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 <sha...@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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Thomas Lumley Professor of Biostatistics University of Auckland ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.