Best practice in mixed effects modelling and multi-model inference in ecology
- Published
- Accepted
- Subject Areas
- Ecology, Evolutionary Studies, Statistics
- Keywords
- GLMM, mixed effects models, model selection, AIC, multi-model inference, overdispersion, model averaging, random effects, collinearity, type I error
- Copyright
- © 2017 Harrison et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Best practice in mixed effects modelling and multi-model inference in ecology. PeerJ Preprints 5:e3113v1 https://doi.org/10.7287/peerj.preprints.3113v1
Abstract
The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues relating to the use of information theory and multi-model inference in ecology, and demonstrate the tendency for data dredging to lead to greatly inflated Type I error rate (false positives) and impaired inference. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Simulation Methodology for Type I error rates and Figure 4
This file contains the methods used to conduct the Type I error rate simulations in the manuscript. For downstream processing of these data, R markdown documents are available on Figshare.