One more thing. You actually talk about "overall model
fit", but also imply looking at relative model fit
among different models.
The latter is possible with GLME, although as far as I
know, only with maximum likelihood estimation.
The former implies absolute model fit, which is not
possible with GLME (for example, there is no
equivalent to an R^2 value). There is a very clear
explanation for this in Hox, J.J. 2002. Multilevel
Analysis: Techniques and Applications.
Nicola
--- 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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