Greetings, everybody. Trying to model a probability of goal from a shot using a bernoulli logit model. I have a working implementation in PyStan in Jupyter Notebook, but the sampling isn’t run. This code trhow me an initialization error.

```
goals_model = """
data {
int<lower=0> N; // number of observations (8238 solo jugadores con >5) (8451 todos los tiros)
int players; // number of players (331 solo jugadores con >5) (426 todos los jugadores con tiros)
int glk; // number of goalkeepers 38
int zones; // number of field zones 8
int time; // number of time frames 7
int res; // types of results (winning, losing, tying)
int loc; // localia
int<lower=1,upper=players> player_id[N];
int<lower=1,upper=glk> glk_id[N];
int<lower=1,upper=zones> cat_zone[N];
int<lower=1,upper=time> time_frame[N];
int<lower=1,upper=res> cat_res[N];
int<lower=1,upper=loc> localia[N];
int goal[N]; // dependent variable
}
parameters {
real alpha; // intercept
vector[players] beta_player; // coefficient associated with each player
vector[glk] beta_glk; // coefficient associated with each goalkeeper
vector[zones] beta_zones; // coefficient associated with each zone
vector[time] beta_time; // coefficient associated with each time frame
vector[res] beta_res; // coefficient associated with each result
vector[loc] beta_loc; // coefficient associated with each type of localia
real epsilon; //Uncertainty / unexplained variance
}
model {
// priors
alpha ~ normal(0,1);
beta_player ~ beta(3.44,7.34);
beta_glk ~ normal(-0.05,0.38);
beta_zones ~ normal(-0.17,0.58);
beta_time ~ normal(-0.23,0.37);
beta_res ~ normal(-0.5,0.5);
beta_loc ~ normal(-0.78,0.6);
goal ~ bernoulli_logit(alpha + beta_player[player_id] + beta_glk[glk_id] +
beta_zones[cat_zone] + beta_time[time_frame] + beta_res[cat_res] + beta_loc[localia]);
}
"""
```

When i tried with this settings, the initialization it does not start.

```
goal_fit = goal_reg.sampling(data=datos,
iter=2000, chains=4,
warmup=500, n_jobs=-1,
seed=42)
```

I hope you can help me.