https://github.com/helske/ggstudent
Continuous confidence interval plots using t-distribution
https://github.com/helske/ggstudent
Last synced: 3 months ago
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Continuous confidence interval plots using t-distribution
- Host: GitHub
- URL: https://github.com/helske/ggstudent
- Owner: helske
- Created: 2019-06-17T12:12:26.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-09-06T10:37:03.000Z (almost 4 years ago)
- Last Synced: 2024-10-29T09:18:56.540Z (9 months ago)
- Language: R
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
ggstudent - Continuous Confidence Interval Plots using t-Distribution
============================================
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](https://arxiv.org/pdf/2002.07671) and [repository](https://github.com/helske/statvis)
Here is an example:
```r
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](https://github.com/wjschne/ggnormalviolin) for creating violin plots based on normal distribution.