[R] fit.mult.impute and quantile regression

roger koenker rkoenker at uiuc.edu
Tue Jun 15 14:26:55 CEST 2004

Having not tried this, it is dangerous to speculate, but it appears to 
me that there
would be no problem passing rq arguments (crucially, only tau, the 
of the quantile of interest) to fit.mult.impute, since the call to the 
"fitter" procedure
includes a ... argument.  The real question would seem to be:  are the 
underlying the imputation procedure consistent with the rq fitting, 
that is are they
assuming something stronger than that the tauth conditional quantile 
function of
y is linear in x?   There seem to be quite a variety of options for the 
in transcan, maybe Frank could advise on this?

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				Champaign, IL 61820

On Jun 15, 2004, at 11:52 AM, <david_foreman at doctors.org.uk> wrote:

> I have a largish dataset (1025) with around .15 of the data missing at 
> random overall, but more like .25 in the dependent variable.  I am 
> interested in modelling the data using quantile regression, but do not 
> know how to do this with multiply imputed data (which is what the 
> dataset seems to need).  The original plan was to use qr (or whatever) 
> from the quantreg package as the 'fitter' argument in Design's 
> fit.mult.impute, but it is not clear whether this would work, 
> especially as fit.mult.impute seems only to work with the default 
> settings of its 'fitter' arguments, which rather defeats the purpose 
> of quantile regression.  Help!!
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