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    <h1>Multivariate Elastic Net Regression</h1>
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    <small class="dont-index">Source: <a href='https://github.com/rauschenberger/joinet/blob/master/R/functions.R'><code>R/functions.R</code></a></small>
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    <div class="hidden name"><code>joinet.Rd</code></div>
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    </div>

    <div class="ref-description">
    
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    <p>Implements multivariate elastic net regression.</p>
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    </div>

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    <pre class="usage"><span class='fu'>joinet</span>(<span class='no'>Y</span>, <span class='no'>X</span>, <span class='kw'>family</span> <span class='kw'>=</span> <span class='st'>"gaussian"</span>, <span class='kw'>nfolds</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>foldid</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
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  <span class='kw'>type.measure</span> <span class='kw'>=</span> <span class='st'>"deviance"</span>, <span class='kw'>alpha.base</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>alpha.meta</span> <span class='kw'>=</span> <span class='fl'>0</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>
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      <th>Y</th>
      <td><p>outputs<strong>:</strong>
numeric matrix with \(n\) rows (samples)
and \(q\) columns (variables),
with positive correlation (see details)</p></td>
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    </tr>
    <tr>
      <th>X</th>
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      <td><p>inputs<strong>:</strong>
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numeric matrix with \(n\) rows (samples)
and \(p\) columns (variables)</p></td>
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    </tr>
    <tr>
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      <th>family</th>
      <td><p>distribution<strong>:</strong>
vector of length \(1\) or \(q\) with entries
<code>"gaussian"</code>, <code>"binomial"</code> or <code>"poisson"</code></p></td>
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    </tr>
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    <tr>
      <th>nfolds</th>
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      <td><p>number of folds</p></td>
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    </tr>
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    <tr>
      <th>foldid</th>
      <td><p>fold identifiers<strong>:</strong>
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vector of length \(n\) with entries between \(1\) and <code>nfolds</code>;
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or <code>NULL</code> (balance)</p></td>
    </tr>
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    <tr>
      <th>type.measure</th>
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      <td><p>loss function<strong>:</strong>
vector of length \(1\) or \(q\) with entries
<code>"deviance"</code>, <code>"class"</code>, <code>"mse"</code> or <code>"mae"</code>
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(see <code><a href='https://www.rdocumentation.org/packages/glmnet/topics/cv.glmnet'>cv.glmnet</a></code>)</p></td>
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    </tr>
    <tr>
      <th>alpha.base</th>
      <td><p>elastic net mixing parameter for base learners<strong>:</strong>
numeric between \(0\) (ridge) and \(1\) (lasso)</p></td>
    </tr>
    <tr>
      <th>alpha.meta</th>
      <td><p>elastic net mixing parameter for meta learner<strong>:</strong>
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numeric between \(0\) (ridge) and \(1\) (lasso)</p></td>
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    </tr>
    <tr>
      <th>...</th>
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      <td><p>further arguments passed to <code><a href='https://www.rdocumentation.org/packages/glmnet/topics/glmnet'>glmnet</a></code></p></td>
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    </tr>
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    </table>
    
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    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>This function returns an object of class <code>joinet</code>.
Available methods include
<code><a href='predict.joinet.html'>predict</a></code>,
<code><a href='coef.joinet.html'>coef</a></code>,
and <code><a href='weights.joinet.html'>weights</a></code>.
The slots <code>base</code> and <code>meta</code> each contain
\(q\) <code><a href='https://www.rdocumentation.org/packages/glmnet/topics/cv.glmnet'>cv.glmnet</a></code>-like objects.</p>
    
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    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

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    <p><strong>correlation:</strong>
The \(q\) outcomes should be positively correlated.
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Avoid negative correlations by changing the sign of the variable.</p>
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<p><strong>elastic net:</strong>
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<code>alpha.base</code> controls input-output effects,
<code>alpha.meta</code> controls output-output effects;
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lasso renders sparse models (<code>alpha</code>\(=1\)),
ridge renders dense models (<code>alpha</code>\(=0\))</p>
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    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>

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    <p>Armin Rauschenberger, Enrico Glaab (2019)
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"joinet: predicting correlated outcomes jointly
to improve clinical prognosis"
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<em>Manuscript in preparation</em>.</p>
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    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>

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    <div class='dont-index'><p><code><a href='cv.joinet.html'>cv.joinet</a></code>, vignette</p></div>
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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'>50</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>
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<span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/matrix'>matrix</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>rnorm</a></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>)
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<span class='no'>Y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/lapply'>replicate</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>q</span>,<span class='kw'>expr</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>rnorm</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>mean</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colSums'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
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<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'>joinet</span>(<span class='kw'>Y</span><span class='kw'>=</span><span class='no'>Y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)</div><span class='co'># NOT RUN {</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/browseVignettes'>browseVignettes</a></span>(<span class='st'>"joinet"</span>) <span class='co'># further examples</span>
<span class='co'># }</span><div class='input'>
</div></pre>
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  </div>
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    <h2>Contents</h2>
    <ul class="nav nav-pills nav-stacked">
      <li><a href="#arguments">Arguments</a></li>
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      <li><a href="#value">Value</a></li>

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      <li><a href="#details">Details</a></li>
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      <li><a href="#references">References</a></li>
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      <li><a href="#see-also">See also</a></li>
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      <li><a href="#examples">Examples</a></li>
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  <p>Developed by <a href='https://rauschenberger.github.io'>Armin Rauschenberger</a>.</p>
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