coffee_data

A sample data set from a course about the analysis of factorial
designs, by Mattan S. Ben-Shachar. See following link for more information:
https://github.com/mattansb/Analysis-of-Factorial-Designs-foR-Psychologists
The data consists of five variables from 120 observations:<ul>
<li><code>ID</code>: A unique identifier for each participant</li>
<li><code>sex</code>: The participant's sex</li>
<li><code>time</code>: The time of day the participant was tested (morning, noon, or afternoon)</li>
<li><code>coffee</code>: Group indicator, whether participant drank coffee or not
("<code>coffee"</code> or <code>"control"</code>).</li>
<li><code>alertness</code>: The participant's alertness score.</li>
</ul>

data

Compute marginal effects and adjusted predictions from statistical
models and returns the result as tidy data frames. These data frames are
ready to use with the 'ggplot2'-package. Effects and predictions can be
calculated for many different models. Interaction terms, splines and
polynomial terms are also supported. The main functions are ggpredict(),
ggemmeans() and ggeffect(). There is a generic plot()-method to plot the
results using 'ggplot2'.

Daniel Lüdecke

ggeffects

Create Tidy Data Frames of Marginal Effects for 'ggplot' from
Model Outputs

Frederik Aust

Sam Crawley

coffee_data function

Sample dataset from a course about analysis of factorial designs — coffee_data

Sample dataset from a course about analysis of factorial designs

coffee_data: Sample dataset from a course about analysis of factorial designs

Description

Arguments

Examples