成对比较
Lasso(编程语言)
图形模型
适度
计算机科学
多元统计
构造(python库)
网络模型
高斯分布
数据挖掘
机器学习
人工智能
量子力学
物理
万维网
程序设计语言
作者
Jonas M B Haslbeck,Denny Borsboom,Lourens Waldorp
标识
DOI:10.1080/00273171.2019.1677207
摘要
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 article, 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 MNMs and propose an ℓ1-regularized nodewise regression approach to estimate them. 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.
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