add_variable_labels

support function to plot variable text labels

In the observational study design stage, matching/weighting methods are
conducted. However, when many background variables are present, the decision as to
which variables to prioritize for matching/weighting is not trivial. Thus, the
joint treatment-outcome variable importance plots are created to guide variable
selection. The joint variable importance plots enhance variable comparisons via
unadjusted bias curves derived under the omitted variable bias framework. The
plots translate variable importance into recommended values for tuning parameters
in existing methods. Post-matching and/or weighting plots can also be used to
visualize and assess the quality of the observational study design. The method
motivation and derivation is presented in "Prioritizing Variables for Observational
Study Design using the Joint Variable Importance Plot" by Liao et al. (2024)
<doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel
(2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Lauren D. Liao

jointVIP

Prioritize Variables with Joint Variable Importance Plot in
Observational Study Design

Samuel D. Pimentel

add_variable_labels function

<dl><dt>p</dt>
<dd>plot made with jointVIP object</dd>
<dt>...</dt>
<dd>encompasses other variables needed</dd></dl>

Arguments

support function to plot variable text labels — add_variable_labels

<dl>

<dt>p</dt>
<dd>plot made with jointVIP object</dd>


<dt>...</dt>
<dd>encompasses other variables needed</dd>

</dl>

add_variable_labels: support function to plot variable text labels

Description

Usage

Value

Arguments