No puedo entender. Nicht versteh. Je ne comprend pas.
Peter Spreeuwenberg wrote:
Liset
Dat moet lukken en hoe sneller je het anlevert hoe eerder je resultaten terug
ziet.
Dus, Ik wacht af.
groet Peter S
Date sent: Sun, 17 Apr 2005 18:07:28 +0100 (BST)
From: Prof Brian Ripley <[EMAIL PROTECTED]>
To: Deepayan Sarkar <[EMAIL PROTECTED]>
Subject: Re: [R] generalized linear mixed models - how to
compare?
Copies to: [email protected],
Nestor Fernandez <[EMAIL PROTECTED]>
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
On Sunday 17 April 2005 08:39, Nestor Fernandez wrote:
I want to evaluate several generalized linear mixed models, including
the null model, and select the best approximating one. I have tried
glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result
in similar parameter estimates but fairly different likelihood
estimates.
My questions:
1- Is it correct to calculate AIC for comparing my models, given that
they use quasi-likelihood estimates? If not, how can I compare them?
2- Why the large differences in likelihood estimates between the two
procedures?
The likelihood reported by glmmPQL is wrong, as it's the likelihood of
an incorrect model (namely, an lme model that approximates the correct
glmm model).
Actually glmmPQL does not report a likelihood. It returns an object of
class "lme", but you need to refer to the reference for how to interpret
that. It *is* support software for a book.
GLMM uses (mostly) the same procedure to get parameter estimates, but as
a final step calculates the likelihood for the correct model for those
estimates (so the likelihood reported by it should be fairly reliable).
Well, perhaps but I need more convincing. The likelihood involves many
high-dimensional non-analytic integrations, so I do not see how GLMM can
do those integrals -- it might approximate them, but that would not be
`calculates the likelihood for the correct model'. It would be helpful to
have a clarification of this claim. (Our experiments show that finding an
accurate value of the log-likelihood is difficult and many available
pieces of software differ in their values by large amounts.)
Further, since neither procedure does ML fitting, this is not a maximized
likelihood as required to calculate an AIC value. And even if it were,
you need to be careful as often one GLMM is a boundary value for another,
in which case the theory behind AIC needs adjustment.
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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