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Your search keyword '"principal balances"' showing total 3 results
Start Over Descriptor "principal balances" Topic compositional data analysis
3 results on '"principal balances"'

1. How to choose a proper representation of compositional data for mineral exploration?

Author

Sadeghi, Behnam, Molayemat, Hossein, and Pawlowsky-Glahn, Vera

Subjects
*PROSPECTING, *PRINCIPAL components analysis, *REAL variables, *RANDOM variables, *GEOCHEMISTRY
Abstract

Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale. [Display omitted] • Geochemical data possess a compositional nature, necessitating meaningful representation • Among compositional tools, clr and olr coordinates are used for representation • Extensive till geochemical data from across Sweden were employed for comparison • Both models exhibited strikingly similar results in terms of anomaly separation • The ilr-based model enhanced accuracy in delineating anomalous zones [ABSTRACT FROM AUTHOR]

Published
2024
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2. Compositional analysis of dietary patterns.

Author

Solans, M, Coenders, G, Marcos-Gragera, R, Castelló, A, Gràcia-Lavedan, E, Benavente, Y, Moreno, V, Pérez-Gómez, B, Amiano, P, Fernández-Villa, T, Guevara, M, Gómez-Acebo, I, Fernández-Tardón, G, Vanaclocha-Espi, M, Chirlaque, MD, Capelo, R, Barrios, R, Aragonés, N, Molinuevo, A, and Vitelli-Storelli, F

Subjects
*MULTIPLE correspondence analysis (Statistics), *DATA analysis
Abstract

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Compositional analysis of dietary patterns

Author

Marina Pollán, Facundo Vitelli-Storelli, Rafael Marcos-Gragera, Adela Castelló, Rocío Capelo, Pilar Amiano, Marta Solans, Gemma Castaño-Vinyals, Mercedes Vanaclocha-Espi, Germà Coenders, M-D Chirlaque, Marcela Guevara, Guillermo Fernández-Tardón, Trinidad Dierssen-Sotos, Amaia Molinuevo, Jesús Castilla, Tania Fernández-Villa, Marc Saez, Beatriz Pérez-Gómez, Manolis Kogevinas, R Barrios, Inés Gómez-Acebo, Yolanda Benavente, Esther Gracia-Lavedan, Nuria Aragonés, and Victor Moreno

Subjects
Statistics and Probability, Adult, Male, Epidemiology, dietary patterns, Biology, Health outcomes, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Nutrient, principal balances, Health Information Management, 32 Ciencias Médicas, Environmental health, Surveys and Questionnaires, Humans, Multicenter Studies as Topic, 030212 general & internal medicine, 0101 mathematics, Dietary patterns, Aged, Aged, 80 and over, Models, Statistical, Principal (computer security), Compositional data analysis, MCC-Spain, Feeding Behavior, Dietary pattern, Middle Aged, Control subjects, Principal balances, Spain, Case-Control Studies, Principal component analysis, Female, epidemiology, Compositional data
Abstract

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was partially funded by the ‘‘Accion Transversal del Cancer’’, approved by the Spanish Ministry Council on 11 October 2007; by the Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP); by the Instituto de Salud Carlos III-FEDER (grant nos PI08/1770, PI08/0533, PI08/1359, PS09/00773-Cantabria, PS09/01286-Leo´n, PS09/ 01903-Valencia, PS09/02078-Huelva, PS09/01662-Granada, PI11/01403, PI11/01810, PI11/02213, PI12/00488, PI14/01219, PI14/0613, PI15/00069, PI15/00914, PI15/01032, PI17/01280, PI09/0914, IJCI-2014-20900); by the Spanish Ministry of Health (grant no. CB06/02/1002); by the Spanish Ministry of Economy and Competitiveness (grant no. MTM2015-65016- C2-1-R); by the Catalan Government-Agency for Management of University and Research Grants (AGAUR) (grant nos 2014SGR551, 2017SGR656, 2017SGR733, 2017SGR723, 2017SGR1085); by the University of Girona (grant no. MPCUdG2016/069, GDRCompetUdG2017/19); by the Fundacio´n Marque´s de Valdecilla (grant no. API 10/09); by the Junta de Castilla y Leo´n (grant no. LE22A10-2); by the Consejerı´a de Salud of the Junta de Andalucı´a (grant nos PI-0571- 2009, PI-0306-2011, salud201200057018tra); by the Conselleria de Sanitat of the Generalitat Valenciana (grant no. AP_061/10); by the Regional Government of the Basque Country; by the Consejerı´a de Sanidad de la Regio´n de Murcia; by the European Commission (FOOD-CT-2006-036224-HIWATE); by the Spanish Association Against Cancer (AECC) Scientific Foundation; by the Fundacio´n Caja de Ahorros de Asturias; and by the University of Oviedo. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.

Published
2019

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