Analyzing Compositional Data with R
By K. G. van den Boogaart, Raimon Tolosana-Delgado. 2013
507 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/2edd9659a4b84938f27c46b182e2072d51035e4d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vera Pawlowsky-Glahn
- Juan José Egozcue
- Michael J. Greenacre
- John Bacon-Shone
- Eric C. Grunsky
- Ionas Erb
- Thomas P. Quinn
- G. Gloor
Citations in the corpus
- Consistent and correctable bias in metagenomic sequencing experiments (2019, methodology): “In fact, our model of bias is equivalent to what is referred to as a compositional perturbation in the CoDA field; many CoDA methods are invariant to compositional perturbations (Aitchison, 2003; van den Boogaart and Tolosana-Delgado, 2013) and thus would be invariant to bias.”
- Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal (2022, background): “ILRs have thus been called the “perfect black box” [83].”
- Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “The clr transformed values can be used as inputs for multivariate hypothesis testing using tools such as MANOVA, regression etc. (Van den Boogaart and Tolosana-Delgado, 2013) and for model building.”