**Model**

`m = brm(bf(acute_therapy ~ year_centered + (1 | town)), data = data, family = hurdle_lognormal(), cores = 3, chains = 3)`

**Conditional effects plot for lognormal part of the hurdle model**

`conditional_effects(m, robust = TRUE)`

Comment: all median values are below 2.0 on y-axis

**Let’s calculate the median value and CIs for the year 0**

`m %>% spread_draws(b_Intercept) %>% median_qi(intercept = exp(b_Intercept))`

2.05 [1.87; 2.26]

Comment: median value is above 2, not matching the conditional effects plot

The same happens, when I calculate means instead of medians, using formula as follows:

exp((b_Intercept) + 1/2 * (sigma)**2)

**What am I doing wrong here and why there is a discrepancy between the two outcomes?**

Or are there better options for making such calculations. E.g. calculation mean Y value in year “-4” and in year “4”? I checked fitted() and predict() function, however, did not understand how to receive predictions for year “-4” for example (including all towns).