EGA: Apply the Exploratory Graph Analysis technique

Description Usage Arguments Author(s) References See Also Examples

Description

Estimates the number of dimensions of a given dataset/instrument using graphical lasso (EBICglasso.qgraph) or the Triangulated Maximally Filtered Graph (TMFG) method and the walktrap community detection algorithm (cluster_walktrap). The glasso regularization parameter is set via EBIC.

Usage

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EGA(data, model = c("glasso", "TMFG"), plot.EGA = TRUE, n = NULL,
  steps = 4, nvar = 4, nfact = 1, load = 0.7, ...)

Arguments

data

A dataframe with the variables to be used in the analysis or a correlation matrix. If the data used is a correlation matrix, the argument n will need to be specified.

model

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

plot.EGA

Logical. If TRUE, returns a plot of the network and its estimated dimensions. Defaults to TRUE

n

Integer. Sample size, if the data provided is a correlation matrix

steps

Number of steps to be used in cluster_walktrap algorithm. Defaults to 4.

nvar

Number of variables to use in the simulation part of the unidimensionality check. Defaults to 4.

nfact

Number of factors to be simulated (part of the unidimensionality check algorithm). Defaults to 1.

load

Factor loadings (used in the unidimensionality check algorithm). Defaults to 0.70.

...

Additional arguments to be passed to EBICglasso.qgraph or TMFG

Author(s)

Hudson F. Golino <hfg9s at virginia.edu>, Alexander P. Christensen <alexpaulchristensen at gmail.com>, Maria Dolores Nieto <acinodam at gmail.com> and Luis E. Garrido <garrido.luiseduardo at gmail.com>

References

Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS one, 12(6), e0174035.. doi: journal.pone.0174035

Golino, H. F., & Demetriou, A. (2017). Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis. Intelligence, 62, 54-70. doi: j.intell.2017.02.007

Golino, H., Shi, D., Garrido, L. E., Christensen, A. P., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2018). Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. PsyArXiv. doi: 10.31234/osf.io/gzcre

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.

Examples

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


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

#summary statistics
summary(ega.wmt)

#plot
plot(ega.wmt)

#estimate EGA
ega.intel <- EGA(data = intelligenceBattery[,8:66], model = "glasso", plot.EGA = TRUE)

#summary statistics
summary(ega.intel)

#plot
plot(ega.intel)

hfgolino/EGA documentation built on Aug. 16, 2019, 2:50 a.m.