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
______________________________________________
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.
