Stat for drawing "spikes" (optionally with points on them) at specific points
on a distribution (numerical or determined as a function of the distribution),
intended for annotating stat_slabinterval()
geometries.
stat_spike(
mapping = NULL,
data = NULL,
geom = "spike",
position = "identity",
...,
at = "median",
p_limits = c(NA, NA),
density = "bounded",
adjust = 1,
trim = TRUE,
expand = FALSE,
breaks = "Sturges",
align = "none",
outline_bars = FALSE,
slab_type = NULL,
limits = NULL,
n = 501,
orientation = NA,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
Set of aesthetic mappings created by aes()
. If specified and
inherit.aes = TRUE
(the default), it is combined with the default mapping
at the top level of the plot. You must supply mapping
if there is no plot
mapping.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame
, and
will be used as the layer data. A function
can be created
from a formula
(e.g. ~ head(.x, 10)
).
Use to override the default connection between stat_spike()
and geom_spike()
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Setting this equal to "dodge"
(position_dodge()
) or "dodgejust"
(position_dodgejust()
) can be useful if
you have overlapping geometries.
Other arguments passed to layer()
. These are often aesthetics, used to set an aesthetic
to a fixed value, like colour = "red"
or linewidth = 3
(see Aesthetics, below). They may also be
parameters to the paired geom/stat. When paired with the default geom, geom_spike()
,
these include:
arrow
grid::arrow()
giving the arrow heads to use on the spike, or NULL
for no arrows.
normalize
How to normalize heights of functions input to the thickness
aesthetic. One of:
"all"
: normalize so that the maximum height across all data is 1
.
"panels"
: normalize within panels so that the maximum height in each panel is 1
.
"xy"
: normalize within the x/y axis opposite the orientation
of this geom so
that the maximum height at each value of the opposite axis is 1
.
"groups"
: normalize within values of the opposite axis and within each
group so that the maximum height in each group is 1
.
"none"
: values are taken as is with no normalization (this should probably
only be used with functions whose values are in [0,1], such as CDFs).
The points at which to evaluate the PDF and CDF of the distribution. One of:
numeric vector: points to evaluate the PDF and CDF of the distributions at.
function or string: function (or name of a function) which,
when applied on a distribution-like object (e.g. a distributional object or a
posterior::rvar()
), returns a vector of values to evaluate the distribution functions at.
a list where each element is any of the above (e.g. a numeric, function, or
name of a function): the evaluation points determined by each element of the
list are concatenated together. This means, e.g., c(0, median, qi)
would add
a spike at 0
, the median, and the endpoints of the qi
of the distribution.
Probability limits (as a vector of size 2) used to determine the lower and upper
limits of theoretical distributions (distributions from samples ignore this parameter and determine
their limits based on the limits of the sample). E.g., if this is c(.001, .999)
, then a slab is drawn
for the distribution from the quantile at p = .001
to the quantile at p = .999
. If the lower
(respectively upper) limit is NA
, then the lower (upper) limit will be the minimum (maximum) of the
distribution's support if it is finite, and 0.001
(0.999
) if it is not finite. E.g., if
p_limits
is c(NA, NA)
, on a gamma distribution the effective value of p_limits
would be
c(0, .999)
since the gamma distribution is defined on (0, Inf)
; whereas on a normal distribution
it would be equivalent to c(.001, .999)
since the normal distribution is defined on (-Inf, Inf)
.
Density estimator for sample data. One of:
A function which takes a numeric vector and returns a list with elements
x
(giving grid points for the density estimator) and y
(the
corresponding densities). ggdist provides a family of functions
following this format, including density_unbounded()
and
density_bounded()
. This format is also compatible with stats::density()
.
A string giving the suffix of a function name that starts with "density_"
;
e.g. "bounded"
for [density_bounded()]
, "unbounded"
for [density_unbounded()]
,
or "histogram"
for density_histogram()
.
Defaults to "bounded"
, i.e. density_bounded()
, which estimates the bounds from
the data and then uses a bounded density estimator based on the reflection method.
Passed to density
: the bandwidth for the density estimator for sample data
is adjusted by multiplying it by this value. See e.g. density_bounded()
for more information.
For sample data, should the density estimate be trimmed to the range of the
data? Passed on to the density estimator; see the density
parameter. Default TRUE
.
For sample data, should the slab be expanded to the limits of the scale? Default FALSE
.
Can be length two to control expansion to the lower and upper limit respectively.
Determines the breakpoints defining bins. 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
.
Determines how to align the breakpoints defining bins. 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
.
For sample data (if density
is "histogram"
) and for discrete analytical
distributions (whose slabs are drawn as histograms), determines
if outlines in between the bars are drawn when the slab_color
aesthetic is used. If FALSE
(the default), the outline is drawn only along the tops of the bars; if TRUE
, outlines in between
bars are also drawn. See density_histogram()
.
(deprecated) The type of slab function to calculate: probability density (or mass) function ("pdf"
),
cumulative distribution function ("cdf"
), or complementary CDF ("ccdf"
). Instead of using slab_type
to
change f
and then mapping f
onto an aesthetic, it is now recommended to simply map the corresponding
computed variable (e.g. pdf
, cdf
, or 1 - cdf
) directly onto the desired aesthetic.
Manually-specified limits for the slab, as a vector of length two. These limits are combined with those
computed based on p_limits
as well as the limits defined by the scales of the plot to determine the
limits used to draw the slab functions: these limits specify the maximal limits; i.e., if specified, the limits
will not be wider than these (but may be narrower). Use NA
to leave a limit alone; e.g.
limits = c(0, NA)
will ensure that the lower limit does not go below 0, but let the upper limit
be determined by either p_limits
or the scale settings.
Number of points at which to evaluate the function that defines the slab.
Whether this geom is drawn horizontally or vertically. One of:
NA
(default): automatically detect the orientation based on how the aesthetics
are assigned. Automatic detection works most of the time.
"horizontal"
(or "y"
): draw horizontally, using the y
aesthetic to identify different
groups. For each group, uses the x
, xmin
, xmax
, and thickness
aesthetics to
draw points, intervals, and slabs.
"vertical"
(or "x"
): draw vertically, using the x
aesthetic to identify different
groups. For each group, uses the y
, ymin
, ymax
, and thickness
aesthetics to
draw points, intervals, and slabs.
For compatibility with the base ggplot naming scheme for orientation
, "x"
can be used as an alias
for "vertical"
and "y"
as an alias for "horizontal"
(ggdist had an orientation
parameter
before base ggplot did, hence the discrepancy).
If FALSE
, the default, missing values are removed with a warning. If TRUE
, missing
values are silently removed.
Should this layer be included in the legends? Default is c(size = FALSE)
, unlike most geoms,
to match its common use cases. FALSE
hides all legends, TRUE
shows all legends, and NA
shows only
those that are mapped (the default for most geoms).
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders()
.
A ggplot2::Stat representing a spike geometry which can be added to a ggplot()
object.
This stat computes slab values (i.e. PDF and CDF values) at specified locations
on a distribution, as determined by the at
parameter.
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.
The spike geom
has a wide variety of aesthetics that control
the appearance of its two sub-geometries: the spike and the point.
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).
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_spike()
)
the following aesthetics are supported by the underlying geom:
Spike-specific (aka Slab-specific) aesthetics
thickness
: The thickness of the slab at each x
value (if orientation = "horizontal"
) or
y
value (if orientation = "vertical"
) of the slab.
side
: Which side to place the slab on. "topright"
, "top"
, and "right"
are synonyms
which cause the slab to be drawn on the top or the right depending on if orientation
is "horizontal"
or "vertical"
. "bottomleft"
, "bottom"
, and "left"
are synonyms which cause the slab
to be drawn on the bottom or the left depending on if orientation
is "horizontal"
or
"vertical"
. "topleft"
causes the slab to be drawn on the top or the left, and "bottomright"
causes the slab to be drawn on the bottom or the right. "both"
draws the slab mirrored on both
sides (as in a violin plot).
scale
: What proportion of the region allocated to this geom to use to draw the slab. If scale = 1
,
slabs that use the maximum range will just touch each other. Default is 0.9
to leave some space.
Color aesthetics
colour
: (or color
) The color of the spike and point sub-geometries.
fill
: The fill color of the point sub-geometry.
alpha
: The opacity of the spike and point sub-geometries.
colour_ramp
: (or color_ramp
) A secondary scale that modifies the color
scale to "ramp" to another color. See scale_colour_ramp()
for examples.
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 the line used to draw the spike sub-geometry.
size
: Size of the point sub-geometry.
stroke
: Width of the outline around the point sub-geometry.
linetype
: Type of line (e.g., "solid"
, "dashed"
, etc) used to draw the spike.
Other aesthetics (these work as in standard geom
s)
width
height
group
See examples of some of these aesthetics in action in vignette("slabinterval")
.
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")
.
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.
n
: For slabs, the number of data points summarized into that slab. If the slab was created from
an analytical distribution via the xdist
, ydist
, or dist
aesthetic, n
will be Inf
.
f
: (deprecated) For slabs, the output values from the slab function (such as the PDF, CDF, or CCDF),
determined by slab_type
. Instead of using slab_type
to change f
and then mapping f
onto an
aesthetic, it is now recommended to simply map the corresponding computed variable (e.g. pdf
, cdf
, or
1 - cdf
) directly onto the desired aesthetic.
See geom_spike()
for the geom underlying this stat.
See stat_slabinterval()
for the stat this shortcut is based on.
Other slabinterval stats:
stat_ccdfinterval()
,
stat_cdfinterval()
,
stat_eye()
,
stat_gradientinterval()
,
stat_halfeye()
,
stat_histinterval()
,
stat_interval()
,
stat_pointinterval()
,
stat_slab()
library(ggplot2)
library(distributional)
library(dplyr)
df = tibble(
d = c(dist_normal(1), dist_gamma(2,2)), g = c("a", "b")
)
# annotate the density at the mode of a distribution
df %>%
ggplot(aes(y = g, xdist = d)) +
stat_slab(aes(xdist = d)) +
stat_spike(at = "Mode") +
# need shared thickness scale so that stat_slab and geom_spike line up
scale_thickness_shared()
# annotate the endpoints of intervals of a distribution
# here we'll use an arrow instead of a point by setting size = 0
arrow_spec = arrow(angle = 45, type = "closed", length = unit(4, "pt"))
df %>%
ggplot(aes(y = g, xdist = d)) +
stat_halfeye(point_interval = mode_hdci) +
stat_spike(
at = function(x) hdci(x, .width = .66),
size = 0, arrow = arrow_spec, color = "blue", linewidth = 0.75
) +
scale_thickness_shared()
# annotate quantiles of a sample
set.seed(1234)
data.frame(x = rnorm(1000, 1:2), g = c("a","b")) %>%
ggplot(aes(x, g)) +
stat_slab() +
stat_spike(at = function(x) quantile(x, ppoints(10))) +
scale_thickness_shared()