In my data, each observation has three count variables, with the second being a subtotal of the first, and the third one being a sub-subtotal. One thing I’d like to estimate is `(total - subtotal) / (total - subsubtotal)`

, but currently `posterior_predict()`

is giving me seemingly absurd values:

```
N <- 100
set.seed(1)
total <- rpois(N, 10)
subtotal <- rbinom(N, total, 0.5)
subsubtotal <- rbinom(N, subtotal, 0.5)
d <- data.frame(total, subtotal, subsubtotal)
bf1 <- brmsformula(subtotal | trials(total) ~ 1)
bf2 <- brmsformula(subsubtotal | trials(subtotal) ~ 1)
m <- brm(bf1 + bf2, d, binomial)
pp <- posterior_predict(m)
stopifnot(all(pp[, , "subtotal"] >= pp[, , "subsubtotal"])) # error
```

I hoped the posterior predictions would take in consideration that `subtotal`

is both the outcome in the first formula and the number of trials in the second formula. However, sometimes the predicted subsubtotal is larger than the predicted subtotal. Did I miss something?

I guess I could work around this issue by expanding the data into `total`

rows per old observation with an ordinal outcome (levels: “not within subtotal”, “within subtotal but not subsubtotal” and “within subtotal”). Is there a better way?

Of course I could create a model with `(total - subtotal) / (total - subsubtotal)`

as the outcome, but I’d like to stick to something like the first model, because when I add predictor variables it provides me odds ratios I’m interested in.

Thank you!

- Operating System: Windows
- brms Version: 2.10.0