The Gaussian graphical model in cross-sectional and time-series data

Sacha Epskamp, Lourens J. Waldorp, Rene Mottus, Denny Borsboom

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1-28
JournalMultivariate Behavioral Research
Publication statusPublished - 16 Apr 2018


  • time-series analysis
  • multilevel modeling
  • multivariate analysis
  • exploratory-data analysis
  • network modeling

Fingerprint Dive into the research topics of 'The Gaussian graphical model in cross-sectional and time-series data'. Together they form a unique fingerprint.

Cite this