Identifying important pairwise logratios in compositional data with sparse principal component analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73634000" target="_blank" >RIV/61989592:15310/25:73634000 - isvavai.cz</a>
Alternative codes found
RIV/62690094:18450/25:50021776
Result on the web
<a href="https://link.springer.com/article/10.1007/s11004-024-10159-0" target="_blank" >https://link.springer.com/article/10.1007/s11004-024-10159-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11004-024-10159-0" target="_blank" >10.1007/s11004-024-10159-0</a>

Result language
angličtina
Original language name
Identifying important pairwise logratios in compositional data with sparse principal component analysis
Original language description
Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible pairs to consider quickly becomes too large even for medium-sized compositions, which may hinder interpretability in further multivariate analysis. Sparse methods can therefore be useful for identifying a few important pairwise logratios (and parts contained in them) from the total candidate set. To this end, we propose a procedure based on the construction of all possible pairwise logratios and employ sparse principal component analysis to identify important pairwise logratios. The performance of the procedure is demonstrated with both simulated and real-world data. In our empirical analysis, we propose three visual tools showing (i) the balance between sparsity and explained variability, (ii) the stability of the pairwise logratios, and (iii) the importance of the original compositional parts to aid practitioners in their model interpretation.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GF22-15684L" target="_blank" >GF22-15684L: Generalized relative data and robustness in Bayes spaces</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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