Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.
翻译:诸如高斯图形模型( GGM) 等对称网络模型( GGM) 是分析多变量数据依赖性的一种强大和直觉的方法。 GGM 的关键假设是, 每种对称互动都独立于所有其他变量的值。 但是, 在心理研究中, 这一点往往不可信。 在本文中, 我们通过允许两个变量( 一组) 以模型中所有其他变量来调节对称互动来扩展 GGGM, 从而引入一个调节网络模型( MNM ) 。 我们展示了如何构建 MNM 并提议一个 L1 常规的无偏向回归法来估算它。 我们提供模拟研究的性能结果, 并显示 MNMs 超越了基于多种样本的方法网络比较测试( NCT) 和 Fused Gigmagal Lasso ( FGL) 来检测调制效果。 最后, 我们提供了关于如何用R 组合 mgm 来估算 MNMs 并讨论可能存在的问题, 与模型区分 。