Mixed-Effects Models in S and S-Plus

ISBN-13: 9780387989570 / Angielski / Twarda / 2000 / 528 str.

Jose C. Pinheiro; J. C. Pinheiro; Josi Pinheiro

Opis

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. The authors present a unified model-building strategy for both models and apply this to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing. Much emphasis is placed on the use of graphical displays at the various phases of the model-building process, starting with exploratory plots of the data and concluding with diagnostic plots to assess the adequacy of a fitted model. The NLME library for analyzing mixed-effects models in S and S-PLUS, developed by the authors, provides the underlying software for implementing the methods presented. This balanced mix of real data examples, modeling software, and theory makes the 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.

Recenzje

"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)

Spis treści

Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of Grouped Data * Fitting LME Models * Extending the Basic LME Model * Nonlinear Mixed-Effects * Theory and Computational Methods for NLME Models * Fitting NLME Models

O autorze

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.