density_histogram function

Histogram density estimator

Histogram density estimator.

Supports automatic partial function application .

density_histogram(
  x,
  weights = NULL,
  breaks = "Scott",
  align = "none",
  outline_bars = FALSE,
  na.rm = FALSE,
  ...,
  range_only = FALSE
)

Arguments

• x: numeric vector containing a sample to compute a density estimate for.

• weights: optional numeric vector of weights to apply to x.

• breaks: Determines the breakpoints defining bins. Defaults to "Scott". Similar to (but not exactly the same as) the breaks argument to graphics::hist(). One of:

  • A scalar (length-1) numeric giving the number of bins
  • A vector numeric giving the breakpoints between histogram bins
  • A function taking x and weights and returning either the number of bins or a vector of breakpoints
  • A string giving the suffix of a function that starts with "breaks_". ggdist provides weighted implementations of the "Sturges", "Scott", and "FD" break-finding algorithms from graphics::hist(), as well as breaks_fixed() for manually setting the bin width. See breaks .

  For example, breaks = "Sturges" will use the breaks_Sturges() algorithm, breaks = 9 will create 9 bins, and breaks = breaks_fixed(width = 1) will set the bin width to 1.

• align: Determines how to align the breakpoints defining bins. Default ("none") performs no alignment. One of:

  • A scalar (length-1) numeric giving an offset that is subtracted from the breaks. The offset must be between 0 and the bin width.
  • A function taking a sorted vector of breaks (bin edges) and returning an offset to subtract from the breaks.
  • A string giving the suffix of a function that starts with "align_" used to determine the alignment, such as align_none(), align_boundary(), or align_center().

  For example, align = "none" will provide no alignment, align = align_center(at = 0)

  will center a bin on 0, and align = align_boundary(at = 0) will align a bin edge on 0.

• outline_bars: Should outlines in between the bars (i.e. density values of 0) be included?

• na.rm: Should missing (NA) values in x be removed?

• ...: Additional arguments (ignored).

• range_only: If TRUE, the range of the output of this density estimator is computed and is returned in the $x element of the result, and c(NA, NA)

  is returned in $y. This gives a faster way to determine the range of the output than density_XXX(n = 2).

Returns

An object of class "density", mimicking the output format of stats::density(), with the following components:

• x: The grid of points at which the density was estimated.
• y: The estimated density values.
• bw: The bandwidth.
• n: The sample size of the x input argument.
• call: The call used to produce the result, as a quoted expression.
• data.name: The deparsed name of the x input argument.
• has.na: Always FALSE (for compatibility).
• cdf: Values of the (possibly weighted) empirical cumulative distribution function at x. See weighted_ecdf().

This allows existing methods for density objects, like print() and plot(), to work if desired. This output format (and in particular, the x and y components) is also the format expected by the density argument of the stat_slabinterval()

and the smooth_ family of functions.

Examples

library(distributional)
library(dplyr)
library(ggplot2)

# For compatibility with existing code, the return type of density_unbounded()
# is the same as stats::density(), ...
set.seed(123)
x = rbeta(5000, 1, 3)
d = density_histogram(x)
d

# ... thus, while designed for use with the `density` argument of
# stat_slabinterval(), output from density_histogram() can also be used with
# base::plot():
plot(d)

# here we'll use the same data as above with stat_slab():
data.frame(x) %>%
  ggplot() +
  stat_slab(
    aes(xdist = dist), data = data.frame(dist = dist_beta(1, 3)),
    alpha = 0.25
  ) +
  stat_slab(aes(x), density = "histogram", fill = NA, color = "#d95f02", alpha = 0.5) +
  scale_thickness_shared() +
  theme_ggdist()

See Also

Other density estimators: density_bounded(), density_unbounded()