You can have one observation per subject with multiple subjects nested in a group. If you only have 1 observation per group, then there is no multilevel structure to your data.

For example, 30 students in a classroom or 20 employees in an office division are appropriate data structures. On the other hand 1 observation per school in each of 30 schools has no grouping structure.

If you look at some of the data in the mlmRev package or other data files in the nlme package and look at their structure, this might be helpful to see exactly how the data might be layed out.

Look at the egsingle or the star data in the mlmRev package to see examples of longitudinal models where eac student has multiple test scores. In egsingle, each student is properly nested in a single school whereas in the star data, students are crossed with teachers and schools.

Use the str(star) to see the data structure. Or, you can do something like head(star) to see the 1st 6 rows and see how the data are layed out.

I hope this helps,
Harold



-----Original Message-----
From: r-help-bounces@stat.math.ethz.ch on behalf of Nantachai Kantanantha
Sent: Sun 7/30/2006 12:33 AM
To: r-help@stat.math.ethz.ch
Subject: [R] Question about data used to fit the mixed model
 
Hi everyone,

I would like to ask a question regarding to the data used to fit the mixed 
model.

I wonder that, for the response variable data used to fit the mixed model 
(either via "spm" or "lme"), we must have several observations per subject 
(i.e. Yij,  i = 1,..,M,  j = 1,.., ni) or it can be just one observation per 
subject (i.e. Yi,  i = 1,...,M). Since we have to specify the groups for 
random effect components, if we have only one observation per subject, then 
each group will have only one observation.

Thank you vert much for your help.
Sincerely yours,

Nantachai

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