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RE: st: RE: continuous to categorical and determination of monotonic categories


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: continuous to categorical and determination of monotonic categories
Date   Wed, 10 Dec 2003 11:12:30 -0000

For binary outcomes, there would seem no point in smearing 
two spikes in the probability distribution into 
two humps. 

. tab category, su(outcome) 

would be one way to summarise the information. 

Nick 
n.j.cox@durham.ac.uk 

Constantine Daskalakis

> >I'll take the motivation here as given,
> >although clearly a separate discussion
> >on merits and demerits is possible.
> >
> >There may be formal machinery to do this, but
> >I'd tend to proceed in an exploratory manner.
> >
> >One key is to look at the conditional distributions
> >and see whether their properties change as expected.
> >
> >Among other tools, you could look at a bundle
> >of cumulative distribution curves or kernel
> >estimates of density functions. -distplot- or -qplot- from
> >SSC have easy handles to draw graphs -by()-.
> 
> Agreed. But is there a non-parametric procedure, say the 
> equivalent of 
> Stata's -ksm- (smoother) for binary outcomes?

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