Flora Le | Searcharxiv

arXiv2024

Bayesian multilevel compositional data analysis with the R package multilevelcoda

Multilevel compositional data, such as data sampled over time that are non-negative and sum to a constant value, are common in various fields. However, there is currently no software specifically built to model compositional data in a multilevel framework. The R package multilevelcoda implements a collection of tools for modelling compositional data in a Bayesian multivariate, multilevel pipeline. The user-friendly setup only requires the data, model formula, and minimal specification of the analysis. This paper outlines the statistical theory underlying the Bayesian compositional multilevel modelling approach and details the implementation of the functions available in multilevelcoda, using an example dataset of compositional daily sleep-wake behaviours. This innovative method can be used to gain robust answers to scientific questions using the increasingly available multilevel compositional data from intensive, longitudinal studies.

stat.ME↗

arXiv2024

Bayesian multilevel compositional data analysis: introduction, evaluation, and application

Multilevel compositional data are data that are repeatedly measured or clustered within groups and are non-negative and sum to a constant value. These data arise in various settings, such as intensive, longitudinal studies using ecological momentary assessments and wearable devices. Examples include 24h sleep-wake behaviours, sleep architecture, and macronutrients. This article presents a novel method for analysing multilevel compositional data using Bayesian inference. We describe the theoretical details of the data and the models, and outline the steps necessary to implement this method. We introduce the R package multilevelcoda to facilitate the application of this method and illustrate using a real data example. An extensive parameter recovery simulation study verified the robust performance of the method. Across all conditions investigated in the simulation study, the fitted models had minimal convergence issues (convergence rate > 99%) and achieved excellent quality parameter estimates and inference, with an average bias of 0.00 (range -0.09, 0.05) and coverage of 0.95 (range 0.93, 0.97). We conclude the article with recommendations on the use of the Bayesian multilevel compositional data analysis. We hope to promote wider application of this method to gain novel and robust answers to scientific questions.

stat.ME↗