Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Marc Hofmann, Cristian Gatu, Erricos J. Kontoghiorghes, Ana Colubi, Achim Zeileis
Title: lmSubsets: Exact Variable-Subset Selection in Linear Regression for R
Abstract: An R package for computing the all-subsets regression problem is presented. The proposed algorithms are based on computational strategies recently developed. A novel algorithm for the best-subset regression problem selects subset models based on a predetermined criterion. The package user can choose from exact and from approximation algorithms. The core of the package is written in C++ and provides an efficient implementation of all the underlying numerical computations. A case study and benchmark results illustrate the usage and the computational efficiency of the package.

Page views:: 2058. Submitted: 2018-05-02. Published: 2020-04-28.
Paper: lmSubsets: Exact Variable-Subset Selection in Linear Regression for R     Download PDF (Downloads: 659)
Supplements:
lmSubsets_0.5.tar.gz: R source package Download (Downloads: 49; 1MB)
v93i03-replication.zip: Replication materials Download (Downloads: 36; 422KB)
DOI: 10.18637/jss.v093.i03

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.