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

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

    <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>,
  <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'>1</span>,
  <span class='no'>...</span>
)</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>Y</th>
      <td><p>outputs<strong>:</strong>
numeric matrix with \(n\) rows (samples)
and \(q\) columns (variables),
with positive correlation (see details)</p></td>
    </tr>
    <tr>
      <th>X</th>
      <td><p>inputs<strong>:</strong>
numeric matrix with \(n\) rows (samples)
and \(p\) columns (variables)</p></td>
    </tr>
    <tr>
      <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>
    </tr>
    <tr>
      <th>nfolds</th>
      <td><p>number of folds</p></td>
    </tr>
    <tr>
      <th>foldid</th>
      <td><p>fold identifiers<strong>:</strong>
vector of length \(n\) with entries between \(1\) and <code>nfolds</code>;
or <code>NULL</code> (balance)</p></td>
    </tr>
    <tr>
      <th>type.measure</th>
      <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>
(see <code><a href='https://rdrr.io/pkg/glmnet/man/cv.glmnet.html'>cv.glmnet</a></code>)</p></td>
    </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>
numeric between \(0\) (ridge) and \(1\) (lasso)</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>further arguments passed to <code><a href='https://rdrr.io/pkg/glmnet/man/glmnet.html'>glmnet</a></code></p></td>
    </tr>
    </table>

    <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://rdrr.io/pkg/glmnet/man/cv.glmnet.html'>cv.glmnet</a></code>-like objects.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

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

    <p>Armin Rauschenberger, Enrico Glaab (2020)
"Predicting correlated outcomes from molecular data"
<em>Manuscript in preparation</em>.</p>
    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>

    <div class='dont-index'><p><code><a href='cv.joinet.html'>cv.joinet</a></code>, vignette</p></div>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"></pre>
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