Statistical Methods By Sp Gupta.epub
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Because the number of clusters in dietary patterns is typically unknown a priori, clustering algorithms are developed and evaluated to find the number of clusters from multivariate data. When there are missing values, imputation methods are typically used to transform categorical data into a continuous or count variable. A variety of different analyses can then be conducted on the data to identify and characterize each pattern with respect to total energy intake, specific nutrients, the state of the environment and other factors. This clustering analysis identifies subpopulations that share similar food patterns and corresponding nutrient intakes. Data-driven dietary patterns are characterized by their frequency of consumption and also nutrient to calorie ratios of foods consumed
Dietary patterns can be modeled using principal component analysis (PCA) and factorial approaches. PCA is a commonly used method of data dimension reduction [9, 19, 67]. A single principal component is assumed to best represent the factors among the original variables, which therefore explain a total of all the information in the dataset. Derived PCA-based dietary patterns have shown a high degree of agreement with those derived by other approaches [9, 61].
Factorial approaches are generally derived by modeling the relative salience of food groups using multinomial logistic regression [15, 18, 19, 21, 40, 55, 59]. Although the mathematical objective of factorial approaches is similar to that of PCA-derived dietary patterns, factor analytic methods, particularly those proposed by Lu, et al. [15] differ from those PCA approaches in key respects. The most significant difference is that the factors are largely defined on the basis of categorical rather than quantitative data [15, 19]. In addition, factors can be ranked based on their relative salience compared to other factors, which allows ranking of dietary patterns according to their relative healthfulness. d2c66b5586
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