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<title>Logistic regression with a continuous response — bilasso • colasso</title>
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    <h1>Logistic regression with a continuous response</h1>
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    <small class="dont-index">Source: <a href='https://github.com/rauschenberger/colasso/blob/master/R/bilasso.R'><code>R/bilasso.R</code></a></small>
    <div class="hidden name"><code>bilasso.Rd</code></div>
    </div>

    <div class="ref-description">
    
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    <p>Implements logistic regression with a continuous response.</p>
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    </div>

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    <pre class="usage"><span class='fu'>bilasso</span>(<span class='no'>y</span>, <span class='no'>cutoff</span>, <span class='no'>X</span>, <span class='kw'>npi</span> <span class='kw'>=</span> <span class='fl'>101</span>, <span class='kw'>pi</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>nsigma</span> <span class='kw'>=</span> <span class='fl'>99</span>,
  <span class='kw'>sigma</span> <span class='kw'>=</span> <span class='kw'>NULL</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='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>y</th>
      <td><p>continuous response<strong>:</strong>
vector of length \(n\)</p></td>
    </tr>
    <tr>
      <th>cutoff</th>
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      <td><p>cutoff point for dichotomising response into classes<strong>:</strong>
value between <code>min(y)</code> and <code>max(y)</code></p></td>
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    </tr>
    <tr>
      <th>X</th>
      <td><p>covariates<strong>:</strong>
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numeric matrix with \(n\) rows (samples)
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and \(p\) columns (variables)</p></td>
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    </tr>
    <tr>
      <th>npi</th>
      <td><p>number of <code>pi</code> values</p></td>
    </tr>
    <tr>
      <th>pi</th>
      <td><p>pi sequence<strong>:</strong>
vector of values in the unit interval;
or <code>NULL</code> (default sequence)</p></td>
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    </tr>
    <tr>
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      <th>nsigma</th>
      <td><p>number of <code>sigma</code> values</p></td>
    </tr>
    <tr>
      <th>sigma</th>
      <td><p>sigma sequence<strong>:</strong>
vector of increasing positive values;
or <code>NULL</code> (default sequence)</p></td>
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    </tr>
    <tr>
      <th>nfolds</th>
      <td><p>number of folds</p></td>
    </tr>
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    <tr>
      <th>foldid</th>
      <td><p>fold identifiers<strong>:</strong>
vector with entries between \(1\) and <code>nfolds</code>;
or <code>NULL</code> (balance)</p></td>
    </tr>
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    <tr>
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      <th>type.measure</th>
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      <td><p>loss function for binary classification
(linear regression uses the deviance)</p></td>
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    </tr>
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    <tr>
      <th>...</th>
      <td><p>further arguments passed to <code><a href='http://www.rdocumentation.org/packages/glmnet/topics/glmnet'>glmnet</a></code></p></td>
    </tr>
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    </table>
    
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    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

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    <p>- INCLUDE note on deviance (not comparable between lin and log models)
- alpha: elastic net parameter<strong>:</strong>
numeric between \(0\) (ridge) and \(1\) (lasso)
- do not use "family"</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>)
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<span class='no'>net</span> <span class='kw'>&lt;-</span> <span class='fu'>bilasso</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='co'>### Add ... to all glmnet::glmnet calls !!! ###</span></div></pre>
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  </div>
  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
    <h2>Contents</h2>
    <ul class="nav nav-pills nav-stacked">
      <li><a href="#arguments">Arguments</a></li>
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      <li><a href="#details">Details</a></li>
      
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      <li><a href="#examples">Examples</a></li>
    </ul>

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