[R-sig-ME] heteroscedastic model in lme4

Thu Jan 15 11:13:12 CET 2009

Dear all,

I would like to analyse some spatial data with mixed model. As I'm
dealing with presence/absence data or counts I should use the bionomial
or poisson family. These families are implemented in lme4 but
correlation structures are not. I'm wondering if the steps from section
5 in Pinheiro and Bates can be applied in case of a GLMM. If one can do
that, should one apply the transformation on the response in the
original scale or the transformed (logit / log) scale?

Another, more approximate, solution might be to code the GLMM as a NLMM.
E.g. glmer(Count ~ A + B + (1|Group), family = poisson) versus
nlme(model = Count ~ exp(mu), fixed = mu ~ A + B, random = mu ~ Group)
Any ideas on that?

Thierry

------------------------------------------------------------------------
----
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
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Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Doran, Harold
Verzonden: vrijdag 19 december 2008 20:52
Aan: Alan Cobo-Lewis; r-sig-mixed-models at r-project.org
Onderwerp: Re: [R-sig-ME] 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

r-sig-mixed-models at 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 at abdn.ac.uk>
>Subject: [R-sig-ME] heteroscedastic model in lme4
>To: "'r-sig-mixed-models at r-project.org'"
>	<r-sig-mixed-models at r-project.org>
>Message-ID:
>
<B9D1301370916C44B5874AF340C18B9B28AE890D50 at 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 at maine.edu

http://www.umaine.edu/visualperception

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