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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Binomial regression |

Date |
Fri, 3 Aug 2007 15:10:52 -0400 |

Constantine et al. -- I think Constantine is not taking the point of Maarten, Bobby, Vince, and Marcello, so my input is unlikely to sway him. Nevertheless, here goes: your model makes little sense when your predictions make no sense--you cannot reasonably assume that there is a constant effect of x on probabilities if predicted probabilities stray outside [0,1]. Hence, convergence failure indicates a failure of your assumptions that the effect of x on probability is linear, as opposed to logistic or some other g(xb) that stays in [0,1]. You can always back out a mean marginal effect of x on predicted probability conditional on the distribution of other explanatory variables, however, to get your estimates in terms of risk differences. Or run a linear probability model, which avoids convergence issues, and damn the consequences. Kit wrote: But the link function is not a CDF. True--but I think Marcello has in mind that g(xb) must have the properties of a CDF to produce predictions interpretable as probabilities. So a linear g=xb must be truncated to g=min(max(xb,0),1) which looks like the CDF of a uniform X. On 8/3/07, Marcello Pagano <pagano@hsph.harvard.edu> wrote: > Anyway, if you have the risks you can, of course, calculate the > differences of the risks. The problem, as you point out, is how this > risk difference is related to other covariates. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Binomial regression***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Binomial regression***From:*Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu>

**Re: st: Binomial regression***From:*Marcello Pagano <pagano@hsph.harvard.edu>

**Re: st: Binomial regression***From:*Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu>

**Re: st: Binomial regression***From:*Marcello Pagano <pagano@hsph.harvard.edu>

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