Direct comparison among models can be problematic, so
you may not like this reply at all. You can really
only use AIC if you use maximum likelihood estimation,
which is theoretically possible with GLME but I don't
think you can do it in Splus at all. I have turned to
R (glmmML) or NLMIXED in SAS to do so, in the past.
I'll be interested in whether other people know of
other methods to do this with glme.
I really like Applied Longitudinal Analysis 2004 by
Fitzmaurice et al. I find it more intuitive than some
of the other texts that talk about GLME.
Nicky
--- A&B Penner <pennerab@gmail.com> wrote:
> Thank you to everyone who replied to my question: I
> will use the glme()
> function from the correlatedData library to perform
> a mixed-effects model on
> my count data.
>
> This brings up a new question. Is there a way to
> compare overall glme model
> fits when the models have different fixed-effect
> terms? For example, lme
> models estimated by maximum likelihood can be
> compared using a
> drop-in-deviance test performed by the anova
> function. Is there anything
> similar for glme models?
>
> Additionally, besides Venables and Ripley 2002 and
> the help file for the
> correlatedData library, are there other basic
> references for performing and
> interpreting generalized linear mixed models?
>
> Many thanks,
> A. Penner
>
> On 10/29/07, Michael O'Connell
> <moconnell@insightful.com> wrote:
> >
> > yes, the correlatedData library and associated
> glme() function postdate
> > the Pinheiro and Bates text.
> >
> > gee() in the correlatedData library will also work
> for these data. I
> > prefer the glme() implementation in this case.
> >
> > Michael
> >
> > ------------------------------
> > *From:* s-news-owner@lists.biostat.wustl.edu
> [mailto:
> > s-news-owner@lists.biostat.wustl.edu] *On Behalf
> Of *Austin, Matt
> > *Sent:* Thursday, October 25, 2007 8:14 PM
> > *To:* A&B Penner; s-news@lists.biostat.wustl.edu
> > *Subject:* Re: [S] lme for count data?
> >
> > In the past I would have suggested looking at
> glmmPQL() in the MASS
> > library or glme() in the correlatedData library if
> you have grouped count
> > data, but I can't find the correlatedData library
> in version 8.
> >
> >
> >
> > Another alternative would be a GEE approach.
> >
> >
> >
> > The MASS text by Venables and Ripley gives a short
> overview of both
> > methods (PQL and GEE) and good references for
> deeper study. It also gives a
> > brief explanation of the difference in
> interpretation between the methods.
> > I believe the correlatedData library and
> associated glme() function postdate
> > the Pinheiro and Bates text.
> >
> >
> >
> >
> >
> > --Matt
> >
> >
> > ------------------------------
> >
> > *From:* s-news-owner@lists.biostat.wustl.edu
> [mailto:
> > s-news-owner@lists.biostat.wustl.edu] *On Behalf
> Of *A&B Penner
> > *Sent:* Thursday, October 25, 2007 1:50 PM
> > *To:* s-news@lists.biostat.wustl.edu
> > *Subject:* [S] lme for count data?
> >
> >
> >
> > Hi,
> >
> > I'd like to fit a mixed-effects model to my data.
> Pinheiro and Bates
> > (2000) write that "These models are intended for
> grouped data in which the
> > response variable is (at least approximately)
> continuous." My response
> > variable is count data with range from 0-4, mean
> 1.7, median 2.0, stdev
> > 0.8, and an approximately normal distribution. Can
> I use the lme function
> > for this data?
> >
> > Thank you,
> > A. Penner
> >
>
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