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BayesLCA: An R Package for Bayesian Latent Class Analysis

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  • White, Arthur
  • Murphy, Thomas Brendan

Abstract

The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian setting. Three methods for fitting the model are provided, incorporating an expectation-maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology behind each of these techniques and discusses some of the technical difficulties associated with them. Methods to remedy these problems are also described. Visualization methods for each of these techniques are included, as well as criteria to aid model selection.

Suggested Citation

  • White, Arthur & Murphy, Thomas Brendan, 2014. "BayesLCA: An R Package for Bayesian Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i13).
    • Handle: RePEc:jss:jstsof:v:061:i13
    DOI: http://hdl.handle.net/10.18637/jss.v061.i13
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    References listed on IDEAS

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    1. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
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    Cited by:

    1. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    2. Francesco Giovinazzi & Daniela Cocchi, 2022. "Social Integration of Second Generation Students in the Italian School System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 287-307, February.
    3. Tariq Kewan & Arda Durmaz & Waled Bahaj & Carmelo Gurnari & Laila Terkawi & Hussein Awada & Olisaemeka D. Ogbue & Ramsha Ahmed & Simona Pagliuca & Hassan Awada & Yasuo Kubota & Minako Mori & Ben Ponvi, 2023. "Molecular patterns identify distinct subclasses of myeloid neoplasia," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Weicong Lyu & Jee-Seon Kim & Youmi Suk, 2023. "Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 3-36, February.
    5. Paula Carroll & Arthur White, 2017. "Identifying Patterns of Learner Behaviour: What Business Statistics Students Do with Learning Resources," INFORMS Transactions on Education, INFORMS, vol. 18(1), pages 1-13, September.
    6. Ye, Mao & Zhang, Peng & Nie, Lizhen, 2018. "Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 32-49.
    7. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference for the DINA Model," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 569-597, October.
    8. Kazuhiro Yamaguchi, 2023. "Bayesian Analysis Methods for Two-Level Diagnosis Classification Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 773-809, December.
    9. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 973-995, December.

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