The Gaussian Graphical Model in Cross-sectional and Time-series Data

Abstract

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 3 kinds of psychological datasets: datasets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered datasets (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.

Accepted pending revision in Multivariate Behavioral Research

References in corpus (8)

• A Tutorial on Regularized Partial Correlation Networks
• Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research
• Major depression as a complex dynamic system
• The huge Package for High-dimensional Undirected Graph Estimation in R
• Generalized Network Psychometrics: Combining Network and Latent Variable Models
• Estimating psychopathological networks: be careful what you wish for
• Covariance Estimation: The GLM and Regularization Perspectives
• Three representations of the Ising model

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• High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
• Graph recovery from graph wave equation