Hello,
I'm trying to fit a mixed-effects model with a single binary predictor
(case/control status in my case), a random intercept (e.g. dependent on
radiologist) and also a random slope (a per-radiologist difference
between cases and controls).
I know how to do that, but what I don't know how to do is both of (1)
allowing the variance to be different for cases and controls (2) forcing
the random effects to be independent
By "both", I mean:
(1) Using lme (from nlme library) I know how to use varGroup as
described in Pinheiro & Bates chapter 5, but in that library, I don't
know how to force the random effects to be independent.
(2) Using lmer (from lme4 library) I can force the random effects to be
independent (using a description published by Bates in the R magazine in
2005) but I don't know how to allow the variance to depend on group.
To be clear, the model I wan to fit is:
Y_{ij} ~ beta_0 + beta_1*disease_{ij} + b_i 0 + b_i1*disease_{ij} +
error_{ij}
where b_i0 and b_i1 are independent Normal
where error_{ij} = Normal(0, sd_case) if disease_{ij}= 1
error_{ij} = Normal(0, sd_control) if disease_{ij}= 2
i is an indicator of radiologist... a single radiologist does multiple
cases and multiple controls.
Thanks, Daryl
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