Hello,

I’m trying to set some weakly informative priors on the mu, ndt, and sigma parameters (on the identity, log, and log link scales, respectively, as a default) for the shifted lognormal distribution but am struggling to understand how to do this. As an example, I have a dependent variable (reaction time, in milliseconds) being predicted by 3 categorical independent variables (age group, bias type, trial type) with the model formula being as below,

Formula: rt_ms ~ age_group * bias_type * trial_type + (1 | subject)

ndt ~ age_group * bias_type * trial_type + (1 | subject)

sigma ~ age_group + bias_type + trial_type + (1 | subject)

The prior summary tells me that there are flat priors on all population-level effects for mu, ndt, and sigma (i.e., class ‘b’) but I would like to set some weakly informative priors.

I’ve been advised that the below should work but am trying to gain more insight and understanding regardless of whether it is right or wrong (or somewhere in between).

c(prior(“student_t(3,0,2.5)”, class = “b”),

prior(“student_t(3,0,2.5)”, dpar = “ndt”),

prior(“student_t(3,0,2.5)”, dpar = “sigma”))

- Operating System: Cent OS Linux (Release 7 (Core) 64-bit)
- brms Version: 2.16.3

Thank you for reading! Also, pinging @martinmodrak