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<h1 class="title toc-ignore">IMaGES R Package and Algorithm</h1>
<h4 class="author"><em>Noah Frazier-Logue, Stephen José Hanson</em></h4>
<h4 class="date"><em>2018-12-14</em></h4>
<p><strong>Version</strong> 0.1</p>
<p><strong>License</strong> GPL (>= 2)</p>
<p><strong>LazyData</strong> true</p>
<p><strong>Repository</strong> CRAN</p>
<p><strong>URL</strong> <a href="https://github.com/noahfl/IMaGES" class="uri">https://github.com/noahfl/IMaGES</a></p>
<p><strong>BugReports</strong> <a href="https://github.com/noahfl/IMaGES/issues" class="uri">https://github.com/noahfl/IMaGES/issues</a></p>
<p><strong>NeedsCompilation</strong> Yes</p>
<div id="images-algorithm" class="section level2">
<h2>IMaGES Algorithm</h2>
<p>This package is an implementation of the IMaGES algorithm of Ramsey, Hanson, Hanson, Halchenko, Poldrack, Glymour (2010) and is adapted from ‘GES’ as implemented in the ‘pcalg’ package. IMaGES (Independent Multi-sample Greedy Equivalence Search) is a score-based algorithm that greedily maximizes a score function similar to the one used in the pcalg implementation of GES. It modifies the scoring by creating a global score across all datasets and uses this score to determine which step from the individual datasets best represents all of the datasets. It accomplishes this by using forward, backward, and turning steps as described below:</p>
<ul>
<li><p><strong>Forward phase</strong> In the forward phase, IMaGES moves through the space of essential graphs in steps that correspond to the addition of a single edge in the space of DAGs; the phase is aborted as soon as the score cannot be augmented any more.</p></li>
<li><p><strong>Backward phase</strong> In the backward phase, the algorithm performs moves that correspond to the removal of a single edge in the space of DAGs until the score cannot be augmented any more.</p></li>
<li><p><strong>Turning phase</strong> In the turning phase, the algorithm performs moves that correspond to the reversal of a single arrow in the space of DAGs until the score cannot be augmented any more.</p></li>
</ul>
<p>During each step, the IMaGES algorithm simulates the addition, deletion, or turning of an edge for each individual dataset. The step that most augments the score (each edge is assigned a SEM BIC score) for the individual dataset is selected, and the global step across all datasets is selected by finding the most commonly recommended step. The algorithm then executes that step and updated the IMScore accordingly. This repeats for (number of nodes)**2 or until the algorithm detects that no steps augment the score for five consecutive iterations (also known as ‘early stopping’).</p>
<p>These operations are carried out and result in a global representative graph and a Markov equivalence class.</p>
<div id="images-in-r" class="section level3">
<h3>IMaGES in R</h3>
<div id="description" class="section level4">
<h4>Description</h4>
<p>Running this on the provided sample data returns an IMaGES object with a named list containing:</p>
<ul>
<li><p><strong>.global</strong> a named list containing <code>.graph</code>, the global graphNEL object, and <code>.params</code>, the structural equation modeling data for the global graph</p></li>
<li><p><strong>.single.graphs</strong> a list containing named lists of the same structure as above that corresponds to each individual dataset passsed into IMaGES</p></li>
<li><p><strong>.markovs</strong> a list containing named lists of the same structure as above that corresponds to each Markov equivalence class (the size of which is specified by the user)</p></li>
<li><p><strong>.means</strong> a list containing the mean values for the structural equation modeling data for each edge in the graph</p></li>
<li><p><strong>.std.errs</strong> a list containing the standard errors for the structural equation modeling data for each edge in the graph</p></li>
</ul>
</div>
<div id="usage" class="section level4">
<h4>Usage</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">require</span>(IMaGES)
<span class="kw">data</span>(IMData)
<span class="co">#run IMaGES</span>
im.fits <-<span class="st"> </span><span class="kw">IMaGES</span>(<span class="dt">matrices=</span>IMData, <span class="dt">penalty=</span><span class="dv">3</span>, <span class="dt">num.markovs=</span><span class="dv">5</span>, <span class="dt">use.verbose=</span><span class="ot">FALSE</span>)
<span class="co">#> [1] "Running..."</span>
<span class="co">#> [1] "Stopping early. IMaGES run has converged on a representative graph."</span>
<span class="co">#> [1] "Done with IMaGES run."</span>
<span class="co">#> [1] "Final IMScore: 220936.078265305"</span></code></pre></div>
</div>
</div>
<div id="plotall" class="section level3">
<h3>plotAll</h3>
<div id="description-1" class="section level4">
<h4>Description</h4>
<p>This function takes the object returned by an IMaGES run and plots the global structure with its structural equation modeling data, as well as the structural equation modeling data for each dataset imposed on the global structure. The function determines the dimensions that most closely represent a square and plots the graphs in that fashion.</p>
</div>
<div id="usage-1" class="section level4">
<h4>Usage</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">require</span>(IMaGES)
## Load predefined data
<span class="kw">data</span>(IMData)
<span class="co">#run IMaGES</span>
im.fits <-<span class="st"> </span><span class="kw">IMaGES</span>(<span class="dt">matrices=</span>IMData, <span class="dt">penalty=</span><span class="dv">3</span>, <span class="dt">num.markovs=</span><span class="dv">5</span>, <span class="dt">use.verbose=</span><span class="ot">FALSE</span>)
<span class="co">#> [1] "Running..."</span>
<span class="co">#> [1] "Stopping early. IMaGES run has converged on a representative graph."</span>
<span class="co">#> [1] "Done with IMaGES run."</span>
<span class="co">#> [1] "Final IMScore: 220936.078265305"</span>
<span class="co">#plot global graph and all individual graphs with own SEM data</span>
<span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">2</span>))
<span class="kw">plotAll</span>(im.fits)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="plotmarkovs" class="section level3">
<h3>plotMarkovs</h3>
<div id="description-2" class="section level4">
<h4>Description</h4>
<p>This function takes the object returned by an IMaGES run and plots the global structure with its structural equation modeling data, as well as the structural equation modeling data for each Markov equivalence class and their respective structures. The function determines the dimensions that most closely represent a square and plots the graphs in that fashion.</p>
</div>
<div id="usage-2" class="section level4">
<h4>Usage</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">require</span>(IMaGES)
## Load predefined data
<span class="kw">data</span>(IMData)
<span class="co">#run IMaGES</span>
im.fits <-<span class="st"> </span><span class="kw">IMaGES</span>(<span class="dt">matrices=</span>IMData, <span class="dt">penalty=</span><span class="dv">3</span>, <span class="dt">num.markovs=</span><span class="dv">5</span>, <span class="dt">use.verbose=</span><span class="ot">FALSE</span>)
<span class="co">#> [1] "Running..."</span>
<span class="co">#> [1] "Stopping early. IMaGES run has converged on a representative graph."</span>
<span class="co">#> [1] "Done with IMaGES run."</span>
<span class="co">#> [1] "Final IMScore: 220936.078265305"</span>
<span class="co">#plot global graph alongside Markov equivalence class</span>
<span class="kw">plotMarkovs</span>(im.fits)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="plotimgraph" class="section level3">
<h3>plotIMGraph</h3>
<div id="description-3" class="section level4">
<h4>Description</h4>
<p>This function takes a graph object returned from IMaGES (takes the form of a named list containing <code>.graph</code> and <code>.params</code>) and plots it. Using <code>plotAll</code> or <code>plotMarkovs</code> is recommended unless you only want to see one specific graph.</p>
</div>
<div id="usage-3" class="section level4">
<h4>Usage</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">require</span>(IMaGES)
## Load predefined data
<span class="kw">data</span>(IMData)
<span class="co">#run IMaGES</span>
im.fits <-<span class="st"> </span><span class="kw">IMaGES</span>(<span class="dt">matrices=</span>IMData, <span class="dt">penalty=</span><span class="dv">3</span>, <span class="dt">num.markovs=</span><span class="dv">5</span>, <span class="dt">use.verbose=</span><span class="ot">FALSE</span>)
<span class="co">#> [1] "Running..."</span>
<span class="co">#> [1] "Stopping early. IMaGES run has converged on a representative graph."</span>
<span class="co">#> [1] "Done with IMaGES run."</span>
<span class="co">#> [1] "Final IMScore: 220936.078265305"</span>
<span class="co">#plot individual graph</span>
<span class="kw">plotIMGraph</span>(im.fits$.single.graph[[<span class="dv">1</span>]])</code></pre></div>
<p><img src="data:image/png;base64,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" 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