I’m trying to code a very basic intercept only model with both prior and predictive checks following the code provided in Chapter 26 of the Stan User guide (https://mc-stan.org/docs/2_23/stan-users-guide/ppcs-chapter.html). I’m getting two errors and have spent the last hour and a half trouble shooting to no success.

Error 1 " Exception: normal_rng: Scale parameter is -9.4569, but must be > 0! (in ‘model3abc45661853_002_intercept_only’ at line 47)"

Error 1 I think is referring to the line `y_prior[N] = normal_rng(prior_nation_coef, prior_tau);`

I think this is telling me that prior_tau must be > 0. Earlier in that generated quantities block I declare prior_tau as having a lower limit of 0, so I’m not sure how I’m getting prior_tau values < 0.

Error 2: “Exception: model3abc2ba239f6_002_intercept_only_namespace::write_array: sd_y_rep is nan, but must be greater than or equal to 0 (in ‘model3abc2ba239f6_002_intercept_only’ at line 38)”

I think Error 2 is referring to the line `sd_y_rep = sd(to_vector(y_rep));`

where I’m trying to calculate the sd of my y_reps. I imagine I’m getting this error because earlier I declared sd_y_rep with a lower limit of 0, but I’m confused as to how the sd() function could be returning a value < 0.

```
data {
int<lower = 1> N; // number of obs
vector[N] Y; //
}
transformed data{
real<lower = 0> mean_y = mean(to_vector(Y));
real<lower = 0> sd_y = sd(to_vector(Y));
}
parameters {
real nation_coef; // national level regression coefs
real<lower=0> tau; // standardeviation of regression coefs
}
model {
//priors
nation_coef ~ normal(0, 5);
tau ~ cauchy(0, 2.5);
// likelihood
Y ~ normal(nation_coef, tau);
}
generated quantities{
// prior predictive check declarations
real prior_nation_coef;
real<lower = 0> prior_tau;
real y_prior[N];
// posterior predictive checks declarations
real y_rep[N];
real mean_y_rep;
real<lower = 0> sd_y_rep;
int<lower = 0, upper = 1> mean_gte;
// Log Likelihoods for WAIC/LOO declare
vector[N] log_lik;
//prior predictive check
prior_nation_coef = normal_rng(0, 5);
prior_tau = cauchy_rng(0, 2.5);
y_prior[N] = normal_rng(prior_nation_coef, prior_tau);
// poster pred check
y_rep[N] = normal_rng(nation_coef, tau);
mean_y_rep = mean(to_vector(y_rep));
sd_y_rep = sd(to_vector(y_rep));
mean_gte = (mean_y_rep >= mean_y);
//log likelihood
for (n in 1:N) {
log_lik[n] = normal_lpdf(Y[n] | nation_coef, tau);
}
}
```

If you want to try this yourself, any random vector of Ys will work, I think to test.

Thank you for your help!