On 07/10/2013 12:45 PM, Josef Perktold wrote:
>
> In statsmodels/emplike/aft_el.py:
>
> > @@ -371,7 +371,7 @@ def predict(self, params, endog=None):
> >          return np.dot(endog, params)
> >
> >
> > -class AFTResults(OptAFT):
> > +class AFTResults(OptAFT_Mixin):
>
> why do you call it |Mixin| if it's the main super class
>
> should there be a super Results class
>
> |AFTResults(EmpLikeResults, OptAFTMixin)|
>
|I don't think so.  Fitting the elAFT returns the results class.  
However, some of the results are computed in the results class (i.e. a 
user might only want to conduct inference on one parameter.  Since this 
can be slow, confidence intervals are only computed when called by the 
user). |
>
> (I'm making comments without having an overview yet.)
>
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