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Bayes Factor Model Comparisons Across Parameter Values for Mixed Models

机译:混合模型参数值之间的贝叶斯因子模型比较

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Abstract Nested data structures, in which conditions include multiple trials and are fully crossed with participants, are often analyzed using repeated-measures analysis of variance or mixed-effects models. Typically, researchers are interested in determining whether there is an effect of the experimental manipulation. These kinds of analyses have different appropriate specifications for the null and alternative models, and a discussion on which is to be preferred and when is sorely lacking. van Doorn et al. (2021) performed three types of Bayes factor model comparisons on a simulated data set in order to examine which model comparison is most suitable for quantifying evidence for or against the presence of an effect of the experimental manipulation. Here, we extend their results by simulating multiple data sets for various scenarios and by using different prior specifications. We demonstrate how three different Bayes factor model comparison types behave under changes in different parameters, and we make concrete recommendations on which model comparison is most appropriate for different scenarios.
机译:抽象的嵌套数据结构,包括多个试验和充分条件交叉与参与者,经常分析使用重复测量方差分析mixed-effects模型。确定是否有一个感兴趣实验操作的影响。有不同种类的分析恰当规范零和替代模型,和讨论首选,是非常缺乏。et al .(2021)执行三种贝叶斯因子模型模拟数据集比较为了检验模型的比较最适合或量化依据的存在的影响实验操作。结果通过模拟多个数据集各种场景和通过使用不同的之前规范。不同的贝叶斯因子模型比较类型不同参数下的行为变化,我们做模型的具体建议比较是最适合于不同场景。

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