EGA for Multidimensional StructuresA basic function to estimate EGA for multidimensional structures.
This function does not include the unidimensional check and it does not
plot the results. This function can be used as a streamlined approach
for quick EGA estimation when unidimensionality or visualization
is not a priority
EGA.estimate(
data,
n = NULL,
corr = c("auto", "cor_auto", "cosine", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("BGGM", "glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
verbose = FALSE,
...
)Returns a list containing:
A matrix containing a network estimated using
link[EGAnet]{network.estimation}
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
Number of cases in data
Matrix or data frame. Should consist only of variables to be used in the analysis
Numeric (length = 1).
Sample size if data provided is a correlation matrix
Character (length = 1).
Method to compute correlations.
Defaults to "auto".
Available options:
"auto" --- Automatically computes appropriate correlations for
the data using Pearson's for continuous, polychoric for ordinal,
tetrachoric for binary, and polyserial/biserial for ordinal/binary with
continuous. To change the number of categories that are considered
ordinal, use ordinal.categories
(see polychoric.matrix for more details)
"cor_auto" --- Uses cor_auto to compute correlations.
Arguments can be passed along to the function
"cosine" --- Uses cosine to compute cosine similarity
"pearson" --- Pearson's correlation is computed for all
variables regardless of categories
"spearman" --- Spearman's rank-order correlation is computed
for all variables regardless of categories
For other similarity measures, compute them first and input them
into data with the sample size (n)
Character (length = 1).
How should missing data be handled?
Defaults to "pairwise".
Available options:
"pairwise" --- Computes correlation for all available cases between
two variables
"listwise" --- Computes correlation for all complete cases in the dataset
Character (length = 1).
Defaults to "glasso".
Available options:
"BGGM" --- Computes the Bayesian Gaussian Graphical Model.
Set argument ordinal.categories to determine
levels allowed for a variable to be considered ordinal.
See ?BGGM::estimate for more details
"glasso" --- Computes the GLASSO with EBIC model selection.
See EBICglasso.qgraph for more details
"TMFG" --- Computes the TMFG method.
See TMFG for more details
Character or
igraph cluster_* function (length = 1).
Defaults to "walktrap".
Three options are listed below but all are available
(see community.detection for other options):
"leiden" --- See cluster_leiden for more details
"louvain" --- By default, "louvain" will implement the Louvain algorithm using
the consensus clustering method (see community.consensus
for more information). This function will implement
consensus.method = "most_common" and consensus.iter = 1000
unless specified otherwise
"walktrap" --- See cluster_walktrap for more details
Boolean (length = 1).
Whether messages and (insignificant) warnings should be output.
Defaults to FALSE (silent calls).
Set to TRUE to see all messages and warnings for every function call
Additional arguments to be passed on to
auto.correlate,
network.estimation,
community.detection, and
community.consensus
Alexander P. Christensen <alexpaulchristensen at gmail.com> and Hudson Golino <hfg9s at virginia.edu>
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.
Introduced unidimensional checks, simulation with 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.
Compared all igraph community detection algorithms, simulation with continuous and polytomous data
Christensen, A. P., Garrido, L. E., Guerra-Pena, K., & Golino, H. (2023).
Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.
Behavior Research Methods.
plot.EGAnet for plot usage in EGAnet
# Obtain data
wmt <- wmt2[,7:24]
# Estimate EGA
ega.wmt <- EGA.estimate(data = wmt)
# Estimate EGA with TMFG
ega.wmt.tmfg <- EGA.estimate(data = wmt, model = "TMFG")
# Estimate EGA with an {igraph} function (Fast-greedy)
ega.wmt.greedy <- EGA.estimate(
data = wmt,
algorithm = igraph::cluster_fast_greedy
)
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