[R-sig-ME] models with no fixed effects

Andy Fugard a.fugard at ed.ac.uk
Thu Sep 11 22:15:21 CEST 2008

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.


Andy Fugard, Postgraduate Research Student
Psychology (Room S6), The University of Edinburgh,
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