A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

Frédéric Bertrand and Myriam Maumy-Bertrand

https://doi.org/10.32614/CRAN.package.SelectBoost

The SelectBoost package implements SelectBoost: a general algorithm to enhance the performance of variable selection methods https://doi.org/10.1093/bioinformatics/btaa855, F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2015),

With the growth of big data, variable selection has become one of the major challenges in statistics. Although many methods have been proposed in the literature their performance in terms of recall and precision are limited in a context where the number of variables by far exceeds the number of observations or in a high correlated setting.

Results: This package implements a new general algorithm which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.

Installation

You can install the released version of SelectBoost from CRAN with:

install.packages("SelectBoost")

You can install the development version of SelectBoost from github with:

devtools::install_github("fbertran/SelectBoost")

If you are a Linux/Unix or a Macos user, you can install a version of SelectBoost with support for doMC from github with:

devtools::install_github("fbertran/SelectBoost", ref = "doMC")

Examples

First example: Simulated dataset

Simulating data

Create a correlation matrix for two groups of variable with an intragroup correlation value of