# Inf- values when attempting to simulate data: sample\_prior = "only" and "posterior\_perdict"

**URL:** <https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533>\
**Category:** Modeling\
**Tags:** techniques, fitting-issues, brms\
**Created:** [July 19, 2021, 1:53am UTC](https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533 "2021-07-19T01:53:21Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![pacey](https://avatars.discourse-cdn.com/v4/letter/p/df788c/32.png) [@pacey](https://discourse.mc-stan.org/u/pacey)\
**Post date:** [July 19, 2021, 1:53am UTC](https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533/1 "2021-07-19T01:53:21Z")

</div>

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

---

<div class="post-metadata">

**Author:** ![stijn](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/stijn/32/105_2.png) [@stijn](https://discourse.mc-stan.org/u/stijn)\
**Post date:** [July 19, 2021, 7:21am UTC](https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533/2 "2021-07-19T07:21:16Z")

</div>

I think one issue could be that the prior is too wide. With `sample_prior = "only"`, I assume you only sample from the prior. A parameter with a normal(0, 100) prior is going to give very high prediction with the exponential transformation (due to the lognormal).

```
quantile(exp(rnorm(1000, 0, 100)), probs = c(0.9, 0.95))

```

```
         90% 95% 
7.292745e+48 2.708517e+66

```

With these kind of values, Stan is overflowing to infinity. (Too wide priors might also be why you need `adapt_delta = .99` and `max_treedepth = 12`. You should also not need 54000 iterations for most applications).

---

<div class="post-metadata">

**Author:** ![pacey](https://avatars.discourse-cdn.com/v4/letter/p/df788c/32.png) [@pacey](https://discourse.mc-stan.org/u/pacey)\
**Post date:** [July 19, 2021, 7:42am UTC](https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533/3 "2021-07-19T07:42:39Z")

</div>

Thanks for your reply and your advice, I will attempt to fix these issues and report back about whether this addresses the problem.

---

<div class="post-metadata">

**Author:** ![pacey](https://avatars.discourse-cdn.com/v4/letter/p/df788c/32.png) [@pacey](https://discourse.mc-stan.org/u/pacey)\
**Post date:** [July 19, 2021, 9:02am UTC](https://discourse.mc-stan.org/t/inf-values-when-attempting-to-simulate-data-sample-prior-only-and-posterior-perdict/23533/4 "2021-07-19T09:02:38Z")

</div>

Fantastic, narrowing priors seems to have solved the issue, I am now getting results for `posterior_predict`, while fit and estimates appear equivalent to previous results. Also lowered iterations as advised and returned `adapt_delta` and `max_treedepth` back to default with no divergent transitions.

```
prior1 <- prior(normal(0, 1), nlpar = "b1", lb = 0) + 
  prior(normal(0, 0.5), nlpar = "b2", lb = 0) 

fit .2 <- brm(bf(y ~ b1^b2, 
                                     b1 ~ 1 + x,
                                     b2 ~ 1,
                                     nl = TRUE),
                                  data = data, 
                                  prior = prior1, 
                                  iter = 4000, 
                                  warmup = 1400,
                                  thin = 4, 
                                  cores = 4,
                                  family = lognormal(),
                                  #control = list(adapt_delta = 0.99,
                                   # max_treedepth = 12), 
                                  file = "fit.2",
                                  sample_prior = "only", 
                                  seed = 1234
) 

```

With result

```
vals <- posterior_predict(fit.2)
vals[[1]] 
[1] 0.6114292

```

Thanks again!