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When Fixed and Random Effects Mismatch: Another Case of Inflation of Evidence in Non-Maximal Models

机译：当固定效应和随机效应不匹配时：非极大模型中证据膨胀的另一种情况

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Abstract Mixed-effects models that include both fixed and random effects are widely used in the cognitive sciences because they are particularly suited to the analysis of clustered data. However, testing hypotheses about fixed effects in the presence of random effects is far from straightforward and a set of best practices is still lacking. In the target article, van Doorn et al. (Computational Brain Behavior, 2022) examined how Bayesian hypothesis testing with mixed-effects models is impacted by particular model specifications. Here, I extend their work to the more complex case of multiple correlated predictors, such as a predictor of interest and a covariate. I show how non-maximal models can display ‘mismatches’ between fixed and random effects, which occur when a model includes random slopes for the effect of interest, but fails to include them for those predictors that correlate with the effect of interest. Bayesian model comparisons with synthetic data revealed that such mismatches can lead to an underestimation of random variance and to inflated Bayes factors. I provide specific recommendations for resolving mismatches of this type: fitting maximal models, eliminating correlations between predictors, and residualising the random effects. Data and code are publicly available in an OSF repository at https://osf.io/njaup.
机译：抽象包括Mixed-effects模型广泛应用于固定和随机效应认知科学,因为他们特别适合集群数据的分析。然而,测试固定影响的假设在随机效应远的存在简单,是一组最佳实践仍然缺乏。et al。(计算大脑和行为,2022)研究了贝叶斯假设检验mixed-effects模型受到特别的影响模式规范。更复杂的情况下多个相关的预测,比如预测和兴趣协变量。显示固定和随机之间的不匹配影响,这发生在当一个模型包括随机的山坡上的利益的影响,但不能包括他们对于那些相关的预测与感兴趣的影响。与合成数据显示这样的不匹配会导致低估的随机方差和膨胀的贝叶斯因子。为解决提供具体建议这种类型的不匹配:配件最大模型,消除指标之间的相关性,和residualising随机效应。公开在OSF存储库吗https://osf.io/njaup。

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