marginaleffects_internal-class

This S4 class is used internally to hold common arguments passed between
functions to simplify the function signatures and reduce redundant argument passing.

internal

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

Vincent Arel-Bundock

marginaleffects

Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis
Tests

Noah Greifer

Etienne Bacher

Grant McDermott

Andrew Heiss

marginaleffects_internal-class function

<dl>
<dt><code>by</code></dt>
<dd>Aggregation/grouping specification</dd></dl>
<dt><code>call</code></dt>
<dd>The original function call</dd>
<dt><code>calling_function</code></dt>
<dd>The name of the calling function (comparisons, predictions, hypotheses)</dd>
<dt><code>comparison</code></dt>
<dd>Comparison function specification</dd>
<dt><code>cross</code></dt>
<dd>Boolean flag for cross-contrasts</dd>
<dt><code>df</code></dt>
<dd>The degrees of freedom</dd>
<dt><code>eps</code></dt>
<dd>Epsilon value for numerical derivatives</dd>
<dt><code>jacobian</code></dt>
<dd>The jacobian matrix or NULL</dd>
<dt><code>jacobian_method</code></dt>
<dd>String naming the differentiation path which produced
the jacobian: "analytic", "analytic+numeric_stage", "numeric", "custom",
or NULL</dd>
<dt><code>model</code></dt>
<dd>The fitted model object</dd>
<dt><code>modeldata</code></dt>
<dd>The model data frame</dd>
<dt><code>newdata</code></dt>
<dd>The new data frame for predictions</dd>
<dt><code>type</code></dt>
<dd>The sanitized type from sanitize_type()</dd>
<dt><code>vcov_model</code></dt>
<dd>The variance-covariance matrix</dd>
<dt><code>wts</code></dt>
<dd>The weights specification</dd>

Slots

Internal S4 class for marginaleffects — marginaleffects_internal-class

<dl>
<dt><code>by</code></dt>
<dd>Aggregation/grouping specification</dd>


<dt><code>call</code></dt>
<dd>The original function call</dd>


<dt><code>calling_function</code></dt>
<dd>The name of the calling function (comparisons, predictions, hypotheses)</dd>


<dt><code>comparison</code></dt>
<dd>Comparison function specification</dd>


<dt><code>cross</code></dt>
<dd>Boolean flag for cross-contrasts</dd>


<dt><code>df</code></dt>
<dd>The degrees of freedom</dd>


<dt><code>eps</code></dt>
<dd>Epsilon value for numerical derivatives</dd>


<dt><code>jacobian</code></dt>
<dd>The jacobian matrix or NULL</dd>


<dt><code>jacobian_method</code></dt>
<dd>String naming the differentiation path which produced
the jacobian: "analytic", "analytic+numeric_stage", "numeric", "custom",
or NULL</dd>


<dt><code>model</code></dt>
<dd>The fitted model object</dd>


<dt><code>modeldata</code></dt>
<dd>The model data frame</dd>


<dt><code>newdata</code></dt>
<dd>The new data frame for predictions</dd>


<dt><code>type</code></dt>
<dd>The sanitized type from sanitize_type()</dd>


<dt><code>vcov_model</code></dt>
<dd>The variance-covariance matrix</dd>


<dt><code>wts</code></dt>
<dd>The weights specification</dd>


</dl>

marginaleffects_internal-class: Internal S4 class for marginaleffects

Description

Arguments

Slots