shade_confidence_interval() plots confidence interval region on top of the visualize() output. It should be used as \ggplot2\ layer function (see examples). shade_ci() is its alias.

Learn more in vignette("infer").

  color = "mediumaquamarine",
  fill = "turquoise",

shade_ci(endpoints, color = "mediumaquamarine", fill = "turquoise", ...)



A 2 element vector or a 1 x 2 data frame containing the lower and upper values to be plotted. Most useful for visualizing conference intervals.


A character or hex string specifying the color of the end points as a vertical lines on the plot.


A character or hex string specifying the color to shade the confidence interval. If NULL then no shading is actually done.


Other arguments passed along to \ggplot2\ functions.


A list of \ggplot2\ objects to be added to the visualize() output.

See also

shade_p_value() to add information about p-value region.


# find the point estimate---mean number of hours worked per week point_estimate <- gss %>% specify(response = hours) %>% calculate(stat = "mean") %>% dplyr::pull() # ...and a null distribution null_dist <- gss %>% # ...we're interested in the number of hours worked per week specify(response = hours) %>% # hypothesizing that the mean is 40 hypothesize(null = "point", mu = 40) %>% # generating data points for a null distribution generate(reps = 1000, type = "bootstrap") %>% # finding the null distribution calculate(stat = "mean") # find a confidence interval around the point estimate ci <- null_dist %>% get_confidence_interval(point_estimate = point_estimate, # at the 95% confidence level level = .95, # using the standard error method type = "se") # and plot it! null_dist %>% visualize() + shade_confidence_interval(ci)
# or just plot the bounds null_dist %>% visualize() + shade_confidence_interval(ci, fill = NULL)
# More in-depth explanation of how to use the infer package if (FALSE) { vignette("infer") }