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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Ensuring identifiability in hierarchical mixed effects Bayesian models.

Kiona Ogle¹, Jarrett J Barber¹

¹School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, 86011, USA.

Ecological Applications : a Publication of the Ecological Society of America

|May 5, 2020

Summary

This summary is machine-generated.

Bayesian statistical models in ecology can face identifiability issues. This study offers practical solutions for diagnosing and fixing these problems in complex models using popular software.

Keywords:

MCMC crossed effects equifinality fixed effects hierarchical model identifiability multi-level model nested effects prior distribution random effects sum-to-zero sweeping

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Area of Science:

Ecology
Statistics
Computational Biology

Background:

Bayesian statistical modeling and Markov chain Monte Carlo (MCMC) are increasingly used by ecologists.
Complex ecological data often requires hierarchical or multi-level models with fixed and/or random effects.
However, subtle implementation problems, particularly with effect identifiability, are common and often overlooked.

Purpose of the Study:

To highlight common implementation pitfalls in Bayesian hierarchical models used in ecology.
To focus on the critical issue of effect identifiability and its impact on inference.
To provide practical solutions for diagnosing and resolving these identifiability issues.

Main Methods:

Utilized random effects regressions on synthetic data to illustrate identifiability problems.
Demonstrated diagnostic techniques for identifying these issues.
Proposed remediation strategies including model reparameterization and specific computational/coding practices.

Main Results:

Identified specific implementation pitfalls that can lead to misinformed inference in complex ecological models.
Showcased how to diagnose and resolve effect identifiability issues.
Provided adaptable code examples for popular software (JAGS, OpenBUGS, Stan).

Conclusions:

Effect identifiability is a crucial, yet often neglected, aspect of Bayesian modeling in ecology.
Model reparameterization and careful computational/coding practices are effective solutions.
The presented methods and code facilitate more robust inference for complex ecological models.