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Re: Re: st: Why does a non-statistically significant covariate in a a regression model become significant in margins?
From
Joerg Luedicke <[email protected]>
To
[email protected]
Subject
Re: Re: st: Why does a non-statistically significant covariate in a a regression model become significant in margins?
Date
Fri, 14 Dec 2012 16:32:09 -0500
Ariel had a follow-up question which he sent to me offlist.
Here is his question:
On Fri, Dec 14, 2012 at 4:08 PM, Ariel Linden, DrPH
<[email protected]> wrote:
> Joerg,
>
> As a quick followup (I can post this on the listserve if you prefer to
> respond publically):
>
> How would I get the predicted values for the binary part of the model via
> margins? I thought perhaps predict(pr), but that doesn't seem to give the
> same results as your manual calculation:
>
> . sum p_c p_t
>
> Variable | Obs Mean Std. Dev. Min Max
> -------------+--------------------------------------------------------
> p_c | 10000 .4449147 .1127882 .1127662 .8525233
> p_t | 10000 .33073 .1019106 .0709849 .7765486
>
> . margins, at(treat=(0 1)) expression(predict(pr))
>
> Predictive margins Number of obs =
> 10000
> Model VCE : OIM
>
> Expression : Pr(y=0), predict(pr)
>
> 1._at : treat = 0
>
> 2._at : treat = 1
>
> ----------------------------------------------------------------------------
> --
> | Delta-method
> | Margin Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> _at |
> 1 | .5550853 .0076111 72.93 0.000 .5401679
> .5700028
> 2 | .66927 .0076781 87.17 0.000 .6542211
> .6843188
> ----------------------------------------------------------------------------
> --
>
My response:
In the inflation part of the model, we predict the probability of y=0.
However, with the marginal predictions of counts using both model
components, we weight the nonzero counts with the probability of y>0,
which is simply 1 - p(y=0). Therefore, we can type:
margins, at(treat=(0 1)) expression(1-(predict(pr)))
in order to get the marginal predictions for p(y>0).
Joerg
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