Lmsubsets: Exact variable-subset selection in linear regression for R

14Citations
Citations of this article
21Readers
Mendeley users who have this article in their library.

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 pre-determined 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.

References Powered by Scopus

A New Look at the Statistical Model Identification

41224Citations
N/AReaders
Get full text

Regression Shrinkage and Selection Via the Lasso

36000Citations
N/AReaders
Get full text

Regularization paths for generalized linear models via coordinate descent

12370Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Bess: An R package for best subset selection in linear, logistic and cox proportional hazards models

30Citations
N/AReaders
Get full text

Biomarkers of environmental enteric dysfunction are not consistently associated with linear growth velocity in rural Zimbabwean infants

19Citations
N/AReaders
Get full text

Modelling impact of site and terrain morphological characteristics on biomass of tree species in Putorana region

8Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Hofmann, M., Gatu, C., Kontoghiorghes, E. J., Colubi, A., & Zeileis, A. (2020). Lmsubsets: Exact variable-subset selection in linear regression for R. Journal of Statistical Software, 93(3). https://doi.org/10.18637/jss.v093.i03

Readers over time

‘19‘20‘21‘22‘23‘2402468

Readers' Seniority

Tooltip

Researcher 7

50%

PhD / Post grad / Masters / Doc 4

29%

Professor / Associate Prof. 2

14%

Lecturer / Post doc 1

7%

Readers' Discipline

Tooltip

Environmental Science 4

44%

Mathematics 3

33%

Chemical Engineering 1

11%

Computer Science 1

11%

Save time finding and organizing research with Mendeley

Sign up for free
0