get_draws

Extract Posterior Draws or Bootstrap Resamples from <code>marginaleffects</code> Objects

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

get_draws function

<dl><dt>x</dt>
<dd>An object produced by a <code>marginaleffects</code> package function, such as <code>predictions()</code>, <code>avg_slopes()</code>, <code>hypotheses()</code>, etc.</dd>
<dt>shape</dt>
<dd>string indicating the shape of the output format:<ul>
<li>"long": long format data frame</li>
<li>"DxP": Matrix with draws as rows and parameters as columns</li>
<li>"PxD": Matrix with draws as rows and parameters as columns</li>
<li>"rvar": Random variable datatype (see <code>posterior</code> package documentation).</li>
</ul></dd></dl>

Arguments

Extract Posterior Draws or Bootstrap Resamples from marginaleffects Objects — get_draws

<dl>

<dt>x</dt>
<dd>An object produced by a <code>marginaleffects</code> package function, such as <code>predictions()</code>, <code>avg_slopes()</code>, <code>hypotheses()</code>, etc.</dd>


<dt>shape</dt>
<dd>string indicating the shape of the output format:<ul>
<li>"long": long format data frame</li>
<li>"DxP": Matrix with draws as rows and parameters as columns</li>
<li>"PxD": Matrix with draws as rows and parameters as columns</li>
<li>"rvar": Random variable datatype (see <code>posterior</code> package documentation).</li>
</ul></dd>

</dl>

get_draws: Extract Posterior Draws or Bootstrap Resamples from `marginaleffects` Objects

Description

Usage

Value

Arguments

Details