Simulation-based regularized logistic regression (reglogit R package)
reglogit is an R package for regularized logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface.
New: the latest version supports
- Polychotomous (3+) outputs through regmlogit and
- Faster computation for sparse design matrices
This software is licensed under the GNU Lesser Public
License (LGPL), version 2 or later.
- Obtain R from cran.r-project.org by selecting the version for your operating system.
- Install the reglogit, mvtnorm and boot packages, from within R.
> install.packages(c("reglogit", "mvtnorm", "boot", "Matrix"))
- Load the library as you would for any R library.
- See the package documentation. A pdf version of the
reference manual, or help pages, as also available.
The help pages can be accessed from within
R. Try starting with...
?reglogit # follow the examples
- For details on the use of this software for estimating player abilities in hockey, please see the paper linked below and our Chicago Hockey Analytics page.
- Simulation-based regularized logistic regression (2012) with Nicholas Polson; Bayesian Analysis, 7(3), pp. 567-590; preprint on arXiv:1005.3430
- Estimating player contribution in hockey with regularized logistic regression (2013) with Shane Jensen, and Matt Taddy. Journal of Quantitative Analysis in Sports, 9(1), pp. 97-111; preprint on arXiv:1209.5026
Please send questions and comments to rbgramacy_AT (_chicagobooth_DOT_edu). Enjoy!
Robert B. Gramacy -- 2013