[R-sig-ME] heteroscedastic model in lme4

Doran, Harold HDoran at air.org
Thu Jan 15 15:10:05 CET 2009 

I would think on the transformed data. In a GLMM an offset is applied on
the transformed data and not on the original data, which is what makes
me think the same would be used here.

> -----Original Message-----
> From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] 
> Sent: Thursday, January 15, 2009 5:13 AM
> To: Doran, Harold; Alan Cobo-Lewis; r-sig-mixed-models at r-project.org
> Subject: RE: [R-sig-ME] heteroscedastic model in lme4
> 
> 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 9500 Geraardsbergen 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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