[R-sig-ME] R-sig-mixed-models Digest, Vol 9, Issue 9

Iasonas Lamprianou lamprianou at yahoo.com
Sun Sep 9 08:16:28 CEST 2007

Dear friends,
first of all I would like to thank everybody, and especially Dr Bates, for all the support on using lmer. I now have another question. When using lmer on binary responses using family=binomial, we do not get the residual variance. In the default option (normal model) we do get the variance for every rndom effect, as well as the residual variance, so that we can compute the % of variance accounted by each effect. How can we find the residual variance in the binomial case, so that we compute the% of variance explained by each random effect? Thank you for your help once again
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

----- Original Message ----
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Sent: Friday, 7 September, 2007 1:00:09 PM
Subject: R-sig-mixed-models Digest, Vol 9, Issue 9

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Today's Topics:

   1. Re: lmer and autocorrelation structures (Douglas Bates)


Message: 1
Date: Thu, 6 Sep 2007 19:35:34 -0500
From: "Douglas Bates" <bates at stat.wisc.edu>
Subject: Re: [R-sig-ME] lmer and autocorrelation structures
To: "Chris O'Brien" <obrienc at email.arizona.edu>
Cc: r-sig-mixed-models at r-project.org
    <40e66e0b0709061735m73a8a30cu6be832f30ef630b2 at mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1

On 9/6/07, Douglas Bates <bates at stat.wisc.edu> wrote:
> On 9/6/07, Douglas Bates <bates at stat.wisc.edu> wrote:
> > On 9/6/07, Chris O'Brien <obrienc at email.arizona.edu> wrote:
> > > Dear mixed-modelers,
> >
> > > I'm interested in modelling a dataset using generalized linear mixed models
> > > (specifying "family=binomial" with random effects) in which there is
> > > temporal autocorrelation in the response. Is there a way to do this in the
> > > new lme4 package?  I've used the "correlation" argument in the nlme library
> > > to specify a correlation structure for least-square means regression, but
> > > this option appears not to be implemented in the new lmer call in the lme4
> > > package.   Is such a thing possible (and appropriate) at this time?
> > > thanks,
> >
> > Possible, but only in the fortune("Yoda") sense.  In other words that
> > capability is not built into the lme4 package.
> >
> > I don't think we can comment on the appropriateness of the model
> > without more information and perhaps a look at the data.  However,
> > until someone writes the code to incorporate an autoregressive
> > structure into lmer, the question is moot.
> P.S. If the reference to fortune("Yoda") is too obscure, try
> install.packages("fortunes"); library(fortunes); fortune("Yoda")

P.P.S.  I have always felt that to qualify as a Yodaism, Simon's first
sentence should have been "R this is.", not "This is R."


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