A brief introduction to mixed effects modelling and multi-model inference in ecology

Harrison, Xavier A.; Donaldson, Lynda; Correa-Cano, Maria Eugenia; Evans, Julian; Fisher, David N.; Goodwin, Cecily E.D.; Robinson, Beth S.; Hodgson, David J.; Inger, Richard

doi:10.7717/peerj.4794

Cited by 2,094Statistical Methods and Bayesian InferenceAgriculture, Soil, Plant ScienceStatistical Methods and Applications

A brief introduction to mixed effects modelling and multi-model inference in ecology

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Harrison, Xavier A., Donaldson, Lynda, Correa-Cano, Maria Eugenia, Evans, Julian, Fisher, David N., Goodwin, Cecily E.D., Robinson, Beth S., Hodgson, David J. +1 more·2018·PeerJ

DOI 10.7717/peerj.4794PMC PMC5970551

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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.

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Recent advances make it possible to fit generalized linear mixed models with a variety of error distributions or to use randomization techniques such as bootstrapping.
“Recent advances in statistical modeling appear to have solved this problem, because it is now possible to fit generalized linear mixed models “glmm” with a variety of error distributions (e.g., binomial, Poisson, zero-inflated Poisson, negative binomial; Harrison et al., 2018; O'Hara, 2009) or to use a range of randomization techniques such as bootstrapping (Good, 2005)”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Harrison et al., 2018
Controlling for overdispersion in counts through a glmm with an observation-level random effect fixed the problem of inflated type I error rates for distributions D2 and D7.
“Controlling for overdispersion in counts through the use of a glmm with an observation-level random effect (Harrison et al., 2018) fixed the problem of inflated type I error rates for distributions D2 and D7 (Fig. 3, indicated in red)”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Harrison et al., 2018
The use of a linear mixed model with different levels of correlations was necessary because data were unbalanced; this allowed all data to be used, and including participant as a random effect ensured each participant had their own baseline level, and the estimated change from baseline to visit 5 can be interpreted as the mean change from this level.
“Furthermore, as data were unbalanced, the use of a linear mixed model with different levels of correlations was necessary.42 This allowed all data to be used, and by including participant as a random effect, it ensured that each participant had their own baseline level, and the estimated change from baseline to visit 5 can be interpreted as the mean change from this level.”
From Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen (2020) · cites this paper as [42]

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Best practice in mixed effects modelling and multi-model inference in ecologyPreprint
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2017) · doi:10.7287/peerj.preprints.3113v1 · Cited by 11 · see citing works
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Multimodel inference in ecology and evolution: challenges and solutions
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Perils and pitfalls of mixed-effects regression models in biology
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Self-citation
Model selection and multimodel inference: a practical information-theoretic approach.
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Authors: Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R.
Affiliations: Zoological Society of London (GB)
University of Exeter (GB)
Environment and Sustainability Institute, University of Exeter, Penryn, UK
University of Ottawa (CA)
Corresponding author: Harrison, Xavier A.
ORCID: Harrison, Xavier A.
Donaldson, Lynda
Evans, Julian
Fisher, David N.
Goodwin, Cecily E.D.
Robinson, Beth S.
Hodgson, David J.
Inger, Richard
Venue: PeerJ
Publisher: PeerJ
Published: 2018-05-23
Volume: 6
Pages: e4794
ISSN: 2167-8359 (ISSN-L)
DOI: 10.7717/peerj.4794
PMC: PMC5970551
OpenAlex: W2783564415
Cited by: 2,094
References: 91
Citations by year: 2026: 227 · 2025: 228 · 2024: 271 · 2023: 246 · 2022: 327 · 2021: 383 · 2020: 261 · 2019: 116 · 2018: 32 · 2014: 1
FWCI: 164.31
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PeerJ (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Institute of Zoology Research Fellowship
NERC studentship (NE/H02249X/1, NE/L501669/1)
University of Exeter and the Animal and Plant Health as part of ‘Wildlife Research Co-Operative’
CONACYT (The Mexican National Council for Science and Technology)
SEP (The Mexican Ministry of Education)
Forestry Commission
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Agriculture, Soil, Plant Science
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: linear mixed models, multi-model inference, model selection, information theory, ecological data analysis, statistical pitfalls

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A brief introduction to mixed effects modelling and multi-model inference in ecology

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