EGA.estimate: Estimates 'EGA' for Multidimensional Structures

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Estimates EGA for Multidimensional Structures

Description

A 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

Usage

EGA.estimate(
  data,
  n = NULL,
  corr = c("auto", "cor_auto", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  model = c("BGGM", "glasso", "TMFG"),
  algorithm = c("leiden", "louvain", "walktrap"),
  verbose = FALSE,
  ...
)

Arguments

data	Matrix or data frame. Should consist only of variables to be used in the analysis
n	Numeric (length = 1). Sample size if data provided is a correlation matrix
corr	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 "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)
na.data	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
model	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
algorithm	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
verbose	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

Value

Returns a list containing:

network	A matrix containing a network estimated using link[EGAnet]{network.estimation}
wc	A vector representing the community (dimension) membership of each node in the network. NA values mean that the node was disconnected from the network
n.dim	A scalar of how many total dimensions were identified in the network
cor.data	The zero-order correlation matrix
n	Number of cases in data

Author(s)

Alexander P. Christensen <alexpaulchristensen at gmail.com> and Hudson Golino <hfg9s at virginia.edu>

References

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.

See Also

plot.EGAnet for plot usage in EGAnet

Examples

# 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
)

EGAnet documentation built on Nov. 18, 2023, 1:07 a.m.