Compositional Cubes: A New Concept for Multi-factorial Compositions

Kamila Fačevicová; Peter Filzmoser; Karel Hron

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arXiv · 2201.10321

Compositional Cubes: A New Concept for Multi-factorial Compositions

Kamila Fačevicová·Peter Filzmoser·Karel Hron

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Abstract

Compositional data are commonly known as multivariate observations carrying relative information. Even though the case of vector or even two-factorial compositional data (compositional tables) is already well described in the literature, there is still a need for a comprehensive approach to the analysis of multi-factorial relative-valued data. Therefore, this contribution builds around the current knowledge about compositional data a general theory of work with k-factorial compositional data. As a main finding it turns out that similar to the case of compositional tables also the multi-factorial structures can be orthogonally decomposed into an independent and several interactive parts and, moreover, a coordinate representation allowing for their separate analysis by standard analytical methods can be constructed. For the sake of simplicity, these features are explained in detail for the case of three-factorial compositions (compositional cubes), followed by an outline covering the general case. The three-dimensional structure is analysed in depth in two practical examples, dealing with systems of spatial and time dependent compositional cubes. The methodology is implemented in the R package robCompositions.

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Kamila Fačevicová, Peter Filzmoser, Karel Hron. 2022-01-25. Compositional Cubes: A New Concept for Multi-factorial Compositions. https://arxiv.org/abs/2201.10321

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