Mixed-e?ects 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-e?ects models is explained by the ?exibility they o?er in modeling the within-group correlation often present in grouped data, by the handling of balanced and unbalanced data in a uni?ed framework, and by the availability of reliable and e?cient software for ?tting them. This book provides an overview of the theory and application of l- ear and nonlinear mixed-e?ects models in the analysis of grouped data. A uni?ed 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.
Mixed-Effects Models in S and S-Plus
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