Generalized Linear Mixed-Effects Modeling Programs in R for Binary Outcomes

Wooyeol Lee, Kevin Grimm

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)824-828
Number of pages5
JournalStructural Equation Modeling
Volume25
Issue number5
DOIs
StatePublished - Sep 3 2018

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Mixed Effects
Binary Outcomes
Modeling
Linear Mixed Effects Model
performance

Keywords

  • generalized linear mixed-effects models
  • R
  • software review

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Cite this

Generalized Linear Mixed-Effects Modeling Programs in R for Binary Outcomes. / Lee, Wooyeol; Grimm, Kevin.

In: Structural Equation Modeling, Vol. 25, No. 5, 03.09.2018, p. 824-828.

Research output: Contribution to journalReview article

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