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 specification
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 assumptions
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 imputation
in transcan, maybe Frank could advise on this?
You are right Roger, fit.mult.impute is generic. It just assumes that impute.transcan can find the imputations to insert, to complete the dataframe. Note that transcan is no longer the function to use for multiple imputations. Use aregImpute. It is flexible as long as additivity holds.
Frank
url: www.econ.uiuc.edu/~roger Roger Koenker email [EMAIL PROTECTED] 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, <[EMAIL PROTECTED]> 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!!
-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University
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