DirichletReg: Dirichlet Regression for Compositional Data in R

Marco Maier

Publication: Working/Discussion PaperWU Working Paper

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Abstract

Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to transform the data. There are two parametrization for the presented model, one using the common Dirichlet distribution's alpha parameters, and a reparametrization of the alpha's to set up a mean-and-dispersion-like model. By applying appropriate link-functions, a GLM-like framework is set up that allows for the analysis of such data in a straightforward and familiar way, because interpretation is similar to multinomial logistic regression. This paper gives a brief theoretical foundation and describes the implementation as well as application (including worked examples) of Dirichlet regression methods implemented in the package DirichletReg (Maier, 2013) in the R language (R Core Team, 2013).
Original languageEnglish
Publication statusPublished - 1 Apr 2014

Publication series

NameResearch Report Series / Department of Statistics and Mathematics
No.125

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 501 not use (legacy)
  • 509 not use (legacy)
  • 102022 Software development
  • 101018 Statistics

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

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