Compositional Cubes: A New Concept for Multi-factorial Compositions
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.
Cite
@article{arxiv.2201.10321,
title = {Compositional Cubes: A New Concept for Multi-factorial Compositions},
author = {Kamila Fačevicová and Peter Filzmoser and Karel Hron},
journal= {arXiv preprint arXiv:2201.10321},
year = {2022}
}