Data for: Missing data imputation via the Expectation-Maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns.

Published: 6 Feb 2020 | Version 1 | DOI: 10.17632/4jc6tdgx4s.1
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Description of this data

NHANES datasets, nutrient intakes and plasma fatty acids. Subsets from Surveys 2003-2004

Experiment data files

This data is associated with the following publication:

Missing data imputation via the expectation-maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns

Published in: Nutrition Research

Latest version

  • Version 1

    2020-02-06

    Published: 2020-02-06

    DOI: 10.17632/4jc6tdgx4s.1

    Cite this dataset

    Ricci, Cristian; Smuts, Cornelius; Malan, Linda; Baumgartner, Jeannine (2020), “Data for: Missing data imputation via the Expectation-Maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns.”, Mendeley Data, v1 http://dx.doi.org/10.17632/4jc6tdgx4s.1

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Nutrition

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