ArticleJournal of applied statistics2023
Three approaches to supervised learning for compositional data with pairwise logratios.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Identifying Important Pairwise Logratios in Compositional Data with Sparse Principal Component Analysis.Mathematical geosciences · 2025Article
- Orthonormal pairwise logratio selection (OPALS) algorithm for compositional data analysis in high dimensions.Bioinformatics advances · 2025Article
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.Current opinion in plant biology · 2023Review
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
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2 authors.
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Abstract
Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that
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