Plotting Point Estimates and Confidence Intervals of Causal Effects

ggcmest is used to plot results of cmest nicely with plotting functions in the ggplot2 package. Additional layers can be added to this plot using other plotting functions in the ggplot2 package.

ggcmest(
  x,
  errorbar.width = 0.3,
  errorbar.size = 0.3,
  errorbar.colour = "black",
  point.size = 1,
  point.colour = "blue",
  refline = TRUE,
  refline.colour = "red",
  refline.size = 0.3
)

Arguments

x	an object of class cmest
errorbar.width	width of errorbars for confidence intervals. Default is 0.3.
errorbar.size	size of errorbars for confidence intervals. Default is 0.3.
errorbar.colour	colour of errorbars for confidence intervals. Default is black.
point.size	size of points for point estimates. Default is 1.
point.colour	colour of points for point estimates. Default is blue.
refline	a logical value. If true, include a reference line at y = 0 when effects are on the difference scale and include a reference line at y = 1 when effects are on the ratio scale. Default is TRUE.
refline.colour	colour of the reference line. Default is red.
refline.size	size of the reference line. Default is 0.3.

See also

cmest, ggplot2.

Examples

library(CMAverse)
library(ggplot2)

x <- cmest(data = cma2020, model = "rb", outcome = "contY", 
exposure = "A", mediator = "M2", basec = c("C1", "C2"), 
EMint = TRUE, mreg = list("multinomial"), yreg = "linear", 
astar = 0, a = 1, mval = list("M2_0"), estimation = "paramfunc", 
inference = "delta")

ggcmest(x) +
theme(axis.text.x = element_text(angle = 45))

ggcmest(x) +
coord_flip(xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")