activity
20212023
most citedThree approaches to supervised learning for compositional data with pairwise logratios

13 citations · 29 across the 6 of their papers we have counts for

collaborators

6 papers

physics.geo-ph2023★ 1 cited

GeoCoDA: Recognizing and Validating Structural Processes in Geochemical Data. A Workflow on Compositional Data Analysis in Lithogeochemistry

Eric Grunsky, Michael Greenacre, Bruce Kjarsgaard

Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-…

stat.ME2022

The chiPower transformation: a valid alternative to logratio transformations in compositional data analysis

Michael Greenacre

The approach to analysing compositional data has been dominated by the use of logratio transformations, to ensure exact subcompositional coherence and, in some situations, exact is…

stat.AP2022★ 4 cited

A Guideline for the Statistical Analysis of Compositional Data in Immunology

Jinkyung Yoo, Zequn Sun, Michael Greenacre +3

The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of v…

stat.ME2022★ 11 cited

Aitchison's Compositional Data Analysis 40 Years On: A Reappraisal

Michael Greenacre, Eric Grunsky, John Bacon-Shone +2

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…

stat.ML2021★ 13 cited

Three approaches to supervised learning for compositional data with pairwise logratios

Germa Coenders, Michael Greenacre

The common approach to compositional data analysis is to transform the data by means of logratios. Logratios between pairs of compositional parts (pairwise logratios) are the easie…

stat.ME2021

Compositional data analysis -- linear algebra, visualization and interpretation

Michael Greenacre

Compositional data analysis is concerned with multivariate data that have a constant sum, usually 1 or 100\%. These are data often found in biochemistry and geochemistry, but also…