For Poisson model with log link (which is probably ok for these data) there
are several 'likelihood' methods available for glme() ie
1) PQL - penalized quasi-likelihood.
2) MQL - (restricted) marginal quasi-likelihood.
3) AGQUAD - adaptive gaussian quadrature
4) LAPLACE - Laplacian approximation to the likelihood.
The number of quadrature points can be specified through the control formal
argument.
For Poisson, we've found PQL to work quite well. For binary data, you should
use AGQUAD or LAPLACE in most situations. You can use AIC with the above
methods.
See the glme() help file and user guide for more information.
For references, you can see Breslow and Clayton (93) or Wolfinger and
O'Connell (93) for some early examples. The book by Geert Verbeke and Geert
Molenberghs is quite good, but a little dated now (2001)
http://www.amazon.com/Linear-Mixed-Models-Longitudinal-Data/dp/0387950273
Michael
-----Original Message-----
From: s-news-owner@lists.biostat.wustl.edu
[mailto:s-news-owner@lists.biostat.wustl.edu] On Behalf Of Nicola Koper
Sent: Thursday, November 01, 2007 4:43 PM
To: A&B Penner; s-news@lists.biostat.wustl.edu
Subject: Re: [S] glme for count data...
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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