I’m fitting an adjacent categories ordinal model with category-specific effects, and I’d like to set different priors on the per-level coefficients. For a reproducible example, consider the following model:
library(brms)
library(foreign)
dat <- read.dta(“https://stats.idre.ucla.edu/stat/data/ologit.dta”)
dat$apply = as.integer(dat$apply)
fit1 = brm(bf(apply~cs(gpa)), family = acat(), data = dat, chains = 2)
GPA has two coefficients, as expected, but prior_summary(fit1) shows only one possible prior location for GPA, which sets the priors for both of the coefficients. How would I go about setting, for example, a uniform(-0.001,0.001) prior on gpa[1] (essentially setting it to 0), while leaving gpa[2] with more traditional prior?
- Operating System: Ubuntu 16.04
- brms Version: 2.7.0