Douglas Bates a ??crit:
I prefer to have a grouping factor constructed with unique levels for
each distinct unit. The only reason I mention constructions like
Treatment:Rat in the original part of this thread is that data are
often provided in that form.
Reusing "subject" labels within another group is awkward and can be
error prone. One of the data sets I examine in the MlmSoftRev
vignette of the mlmRev package is called Exam and has student
identifiers within schools. The student identifiers are not unique
but the school:student combination should be. It isn't. These data
have been analyzed in scores of books and articles and apparently none
of the other authors bothered to check this. There are some
interesting ramifications such as some of the schools that are
classified as mixed-sex schools are likely single-sex schools because
the only student of one of the sexes in that school is apparently
mislabelled.
BTW, in your example you have only one observation per level of 'obs'
so you can't use obs as a grouping factor as this variance component
would be completely confounded with the per-observation noise.
Douglas Bates a ??crit:
The difference between models like
lmer(Glycogen~Treatment+(1|Rat)+(1|Rat:Liver))
and
lmer(Glycogen~Treatment+(1|Treatment:Rat)+(1|Treatment:Rat:Liver))
is more about the meaning of the levels of "Rat" than about the
meaning of "Treatment". As I understood it there are three different
rats labelled 1. There is a rat 1 on treatment 1 and a rat 1 on
treatment 2 and a rat 1 on treatment 3. Thus the levels of Rat do not
designate the "experimental unit", it is the levels of Treatment:Rat
that do this.
--
Ken Knoblauch
Inserm U371
Cerveau et Vision
Dept. of Cognitive Neuroscience
18 avenue du Doyen L??pine
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France
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