<p>Positive Predictive Values are the proportions of positive
results in statistics and diagnostic tests that are true positive results.</p>

classif

math

Provides a novel framework to able to automatically develop and deploy
an accurate Multiple Classifier System based on the feature-clustering
distribution achieved from an input dataset. 'D2MCS' was developed focused on
four main aspects: (i) the ability to determine an effective method to
evaluate the independence of features, (ii) the identification of the
optimal number of feature clusters, (iii) the training and tuning of ML
models and (iv) the execution of voting schemes to combine the outputs of
each classifier comprising the Multiple  Classifier System.

Miguel Ferreiro-D<c3><ad>az

D2MCS

Data Driving Multiple Classifier System

David Ruano-Ordás

Miguel Ferreiro-Díaz

José Ramón Méndez

University of Vigo 

PPV function

<p><code>D2MCS::MeasureFunction</code> -&gt; <code>PPV</code></p>

Super class


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/PPV%24new()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::PPV$new()"><code>PPV$new()</code></a></p></li>
<li><p><a href="/link/PPV%24compute()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::PPV$compute()"><code>PPV$compute()</code></a></p></li>
<li><p><a href="/link/PPV%24clone()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::PPV$clone()"><code>PPV$clone()</code></a></p></li>
</ul></p><p></p></div><p><hr>
<a id="method-PPV-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>Method for initializing the object arguments during runtime.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>PPV$new(performance.output = NULL)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>performance.output</code></dt>
<dd><p>An optional <code>ConfMatrix</code> parameter
to define the type of object used as basis to compute the <strong>PPV</strong>
measure.</p></dd></dl></div></p><p>
</p><p></p>
<p></p><p></p><p><hr>
<a id="method-PPV-compute"></a></p><div class="section" id="method-compute-">
<h3>Method <code>compute()</code></h3>
<p>The function computes the <strong>PPV</strong> achieved by the M.L.
model.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>PPV$compute(performance.output = NULL)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>performance.output</code></dt>
<dd><p>An optional <code>ConfMatrix</code> parameter
to define the type of object used as basis to compute the <strong>PPV</strong>
measure.</p></dd></dl></div></p><p>
</p><p></p>

<div class="section" id="details">
<h4>Details</h4>
<p>This function is automatically invoke by the
ClassificationOutput object.</p>
</div><p></p><p><div class="section" id="returns">
<h4>Returns</h4>
<p>A <a href="/link/numeric?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::numeric">numeric</a> vector of size 1 or <a href="/link/NULL?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::NULL">NULL</a> if an error
occurred.</p>
</div></p><p></p><p><hr>
<a id="method-PPV-clone"></a></p><div class="section" id="method-clone-">
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>PPV$clone(deep = FALSE)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd></dl></div></p><p>
</p><p></p>
<p></p><p>
</p>

Methods

Computes the Positive Predictive Value. — PPV


<div class='section' id='public-methods'>
<h3>Public methods</h3>

<ul>
<li><p><a href='#method-PPV-new'><code>PPV$new()</code></a></p></li>
<li><p><a href='#method-PPV-compute'><code>PPV$compute()</code></a></p></li>
<li><p><a href='#method-PPV-clone'><code>PPV$clone()</code></a></p></li>
</ul>

</div><p><hr>
<a id="method-PPV-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>Method for initializing the object arguments during runtime.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>PPV$new(performance.output = NULL)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>performance.output</code></dt>
<dd><p>An optional <code>ConfMatrix</code> parameter
to define the type of object used as basis to compute the <strong>PPV</strong>
measure.</p></dd>


</dl><p></div></p>
</div>

</div><p><hr>
<a id="method-PPV-compute"></a></p><div class='section' id='method-compute-'>
<h3>Method <code>compute()</code></h3>
<p>The function computes the <strong>PPV</strong> achieved by the M.L.
model.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>PPV$compute(performance.output = NULL)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>performance.output</code></dt>
<dd><p>An optional <code>ConfMatrix</code> parameter
to define the type of object used as basis to compute the <strong>PPV</strong>
measure.</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='details'>
<h4>Details</h4>
<p>This function is automatically invoke by the
ClassificationOutput object.</p>
</div>

<div class='section' id='returns'>
<h4>Returns</h4>
<p>A <a href='https://rdrr.io/r/base/numeric.html'>numeric</a> vector of size 1 or <a href='https://rdrr.io/r/base/NULL.html'>NULL</a> if an error
occurred.</p>
</div>

</div><p><hr>
<a id="method-PPV-clone"></a></p><div class='section' id='method-clone-'>
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>PPV$clone(deep = FALSE)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd>


</dl><p></div></p>
</div>

</div>


PPV: Computes the Positive Predictive Value.

Description

Arguments

Super class

Methods

Public methods

Method `new()`

Usage

Arguments

Method `compute()`

Usage

Arguments

Details

Returns

Method `clone()`

Usage

Arguments

Details

See Also

Description

Arguments

Super class

Methods

Public methods

Method new()

Usage

Arguments

Method compute()

Usage

Arguments

Details

Returns

Method clone()

Usage

Arguments

Details

See Also

Method `new()`

Method `compute()`

Method `clone()`