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

[Highland Statistics Ltd]

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Message: 5
Date: Fri, 16 Oct 2015 22:24:33 +0300
From: Shadiya Al Hashmi <saah500 at york.ac.uk>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] GLMM model failing to converge
Message-ID:
	<CACrevpk7TyMzb_LfVyq+OCF0pK92AaNp9LSjVZjWhNnrEDM=5g at mail.gmail.com>
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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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Shadiya,


You answered your own question already....your model is too complicated. Simplify your categorical covariates.
And simplify the set of covariates. And try to have at least 15 observations per regression parameter.
And do a good data exploration to see which of these categorical covariates may be linked to each other.

And without reproducible code + data I doubt whether anyone is going to give you a more detailed answer.


Alain Zuur