`shade_p_value()`

plots p-value region(s) (using "area under the curve"
approach) on top of the `visualize()`

output. It should be used as
\ggplot2\ layer function (see examples). `shade_pvalue()`

is its alias.

Learn more in `vignette("infer")`

.

shade_p_value(obs_stat, direction, color = "red2", fill = "pink", ...) shade_pvalue(obs_stat, direction, color = "red2", fill = "pink", ...)

obs_stat | A numeric value or 1x1 data frame corresponding to what the observed statistic is. |
---|---|

direction | A string specifying in which direction the shading should
occur. Options are |

color | A character or hex string specifying the color of the observed statistic as a vertical line on the plot. |

fill | A character or hex string specifying the color to shade the
p-value region. If |

... | Other arguments passed along to \ggplot2\ functions. |

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

output.

`shade_confidence_interval()`

to add information about confidence
interval.

# 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") # shade the p-value of the point estimate null_dist %>% visualize() + shade_p_value(obs_stat = point_estimate, direction = "two-sided")