van Doorn Johnny, Aust Frederik, Haaf Julia M, Stefan Angelika M, Wagenmakers Eric-Jan
Department of Psychological Methods, University of Amsterdam, Valckeniersstraat 59, 1018 XA Amsterdam, the Netherlands.
Comput Brain Behav. 2023;6(1):127-139. doi: 10.1007/s42113-022-00158-x. Epub 2023 Feb 14.
In van Doorn et al. (2021), we outlined a series of open questions concerning Bayes factors for mixed effects model comparison, with an emphasis on the impact of aggregation, the effect of measurement error, the choice of prior distributions, and the detection of interactions. Seven expert commentaries (partially) addressed these initial questions. Surprisingly perhaps, the experts disagreed (often strongly) on what is best practice-a testament to the intricacy of conducting a mixed effect model comparison. Here, we provide our perspective on these comments and highlight topics that warrant further discussion. In general, we agree with many of the commentaries that in order to take full advantage of Bayesian mixed model comparison, it is important to be aware of the specific assumptions that underlie the to-be-compared models.
在范·多恩等人（2021年）的研究中，我们概述了一系列关于混合效应模型比较的贝叶斯因子的开放性问题，重点关注聚集的影响、测量误差的效应、先验分布的选择以及交互作用的检测。七篇专家评论（部分地）探讨了这些初始问题。也许令人惊讶的是，专家们（常常是强烈地）对于什么是最佳实践存在分歧——这证明了进行混合效应模型比较的复杂性。在此，我们阐述我们对这些评论的看法，并突出值得进一步讨论的主题。总体而言，我们同意许多评论的观点，即要充分利用贝叶斯混合模型比较，重要的是要了解待比较模型所基于的具体假设。
