In the GLM: Hierarchical Linear Regression article a hierarchical regression model is used to predict radon levels in different counties.

By using `pm.sample`

the parameter’s distributions for each country can be fitted. **Now that I know the parameters, how can I sample the model for a specific county only?**

## Full details

Full details. Code from article:

```
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import pandas as pd
import theano
data = pd.read_csv(pm.get_data('radon.csv'))
data['log_radon'] = data['log_radon'].astype(theano.config.floatX)
county_names = data.county.unique()
county_idx = data.county_code.values
n_counties = len(data.county.unique())
with pm.Model() as hierarchical_model:
# Hyperpriors for group nodes
mu_a = pm.Normal('mu_a', mu=0., sd=100**2)
sigma_a = pm.HalfCauchy('sigma_a', 5)
mu_b = pm.Normal('mu_b', mu=0., sd=100**2)
sigma_b = pm.HalfCauchy('sigma_b', 5)
# Intercept for each county, distributed around group mean mu_a
# Above we just set mu and sd to a fixed value while here we
# plug in a common group distribution for all a and b (which are
# vectors of length n_counties).
a = pm.Normal('a', mu=mu_a, sd=sigma_a, shape=n_counties)
# Intercept for each county, distributed around group mean mu_a
b = pm.Normal('b', mu=mu_b, sd=sigma_b, shape=n_counties)
# Model error
eps = pm.HalfCauchy('eps', 5)
radon_est = a[county_idx] + b[county_idx] * data.floor.values
# Data likelihood
radon_like = pm.Normal('radon_like', mu=radon_est, sd=eps, observed=data.log_radon)
```

Then to fit the model and determine parameter distributions:

```
with hierarchical_model:
hierarchical_trace = pm.sample(draws=2000, n_init=1000)
```

**How can I now sample the model for a specific county only, for example AITKIN?**