
On Sep 11, 2008, at 2:15 PM, Andy Fugard wrote:

> 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.
>
> A
>
> -- 
> Andy Fugard, Postgraduate Research Student
> Psychology (Room S6), The University of Edinburgh,
>  7 George Square, Edinburgh EH8 9JZ, UK
> +44 (0)78 123 87190   http://figuraleffect.googlepages.com/
>
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> Scotland, with registration number SC005336.
>
>


In the context in which this arose, I was interested in assessing the  
evidence for an overall positive difference score (i.e., that E(Y)>0),  
and my strategy was to compare the fit of two models, essentially, D~0+ 
(1|Subject) and D~1+(1|Subject), using AIC values. To get a sensible  
assessment of the evidence for the fixed effect, it seemed to me that  
one would want to have the same random effects in the two models being  
compared. The second model is clearly more obvious, but the  
interpretation of D~0+(1|Subject) could be that subjects differ  
randomly in their response to the treatment, but that there is no  
consistent effect in the population.
- Pete
---
Peter Dixon
Department of Psychology
University of Alberta
Edmonton, Alberta, Canada
T6G 2E9
780-492-2318
780-492-1768 (fax)
peter.dixon@ualberta.ca



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