Hi all, I figured out why this was happening. It is because my actual code was:
lmer(Y~X + (1|as.factor(labs)),data=DATA) In this case, the as.factor function looks for object 'labs' not object 'DATA$labs.' Scope is something you hear about don't worry about until it bites you on your ass I guess. JJ On Wed, Aug 18, 2010 at 5:52 PM, David Winsemius <dwinsem...@comcast.net>wrote: > > On Aug 18, 2010, at 6:45 PM, Peter Ehlers wrote: > > On 2010-08-18 11:49, Johan Jackson wrote: >> >>> No, apologies (good catch David!), I merely copied the script >>> incorrectly. >>> It was >>> >>> lmer(Y~X + (1|labs),data=DATA) >>> >>> in my original script. So my question still stands: is it expected >>> behavior >>> for lmer to access the object 'labs' rather than the object 'DATA$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? >> > > This makes me wonder if there couldn't be a Wiki page where questioners > could be referred that would illustrate the quick and easy construction of > examples that could test such theories? I would imagine that in (this > instance) the page would start with the data.frame that were on the help > page for lmer() (for example) and then put in the workspace a mangled copy > of a vector that migh exhibit the pathological structure that might exist in > the OP's version of "labs" and then run lmer() to see if such an "unexpected > behavior" might be exhibited. > > Just an idea. (I've never managed to get any R-Wiki contributions accepted > through the gauntlet that it puts up.) > > -- > David. > > >> -Peter Ehlers >> >> >>> >>> >>> On Wed, Aug 18, 2010 at 11:29 AM, David Winsemius<dwinsem...@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<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. >>>>> >>>>> >>>> David Winsemius, MD >>>> West Hartford, CT >>>> >>>> >>>> > [[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.
