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The R package ggstudent provides an extension to ggplot2
for creating two types of continuous confidence interval plots (Violin
CI and Gradient CI plots), typically for the sample mean. These plots
contain multiple user-defined confidence areas with varying colours,
defined by the underlying t-distribution used to compute standard
confidence intervals for the mean of the normal distribution when the
variance is unknown.
Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution.
See also the related paper and repository
Here is an example:
library("magrittr")
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))
g <- scales::seq_gradient_pal("#e5f5f9", "#2ca25f")
p + scale_fill_manual(values = g(seq(0, 1, length = n))) + theme_bw()
See also the R package ggnormalviolin for creating violin plots based on normal distribution.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.