logistf.mod.control

Sets parameters for <code>logistf</code> calls.

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>.

Georg Heinze

logistf

Firth's Bias-Reduced Logistic Regression

Meinhard Ploner

Daniela Dunkler

Harry Southworth

Lena Jiricka

Gregor Steiner

logistf.mod.control function

<dl><dt>tau</dt>
<dd>Penalization parameter (default = 0.5)</dd>
<dt>terms.fit</dt>
<dd>A numeric vector of terms to fit. Intercept has to be included if needed.</dd></dl>

Arguments

Controls additional parameters for logistf — logistf.mod.control

<dl>

<dt>tau</dt>
<dd>Penalization parameter (default = 0.5)</dd>


<dt>terms.fit</dt>
<dd>A numeric vector of terms to fit. Intercept has to be included if needed.</dd>

</dl>

logistf.mod.control: Controls additional parameters for `logistf`

Description

Usage

Value

Arguments

Examples