Thanks Kieran,
I was beginning to think that the difference lay between continuous
and categorical predictors - i had shown that a model with a 4-level
categorical predictor could be factored into logits, whilst treating
the same variable as continuous meant that this was not possible.
I now see that including *two* categorical predictors also results in
the logits giving a different answer. Hence it does appear to be
model complexity rather than variable type.
I have a Kleinbaum paper in front of me (IJE 26(6), pp1323-1333)
but I will attempt to track down the book you mention.
In the meantime I think I have learned enough to drop this from
from my lecture as it is nothing more than a distraction.
all the best, Jon
On Tue, August 18, 2009 7:47 pm, Kieran McCaul wrote:
> Hi Jon,
>
> I think your belief may be wrong.
> I think that when you only have one binary predictor then the results
> from a multinomial logistic regression will agree with the results of a
> series of logistic regressions, but in more complex models this is not
> so.
>
> From memory (I haven't got the book with me) Kleinbaum & Klein discuss
> this.
>
> Kleinbaum DG and Klein M (2005). Logistic Regression: A Self-Learning
> Text. 2nd Ed. Springer.
>
> ______________________________________________
> Kieran McCaul MPH PhD
> WA Centre for Health & Ageing (M573)
> University of Western Australia
> Level 6, Ainslie House
> 48 Murray St
> Perth 6000
> Phone: (08) 9224-2701
> Fax: (08) 9224 8009
> email: [email protected]
> http://myprofile.cos.com/mccaul
> http://www.researcherid.com/rid/B-8751-2008
> ______________________________________________
> If you live to be one hundred, you've got it made.
> Very few people die past that age - George Burns
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Jon Heron
> Sent: Wednesday, 19 August 2009 1:31 AM
> To: [email protected]
> Subject: st: MLOGIT versus a set of LOGIT models [re-posting]
>
> Re-posted following a reading of the relevant FAQ
>
>
>
> Dear Statalisters,
>
>
> (I am using Stata/MP v10.1, born 02 Feb 2009)
>
> It was my belief that the regression estimates from a multinomial
> logistic
> regression model -mlogit- could be replicated through a set of simple
> logit
> models with the appropriately derived binary outcomes.
>
> Whilst attempting to demonstrate this fact for some teaching material
> on
> polytomous IRT that i am writing, I moved from my usual categorical
> predictors to a continuous covariate + discovered that the above
> equivalence
> no longer held.
>
> for instance, with a 4-level outcome (ghq1) and either a binary
> predictor
> (ghq3_bin) or a 4-level predictor treated as a continuous variable
> (ghq3),
> I fitted models with the two commands
>
> ******************************
> mlogit ghq1 ghq3_bin, baseoutcome(0)
> mlogit ghq1 ghq3, baseoutcome(0)
> ******************************
>
> the former can be replicated using logits, whilst the latter cannot.
> I am struggling to understand why this should be.
>
>
> I would very much appreciate any advice you can give,
>
>
>
>
> Jon
> --
> Dr Jon Heron
> ALSPAC Stats Team Leader
> Department of Social Medicine
> University of Bristol
> Oakfield House
> Oakfield Grove
> Bristol
> BS8 2BN
>
>
> *
> * For searches and help try:
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>
>
>
> *
> * For searches and help try:
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>
--
Dr Jon Heron
ALSPAC Stats Team Leader
Department of Social Medicine
University of Bristol
Oakfield House
Oakfield Grove
Bristol
BS8 2BN
*
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