compare.EGA.plots

<code><a rd-options="" href="/link/EGAnet?package=EGAnet&version=1.0.0" data-mini-rdoc="EGAnet::EGAnet">EGAnet</a></code> objects

Character vector.
Labels for each <code><a rd-options="" href="/link/EGAnet?package=EGAnet&version=1.0.0" data-mini-rdoc="EGAnet::EGAnet">EGAnet</a></code> object

labels

Numeric.
Number of rows to spread plots across

rows

Numeric.
Number of columns to spread plots down

columns

Identifies redundant nodes in the network based on several
measures. Computes the weighted topological overlap between
each node and every other node in the network. The weighted topological
overlap is implemented using the method from Nowick et al. (2009; see references)
and the function <a rd-options="wTO" href="/link/wTO?package=EGAnet&version=1.0.0&to=wTO" data-mini-rdoc="wTO::wTO">wTO</a> from the wTO package.

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA is part of a new area called network psychometrics that uses undirected network models for the assessment of psychometric properties. EGA estimates the number of dimensions (or factors) using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community detection algorithm. A bootstrap method for verifying the stability of the dimensions and items in those dimensions is available. The fit of the structure suggested by EGA can be verified using Entropy Fit Indices. A novel approach called Unique Variable Analysis (UVA) can be used to identify and reduce redundant variables in multivariate data. Network loadings, which are roughly equivalent to factor loadings when the data generating model is a factor model, are available. Network scores can also be computed using the network loadings. Dynamic EGA (dynEGA) will estimate dimensions from time series data for individual, group, and sample levels. Golino, H., & Epskamp, S. (2017) <doi:10.1371/journal.pone.0174035>. Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2020) <doi:10.31234/osf.io/gzcre>. Christensen, A. P., & Golino, H. (under review) <doi:10.31234/osf.io/hz89e>. Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020) <doi:10.31234/osf.io/mtka2>. Christensen, A. P. & Golino, H. (2021) <doi:10.3390/psych3030032>. Christensen, A. P., Garrido, L. E., & Golino, H. (under review) <doi:10.31234/osf.io/4kra2>. Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (under review) <doi:10.31234/osf.io/tfs7c>.

Hudson Golino

EGAnet

Exploratory Graph Analysis <e2><80><93> a Framework for Estimating the
Number of Dimensions in Multivariate Data using Network
Psychometrics

Alexander Christensen

Robert Moulder

Luis E. Garrido

compare.EGA.plots function

<code><a rd-options='' href='EGAnet'>EGAnet</a></code> objects

Character vector.
Labels for each <code><a rd-options='' href='EGAnet'>EGAnet</a></code> object

Identifies redundant nodes in the network based on several
measures. Computes the weighted topological overlap between
each node and every other node in the network. The weighted topological
overlap is implemented using the method from Nowick et al. (2009; see references)
and the function <a rd-options='wTO' href='wTO'>wTO</a> from the wTO package.

compare.EGA.plots: Visual Compares `EGAnet` plots

Description

Usage

Arguments

Value

Examples