EGAEstimates 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.
EGA.estimate(
data,
n = NULL,
corr = c("cor_auto", "pearson", "spearman"),
model = c("glasso", "TMFG"),
model.args = list(),
algorithm = c("walktrap", "leiden", "louvain"),
algorithm.args = list(),
consensus.method = c("highest_modularity", "most_common", "iterative", "lowest_tefi"),
consensus.iter = 100,
...
)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
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
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.
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:
walktrap
Computes the Walktrap algorithm using cluster_walktrap
leiden
Computes the Leiden algorithm using cluster_leiden
louvain
Computes the Louvain algorithm using cluster_louvain
List.
A list of additional arguments for cluster_walktrap, cluster_louvain,
or some other community detection algorithm function (see examples)
Character.
What consensus clustering method should be used?
Defaults to "highest_modularity".
Current options are:
highest_modularity
Uses the community solution that achieves the highest modularity
across iterations
most_common
Uses the community solution that is found the most
across iterations
iterative
Identifies the most common community solutions across iterations
and determines how often nodes appear in the same community together.
A threshold of 0.30 is used to set low proportions to zero.
This process repeats iteratively until all nodes have a proportion of
1 in the community solution.
lowest_tefi
Uses the community solution that achieves the lowest tefi
across iterations
Numeric.
Number of iterations to perform in consensus clustering for the Louvain algorithm
(see Lancichinetti & Fortunato, 2012).
Defaults to 100
Additional arguments.
Used for deprecated arguments from previous versions of EGA
Alexander P. Christensen <alexpaulchristensen at gmail.com> and Hudson Golino <hfg9s at virginia.edu>
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).
# 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.
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.
# Obtain data
wmt <- wmt2[,7:24]
if (FALSE) {
# Estimate EGA
ega.wmt <- EGA.estimate(data = wmt)
# Estimate EGAtmfg
ega.wmt.tmfg <- EGA.estimate(data = wmt, model = "TMFG")
# Estimate EGA with Louvain algorithm
ega.wmt.louvain <- EGA.estimate(data = wmt, algorithm = "louvain")
# Estimate EGA with Spinglass algorithm
ega.wmt.spinglass <- EGA.estimate(
data = wmt,
algorithm = igraph::cluster_spinglass # any {igraph} algorithm
)}
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