I am trying to fit GLMM's to my data using the glmer function available in R's lme4 package. The data is available at: https://onedrive.live.com/redir?resid=1B727FC7180E87DF%21118

I keep getting warning messages. Can anybody help me get rid of them.

I am re-posting this from StackOverflow after someone's kind suggestion. That person also suggested that the main of the issue may be low number of virus positive samples n=12. Which I suspected. But I am also wandering if linear separation could be an issue, as all the virus positives occur in the low food group. Can these problems be resolved using GLMMs or should I think of other statistical tests?

Tried fitting the model:

`Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment+(1|Set), family=binomial,data=data,method = "ML") `

to get the warning messages

`Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.001101 (tol = 0.001, component 3) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge: degenerate Hessian with 4 negative eigenvalues `

After running code with more iterations of the model, I still get the same warning messages:

`Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment+(1|Set),data=data, family=binomial,control=glmerControl(optCtrl=list(maxfun=1e9))) `

I then looked on-line and that people had similar problems and tried the optmizer `bobyqa`

:

`Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment(1|Set),data=data,family=binomial, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e9))) `

I then got the very similar warning messages:

`Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00393532 (tol = 0.001, component 2) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge: degenerate Hessian with 2 negative eigenvalues `

I then thought of simplifying the model and tried no interactions between explanatory variables, with the code:

`Food.Plus.Treatment.glmer<-glmer(Virus_DNA~Food+Treatment(1|Set),family=binomial, data=data) `

and

`Food.Plus.Treatment.glmer<-glmer(Virus_DNA~Food+Treatment(1|Set),family=binomial, data=data,control=glmerControl(optCtrl=list(maxfun=1e9))) `

Only to get the warning messages :

`Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00248016 (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 `

So I tried this simplified model with the optimizers `bobyqa`

and `Nelder_Mead`

, as well as the optimzers `nlminb`

and `L-BFGS-B`

from the package `optimx`

.

All but the `bobyqa`

optimizers produce variations on the 2 warning messages. The `bobyqa`

optimizer produces the 1 warning message:

`Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00139574 (tol = 0.001, component 2) `

P.S. This is my first post on here I hope I have provided enough information without being verbose and it is correctly formatted.

**Contents**hide

#### Best Answer

Variable `Set`

has only 6 categories. My guess is that this is not enough information to appropriately estimate the variance of the random effects. Most books on multilevel modeling suggest sample sizes of $geq 15$ for level two units. You might want to include `Set`

as a fixed effect and estimate a simpler `glm()`

model.

Here are a few references:

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