plot.prepost_mi

Plot method for visualizing imputation diagnostics

Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2024), Biostatistics 25(4): 1094-1111.

Ravi Varadhan

resilience

Predictors of Resilience to a Stressor in a Single-Arm Study

Jiafeng Zhu

plot.prepost_mi function

<dl><dt>x</dt>
<dd>An object of class <code>prepost_resilience_mi_list</code>.</dd>
<dt>type</dt>
<dd>Type of plot: <code>"estimates"</code> (default) or <code>"fmi"</code>.</dd>
<dt>...</dt>
<dd>Additional arguments passed to plotting functions.</dd></dl>

Arguments

Plot Results from Multiply Imputed Resilience Analysis — plot.prepost_mi

<dl>

<dt>x</dt>
<dd>An object of class <code>prepost_resilience_mi_list</code>.</dd>


<dt>type</dt>
<dd>Type of plot: <code>"estimates"</code> (default) or <code>"fmi"</code>.</dd>


<dt>...</dt>
<dd>Additional arguments passed to plotting functions.</dd>

</dl>

plot.prepost_mi: Plot Results from Multiply Imputed Resilience Analysis

Description

Usage

Arguments