.fitFRESA

Returns the summary of the fit

summary.bootstrapValidation_Bin

Generate a report of the results obtained using the bootstrapValidation_Bin function

updateModel.Res

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

timeSerieAnalysis

Fit the listed time series variables to a given model

updateModel.Bin

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

update.uniRankVar

Update the univariate analysis using new data

signatureDistance

Distance to the signature template

predictionStats

Prediction Evaluation

Univariate analysis of features (additional values returned)

trajectoriesPolyFeatures

Extract the per patient polynomial Coefficients of a feature trayectory

univariateRankVariables

Univariate analysis of features

predict.FRESAsignature

Predicts CVsignature models

reportEquivalentVariables

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

Predicts GMVECluster clusters

residualForFRESA

Return residuals from prediction

BSWiMS model selection

FRESA.CAD-package

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

Data frame used in several examples of this package

getMedianSurvCalibratedPrediction

Binary Predictions Calibration of Random CV

Cross-validated Signature

EmpiricalSurvDiff

Estimate the LR value and its associated p-values

CalibrationProbPoissonRisk

Baseline hazard and interval time Estimations

Hybrid Hierarchical Modeling

Automated model selection