Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained.
|Depends:||R (≥ 3.0.0)|
|Author:||Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb]|
|Maintainer:||Georg Heinze <georg.heinze at meduniwien.ac.at>|
|License:||GPL-2 | GPL-3 [expanded from: GPL]|
|CRAN checks:||logistf results|
|Windows binaries:||r-devel: logistf_1.22.zip, r-release: logistf_1.22.zip, r-oldrel: logistf_1.22.zip|
|OS X El Capitan binaries:||r-release: logistf_1.22.tgz|
|OS X Mavericks binaries:||r-oldrel: logistf_1.22.tgz|
|Old sources:||logistf archive|
|Reverse imports:||apricom, AUtests, pogit, Surrogate|
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