# Identifying outliers in logistic regression model

I'm looking to identify outliers in a logistic regression model, e.g.

mod1 <- glmer(proportion ~ a*b*c + (1 | ID), family = binomial, weights =
n, data = my_dat)


The model itself converges. However, when I try to determine influence using influence.ME (in order to calculate Cook's and DFBETAS), the operation fails to converge:

mod1.est <- influence(mod1, obs = T)


And produces a warning message:

In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
Model failed to converge with max|grad| = 0.00125887 (tol = 0.001, component
1)


I've tried the following fixes, but the warning message remains the same:

1. I converted my ID variable from an integer to a factor (it becomes a factor with three levels, 67 observations at each level):

my_dat$ID <- as.factor(my_dat$ID)

2. I inputted ID as the grouping variable in the influence function:

mod1.est2 <- influence(mod1, group = "ID")


Any advice about what might be going on and/or how to identify outliers using a different method would be much appreciated. Thank you!