What are quantiles in ggplot stat_quantile?

Question

Here is my reproducible data:

library("ggplot2")
library("ggplot2movies")
library("quantreg")    
set.seed(2154)
msamp <- movies[sample(nrow(movies), 1000), ]

I am trying to become acquainted with stat_quantile but the example from the documentation raises a couple of questions.

mggp <- ggplot(data=msamp, mapping=aes(x=year, y=rating)) + 
    geom_point() + 
    stat_quantile(formula=y~x, quantiles=c(0, 0.25, 0.50, 0.75, 1)) + 
    theme_classic(base_size = 12) + 
    ylim(c(0,10))
mggp

To my understanding quantiles split the data into parts that are smaller than the defined cut-off values, correct? If I define quantiles like in the following code I get five lines. Why? What do they represent?
It seems that the quantiles are calculated based on the dependent variable on the y-axis (rating). Is it possible to reverse this? I mean to split the data based on quantiles in 'year'?

This is quantile regression, not just "quantiles" per se. You probably just need to spend some time reading the documentation of the **quantreg** package. There is an associated book that is sort of the definitive one. `stat_quantile` in particular is fitting `quantreg::rq` models. — joran, Dec 28 '15 at 19:56

score 3 · Answer 1 · answered May 11 '16 at 11:06

This function performs quantile regression, and each line is an indicator of the

From Wikipedia:

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares results in estimates that approximate the conditional mean of the response variable given certain values of the predictor variables, quantile regression aims at estimating either the conditional median or other quantiles of the response variable.

Thus each line in the regression plot is an estimate of the quantile value, e.g. median, 75th and 100th percentile.

You can find a detailed technical discussion in the vignette of the quantreg package.

What are quantiles in ggplot stat_quantile?

1 Answers1