Includes bibliographical references (pages 689-694) and index.
Contents:
Bayesian multilevel modeling of categorical response variables. Review of basic statistics -- Logistic regression for binary data -- Proportional odds models for ordinal response variables -- Generalized ordinal logistic regression models and partial proportional odds models -- Other ordinal logistic regression models -- Multinomial logistic regression models -- Poisson regression models -- Negative binomial regression models and zero-inflated models -- Multilevel modeling for continuous response variables -- Multilevel modeling for binary response variables -- Multilevel modeling for ordinal response variables -- Multilevel modeling for count response variables -- Multilevel modeling for nominal response variables -- Bayesian generalized linear models -- Bayesian multilevel modeling of categorical response variables.
Summary:
"Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains PowerPoint slides and solutions for the end-of-chapter exercises on the instructor site, and datasets and R commands used in the book on the student site"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.