Propensity score plotting for Matched objects

This function plots the distribution of propensity scores from each matched set of a Matched object.

plotPropensity(
  x,
  sets = c("focal", "matched", "pool", "unmatched"),
  type = NULL,
  log = NULL,
  ...
)

# S4 method for Matched,character_OR_missing,character_OR_missing,character_OR_missing
plotPropensity(x, sets, type, log, thresh = 12)

Arguments

x

Matched object

sets

Character vector describing which matched set(s) to include in the plot. Options are 'focal', 'matched', 'pool', or 'unmatched'. Multiple options are accepted.

type

Character naming the plot type. Available options are one of either 'ridges', 'jitter', 'lines', or 'bars'. Note that for large datasets, use of 'jitter' is discouraged because the large density of points can stall the R-graphics device.

log

Character vector describing which axis or axes to apply log-transformation. Available options are 'x' and/or 'y'.

...

Additional arguments.

thresh

Integer describing the number of unique values required to classify a numeric variable as discrete (and convert it to a factor). If the number of unique values exceeds thresh then the variable is considered continuous.

Value

Returns a plot of propensity score distributions among matched sets.

Details

plotPropensity uses the thresh argument to determine whether to plot propensity scores as continuous (line plot) or catetgorical (bar plot). These settings can also be overwritten manually.

See also

plotCovariate() to plot covariate distributions.

Examples

## Matched example dataset
set.seed(123)
mdf <- makeExampleMatchedDataSet(matched = TRUE)

## Visualize propensity scores
plotPropensity(mdf)

plotPropensity(mdf,
              sets = c('focal', 'matched', 'pool'))

plotPropensity(mdf,
              sets = c('focal', 'matched', 'pool'),
              type = 'ridges')
#> Picking joint bandwidth of 0.0146

plotPropensity(mdf,
              sets = c('focal', 'matched', 'pool'),
              type = 'jitter')