Stats for computing densities and CDFs + intervals from samples for use with geom_slabinterval(). Useful for creating eye plots, half-eye plots, CCDF bar plots etc.

stat_sample_slabinterval(
  mapping = NULL,
  data = NULL,
  geom = "slabinterval",
  position = "identity",
  ...,
  slab_type = c("pdf", "cdf", "ccdf", "histogram"),
  adjust = 1,
  trim = TRUE,
  breaks = "Sturges",
  outline_bars = FALSE,
  orientation = NA,
  limits = NULL,
  n = 501,
  interval_function = NULL,
  interval_args = list(),
  point_interval = median_qi,
  .width = c(0.66, 0.95),
  na.rm = FALSE,
  show.legend = c(size = FALSE),
  inherit.aes = TRUE
)

stat_halfeye(...)

stat_eye(..., side = "both")

stat_ccdfinterval(
  ...,
  slab_type = "ccdf",
  justification = 0.5,
  side = "topleft",
  normalize = "none"
)

stat_cdfinterval(
  ...,
  slab_type = "cdf",
  justification = 0.5,
  side = "topleft",
  normalize = "none"
)

stat_gradientinterval(
  mapping = NULL,
  data = NULL,
  geom = "slabinterval",
  position = "identity",
  ...,
  justification = 0.5,
  thickness = 1,
  show.legend = c(size = FALSE, slab_alpha = FALSE),
  inherit.aes = TRUE
)

stat_histinterval(..., slab_type = "histogram")

stat_slab(
  mapping = NULL,
  data = NULL,
  geom = "slab",
  position = "identity",
  ...,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes() or 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.

data

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)).

geom

Use to override the default connection between stat_slabinterval and geom_slabinterval()

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

Other arguments passed to layer(). They may also be arguments to the paired geom (e.g., geom_pointinterval())

slab_type

The type of slab function to calculate: probability density (or mass) function ("pdf"), cumulative distribution function ("cdf"), complementary CDF ("ccdf"), or histogram ("histogram".

adjust

If slab_type is "pdf", bandwidth for the density estimator is adjusted by multiplying it by this value. See density() for more information.

trim

If slab_type is "pdf", should the density estimate be trimmed to the range of the input data? Default TRUE.

breaks

If slab_type is "histogram", the breaks parameter that is passed to hist() to determine where to put breaks in the histogram.

outline_bars

If slab_type is "histogram", outline_bars 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.

orientation

Whether this geom is drawn horizontally ("horizontal") or vertically ("vertical"). The default, NA, automatically detects the orientation based on how the aesthetics are assigned, and should generally do an okay job at this. When horizontal (resp. vertical), the geom uses the y (resp. x) aesthetic to identify different groups, then for each group uses the x (resp. y) aesthetic and the thickness aesthetic to draw a function as an slab, and draws points and intervals horizontally (resp. vertically) using the xmin, x, and xmax (resp. ymin, y, and ymax) aesthetics. 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" (tidybayes had an orientation parameter before ggplot did, and I think the tidybayes naming scheme is more intuitive: "x" and "y" are not orientations and their mapping to orientations is, in my opinion, backwards; but the base ggplot naming scheme is allowed for compatibility).

limits

Limits for slab_function, as a vector of length two. These limits are combined with those computed by the limits_function 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.

n

Number of points at which to evaluate slab_function

interval_function

Custom function for generating intervals (for most common use cases the point_interval argument will be easier to use). This function takes a data frame of aesthetics and a .width parameter (a vector of interval widths), and returns a data frame with columns .width (from the .width vector), .value (point summary) and .lower and .upper (endpoints of the intervals, given the .width). Output will be converted to the appropriate x- or y-based aesthetics depending on the value of orientation. If interval_function is NULL, point_interval is used instead.

interval_args

Additional arguments passed to interval_function or point_interval.

point_interval

A function from the point_interval() family (e.g., median_qi, mean_qi, etc). This function should take in a vector of value, and should obey the .width and .simple_names parameters of point_interval() functions, such that when given a vector with .simple_names = TRUE should return a data frame with variables .value, .lower, .upper, and .width. Output will be converted to the appropriate x- or y-based aesthetics depending on the value of orientation. See the point_interval() family of functions for more information.

.width

The .width argument passed to interval_function or point_interval.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

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).

inherit.aes

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().

side

Which side to draw 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).

justification

Justification of the interval relative to the slab, where 0 indicates bottom/left justification and 1 indicates top/right justification (depending on orientation). If justification is NULL (the default), then it is set automatically based on the value of side: when side is "top"/"right" justification is set to 0, when side is "bottom"/"left" justification is set to 1, and when side is "both" justification is set to 0.5.

normalize

How to normalize heights of functions input to the thickness aesthetic. If "all" (the default), normalize so that the maximum height across all data is 1; if "panels", normalize within panels so that the maximum height in each panel is 1; if "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; if "groups", normalize within values of the opposite axis and within groups so that the maximum height in each group is 1; if "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).

thickness

Override for the thickness aesthetic in geom_slabinterval(): the thickness of the slab at each x / y value of the slab (depending on orientation).

Value

A ggplot2::Stat representing a slab or combined slab+interval geometry which can be added to a ggplot() object.

Details

A highly configurable stat for generating a variety of plots that combine a "slab" that summarizes a sample plus an interval. Several "shortcut" stats are provided which combine multiple options to create useful geoms, particularly eye plots (a combination of a violin plot and interval), half-eye plots (a density plus interval), and CCDF bar plots (a complementary CDF plus interval). These can be handy for visualizing posterior distributions in Bayesian inference, amongst other things.

The shortcut stat names follow the pattern stat_[name].

Stats include:

  • stat_eye: Eye plots (violin + interval)

  • stat_halfeye: Half-eye plots (density + interval)

  • stat_ccdfinterval: CCDF bar plots (CCDF + interval)

  • stat_cdfinterval: CDF bar plots (CDF + interval)

  • stat_gradientinterval: Density gradient + interval plots

  • stat_histinterval: Histogram + interval plots

  • stat_pointinterval: Point + interval plots

  • stat_interval: Interval plots

Aesthetics

These stats support the following aesthetics:

  • x

  • y

  • datatype

  • thickness

  • size

  • group

In addition, in their default configuration (paired with geom_slabinterval()) the following aesthetics are supported by the underlying geom:

  • x

  • y

  • datatype

  • alpha

  • colour

  • linetype

  • fill

  • shape

  • stroke

  • point_colour

  • point_fill

  • point_alpha

  • point_size

  • size

  • interval_colour

  • interval_alpha

  • interval_size

  • interval_linetype

  • slab_size

  • slab_colour

  • slab_fill

  • slab_alpha

  • slab_linetype

  • ymin

  • ymax

  • xmin

  • xmax

  • width

  • height

  • thickness

  • group

See examples of some of these aesthetics in action in vignette("slabinterval"). Learn more about the sub-geom aesthetics (like interval_color) in the scales documentation. Learn more about basic ggplot aesthetics in vignette("ggplot2-specs").

Computed Variables

  • 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.

  • f: For slabs, the output values from the slab function (such as the PDF, CDF, or CCDF), determined by slab_type.

  • pdf: For slabs, the probability density function.

  • cdf: For slabs, the cumulative distribution function.

  • n: For slabs, the number of data points summarized into that slab.

See also

See geom_slabinterval() for more information on the geom these stats use by default and some of the options they have. See stat_dist_slabinterval() for the versions of these stats that can be used on analytical distributions. See vignette("slabinterval") for a variety of examples of use.

Examples

library(dplyr) library(tidyr) library(ggplot2) # consider the following example data: set.seed(1234) df = tribble( ~group, ~subgroup, ~value, "a", "h", rnorm(500, mean = 5), "b", "h", rnorm(500, mean = 7, sd = 1.5), "c", "h", rnorm(500, mean = 8), "c", "i", rnorm(500, mean = 9), "c", "j", rnorm(500, mean = 7) ) %>% unnest(value) # here are vertical eyes: df %>% ggplot(aes(x = group, y = value)) + stat_eye()
# note the sample size is not automatically incorporated into the # area of the densities in case one wishes to plot densities against # a reference (e.g. a prior generated by a stat_dist_... function). # But you may wish to account for sample size if using these geoms # for something other than visualizing posteriors; in which case # you can use stat(f*n): df %>% ggplot(aes(x = group, y = value)) + stat_eye(aes(thickness = stat(f*n)))
# see vignette("slabinterval") for many more examples.