SelectBoost {SelectBoost}

SelectBoost

Description

Motivation: 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.

References

F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2020). SelectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Bioinformatics. doi:10.1093/bioinformatics/btaa855

SelectBoost was used to decypher networks in C. Schleiss, [...], M. Maumy-Bertrand, S. Bahram, F. Bertrand, and L. Vallat. (2021). Temporal multiomic modelling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia. Leukemia.

Examples

set.seed(314)
xran=matrix(rnorm(75),15,5)
ybin=sample(0:1,15,replace=TRUE)
yran=rnorm(15)

#For quick test purpose, not meaningful, should be run with greater value of B
#(disabling parallel computing as well)
res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE)


fastboost(xran,yran)
#Customize resampling levels
fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE)

#Binary logistic regression
fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)