The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

作者：Sacha Epskamp, Lourens Waldorp, René Mõttus, Denny Borsboom · 发表于：Multivariate Behavioral Research · 年份：2018 · DOI：10.1080/00273171.2018.1454823 · 被引用次数：1221 · 研究领域：Mental Health Research Topics、Cognitive Science and Mapping、Sensory Analysis and Statistical Methods

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 means-the 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.