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

Name: Data for: Missing data imputation via the Expectation-Maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns.
Creator: Cristian Ricci
Published: 2020-02-06T04:37:45.893Z
Keywords: Nutrition

Ricci, Cristian; Smuts, Cornelius; Malan, Linda; Baumgartner, Jeannine

doi:10.17632/4jc6tdgx4s.1

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 February 2020| Version 1 | DOI: 10.17632/4jc6tdgx4s.1

Contributors:

Cristian Ricci,

Cornelius Smuts,

Linda Malan,

Jeannine Baumgartner

Description

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

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

Description

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