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AW: [Fwd: st: AW: GLM family and link (default)]


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   AW: [Fwd: st: AW: GLM family and link (default)]
Date   Mon, 14 Jun 2010 15:21:19 +0200

<> 

It is quite easy to replicate "mmolina"´s problem, btw:


*************
sysuse auto, clear
set seed 43254

prob foreign weight length trunk turn /* 
*/  displacement headroom price mpg rep78  /* 
*/ if runiform()<.3

glm foreign weight length trunk turn  /* 
*/ displacement headroom price mpg rep78 /* 
*/  if e(sample), family(binomial 1) /* 
*/  link(probit)
*************



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Maarten buis
Gesendet: Montag, 14. Juni 2010 15:09
An: statalist@hsphsun2.harvard.edu
Betreff: Re: [Fwd: st: AW: GLM family and link (default)]

--- On Mon, 14/6/10, mmolina@uniroma3.it wrote:
> They are not 33 observations but these are the remaining...

It doesn't matter how large your dataset is, all that counts
is how many observations are used in your estimation, and
that is 33 (and only 22 in your probit model), which is a 
major problem when you want to estimate 13 parameters. Anyhow, 
the linear probability model is not the most obvious solution 
to your problem: 

First, simplify your model by using much less variables, 
I'd say an absolute maximum of 3 variables (10 obs per 
variable). 

Second, use -exlogisitc- if you want to retain the variables 
that perfectly predict your outcome, which as I stated before
was the reason that your -probit- model was mis-behaving.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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