lme4/glmer convergence warnings
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