Prioritize Variables with Joint Variable Importance Plot in Observational Study Design
Obtains a summary post_jointVIP object
support function to plot bias curves
support function to plot variable text labels
plot the bootstrap version of the jointVIP object
support function for ceiling function with decimals
Check measures Check to see if there is any missing values or variable...
create jointVIP object
create post_jointVIP object
support function for floor function with decimals
Calculate bootstrapped variation additional tool to help calculate the...
Prepare data frame to plot standardized omitted variable bias Marginal...
Post-measures data frame to plot post-standardized omitted variable bi...
support function for one-hot encoding
plot the jointVIP object
plot the post_jointVIP object this plot uses the same custom options a...
Obtains a print for jointVIP object
Obtains a print for post_jointVIP object
Obtains a summary jointVIP object
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.
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