Density bins and histogram bins as data frames

Generates a data frame of bins representing the kernel density (or histogram) of a vector, suitable for use in generating predictive distributions for visualization. These functions were originally designed for use with the now-deprecated predict_curve(), and may be deprecated in the future.

density_bins(x, n = 101, ...)

histogram_bins(x, n = 30, breaks = n, ...)

Arguments

x: A numeric vector
n: Number of bins
...: Additional arguments passed to density() or hist().
breaks: Used to set bins for histogram_bins. Can be number of bins (by default it is set to the value of n) or a method for setting bins. See the breaks argument of hist().

Value

A data frame representing bins and their densities with the following columns:

mid: Bin midpoint
lower: Lower endpoint of each bin
upper: Upper endpoint of each bin
density: Density estimate of the bin

Details

These functions are simple wrappers to density() and hist() that compute density estimates and return their results in a consistent format: a data frame of bins suitable for use with the now-deprecated predict_curve().

density_bins computes a kernel density estimate using density().

histogram_bins computes a density histogram using hist().

Author

Matthew Kay

Examples

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library(ggplot2)
library(dplyr)
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library(modelr)

theme_set(theme_light())

m_mpg = brm(mpg ~ hp * cyl, data = mtcars)
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step = 1
mtcars %>%
  group_by(cyl) %>%
  data_grid(hp = seq_range(hp, by = step)) %>%
  add_predicted_draws(m_mpg) %>%
  summarise(density_bins(.prediction), .groups = "drop") %>%
  ggplot() +
  geom_rect(aes(
    xmin = hp - step/2, ymin = lower, ymax = upper, xmax = hp + step/2,
    fill = ordered(cyl), alpha = density
  )) +
  geom_point(aes(x = hp, y = mpg, fill = ordered(cyl)), shape = 21, data = mtcars) +
  scale_alpha_continuous(range = c(0, 1)) +
  scale_fill_brewer(palette = "Set2")


# }

Arguments

Value

Details

See also

Author

Examples