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@r-project.org on behalf of Alan Cobo-Lewis
Sent: Fri 12/19/2008 11:19 AM
To: r-sig-mixed-models@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


r-sig-mixed-models@r-project.org on Friday, December 19, 2008 at 6:00 AM -0500 wrote:
>Message: 1
>Date: Thu, 18 Dec 2008 11:23:46 +0000
>From: "Renwick, A. R." <a.renwick@abdn.ac.uk>
>Subject: [R-sig-ME] heteroscedastic model in lme4
>To: "'r-sig-mixed-models@r-project.org'"
>	<r-sig-mixed-models@r-project.org>
>Message-ID:
>	<B9D1301370916C44B5874AF340C18B9B28AE890D50@VMAILB.uoa.abdn.ac.uk>
>Content-Type: text/plain; charset="us-ascii"
>
>I have been using the nlme package to run some LMM's, however I would like to try rerunning them using the lme4 package so that I can use mcmc sampling.  The data I am using shows some heteroscesdasticity of the within error group and so I have
>been using the 'weights' argument and the varIdent variance function structure to allow different variances for each level of my factor (patch width).
>
>My problem is how to code for a heteroscedastic model in lme4 and any suggestion wouuld be much apprecaited.
>
>The code I used in the nlme package:
>
># model fit using "REML"
>mrem<-lme(log(Nhat+1)~Group + GreenPerc + sess + crop + VegDensity + Group:sess + Group:VegDensity ,random=~1|Site, data=all,
>       method="REML",correlation=NULL,weights=varIdent(form=~1|width))
>
>
>Many thanks,
>Anna
>
>Anna Renwick
>Institute of Biological & Environment Sciences
>University of Aberdeen
>Zoology Building
>Tillydrone Avenue
>Aberdeen
>AB24 2TZ
>
>
>The University of Aberdeen is a charity registered in Scotland, No SC013683.


--
Alan B. Cobo-Lewis, Ph.D.		(207) 581-3840 tel
Department of Psychology		(207) 581-6128 fax
University of Maine
Orono, ME 04469-5742     		alanc@maine.edu

http://www.umaine.edu/visualperception

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