Covariate plotting for Matched objects

This function plots the distributions of a covariate from each matched set of a Matched object.

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

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

Arguments

x

Matched object

covar

Character naming the covariate to plot. If multiple are provided, only the first one is used.

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 a covariate's distribution among matched sets.

Details

By default, plotCovariate will sense the class of covariate and make a plot best suited to that data type. For example, if the covariate class is categorical in nature then the type argument defaults to 'bars'. type is set to 'lines' for continuous covariates. These settings can also be overwritten manually.

See also

plotPropensity() to plot propensity scores.

Examples

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

## Visualize covariates
plotCovariate(mdf)

plotCovariate(mdf, covar = 'feature3')

plotCovariate(mdf,
              covar = 'feature2',
              sets = c('focal', 'matched', 'pool'))

plotCovariate(mdf,
              covar = 'feature2',
              sets = c('focal', 'matched', 'pool'),
              type = 'ridges')
#> Picking joint bandwidth of 0.553

plotCovariate(mdf,
              covar = 'feature2',
              sets = c('focal', 'matched', 'pool'),
              type = 'jitter')