I’m having trouble fitting a multivariate model using `stan_mvmer`

.

I’m trying to fit two different GLMs, which I assume are correlated. The response variables `y_1,y_2`

are count vectors, with integer exposures `n_1,n_2`

.

Following this vignette, I’ve defined rates `rate_i <- y_i/n_i`

and want to use the `weights`

parameter to account for the different exposures. I tried:

```
f <- stan_mvmer(
formula = list(
rate_1 ~ feature_2 + feature_3 + (1|feature_1),
rate_2 ~ feature_2 + feature_4 + (1|feature_1)),
data = df,
family = list(binomial, binomial),
weights = list(df$n_1,df$n_2),
chains = 1, seed = 12345, iter = 1000)
```

But when I ran this command, I got nothing; when I commented the weights statement, the model was fitted (but the results were quite meaningless, since the exposures were ignored)

EDIT: I’ve tried using the `cbind(y_i,n_i-y_i)`

syntax and got the following error message:

```
Error: All outcome values must be 0 or 1 for Bernoulli models.
```

- Operating System: Windows
- rstanarm Version: 2.18.2