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

[ONKELINX, Thierry]

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

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