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. 2023 Apr 11;62(03-04):119–129. doi: 10.1055/a-2048-7692

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The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ )

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.

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Fig. 5 — Correlation plots for the Golub data and difference between the correlation matrix estimated from the original data and the mean correlation matrix estimated by 500 runs of Mock Data Generation (modgo). ( A ) Correlation matrix for the original data; ( B ) mean correlation matrix of 500 datasets simulated by modgo; ( C ) mean correlation matrix of 500 datasets simulated by modgo, when all correlations were estimated with the Pearson correlation coefficient; ( D) mean correlation matrix of 500 datasets simulated by modgo, when a large tolerance of 10 is used for generating the normally distributed random variables was used; ( D ) mean correlation matrix of 500 datasets simulated by modgo; ( E ) difference between the correlation matrix estimated from the original data and the mean correlation matrix estimated by 500 runs of modgo; ( F ) difference between the correlation matrix estimated from the original data and the mean correlation matrix estimated by 500 runs of modgo, when correlations were estimated using Pearson correlation; ( G ) difference between the correlation matrix estimated from the original data and the mean correlation matrix estimated by 500 runs of modgo, when a large tolerance was used in generating the normally distributed random variables.