Firth's Bias-Reduced Logistic Regression
Add or Drop All Possible Single Terms to/from a logistf Model
Analysis of Penalized Deviance for logistf Models
Backward Elimination/Forward Selection of Model Terms in logistf Model...
Confidence Intervals after Multiple Imputation: Combination of Likelih...
Combine Profile Likelihoods from Imputed-Data Model Fits
Emmeans support for logistf
FLAC - Firth's logistic regression with added covariate
FLIC - Firth's logistic regression with intercept correction
Firth's Bias-Reduced Logistic Regression
Control Parameters for logistf
Controls additional parameters for logistf
Firth's Bias-Reduced Logistic Regression
Penalized likelihood ratio test
Control Parameters for logistf Profile Likelihood Confidence Interval ...
plot Method for logistf Likelihood Profiles
Predict Method for flac Fits
Predict Method for flic Fits
Predict Method for logistf Fits
Compute Profile Penalized Likelihood
Pseudo Variance Modification of Rubin's Rule
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, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.