How to convert hurdle lognormal model posterior values to actual values of y variable?

Multiple intercepts in posterior cause confusion for me.

y - received hours
period - factor, 3 levels, I, II, III
region - factor, 2 levels, A, B

m = brm(bf(hours ~ period + (period | region), hu ~ period + (period | region)), data = data, family = hurdle_lognormal())

Could you please tell me how to convert posterior values from hurdle_lognormal model to

  1. received hours (lognormal part) for region A and period I
  2. probability of zero therapy (binomial part) for region B and period III

Model posterior
post = posterior_samples(m)

Column names of posterior

My own solution for question 1: region A, period I
lognormal model intercept + regionA intercept?
exp((b_Intercept + r_county[regionA,Intercept) + 1/2 * (sigma)**2))

My own solution for question 2: region B, period III
hurdle model intercept + regionB period III?
inv_logit_scaled(b_hu_Intercept + r_county__hu[regionB,periodIII)

Hey there!

Not a brms expert here, but…

I think you can just use posterior_predict with the newdata argument set to a df with the specific cases, right?