plot

plot.sensitivity

S3 methods visualizing results for some objects generated by <code>sensitivity</code>.

We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (<doi:10.1177/00491241211067516>; <doi:10.1177/00811750231183711>). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 <doi:10.1515/jci-2022-0031>). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) <doi:10.48550/arXiv.2506.19010>.

Suyeon Kang

causal.decomp

Causal Decomposition Analysis

Soojin Park

Karen Xu

plot function

<dl><dt>x</dt>
<dd>an object of class <code>sensitivity</code>.</dd>
<dt>xlim</dt>
<dd>limits of the x-axis of contour plots.</dd>
<dt>ylim</dt>
<dd>limits of the y-axis of contour plots.</dd>
<dt>...</dt>
<dd>Other arguments for future usage.</dd></dl>

Arguments

Visualize sensitivity Objects — plot

<dl>

<dt>x</dt>
<dd>an object of class <code>sensitivity</code>.</dd>


<dt>xlim</dt>
<dd>limits of the x-axis of contour plots.</dd>


<dt>ylim</dt>
<dd>limits of the y-axis of contour plots.</dd>


<dt>...</dt>
<dd>Other arguments for future usage.</dd>

</dl>

plot: Visualize sensitivity Objects

Description

Usage

Arguments