23: Mixed-Effects Models

Author

Affiliation

Smit, A. J.

University of the Western Cape

1 Introduction

Mixed-effects models are the direct solution to the problem of non-independence and data dependence outlined in the previous chapter. These models provide a powerful and flexible framework for analyzing hierarchical, nested, or longitudinal data, which are ubiquitous in ecology.

2 Key Concepts

• Random Intercepts and Slopes: The core of mixed models. Understanding a random intercept as allowing the baseline response to vary among groups, and a random slope as allowing the effect of a predictor to vary among groups.
• Group-Level Variation: Explicitly modeling the variation among groups (e.g., sites, individuals, years) instead of treating it as a nuisance.
• Partial Pooling: How mixed models strike a balance between completely pooling all data (ignoring group structure) and analyzing each group separately.
• Ecological Examples: Applying mixed models to common ecological scenarios.

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