Volume 45, Issue 4
ORIGINAL ARTICLE

General approach to coordinate representation of compositional tables

Kamila Fačevicová

Corresponding Author

E-mail address: kamila.facevicova@gmail.com

Department of Mathematics, Palacký University Olomouc

Correspondence: Kamila Facevicova, Department of Mathematics, Faculty of Education, Palacký University, Žižkovo nám. 5, CZ‐77140 Olomouc, Czech Republic.

Email: kamila.facevicova@gmail.com

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Karel Hron

Department of Mathematical Analysis and Applications of Mathematics, Palacký University Olomouc

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Valentin Todorov

United Nations Industrial Development Organisation

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Matthias Templ

Institute of Data Analysis and Process Design, Zurich University of Applied Sciences

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First published: 23 April 2018
Citations: 3

Abstract

Compositional tables can be considered a continuous counterpart to the well‐known contingency tables. Their cells, which generally contain positive real numbers rather than just counts, carry relative information about relationships between two factors. Hence, compositional tables can be seen as a generalization of (vector) compositional data. Due to their relative character, compositions are commonly expressed in orthonormal coordinates using a sequential binary partition prior to being further processed by standard statistical tools. Unfortunately, the resulting coordinates do not respect the two‐dimensional nature of compositional tables. Information about relationship between factors is thus not well captured. The aim of this paper is to present a general system of orthonormal coordinates with respect to the Aitchison geometry, which allows for an analysis of the interactions between factors in a compositional table. This is achieved using logarithms of odds ratios, which are also widely used in the context of contingency tables.

Number of times cited according to CrossRef: 3

  • Robust principal component analysis for compositional tables, Journal of Applied Statistics, 10.1080/02664763.2020.1722078, (1-20), (2020).
  • Convex clustering method for compositional data modeling, Soft Computing, 10.1007/s00500-020-05355-z, (2020).
  • Compositional Tables, Applied Compositional Data Analysis, 10.1007/978-3-319-96422-5_12, (227-243), (2018).

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