[R-sig-ME] R-sig-mixed-models Digest, Vol 8, Issue 10

Iasonas Lamprianou lamprianou at yahoo.com
Tue Aug 21 12:35:05 CEST 2007

Hi all, I have a question regarding the paper
Estimating the Multilevel Rasch Model: With the
lme4 Package
by Doran, Bates etc al.
I would be greateful if someone could answer these questions, I am still a beginner.

In page 10, they fitted a model with no intercept. (1) What might be the motivation for this?  (1) How would the interpretation be different if they had included am intercept? (3) How may I derive the table of residuals (observed-predicted)? (4) How can I estimate/evaluate the fit of individual items, subjects or companies?
I know that I have many questions, but I really love lme4 and I would like to routinely use it in my research. I have a great education dataset which I would love to analyse. Any experts wishing a joint publication? My motivation is to learn as much as possible during the collaboration for this paper.

Dr. Iasonas Lamprianou
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044 161 275 3485
iasonas.lamprianou at manchester.ac.uk

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Today's Topics:

   1. Re: lmer, intercepts and offsets (Daniel Farewell)


Message: 1
Date: Tue, 14 Aug 2007 16:39:02 +0000 (GMT)
From: Daniel Farewell <farewelld at cf.ac.uk>
Subject: Re: [R-sig-ME] lmer, intercepts and offsets
To: r-sig-mixed-models at r-project.org
Message-ID: <523651.84987.qm at web27101.mail.ukl.yahoo.com>
Content-Type: text/plain; charset=iso-8859-1

This is a follow-up to a thread from back in March ("lmer, intercepts and offsets"). I'm hoping (at least) to better understand how lmer works.

I'd like to "trick" lmer into thinking it has converged to certain parameter estimates. This is straightforward for variance components, making use of the 'start' parameter and using 'control' to set the number of the various kinds of iteration to zero.

My ultimate goal is to extract posterior second moments from a model "fit" where I have specified both the variance components and the fixed effects.

Obviously it is possible to dig inside the fitted model and manually alter the fixed effects, but this has no impact on the result of a call to ranef, presumably because the posterior means (and variances?) have already been calculated, and are sitting in the ranef slot of the fitted model.

My question is this: at what stage do the random effects get calculated? Simplifying greatly, at some stage lmer must calculate betahat(Omega) (the fixed effects) and ranef(Omega) (the estimated random effects) for the converged value of Omega. Does ranef(Omega) depend on the result of betahat(Omega)? If so, then presumably tinkering with the betahat(Omega) results at the appropriate point inside lmer would result in what I want. If not (that is, if the dependence on the fixed effects is indirect) what needs tinkering with?

Is there a good reason why lmer does not allow models with no fixed effects at all? With the right offset, this would be another way to achieve the same result.

[[replacing trailing spam]]

Daniel Farewell


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