The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
By Sacha Epskamp, Lourens J. Waldorp, R. Mõttus, Denny Borsboom. Multivariate Behavioral Research, 2016
897 citations recorded by the source corpus.
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 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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Food Science
View Paper: https://www.semanticscholar.org/paper/37968e0eddbb55dc31a8607b6bf8ca32ac012e29
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alexander P. Christensen
- David M. Condon
- L. E. Garrido
- Samuel Greiff
- Tal Yarkoni
- M. Back
- Wendy Johnson
- William Revelle
In Experimental and Cognitive Psychology
- Sacha Epskamp
- Hudson F. Golino
- Alexander P. Christensen
- David M. Condon
- R. Mõttus
- G. Costantini
- William Revelle
- M. Back
In Food Science
- L. E. Garrido
- Aidan G. C. Wright
- Tal Yarkoni
- M. Back
- William Revelle
- Alexander P. Christensen
- R. Mõttus
- Hudson F. Golino
Citations in the corpus
- Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “The GLASSO is a regularization technique that is very fast to estimate both the model structure and the parameters of a sparse GGM (Epskamp et al., 2018).”
- Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits (2020, background): “…point t are associated with the levels of Y concurrently (at time t ) or even prospectively (e.g., can predict how Y will change from t to t+1 ; e.g., Epskamp et al., 2018) is useful for bolstering evidence of causal associations, also allowing to separate within-and between-individual variances.”
- Mental Health and Social Contact During the COVID-19 Pandemic: An Ecological Momentary Assessment Study (2020, neutral): “There are many more auxiliaries, including specific assumptions that come with the use of particular statistical models such as the VAR model (Epskamp et al., 2018; Fried, 2021), which does not investigate whether changes in x predict changes in y but whether deviations from a person-wise average…”