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Exploration of geochemical data with compositional canonical biplots
Jan Graffelman1,2, Vera Pawlowsky-Glahn3, Juan José Egozcue4
1Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Avinguda Diagonal 647, Barcelona 08028, Spain.
Journal of Geochemical Exploration
|January 29, 2021
Summary
This study introduces compositional canonical correlation analysis (CoDA-CCO) to explore relationships in geochemical data. CoDA-CCO, using log-ratio transformations and biplots, effectively reveals key compositional associations.
Area of Science:
- Geochemistry
- Statistical Analysis
- Data Science
Background:
- Analyzing relationships between multiple chemical compositions is crucial in geochemistry.
- Traditional methods may not adequately handle the complexities of compositional data.
Purpose of the Study:
- To develop a compositional approach to canonical correlation analysis (CoDA-CCO).
- To introduce and evaluate compositional canonical biplots for visualizing compositional relationships.
Main Methods:
- Utilized centered log-ratio (CLR) and pairwise log-ratio transformations.
- Employed generalized inverses to handle structurally singular covariance matrices.
- Developed and analyzed compositional canonical biplots.
Main Results:
- CoDA-CCO effectively identifies salient relationships between geochemical compositions.
- Compositional canonical biplots provide powerful visualization tools.
- Demonstrated the utility of the method on a European floodplain dataset.
Conclusions:
- The proposed CoDA-CCO method offers a robust framework for analyzing compositional data in geochemistry.
- Compositional canonical biplots are effective for exploring complex multivariate relationships.
Keywords:
BiplotBiplot linkBiplot rayCanonical loadingsCanonical weightsFloodplain sedimentGeneralized least squaresGoodness-of-fitLog-ratio transformationMoore-Penrose inverseX-ray fluorescence spectrometryMore Related Videos
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