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I am trying to use the lme4 package for a glmm and am getting a convergence code of 0 and a statement: Model failed to converge with max|grad| = 0.00791467 (tol = 0.001, component 1). I am interested in using the lme4 package because I would like to have AIC values to determine the appropriate model as I add in additional covariates.

Two weeks ago when I tried the same approach I got a warning message that the model failed to converge because of the max|grad| issue, but am not getting the warning message this time, just the statement at the end of the summary output.

Does this mean that the model is not converging? I also used the glmmPQL method. The coefficient parameter estimates are similar between the two model types.

Here is glmer (lme4) model code. I increased the maxfun to deal with other issues I had when I ran the model last time. 

l1<-glmer(Meat_Weight~logsh+SAMS_region_2015+(1|StationID),
        family="Gamma"(link="log"),data=datad,control=glmerControl(optCtrl=list(maxfun=100000)))

Here is the glmmPQL code. 

m1<-glmmPQL(fixed=Meat_Weight~logsh+SAMS_region_2015,random=~1|StationID,
        family=Gamma(link="log"),data=datad)

I am sure this is not information to diagnosis the problem, but if anyone has suggestions I can provide more data.

Thanks

user41509
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  • Please don't cross-post; I've [answered this on r-sig-mixed-models](http://article.gmane.org/gmane.comp.lang.r.lme4.devel/13931) – Ben Bolker Nov 07 '15 at 20:14
  • I posted this message here first before I found the r-sig-mixed-models mailing list. – user41509 Nov 09 '15 at 17:33
  • For reference, this is how @BenBolker replied on that mailing list: "Have you looked at ?convergence ? The bottom line (as commented on here recently by Doug Bates in this forum) is that the convergence tests give a lot of false positives; I have thought a lot about removing them, or at least about increasing the tolerances considerably, but have been afraid to make changes that would lead to..." – DirtStats Aug 30 '17 at 18:01
  • "...a lot more false *negatives* (i.e. unreported problems with models) without a lot more time & effort evaluating these rules and makng the decision carefully (which I don't have right now ...) Especially if you are getting similar-enough results between glmmPQL and glmer, I would feel free to ignore the warnings." – DirtStats Aug 30 '17 at 18:01
  • For a more thorough explanation of @BenBolker 's thoughts on the matter, see also https://stackoverflow.com/a/21370041/2626562 – DirtStats Aug 30 '17 at 18:10

1 Answers1

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Try to change the optimizer

    l1<-glmer(Meat_Weight~logsh+SAMS_region_2015+(1|StationID),
    family="Gamma"(link="log"),data=datad, control = glmerControl(optimizer="bobyqa"))
Pablo Vilas
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