Olivier Ma - 11 months ago 91

Python Question

In the Pymc3 example for multilevel linear regression (the example is here, with the

`radon`

The hierarchical model given in the example is like this:

`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)

hierarchical_trace = pm.sample(2000)

And I'm trying to make some change to the priors

`with pm.Model() as correlation_model:`

# Hyperpriors for group nodes

mu_a = pm.Normal('mu_a', mu=0., sd=100**2)

mu_b = pm.Normal('mu_b', mu=0., sd=100**2)

# here I want to model a and b together

# I borrowed some code from a multivariate normal model

# but the code does not work

sigma = pm.HalfCauchy('sigma', 5, shape=2)

C_triu = pm.LKJCorr('C_triu', n=2, p=2)

C = T.fill_diagonal(C_triu[np.zeros((2,2), 'int')], 1)

cov = pm.Deterministic('cov', T.nlinalg.matrix_dot(sigma, C, sigma))

tau = pm.Deterministic('tau', T.nlinalg.matrix_inverse(cov))

a, b = pm.MvNormal('mu', mu=(mu_a, mu_b), tau=tau,

shape=(n_counties, 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)

correlation_trace = pm.sample(2000)

Here is the error message I got:

`File "<ipython-input-108-ce400c54cc39>", line 14, in <module>`

tau = pm.Deterministic('tau', T.nlinalg.matrix_inverse(cov))

File "/home/olivier/anaconda3/lib/python3.5/site-packages/theano/gof/op.py", line 611, in __call__

node = self.make_node(*inputs, **kwargs)

File "/home/olivier/anaconda3/lib/python3.5/site-packages/theano/tensor/nlinalg.py", line 73, in make_node

assert x.ndim == 2

AssertionError

Clearly I've made some mistakes about the covariance matrix, but I'm new to

`pymc3`

`theano`

The full replicable code and data can be seen on the example page (link given above). I didn't include it here because it's too long and also I thought those familiar with

`pymc3`

Answer Source

You forgot to add one line when creating the covariance matrix you miss-specified the shape of the MvNormal. Your model should look something like this:

```
with pm.Model() as correlation_model:
mu = pm.Normal('mu', mu=0., sd=10, shape=2)
sigma = pm.HalfCauchy('sigma', 5, shape=2)
C_triu = pm.LKJCorr('C_triu', n=2, p=2)
C = tt.fill_diagonal(C_triu[np.zeros((2,2), 'int')], 1.)
sigma_diag = tt.nlinalg.diag(sigma) # this line
cov = tt.nlinalg.matrix_dot(sigma_diag, C, sigma_diag)
tau = tt.nlinalg.matrix_inverse(cov)
ab = pm.MvNormal('ab', mu=mu, tau=tau, shape=(n_counties, 2))
eps = pm.HalfCauchy('eps', 5)
radon_est = ab[:,0][county_idx] + ab[:,1][county_idx] * data.floor.values
radon_like = pm.Normal('radon_like', mu=radon_est, sd=eps, observed=data.log_radon)
trace = pm.sample(2000)
```

Notice that alternatively, you can evaluate the correlation of the intercept and the slope from the posterior of `hierarchical_model`

. You can use a frequentist method or build another Bayesian model, that takes as the observed data the result of `hierarchical_model`

. May be this could be faster.

EDIT

If you want to evaluate the correlation of two variables from the posterior you can do something like.

```
chain = hierarchical_trace[100:]
x_0 = chain['mu_a']
x_1 = chain['mu_b']
X = np.vstack((x_0, x_1)).T
```

and then you can run the following model:

```
with pm.Model() as correlation:
mu = pm.Normal('mu', mu=0., sd=10, shape=2)
sigma = pm.HalfCauchy('sigma', 5, shape=2)
C_triu = pm.LKJCorr('C_triu', n=2, p=2)
C = tt.fill_diagonal(C_triu[np.zeros((2,2), 'int')], 1.)
sigma_diag = tt.nlinalg.diag(sigma)
cov = tt.nlinalg.matrix_dot(sigma_diag, C, sigma_diag)
tau = tt.nlinalg.matrix_inverse(cov)
yl = pm.MvNormal('yl', mu=mu, tau=tau, shape=(2, 2), observed=X)
trace = pm.sample(5000, pm.Metropolis())
```

You can replace x_0 and x_1 according to your needs. For example you may want to do:

```
x_0 = np.random.normal(chain['mu_a'], chain['sigma_a'])
x_1 = np.random.normal(chain['mu_b'], chain['sigma_b'])
```