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Re: st: possible problem with GLM command


From   "Patrick Mcelduff" <Patrick.Mcelduff@man.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: possible problem with GLM command
Date   Tue, 3 Feb 2004 09:16:58 +0000

Dear Joe

Thanks for you suggestion - it did solve my problem. 

regards
Patrick

> Just a quick thought as to what may be the problem. Using the log link
> with the bernoulli family (binomial with a response of 1 or 0) results
> in fitted values that may lie outside the 1/0 bounds, which is an
> assumption of the Bernoulli parameterization of the binomial family.
> Because of this, and depending on the specific nature of the data,
> convergence problems may occur. You may find that using the -irls-
> option clears up the problem. 
> 
> James Hardin, who co-authored the glm command with me, wrote a program
> for binomial models with non-standard links some years back. It was
> published in the STB. Fitted values that exceeded 1 or 0 were
> internally truncated at each iteration. I forget the name of the
> program, but you can probably find it by using the -lookup binomial-
> command in Stata. It should work. 
> 
> Joe Hilbe
> 
> > Dear all
> > 
> > Is anyone aware of a problem with the GLM command?
> > 
> > I have recommended to a number of my collegues that they fit a GLM
> > with a log link and binomial error term when analysing cross-
> > sectional data with a dichotomous outcome. The benefit of this is
> > that the exponental of the paramater estimate is equivalent to the
> > relative prevalence.  [I'm not looking for a debate on the wisdom of
> > this].
> > 
> > A number of them have reported a problem with their models when they
> > have several independent variables. For example I have a model with
> > three categorical predictors: agegroup (3 levels), sex (2 levels)
> > and housing (2 levels). 
> > 
> > The problem is that I get a message indicating that the log 
> > likelihood function is not concave. Although this is possible I am
> > able to run the model without any problems is SAS. Furthermore,
> > there doesn't seem to be anything in the data that suggest there
> > would be a problem, i.e. all covariate patterns have a reasonable
> > number of observations for each level of the outcome of interest.
> > Surprisingly to me, the problem goes away if I add an interaction
> > term for sex and housing, although the interaction term is not
> > statistically significant and the data suggest it should be.
> > 
> > Any suggestions are greatly appreciated.
> > 
> > Thanks
> > Patrick
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Patrick McElduff
Lecturer
The Medical School
The University of Manchester
Oxford Road,
Manchester M13 9PT UK

Email: patrick.mcelduff@man.ac.uk
Phone: +44 0161 275 5953
Fax:   +44 0161 275 7712
*
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