plotPs shows the propensity (or preference) score distribution

plotPs(
data,
unfilteredData = NULL,
scale = "preference",
type = "density",
binWidth = 0.05,
targetLabel = "Target",
comparatorLabel = "Comparator",
showCountsLabel = FALSE,
showAucLabel = FALSE,
showEquiposeLabel = FALSE,
equipoiseBounds = c(0.3, 0.7),
unitOfAnalysis = "subjects",
title = NULL,
fileName = NULL
)

## Arguments

data A data frame with at least the two columns described below To be used when computing preference scores on data from which subjects have already been removed, e.g. through trimming and/or matching. This data frame should have the same structure as data. The scale of the graph. Two scales are supported:  scale = 'propensity' or scale = 'preference'. The preference score scale is defined by Walker et al (2013). Type of plot. Four possible values: type = 'density' or type = 'histogram' or type = 'histogramCount' or type = 'histogramProportion'. 'histogram' defaults to 'histogramCount'. For histograms, the width of the bins A label to us for the target cohort. A label to us for the comparator cohort. Show subject counts? Show the AUC? Show the percentage of the population in equipoise? The bounds on the preference score to determine whether a subject is in equipoise. The unit of analysis in the input data. Defaults to 'subjects'. Optional: the main title for the plot. Name of the file where the plot should be saved, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

## Value

A ggplot object. Use the ggsave function to save to file in a different format.

## Details

The data frame should have a least the following two columns:

 treatment (integer) Column indicating whether the person is in the target (1) or comparator (0) group propensityScore (numeric) Propensity score

## References

Walker AM, Patrick AR, Lauer MS, Hornbrook MC, Marin MG, Platt R, Roger VL, Stang P, and Schneeweiss S. (2013) A tool for assessing the feasibility of comparative effectiveness research, Comparative Effective Research, 3, 11-20

## Examples

treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
plotPs(data)