FRESA.CAD
Feature Selection Algorithms for Computer Aided Diagnosis.
Set of functions for: Conditioning, Feature Selection, Machine Learning, Cross-Validation, and Visual Evaluation
Table of Contents
Overview
The design of diagnostic or prognostic multivariate models via the selection of significantly discriminant features is complex.
FRESA.CAD provides a series of functions for: Data conditioning, Feature Selection, Machine Learning, Benchmarking, Visualization and Reporting.
| Category | Function(s) | Purpose |
|---|---|---|
| Conditioning/Preprocessing | nearestNeighborImpute() | Impute missing values |
| Conditioning/Preprocessing | FRESA.Scale() | Data Scale/Normalization |
| Conditioning/Preprocessing | featureAdjustment() | Adjust variables removing collinearity |
| Conditioning/Preprocessing | IDeA()/ILAA() | Multicollinearity Mitigation |
| Feature Selection | uniRankVar() | Univariate Analysis |
| Feature Selection | BSWiMS.model() | Linear Model Subset Selection |
| Feature Selection | univariate_BinEnsemble() | Ensemble Select Top Features |
| Feature Selection | univariate... | Filter Select Top Features ... |
| Machine Learning | BSWiMS.model() | Bootstrap Modeling |
| Machine Learning | filteredFit() | Pipeline ML: Scale/Filter/Transform/Learn |
| Machine Learning | HLCM()/HLCM_EM() | Latent-Class Based Modeling |
| Machine Learning | GMVECluster() | Unsupervised Clustering via GMVE |
| Benchmarking / Evaluation | RandomCV() | Random Holdout Validation |
| Benchmarking / Evaluation | BinaryBenchmark() | Binary Model Evaluation |
| Benchmarking / Evaluation | OrdinalBenchmark() | Ordinal Model Evaluation |
| Benchmarking / Evaluation | CoxBenchmark() | Cox-based Model Evaluation |
| Visualization / Reporting | RRPlot() | Survival Model Evaluation |
| Visualization / Reporting | predictionStats_binary() | Report Cross Validation Results Binary |
| Visualization / Reporting | predictionStats_Ordinal() | Report Cross Validation Results Ordinal |
| Visualization / Reporting | predictionStats_survival() | Report Cross Validation Results Survival |
Besides the above listed functions the library provides predictors and wrappers of common machine learning methods, and many other auxiliary functions.
Installation
You can install the official release of the package from CRAN using:
install.packages("FRESA.CAD")To install the development version from GitHub, use:
# Install 'devtools' package if you haven't already
install.packages("devtools")
# Install the package from GitHub
devtools::install_github("https://github.com/joseTamezPena/FRESA.CAD")Usage
#Load the package
library(FRESA.CAD)
#For comprehensive evaluaiton of confusion tables
library("epiR")
# Example usage
data(stagec,package = "rpart")
options(na.action = 'na.pass')
dataCancer <- cbind(pgstat = stagec$pgstat,
pgtime = stagec$pgtime,
as.data.frame(
model.matrix(Surv(pgtime,pgstat) ~ .,stagec))[-1])
#Impute missing values
dataCancerImputed <- nearestNeighborImpute(dataCancer)
data(cancerVarNames)
UniRankFeaturesRaw <- univariateRankVariables(variableList = cancerVarNames,
formula = "pgstat ~ 1+pgtime",
Outcome = "pgstat",
data = dataCancer,
categorizationType = "Raw",
type = "LOGIT",
rankingTest = "zIDI",
description = "Description",
uniType="Binary")
print(UniRankFeaturesRaw)
# A simple BSIWMS Model
BSWiMSModel <- BSWiMS.model(formula = Surv(pgtime, pgstat) ~ 1, dataCancerImputed)
#The list of all models of the bootstrap forward selection
print(BSWiMSModel$forward.selection.list)
#With FRESA.CAD we can do a leave-one-out using the list of models
pm <- ensemblePredict(BSWiMSModel$forward.selection.list,
dataCancer,predictType = "linear",type="LOGIT",Outcome="pgstat")
#Ploting the ROC with 95
pm <- plotModels.ROC(cbind(dataCancer$pgstat,
pm$ensemblePredict),
main=("LOO Forward Selection Median Predict"))
#The plotModels.ROC provides the diagnosis confusion matrix.
summary(epi.tests(pm$predictionTable))
More examples of FRESA.CAD usage can be found at: https://rpubs.com/J_Tamez
Contributing
Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:
- Fork the repository.
- Create a new branch: git checkout -b feature/new-feature.
- Make your changes and commit them: git commit -m 'Add new feature'.
- Push to the branch: git push origin feature/new-feature.
- Submit a pull request.
License
This project is licensed under the LGPL>=2.0.
Contact
For any questions or feedback, feel free to contact us at:
Email: jose.tamezpena@tec.mx
Twitter: @tamezpena