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. 2022 Mar 23;22:111. doi: 10.1186/s12883-022-02633-1

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Factor selection using the least absolute shrinkage and selection operator (LASSO) logistic regression. A The LASSO coefficient profiles of the 40 candidate variables. The binomial deviance is plotted versus log (λ). B Tuning parameter (λ) selection in the LASSO logistic regression performed using tenfold cross-validation via the minimum criteria. A coefficient profile plot is produced versus the log (λ). The left vertical line represents the minimum error, and the right vertical line represents the cross-validated error within 1 standard error of the minimum