Dear Tom,
Thanks so much. Your solution is great, but if I
remember correctly, I think Stata has a ready and very
simple command for post-estimation graphing of the
predicted effects. I will try to look further myself.
Thanks so much again.
Best wishes,
yumin
--- Thomas Trikalinos <[email protected]> wrote:
> So you need predicted values on the
> VariableOfInterest adjusting at the
> mean level of continuous covariates, and the
> reference category of
> categoric covariates.
>
> A simple but not so elegant solution is
>
> . gen PredY = Constant + beta1* VariableOfInterest1
> +
> beta2*MeanContinuousCovariate2 (or the
> corresponding analogue for a
> logit/probit/poisson etc regression)
>
> [first run . egen MeanContinuousCovariate2 =
> mean(ContinuousCovariate2)]
>
>
>
> Constant and beta1, beta2 are from the regression
> output. All
> Categorical covariate terms are zero (this would be
> your reference
> category, right?) and all the continuous covariate
> terms are
> incorporated using their mean level. This way you
> adjust for the
> reference category for categoric covariates and for
> the mean value of
> continuous covariates.
>
> You most probably have more than one continuous
> covariates; just put in
> as many terms as you need. If you have different
> functions of the
> VariableOfInterest (eg quadratic or cubic terms) put
> them in as more
> VariablesOfInterest.
>
> This is a crude workaround I use. I'm confident that
> people know
> something better and more elegant, though...
>
> Cheers
> Tom
>
>
> On May 8, 2004, at 1:37, yumin sheng wrote:
>
> > Dear all,
> >
> > This is a real simple question, but I just
> couldn't
> > remember the command off the top of my head. Can
> > anyone tell me how to plot the predicted effects
> of a
> > variable after running the regressions, while
> holding
> > all the other independent variables constant.
> Thanks a
> > bundle!
> >
> > Best,
> > yumin
> >
> >
> >
> >
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