heteroscedastic model in lme4

This isn't an entirely accurate statement. nlme has built-in
functions that implement the methods for correlational and variance
structures as described in section 5 of Pinhiero and Bates. lme4
doesn't have these functions built in as does nlme, but those same
methods can be implemented by the user and then the data can be
analyzed using functions in lme4. So, functions in lme4 can "handle"
the same issues as nlme, it just requires the user to perform the
steps described in PB section 5 et seq on their own.

-----Original Message----- From:
r-sig-mixed-models-bounces at r-project.org on behalf of Alan Cobo-Lewis
 Sent: Fri 12/19/2008 11:19 AM To: r-sig-mixed-models at r-project.org 
Subject: Re: [R-sig-ME] heteroscedastic model in lme4


Anna,

lme4 cannot handle certain kinds of heteroscedasticity, but I believe
it can handle the kind you have in mind. Search the
r-sig-mixed-models archive for a discussion involving me and David
Afshartous, especially the summary message titled "[R-sig-ME] random
effect variance per treatment group in lmer" that David posted
07/13/2007 04:18:08 PM

I can't be certain that the suggestion below would work without
knowing more about your design, but if width were a factor with three
levels then you might try setting up indicator variables Wind1,
Wind2, and Wind3 (that each take on the value 1 when a site is at the
indicator's target width and 0 otherwise) and then fit the model with
something like mrem <- lmer( log(Nhat+1)~Group + GreenPerc + sess +
crop + VegDensity + Group:sess + Group:VegDensity + (0+Wind1|site) +
(0+Wind2|site) + (0+Wind3|site), data=all, method="REML" )

alan