The ridge regression estimator, one of the commonly used alternatives to the conventional ordinary least squares estimator, avoids the adverse effects in the situations when there exists some considerable degree of multicollinearity among the regressors. There are many software packages available for estimation of ridge regression coefficients. However, most of them display limited methods to estimate the ridge biasing parameters without testing procedures. Our developed package, lmridge can be used to estimate ridge coefficients considering a range of different existing biasing parameters, to test these coefficients with more than 25 ridge related statistics, and to present different graphical displays of these statistics.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-060.zip
lmridge, ridge, MASS, lrmest, ltsbase, penalized, glmnet, RXshrink, rrBLUP, RidgeFusion, bigRR, lpridge, genridge, CoxRidge
MachineLearning, Survival, Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Psychometrics, Robust, SocialSciences
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For attribution, please cite this work as
Ullah, et al., "lmridge: A Comprehensive R Package for Ridge Regression", The R Journal, 2018
BibTeX citation
@article{RJ-2018-060,
author = {Ullah, Muhammad Imdad and Aslam, Muhammad and Altaf, Saima},
title = {lmridge: A Comprehensive R Package for Ridge Regression},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-060},
doi = {10.32614/RJ-2018-060},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {326-346}
}