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 <mmal...@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.jack...@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. > > > [[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.
