Hi everyone,
I just got an email implying that I was putting down
Michael because he was from Insightful. I just wanted
to clarify that I truly meant the complete opposite.
When I said, "I see Michael is from insightful so
maybe he knows more than I do!", what I meant was,
he's obviously a statistician and therefore he knows
something I don't about this. I did NOT mean it in a
sarcastic way, which I'm guessing is how it was
interpreted.
It's hard to sort these things out by email when we
can't read each other's body language! But I think
we're all on the same side here, trying to give useful
and accurate advice.
Nicky
--- Nicola Koper <nkoper@yahoo.com> wrote:
> 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,
>
=== message truncated ===
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