logistf: Firth's Bias-Reduced Logistic Regression
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>.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
mDAG |
| Reverse imports: |
AUtests, ExactMed, GWASTools, multisite.accuracy, PhenStat, pogit, rnaEditr, SeqVarTools, Surrogate |
| Reverse suggests: |
EHR, ggeffects, insight, metamisc, phyr |
| Reverse enhances: |
MuMIn |
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