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
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______________________________________________
R-help@r-project.org mailing list
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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
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
