PSAgraphics: An R Package to Support Propensity Score Analysis

Propensity score analysis is a technique for adjusting for selection bias in observational data. Estimated propensity scores (probability of treatment given observed covariates) are used for stratification of observations. Within strata covariates should be more balanced between the two treatments than without the stratification. PSAgraphics is an R package that provides flexible graphical tools to assess within strata balance between treatment groups, as well as how covariate distributions differ across strata. Additional graphical tools facilitate estimation of treatment effects having adjusted for covariate differences. Several new and conventional numerical measures of balance are also provided.

PSAgraphics: An R Package to Support Propensity Score Analysis

Propensity score analysis is a technique for adjusting for selection bias in observational data. Estimated propensity scores (probability of treatment given observed covariates) are used for stratification of observations. Within strata covariates should be more balanced between the two treatments than without the stratification. PSAgraphics is an R package that provides flexible graphical tools to assess within strata balance between treatment groups, as well as how covariate distributions differ across strata. Additional graphical tools facilitate estimation of treatment effects having adjusted for covariate differences. Several new and conventional numerical measures of balance are also provided

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Description This collection of functions in granovaGG provides what we call elemental graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use ggplot2, Hadley Wickham’s package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w (a graphic for one way anova); two other functions (granovagg.ds and granovagg.contr) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w,which entails dynamic displays of data, is not currently part of granovaGG.) The granovaGG functions are to display data for any number of groups, regardless of their sizes (however, ver