9 Nonlinear and Generalized Linear Mixed Models
This chapter fuses the flexibility of nonlinear and generalized models with random-effects structures, yielding NLMMs and GLMMs. We articulate their marginal and conditional interpretations, describe approximation strategies, and address identifiability in high-level random coefficients. The chapter systematically compares NLMMs and GLMMs, illustrating when each is preferred, and provides a full estimation workflow—model building, convergence assessment, and inferential reporting.
Nonlinear Mixed Models (NLMMs) and Generalized Linear Mixed Models (GLMMs) extend traditional models by incorporating both fixed effects and random effects, allowing for greater flexibility in modeling complex data structures.
- NLMMs extend nonlinear models to include both fixed and random effects, accommodating nonlinear relationships in the data.
- GLMMs extend generalized linear models to include random effects, allowing for correlated data and non-constant variance structures.
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| Vol. | Title | Link |
|---|---|---|
| 1 | Foundations of Data Analysis | Buy on Springer |
| 2 | Regression Techniques for Data Analysis | Buy on Springer |
| 3 | Advanced Modeling and Data Challenges | Buy on Springer |
| 4 | Experimental Design | Buy on Springer |