R/cut_cdf_qi.R

In ggdist: Visualizations of Distributions and Uncertainty

# Helper function for splitting up a slab into its intervals
#
# Author: mjskay
###############################################################################


#' Categorize values from a CDF into quantile intervals
#'
#' Given a vector of probabilities from a cumulative distribution function (CDF)
#' and a list of desired quantile intervals, return a vector categorizing each
#' element of the input vector according to which quantile interval it falls into.
#' **NOTE:** While this function can be used for (and was originally designed for)
#' drawing slabs with intervals overlaid on the density, this is can now be
#' done more easily by mapping the `.width` or `level` computed variable to
#' slab fill or color. See **Examples**.
#'
#' @param p A numeric vector of values from a cumulative distribution function,
#' such as values returned by `p`-prefixed distribution functions in base R (e.g. [pnorm()]),
#' the [cdf()] function, or values of the `cdf` computed aesthetic from the
#' [stat_slabinterval()] family of stats.
#' @param .width vector of probabilities to use that determine the widths of the resulting intervals.
#' @param labels One of:
#'   - `NULL` to use the default labels (`.width` converted to a character vector).
#'   - A character vector giving labels (must be same length as `.width`)
#'   - A function that takes numeric probabilities as input and returns labels as output
#'     (a good candidate might be [scales::percent_format()]).
#'
#' @return
#' An [ordered] factor of the same length as `p` giving the quantile interval to
#' which each value of `p` belongs.
#'
#' @seealso See [stat_slabinterval()] and
#' its shortcut stats, which generate `cdf` aesthetics that can be used with
#' [cut_cdf_qi()] to draw slabs colored by their intervals.
#' @examples
#'
#' library(ggplot2)
#' library(dplyr)
#' library(scales)
#' library(distributional)
#'
#' theme_set(theme_ggdist())
#'
#' # NOTE: cut_cdf_qi() used to be the recommended way to do intervals overlaid
#' # on densities, like this...
#' tibble(x = dist_normal(0, 1)) %>%
#'   ggplot(aes(xdist = x)) +
#'   stat_slab(
#'     aes(fill = after_stat(cut_cdf_qi(cdf)))
#'   ) +
#'   scale_fill_brewer(direction = -1)
#'
#' # ... however this is now more easily and flexibly accomplished by directly
#' # mapping .width or level onto fill:
#' tibble(x = dist_normal(0, 1)) %>%
#'   ggplot(aes(xdist = x)) +
#'   stat_slab(
#'     aes(fill = after_stat(level)),
#'     .width = c(.66, .95, 1)
#'   ) +
#'   scale_fill_brewer()
#'
#' # See vignette("slabinterval") for more examples. The remaining examples
#' # below using cut_cdf_qi() are kept for posterity.
#'
#' # With a halfeye (or other geom with slab and interval), NA values will
#' # show up in the fill scale from the CDF function applied to the internal
#' # interval geometry data and can be ignored, hence na.translate = FALSE
#' tibble(x = dist_normal(0, 1)) %>%
#'   ggplot(aes(xdist = x)) +
#'   stat_halfeye(aes(
#'     fill = after_stat(cut_cdf_qi(cdf, .width = c(.5, .8, .95, 1)))
#'   )) +
#'   scale_fill_brewer(direction = -1, na.translate = FALSE)
#'
#' # we could also use the labels parameter to apply nicer formatting
#' # and provide a better name for the legend, and omit the 100% interval
#' # if desired
#' tibble(x = dist_normal(0, 1)) %>%
#'   ggplot(aes(xdist = x)) +
#'   stat_halfeye(aes(
#'     fill = after_stat(cut_cdf_qi(
#'       cdf,
#'       .width = c(.5, .8, .95),
#'       labels = percent_format(accuracy = 1)
#'     ))
#'   )) +
#'   labs(fill = "Interval") +
#'   scale_fill_brewer(direction = -1, na.translate = FALSE)
#'
#' @export
cut_cdf_qi = function(p, .width = c(.66, .95, 1), labels = NULL) {
  .width = sort(.width)

  if (is.function(labels)) {
    labels = labels(.width)
  } else if (is.null(labels)) {
    labels = .width
  }

  cut(abs(1 - p*2), labels = labels, breaks = c(0, .width), include.lowest = TRUE, ordered_result = TRUE)
}