[R-sig-ME] GLMM model failing to converge

[Shadiya]

Thanks a lot Ben for the swift response. Will definitely try out different optimizers and check lmerControl.

Best wishes,

Shadiya

Sent from my iPhone

> On Oct 16, 2015, at 10:53 PM, Ben Bolker <bbolker at gmail.com> wrote:
> 
>> On Fri, Oct 16, 2015 at 3:24 PM, Shadiya Al Hashmi <saah500 at york.ac.uk> wrote:
>> Hello,
>> 
>> 
>> I’m novice in using R in general and generalized logistic regression models
>> with mixed effects in particular.
>> 
>> At any rate, I’m testing how close the linguistic perception (response
>> vowels) of different Turkish listeners (T [monolingual Turkish speakers],
>> TA [bilingual Turkish-Arabic speakers] and TQ [Turkish speakers with some
>> knowledge of Arabic through Quran recitation]) is to observed mappings
>> (predicted vowels) in my research qualitative corpus. In the data, this is
>> reflected in the binary variable match (1=match, 0=mismatch).
>> 
>> 
>> Having said this, my dependent variable is ‘match’ which interacts with
>> some +20 independent variables, some of which are factors with up to 12
>> levels.
>> 
>> 
>> Now, the basic model I’ve used is as follows and works just fine.
>> 
>> 
>> m0.1 <- glmer(match ~ Listgp + (1|Listener), data = PATdata1, family =
>> "binomial")
>> 
>> However, all subsequent models such as the one below crash.
>> 
>> cf. m0.4 <- glmer(match~ Listgp + stimulus + st.context + st.length + age +
>> gender + level.of.education + reading.A + comprehension.A + speaking.A +
>> writing.A + (1|Listener), data = PATdata1, family = "binomial")
> 
>  What does "crash" mean? Precision is important here -- you could mean:
> 
> * a warning (which should certainly concern you, but it might be a
> false positive ...) -- in this case you *will* get a result, which you
> can use if you conclude that the warnings don't actually represent a
> serious problem;
> * an error -- in this case you won't get an answer at all, you need to
> deal with/work around the error before you can get results;
> * a true crash, where the R process actually stops.  This is by
> definition a bug in the package, or (much more unlikely) in R itself.
> 
> 
>> 
>> Once I start parsing in the other factors especially the ones with
>> mutli-levels such as ‘stimulus’ , the model fails to converge and I
>> get a number of warning messages as follows.
>> 
>> 1.    fixed-effect model matrix is rank deficient so dropping 4
>> columns / coefficients’
> 
>  This means you have collinear predictors -- most likely, some
> combinations of factors are aliased with each other.
> 
>> 
>> 2.    In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>> 
>>  Model failed to converge with max|grad| = 0.151201 (tol = 0.001,
>> component 7).
> 
>  This is a "medium-sized" gradient; it may be OK, hard to know.  How big
> is your data set?
>   As suggested in ?convergence, the gold standard is to try your model
> with one or more different optimizers and see if it gets to a sufficiently
> (for your purposes) similar answer.
> 
>> 3. (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf,  :
>> 
>>  failure to converge in 10000 evaluations
> 
>  See ?lmerControl for advice about how to increase the number of
> evaluations.
> 
>> 
>> 
>> 
>> Any advice on how to go about this?
>> 
>> 
>> Thank you,
>> 
>> 
>> Shadiya
>> 
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>> 
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