How can I extend the quantile regression lines geom_quantile to forecast in ggplot?-CodePudding

I am trying to plot the quantile regression lines for a set of data. I would like to extend the quantile regression lines from geom_quantile() in order to show how they forecast similar to using stat_smooth() with the fullrange argument set to TRUE. However, there is no fullrange argument for geom_quantile(). As an example, see below:

data("mpg")
library(ggplot2)
library(dplyr)

m <-
   ggplot(mpg, aes(displ,1/ hwy))  
   geom_point()
m   geom_quantile()  
   scale_x_continuous(limits = c(1,9),
                      breaks = waiver(),
                      n.breaks = 8)

p <-
   ggplot(mpg, aes(displ,1/ hwy))  
   geom_point()
p   stat_smooth(method = lm, fullrange = TRUE, se = FALSE, color = "red")  
   scale_x_continuous(limits = c(1,9),
                      breaks = waiver(),
                      n.breaks = 8)

m1 <-
   ggplot(mpg, aes(displ,1/ hwy))  
   geom_point()
m1   geom_quantile(fullrange = TRUE)  
   scale_x_continuous(limits = c(1,9),
                      breaks = waiver(),
                      n.breaks = 8)

The first part for m gives the quantile regression lines over the set of data. For p, I can show the linear regression line forecast out to a displacement of say 9. For m1 though, I can't extend out the regression lines. Would there be a way for me to tell ggplot to do this kind of forecast? Easier is preferable, of course, but I'll consider any suggestions. Thanks in advance!

CodePudding user response：

Under the hood, geom_quantile uses quantreg::rq, and it's very straightforward to use it directly to produce the same effect using geom_abline:

mod  <- quantreg::rq(I(1/hwy) ~ displ, tau = c(0.25, 0.5, 0.75), data = mpg)
r_df <- setNames(as.data.frame(t(coef(mod))), c("intercept", "gradient"))

m1   geom_abline(data = r_df, aes(slope = gradient, intercept = intercept))  
     scale_x_continuous(limits = c(1,9),
                      breaks = waiver(),
                      n.breaks = 8)