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PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes

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  • Liverani, Silvia
  • Hastie, David I.
  • Azizi, Lamiae
  • Papathomas, Michail
  • Richardson, Sylvia

Abstract

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non- parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.

Suggested Citation

  • Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
  • Handle: RePEc:jss:jstsof:v:064:i07
    DOI: http://hdl.handle.net/10.18637/jss.v064.i07
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    References listed on IDEAS

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    7. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    8. Lauren Hoskovec & Wande Benka-Coker & Rachel Severson & Sheryl Magzamen & Ander Wilson, 2021. "Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
    9. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
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    11. Eric Coker & Robert Gunier & Asa Bradman & Kim Harley & Katherine Kogut & John Molitor & Brenda Eskenazi, 2017. "Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years," IJERPH, MDPI, vol. 14(5), pages 1-20, May.
    12. Silvia Liverani & Lucy Leigh & Irene L. Hudson & Julie E. Byles, 2021. "Clustering method for censored and collinear survival data," Computational Statistics, Springer, vol. 36(1), pages 35-60, March.
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    15. Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.

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