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). > > Iâm 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. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
