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??

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@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@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@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.
> >
>

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