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 language | English (US) |
|---|---|
| Pages (from-to) | 824-828 |
| Number of pages | 5 |
| Journal | Structural Equation Modeling |
| Volume | 25 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 3 2018 |
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)