vcov.brglmFit

a fitted model object, typically. Sometimes also a
 <code><a rd-options="" href="/link/summary?package=brglm2&version=0.6.0" data-mini-rdoc="brglm2::summary">summary</a>()</code> object of such a fitted model.

object

character specyfying for which component of the model coefficients shoould be extracted.

model

additional arguments for method functions. For the
 <code><a rd-options="" href="/link/glm?package=brglm2&version=0.6.0" data-mini-rdoc="brglm2::glm">glm</a></code> method this can be used to pass a
 <code>dispersion</code> parameter.

Return the variance-covariance matrix for the regression parameters
in a <code><a rd-options="" href="/link/brglmFit?package=brglm2&version=0.6.0" data-mini-rdoc="brglm2::brglmFit">brglmFit</a></code> object

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. (2016) <arXiv:1604.04768>, 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) <http://www.jstor.org/stable/2345592>. See Kosmidis et al (2019) <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 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). '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

vcov.brglmFit function

a fitted model object, typically. Sometimes also a
 <code><a rd-options='' href='summary'>summary</a>()</code> object of such a fitted model.

additional arguments for method functions. For the
 <code><a rd-options='' href='glm'>glm</a></code> method this can be used to pass a
 <code>dispersion</code> parameter.

Return the variance-covariance matrix for the regression parameters
in a <code><a rd-options='' href='brglmFit'>brglmFit</a></code> object

Return the variance-covariance matrix for the regression parameters
in a <code><a rd-options='' href='brglmFit'>brglmFit</a></code> object — vcov.brglmFit

Return the variance-covariance matrix for the regression parameters
in a <code><a rd-options='' href='brglmFit'>brglmFit</a></code> object

Return the variance-covariance matrix for the regression parameters
in a <code><a rd-options="" href="/link/brglmFit?package=brglm2&version=0.6.0" data-mini-rdoc="brglm2::brglmFit">brglmFit</a></code> object

vcov.brglmFit: Return the variance-covariance matrix for the regression parameters in a `brglmFit` object

Description

Usage

Arguments