weighting_plot: Propensity score weighting plot

In jbryer/psa: Applied Propensity Score Analysis with R

Propensity score weighting plot

Description

Propensity score weighting plot

Usage

weighting_plot(
  ps,
  treatment,
  outcome,
  estimand = "ATE",
  null_hypothesis = 0,
  method = "loess",
  se = TRUE,
  level = 0.95,
  colors = c("#fc8d62", "#66c2a5"),
  point_size_range = c(1, 8),
  xlab = "Propensity Score",
  ylab = "Outcome",
  treat_lab = "Treatment",
  size_lab = "PS Weight",
  plot_points = TRUE
)

Arguments

ps	vector of propensity scores.
treatment	vector of treatment indicators.
outcome	vector of outcome values.
estimand	the estimand to use, either ATE, ATT, ATC, or ATM.
null_hypothesis	the value of the null hypothesis (typically zero). A horizontal line will be drawn to compare with regression line.
method	the method to use for the regression line. Typically loess, gan, or lm.
se	Display confidence interval around smooth? (TRUE by default, see level to control.)
level	Level of confidence interval to use (0.95 by default).
colors	vector of colors to use for control and treatment, respectively.
point_size_range	range of the point sizes. Needs to be a vector of length two.
xlab	label for the x-axis.
ylab	label for the y-axis.
treat_lab	label for the legend of group colors.
size_lab	label for the legend of point sizes.
plot_points	whether to plot the individual points.

Value

a ggplot2 expression.

Examples

if(require(Matching)) {
data(lalonde, package = 'Matching')
lr_out <- glm(treat ~ age + I(age^2) + educ + I(educ^2) + black + 
              hisp + married + nodegr + re74  + I(re74^2) + re75 + I(re75^2) +
              u74 + u75,
			  data = lalonde, 
			  family = binomial(link = 'logit'))
lalonde$ps <- fitted(lr_out)
weighting_plot(ps = lalonde$ps,
					treatment = lalonde$treat,
					outcome = log(lalonde$re78))
}

jbryer/psa documentation built on Nov. 17, 2023, 8:21 a.m.