A permutation test of differences between externally or internally defined groupings in compositional data sets

Testing group differences in compositional data, that is, multivariate data referring to parts of a whole, requires focussing on the relative information between components. This is commonly achieved by mapping the data into a sensible logratio coordinate system. Groupings are often defined by an externally given factor but can also emerge from internal features of the data, such as distinct zero patterns, which may reflect an underlaying structure of subpopulations. This work introduces the PERLOG test, a novel non-parametric permutation test to identify significant groupings based on pairwise logratios, the fundamental units of compositional information. The method is suitable for both externally and internally defined groupings. In particular, the case of groups defined according to zero patterns is discussed as a prominent example of the latter. The performance of the proposal as a statistical test and its advantages over conventional multivariate tests are demonstrated through simulation. Real-world applications are illustrated using data from studies on movement behaviours and time-use epidemiology.