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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