[R] Nested Design Coding Question
Steeno, Gregory S
gregory_s_steeno at groton.pfizer.com
Wed Feb 19 17:29:03 CET 2003
I'm a SAS user who is slowly but surely migrating over to R. I'm trying to
find the proper code to analyze a nested design. I have four
classification variables, L (fixed), A (random within L), D (random within
L), and I (random within L). The model I'm interested in is
L A(L) D(L) I(L) A:D:I(L),
where the interaction is interpreted as the lack-of-fit term. I've tried
variants of the lme function similar to these,
lme(response~L, data, random=~Lab/(A+L+I+A:D:I),
lme(response~1, data, random=~Lab/(A+L+I+A:D:I),
lme(response~L, data, random=~1/(A+L+I+A:D:I).
All give results different from SAS, and all give warning messages regarding
either false- or non-convergence.
For reference, the abbreviated SAS code is,
model response = L;
random A(L) D(L) I(L) A:D:I(L);
Can anyone shed some light? I'd be very appreciative.
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