DirichletReg: Dirichlet Regression for Compositional Data in R

Marco Maier

Publikation: Working/Discussion PaperWU Working Paper

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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).
PublikationsstatusVeröffentlicht - 1 Apr. 2014


ReiheResearch Report Series / Department of Statistics and Mathematics

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 501
  • 509
  • 102022 Softwareentwicklung
  • 101018 Statistik

WU Working Paper Reihe

  • Research Report Series / Department of Statistics and Mathematics