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Plot a Probability Mass Function as Lollipops

Source: R/geom_pmf.R
geom_pmf.Rd

geom_pmf() creates a ggplot2 layer that plots a probability mass function (PMF) using a lollipop representation. Vertical segments extend from zero up to the probability value at each integer support value and a point is drawn at the top. Shading modes mirror those of geom_pdf(): a cumulative threshold (p), a two-sided interval (p_lower/p_upper), or a highest density region (shade_hdr). Non-shaded lollipops are rendered in grey with dashed segments.

Usage

geom_pmf(
  mapping = NULL,
  data = NULL,
  stat = StatPMF,
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  fun,
  xlim = NULL,
  support = NULL,
  point_size = 2.5,
  stick_linewidth = 0.25,
  stick_linetype = "dashed",
  color = "black",
  args = list(),
  p = NULL,
  lower.tail = TRUE,
  p_lower = NULL,
  p_upper = NULL,
  shade_outside = FALSE,
  shade_hdr = NULL
)

StatPMF

GeomPMF

Format

An object of class StatPMF (inherits from Stat, ggproto, gg) of length 3.

An object of class GeomPMF (inherits from GeomPoint, Geom, ggproto, gg) of length 2.

Arguments

mapping

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.

data

The data to be displayed in this layer. There are three options:

  • NULL (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)).

stat

The statistical transformation to use on the data for this layer. When using a geom_*() function to construct a layer, the stat argument can be used to override the default coupling between geoms and stats. The stat argument accepts the following:

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

...

Other parameters passed on to ggplot2::layer().

na.rm

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

show.legend

Logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display. To include legend keys for all levels, even when no data exists, use TRUE. If NA, all levels are shown in legend, but unobserved levels are omitted.

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. annotation_borders().

fun

A function to compute the PMF (e.g. dbinom or dpois). The function must accept a numeric vector as its first argument and return non-negative probability values. (Ideally, the probabilities sum to 1 over the support.)

xlim

A numeric vector of length 2 specifying the range (of x values) over which to evaluate the PMF. If not provided, a default range of 0 to 10 is used.

support

An optional integer or numeric vector giving the exact support points to evaluate. When supplied, xlim is ignored.

point_size

Size of the points at the top of each lollipop (defaults to 2.5).

stick_linewidth

Linewidth of the vertical sticks (defaults to 0.25).

stick_linetype

Linetype of the vertical sticks (defaults to "dashed").

color

Color for the shaded points and segments (defaults to "black").

args

A named list of additional arguments to pass to fun.

p

(Optional) A numeric value between 0 and 1 specifying a cumulative probability threshold. When lower.tail = TRUE (the default), lollipops up to the corresponding quantile are shaded; when FALSE, the upper tail is shaded.

lower.tail

Logical; controls the direction of p-based shading. Defaults to TRUE.

p_lower

(Optional) Lower cumulative probability bound for two-sided shading. Used with p_upper.

p_upper

(Optional) Upper cumulative probability bound for two-sided shading. Used with p_lower.

shade_outside

Logical; if TRUE, shading is applied to the tails outside the p_lower/p_upper interval rather than inside. Defaults to FALSE.

shade_hdr

(Optional) A numeric value between 0 and 1 specifying the target coverage of the highest density region (HDR) to shade – the smallest set of support points containing at least the specified probability mass. Because a discrete distribution may not achieve the exact coverage, the smallest HDR with coverage >= shade_hdr is used and a message is issued via cli::cli_inform() reporting both the specified and actual coverage whenever they differ.

Value

A ggplot2 layer.

Examples

# Basic PMF
ggplot() +
  geom_pmf(fun = dbinom, xlim = c(0, 10), args = list(size = 10, prob = 0.25))


# Shade the lower tail up to the 80th percentile
ggplot() +
  geom_pmf(fun = dbinom, xlim = c(0, 10), args = list(size = 10, prob = 0.5),
    p = 0.8)


# Shade the 80% HDR
ggplot() +
  geom_pmf(fun = dbinom, xlim = c(0, 10), args = list(size = 10, prob = 0.5),
    shade_hdr = 0.8)
#> ! shade_hdr: 80% is not exactly achievable for this discrete distribution.
#> ℹ Using smallest HDR with coverage >= 80%: actual coverage = 89.1%.