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<title>Extract estimated coefficients — coef.cornet • cornet</title>
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<meta property="og:title" content="Extract estimated coefficients — coef.cornet" />
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<meta property="og:description" content="Extracts estimated coefficients from linear and logistic regression,
under the penalty parameter that minimises the cross-validated loss." />
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    <h1>Extract estimated coefficients</h1>
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    <small class="dont-index">Source: <a href='https://github.com/rauschenberger/cornet/blob/master/R/functions.R'><code>R/functions.R</code></a></small>
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    <div class="hidden name"><code>coef.cornet.Rd</code></div>
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    </div>

    <div class="ref-description">
    
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    <p>Extracts estimated coefficients from linear and logistic regression,
under the penalty parameter that minimises the cross-validated loss.</p>
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    <pre class="usage"><span class='co'># S3 method for cornet</span>
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<span class='fu'>coef</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
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    <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>
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      <td><p><a href='cornet.html'>cornet</a> object</p></td>
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    </tr>
    <tr>
      <th>...</th>
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      <td><p>further arguments (not applicable)</p></td>
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    </tr>
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    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>This function returns a matrix with \(n\) rows and two columns,
where \(n\) is the sample size. It includes the estimated coefficients
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from linear regression (1st column, <code>"beta"</code>)
and logistic regression (2nd column, <code>"gamma"</code>).</p>
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    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
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    <pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>200</span>
<span class='no'>y</span> <span class='kw'>&lt;-</span> <span class='fu'>rnorm</span>(<span class='no'>n</span>)
<span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='fu'>matrix</span>(<span class='fu'>rnorm</span>(<span class='no'>n</span>*<span class='no'>p</span>),<span class='kw'>nrow</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>ncol</span><span class='kw'>=</span><span class='no'>p</span>)
<span class='no'>net</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='cornet.html'>cornet</a></span>(<span class='kw'>y</span><span class='kw'>=</span><span class='no'>y</span>,<span class='kw'>cutoff</span><span class='kw'>=</span><span class='fl'>0</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)
<span class='fu'>coef</span>(<span class='no'>net</span>)</div><div class='output co'>#&gt; 201 x 2 sparse Matrix of class "dgCMatrix"
#&gt;                     beta     gamma
#&gt; (Intercept)  0.094379197 0.1648755
#&gt; V1           .           .        
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#&gt; V200        -0.075663233 .        </div><div class='input'>
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</div></pre>
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    <h2>Contents</h2>
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      <li><a href="#arguments">Arguments</a></li>
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      <li><a href="#value">Value</a></li>
      
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