[R-sig-ME] False convergence in a mixed model.

[Luca Borger]

Hello,

I've found that sometimes you can get the models to converge by 
increasing the value for nAGQ (the default is equal to one, see ?glmer). 
However, even if this will lead your model to (apparently) converge 
well, I would seriously consider Thierry's remarks.

HTH

Luca


-------------------------------------------
Luca Borger
Postdoctoral Research Fellow
CNRS - Centre d'Etudes Biologiques de Chizé
Villiers-en-Bois
79360 Beauvoir-sur-Niort
France

Tel: +33 (0)549 09 96 13
Fax: +33 (0)549 09 65 26

email: lborger at cebc.cnrs.fr
http://www.researcherid.com/rid/C-6003-2008
http://cnrs.academia.edu/LucaBorger
-------------------------------------------


Le 14/12/2011 11:03, ONKELINX, Thierry a écrit :
> Dear Staffan,
>
> False convergence is often due to a model that is too complex for the data. Do you have enough data to support that four-way interaction. And if so do you really want a four-way interaction?
> Another problem might be complete separation: for a given combination of factors all responses are either 0 or 1
>
> Best regards,
>
> Thierry
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
> team Biometrie&  Kwaliteitszorg / team Biometrics&  Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> 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 staffan at myrica.se
> Verzonden: dinsdag 13 december 2011 10:34
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] False convergence in a mixed model.
>
> Hi there!
>
> I´m a quite new R-user, ecpecially when it comes to mixed models. Currently I´m doing my master thesis and I have built a glmer model as seen below. But I recive a warning message "Warning message: In mer_finalize(ans): false convergence (8)" and can´t get rid of it.
> I have gone trough the mailing list on this subject and googled, but the answers I find (which offcourse are good) are to difficult for me to interpret.
>
> Now I´m sending you this email in hope of a more simple answer that I can interpret.
>
> My initial data looks like this;
> X               Y         Status     Size                 Density  Trial        Summarize( I have 3 densities 8,16 and 32, 12 trials A-L, status is either 1 or 0, and X,Y and size are measures.
>
>
>
>
>
>
>
> 27,7
>
> 903
>
> 1
>
> 2.85454545454545
>
> 32
>
> C
>
>
> 1
>
> 855
>
> 0
>
> 3.5
>
> 8
>
> D
>
>
> 54,7
>
> 796
>
> 0
>
> 3.12727272727273
>
> 32
>
> C
>
>
> 113,6
>
> 3
>
> 1
>
> 2.94545454545455
>
> 16
>
> B
>
>
> 32,5
>
> 863
>
> 0
>
> 3.09433962264151
>
> 32
>
> C
>
>
> 283,5
>
> 808
>
> 0
>
> 3.07142857142857
>
> 8
>
> D
>
>
> 281,5
>
> 898
>
> 1
>
> 3.16363636363636
>
> 8
>
> D
>
>
> 8,6
>
> 845
>
> 0
>
> 2.90909090909091
>
> 32
>
> C
> and I use the model:  model1<-glmer(Status~1+(X*Y*Size*Density)+(1|Trial),data=data,family=binomial)
>
> In other answers I have read about "verbose=T" but this does not change the warning. Neither does "scale=T".
> Something about "mer_optimize" is also spoken about, but I do not understand what I am supposed to do with that.
> I get some significans in the model, and the very pin pointed question is....can I trust these results even tough the warning?
>
> I´m soon about to present my project, so I would very much appritiate a quick answer.
>
> A Huge thanks!
> //Staffan svanberg
>
> 	[[alternative HTML version deleted]]
>
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