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 regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. 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.
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 regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. 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.
| Original language | English |
|---|---|
| Article number | e4794 |
| Pages (from-to) | 1-32 |
| Number of pages | 32 |
| Journal | PeerJ |
| Volume | 6 |
| Early online date | 27 Apr 2018 |
| DOIs | |
| Publication status | Published - 23 May 2018 |
Bibliographical note
AcknowledgementsThis paper is the result of a University of Exeter workshop on best practice for the application of mixed effects models and model selection in ecological studies
Funding
Xavier A. Harrison was funded by an Institute of Zoology Research Fellowship. David Fisher was funded by NERC studentship NE/H02249X/1. Lynda Donaldson was funded by NERC studentship NE/L501669/1. Beth S. Robinson was funded by the University of Exeter and the Animal and Plant Health Agency as part of ‘Wildlife Research Co-Operative’. Maria Correa-Cano was funded by CONACYT (The Mexican National Council for Science and Technology) and SEP (The Mexican Ministry of Education). Cecily Goodwin was funded by the Forestry Commission and NERC studentship NE/L501669/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords
- GLMM
- Mixed effects models
- model selection
- AIC
- multimodel inference
- overdispersion
- model averaging
- random effects
- collinearity
- type 1 error
