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

[Peter Ehlers]

On 2010-08-18 11:49, Johan Jackson wrote:
> No, apologies (good catch David!), I merely copied the script incorrectly.
> It was
>
> lmer(Y~X + (1|labs),data=DATA)
>
> in my original script. So my question still stands: is it expected behavior
> for lmer to access the object 'labs' rather than the object 'DATA$labs' when
> using the data= argument?
>
> JJ
>

I don't think that's expected behaviour, nor do I think that it occurs.
There must be something else going on. Can you produce this with a
small reproducible example?

   -Peter Ehlers

>
>
>
> On Wed, Aug 18, 2010 at 11:29 AM, David Winsemius<dwinsemius at comcast.net>wrote:
>
>>
>> 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]]
>>>>>
>>>>> ______________________________________________
>>>>> 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.
>>>>>
>>>>>
>>>>
>>>         [[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
>>
>>