Hi,
Someone correct me if I am wrong, but I am sure that
AIC CANNOT be used with those methods, because none of
them are based on a true log-likelihood; all the
likelihoods are approximate (e.g. penalized
QUASI-likelihood, because it's not a true likelihood,
Laplacian APPROXIMATION to the likelihood, etc). I see
Michael is from insightful so maybe he knows more than
I do! But, I was definately explicitly taught that you
cannot use AIC with any of these.
If that is incorrect, could you direct me to a
publication (NOT from Splus or insightful; instead, an
independent text or journal publication) that
demonstrates that you can use AIC with these
approximate methods?
Thanks,
Nicky
--- Michael O'Connell <moconnell@insightful.com>
wrote:
> 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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