stat_pointinterval: Point + multiple-interval plot (shortcut stat) in ggdist: Visualizations of Distributions and Uncertainty (2024)

stat_pointinterval

R Documentation

Point + multiple-interval plot (shortcut stat)

Description

Shortcut version of stat_slabinterval() with geom_pointinterval() forcreating point + multiple-interval plots.

Roughly equivalent to:

stat_slabinterval( geom = "pointinterval", show_slab = FALSE)

Usage

stat_pointinterval( mapping = NULL, data = NULL, geom = "pointinterval", position = "identity", ..., point_interval = "median_qi", .width = c(0.66, 0.95), orientation = NA, na.rm = FALSE, show.legend = c(size = FALSE), inherit.aes = TRUE)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and`inherit.aes = TRUE` (the default), it is combined with the default mappingat the top level of the plot. You must supply `mapping` if there is no plotmapping.
`data`	The data to be displayed in this layer. There are threeoptions: If `NULL`, the default, the data is inherited from the plotdata as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plotdata. 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`, andwill be used as the layer data. A `function` can be createdfrom a `formula` (e.g. `~ head(.x, 10)`).
`geom`	Use to override the default connection between`stat_pointinterval()` and `geom_pointinterval()`
`position`	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 ifyou have overlapping geometries.
`...`	Other arguments passed to `layer()`. These are often aesthetics, used to set an aestheticto a fixed value, like `colour = "red"` or `linewidth = 3` (see Aesthetics, below). They may also beparameters to the paired geom/stat. When paired with the default geom, `geom_pointinterval()`,these include: `interval_size_domain` A length-2 numeric vector giving the minimum and maximum of the values of the `size` and `linewidth` aesthetics that will betranslated into actual sizes for intervals drawn according to `interval_size_range` (see the documentationfor that argument.) `interval_size_range` A length-2 numeric vector. This geom scales the raw size aesthetic values when drawing interval and pointsizes, as they tend to be too thick when using the default settings of `scale_size_continuous()`, which givesizes with a range of `c(1, 6)`. The `interval_size_domain` value indicates the input domain of raw sizevalues (typically this should be equal to the value of the `range` argument of the `scale_size_continuous()`function), and `interval_size_range` indicates the desired output range of the size values (the min and max ofthe actual sizes used to draw intervals). Most of the time it is not recommended to change the value of thisargument, as it may result in strange scaling of legends; this argument is a holdover from earlier versionsthat did not have size aesthetics targeting the point and interval separately. If you want to adjust thesize of the interval or points separately, you can also use the `linewidth` or `point_size`aesthetics; see scales. `fatten_point` A multiplicative factor used to adjust the size of the point relative to the size of thethickest interval line. If you wish to specify point sizes directly, you can also use the `point_size`aesthetic and `scale_point_size_continuous()` or `scale_point_size_discrete()`; sizesspecified with that aesthetic will not be adjusted using `fatten_point`.
`point_interval`	A function from the `point_interval()` family (e.g., `median_qi`,`mean_qi`, `mode_hdi`, etc), or a string giving the name of a function from that family(e.g., `"median_qi"`, `"mean_qi"`, `"mode_hdi"`, etc; if a string, the caller's environment is searchedfor the function, followed by the ggdist environment). This function determines the point summary(typically mean, median, or mode) and interval type (quantile interval, `qi`;highest-density interval, `hdi`; or highest-density continuous interval, `hdci`). Output willbe 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 `point_interval`: a vector of probabilities to usethat determine the widths of the resulting intervals. If multiple probabilities are provided,multiple intervals per group are generated, each with a different probability interval (andvalue of the corresponding `.width` and `level` generated variables).
`orientation`	Whether this geom is drawn horizontally or vertically. One of: `NA` (default): automatically detect the orientation based on how the aestheticsare assigned. Automatic detection works most of the time. `"horizontal"` (or `"y"`): draw horizontally, using the `y` aesthetic to identify differentgroups. For each group, uses the `x`, `xmin`, `xmax`, and `thickness` aesthetics todraw points, intervals, and slabs. `"vertical"` (or `"x"`): draw vertically, using the `x` aesthetic to identify differentgroups. For each group, uses the `y`, `ymin`, `ymax`, and `thickness` aesthetics todraw points, intervals, and slabs. For compatibility with the base ggplot naming scheme for `orientation`, `"x"` can be used as an aliasfor `"vertical"` and `"y"` as an alias for `"horizontal"` (ggdist had an `orientation` parameterbefore base ggplot did, hence the discrepancy).
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missingvalues 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 onlythose 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 functionsthat define both data and aesthetics and shouldn't inherit behaviour fromthe default plot specification, e.g. `borders()`.

Details

To visualize sample data, such as a data distribution, samples from abootstrap distribution, or a Bayesian posterior, you can supply samples tothe x or y aesthetic.

To visualize analytical distributions, you can use the xdist or ydistaesthetic. For historical reasons, you can also use dist to specify the distribution, thoughthis 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 distributionalpackage (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 theinput data frame.
dist can be a character vector giving the distribution name. Then the arg1, ... arg9aesthetics (or args as a list column) specify distribution arguments. Distribution namesshould 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 argsvalues from human-readable distribution specs (like "normal(0,1)"). Such specs are alsoproduced 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 outputof those functions.

Value

A ggplot2::Stat representing a point + multiple-interval geometry which canbe added to a ggplot() object.

Computed Variables

The following variables are computed by this stat and made available foruse 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.
See Also
What is Heart Rate Variability (HRV) & why does it matter? | Firstbeat Blog 2.11: Assessment of Cardiovascular Function ECG Rate Interpretation RR Intervals, Heart Rate, and HRV Howto
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_maxfor 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_maxfor the CDF at the lower and upper ends of the interval.

Aesthetics

The slab+interval stats and geoms have a wide variety of aesthetics that controlthe appearance of their three sub-geometries: the slab, the point, andthe interval.

These stats 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 distributionalobject (e.g. dist_normal()) or a posterior::rvar() object.
ydist: When using analytical distributions, distribution to map on the y axis: a distributionalobject (e.g. dist_normal()) or a posterior::rvar() object.
dist: When using analytical distributions, a name of a distribution (e.g. "norm"), adistributional 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_pointinterval())the following aesthetics are supported by the underlying geom:

Interval-specific aesthetics

xmin: Left end of the interval sub-geometry (if orientation = "horizontal").
xmax: Right end of the interval sub-geometry (if orientation = "horizontal").
ymin: Lower end of the interval sub-geometry (if orientation = "vertical").
See Also
Heart Rate Variability (HRV)
ymax: Upper end of the interval sub-geometry (if orientation = "vertical").

Point-specific aesthetics

shape: Shape type used to draw the point sub-geometry.

Color aesthetics

colour: (or color) The color of the interval and point sub-geometries.Use the slab_color, interval_color, or point_color aesthetics (below) toset sub-geometry colors separately.
fill: The fill color of the slab and point sub-geometries. Use the slab_fillor point_fill aesthetics (below) to set sub-geometry colors separately.
alpha: The opacity of the slab, interval, and point sub-geometries. Use the slab_alpha,interval_alpha, or point_alpha aesthetics (below) to set sub-geometry colors separately.
colour_ramp: (or color_ramp) A secondary scale that modifies the colorscale to "ramp" to another color. See scale_colour_ramp() for examples.
fill_ramp: A secondary scale that modifies the fillscale to "ramp" to another color. See scale_fill_ramp() for examples.

Line aesthetics

linewidth: Width of the line used to draw the interval (except with geom_slab(): thenit is the width of the slab). With composite geometries including an interval and slab,use slab_linewidth to set the line width of the slab (see below). For interval, rawlinewidth values are transformed according to the interval_size_domain and interval_size_rangeparameters of the geom (see above).
size: Determines the size of the point. If linewidth is not provided, size willalso determines the width of the line used to draw the interval (this allows line width andpoint size to be modified together by setting only size and not linewidth). Rawsize values are transformed according to the interval_size_domain, interval_size_range,and fatten_point parameters of the geom (see above). Use the point_size aesthetic(below) to set sub-geometry size directly without applying the effects ofinterval_size_domain, interval_size_range, and fatten_point.
stroke: Width of the outline around the point sub-geometry.
linetype: Type of line (e.g., "solid", "dashed", etc) used to draw the intervaland the outline of the slab (if it is visible). Use the slab_linetype orinterval_linetype aesthetics (below) to set sub-geometry line types separately.

Interval-specific color/line override aesthetics

interval_colour: (or interval_color) Override for colour/color: the color of the interval.
interval_alpha: Override for alpha: the opacity of the interval.
interval_linetype: Override for linetype: the line type of the interval.

Point-specific color/line override aesthetics

point_fill: Override for fill: the fill color of the point.
point_colour: (or point_color) Override for colour/color: the outline color of the point.
point_alpha: Override for alpha: the opacity of the point.
point_size: Override for size: the size of the point.

Deprecated aesthetics

interval_size: Use interval_linewidth.

Other aesthetics (these work as in standard geoms)

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 thescales documentation. Learn more about basic ggplot aesthetics invignette("ggplot2-specs").

Examples

library(dplyr)library(ggplot2)library(distributional)theme_set(theme_ggdist())# ON SAMPLE DATAset.seed(1234)df = data.frame( group = c("a", "b", "c"), value = rnorm(1500, mean = c(5, 7, 9), sd = c(1, 1.5, 1)))df %>% ggplot(aes(x = value, y = group)) + stat_pointinterval()# ON ANALYTICAL DISTRIBUTIONSdist_df = data.frame( group = c("a", "b", "c"), mean = c( 5, 7, 8), sd = c( 1, 1.5, 1))# Vectorized distribution types, like distributional::dist_normal()# and posterior::rvar(), can be used with the `xdist` / `ydist` aestheticsdist_df %>% ggplot(aes(y = group, xdist = dist_normal(mean, sd))) + stat_pointinterval()