[R] what does it mean when my main effect 'disappears' when using lme4?

[David Winsemius]

On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote:

> Hi all,
>
> Thanks for the replies (including off list).  I have since resolved  
> the
> discrepant results. I believe it has to do with R's scoping rules -  
> I had an
> object called 'labs' and a variable in the dataset (DATA) called  
> 'labs', and
> apparently (to my surprise), when I called this:
>
> lmer(Y~X + (1|labs),dataset=DATA)
>
> lmer was using the object 'labs' rather than the object 'DATA$labs'.  
> Is this
> expected behavior??

help(lmer, package=lme4)

It would be if you use the wrong data argument for lmer(). I doubt  
that the argument "dataset" would result in lmer processing "DATA".   
My guess is that the function also accessed objects "Y" and "X" from  
the calling environment rather than from within "DATA".


>
> This would have been fine, except I had reordered DATA in the  
> meantime!
>
> Best,
>
> JJ
>
> On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfort <mmalten at gmail.com 
> >wrote:
>
>> One difference is that the random effect in lmer is assumed --
>> implicitly constrained, as I understand it -- to
>> be a bell curve.  The fixed effect model does not have that  
>> constraint.
>>
>> How are the values of "labs" effects distributed in your lm model?
>>
>> On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson
>> <johan.h.jackson at gmail.com> wrote:
>>> Hello,
>>>
>>> Setup: I have data with ~10K observations. Observations come from 16
>>> different laboratories (labs). I am interested in how a continuous
>> factor,
>>> X, affects my dependent variable, Y, but there are big differences  
>>> in the
>>> variance and mean across labs.
>>>
>>> I run this model, which controls for mean but not variance  
>>> differences
>>> between the labs:
>>> lm(Y ~ X + as.factor(labs)).
>>> The effect of X is highly significant (p < .00001)
>>>
>>> I then run this model using lme4:
>>> lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs
>>> lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs.
>>>
>>> For both of these latter models, the effect of X is non- 
>>> significant (|t|
>> <
>>> 1.5).
>>>
>>> What might this be telling me about my data? I guess the second (X| 
>>> labs)
>> may
>>> tell me that there are big differences in the slope across labs,  
>>> and that
>>> the slope isn't significant against the backdrop of 16 slopes that  
>>> differ
>>> quite a bit between each other. Is that right? (Still, the  
>>> enormous drop
>> in
>>> p-value is surprising!). I'm not clear on why the first (1|labs),
>> however,
>>> is so discrepant from just controlling for the mean effects of labs.
>>>
>>> Any help in interpreting these data would be appreciated. When I  
>>> first
>> saw
>>> the data, I jumped for joy, but now I'm muddled and uncertain if I'm
>>> overlooking something. Is there still room for optimism (with  
>>> respect to
>> X
>>> affecting Y)?
>>>
>>> JJ
>>>
>>>       [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
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>>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

David Winsemius, MD
West Hartford, CT