[R] Quantile regression (rq) and complex samples

[Thomas Lumley]

You could use the survey package to run the bootstrapping, if you mean
the Rao & Wu bootstrap that samples n-1 of n PSUs in each replicate.

Set up a survey design object with bootstrap replicate weights: use
svrepdesign() if you already have replicate weights, use svydesign()
and then as.svrepdesign() to get R to make the replicate weights for
you.

Then use withReplicates() to run rq() for each set of replicate
weights and compute the variance.

       -thomas


On Thu, Jan 27, 2011 at 11:18 AM, James Shaw <shawjw at gmail.com> wrote:
> I am new to R and am interested in using the program to fit quantile
> regression models to data collected from a multi-stage probability
> sample of the US population.  The quantile regression package, rq, can
> accommodate person weights.  However, it is not clear to me that
> boot.rq is appropriate for use with multi-stage samples (i.e., is
> capable of sampling primary sampling units instead of survey
> respondents).  I would like to apply Rao's rescaling bootstrap
> procedure and poststratify the weights to population control totals in
> each bootstrap replicate.  I know how to do all of this in Stata but
> have not yet seen any means of doing so in R.  I  presume I could do
> what is needed using batch processing but was hoping that there might
> be a way to pass the rq parameter estimates to a package that performs
> resampling variance estimation in order to simplify the task.  Any
> programming suggestions or directions to informational resources would
> be greatly appreciated.
>
> Jim
>
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-- 
Thomas Lumley
Professor of Biostatistics
University of Auckland