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RE: st: RE: continuous to categorical and determination of monotonic categories
For binary outcomes, there would seem no point in smearing
two spikes in the probability distribution into
. tab category, su(outcome)
would be one way to summarise the information.
> >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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