EGA.estimate {EGAnet}R Documentation

A Sub-routine Function for EGA


Estimates the number of dimensions of a given dataset or correlation matrix using the graphical lasso (EBICglasso.qgraph) or the Triangulated Maximally Filtered Graph (TMFG) network estimation methods.


  n = NULL,
  model = c("glasso", "TMFG"),
  model.args = list(),
  algorithm = c("walktrap", "louvain"),
  algorithm.args = list(),
  corr = c("cor_auto", "pearson", "spearman"),
  verbose = TRUE,



Matrix or data frame. Variables (down columns) or correlation matrix. If the input is a correlation matrix, then argument n (number of cases) is required


Integer. Sample size if data provided is a correlation matrix


Character. A string indicating the method to use.

Current options are:

  • glasso Estimates the Gaussian graphical model using graphical LASSO with extended Bayesian information criterion to select optimal regularization parameter. This is the default method

  • TMFG Estimates a Triangulated Maximally Filtered Graph


List. A list of additional arguments for EBICglasso.qgraph or TMFG


A string indicating the algorithm to use or a function from igraph Current options are:


List. A list of additional arguments for cluster_walktrap, cluster_louvain, or some other community detection algorithm function (see examples)


Type of correlation matrix to compute. The default uses cor_auto. Current options are:

  • cor_auto Computes the correlation matrix using the cor_auto function from qgraph.

  • pearson Computes Pearson's correlation coefficient using the pairwise complete observations via the cor function.

  • spearman Computes Spearman's correlation coefficient using the pairwise complete observations via the cor function.


Boolean. Should network estimation parameters be printed? Defaults to TRUE. Set to FALSE for no print out


Additional arguments. Used for deprecated arguments from previous versions of EGA


Two community detection algorithms, Walktrap (Pons & Latapy, 2006) and Louvain (Blondel et al., 2008), are pre-programmed because of their superior performance in simulation studies on psychological data generated from factor models (Christensen & Golino; 2020; Golino et al., 2020). Notably, any community detection algorithm from the igraph can be used to estimate the number of communities (see examples).


Returns a list containing:

A symmetric network estimated using either the EBICglasso.qgraph or TMFG


A vector representing the community (dimension) membership of each node in the network. NA values mean that the node was disconnected from the network


A scalar of how many total dimensions were identified in the network

The zero-order correlation matrix


Alexander P. Christensen <alexpaulchristensen at> and Hudson Golino <hfg9s at>


# Louvain algorithm
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008.

# Compared all igraph community detections algorithms, introduced Louvain algorithm, simulation with continuous and polytomous data
Christensen, A. P., & Golino, H. (under review). Estimating factors with psychometric networks: A Monte Carlo simulation comparing community detection algorithms. PsyArXiv.

# Original simulation and implementation of EGA
Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12, e0174035.

Golino, H. F., & Demetriou, A. (2017). Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis. Intelligence, 62, 54-70.

# Current implementation of EGA, introduced unidimensional checks, continuous and dichotomous data
Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2020). Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25, 292-320.

# Walktrap algorithm
Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10, 191-218.

See Also

bootEGA to investigate the stability of EGA's estimation via bootstrap and CFA to verify the fit of the structure suggested by EGA using confirmatory factor analysis.


# Estimate EGA
ega.wmt <- EGA.estimate(data = wmt2[,7:24], model = "glasso")

# Estimate EGAtmfg
ega.wmt <- EGA.estimate(data = wmt2[,7:24], model = "TMFG")

# Estimate EGA with Spinglass
ega.wmt <- EGA.estimate(data = wmt2[,7:24], model = "glasso",
algorithm = igraph::cluster_spinglass)

[Package EGAnet version 1.1.0 Index]