09_FilconPyMC_ex9.2.B.py

"""
More Hierarchical models. The filtration-condensation experiment. 
Hyperparameters for kappafor all conditions.
"""
import numpy as np
import pymc3 as pm
import sys
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')

# Data
# For each subject, specify the condition s/he was in,
# the number of trials s/he experienced, and the number correct.
ncond = 4
nSubj = 40
trials = 64

N = np.repeat([trials], (ncond * nSubj))
z = np.array([45, 63, 58, 64, 58, 63, 51, 60, 59, 47, 63, 61, 60, 51, 59, 45,
61, 59, 60, 58, 63, 56, 63, 64, 64, 60, 64, 62, 49, 64, 64, 58, 64, 52, 64, 64,
64, 62, 64, 61, 59, 59, 55, 62, 51, 58, 55, 54, 59, 57, 58, 60, 54, 42, 59, 57,
59, 53, 53, 42, 59, 57, 29, 36, 51, 64, 60, 54, 54, 38, 61, 60, 61, 60, 62, 55,
38, 43, 58, 60, 44, 44, 32, 56, 43, 36, 38, 48, 32, 40, 40, 34, 45, 42, 41, 32,
48, 36, 29, 37, 53, 55, 50, 47, 46, 44, 50, 56, 58, 42, 58, 54, 57, 54, 51, 49,
52, 51, 49, 51, 46, 46, 42, 49, 46, 56, 42, 53, 55, 51, 55, 49, 53, 55, 40, 46,
56, 47, 54, 54, 42, 34, 35, 41, 48, 46, 39, 55, 30, 49, 27, 51, 41, 36, 45, 41,
53, 32, 43, 33])
condition = np.repeat([0,1,2,3], nSubj)

# Specify the model in PyMC
with pm.Model() as model:
    # define the hyper-hyperparameters for kappa
    mean_gamma = pm.Uniform('mean_gamma', 0, 30)
    sd_gamma = pm.Uniform('sd_gamma', 0, 30)
    s_kappa = mean_gamma**2/sd_gamma**2
    r_kappa = mean_gamma/sd_gamma**2
    # define the hyperparameters
    kappa = pm.Gamma('kappa', s_kappa, r_kappa)
    mu = pm.Beta('mu', 1, 1, shape=ncond)
    # define the prior
    theta = pm.Beta('theta', mu[condition] * kappa, (1 - mu[condition]) * kappa, shape=len(z))
    # define the likelihood
    y = pm.Binomial('y', p=theta, n=N, observed=z)
    trace = pm.sample(2000, tune=1000)

## Check the results.
burnin = 0  # posterior samples to discard

## Print summary for each trace
#pm.df_summary(trace[burnin:])
#pm.df_summary(trace)

## Check for mixing and autocorrelation
#pm.autocorrplot(trace, varnames=['mu', 'kappa'])

## Plot KDE and sampled values for each parameter.
#pm.traceplot(trace[burnin:])
pm.traceplot(trace)

# Create arrays with the posterior sample
mu1_sample = trace['mu'][:,0][burnin:]
mu2_sample = trace['mu'][:,1][burnin:]
mu3_sample = trace['mu'][:,2][burnin:]
mu4_sample = trace['mu'][:,3][burnin:]

# Plot differences among filtrations experiments
fig, ax = plt.subplots(1, 3, figsize=(15, 6))
pm.plot_posterior((mu1_sample-mu2_sample), ax=ax[0], ref_val=0, color='skyblue')
ax[0].set_xlabel(r'$\mu1-\mu2$')

# Plot differences among condensation experiments
pm.plot_posterior((mu3_sample-mu4_sample), ax=ax[1], ref_val=0, color='skyblue')
ax[1].set_xlabel(r'$\mu3-\mu4$')

# Plot differences between filtration and condensation experiments
a = (mu1_sample+mu2_sample)/2 - (mu3_sample+mu4_sample)/2
pm.plot_posterior(a, ax=ax[2], ref_val=0, color='skyblue')
ax[2].set_xlabel(r'$(\mu1+\mu2)/2 - (\mu3+\mu4)/2$')

plt.tight_layout()
plt.savefig('Figure_9.18_lower.png')
plt.show()