Dear Xiyue and Thiery,
While more data points may affect the estimation process in that way
they do not seem to affect the fixed-effects estimates in that way. To
be more precise, the fixed effect estimate seems to correspond to the
unweighted mean (i.e., the mean in which each level of the random effect
is weighted equally) and not to the weighted mean (in which each data
point is weighted equally).
I had a similar problem some time ago:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q3/022478.html
Thanks to the help of Jake Westfall I was able to get the desired result
(i.e., a fixed-effect estimate corresponding to the weighted mean), by
adding group size as fixed effect to my model, see:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q3/022481.html
There might be other approaches to achieve this as well (i.e., some
post-fit weighting), but I am not sure how to implement this (perhaps
using lsmeans somehow).
I hope this helps,
Henrik
Am 17.10.2016 um 11:09 schrieb Thierry Onkelinx:
Dear Xiyue,
Don't think in terms of cells but in terms of observations. The model
tries
to minimise the residuals. So combinations with more observations have
more
residuals and thus a stronger impact on the MSE.
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able
to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
2016-10-12 19:48 GMT+02:00 Xiyue Liao
<liaoxiyue2011 at gmail.com>:
Hi,
I'm using lmer in the R package lme4 to do a one-way anova analysis
with a
fixed effect term and a random effect term. So the fixed effect is about
four medical conditions and the random effect is about randomly sampled
donors. Now for some combinations of donors and medical conditions,
there
are more than one measurement, which makes the whole design
unbalanced. I
think that lmer can handle such a case, and I have run the code
without any
error message. However, I don't understand how this routine put
weight on
the cells with more measurements than other cells. Could you give me
some
hint?
Thanks in advance for your help.
Sincerely,
Xiyue
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