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").

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

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

## Arguments

endpoints 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.

## Value

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

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

## Examples

# 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() +