Linear Mixed-Effects Models Using R: A Step-by-Step Approach [Paperback]

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, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linearmodels presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.

Introduction.- Linear Models for Independent Observations.- Linear Fixed-effects Models for Correlated Data.- Linear Mixed-effects Models.

From the reviews:

Overall, this is very well-written book that covers both LMs and LMMs. Most of the R codes have been checked and work well. The R package nlmeU created by the authors provides great convenience for readers to explore the data in the textbook. Given the extensive applications of LM and LMM, the book should be very appealing to the readers of Technometrics.
Techonometrló0