Hi, let me say straight up I am new to this sort of modeling, apologies for any faux pas both in asking this question and my modeling.

I am currently trying to simulate data for a non-linear model I have developed, with the end goal of sharing the model for others to use (perhaps as a shiny app, but again, new territory for me) using the brms package in R.

I had hoped to summarize an equation that I could share, however due to the apparently complex relationship between the model supplied estimates and predictors, I realized it would probably be easier to just share the model in question.

My new issue is that I cannot just share the model as it is built on confidential data.

After reading a number of topics on this site, I saw that you should be able to use `sample_prior = "only"`

and then use `posterior_predict`

to get the simulated data, but when I do this, the values returned are all ‘-Inf’ values. Here is my code for the model below:

```
prior1 <- prior(normal(0, 100), nlpar = "b1", lb = 0) +
prior(normal(0, 10), nlpar = "b2", lb = 0)
fit <- brm(bf(y~ b1^b2,
b1 ~ 1 + x
b2 ~ 1,
nl = TRUE),
data = data,
prior = prior1,
iter = 54000,
warmup = 22000,
thin = 54,
cores = 4,
family = lognormal(),
control = list(adapt_delta = 0.99,
max_treedepth = 12),
file = "fit",
sample_prior = "only",
save_all_pars = T,
seed = 1234)
str(posterior_predict(fit))
```

I get these results:

```
num [1:2372, 1:2619] 27 84 116.8 168 98.9 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : NULL
```

```
> vals <- (posterior_predict(fit))
> vals[[1]]
[1] Inf
```

Unfortunately I cannot share the underlying data here either.

I would be grateful for any advice or insight at all. Ideally a solution would not involve redoing the model, if possible (but understand it may just have to be if I’ve done something incorrectly).

Thanks