[R-sig-ME] (no subject)

[Dan McCloy]

The GLMM FAQ has some information about specifying nested random effects.
See especially the "model specification" table [1] and the "nested or
crossed" section [2].  Something like this may be what you're looking for:

grade ~ Time + (Time | SubjectGroup / Subject) + (Time | ItemGroup / Item)

Given that you've assigned groups randomly, I'm unsure if there will be
much benefit to modeling the groups this way (unless maybe "grade" for one
subject/item is somehow influenced by the other subjects or items in the
group?).  Anyway, the dataset is small enough that it will be easy to try
and see what happens.  Also note that if "Time" has only two values, it can
be treated as a factor (like "pre" and "post" treatment).

[1]:
https://rawgit.com/bbolker/mixedmodels-misc/master/glmmFAQ.html#model-specification
[2]:
https://rawgit.com/bbolker/mixedmodels-misc/master/glmmFAQ.html#nested-or-crossed

-- dan

Daniel McCloy
http://dan.mccloy.info/
Postdoctoral Research Associate
Institute for Learning and Brain Sciences
University of Washington


On Thu, Aug 4, 2016 at 2:22 AM, Meir Barneron <meir.barneron at gmail.com>
wrote:

> Hi everyone,
>
> I am relatively new in MEMs, and trying to tigure out what is the best
> model fo my data. The data itself is relatively simple but the design more
> complicated..
>
> To make it simpler, I am interested in investigating if there is a
> difference between two measures made at two point in time (1 and 2), that
> is all. My dependent variable is a grade. I do not enter into details in
> order to keep it as simple as possible. My theory predicts that the grades
> will be smaller at time 2 compared to time 1.
>
> Basically I have 30 subjects, and 100 Items and I want to make sure that
> there is an effect after controlling for subjects and items. Here is the
> design.
> Before the experiment I randomly selected 30 subjects from a pool. I also
> randomly selected 100 items from a pool. Next,  I randomly divided the 30
> subjects into 5 groups of 6 subjects. I also randomly divided the 100
> items into 5 groups of 20 items. The groups do not have any theoretical
> relations and all was divided totally randomly.
>
> Then I assigned one group of 20 items to one group of 6 subjects. Within
> each group, each 6 subjects saw each 20 items. For each Items, each subject
> gave me one grade at Time 1, and one at Time 2.
>
> My question is how to model this design. One possibility I have tried
> if to ignore the group and took into account only the subjects and the
> items.
> This is my syntax:
> model1 <- lmer(Grade ~ 1 + Time +
>                  (1 + Time | Subject) +
>                  (1 + Time | Item),
>                REML=F, data = NITE1)
>
> Does anyone have an idea how to take the "group" into account?
> Alternaltvely, do you think the model I built is sufficient?
>
> Thank you in advance
>
> Meir
>
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>
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