Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: Re: Re: st: Why does a non-statistically significant covariate in a a regression model become significant in margins? |

Date |
Fri, 14 Dec 2012 16:40:59 -0500 |

Thank you, once again Joerg! This now tells the complete story and is extremely insightful! Ariel 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 <ariel.linden@gmail.com> 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**Re: Re: st: Why does a non-statistically significant covariate in a a regression model become significant in margins?** - Next by Date:
**Re: st: Re:RE: inequality measures, and dynamic decomposition of inequality** - Previous by thread:
**re: Re: st: Why does a non-statistically significant covariate in a a regression model become significant in margins?** - Next by thread:
**st: logit for case control** - Index(es):