Multilevel modeling using R - Finch, W. Holmes (William Holmes); Bolin, Jocelyn E.; Kelley, Ken (Professor of information technology) (New York, NY : CRC Press, 2019)
Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.
MGLMs for a Dichotomous Outcome VariableRandom Intercept Logistic Regression; Random Coefficients Logistic Regression; Inclusion of Additional Level-1 and Level-2 Effects in MGLM; MGLM for an Ordinal Outcome Variable; Random Intercept Logistic Regression; MGLM for Count Data; Random Intercept Poisson Regression; Random Coefficient Poisson Regression; Inclusion of Additional Level-2 Effects to the Multilevel Poisson Regression Model; Summary; 9: Bayesian Multilevel Modeling; MCMCglmm for a Normally Distributed Response Variable; Including Level-2 Predictors with MCMCglmm; User Defined Priors
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Keywords: Social sciences -- Statistical methods; Multivariate analysis; R (Computer program language); MATHEMATICS / Probability & Statistics / General