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Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics) by Andrzej Galecki, Tomasz Burzykowski is a nonfiction available to read on EtoBox.

What is Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics) about?

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 linear models 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, metho

Who reads Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)?

Author

Andrzej Galecki, Tomasz Burzykowski

Publisher

Springer New York; Imprint: Springer

Published

2013

Language

EN

ISBN

9781461439004

Category

nonfiction

Subjects

Mathematics, Computer Science, Stem

Updated

2026-03-24