MatchingFrontier (version 1.0.0)

plotEstimates: Plot estimates along the frontier.

Description

plotEstimates() plots estimates along the frontier.

Usage

plotEstimates(estimates.object, xlab = 'Number of Observations Pruned', ylab = 'Estimate', main = 'Effects Plot', xlim = NULL, ylim = NULL, mod.dependence.col = rgb(255,0,0,127, maxColorValue=255), mod.dependence.border.col = rgb(255,0,0,200, maxColorValue=255), line.col = rgb(102,0,0,255, maxColorValue=255), ...)

Arguments

estimates.object

An object generated by estimateEffects()

xlab

The label for the x-axis. Defaults to 'Number of Observations Pruned'.

ylab

The label for the y-axis. Defaults to 'Estimate'.

main

The main label. Defaults to 'Effects Plot'.

xlim

The x-axis limits.

ylim

The y-axis limits.

...

Additional arguments to be passed to plot.

mod.dependence.col

The color to shade the model dependence region.

mod.dependence.border.col

The model dependence region border color.

line.col

The color of the line displaying point estimates.

Details

plotEstimates() wraps plot and uses ... to pass additional arguments to the base plot() function, like color, axis range, etc.

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',
                            outcome = 're78',
                            match.on = match.on)

base.form <- as.formula('re78 ~ treat + age + education
                         + black + hispanic + married +
                         nodegree + re74 + re75')
## Not run: 
# my.estimates <- estimateEffects(my.frontier,
#                                 're78 ~ treat',
#                                 mod.dependence.formula = base.form,
#                                 continuous.vars = c('age', 'education', 're74', 're75'),
#                                 prop.estimated = .1,
#                                 means.as.cutpoints = TRUE)
# 
# plotEstimates(my.estimates)
# ## End(Not run)