Density estimator that picks density_bounded()
or density_unbounded()
depending on trim
.
Supports automatic partial function application.
density_auto(
x,
weights = NULL,
n = 512,
bandwidth = "nrd0",
adjust = 1,
kernel = "gaussian",
trim = FALSE,
...
)
numeric vector containing a sample to compute a density estimate for.
optional numeric vector of weights to apply to x
.
numeric: the number of grid points to evaluate the density estimator at.
bandwidth of the density estimator. One of:
a numeric: the bandwidth, as the standard deviation of the kernel
a function: a function taking x
(the sample) and returning the bandwidth
a string: the suffix of the name of a function starting with "bw."
that
will be used to determine the bandwidth. See bw.nrd0()
for a list.
numeric: the bandwidth for the density estimator is multiplied
by this value. See stats::density()
.
string: the smoothing kernel to be used. This must partially
match one of "gaussian"
, "rectangular"
, "triangular"
, "epanechnikov"
,
"biweight"
, "cosine"
, or "optcosine"
. See stats::density()
.
Should the density estimate be trimmed to the bounds of the data?
If TRUE
, uses density_bounded()
, if FALSE
, uses density_unbounded()
.
Additional arguments passed to density_bounded()
or density_unbounded()
.
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).
This allows existing methods (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.
Other density estimators:
density_bounded()
,
density_unbounded()
library(distributional)
library(dplyr)
library(ggplot2)
set.seed(123)
x = rbeta(5000, 1, 3)
# here we'll use the same data as above, but pick either density_bounded()
# or density_unbounded() (which is equivalent to stats::density()). Notice
# how the bounded density (green) is biased near the boundary of the support,
# while the unbounded density is not.
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 = "auto", trim = TRUE, fill = NA, color = "#d95f02", alpha = 0.5) +
stat_slab(aes(x), density = "auto", trim = FALSE, fill = NA, color = "#1b9e77", alpha = 0.5) +
scale_thickness_shared() +
theme_ggdist()