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.
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