lme4/glmer convergence warnings

Hi Ben

Thanks for your reply. The code you posted generates the following:

  Min.   1st Qu.    Median     Mean   3rd Qu.     Max.
0.001474 0.023920 0.045420 0.255600 0.068600 2.114000

This model was fitted with the raw data (not standardised continuous
data) and without removing small clusters.

Thanks again
Robert Long




On 02/04/2014 14:05, Ben Bolker wrote:

   I think this is a false positive, caused by our recent introduction of
new convergence tests. There's been lots of discussion of this on the
list recently.

    I have a new trouble-shooting idea:

if g0 is your fitted model, can you see what happens if you scale the
estimated gradients by the curvature/standard errors?

gg <- g0 at optinfo$derivs$grad
hh <- g0 at optinfo$derivs$Hessian
vv <- sqrt(diag(solve(hh/2)))
summary(abs(gg*vv))


On 14-04-02 06:40 AM, W Robert Long wrote:

I should perhaps also mention that of the 9 covariates, 3 are continous
and I have tried standardising them. Of the remaining 6, 5 are binary
and the last one is ordinal.

On 02/04/2014 11:28, W Robert Long wrote:

Hi all

I am running a simple random intercepts model using lme4 on
approximately 70,000 observations, with 250 clusters. The code looks
like

glmer(Y~x1+x2+x3+x4+x5+x6+x7+x8+x9+(1|clusdID),
      data=dt1, family=binomial(link=logit))

and I receive the following warnings:

Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv,  :
    Model failed to converge with max|grad| = 4847.75 (tol = 0.001)
2: In if (resHess$code != 0) { :
    the condition has length > 1 and only the first element will be
used
3: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv,  :
    Model is nearly unidentifiable: very large eigenvalue
   - Rescale variables?;Model is nearly unidentifiable: large
eigenvalue
ratio
   - Rescale variables?

There are some small clusters (<10 obs per cluster), but even removing
those, the warnings remain.

Using Stata -xtmelogit- there are no warnings and the output is almost
identical to glmer() so this gives me some comfort, yet I still worry
about these warnings from glmer.

I have tried setting nAGQ as high as 10, to no avail.

Could anyone suggest what I can look for or change ? The data are
confidential so I can't easily make a reprodicible example.

Thanks in advance
Robert Long