Mixed Effects Models involve measurements made over time on an individual in an experiment. This book presents the most recent techniques for analyzing this type of data in the statistical software program S-PLUS. It will be of interest to researchers and graduate students in statistics, biostatistics, and epidemiology.

This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data.

Autorentext
Mixed Effects Models involve measurements made over time on an
individual in an experiment. This book presents the most recent
techniques for analyzing this type of data in the statistical software
program S-PLUS. It will be of interest to researchers and graduate
students in statistics, biostatistics, and epidemiology.

Klappentext

Over 170 figures are included in the book. the material covered in the book is self-contained . The balanced mix of real data examples, modeling software, and theory makes this book a useful reference for practitioners who use, or intend to use, mixed-effects models in their data analyses. It can also be used as a text for a one-semester graduate-level applied course in mixed-effects models. (E.M.Psyadlo, zbMATH 0953.62065, 2022)

Inhalt
Linear Mixed-Effects Models.- Linear Mixed-Effects Models: Basic Concepts and Examples.- Theory and Computational Methods for Linear Mixed-Effects Models.- Describing the Structure of Grouped Data.- Fitting Linear Mixed-Effects Models.- Extending the Basic Linear Mixed-Effects Model.- Nonlinear Mixed-Effects Models.- Nonlinear Mixed-effects Models: Basic Concepts and Motivating Examples.- Theory and Computational Methods for Nonlinear Mixed-Effects Models.- Fitting Nonlinear Mixed-Effects Models.