Abstract
As shown in the examples in Chapter 6, nonlinear mixed-effects models offer a flexible tool for analyzing grouped data with models that depend nonlinearly upon their parameters. As nonlinear models are usually based on a mechanistic model of the relationship between the response and the covariates, their parameters can have a theoretical interpretation and are often of interest in their own right. In this chapter, we describe in detail the facilities in the nlme library for fitting nonlinear mixed-effects models.
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(2000). Fitting Nonlinear Mixed-Effects Models. In: Mixed-Effects Models in Sand S-PLUS. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0318-1_8
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DOI: https://doi.org/10.1007/978-1-4419-0318-1_8
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