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

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 ca...

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Veröffentlicht in:Multivariate behavioral research Jg. 53; H. 4; S. 453 - 480
Hauptverfasser: Epskamp, Sacha, Waldorp, Lourens J., Mõttus, René, Borsboom, Denny
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Routledge 04.07.2018
Taylor & Francis Ltd
Schlagworte:
Between-subjects design
Computer Simulation
Correlation coefficients
Covariance
Cross-Sectional Studies
Data analysis
Data Interpretation, Statistical
Datasets
Economic models
Empirical analysis
exploratory-data analysis
Humans
Mathematical models
Models, Statistical
multilevel modeling
multivariate analysis
network modeling
Software
Surveys and Questionnaires
Time Factors
Time series
Time-series analysis
multilevel modeling
Time-series analysis
exploratory-data analysis
network modeling
multivariate analysis
ISSN:0027-3171, 1532-7906, 1532-7906
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Zusammenfassung: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.
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ISSN:0027-3171
1532-7906
1532-7906
DOI:10.1080/00273171.2018.1454823