TY  - JOUR
SP  - 19
VL  - 10
IS  - 1
N1  - © The Author(s), 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
UR  - https://doi.org/10.6339/JDS.2012.10(1).1013
SN  - 1680-743X
A1  - Bountziouka, Vassiliki
A1  - Panagiotakos, Demosthenes B
JF  - Journal of Data Science
AV  - public
Y1  - 2022/08/04/
EP  - 36
TI  - The Role of Rotation Type used to Extract Dietary Patterns through Principal Component Analysis, on their Short-Term Repeatability
PB  - School of Statistics, Renmin University of China
N2  - Principal components analysis (PCA) is a widely used technique in nutritional epidemiology, to extract dietary patterns. To improve the interpretation of the derived patterns, it has been suggested to rotate the axes defined by PCA. This study aimed to evaluate whether rotation influences the repeatability of these patterns. For this reason PCA was applied in nutrient data of 500 participants (37 ± 15 years, 38% male) who were voluntarily enrolled in the study and asked to complete a semi-quantitative food frequency questionnaire (FFQ), twice within 15 days. The varimax and the quartimax orthogonal rotation methods, as well as the non-orthogonal promax and the oblimin methods were applied. The degree of agreement between the similar extracted patterns by each rotation method was assessed using the Bland and Altman method and Kendall?s tau-b coefficient. Good agreement was observed between the two administrations of the FFQ for the un-rotated components, while low-to-moderate agreement was observed for all rotation types (the quartimax and the oblimin method lead to more repeatable results). To conclude, when rotation is needed to improve food patterns? interpretation, the quartimax and the oblimin methods seems to produce more robust results.
ID  - discovery10202984
KW  - Multivariate analysis
KW  -  principal
KW  -  components analysis
KW  -  rotation type
ER  -