rggplot2ggridges

How to replace quantile intervals in ridge plot with my own intervals?

With the R code below,

library(ggplot2)
library(ggridges)
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.025, 0.975), alpha = 0.7)

I get the following ridge plot

enter image description here

I would like to replace the two quantile lines (2.5% and 97.5%) under each density plot with two different lines based on the following code:

require(coda)
aggregate(Sepal.Length ~ Species, iris, function(x) HPDinterval(as.mcmc(x), 0.975))

In other words, I wish to put those vertical lines at the following locations:

     Species Sepal.Length.1 Sepal.Length.2
1     setosa            4.3            5.8
2 versicolor            4.9            7.0
3  virginica            4.9            7.9

How to achieve this?

Solution

  • The inverse of quantile is ecdf, so you can back-transform your lengths into quantiles to feed to stat_density_ridges:

    aggregate(Sepal.Length ~ Species, iris, function(x) {
  ecdf(x)(HPDinterval(as.mcmc(x), 0.975))})
#>      Species Sepal.Length.1 Sepal.Length.2
#> 1     setosa           0.02           1.00
#> 2 versicolor           0.02           1.00
#> 3  virginica           0.02           1.00

    And you will see that the quantiles you are looking for are 0.02 and 1, so now you can plot:

    ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.02, 1), 
                      alpha = 0.7)

    enter image description here

    Note also that the function you are passing to aggregate is the same as passing range

    aggregate(Sepal.Length ~ Species, iris, range)
#>      Species Sepal.Length.1 Sepal.Length.2
#> 1     setosa            4.3            5.8
#> 2 versicolor            4.9            7.0
#> 3  virginica            4.9            7.9

    So you could simply pass 0 and 1 as quantiles, which matches the numbers in your table perfectly:

    ggplot(iris, aes(x = Sepal.Length, y = Species)) +
    stat_density_ridges(quantile_lines = TRUE, quantiles = c(0, 1), 
                        alpha = 0.7)

    enter image description here

    Edit

    If the quantiles returned by your function are all different, you could use multiple geom layers, which you could have in a list, like this:

    library(ggridges)

ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  lapply(split(iris, iris$Species)[3:1], function(x) {
  stat_density_ridges(quantile_lines = TRUE, 
  quantiles = ecdf(x$Sepal.Length)(HPDinterval(as.mcmc(x$Sepal.Length), 0.75)), 
  alpha = 0.7, data = x)})

    enter image description here