[R] lmer (or lme) with heteroscedasticity

[ONKELINX, Thierry]

Dear Daryl,

1) use pdDiag to get indepedent random effects.
lme(Y ~ disease, random = list(radiologist = pdDiag(~disease)), weight =
varGroup(~disease))

2) lmer can't handle variance structures like nlme can. I believe it is
on Douglas Bates to do list. But rather at the bottom of it.

HTH,

Thierry

PS The R-SIG-Mixed models is a more appropriate list for this kind of
questions.


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

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~ Roger Brinner

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-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Daryl Morris
Verzonden: dinsdag 30 juni 2009 8:17
Aan: r-help at r-project.org
Onderwerp: [R] lmer (or lme) with heteroscedasticity

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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