Impact of Covariates in Compositional Models and Simplicial Derivatives


  • Joanna Morais Toulouse School of Economics
  • Christine Thomas-Agnan Toulouse School of Economics



In the framework of Compositional Data Analysis, vectors carrying relative information, also called compositional vectors, can appear in regression models either as dependent or as explanatory variables. In some situations, they can be on both sides of the regression equation. Measuring the marginal impacts of covariates in these types of models is not straightforward since a change in one component of a closed composition automatically affects the rest of the composition.

Previous work by the authors has shown how to measure, compute and interpret these marginal impacts in the case of linear regression models with compositions on both sides of the equation. The resulting natural interpretation is in terms of an elasticity, a quantity commonly used in econometrics and marketing applications. They also demonstrate the link between these elasticities and simplicial derivatives.

The aim of this contribution is to extend these results to other situations, namely when the compositional vector is on a single side of the regression equation. In these cases, the marginal impact is related to a semi-elasticity and also linked to some simplicial derivative. Moreover we consider the possibility that a total variable is used as an explanatory variable, with several possible interpretations of this total and we derive the elasticity formulas in that case.

Author Biography

Joanna Morais, Toulouse School of Economics

Decision Mathematics and Statistics department




How to Cite

Morais, J., & Thomas-Agnan, C. (2021). Impact of Covariates in Compositional Models and Simplicial Derivatives. Austrian Journal of Statistics, 50(2), 1–15.



Special Issue on Compositional Data Analysis (CoDaWork 2019)