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. 2023 Sep 8;24(1):123. doi: 10.1186/s10194-023-01638-6

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© The Author(s) 2023

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 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. 4 — Bayesian posterior distributions for sleep variables predicting migraine attacks. Larger deviations from typical sleep (A M = 0.06, 95% highest density interval (HDI) [0.04 – 0.08]) and a greater number of sleep interruptions (B M = 0.16, 95% HDI [0.11 –0.21]) predict occurrence of a next day migraine attack, but total hours slept does not (C M = -0.00, 95% HDI [-0.04 – 0.04]). Density plots show posterior distributions for the population means of the three tested sleep-related predictor variables on whether a migraine attack occurred or not. The x-axis represents b-value estimates of the parameter. Error bars indicate the 95% highest density interval, with the central point indicating the mean of the posterior distribution