summary.predictions

Summarize a <code>predictions</code> object

Compute and plot adjusted predictions, contrasts, marginal effects, and marginal means for over 70 classes of statistical models in R. Conduct linear and non-linear hypothesis tests using the delta method.

Vincent Arel-Bundock

marginaleffects

Marginal Effects, Marginal Means, Predictions, and Contrasts

Marcio Augusto Diniz

Noah Greifer

summary.predictions function

<dl><dt>object</dt>
<dd>An object produced by the <code>predictions</code> function</dd>
<dt>conf_level</dt>
<dd>numeric value between 0 and 1. Confidence level to use to build a confidence interval.</dd>
<dt>transform_avg</dt>
<dd>A function applied to the estimates and confidence intervals after the unit-level estimates have been averaged.</dd>
<dt>...</dt>
<dd>Additional arguments are passed to the <code><a href="/link/predict()?package=marginaleffects&version=0.8.1" data-mini-rdoc="marginaleffects::predict()">predict()</a></code> method
supplied by the modeling package.These arguments are particularly useful
for mixed-effects or bayesian models (see the online vignettes on the
<code>marginaleffects</code> website). Available arguments can vary from model to
model, depending on the range of supported arguments by each modeling
package. See the "Model-Specific Arguments" section of the
<code>?marginaleffects</code> documentation for a non-exhaustive list of available
arguments.</dd></dl>

Arguments

Summarize a predictions object — summary.predictions

<dl>

<dt>object</dt>
<dd>An object produced by the <code>predictions</code> function</dd>


<dt>conf_level</dt>
<dd>numeric value between 0 and 1. Confidence level to use to build a confidence interval.</dd>


<dt>transform_avg</dt>
<dd>A function applied to the estimates and confidence intervals after the unit-level estimates have been averaged.</dd>


<dt>...</dt>
<dd>Additional arguments are passed to the <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> method
supplied by the modeling package.These arguments are particularly useful
for mixed-effects or bayesian models (see the online vignettes on the
<code>marginaleffects</code> website). Available arguments can vary from model to
model, depending on the range of supported arguments by each modeling
package. See the "Model-Specific Arguments" section of the
<code>?marginaleffects</code> documentation for a non-exhaustive list of available
arguments.</dd>

</dl>

summary.predictions: Summarize a `predictions` object

Description

Usage

Value

Arguments

See Also