Nonlinear Mixed-effects Models: Basic Concepts and Motivating Examples

Part of the Statistics and Computing book series (SCO)

Chapter Summary

This chapter gives an introductory overview of the nonlinear mixed-effects model, describing its basic concepts and assumptions and relating it to the linear mixed-effects model described in the first part of the book. Real-life examples from pharmacokinetics studies and an agricultural experiment are used to illustrate the use of the nlme function in S, and its associated methods, for fitting and analyzing NLME models.

The many similarities between NLME and LME models allow most of the lme methods defined in the first part of the book to also be used with the nlme objects introduced in this section. There are, however, important differences between the two models, and the methods used to fit them, which translate into more complex estimation algorithms and less accurate inference for NLME models.

The purpose of this chapter is to present the motivation for using NLME models with grouped data and to set the stage for the following two chapters in the book, dealing with the theory and computational methods for NLME models (Chapter 7) and the nonlinear modeling facilities in the nlme library (Chapter 8).


Soybean Plant Standardize Residual Subject Structure Previous Item Biexponential Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag New York, Inc. 2000

Personalised recommendations