Question

In my statistics class we use Stata and since I’m an R user I want to do the same things in R. I’ve gotten the right results but it seems like a somewhat awkward way of getting something as simple as confidence intervals.

Here’s my crude solution:

library(quantreg)
na = round(runif(100, min=127, max=144))
f <- rq(na~1, tau=.5, data=ds)
s <- summary.rq(f, se="boot", R=1000)
coef(s)[1]
coef(s)[1]+ c(-1,1)*1.96*coef(s)[2]

I’ve also experimented a little at the boot package but I haven’t gotten it to work:

library(boot)
b <- boot(na, function(w, i){ 
        rand_bootstrap_sample = w[i]
        f <- rq(rand_bootstrap_sample~1, tau=.5)
        return(coef(f)) 
        }, R=100)
boot.ci(b)

Gives an error:

Error in bca.ci(boot.out, conf, index[1L], L = L, t = t.o, t0 = t0.o, :
estimated adjustment ‘a’ is NA

My questions:

• What I wan’t is to know if there is another better way of getting the confidence interval
• why is the bootstrap code complaining?

Answer 1

Your example does not give an error message for me (Windows 7/64,R 2.14.2), so it could be a problem of random seeds. So if you post an example using some random method, better add a line set.seed; see example.

Note that the error message refers to the bca type of boot.ci; since this one often complains, deselect it by giving type explicitly.

I do not know exactly why you use the rather complex rq in the bootstrap. If you really wanted to profile rq, forget the simple example below, but please give some more details.

library(boot)
set.seed(4711)
na = round(runif(100, min=127, max=144))

b <- boot(na, function(w, i) median(w[i]), R=1000)
boot.ci(b,type=c("norm","basic","perc"))