Computes the Jensen-Shannon Distance between two networks

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 – a Framework for Estimating the
Number of Dimensions in Multivariate Data using Network
Psychometrics

Alexander Christensen

Robert Moulder

Luis E. Garrido

Laura Jamison

jsd function

<dl><dt>network1</dt>
<dd>Matrix or data frame.
Network to be compared</dd>
<dt>network2</dt>
<dd>Matrix or data frame.
Second network to be compared</dd>
<dt>method</dt>
<dd>Character.
Method to compute Jensen-Shannon Distance.
Defaults to <code>"spectral"</code>.
Options:
<ul>
<li><code>"kld"</code>
Uses Kullback-Leibler Divergence</li>
<li><code>"spectral"</code>
Uses eigenvalues of combinatiorial Laplacian matrix to compute
Von Neumann entropy</li>
</ul></dd></dl>

Arguments

Hudson Golino &lt;hfg9s at virginia.edu&gt; &amp; Alexander P. Christensen &lt;alexander.christensen at Vanderbilt.Edu&gt;

Author

Jensen-Shannon Distance — jsd

<dl>

<dt>network1</dt>
<dd>Matrix or data frame.
Network to be compared</dd>


<dt>network2</dt>
<dd>Matrix or data frame.
Second network to be compared</dd>


<dt>method</dt>
<dd>Character.
Method to compute Jensen-Shannon Distance.
Defaults to <code>"spectral"</code>.
Options:
<ul>
<li><code>"kld"</code>
Uses Kullback-Leibler Divergence</li>
<li><code>"spectral"</code>
Uses eigenvalues of combinatiorial Laplacian matrix to compute
Von Neumann entropy</li>
</ul></dd>

</dl>

jsd: Jensen-Shannon Distance

Description

Usage

Value

Arguments

Author

Examples