Johan Jackson
2010-Aug-18 00:50 UTC
[R] what does it mean when my main effect 'disappears' when using lme4?
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]]
Mitchell Maltenfort
2010-Aug-18 01:17 UTC
[R] what does it mean when my main effect 'disappears' when using lme4?
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. >