Compositional Data Analysis

By Michael J. Greenacre. Annual Review of Statistics and Its Application, 2021

143 citations recorded by the source corpus.

Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1% or 100%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.

Subfields: Artificial Intelligence, Environmental Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/d93f7f2d324a46eebc04f555083fdd733a085f7d

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Citations in the corpus

Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal (2022, background): “The general idea is to study the behavior of random subcompositions of different sizes and see the effect on either the geometry of the subcompositional parts or on results such as effect sizes and p-values, if the parts are used in modelfitting (an example of the latter in a regression context is g”