reglogit (version 1.2-2)

reglogit-package: Simulation-based Regularized Logistic Regression

Description

Regularized (polychotomous) 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.

Details

ll{ Package: reglogit Type: Package Version: 1.0 Date: 2011-08-05 License: LGPL LazyLoad: yes }

See the documentation for the reglogit function

References

R.B. Gramacy, N.G. Polson. Simulation-based regularized logistic regression. (2010); arXiv:1005.3430; http://arxiv.org/abs/1005.3430

See Also

reglogit, blasso and regress

Examples

## see the help file for the reglogit function