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

[Shadiya Al Hashmi]

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")

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’

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).

3. (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf,  :

  failure to converge in 10000 evaluations



Any advice on how to go about this?


Thank you,


Shadiya

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