The development of John Aitchison's approach to compositional data analysis is followed since his paper read to the Royal Statistical Society in 1982. Aitchison's logratio approach, which was proposed to solve the problematic aspects of working with data with a fixed sum constraint, is summarized and reappraised. It is maintained that the principles on which this approach was originally built, the main one being subcompositional coherence, are not required to be satisfied exactly -- quasi-coherence is sufficient in practice. This opens up the field to using simpler data transformations with easier interpretations and also for variable selection to be possible to make results parsimonious. The additional principle of exact isometry, which was subsequently introduced and not in Aitchison's original conception, imposed the use of isometric logratio transformations, but these have been shown to be problematic to interpret. If this principle is regarded as important, it can be relaxed by showing that simpler transformations are quasi-isometric. It is concluded that the isometric and related logratio transformations such as pivot logratios are not a prerequisite for good practice, and this conclusion is fully supported by a case study in geochemistry provided as an appendix.
翻译:John Aitchison自1982年向皇家统计学会宣读其论文以来,一直遵循了John Aitchison的构成数据分析方法的发展。Aitchison的logratio 方法旨在解决使用固定总额限制的数据的难题,该方法得到总结和重新评价,认为最初建立这一方法所依据的原则,主要是分组一致性的原则,不需要完全满足 -- -- 近似一致性在实践中已经足够。这打开了字段,可以使用简单的数据转换,提供更简单的解释,也可以让变量选择变得容易产生结果。后来引入的、而不是Aitchison最初设想的精确测量补充原则,强制使用等分数转换,但事实证明,这些是难以解释的原则。如果认为这一原则很重要,那么通过显示更简单的转换是准测量性即可放松。结论是,如pivotlogratio等等等等等等等等等异度和相关对数转换不是良好做法的先决条件,因此,精确的精确测量原则是良好做法的先决条件,而不是在Aitchison的初始概念中采用的,而这一结论则得到一个案例研究的充分支持。