Message-ID: <x2wvi89cp5.fsf@blueberry.kubism.ku.dk>
Date: 2000-07-27T08:53:10Z
From: Peter Dalgaard
Subject: Questions about deviance
In-Reply-To: Prof Brian D Ripley's message of "Thu, 27 Jul 2000 09:19:59 +0100 (BST)"
Prof Brian D Ripley <ripley at stats.ox.ac.uk> writes:
> On Mon, 24 Jul 2000, halvorsen wrote:
>
> > I have experimented with the cheese data example from McCullagh&Nelder,
> > page 175. With a proportional odds model they obtain a residual deviance
> > of
> > 20.31.
> >
> > Estimating the same model with polr(MASS) gives a residual deviance of
> > 762.11 !, while using ordglm(gnlm) gives a deviance of 523.94. Can
> > anybody explain these differences?
>
> It depends on the choice of saturated model. See MASS3 pp 230-1, including
> how to compare them. It is a standard problem with discrete glms, and for
> this problem (which is not a glm). I maintain that our choice, which
> amounts to minus twice sum of log predicted probabilities, is by far the
> most interpretable. (Peter McCullagh had a thing about their lack of
> value, but he is not supported by the whole prediction assessment
> industry.)
I think Peter McC's point was that they are useless as measures of
discrepancy between model and data, which is pretty obvious once you
realise that the deviance for the model with p=.5 for all observations
is a constant, so the deviance for any data is equivalent to the test
for "all p==.5".
--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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