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
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