R help - Jun 2014 - Why have my glmms stopped converging (lme4)

I have four sets of glmms (binomial, logit-linked) which I have run in
various incarnations with no problems over the last weeks. All converged,
data assumptions checked, reasonable goodness-of-fit (0.75-85). They are
based on three different data sets. Today, I wanted to rerun one of them
after amending the data set slightly, and I got the following error message:

Warning message:In checkConv(attr(opt, "derivs"), opt$par, ctrl
control$checkConv,  :   Hessian is numerically singular: parameters are not
uniquely determined

I tried re-running the model with the older unchanged version of the data
on which the model previously converged, and got the same message. I have
not change the model specification at all. I then re-ran my other models
which use different data sets to see what would happen, and I got the
following message for each:

Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, 
:
  Model failed to converge with max|grad| = 0.00846421 (tol = 0.001)

I went back to previous versions of the model and  older versions of the
data sets and I’m still getting these error messages, but only for mixed
models. For models with only fixed effects, they are as before. But the
point is that my mixed models were converging before.

Any ideas on what is going on? I might be missing something obvious, but it
really seems like this came out of nowhere.

Further information about my models:
I used the following commands:
library(lme4)
mod<- glmer(y ~ x1 + x2 + x3 + x4 + x5 + (1|x6) + (1|x7) +(1|x8) , data data,
family = binomial (logit))
I am looking at the effects of ecological factors on the presence of
pathogens in wildlife. Most of my variables are categorical. Fixed factors
are reproductive status, month, presence of particular ectoparasites, and a
body condition index. Random factors are site, year, and observer (to
account for potential observer-biased condition index).

I’m running R 3.1.0 in Mavericks.

This is my first time posting in this list, so I hope my question is
acceptably formulated.

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Heather Baldwin <heather.baldwin <at> uni-ulm.de> writes:
> 
>  I have four sets of glmms (binomial, logit-linked) which I have run in
> various incarnations with no problems over the last weeks. All converged,
> data assumptions checked, reasonable goodness-of-fit (0.75-85). They are
> based on three different data sets. Today, I wanted to rerun one of them
> after amending the data set slightly, and I got the
>  following error message:
> 
> Warning message:In checkConv(attr(opt, "derivs"), opt$par, ctrl
> control$checkConv,  :   Hessian is numerically
> singular: parameters are not
> uniquely determined
> 
> I tried re-running the model with the older unchanged version of the data
> on which the model previously converged, and got the same message. I have
> not change the model specification at all. I then re-ran my other models
> which use different data sets to see what would happen, and I got the
> following message for each:
> 
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv,  :
>   Model failed to converge with max|grad| = 0.00846421 (tol = 0.001)
> 
> I went back to previous versions of the model and  older versions of the
> data sets and I'm still getting these error messages, but only for
mixed
> models. For models with only fixed effects, they are as before. But the
> point is that my mixed models were converging before.
> 
> Any ideas on what is going on? I might be missing something obvious, but it
> really seems like this came out of nowhere.
   You probably updated your version of lme4 to 1.1-6, which has
stronger tests for convergence, some of which are false positives.  In
other words, it's not that your models stopped converging (if you were
to compare to older results I strongly suspect you would see precisely
the same fits), but that they started complaining about convergence.

  The max|grad| warning is completely expected and most likely
a false positive: see https://github.com/lme4/lme4/blob/master/README.md .

  The singular Hessian warnings are a bit more interesting. They
_might_ be caused by strongly different scaling in different 
predictor variables, or they might actually indicate a real convergence
issue.  If you'd like to follow this up, please contact me off-list
(or post a message with a link to data on r-sig-mixed-models
@ r-project.org)
   

 > > Further
information about my models: > I used the following commands: >
library(lme4) > mod<- glmer(y ~ x1 + x2 + x3 + x4 + x5 + (1|x6) +
(1|x7) +(1|x8) , data = > data, family = binomial (logit)) > I am
looking at the effects of ecological factors on the presence of >
pathogens in wildlife. Most of my variables are categorical. Fixed
factors > are reproductive status, month, presence of particular
ectoparasites, and a > body condition index. Random factors are site,
year, and observer (to > account for potential observer-biased
condition index).  > > I???m running R 3.1.0 in Mavericks.  > >
This is my first time posting in this list, so I hope my question is >
acceptably formulated. 

  You should try to avoid posting in HTML, and you should probably
have selected the r-sig-mixed-models (@r-project.org) mailing list.