I’ve ended up digging into quantile regression a bit lately (see this excellent gentle introduction to quantile regression

for ecologists [pdf] for what it is and some great reasons why to use it -see also here and here). In R this is done via the quantreg package, which is pretty nice, and has some great plotting diagnostics, etc. But what it doesn’t have out of the box is a way to simply plot your data, and then overlay quantile regression lines at different levels of tau.

The documentation has a nice example of how to do it, but it’s long tedious code. And I had to quickly whip up a few plots for different models.

So, meh, I took the tedious code and wrapped it into a quickie function. Which I dorp here for your delectation. Unless you have some better fancier way to do it (which I’d love to see – especially for ggplot….)

Here’s the function:

```
quantRegLines <- function(rq_obj, lincol="red", ...){
#get the taus
taus <- rq_obj$tau
#get x
x <- rq_obj$x[,2] #assumes no intercept
xx <- seq(min(x, na.rm=T),max(x, na.rm=T),1)
#calculate y over all taus
f <- coef(rq_obj)
yy <- cbind(1,xx)%*%f
if(length(lincol)==1) lincol=rep(lincol, length(taus))
#plot all lines
for(i in 1:length(taus)){
lines(xx,yy[,i], col=lincol[i], ...)
}
}
```

And an example use.

```
data(engel)
attach(engel)
taus <- c(.05,.1,.25,.75,.9,.95)
plot(income,foodexp,xlab="Household Income",
ylab="Food Expenditure",
pch=19, col=alpha("black", 0.5))
rq_fit <- rq((foodexp)~(income),tau=taus)
quantRegLines(rq_fit)
```

Oh, and I set it up to make pretty colors in plots, too.

```
plot(income, foodexp, xlab = "Household Income",
ylab = "Food Expenditure",
pch = 19, col = alpha("black", 0.5))
quantRegLines(rq_fit, rainbow(6))
legend(4000, 1000, taus, rainbow(6), title = "Tau")
```

All of this is in a repo over at github (natch), so, fork and play.