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<meta property="og:description" content="The R package joinet implements multivariate
ridge and lasso regression using stacked generalisation.
This multivariate regression typically outperforms
univariate regression at predicting correlated outcomes.
It provides predictive and interpretable models
in high-dimensional settings." />
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    <h1>Multivariate Elastic Net Regression</h1>
    <small class="dont-index">Source: <a href='https://github.com/rauschenberger/joinet/blob/master/R/pkgname.R'><code>R/pkgname.R</code></a></small>
    <div class="hidden name"><code>joinet-package.Rd</code></div>
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    <div class="ref-description">
    
    <p>The R package <code>joinet</code> implements multivariate
ridge and lasso regression using stacked generalisation.
This multivariate regression typically outperforms
univariate regression at predicting correlated outcomes.
It provides predictive and interpretable models
in high-dimensional settings.</p>
    
    </div>

        
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Use function <code><a href='joinet.html'>joinet</a></code> for model fitting.
Type <code><a href='https://www.rdocumentation.org/packages/base/topics/library'>library(joinet)</a></code> and then <code><a href='joinet.html'>?joinet</a></code> or
<code>help("joinet)"</code> to open its help file.</p>
<p>See the vignette for further examples.
Type <code><a href='../articles/joinet.html'>vignette("joinet")</a></code> or <code><a href='https://www.rdocumentation.org/packages/utils/topics/browseVignettes'>browseVignettes("joinet")</a></code>
to open the vignette.</p>
    
    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>

    <p>Armin Rauschenberger and Enrico Glaab (2019).
"joinet: predicting correlated outcomes jointly to improve clinical prognosis".
<em>Manuscript in preparation</em>.</p>
<p><a href='mailto:armin.rauschenberger@uni.lu'>armin.rauschenberger@uni.lu</a></p>
    

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='co'>#--- data simulation ---</span>
<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>
<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>)
<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>])))
<span class='co'># n samples, p inputs, q outputs</span>

<span class='co'>#--- model fitting ---</span>
<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='joinet.html'>joinet</a></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>)
<span class='co'># slot "base": univariate</span>
<span class='co'># slot "meta": multivariate</span>

<span class='co'>#--- make predictions ---</span>
<span class='no'>y_hat</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/predict'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)
<span class='co'># n x q matrix "base": univariate</span>
<span class='co'># n x q matrix "meta": multivariate </span>

<span class='co'>#--- extract coefficients ---</span>
<span class='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>object</span>)
<span class='co'># effects of inputs on outputs</span>
<span class='co'># q vector "alpha": intercepts</span>
<span class='co'># p x q matrix "beta": slopes</span>

<span class='co'>#--- model comparison ---</span>
<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='cv.joinet.html'>cv.joinet</a></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>)
<span class='co'># cross-validated loss</span>
<span class='co'># row "base": univariate</span>
<span class='co'># row "meta": multivariate</span></div></pre>
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