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    <h1>Function to summarize MCMC run generated by mcmcabn</h1>
    
    <div class="hidden name"><code>summary.Rd</code></div>
    </div>

    <div class="ref-description">
    
    <p>Summary method for mcmcabn objects.</p>
    
    </div>

    <pre class="usage"># S3 method for mcmcabn
summary(object,
     quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975),
     lag.max = 10,
     &#8230;)</pre>
    
    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>object</th>
      <td><p>object of class <code>mcmcabn</code>.</p></td>
    </tr>
    <tr>
      <th>quantiles</th>
      <td><p>numeric vector of probabilities with values in [0,1]. (Values up to 2e-14 outside that range are accepted and moved to the nearby endpoint.)</p></td>
    </tr>
    <tr>
      <th>lag.max</th>
      <td><p>maximum lag at which to calculate the acf. Default is set to 10.</p></td>
    </tr>
    <tr>
      <th>&#8230;</th>
      <td><p>arguments to be passed to methods.</p></td>
    </tr>
    </table>
    
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>The summary function for mcmcabn objects returns multiple summary metrics for assesing the quality of the MCMC run.</p>
    
    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>This method print: the number of Burn in steps, the number of MCMC steps, the thinning, the maximum achieved score, the empirical mean of the MCMC samples, the empirical standard deviation of the MCMC samples, the user defined quantiles of the posterior network score, the global acceptance rate, a table of the accepted and rejected moves in function of the methods used, the sample size adjusted for autocorrelation and the autocorrelations by lag.</p>
    
    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>

    <p>Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1 - 22. doi:http://dx.doi.org/10.18637/jss.v035.i03.</p>
    

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><span class='co'># NOT RUN {</span>
<span class='co'>## Example from the asia dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/data'>data</a></span>(<span class='st'>"mcmc_run_asia"</span>)

<span class='co'>#summary the MCMC run</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>mcmc.out.asia</span>)
<span class='co'># }</span></pre>
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    <h2>Contents</h2>
    <ul class="nav nav-pills nav-stacked">
      <li><a href="#arguments">Arguments</a></li>
      
      <li><a href="#details">Details</a></li>

      <li><a href="#value">Value</a></li>

      <li><a href="#references">References</a></li>
      
      <li><a href="#examples">Examples</a></li>
    </ul>

    <h2>Author</h2>
    <p>Gilles Kratzer</p>
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