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Convergence failure with glmer().

2 messages · Rolf Turner, Ben Bolker

Original
Rolf Turner
# 
This is kind of a follow-up to a question that I asked a few days ago 
with the subject line "Bernoulli glmm question".

[The responses to my question more-or-less confirmed that I was 
basically on the right track with my model and [g]lmer() syntax.
The suggestion (in retrospect "obvious") was made that I should include 
a "word" random effect in my model.  It was also suggested that I should 
include some interaction terms.  However no result "came back" after 
over an hour of waiting, so I killed the job and gave up on this.]

However --- yesterday, after a few hiccups, I installed the latest 
version of lme4 (version 1.1-5) from CRAN.

Now the model that I successfully fitted with my rather elderly version 
(cannot remember the version number; I *think* I could dig it up from 
backups if it is of any interest) of lme4 results in a warning:
I tried switching the optimizer to "Nelder-Mead" and got an apparently 
"even worse" warning:
For the record, my call was:

fit <- glmer(y ~ sex+type+(1|student)+1|word),family=binomial,data=X,
              control=glmerControl(optimizer="Nelder_Mead"))

This is just a warning, not an error, but it does seem to indicate that 
the fit obtained is not (quite?) correct.

Is there anything that I can *do* about this?  I've tried reading the 
help files, but failed to discern anything useful in this regard.  I 
would appreciate suggestions.

If anyone is sufficiently interested, I can make the data set available 
to them.  It's a wee bit on the large side, but; 10314 records.

Thanks for any words of wisdom.

cheers,

Rolf Turner
Ben Bolker
#
On 14-03-18 01:59 AM, Rolf Turner wrote:
My guess is that this is a false positive.  Some trouble-shooting
thoughts:

 * are the parameters you're getting similar to those you got before?
 * if you install lme4.0 (from http://lme4.r-forge.r-project.org/repos),
do you get similar parameters to those you got before?
 * if you extract the parameters from lme4.0, make a deviance function
from lme4 using devFunOnly, and compare, do you get similar or better
(lower) deviances for the new lme4-fit parameters?
 * is your fit singular, i.e. are any of the 'theta' parameters
corresponding to diagonal elements (I think in your case you only have
scalar random effects, so this would correspond to any of them) equal or
close to zero?  Do the components of the gradient that have large
absolute value (which(abs(fit at optinfo#derivs$grad)>0.002)) correspond to
the singular theta parameters?

  Ben Bolker