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.
|Author||Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb]|
|Date of publication||2016-12-19 17:15:26|
|Maintainer||Georg Heinze <email@example.com>|
add1.logistf: Add or Drop All Possible Single Terms to/from a 'logistf'...
anova.logistf: Analysis of Penalized Deviance for 'logistf' Models
backward: Backward Elimination of Model Terms in 'logistf' Models
CLIP.confint: Confidence Intervals after Multiple Imputation: Combination...
CLIP.profile: Combine Profile Likelihoods from Imputed-Data Model Fits
is.logistf: Check 'logistf' Objects
logistf: Firth's Bias-Reduced Logistic Regression
logistf.control: Control Parameters for 'logistf'
logistf-package: Firth's Bias-Reduced Logistic Regression
logistftest: Penalized Likelihood Ratio Test
plot.logistf.profile: 'plot' Method for 'logistf' Likelihood Profiles
print.logistf: 'print' Method for 'logistf' Objects
print.logistftest: 'print' method for 'logistftest' objects
profile.logistf: Compute Profile Penalized Likelihood
PVR.confint: Pseudo-Variance Modification of Rubin's Rules
sex2: Urinary Tract Infection in American College Students
summary.logistf: 'summary' Method for 'logistf' Objects
vcov.logistf: 'vcov' Method for 'logistf' Objects