Mixed-Effects Models in S and S-PLUS

Abstract

Mixed-effects models provide a flexible and powerful tool for the analysis of grouped data, which arise in many areas as diverse as agriculture, biology, economics, manufacturing, and geophysics.Examples of grouped data include longitudinal data, repeated measures, blocked designs, and multilevel data.The increasing popularity of mixed-effects models is explained by the flexibility they offer in modeling the within-group correlation often present in grouped data, by the handling of balanced and unbalanced data in a unified framework, and by the availability of reliable and efficient software for fitting them.This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data.A unified model-building strategy for both linear and nonlinear models is presented and applied to the analysis of over 20 real datasets from a wide variety of areas, including pharmacokinetics, agriculture, and manufacturing.A strong 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.Over 170 figures are included in the book.The class of mixed-effects models considered in this book assumes that both the random effects and the errors follow Gaussian distributions.These models are intended for grouped data in which the response variable is (at least approximately) continuous.This covers a large number of practical applications of mixed-effects models, but does not include, for example, generalized linear mixed-effects models (Diggle, Liang and Zeger, 1994).

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