Here is my model

```
set.seed(1)
n <- 50 # number of reps/group
mu_n <- 0 # means for each group
mu_c1 <- 0.2
mu_c2 <- 0.3
mu_c3 <- 0.3
mu_c4 <- 0.4
d <- tibble(group = rep(c("normal", "covid1", "covid2", "covid3", "covid4"), each = n),
cases = rep(c (1, 2, 3, 4, 5), each = n)) %>%
#as.factor(cases) %>%
mutate(y = c(rnorm(n , mean = mu_n, sd = 1),rnorm(n , mean = mu_c1, sd = 1),rnorm(n , mean = mu_c2, sd = 1),rnorm(n , mean = mu_c3, sd = 1),rnorm(n , mean = mu_c4, sd = 1)))
as.factor(d$cases)
```

```
fit <-
brm(data = d,
family = gaussian,
y ~0+ cases,
prior = c(prior(normal(0,2), class = b),
prior(student_t(3,1,1), class = sigma)),
seed =1)
```

```
sim_d_and_fit <-
function
(seed, n, probs=c(0.05, 0.95),) {
mu_n <- 0 # means for each group
mu_c1 <- 0.2
mu_c2 <- 0.3
mu_c3 <- 0.3
mu_c4 <- 0.4
set.seed(seed)
d <- tibble(group = rep(c("normal", "covid1", "covid2", "covid3", "covid4"), each = n),
cases = rep(c (1, 2, 3, 4, 5), each = n)) %>%
#as.factor(cases) %>%
mutate(y = c(rnorm(n , mean = mu_n, sd = 1),rnorm(n , mean = mu_c1, sd = 1),rnorm(n , mean = mu_c2, sd = 1),rnorm(n , mean = mu_c3, sd = 1),rnorm(n , mean = mu_c4, sd = 1)))
as.factor(d$cases)
update(fit,
newdata = d,
seed = seed) %>%
fixef(probs = probs) %>%
data.frame() %>%
rownames_to_column("parameter") %>%
filter(parameter =="cases")
```

```
n_sim <-100
s1 <-
tibble(seed =1:n_sim) %>%
mutate(b1 = map(seed, sim_d_and_fit, n =60)) %>%
unnest(b1)
```

My question is how can I measure the UI of the difference between the normal group and covid4 group?? and what does b1 represent here?