Package 'ggstudent'

Student CI plot

Description

A Student CI plot (or Violin CI plot) is a mirrored density plot similar to violin plot but instead of kernel density estimate it is based on the density of the t-distribution. It can be though of as a continuous "confidence interval density" (hence the name), which could reduce the dichotomous interpretations due to a fixed confidence level. geom_student can also be used to draw Gradient CI plots (using argument type), which replaces the violin shaped density with a rectangle.

Usage

geom_student(mapping = NULL, data = NULL, position = "identity",
  width = 0.25, type = "density", scale = TRUE, draw_lines = NULL,
  draw_mean = TRUE, 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)).
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
width	Scaling parameter for the width of the violin/rectangle.
type	Type of the plot. The default is density which draws violin style density plot, whereas "box" draws a rectangle shaped gradient plot.
scale	If "TRUE" (default), violins/rectangles are scaled according to the maximum width of the groups (max(dt(0, df) / se)).
draw_lines	If not NULL (default), draw horizontal lines at the given quantiles of the density estimate.
draw_mean	If TRUE (default), draw horizontal line at mean.
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.
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().
...	Other arguments passed to layer(), such as fixed aesthetics.

Value

A ggplot object.

References

Helske, J, S Helske, M Cooper, A Ynnerman, and L Besançon (2020). Are You Sure You’re Sure? - Effects of Visual Representation on the Cliff Effect in Statistical Inference. Under review. https://arxiv.org/abs/2002.07671

Examples

library("dplyr")
library("ggplot2")
library("scales")

ci_levels <- c(0.999, 0.95, 0.9, 0.8, 0.5)
n <- length(ci_levels)
ci_levels <- factor(ci_levels, levels = ci_levels)
PlantGrowth %>% dplyr::group_by(group) %>%
  dplyr::summarise(
    mean = mean(weight),
    df = dplyr::n() - 1,
    se = sd(weight)/sqrt(df + 1)) %>%
 dplyr::full_join(
   data.frame(group =
     rep(levels(PlantGrowth$group), each = n),
     level = ci_levels), by = "group") -> d

p <- ggplot(data = d, aes(group)) +
 geom_student(aes(mean = mean, se = se, df = df,
   level = level, fill = level), draw_lines = c(0.95, 0.5))
p
g <- scales::seq_gradient_pal("#e5f5f9", "#2ca25f")
p + scale_fill_manual(values=g(seq(0,1,length = n))) + theme_bw()

p2 <- ggplot(data = d, aes(group)) +
 geom_student(aes(mean = mean, se = se, df = df,
   level = level, fill = level), type = "box", draw_lines = c(0.95, 0.5))
p2

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