On 2010-08-18 18:41, Johan Jackson wrote:
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$labs.'
Scope is something you hear about don't worry about until it bites you on
your ass I guess.
JJ
Now I agree with you and I don't think that lmer() should do that.
Confirmed using the sleepstudy data:
library(lme4) # lme4_0.999375-34 Matrix_0.999375-42
sleepstudy$subj <- rep(1:18, each=10)
fm <- lmer(Reaction ~ Days + (1|as.factor(subj)), data=sleepstudy)
# Error in inherits(x, "factor") : object 'subj' not found
and, of course, if you have a variable 'subj' in your workspace,
then that will be used. It appears that as.factor() takes precedence
over 'data=', as you surmise.
I haven't had time to look into the lmer code to see what gives and it
may well be a design decision that I'm not aware of. I can't see
anything in the help page that refers to this effect.
-Peter Ehlers
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
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______________________________________________
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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
--
Peter Ehlers
University of Calgary
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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