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. 2019 Feb 28;9:3046. doi: 10.1038/s41598-019-39377-x

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© The Author(s) 2019

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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The MN-MS model performs slightly worse than the Poisson regression models in identifying the true non-zero slopes (left panel) but performs substantially better in identifying the true zero slopes (right panel). Results for the Poisson model without model selection (Poisson no MS; purple), with AIC model selection (Poisson AIC MS; red), and the MN model with model selection (MN-MS; blue) are displayed. A 1:1 line was added for reference (dashed diagonal line), where results closer to this line indicate better performance. Circles represent the median, thick lines represent the 20–80% range, while thin lines represent the full range (minimum to maximum) based on 10 datasets. Left panel: The x-axis displays the true number of non-zero slopes used to generate the data while the y-axis reveals how many of these slopes were correctly identified to be non-zero and were estimated with the correct sign. Right panel: The x-axis displays the true number of zero slopes used to generate the data while the y-axis reveals how many of these slopes were correctly identified to be zero.