plot_diagnose

Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2022) <doi:10.1017/pan.2020.40>, and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.

Naoki Egami

factorEx

Design and Analysis for Factorial Experiments

Brandon de la Cuesta

Kosuke Imai

plot_diagnose function

<dl><dt>x</dt>
<dd>An object of class "pAMCE", a result of a call to 'model_pAMCE.'</dd>
<dt>factor_name</dt>
<dd>A factor for which the function diagnoses modeling assumptions.</dd>
<dt>legend_pos</dt>
<dd>Position of the legend. Default is 'topright'.</dd>
<dt>target_dist_name</dt>
<dd>Names of the target profile distributions to be used.</dd>
<dt>xlim</dt>
<dd>Range for the x-axis.</dd>
<dt>mar</dt>
<dd>Space on the left side of the plot. Default is 12.</dd></dl>

Arguments

Plotting diagnostic checks — plot_diagnose

<dl>

<dt>x</dt>
<dd>An object of class "pAMCE", a result of a call to 'model_pAMCE.'</dd>


<dt>factor_name</dt>
<dd>A factor for which the function diagnoses modeling assumptions.</dd>


<dt>legend_pos</dt>
<dd>Position of the legend. Default is 'topright'.</dd>


<dt>target_dist_name</dt>
<dd>Names of the target profile distributions to be used.</dd>


<dt>xlim</dt>
<dd>Range for the x-axis.</dd>


<dt>mar</dt>
<dd>Space on the left side of the plot. Default is 12.</dd>

</dl>

plot_diagnose: Plotting diagnostic checks

Description

Usage

Value

Arguments