Visually weighted quantile regressions. Initial code by Martin Johnsson.

README.md

An extrapolation of this blog post.

vwqreg.R

library(quantreg)
model.rq <- rq(Temp ~ Wind, airquality, tau=1:99/100)
quantile.regressions <- data.frame(t(coef(model.rq)))
colnames(quantile.regressions) <- c("intercept", "slope")
quantile.regressions$quantile <- rownames(quantile.regressions)
quantile.regressions

library(ggplot2)
quantile.regressions$x = 1 - abs(as.numeric(substring(quantile.regressions$quantile, 6)) - .5)
qplot(x=Wind, y=Temp, data=airquality, geom = "blank") +
  geom_abline(aes(intercept=intercept, slope=slope,
                  colour=x^8), data=quantile.regressions) +
  geom_point(alpha = .5) +
  scale_color_gradient2("", low = "yellow", high = "orangered", limits = c(0, 1)) +
  theme(panel.grid.major = element_blank())

# want more yellow and less white?
qplot(x=Wind, y=Temp, data=airquality, geom = "blank") +
  geom_abline(aes(intercept=intercept, slope=slope,
                  colour=x^8), data=quantile.regressions) +
  geom_point(alpha = .5) +
  scale_color_gradient2("", low = "white", midpoint = .5, mid = "yellow", high = "orangered", limits = c(0, 1)) +
  theme(panel.grid.major = element_blank())