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st: RE: Re: curious behaviour of -glm-


From   "Mak, Timothy" <timothy.mak07@imperial.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Re: curious behaviour of -glm-
Date   Thu, 4 Jun 2009 18:46:36 +0100

No, in the binomial case, the data are sufficient. Eg. 

input y freq
0 90
1 10
end

logit y [fw=freq]

The above is the same model as the glm case. Also, can get equivalent estimates with: 

cii 100 10, wald

Yours, 

Tim

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Martin Weiss
Sent: 04 June 2009 17:45
To: statalist@hsphsun2.harvard.edu
Subject: st: Re: curious behaviour of -glm-

Which "other Stata commands" are you referring to? One of the most popular 
ones exits with the same error message (which, BTW, is very clear: GET MORE 
DATA!)

***
clear*

inp x
2
end

reg x
***

HTH
Martin
_______________
----- Original Message ----- 
From: "Mak, Timothy" <timothy.mak07@imperial.ac.uk>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, June 04, 2009 2:29 PM
Subject: st: curious behaviour of -glm-


> Hi list,
>
>
>
> I'm just curious why -glm, fam(bin) - doesn't seem to handle certain 
> extreme scenarios in accordance with other Stata commands, like give you 
> appropriate warning messages...
>
>
>
> Eg.
>
>
>
> . input r n
>
>
>
>             r          n
>
>  1. 10 100
>
>  2. end
>
>
>
> . glm r, fam(bin n)
>
> insufficient observations
>
> r(2001);
>
>
>
> The model should be estimable...
>
>
>
> Also:
>
>
>
>
>
> . input r n x
>
>
>
>             r          n          x
>
>  1. 10 100 1
>
>  2. 0 100 0
>
>  3. end
>
>
>
> . glm r x, fam(bin n)
>
>
>
> Iteration 0:   log likelihood = -2.2079271
>
> Iteration 1:   log likelihood = -2.0261494
>
> Iteration 2:   log likelihood = -2.0259832
>
> Iteration 3:   log likelihood =  -2.025974
>
> Iteration 4:   log likelihood =  -2.025974
>
>
>
> Generalized linear models                          No. of obs      = 
> 2
>
> Optimization     : ML                              Residual df     = 
> 0
>
>                                                   Scale parameter = 
> 1
>
> Deviance         =  2.00000e-08                    (1/df) Deviance = 
> .
>
> Pearson          =  1.00000e-08                    (1/df) Pearson  = 
> .
>
>
>
> Variance function: V(u) = u*(1-u/n)                [Binomial]
>
> Link function    : g(u) = ln(u/(n-u))              [Logit]
>
>
>
>                                                   AIC             = 
> 4.025974
>
> Log likelihood   = -2.025973987                    BIC             = 
> 2.00e-08
>
>
>
> ------------------------------------------------------------------------------
>
>             |                 OIM
>
>           r |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
> Interval]
>
> -------------+----------------------------------------------------------------
>
>           x |   23.87722      10000     0.00   0.998    -19575.76 
> 19623.52
>
>       _cons |  -26.07444      10000    -0.00   0.998    -19625.71 
> 19573.56
>
> ------------------------------------------------------------------------------
>
>
>
> The model runs as if there was convergence, but of course there can't 
> be...
>
>
>
> Tim
>
>
>
> *
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> 

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