I’m not sure if I can ask this in PyMC’s discourse, but I’m helping someone that built a model in PyMC and I reproduced the results in Stan. The results in Stan look way more like we would expect, given the domain knowledge. Can anyone help in identifying perhaps what might cause the difference?

Note, I added temporary variable names to obfuscate sensitive information.

```
data {
int<lower = 0> sample_size;
int<lower = 0> B;
int<lower = 0> P;
int<lower = 0> T;
int<lower = 0> E;
int<lower = 0> R;
real y[sample_size];
int t[sample_size];
int b[sample_size];
int p[sample_size];
int e[sample_size];
int r[sample_size];
}
parameters{
vector[T] varT;
vector[B] varB;
vector[P] VarP;
vector[E] varE;
vector[R] VarR;
real<lower = 0> c;
real<lower = 0> sigma;
}
model{
real mu[sample_size];
varT ~ normal(0, 1);
varP ~ normal(0 , 1);
varE ~ normal(0 , 1);
varR ~ normal(0 , 1);
c ~ normal(30, 7.2);
sigma ~ exponential(2);
varB ~ normal(0 , 1);
for (i in 1:sample_size){
mu[i] = c + varP[p[i]] + varT[t[i]] + varE[e[i]] + varR[r[i]] + varB[b[i]];
y[i] ~ normal(mu[i], sigma);
}
}
```

This gives the output:

In PyMC the code is:

```
with pm.Model() as basic_model:
intercept = pm.Normal('c', mu=30, sd=7)
varT = pm.Normal('varT', 0, sd=1, shape=(data['varT'].nunique(),1))
varP = pm.Normal('varP', 0, sd=1, shape=(data['varP'].nunique(),1))
varE = pm.Normal('varE', 0, sd=1, shape=(data['varE'].nunique(),1))
varB = pm.Normal('varB', 0, sd=1, shape=(data['varB'].nunique(),1))
varR = pm.Normal('varR', 0, sd=1, shape=(data['varR'].nunique(),1))
sigma = pm.Exponential('sigma', 2)
growth_mu = pm.Deterministic('Growth_mu', c + varT[data['varT_cat'].values] +
varP[data['varP_cat'].values] +
varE[data['varE_cat'].values] +
varB[data['varB_cat'].values] +
varR[data['varR_cat'].values])
out = pm.Normal('Y', mu=growth_mu, sd=sigma, observed=data[['Y', 'varT_cat', 'varP_cat', 'varE_cat', 'varB_cat', 'varR_cat']])
```

This gives the output, with variables in the **same order** :

The intercept at the bottom is definitely not what one would expect (given the domain knowledge at least). There is also way more variation than expected and a lot of the effects have means and median below 0, also not expected.