Multivariate Generalized Linear Mixed Models Using R

Damon Mark Berridge, Robert Crouchley

April 25, 2011 by CRC Press
Reference - 304 Pages - 18 B/W Illustrations
ISBN 9781439813263 - CAT# K10680


Add to Wish List
FREE Standard Shipping!


  • Provides a rigorous yet accessible account of key issues that arise in the fitting of MGLMMs
  • Uses the statistical software package SabreR, which efficiently fits MGLMMs
  • Presents real-life examples from a wide range of application areas
  • Includes download instructions, training materials, and much more on a website dedicated to Sabre


Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.

A Unified Framework for a Broad Class of Models
The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples.

Improve Your Longitudinal Study
In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.