论文标题
网络，回归，部分相关和潜在变量模型之间的关系
Relations between networks, regression, partial correlation, and latent variable model
论文作者
论文摘要
高斯图形模型（GGM）已成为分析心理变量网络的流行工具。在本期刊最近的一篇论文中，《福布斯》，《赖特》，马克和克鲁格（FWMK）表达了一个担忧，即从部分相关性估计的GGM错误地删除了其选民共享的差异。如果是真的，这种担忧会对GGM的应用产生重大影响。确实，如果部分相关仅捕获独特的协方差，则来自一维潜在可变模型ULVM的数据应与空网络（无边缘）相关联，因为ULVM中没有独特的协方差。我们知道这不是真的，这表明FWMK缺少他们的主张。我们引入了ULVM和GGM之间的连接，并使用该连接证明我们找到了与ULVM相关的完全连接的，而不是空的网络。然后，我们使用GGM和线性回归之间的关系表明部分相关确实不会消除共同的方差。
The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent paper in this journal, Forbes, Wright, Markon, and Krueger (FWMK) voiced the concern that GGMs that are estimated from partial correlations wrongfully remove the variance that is shared by its constituents. If true, this concern has grave consequences for the application of GGMs. Indeed, if partial correlations only capture the unique covariances, then the data that come from a unidimensional latent variable model ULVM should be associated with an empty network (no edges), as there are no unique covariances in a ULVM. We know that this cannot be true, which suggests that FWMK are missing something with their claim. We introduce a connection between the ULVM and the GGM and use that connection to prove that we find a fully-connected and not an empty network associated with a ULVM. We then use the relation between GGMs and linear regression to show that the partial correlation indeed does not remove the common variance.
