[R-sig-ME] Convergence warning message

[Christopher David Desjardins]

I am trying to fit a mixed effects binomial model.

The data consists of
- A dependent variable consisting of Bernoulli trials (outcome)
- A time variable (time), which has been mean centered
- An id variable (id)
- A categorical covariate (cat_cov)
- A blocking variable (block) which id is nested in. I realize in the model
below that it should be (1 | id/block) but I am just trying to troubleshoot
my problem at the moment.

When I run the following:

example_data <- read.csv("https://cddesja.github.io/example_data.csv",
header  = T)
example_data$cat_cov <- as.factor(example_data$cat_cov)
example_data$id <- as.factor(example_data$id)
example_data$block <- as.factor(example_data$block)
main_effects <- glmer(outcome ~ 1 + cat_cov + time + I(time^2) + (1 | id),
data = example_data, family = "binomial")

That last line of code gives a warning message:

> main_effects <- glmer(outcome ~ 1 + cat_cov + time + I(time^2) + (1 |
id), data = example_data, family = "binomial")
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 4.36001 (tol = 0.001, component
1)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?

I am not exactly sure how to proceed. I know the issue is with cat_cov,
though it's unclear to me why. If I swap out in a different categorical
covariate in the model, not included in that data set, I don't get this
message. I am not running into complete separation with cat_cov.  So, I'm a
little perplexed.

Any advice on what I should do or something I could look at it would be
very helpful.

Thanks,
Chris
-- 
https://cddesja.github.io/

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