get_model_matrix

get_model_matrix.default

internal

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 80 classes of statistical 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.

Vincent Arel-Bundock

marginaleffects

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

Marcio Augusto Diniz

Noah Greifer

Etienne Bacher

get_model_matrix function

<dl><dt>model</dt>
<dd>Model object</dd>
<dt>newdata</dt>
<dd>Grid of predictor values at which we evaluate the slopes.<ul>
<li><code>NULL</code> (default): Unit-level slopes for each observed value in the original dataset. See <code>insight::get_data()</code></li>
<li>data frame: Unit-level slopes for each row of the <code>newdata</code> data frame.</li>
<li><code>datagrid()</code> call to specify a custom grid of regressors. For example:<ul>
<li><code>newdata = datagrid(cyl = c(4, 6))</code>: <code>cyl</code> variable equal to 4 and 6 and other regressors fixed at their means or modes.</li>
<li>See the Examples section and the <code>datagrid()</code> documentation.</li>
</ul></li>
<li>string:<ul>
<li>"mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode.</li>
<li>"median": Marginal Effects at the Median. Slopes when each predictor is held at its median or mode.</li>
<li>"marginalmeans": Marginal Effects at Marginal Means. See Details section below.</li>
<li>"tukey": Marginal Effects at Tukey's 5 numbers.</li>
<li>"grid": Marginal Effects on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors).</li>
</ul></li>
</ul></dd></dl>

Arguments

Get a named model matrix — get_model_matrix

<dl>

<dt>model</dt>
<dd>Model object</dd>


<dt>newdata</dt>
<dd>Grid of predictor values at which we evaluate the slopes.<ul>
<li><code>NULL</code> (default): Unit-level slopes for each observed value in the original dataset. See <code>insight::get_data()</code></li>
<li>data frame: Unit-level slopes for each row of the <code>newdata</code> data frame.</li>
<li><code>datagrid()</code> call to specify a custom grid of regressors. For example:<ul>
<li><code>newdata = datagrid(cyl = c(4, 6))</code>: <code>cyl</code> variable equal to 4 and 6 and other regressors fixed at their means or modes.</li>
<li>See the Examples section and the <code>datagrid()</code> documentation.</li>
</ul></li>
<li>string:<ul>
<li>"mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode.</li>
<li>"median": Marginal Effects at the Median. Slopes when each predictor is held at its median or mode.</li>
<li>"marginalmeans": Marginal Effects at Marginal Means. See Details section below.</li>
<li>"tukey": Marginal Effects at Tukey's 5 numbers.</li>
<li>"grid": Marginal Effects on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors).</li>
</ul></li>
</ul></dd>

</dl>

Get a named model matrix

get_model_matrix: Get a named model matrix

Description

Usage

Arguments