[R-sig-ME] Warnings BootMer bootstrap glmer lme4

[D.J. Damen]

Dear all,

Currently, I am using the BootMer function to bootstrap my final model (fitted with GLMER from the lme4 package) to estimate the confidence intervals and p-values. Since I am alternating my reference categories, I have to bootstrap several models, and now the bootstrap of one model (below) returns with 50 or more warnings. Is there a standard procedure to follow when this happens? Should I trust the bootstrap when warnings pop up?

I am fitting a binomial model with the (centered and treatment coded) predictors c.con.tr (3 levels), c.type.tr (2 levels), and c.diff.tr (2 levels), and the dependent variable “contrast” (1 = mentioned; 0 = not mentioned).

Details about the model I want to bootstrap, the bootstrap function and the warnings are presented below:

Model:

m0bcc <-glmer (contrast~c.con.tr*c.type.tr*c.diff.tr+(1+c.type.tr+c.diff.tr|id)+(1+c.con.tr+c.type.tr|item.new), data=mydata, family=binomial (link='logit'), control=glmerControl(optimizer="bobyqa"))


Bootstrap function:

FUN <- function(fit) {
  return(fixef(fit))
}

boost <- function(mdl){
  name <- match.call()$mdl
  boosted <- bootMer(mdl, FUN, nsim = 100)
  save(boosted,file=paste(name,"_boosted",sep=""))
}

p_boosted <- function(mdl,boosted){
  for (n in 1:length(fixef(mdl))){
    print(fixef(mdl)[n])
    (bMCI <- boot.ci(boosted, conf = c(0.95, 0.99, 0.999), index=n, type="norm"))
    print(bMCI)
  }
}

Warnings: ( posted the first 10 warnings, in total there were 50 (or more?).

mdl <- m0bcc
> boosted <- bootMer(mdl, FUN, nsim = 100)
There were 50 or more warnings (use warnings() to see the first 50)

1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  unable to evaluate scaled gradient
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  unable to evaluate scaled gradient
4: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  Model failed to converge: degenerate  Hessian with 2 negative eigenvalues
5: In optwrap(object at optinfo$optimizer, ff, x0, lower = lower,  ... :
  convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded
6: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  Model failed to converge with max|grad| = 0.00176467 (tol = 0.001, component 1)
7: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  unable to evaluate scaled gradient
8: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
9: In optwrap(object at optinfo$optimizer, ff, x0, lower = lower,  ... :
  convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded
10: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :
  Model failed to converge with max|grad| = 0.00258682 (tol = 0.001, component 1)
11: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  ... :


I couldn’t find whether I should decrease the random effects structure of my fitted model to decrease these bootstrap warnings, but since my fitted glmer model did converge, I am hesitant to do so anyway. I hope somebody is able to shed some light on this matter.

Thank you very much in advance.

Best, Debby

Debby Damen
PhD Student

Department of Communication and Information Science

Tilburg University
Warandelaan 2, room D410
5037 AB Tilburg

T. +31 13 466  8245
M. d.j.damen at uvt.nl<mailto:d.j.damen at uvt.nl>

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