
On Sep 11, 2008, at 10:15 PM, r-sig-mixed-models-request@r-project.org  
wrote:
------------------------------
------------------------------

> Message: 7
> Date: Thu, 11 Sep 2008 21:15:21 +0100
> From: Andy Fugard <a.fugard@ed.ac.uk>
> Subject: Re: [R-sig-ME] models with no fixed effects
> To: Peter Dixon <peter.dixon@ualberta.ca>
> Cc: r-sig-mixed-models@r-project.org
> Message-ID: <48C97C59.6050103@ed.ac.uk>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
> Peter Dixon wrote:
>> On Sep 11, 2008, at 1:15 PM, Douglas Bates wrote:
>>
>>> I should definitely add a check on p to the validate method.  (In  
>>> some
>>> ways I'm surprised that it got as far as mer_finalize before kicking
>>> an error).  I suppose that p = 0 could be allowed and I could add 
>>> some
>>> conditional code in the appropriate places but does it really make
>>> sense to have p = 0?  The random effects are defined to have mean
>>> zero.  If you have p = 0 that means that E[Y] = 0.  I would have
>>> difficulty imagining when I would want to make that restriction.
>>>
>>> Let me make this offer - if someone could suggest circumstances in
>>> which such a model would make sense, I will add the appropriate
>>> conditional code to allow for p = 0. For the time being I will just
>>> add a requirement of  p >  0 to the validate method.
>>
>> I think it would make sense to consider a model in which E[Y] = 0 
>> when
>> the data are (either explicitly or implicitly) difference scores. (In
>> fact, I tried to fit such a model with lmer a few months ago and ran
>> into exactly this problem.)
>
> Wouldn't you still need the intercept?  The fixed effect tells you
> whether on average the difference differs from zero.  The random  
> effect
> estimates tell you by how much each individual's difference differs  
> from
> the mean difference.

If you are looking for correlations between difference scores, the  
model needs to be constrained to pass through the intercept. Otherwise 
the fit is dependent on the direction in which the differences are  
calculated (positive/negative scores). I ran into this error earlier  
this week while trying a mixed model to evaluate some phylogenetically- 
independent contrasts.

Jeff



__________________________________

Jeff R. Powell, Ph.D.
Postdoctoral Fellow
Freie Universität Berlin
Intitut für Biologie - Ökologie der Pflanzen
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14195 Berlin
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