summary.brglmFit

an object of class <code>"glm"</code>, usually, a result of a
 call to <code><a rd-options="stats" href="/link/glm?package=brglm2&version=0.7.1&to=stats" data-mini-rdoc="stats::glm">glm</a></code>.

object

the dispersion parameter for the family used.
 Either a single numerical value or <code>NULL</code> (the default), when
 it is inferred from <code>object</code> (see &#8216;Details&#8217;).

dispersion

logical; if <code>TRUE</code>, the correlation matrix of
 the estimated parameters is returned and printed.

correlation

logical. If <code>TRUE</code>, print the correlations in
 a symbolic form (see <code><a rd-options="stats" href="/link/symnum?package=brglm2&version=0.7.1&to=stats" data-mini-rdoc="stats::symnum">symnum</a></code>) rather than as numbers.

symbolic.cor

further arguments passed to or from other methods.

<code>summary</code> method for <code><a rd-options="" href="/link/brglmFit?package=brglm2&version=0.7.1" data-mini-rdoc="brglm2::brglmFit">brglmFit</a></code> objects

Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.

Ioannis Kosmidis

brglm2

Bias Reduction in Generalized Linear Models

Kjell Konis

Nicola Sartori

summary.brglmFit function

an object of class <code>"glm"</code>, usually, a result of a
 call to <code><a rd-options='stats' href='glm'>glm</a></code>.

logical. If <code>TRUE</code>, print the correlations in
 a symbolic form (see <code><a rd-options='stats' href='symnum'>symnum</a></code>) rather than as numbers.

<code>summary</code> method for <code><a rd-options='' href='brglmFit'>brglmFit</a></code> objects

<code>summary</code> method for <code><a rd-options='' href='brglmFit'>brglmFit</a></code> objects — summary.brglmFit

<code>summary</code> method for <code><a rd-options='' href='brglmFit'>brglmFit</a></code> objects

<code>summary</code> method for <code><a rd-options="" href="/link/brglmFit?package=brglm2&version=0.7.1" data-mini-rdoc="brglm2::brglmFit">brglmFit</a></code> objects

summary.brglmFit: `summary` method for `brglmFit` objects

Description

Usage

Arguments

Details

See Also

Examples