文档介绍：To Elisa and Laura
To Mary Ellen, Barbara, and Michael
Preface
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 in-
clude 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 lin-
ear 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 va-
riety of areas, including ics, 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).
viii Preface
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