[R-sig-eco] nlme model specification

[Nicholas Lewin-Koh]

Hi,
Last month, or so, Doug was talking about comparing models 
using likelihood ratio tests, anova(m1,m2) and pointed out
that the way things are calculated in lmer the ML and REML estimates
are equivalent. I am not sure if this is because the bias in the REML
estimates cancels out or if the estimation procedures in lmer get them
to the same place. I wish I could find the post. However this has to do
with
the likelihood vs. restricted profile likelihood for the whole model
and may not be relevant to the parameter estimates.

Nicholas

> ------------------------------
> 
> Message: 2
> Date: Sun, 25 May 2008 11:31:39 -0700
> From: "Kingsford Jones" <kingsfordjones at gmail.com>
> Subject: Re: [R-sig-eco] nlme model specification
> To: "Ruben Roa Ureta" <rroa at udec.cl>
> Cc: r-sig-ecology at r-project.org
> Message-ID:
> 	<2ad0cc110805251131q48629679oc8b29eb2296fe320 at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
> 
> On Sun, May 25, 2008 at 7:39 AM, Ruben Roa Ureta <rroa at udec.cl> wrote:
> > [snip]
> >
> >> I think you're right that there is some shaky ground here, and Doug
> >> Bates has pointed out some issues on the R-sig-mixed-models list (I
> >> can't seem to find the thread right now).  One of the issues is that
> >> mixed models are generally fit with REML, which is not ML and
> >> therefore does not technically conform to the derivations of the *IC.
> >> If you fit a mixed model with ML instead, bias is introduced.
> >
> > Bates think that the maximum log likelihood is not a problem with mixed
> > models when fit using ML:
> > http://finzi.psych.upenn.edu/R/Rhelp02a/archive/117488.html
> > though he does see a problem with the counting of parameters.
> > Sorry if I am a bit lost coming late to this thread.
> 
> 
> Thanks Rubin -- that's the link I was looking for.
> 
> When I wrote the paragraph above I was hoping I wasn't misrepresenting
> what Bates said, and I don't think I did.  The problem is that if you
> fit with ML you introduce downward bias in the estimates of the
> variance components -- that's why REML is the default method.  And, as
> you mentioned, you still have the problem of specifying the number of
> parameters estimated.  How much all of this matters, I'm not sure --
> perhaps the paper Jarrett referred to will help clarify things.
> 
> best,
> Kingsford
> 
> >
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> 
>