I am trying to fit a generalised mixed effects model, but I am having convergence problems. The model I want to fit is
mod2 <- glmer(accuracy ~ reps * time + (1 | ID) + (1 | item), family = 'binomial', data = xdata)
The error I have is this one:
Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0167064 (tol = 0.001, component 1)
My predictors are time and number of repetitions. Time levels are immediate, 1 day and 1 week. The number of repetitions are 2, 4 or 6. I thought these should be treated as categorical, but now I'm not sure.
I have 240 observations at each combination of time/number of repetitions. I have 20 participants at each number of repetitions (between subjects design) and all of them were tested at the 3 time intervals (within subjects). I have 36 items, and a different subset of 12 was tested at each time delay. The order of these subsets was randomised across participants.
Most people I asked told me to scale my predictors and if this does not work, to try and optimiser such as bobyqa.
I tried to scale them in R but I get this error:
Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric
That makes me think it is not even possible to scale them. Should I make the continuous even if I only have three points for each one?
Because the subjects are nested within the number of repetitions, another solution could be to add (1 | reps:ID) but I don't know if it makes sense.
Thank you for reading!