Hello,
I was asked to forward my question to this mailing list. I would be glad
to get some hints regarding my questions below:
I am an quit inexperienced R user but I try hard to learn fast. My
problem is that while I try to run lme4 with the model:
model1 = glmer(Survival ~ Kohorte+KJ+Saison+KAE+(1|ID), family =
binomial(link = "logit"),
data=phenos,control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)))
I always get the warning :
1:In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.75662 (tol = 0.001,
component 2)
2:In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative
eigenvalues
I know that my data has the promblem that the binary response variable has
a skewed distribution (1.933 "0" vs. 68.031 "1"). And that this could be
the reason for the converging problem and that I might look for
alternatives.(btw. do you consider some kind of minimum proportion for the
"minor observation" in this case "0" (2.8%) when you fit a model e.g. for
diseases with low incidence)
But beside of the reason for the converging problem I am wondering because
I tried to change the number of iterations using :
control=glmerControl(optCtrl=list(maxfun=...)).The used time which leads to
the error message and also the ...with max|grad| = 0.75662...are always
the same - no matter what value for maxfun I choose. So I am not sure how
to control the number of iterations that are really done by R and whether
the amount changes with different defined maxfun values in my case.
Thank you for your time and looking forward hearing from you,
Jakob G?hrken
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