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:
I converted my
IDvariable 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)
IDas 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!