simpleNodeSampler

<p>Class for defining a simple node sampler</p>
<p>Class for defining a simple node sampler</p>

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0
due to missing information between node pairs), it is possible to account for the underlying process
that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
adjusts the popular stochastic block model from network data sampled under various missing data conditions,
as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Julien Chiquet

missSBM

Handling Missing Data in Stochastic Block Models

Pierre Barbillon

Timothée Tabouy

Jean-Benoist Léger

François Gindraud

großBM team 

simpleNodeSampler function

<p><code>missSBM::networkSampling</code> -&gt; <code>missSBM::networkSampler</code> -&gt; <code>missSBM::nodeSampler</code> -&gt; <code>simpleNodeSampler</code></p>

Super classes


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/simpleNodeSampler%24new()?package=missSBM&version=1.0.5" data-mini-rdoc="missSBM::simpleNodeSampler$new()"><code>simpleNodeSampler$new()</code></a></p></li>
<li><p><a href="/link/simpleNodeSampler%24clone()?package=missSBM&version=1.0.5" data-mini-rdoc="missSBM::simpleNodeSampler$clone()"><code>simpleNodeSampler$clone()</code></a></p></li>
</ul></p><p></p></div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampling" data-id="print"><a href="/link/missSBM%3A%3AnetworkSampling%24print()?package=missSBM&version=1.0.5" data-mini-rdoc="missSBM::missSBM::networkSampling$print()"><code>missSBM::networkSampling$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampling" data-id="show"><a href="/link/missSBM%3A%3AnetworkSampling%24show()?package=missSBM&version=1.0.5" data-mini-rdoc="missSBM::missSBM::networkSampling$show()"><code>missSBM::networkSampling$show()</code></a></span></li>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampler" data-id="rSamplingMatrix"><a href="/link/missSBM%3A%3AnetworkSampler%24rSamplingMatrix()?package=missSBM&version=1.0.5" data-mini-rdoc="missSBM::missSBM::networkSampler$rSamplingMatrix()"><code>missSBM::networkSampler$rSamplingMatrix()</code></a></span></li>
</ul>
</details></p><p><hr>
<a id="method-simpleNodeSampler-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>constructor for networkSampling</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>simpleNodeSampler$new(
  parameters = NA,
  nbNodes = NA,
  directed = FALSE,
  covarMatrix = NULL,
  intercept = 0
)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>parameters</code></dt>
<dd><p>the vector of parameters associated to the sampling at play</p></dd></dl></div></p><p>
<dt><code>nbNodes</code></dt>
<dd><p>number of nodes in the network</p></dd></p><p>
<dt><code>directed</code></dt>
<dd><p>logical, directed network of not</p></dd></p><p>
<dt><code>covarMatrix</code></dt>
<dd><p>a matrix of covariates used</p></dd></p><p>
<dt><code>intercept</code></dt>
<dd><p>double, intercept term used to compute the probability of sampling in the presence of covariates. Default 0.</p></dd></p><p>
</p><p></p>
<p></p><p></p><p><hr>
<a id="method-simpleNodeSampler-clone"></a></p><div class="section" id="method-clone-">
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>simpleNodeSampler$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

Class for defining a simple node sampler — simpleNodeSampler


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

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

</div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampling" data-id="print"><a href='../../missSBM/html/networkSampling.html#method-networkSampling-print'><code>missSBM::networkSampling$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampling" data-id="show"><a href='../../missSBM/html/networkSampling.html#method-networkSampling-show'><code>missSBM::networkSampling$show()</code></a></span></li>
<li><span class="pkg-link" data-pkg="missSBM" data-topic="networkSampler" data-id="rSamplingMatrix"><a href='../../missSBM/html/networkSampler.html#method-networkSampler-rSamplingMatrix'><code>missSBM::networkSampler$rSamplingMatrix()</code></a></span></li>
</ul>
</details>

<hr>
<a id="method-simpleNodeSampler-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>constructor for networkSampling</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>simpleNodeSampler$new(
  parameters = NA,
  nbNodes = NA,
  directed = FALSE,
  covarMatrix = NULL,
  intercept = 0
)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>parameters</code></dt>
<dd><p>the vector of parameters associated to the sampling at play</p></dd>


<dt><code>nbNodes</code></dt>
<dd><p>number of nodes in the network</p></dd>


<dt><code>directed</code></dt>
<dd><p>logical, directed network of not</p></dd>


<dt><code>covarMatrix</code></dt>
<dd><p>a matrix of covariates used</p></dd>


<dt><code>intercept</code></dt>
<dd><p>double, intercept term used to compute the probability of sampling in the presence of covariates. Default 0.</p></dd>


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

</div><p><hr>
<a id="method-simpleNodeSampler-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>simpleNodeSampler$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>


simpleNodeSampler: Class for defining a simple node sampler

Description

Arguments

Super classes

Methods

Public methods

Method `new()`

Usage

Arguments

Method `clone()`

Usage

Arguments

Description

Arguments

Super classes

Methods

Public methods

Method new()

Usage

Arguments

Method clone()

Usage

Arguments

Method `new()`

Method `clone()`