In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDselect package introduces a new forward stepwise based selection procedure to select the best model in different regression frameworks (parametric or nonparametric). The developed methodology, which can be equally applied to linear models, generalized linear models or generalized additive models, aims to introduce solutions to the following two topics: i) selection of the best combination of q variables by using a step-by-step method; and, perhaps, most importantly, ii) search for the number of covariates to be included in the model based on bootstrap resampling techniques. The software is illustrated using real and simulated data.
meifly, leaps, subselect, leaps, subselect, lars, glmnet, glmulti, bestglm, mgcv, FWDselect
ChemPhys, SocialSciences, MachineLearning, Bayesian, Econometrics, Environmetrics, Survival
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For attribution, please cite this work as
Sestelo, et al., "FWDselect: An R Package for Variable Selection in Regression Models", The R Journal, 2016
BibTeX citation
@article{RJ-2016-009,
author = {Sestelo, Marta and Villanueva, Nora M. and Meira-Machado, Luis and Roca-Pardiñas, Javier},
title = {FWDselect: An R Package for Variable Selection in Regression Models},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-009},
doi = {10.32614/RJ-2016-009},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {132-148}
}