plotPrunedMeans: Plot covariate means for the removed observations along the...

In ChristopherLucas/MatchingFrontier: Computation of the Balance - Sample Size Frontier in Matching Methods for Causal Inference

Description

plotMeans() plots means for the removed observations along the frontier.

Usage

plotPrunedMeans(frontier.object,
          xlab = 'Number of Observations Pruned',
          main = 'Means Plot',
          xlim = c(1,max(frontier.object$frontier$Xs)),
          ylim = c(0, 1),
          cols = rainbow(length(frontier.object$match.on)),
          diff.in.means = FALSE,
          ...)

Arguments

frontier.object	An object generated by makeFrontier()
xlab	The label for the x-axis. Defaults to 'Number of Observations Pruned'.
main	The main label. Defaults to Means Plot'.
xlim	The x-axis limits. Defaults to the range of the frontier.
ylim	The y-axis limits. Defaults to (0, 1).
cols	The line colors. Defaults to the rainbow palette.
diff.in.means	If TRUE, means are the difference in means between treated and control groups. If FALSE (the default), means are the covariate means pooling across treated and control.
...	Additional arguments to be passed to plot.

Details

plotMeans() wraps plot and uses ... to pass additional arguments to the base plot() function.

References

King, Gary, Christopher Lucas, and Richard Nielsen. "The Balance-Sample Size Frontier in Matching Methods for Causal Inference." (2015).

Examples

data(lalonde)

match.on <- colnames(lalonde)[!(colnames(lalonde) %in% c('re78', 'treat'))]
my.frontier <- makeFrontier(dataset = lalonde,
                            treatment = 'treat',
                            match.on = match.on)

plotPrunedMeans(my.frontier)

ChristopherLucas/MatchingFrontier documentation built on June 10, 2020, 6:01 p.m.