Operating System: Window 10
Interface Version: 2.15.3
Compiler/Toolkit: I am not sure.
I am new in using stan_lmer and I am trying to run the following model for a self-paced reading experiment:
There are three level of the variable condition.c, thus I am using a sliding contrast to compared level 1 to level 2, and level 2 to level 3; Code.c is sentence region and has two levels. I want to know how individual differences modulate the differences between those conditions and their interaction conditions.
m1 <- stan_lmer(formula = log(reading_time) ~
(condition.c + code.c + condition.c : code.c)*
(1 + condition.c + code.c + condition.c : code.c| participant) +
(1 + condition.c + code.c + condition.c : code.c| item),
prior_intercept = normal(0, 10),
prior = normal(0, 1),
prior_covariance = decov(regularization = 2),
data = data,
chains = 4,
iter = 2000,
cores = 4,
adapt_delta = .9999)
After running the model with default adapt_delta, I got a message suggesting to increase adapt_delta. I did but I still get the warning and at least 2 divergent transition after warmup.
I looked up some alternative and I found a suggestion that I should reparameterize the model, however, this alternative seems applicable for the stan() function and not for the stan_lmer() or stan_glmer() functions.
I would like to know if there is an alternative to solve this issue and avoid divergent transitions using stan_lmer.