Qgraph: Network visualizations of relationships in psychometric data

By Sacha Epskamp, A. Cramer, Lourens J. Waldorp, Verena D. Schmittmann, Denny Borsboom. 2012

4,464 citations recorded by the source corpus.

We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and confirmatory factor analysis, using sem and lavaan; the output of these packages is automatically visualized in qgraph, which may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f134d026032538968185abaeab7871984cf84c3f

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Citations in the corpus

Unpacking Rumination and Executive Control: A Network Perspective (2017, methodology): “We used the R package qgraph (Epskamp et al., 2012) that automatically implements the graphical LASSO regularization in combination with extended Bayesian information criterion (EBIC) model selection (Foygel & Drton, 2011).”
A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “Nodes are placed using an modified version of the FruchtermanReingold algorithm (Fruchterman and Reingold, 1991) for weighted networks (Epskamp et al., 2012).”
Social Anxiety Disorder as a Densely Interconnected Network of Fear and Avoidance for Social Situations (2017, methodology): “We used the R package qgraph (Epskamp et al. 2012) that automatically implements the graphical LASSO regularization in combination with an extended Bayesian Information Criterion (EBIC) model selection (Foygel and Drton 2011).”
Development of Indirect Measures of Conscientiousness: Combining a Facets Approach and Network Analysis (2015, background): “…way of quantifying the net pairwise relationships among several variables (Krämer et al., 2009); (2) a graphical summary of these relationships (Epskamp et al., 2012), which can be more easily understood even when the pattern of relationships is not a simple one; and (3) a representation that…”
Estimating psychological networks and their accuracy: A tutorial paper (2016, positive): “…two steps: (1) estimate a statistical model on data, from which some parameters can be represented as a weighted network between observed variables, and (2), analyze the weighted network structure using measures taken from graph theory (Newman, 2010) to infer, for instance, the most central nodes.”
What do centrality measures measure in psychological networks? (2019, methodology): “These three centrality indices are implemented in the R package qgraph (Epskamp et al., 2012) and are by far the most commonly used to analyze psychological networks.”
Identifying Highly Influential Nodes in the Complicated Grief Network (2016, positive): “We used the R package qgraph (Epskamp et al., 2012) to calculate two networks: an association network in which edges represented the zero-order correlation between nodes and a graphical lasso (least absolute shrinkage and selection operator) network in which edges represented the regularized partial”
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, methodology): “In the open-source statistical programming language R (R Core Team, 2017), automated procedures have been implemented in the corpcor package (Schafer et al., 2017) and the qgraph (Epskamp et al., 2012) package.”