[R-sig-ME] parameterization for partly nested design with repeated measurements

[Kay Cecil Cichini]

dear mr. bates,

many thanks for answering to my mail.
"1|school/class" is the same as "1|school + 1|class", isn't it? - this  
would be the second model i mentioned.

yours,
kay cichini



Zitat von Douglas Bates <bates at stat.wisc.edu>:

> On Fri, Nov 12, 2010 at 5:09 AM, Kay Cecil Cichini
> <Kay.Cichini at uibk.ac.at> wrote:
>> dear listers,
>>
>> i'd very much appreciate help with setting up the right parameterization for
>> the following design:
>> 4 regions, in each region 3 to 12 schools, at each school 2-4 classes and
>> each class tested before and after intervention, yielding a bimomial outcome
>> (pupils that passed / not passed a test).
>>
>> i'm interested in differences between before and after (factor = "interv")
>> intervention outcomes (X = passed, n = passed + not passed) and in the
>> interaction region * interv.
>>
>> i tried with:
>> glmer (cbind(X, n - X) ~ region * interv + (region | school / class), family
>> = binomial)
>
> This model is generating an interaction between the fixed-effects
> factor "region" and the random-effects factor "school", which doesn't
> make sense because each school occurs within only one region.
>
> The simplest way to establish the desired structure is to create the
> region, school and class factors so they follow the "each distinct
> structure corresponds to a distinct level of the factor" rule.  For
> example, if you call the regions "A", "B", "C" and "D" and you call
> the schools "A01", ... "A10", "B01", ..., "B06", "C01", ..., "C12" and
> you call the classes "A01a", "A01b", "A01c", ..., "C12d" then you can
> specify the model very easily as
>
> glmer(cbind(X, n - X) ~ region * interv + (1|school) + (1|class),
> family = binomial)
>
> Most of the confusion about model specification comes from the
> unfortunate practice of labeling the schools as "01", ..., "12"
> without taking into account that school 1 in region A is not
> associated in any way with school 1 in region C.  In other words, all
> you need to do is to disambiguate the names of the schools and the
> classrooms.
>
>
>> and with:
>> glmer (cbind(X, n - X) ~ region * interv + (1 | school / class), family =
>> binomial)
>>
>>
>> i'd be happy about comments on the parameterization or any ideas.
>>
>> yours,
>> kay
>>
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>