density_histogram: Histogram density estimator

In mjskay/ggdist: Visualizations of Distributions and Uncertainty

Histogram density estimator

Description

Histogram density estimator.

Supports automatic partial function application with waived arguments.

Usage

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

Arguments

x	<numeric> Sample to compute a density estimate for.
weights	<numeric | NULL> Optional weights to apply to x.
breaks	<numeric | function | string> Determines the breakpoints defining bins. Default "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	<scalar numeric | function | string> 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	<scalar logical> Should outlines in between the bars (i.e. density values of 0) be included?
right_closed	<scalar logical> Should the right edge of each bin be closed? For a bin with endpoints L and U: if TRUE, use (L, U]: the interval containing all x such that L < x \le U. if FALSE, use [L, U): the interval containing all x such that L \le x < U. Equivalent to the right argument of hist() or the left.open argument of findInterval().
outermost_closed	<scalar logical> Should values on the edges of the outermost (first or last) bins always be included in those bins? If TRUE, the first edge (when right_closed = TRUE) or the last edge (when right_closed = FALSE) is treated as closed. Equivalent to the include.lowest argument of hist() or the rightmost.closed argument of findInterval().
na.rm	<scalar logical> Should missing (NA) values in x be removed?
...	Additional arguments (ignored).
range_only	<scalar logical> 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).

Value

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.

See Also

Other density estimators: density_bounded(), density_unbounded()

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()

mjskay/ggdist documentation built on Nov. 21, 2024, 10:46 a.m.