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

[Mitchell Maltenfort]

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
>
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