Mixed-Effects Models in S and S-PLUS

Mixed-effects models provide a—exibleand powerful toolfor theanalysis of grouped data, which arise in many areas as diverse as agriculture, biology, economics, manufacturing, and geophysics. Examples of grouped data - clude longitudinal data, repeatedmeasures, blocked designs, and multilevel data. The increasing popularity of mixed-effects models is explained by the—exibility 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—tting them. This book provides an overview of the theory and application of l- ear and nonlinear mixed-effects models in the analysis of grouped data. A unified model-building strategy for both linear and nonlinear models is presentedandappliedtotheanalysisofover20realdatasetsfromawide- riety 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 withdiagnostic plots toassess the adequacy ofa—tted model. Over 170—gures are included in the book.