Karel Hron

TL;DR: It turns out that the procedure using ilr‐transformed data and robust PCA delivers superior results to all other approaches, demonstrating that due to the compositional nature of geochemical data PCA should be carried out without an appropriate transformation.

TL;DR: It can be demonstrated that data closure must be overcome prior to calculating even simple statistical measures like mean or standard deviation or plotting graphs of the data distribution, e.g. a histogram.

TL;DR: The R-package robCompositions (Templ et al., 2009) contains functions for robust statistical methods designed for compositional data, like principal component analysis, factor analysis, and discriminant analysis.

TL;DR: The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

TL;DR: The results show that the proposed methods outperform standard imputation methods in the presence of outliers, and the model-based method with robust regressions is preferable.