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. 2020 Jan 16;10:438. doi: 10.1038/s41598-019-57247-4

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Consequences of multiplicative (correlated and uncorrelated) measurement error for data analysis. (A) Scatter plot of the overlayed view of the first two components of two PCA models of simulated data sets; one without multiplicative error and one with multiplicative error. For visualization purposes, the scores are plotted in the same graph, but the subspaces spanned by the first two principal components for the two data sets are of course different. The labels on both axes also present the percentage explained variation for the two analyses. (B) Loading plot for the error free data. (C) Loading plot for the data with multiplicative error. See Material and Methods section 6.5.3 for details on the simulations.