The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Data Collection
2.3. Data Cleaning
2.4. Data Transformation
2.5. Principal Component Analysis
2.6. Clustering and Consolidation
2.7. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Maugeri, A.; Barchitta, M.; Favara, G.; La Mastra, C.; La Rosa, M.C.; Magnano San Lio, R.; Agodi, A. The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns. Nutrients 2023, 15, 195. https://doi.org/10.3390/nu15010195
Maugeri A, Barchitta M, Favara G, La Mastra C, La Rosa MC, Magnano San Lio R, Agodi A. The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns. Nutrients. 2023; 15(1):195. https://doi.org/10.3390/nu15010195
Chicago/Turabian StyleMaugeri, Andrea, Martina Barchitta, Giuliana Favara, Claudia La Mastra, Maria Clara La Rosa, Roberta Magnano San Lio, and Antonella Agodi. 2023. "The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns" Nutrients 15, no. 1: 195. https://doi.org/10.3390/nu15010195
APA StyleMaugeri, A., Barchitta, M., Favara, G., La Mastra, C., La Rosa, M. C., Magnano San Lio, R., & Agodi, A. (2023). The Application of Clustering on Principal Components for Nutritional Epidemiology: A Workflow to Derive Dietary Patterns. Nutrients, 15(1), 195. https://doi.org/10.3390/nu15010195