The chiPower transformation: a valid alternative to logratio transformations in compositional data analysis

• Published in: Advances in data analysis and classification 2024-09, Vol.18 (3), p.769-796
• Main Author: Greenacre, Michael
• Format: Article
• Language: English
• Subjects: Chemistry and Earth Sciences; Coherence; Computer Science; Data analysis; Data Mining and Knowledge Discovery; Dimensional analysis; Economics; Finance; Generalized linear models; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Parameter identification; Physics; Regular Article; Statistical models; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Supervised learning
• ISSN: 1862-5347; 1862-5355
• DOI: 10.1007/s11634-024-00600-x

The approach to analysing compositional data has been dominated by the use of logratio transformations, to ensure exact subcompositional coherence and, in some situations, exact isometry as well. A problem with this approach is that data zeros, found in most applications, have to be replaced to allow the logarithmic transformation. An alternative new approach, called the ‘chiPower’ transformation, which allows data zeros, is to combine the standardization inherent in the chi-square distance in correspondence analysis, with the essential elements of the Box-Cox power transformation. The chiPower transformation is justified because it defines between-sample distances that tend to logratio distances for strictly positive data as the power parameter tends to zero, and are then equivalent to transforming to logratios. For data with zeros, a value of the power can be identified that brings the chiPower transformation as close as possible to a logratio transformation, without having to substitute the zeros. Especially in the area of high-dimensional data, this alternative approach can present such a high level of coherence and isometry as to be a valid approach to the analysis of compositional data. Furthermore, in a supervised learning context, if the compositional variables serve as predictors of a response in a modelling framework, for example generalized linear models, then the power can be used as a tuning parameter in optimizing the accuracy of prediction through cross-validation. The chiPower-transformed variables have a straightforward interpretation, since they are identified with single compositional parts, not ratios.