Graphical modeling as learning the conditional dependence structure of multivariate relations¶

Theory · All theories
Evidence · 2 claims (2 for) · 1 study (1 design), q1 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

The article frames graphical modeling as a framework for characterizing multivariate relations: "The basic idea is to characterize multivariate relations by learning the conditional dependence structure." Variables such as cortical regions or psychological symptoms serve as nodes, and "the featured connections linking nodes are edges that graphically represent the conditional dependence structure". The article applies this framework across domains including cognitive neuroscience (brain connectivity) and clinical psychology (symptom interrelations).

Design Implications¶

Context¶

Requirements¶

• Multivariate data whose conditional dependence structure among variables is of research interest

Constraints¶

Target Learners¶

• researchers in psychology, neuroscience, and the social-behavioral sciences

Target Learning Objectives¶

• understanding multivariate relations and conditional dependence structures among variables

Claims¶

• Ggmnonreg Designed For Low Dimensional Data [+W]
• Regularization-based graphical modeling has key drawbacks, including difficulty obtaining valid parameter uncertainty and inflated false positive rates [+W]

Related Theories¶

• Graphical model framework for deriving efficient MML estimation schemes for multidimensional IRT models

Examples¶

• GGMnonreg: an R package for non-regularized Gaussian graphical modeling of low-dimensional data

Key Sources¶

• Donald Williams. (2021). GGMnonreg: Non-regularized Gaussian graphical models in R. Journal of Open Source Software, 6(67), 3308. https://doi.org/10.21105/joss.03308