Generalized Linear Mixed-Effects Modeling Programs in R for Binary Outcomes. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Lee, Wooyeol; Grimm, Kevin J.
• Is Part Of: Structural equation modeling. Volume 25:Issue 5(2018)
• Page Start: 824
• Page End: 828
• Publication Date: 2018-09-03
• Subjects: generalized linear mixed-effects models -- R -- software review; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1500141
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : We review, examine the performance, and discuss the relative strengths and weaknesses of various R functions for the estimation of generalized linear mixed-effects models (GLMMs) for binary outcomes. The R functions reviewed include glmer in the package lme4, hglm2 in the package hglm, MCMCglmm in the package MCMCglmm, and inla in the package INLA. We illustrate the use of these functions through an empirical example and provide sample code.