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Full Description
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 StudyIn 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.
Contents
Introduction. Generalized Linear Models for Continuous/Interval Scale Data. Generalized Linear Models for Other Types of Data. Family of Generalized Linear Models. Mixed Models for Continuous/Interval Scale Data. Mixed Models for Binary Data. Mixed Models for Ordinal Data. Mixed Models for Count Data. Family of Two-Level Generalized Linear Models. Three-Level Generalized Linear Models. Models for Multivariate Data. Models for Duration and Event History Data. Stayers, Non-Susceptibles, and Endpoints. Handling Initial Conditions/State Dependence in Binary Data. Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models. Appendices. Bibliography.
