GOF_model_info_extractor

<p>R6 does not offer interfaces. Hence all methods
are considered as abstract.</p>

Bootstrap based goodness-of-fit tests. It allows
to perform rigorous statistical tests to check if a chosen
model family is correct based on the marked empirical
process. The implemented algorithms are described in
(Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>)
and can be applied
to generalized linear models without any further implementation
effort. As far as certain linearity conditions are fulfilled
the resampling scheme are also applicable beyond generalized
linear models. This is reflected in the software architecture
which allows to reuse the resampling scheme by implementing
only certain interfaces for models that are not supported
natively by the package.

Marsel Scheer

bootGOF

Bootstrap Based Goodness-of-Fit Tests

Gerhard Dikta

GOF_model_info_extractor function


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/GOF_model_info_extractor%24yhat()?package=bootGOF&version=0.1.0" data-mini-rdoc="bootGOF::GOF_model_info_extractor$yhat()"><code>GOF_model_info_extractor$yhat()</code></a></p></li>
<li><p><a href="/link/GOF_model_info_extractor%24y_minus_yhat()?package=bootGOF&version=0.1.0" data-mini-rdoc="bootGOF::GOF_model_info_extractor$y_minus_yhat()"><code>GOF_model_info_extractor$y_minus_yhat()</code></a></p></li>
<li><p><a href="/link/GOF_model_info_extractor%24beta_x_covariates()?package=bootGOF&version=0.1.0" data-mini-rdoc="bootGOF::GOF_model_info_extractor$beta_x_covariates()"><code>GOF_model_info_extractor$beta_x_covariates()</code></a></p></li>
<li><p><a href="/link/GOF_model_info_extractor%24clone()?package=bootGOF&version=0.1.0" data-mini-rdoc="bootGOF::GOF_model_info_extractor$clone()"><code>GOF_model_info_extractor$clone()</code></a></p></li>
</ul></p><p></p></div><p><hr>
<a id="method-yhat"></a></p><div class="section" id="method-yhat-">
<h3>Method <code>yhat()</code></h3>
<p>Abstract function that estimates/predicts the
  the dependent variable in <code>model</code></p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>GOF_model_info_extractor$yhat(model)</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>model</code></dt>
<dd><p>fitted model</p></dd></dl></div></p><p>
</p><p></p>

<div class="section" id="returns">
<h4>Returns</h4>
<p>estimate/prediction of the dependent variable
  fitted by <code>model</code></p>
</div><p></p><p></p><p><hr>
<a id="method-y_minus_yhat"></a></p><div class="section" id="method-y-minus-yhat-">
<h3>Method <code>y_minus_yhat()</code></h3>
<p>abstract function that calculates the residuals
  on the scale of the dependent variable.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>GOF_model_info_extractor$y_minus_yhat(model)</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>model</code></dt>
<dd><p>fitted model</p></dd></dl></div></p><p>
</p><p></p>

<div class="section" id="returns">
<h4>Returns</h4>
<p>residuals on the scale of the dependent variable</p>
</div><p></p><p></p><p><hr>
<a id="method-beta_x_covariates"></a></p><div class="section" id="method-beta-x-covariates-">
<h3>Method <code>beta_x_covariates()</code></h3>
<p>abstract function that calculates the inner product
  of estimated parameters and the independent variables.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>GOF_model_info_extractor$beta_x_covariates(model)</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>model</code></dt>
<dd><p>fitted model</p></dd></dl></div></p><p>
</p><p></p>

<div class="section" id="returns">
<h4>Returns</h4>
<p>inner product of the estimated parameters and the
  independent variables.</p>
</div><p></p><p></p><p><hr>
<a id="method-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>GOF_model_info_extractor$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

R6 Class representing model information — GOF_model_info_extractor


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

<ul>
<li><p><a href='#method-yhat'><code>GOF_model_info_extractor$yhat()</code></a></p></li>
<li><p><a href='#method-y_minus_yhat'><code>GOF_model_info_extractor$y_minus_yhat()</code></a></p></li>
<li><p><a href='#method-beta_x_covariates'><code>GOF_model_info_extractor$beta_x_covariates()</code></a></p></li>
<li><p><a href='#method-clone'><code>GOF_model_info_extractor$clone()</code></a></p></li>
</ul>

</div><p><hr>
<a id="method-yhat"></a></p><div class='section' id='method-yhat-'>
<h3>Method <code>yhat()</code></h3>
<p>Abstract function that estimates/predicts the
  the dependent variable in <code>model</code></p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>GOF_model_info_extractor$yhat(model)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>model</code></dt>
<dd><p>fitted model</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='returns'>
<h4>Returns</h4>
<p>estimate/prediction of the dependent variable
  fitted by <code>model</code></p>
</div>

</div><p><hr>
<a id="method-y_minus_yhat"></a></p><div class='section' id='method-y-minus-yhat-'>
<h3>Method <code>y_minus_yhat()</code></h3>
<p>abstract function that calculates the residuals
  on the scale of the dependent variable.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>GOF_model_info_extractor$y_minus_yhat(model)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>model</code></dt>
<dd><p>fitted model</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='returns'>
<h4>Returns</h4>
<p>residuals on the scale of the dependent variable</p>
</div>

</div><p><hr>
<a id="method-beta_x_covariates"></a></p><div class='section' id='method-beta-x-covariates-'>
<h3>Method <code>beta_x_covariates()</code></h3>
<p>abstract function that calculates the inner product
  of estimated parameters and the independent variables.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>GOF_model_info_extractor$beta_x_covariates(model)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>model</code></dt>
<dd><p>fitted model</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='returns'>
<h4>Returns</h4>
<p>inner product of the estimated parameters and the
  independent variables.</p>
</div>

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


GOF_model_info_extractor: R6 Class representing model information

Description

Arguments

Methods

Public methods

Method `yhat()`

Usage

Arguments

Returns

Method `y_minus_yhat()`

Usage

Arguments

Returns

Method `beta_x_covariates()`

Usage

Arguments

Returns

Method `clone()`

Usage

Arguments

Description

Arguments

Methods

Public methods

Method yhat()

Usage

Arguments

Returns

Method y_minus_yhat()

Usage

Arguments

Returns

Method beta_x_covariates()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

Method `yhat()`

Method `y_minus_yhat()`

Method `beta_x_covariates()`

Method `clone()`