A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS


  • Yunli Liu University of Virginia Author
  • Xin Tong University of Virginia Author




Bayesian analysis, Compositional data, Lasso, Spike and slab lasso


This tutorial offers an exploration of advanced Bayesian methodologies for compositional data analysis, specifically the Bayesian Lasso and Bayesian Spike-and-Slab Lasso (SSL) techniques. Our focus is on a novel Bayesian methodology that integrates Lasso and SSL priors, enhancing both parameter estimation and variable selection for linear regression with compositional predictors. The tutorial is structured to streamline the learning process, breaking down complex analyses into a series of straightforward steps. We demonstrate these methods using R and JAGS, employing simulated datasets to illustrate key concepts. Our objective is to provide a clear and comprehensive understanding of these sophisticated Bayesian techniques, preparing readers to adeptly navigate and apply these methods in their own compositional data analysis endeavors.

Author Biography

  • Xin Tong, University of Virginia

    Dr. Xin (Cynthia) Tong is an associate professor in the Department of Psychology at the University of Virginia. Methodologically, her research is focused on Bayesian methodology, statistical computing, robust and interpretable longitudinal studies, and missing data analysis. Substantively, she is interested in longitudinal development of cognitive ability and achievement skills, healthcare analytics, and sustainability research. Her most recent research is on Bayesian quantile longitudinal analysis, funded by NSF.

    Dr. Tong is a faculty fellow of the LIFE Academy.




How to Cite

Liu, Y., & Tong, X. (2024). A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS. Journal of Behavioral Data Science, 4(1), 81-104. https://doi.org/10.35566/jbds/tongliu