In certain fields where compositional data are studied, the compositional components, called parts, can be combined into certain subsets, called amalgamations, that are based on domain knowledge. Furthermore, these subsets can form a natural hierarchy of amalgamations subdividing into sub-amalgamations. The authors, a statistician and a biochemist, demonstrate how to create a hierarchy of amalgamations in the context of fatty acid compositions in a sample of marine organisms. Following a tradition in compositional data analysis, these amalgamations are transformed to logratios, and their usefulness as new variables is quantified by the percentage of total logratio variance that they explain. This method is proposed as an alternative method of variable selection in compositional data analysis.

翻译：在研究成分数据的某些领域中，称为部分的成分组分可以根据领域知识组合成特定的子集，称为合并。此外，这些子集可以形成一种自然的合并层次结构，进一步细分为子合并。本文作者（一位统计学家和一位生物化学家）以海洋生物样本中的脂肪酸组成为背景，展示了如何构建合并的层次结构。遵循成分数据分析的传统，这些合并被转换为对数比，并通过它们解释的总对数比方差百分比来量化其作为新变量的有效性。该方法被提出作为成分数据分析中变量选择的替代方法。