Dear List,

I'm a PhD student writing to ask for some help on specifying a model in R.
I'm currently using the lme4 package.

I'm trying to analyse how migration effects womens fertility rates in a
sample of 22 groups (villages). I have a sample of ~1500 women who were
born and continue to live in one of the 22 groups. I treat the model as
women clustered within groups. Each woman has an origin group ID and a
current group ID, so for women who never migrated these ID numbers will be
the same. I want to obtain variance parameters for the different groupings,
to see whether origin or current group has a greater effect on fertility
rates, with a view to then seeing if other group-level predictors explain
some of the variance, but I'm not sure if I have specified the model
correctly, or if I need to make some changes to the data (for example
weighting the memberships somehow).

I started with (what I think is) a cross-classified model of the form
(y~1+(1|originGroupID)+(1|currentGroupID)),
but given that the groups themselves are exactly the same thing, and that
membership in them overlaps, I am worried that this will cause problems for
estimating the group variances and covariances, or that this specification
perhaps doesn't make sense. Should the groups be coded differently?

If anyone could point me in the direction of some references specific to
this kind of overlapping structure, or indeed offer some advice as to
different ways I could specify the model, or recode the data somehow, I
would be extremely grateful.

Many thanks,

Heidi
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