Christian Hennig <fm3a004 at math.uni-hamburg.de> writes:
> I am faced with the following model:
>
> y=E+P+M+H+PxE+Error
>
> y is a response, E and P are factors with fixed effects.
> M is a random effect nested in P and H is a random effect nested in M.
> PxE is interaction of P and E.
>
> It seems that I should fit such a model with the lme function of library
> nlme, but I was not able to figure out from the help page how to specify
the
> formula. In the nlme documentation, the term "nested" is always
associated
> with a "grouping", and I do not know what my grouping is here.
For a complicated model like this you may find it worthwhile looking
at the examples in
@Book{pinh:bate:2000,
author = {Jos\'{e} C. Pinheiro and Douglas M. Bates},
title = {Mixed-Effects Models in \textsf{S} and \textsf{S-PLUS}},
publisher = {Springer},
year = 2000,
series = {Statistics and Computing}
}
The grouping refers to the groups in the data with which random
effects are associated. If the levels of the M factor are distinct
for different levels of P then you can fit your model as
lme(y ~ E * P, data = mydata, random = ~ 1 | M/H)
The last argument indicates that there will be an additive scalar
random effect for M and for H within M.
If you do not have distinct levels for M within P you can create a new
factor with
getGroups(~ 1 | P/M, data = mydata, level = 2)
and assign it as the grouping factor.
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