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logistf (version 1.23.1)

Firth's Bias-Reduced Logistic Regression

Description

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.

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Version

Install

install.packages('logistf')

Monthly Downloads

12,154

Version

1.23.1

License

GPL

Maintainer

Georg Heinze

Last Published

September 4th, 2020

Functions in logistf (1.23.1)

logistf.control

Control Parameters for logistf
profile.logistf

Compute Profile Penalized Likelihood
print.logistftest

print method for logistftest objects
logistf

Firth's Bias-Reduced Logistic Regression
CLIP.profile

Combine Profile Likelihoods from Imputed-Data Model Fits
vcov.logistf

vcov Method for logistf Objects
CLIP.confint

Confidence Intervals after Multiple Imputation: Combination of Likelihood Profiles
PVR.confint

Pseudo-Variance Modification of Rubin`s Rules
add1.logistf

Add or Drop All Possible Single Terms to/from a logistf Model
logistpl.control

Control Parameters for logistf Profile Likelihood Confidence Interval Estimation
logistftest

Penalized Likelihood Ratio Test
backward

Backward Elimination of Model Terms in logistf Models
plot.logistf.profile

plot Method for logistf Likelihood Profiles
anova.logistf

Analysis of Penalized Deviance for logistf Models
logistf-package

Firth's Bias-Reduced Logistic Regression
is.logistf

Check logistf Objects
print.logistf

print Method for logistf Objects
sex2

Urinary Tract Infection in American College Students
summary.logistf

summary Method for logistf Objects