density_bounded | R Documentation |
Bounded density estimator using the reflection method.
Supports automatic partial function application.
density_bounded(
x,
weights = NULL,
n = 512,
bandwidth = "dpi",
adjust = 1,
kernel = "gaussian",
trim = FALSE,
bounds = c(NA, NA),
bounder = "cdf",
adapt = 1,
na.rm = FALSE,
...,
range_only = FALSE
)
x |
numeric vector containing a sample to compute a density estimate for. |
weights |
optional numeric vector of weights to apply to |
n |
numeric: the number of grid points to evaluate the density estimator at. |
bandwidth |
bandwidth of the density estimator. One of:
|
adjust |
numeric: the bandwidth for the density estimator is multiplied
by this value. See |
kernel |
string: the smoothing kernel to be used. This must partially
match one of |
trim |
Should the density estimate be trimmed to the bounds of the data? |
bounds |
length-2 vector of min and max bounds. If a bound is |
bounder |
Method to use to find missing (
|
adapt |
(very experimental) The name and interpretation of this argument
are subject to change without notice. Positive integer. If |
na.rm |
Should missing ( |
... |
Additional arguments (ignored). |
range_only |
If |
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.
Cooke, P. (1979). Statistical inference for bounds of random variables. Biometrika 66(2), 367–374. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/66.2.367")}.
Loh, W. Y. (1984). Estimating an endpoint of a distribution with resampling methods. The Annals of Statistics 12(4), 1543–1550. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/aos/1176346811")}
Other density estimators:
density_histogram()
,
density_unbounded()
library(distributional)
library(dplyr)
library(ggplot2)
# For compatibility with existing code, the return type of density_bounded()
# is the same as stats::density(), ...
set.seed(123)
x = rbeta(5000, 1, 3)
d = density_bounded(x)
d
# ... thus, while designed for use with the `density` argument of
# stat_slabinterval(), output from density_bounded() can also be used with
# base::plot():
plot(d)
# 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 = "bounded", fill = NA, color = "#d95f02", alpha = 0.5) +
stat_slab(aes(x), density = "unbounded", fill = NA, color = "#1b9e77", alpha = 0.5) +
scale_thickness_shared() +
theme_ggdist()
# We can also supply arguments to the density estimators by using their
# full function names instead of the string suffix; e.g. we can supply
# the exact bounds of c(0,1) rather than using the bounds of the data.
data.frame(x) %>%
ggplot() +
stat_slab(
aes(xdist = dist), data = data.frame(dist = dist_beta(1, 3)),
alpha = 0.25
) +
stat_slab(
aes(x), fill = NA, color = "#d95f02", alpha = 0.5,
density = density_bounded(bounds = c(0,1))
) +
scale_thickness_shared() +
theme_ggdist()
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