[R] Fwd: Making rq and bootcov play nice

[roger koenker]

John,

You can make a local version of bootcov which either:

	deletes these arguments from the call to fitter, or
	modify the switch statement to include rq.fit,

the latter would need to also modify rq() to return a fitFunction
component, so the first option is simpler.   One of these days I'll
incorporate clustered se's into summary.rq, but meanwhile
this seems to be a good alternative.

Roger

url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    rkoenker at uiuc.edu            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801



On Jul 23, 2009, at 8:10 PM, John Gardner wrote:

> I have a quick question, and I apologize in advance if, in asking, I
> expose my woeful ignorance of R and its packages. I am trying to use
> the bootcov function to estimate the standard errors for some
> regression quantiles using a cluster bootstrap. However, it seems that
> bootcov passes arguments that rq.fit doesn't like, preventing the
> command from executing. Here is an example:
>
> e<-bootcov(rq(y~x),clust,B=10,fitter=rq.fit)
>
> (where clust is my clustering variable) results in
>
> Error in rq.fit.br(x, y, tau = tau, ...) :
> unused argument(s) (maxit = 15, penalty.matrix = NULL)
>
> In contrast, the lm.fit function seems to just ignore these arguments,
> resulting in the following warning:
>
> 10: In fitter(X[obs, , drop = FALSE], Y[obs, , drop = FALSE], maxit  
> = maxit,  :
> extra arguments maxitpenalty.matrix are just disregarded.
>
> Is there a way that I can either (a) modify bootcov so that it doesn't
> pass these arguments or (b) modify rq so that it ignores them?
>
> Thanks in advance,
> John Gardner
>
> ______________________________________________
> R-help at 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.