Hello,

I’m trying to implement ‘prior predictive checks’. And I got an error message saying this:

“Must use algorithm=“Fixed_param” for model that has no parameters.”

```
stan_mod3 <- sampling(Prior_Predictive,
data = d3, chains = 4,
control = list(max_treedepth = 15,
adapt_delta = 0.8),
iter = 4000)
SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
[1] "Error in sampler$call_sampler(args_list[[i]]) : "
" Must use algorithm=\"Fixed_param\" for model that has no parameters."
[1] "error occurred during calling the sampler; sampling not done"
When I added ‘fixed_param = TRUE’ option, I got this error message:
"passing unknown arguments: fixed_param."
stan_mod3 <- sampling(Prior_Predictive,
data = d3, chains = 4,
control = list(max_treedepth = 15,
adapt_delta = 0.8),
iter = 4000,
fixed_param = TRUE)
```

Now I have had the following error message:

Error in checkForRemoteErrors(val) :

4 nodes produced errors; first error: passing unknown arguments: fixed_param.

Here’s Stan codes I used:

```
data {
int<lower=1> n_days;
real mu_start;
real mu_finish;
int y1_n;
int y2_n;
int y3_n;
int y4_n;
int y5_n;
real y1_values[y1_n];
real y2_values[y2_n];
real y3_values[y3_n];
real y4_values[y4_n];
real y5_values[y5_n];
int y1_days[y1_n];
int y2_days[y2_n];
int y3_days[y3_n];
int y4_days[y4_n];
int y5_days[y5_n];
real y1_se[y1_n];
real y2_se[y2_n];
real y3_se[y3_n];
real y4_se[y4_n];
real y5_se[y5_n];
}
generated quantities {
vector<lower = 0>[n_days] mu;
real d[5];
real<lower = 0> sigma;
mu[1] = normal_rng(mu_start, 0.01);
sigma = normal_rng(0.5, 0.5);
mu[n_days] = normal_rng(mu_finish, 0.01);
d[5] = normal_rng(0, 7.5);
vector[n_days] y_sim;
for(i in 1:n_days) {
for(j in 1:5) {
y_sim[i] = normal_rng(mu[i] + d[j], sigma);
}
}
}
```

Any help would be appreciated.