stat_lineribbon | R Documentation |
A combination of stat_slabinterval()
and geom_lineribbon()
with sensible defaults
for making line + multiple-ribbon plots. While geom_lineribbon()
is intended for use on data
frames that have already been summarized using a point_interval()
function,
stat_lineribbon()
is intended for use directly on data frames of draws or of
analytical distributions, and will perform the summarization using a point_interval()
function.
Roughly equivalent to:
stat_slabinterval( aes( group = after_stat(level), fill = after_stat(level), order = after_stat(level), size = NULL ), geom = "lineribbon", .width = c(0.5, 0.8, 0.95), show_slab = FALSE, show.legend = NA )
stat_lineribbon(
mapping = NULL,
data = NULL,
geom = "lineribbon",
position = "identity",
...,
.width = c(0.5, 0.8, 0.95),
point_interval = "median_qi",
orientation = NA,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
Use to override the default connection between
|
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Setting this equal to |
... |
Other arguments passed to
|
.width |
The |
point_interval |
A function from the |
orientation |
Whether this geom is drawn horizontally or vertically. One of:
For compatibility with the base ggplot naming scheme for |
na.rm |
If |
show.legend |
Should this layer be included in the legends? |
inherit.aes |
If |
To visualize sample data, such as a data distribution, samples from a
bootstrap distribution, or a Bayesian posterior, you can supply samples to
the x
or y
aesthetic.
To visualize analytical distributions, you can use the xdist
or ydist
aesthetic. For historical reasons, you can also use dist
to specify the distribution, though
this is not recommended as it does not work as well with orientation detection.
These aesthetics can be used as follows:
xdist
, ydist
, and dist
can be any distribution object from the distributional
package (dist_normal()
, dist_beta()
, etc) or can be a posterior::rvar()
object.
Since these functions are vectorized,
other columns can be passed directly to them in an aes()
specification; e.g.
aes(dist = dist_normal(mu, sigma))
will work if mu
and sigma
are columns in the
input data frame.
dist
can be a character vector giving the distribution name. Then the arg1
, ... arg9
aesthetics (or args
as a list column) specify distribution arguments. Distribution names
should correspond to R functions that have "p"
, "q"
, and "d"
functions; e.g. "norm"
is a valid distribution name because R defines the pnorm()
, qnorm()
, and dnorm()
functions for Normal distributions.
See the parse_dist()
function for a useful way to generate dist
and args
values from human-readable distribution specs (like "normal(0,1)"
). Such specs are also
produced by other packages (like the brms::get_prior
function in brms); thus,
parse_dist()
combined with the stats described here can help you visualize the output
of those functions.
A ggplot2::Stat representing a line + multiple-ribbon geometry which can
be added to a ggplot()
object.
The following variables are computed by this stat and made available for
use in aesthetic specifications (aes()
) using the after_stat()
function or the after_stat
argument of stage()
:
x
or y
: For slabs, the input values to the slab function.
For intervals, the point summary from the interval function. Whether it is x
or y
depends on orientation
xmin
or ymin
: For intervals, the lower end of the interval from the interval function.
xmax
or ymax
: For intervals, the upper end of the interval from the interval function.
.width
: For intervals, the interval width as a numeric value in [0, 1]
.
For slabs, the width of the smallest interval containing that value of the slab.
level
: For intervals, the interval width as an ordered factor.
For slabs, the level of the smallest interval containing that value of the slab.
pdf
: For slabs, the probability density function (PDF).
If options("ggdist.experimental.slab_data_in_intervals")
is TRUE
:
For intervals, the PDF at the point summary; intervals also have pdf_min
and pdf_max
for the PDF at the lower and upper ends of the interval.
cdf
: For slabs, the cumulative distribution function.
If options("ggdist.experimental.slab_data_in_intervals")
is TRUE
:
For intervals, the CDF at the point summary; intervals also have cdf_min
and cdf_max
for the CDF at the lower and upper ends of the interval.
The line+ribbon stat
s and geom
s have a wide variety of aesthetics that control
the appearance of their two sub-geometries: the line and the ribbon.
These stat
s support the following aesthetics:
x
: x position of the geometry (when orientation = "vertical"
); or sample data to be summarized
(when orientation = "horizontal"
with sample data).
y
: y position of the geometry (when orientation = "horizontal"
); or sample data to be summarized
(when orientation = "vertical"
with sample data).
weight
: When using samples (i.e. the x
and y
aesthetics, not xdist
or ydist
), optional
weights to be applied to each draw.
xdist
: When using analytical distributions, distribution to map on the x axis: a distributional
object (e.g. dist_normal()
) or a posterior::rvar()
object.
ydist
: When using analytical distributions, distribution to map on the y axis: a distributional
object (e.g. dist_normal()
) or a posterior::rvar()
object.
dist
: When using analytical distributions, a name of a distribution (e.g. "norm"
), a
distributional object (e.g. dist_normal()
), or a posterior::rvar()
object. See Details.
args
: Distribution arguments (args
or arg1
, ... arg9
). See Details.
In addition, in their default configuration (paired with geom_lineribbon()
)
the following aesthetics are supported by the underlying geom:
Ribbon-specific aesthetics
xmin
: Left edge of the ribbon sub-geometry (if orientation = "horizontal"
).
xmax
: Right edge of the ribbon sub-geometry (if orientation = "horizontal"
).
ymin
: Lower edge of the ribbon sub-geometry (if orientation = "vertical"
).
ymax
: Upper edge of the ribbon sub-geometry (if orientation = "vertical"
).
order
: The order in which ribbons are drawn. Ribbons with the smallest mean value of order
are drawn first (i.e., will be drawn below ribbons with larger mean values of order
). If
order
is not supplied to geom_lineribbon()
, -abs(xmax - xmin)
or -abs(ymax - ymax)
(depending on orientation
) is used, having the effect of drawing the widest (on average)
ribbons on the bottom. stat_lineribbon()
uses order = after_stat(level)
by default,
causing the ribbons generated from the largest .width
to be drawn on the bottom.
Color aesthetics
colour
: (or color
) The color of the line sub-geometry.
fill
: The fill color of the ribbon sub-geometry.
alpha
: The opacity of the line and ribbon sub-geometries.
fill_ramp
: A secondary scale that modifies the fill
scale to "ramp" to another color. See scale_fill_ramp()
for examples.
Line aesthetics
linewidth
: Width of line. In ggplot2 < 3.4, was called size
.
linetype
: Type of line (e.g., "solid"
, "dashed"
, etc)
Other aesthetics (these work as in standard geom
s)
group
See examples of some of these aesthetics in action in vignette("lineribbon")
.
Learn more about the sub-geom override aesthetics (like interval_color
) in the
scales documentation. Learn more about basic ggplot aesthetics in
vignette("ggplot2-specs")
.
See geom_lineribbon()
for the geom underlying this stat.
Other lineribbon stats:
stat_ribbon()
library(dplyr)
library(ggplot2)
library(distributional)
theme_set(theme_ggdist())
# ON SAMPLE DATA
set.seed(12345)
tibble(
x = rep(1:10, 100),
y = rnorm(1000, x)
) %>%
ggplot(aes(x = x, y = y)) +
stat_lineribbon() +
scale_fill_brewer()
# ON ANALYTICAL DISTRIBUTIONS
# Vectorized distribution types, like distributional::dist_normal()
# and posterior::rvar(), can be used with the `xdist` / `ydist` aesthetics
tibble(
x = 1:10,
sd = seq(1, 3, length.out = 10)
) %>%
ggplot(aes(x = x, ydist = dist_normal(x, sd))) +
stat_lineribbon() +
scale_fill_brewer()
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