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    <h1>Model comparison</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>cv.joinet.Rd</code></div>
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    </div>

    <div class="ref-description">
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    <p>Compares univariate and multivariate regression.</p>
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    <pre class="usage"><span class='fu'>cv.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.ext</span> <span class='kw'>=</span> <span class='fl'>5</span>,
  <span class='kw'>nfolds.int</span> <span class='kw'>=</span> <span class='fl'>10</span>,
  <span class='kw'>foldid.ext</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>foldid.int</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>,
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  <span class='kw'>alpha.meta</span> <span class='kw'>=</span> <span class='fl'>1</span>,
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  <span class='kw'>compare</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
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  <span class='kw'>mice</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
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  <span class='kw'>cvpred</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
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  <span class='kw'>times</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
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  <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>
    </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>
    </tr>
    <tr>
      <th>nfolds.ext</th>
      <td><p>number of external folds</p></td>
    </tr>
    <tr>
      <th>nfolds.int</th>
      <td><p>number of internal folds</p></td>
    </tr>
    <tr>
      <th>foldid.ext</th>
      <td><p>external fold identifiers<strong>:</strong>
vector of length \(n\) with entries
between \(1\) and <code>nfolds.ext</code>;
or <code>NULL</code></p></td>
    </tr>
    <tr>
      <th>foldid.int</th>
      <td><p>internal fold identifiers<strong>:</strong>
vector of length \(n\) with entries
between \(1\) and <code>nfolds.int</code>;
or <code>NULL</code></p></td>
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    </tr>
    <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://rdrr.io/pkg/glmnet/man/cv.glmnet.html'>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>
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      <th>compare</th>
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      <td><p>experimental arguments<strong>:</strong>
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character vector with entries "mnorm", "spls", "mrce",
"sier", "mtps", "rmtl", "gpm" and others
(requires packages <code>spls</code>, <code>MRCE</code>, <code>SiER</code>, <code>MTPS</code>, <code>RMTL</code> or <code>GPM</code>)</p></td>
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    </tr>
    <tr>
      <th>mice</th>
      <td><p>missing data imputation<strong>:</strong>
logical (<code>mice=TRUE</code> requires package <code>mice</code>)</p></td>
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    </tr>
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    <tr>
      <th>cvpred</th>
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      <td><p>return cross-validated predicitions: logical</p></td>
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    </tr>
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    <tr>
      <th>...</th>
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      <td><p>further arguments passed to <code><a href='https://rdrr.io/pkg/glmnet/man/glmnet.html'>glmnet</a></code>
and <code><a href='https://rdrr.io/pkg/glmnet/man/cv.glmnet.html'>cv.glmnet</a></code></p></td>
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    </tr>
    </table>
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    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>This function returns a matrix with \(q\) columns,
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including the cross-validated loss from the univariate models
(<code>base</code>), the multivariate models (<code>meta</code>),
and the intercept-only models (<code>none</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"></pre>
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      <h2 data-toc-skip>Contents</h2>
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      <footer>
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  <p>Developed by <a href='https://rauschenberger.github.io'>Armin Rauschenberger</a>.</p>
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  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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