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st: -binreg-

From   Jay Kaufman <>
To   Stata <>
Subject   st: -binreg-
Date   Thu, 21 Nov 2002 13:28:10 -0500

The -binreg- routine fits generalized linear models for the 
binomial family.  It is presumably preferred over fitting the same
model in -glm-, not only for the convenience of not having to 
specify the distributional family in the command line, but also 
because in iteratively seeking the estimates it checks to make sure 
that they are consistent with the range of allowable probabilities
(i.e. 0 to 1), as described on page 138 of the manual [Ref A-G].  
So my question is, why does -binreg- appear to be so 
bad at this checking?  

Take a very simple model using the auto.dta. 

. use "C:\Stata\auto.dta", clear
(1978 Automobile Data)

. binreg  foreign mpg, rr

Residual df  =        72                   No. of obs =        74
Pearson X2   =  73.88014                   Deviance   =  78.99933
Dispersion   =  1.026113                   Dispersion =  1.097213

Bernoulli distribution, log link
        |                 EIM
foreign | Risk Ratio   Std. Err.    z    P>|z|    [95% Conf. Interval]
    mpg |   1.097213   .0109901   9.26   0.000    1.075883    1.118966

. predict phat, mu

. sum phat

    Variable |     Obs        Mean   Std. Dev.       Min        Max
        phat |      74    .3008965     .22691   .1072727   1.580984

Clearly a predicted probability > 1.5 is not a good estimate.  Did
I do something wrong?  Or did -binreg- do something wrong?  Or is 
this simply another example of why linear models of the logit and
probit have dominated analysis of binary data for decades?  

By the way, note that if I fit the exact same model using -glm-, 
this same observation gets a predicted probability of 1.43, so 
-binreg- actually seems to do worse.  

Jay S. Kaufman, Ph.D         
Department of Epidemiology   
UNC School of Public Health  
2104C McGavran-Greenberg Hall
Pittsboro Road, CB#7435   
Chapel Hill, NC 27599-7435  
phone:  919-966-7435         
fax:    919-966-2089         
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