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 Iâm 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).
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.
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