Hi!
First of all, thanks for taking time into my questions, I really appreciate it!
 
#I tried to increase the nAGQ without success however.
 
#The reason I have the four-way interaction is that I wanted to start of with a full model which would be the model:
“model1<-glmer(Status~1+(X*Y*Size*Density)+(1|Trial),data=data,family=binomial)”
I have then used stepwise backward elimination of non-significant terms, the four-way interaction is the first one to go. Even though I take away this term and some more terms the warning message is still there.
I believe that I have enough data, with 112 observations on each measured variable (X, Y and size).
 
#“Another problem might be complete separation: for a given combination of factors all responses are either 0 or 1”
I do not fully understand your point (I appologize). Do you mean that e.g. the interaction X*Y explains the probability of survival to 100 % (1) or not at al (0)? If it is not much of a problem, I would appreciate an example.
The response variable “survival” is either 1 (alive) or 0 (dead).
 
Since I really feel bad, trying to interpret my results without being sure I done my model correctly I gave google some more time and came up with the following:
“…if the correlations are "high" it tells you that your model may 
be near unidentifiable = the model parameters may not be effectively estimated from the data.”
My variables are very highly correlated in the model summary, in a range 0.806-0.998, I guess this has to do with the warning message and Thierry’s point above, and another proof to my model being incorrect…?
 
I would love to understand how I could improve my model and get rid of that warning, however, the basic question is: Can I interpret the results I have and feel confident about the significant terms?
 
Best regards
//Staffan 
(since the data did not make sense in the reply, I ad it once more so that you can see the structure)




X 

Y 

survival

size

density

trial 


1

391

0

3.41818181818182

8

A


167.9

874

1

3.01785714285714

8

A


40.6

874

1

2.95374013014014

8

A


54.3

739

0

2.76363636363636

32

C


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
 
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.


----Ursprungligt meddelande----
Från: lborger@cebc.cnrs.fr
Datum: 2011-12-14 11:50
Till: 
Kopia: "staffan@myrica.se"<staffan@myrica.se>, "r-sig-mixed-models@r-project.org"<r-sig-mixed-models@r-project.org>
Ärende: Re: [R-sig-ME] False convergence in a mixed model.

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@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&amp;  Kwaliteitszorg / team Biometrics&amp;  Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> Thierry.Onkelinx@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@r-project.org [mailto:r-sig-mixed-models-bounces@r-project.org] Namens staffan@myrica.se
> Verzonden: dinsdag 13 december 2011 10:34
> Aan: r-sig-mixed-models@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]]
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models



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