Mixed-Effects Models in S and S-PLUS

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Springer Science & Business Media, 15.04.2009 - 530 Seiten
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This paperback edition is a reprint of the 2000 edition.

This book provides 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. 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 areincluded in the book.

The NLME package for analyzing mixed-effects models in R and S-PLUS, developed by the authors, provides the underlying software for implementing the methods presented in the text, being described and illustrated in detail throughout the book.

The balanced mix of real data examples, modeling software, and theory makes this book a useful reference for practitioners using mixed-effects models in their data analyses. It can also be used as a text for a one-semester graduate-level applied course in mixed-effects models. Researchers in statistical computing will also find this book appealing for its presentation of novel and efficient computational methods for fitting linear and nonlinear mixed-effects models.

José C. Pinheiro is a Senior Biometrical Fellow at Novartis Pharmaceuticals, having worked at Bell Labs during the time this book was produced. He has published extensively in mixed-effects models, dose finding methods in clinical development, and other areas of biostatistics.

Douglas M. Bates is Professor of Statistics at the University of Wisconsin-Madison. He is the author, with Donald G. Watts, of Nonlinear Regression Analysis and Its Applications, a Fellow of the American Statistical Association, and a former chair of the Statistical Computing Section.

 

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Inhalt

Linear MixedEffects Models 1
3
Theory and Computational Methods for LME Models
57
Describing the Structure of Grouped Data
97
Fitting Linear MixedEffects Models
133
Extending the Basic Linear MixedEffects Model
201
Basic Concepts and Motivating
272
Theory and Computational Methods for NLME Models
305
Fitting Nonlinear MixedEffects Models
337
fixef
459
gapply
460
getGroups
461
gls
462
gnls
464
groupedData
466
gsummary
469
intervals
471

References
415
A Data Used in Examples and Exercises
423
OvaryCounts of Ovarian Follicles
437
PBGEffect of Phenylbiguanide on Blood Pressure
438
PBIBA Partially Balanced Incomplete Block Design
439
PhenobarbPhenobarbitol Kinetics
440
QuinidineQuinidine Kinetics
441
RailEvaluation of Stress in Rails
443
SpruceGrowth of Spruce Trees
444
WaferModeling of Analog MOS Circuits
448
B S Functions and Classes 451
450
ACF lme
452
anova lme
453
coef lme
455
coef lmList
457
fitted lme
458
intervals lmList
473
lme
474
lmeControl
476
lmList
478
logLik
479
nlmeControl
483
nlsList
485
pairs lme
486
plot lme
488
plot nfnGroupedData
490
plot nmGroupedData
492
plot Variogram
494
predict lme
495
A Collection of SelfStarting Nonlinear Regression
511
Index
523
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