Linear Mixed-Effects Models Using R A Step-by-Step Approach
Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, a
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Andrzej Gałecki • Tomasz Burzykowski
Linear Mixed-Effects Models Using R A Step-by-Step Approach
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Andrzej Gałecki University of Michigan 300 North Ingalls Building Ann Arbor Michigan USA
Tomasz Burzykowski Center for Statistics Hasselt University Agoralaan D Diepenbeek Belgium
ISSN 1431-875X ISBN 978-1-4614-3899-1 ISBN 978-1-4614-3900-4 (eBook) DOI 10.1007/978-1-4614-3900-4 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012941857 © Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Bliskim mojemu sercu Oli i Łukaszowi Rodzinie i Nauczycielom Dekadentom – A.T.G. Moim najbli˙zszym i przyjaciołom – T.B. In memory of Tom Ten Have
Preface
Linear mixed-effects models (LMMs) are powerful modeling tools that allow for the analysis of datasets with complex, hierarchical structures. Intensive research during the past decade has led to a better understanding of their properties. The growing body of literature, including recent monographs, has considerably increased their popularity among applied researchers. There are several statistical software packages containing routines for LMMs. These include, for instance, SAS, SPSS, STAT
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