The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
复制标题
DOI：
10.1080/00273171.2018.1454823
复制
发表时间：
2018-01-01
影响因子：
3.8
通讯作者：
Borsboom, Denny
中科院分区：
文献类型：
--
作者：
Epskamp, Sacha;Waldorp, Lourens J.;Borsboom, Denny
我们讨论了高斯图形模型（GGM;一个无向网络的偏相关系数），并详细介绍了其实用程序作为一个探索性的数据分析工具。GGM显示哪些变量相互预测，允许协方差结构的稀疏建模，并可以突出观察到的变量之间的潜在因果关系。我们描述了三种心理数据集的效用：假设连续情况独立的数据集（例如，横截面数据），按时间排序的数据集（例如，n = 1时间序列），以及2个的混合（例如，n > 1时间序列）。在时间序列分析中，GGM可以用来模拟向量自回归分析（VAR）的残差结构，也称为图形VAR。两个网络模型，然后可以得到：一个时间网络和同期网络。在分析多个受试者的数据时，也可以在受试者间网络的平稳均值的协方差结构上形成GGM。我们讨论了这些模型的解释，并提出了估计方法来获得这些网络，我们在R包graphicalVAR和mlVAR中实现。这些方法在两个实证例子中展示，这些方法的模拟研究包括在补充材料中。
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary meansthe between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.