Mathematical Geosciences

, 41:905 | Cite as

Correlation Analysis for Compositional Data

  • Peter Filzmoser
  • Karel Hron


Compositional data need a special treatment prior to correlation analysis. In this paper we argue why standard transformations for compositional data are not suitable for computing correlations, and why the use of raw or log-transformed data is neither meaningful. As a solution, a procedure based on balances is outlined, leading to sensible correlation measures. The construction of the balances is demonstrated using a real data example from geochemistry. It is shown that the considered correlation measures are invariant with respect to the choice of the binary partitions forming the balances. Robust counterparts to the classical, non-robust correlation measures are introduced and applied. By using appropriate graphical representations, it is shown how the resulting correlation coefficients can be interpreted.


Correlation analysis Ilr transformation Log-ratio transformation Compositional data Balances Subcompositions Amalgamation Robust statistics 


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Copyright information

© International Association for Mathematical Geosciences 2008

Authors and Affiliations

  1. 1.Dept. of Statistics and Probability TheoryVienna University of TechnologyViennaAustria
  2. 2.Dept. of Mathematical Analysis and Applications of MathematicsPalacký University OlomoucOlomoucCzech Republic

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