False convergence in a mixed model.
staffan at ... <staffan at ...> writes:
#I tried to increase the nAGQ without success however. #The reason I have the four-way interaction is that I wanted to start off with a full model which would be the model: model1<-glmer(Status~1+(X*Y*Size*Density)+(1|Trial), data=data,family=binomial)
the "1" is redundant (the intercept is included by default), but also harmless
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).
It depends: "enough data" can depend on the structure of the data as well as the raw amount. See various discussions on this list of the potential dangers of backward stepwise regression ...
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).
Here is an example: X Y Status 1.1 1.1 0 1.2 1.2 0 1.1 5 1 4.3 1.2 0 4.2 1.7 1 5.2 2.4 1 5.4 1.7 0 5.7 6.5 1 6.2 6.1 1 6.5 6.4 0 If you plot this out, you'll see that if X and Y are both small, then the Status is always zero. This leads to
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.
Can you post the full data set somewhere?