Computes the Jensen-Shannon Distance between two networks

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
and psychometric assessment. EGA estimates the number of dimensions in
psychological data using network estimation methods and community detection
algorithms. A bootstrap method is provided to assess the stability of dimensions
and items. Fit is evaluated using the Entropy Fit family of indices. Unique
Variable Analysis evaluates the extent to which items are locally dependent (or
redundant). Network loadings provide similar information to factor loadings and
can be used to compute network scores. A bootstrap and permutation approach are
available to assess configural and metric invariance. Hierarchical structures
can be detected using Hierarchical EGA. Time series and intensive longitudinal
data can be analyzed using Dynamic EGA, supporting individual, group, and
population level assessments.

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

Dingjing Shi

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 (length = 1).
Method to compute Jensen-Shannon Distance.
Defaults to <code>"spectral"</code>.
Available options:
<ul>
<li><code>"kld"</code> --- Uses Kullback-Leibler Divergence</li>
<li><code>"spectral"</code> --- Uses eigenvalues of combinatorial Laplacian matrix to compute
Von Neumann entropy</li>
</ul></dd>
<dt>signed</dt>
<dd>Boolean. (length = 1).
Should networks be remain signed?
Defaults to <code>TRUE</code></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 (length = 1).
Method to compute Jensen-Shannon Distance.
Defaults to <code>"spectral"</code>.
Available options:
<ul>
<li><code>"kld"</code> --- Uses Kullback-Leibler Divergence</li>
<li><code>"spectral"</code> --- Uses eigenvalues of combinatorial Laplacian matrix to compute
Von Neumann entropy</li>
</ul></dd>


<dt>signed</dt>
<dd>Boolean. (length = 1).
Should networks be remain signed?
Defaults to <code>TRUE</code></dd>

</dl>

jsd: Jensen-Shannon Distance

Description

Usage

Value

Arguments

Author

Examples