I have a method comparison problem, comparing Labs where a set of compounds are
assayed on 3 different dates for each lab. Both labs will be used to assess
compounds in the future, so the scientists will potentially contrast a compound
at assayed at Lab A with one assayed at Lab B, This implies I ought to regard
the Lab*Compound interaction as random. I also have the date within Lab as a
random term and the Compound*date as random (and as separate variances for each
Lab).
If I regard the Compound*Lab effect as fixed this code works
lme.out <- lme ( data=data, Resp ~ Lab + Compound + Compound:Lab,
random = pdIdent(~Lab-1|Date) ,
weights = varIdent(form=~1|Lab)
)
The trouble is when I try to regard it as random, eg.
lme2.out <- lme ( data=data, Resp ~ Lab + Compound,
random = list( ~Compound:Lab, pdIdent(~Lab-1|Date) ),
weights = varIdent(form=~1|Lab)
)
It appears as if the random interaction is not allowed ... Is this right ? Is
there a way to fit the interaction as random together with the other random
terms ?
I have tried lme4 but note that "lme4 does not currently implement nlme's
features for modeling heteroscedasticity" but "does implement crossed random
effects". No joy in my hands though. Nor with lmer ...
Any help gratefully received, thanks,
Brian (trying to convert from SAS !)
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