def mod_2():
# Data
y0, n0, y1, n1 = data.full()
# y_j = np.log(y0/(n0 - y0)) - np.log(y1/(n1 - y1))
# V_j = 1.0/y1 + 1.0/(n1 - y1) + 1.0/y0 + 1.0/(n0 - y1)
# Priors
# tau_sq = pymc.Uniform('tau_sq', lower=0., upper=1., value=0.5)
tau_sq = pymc.Beta('tau_sq', alpha=1, beta=6, value=0.01)
theta = pymc.Normal('theta', mu = 0, tau=10**-6, value=0)
p1p = pymc.Uniform('p1p', lower=0., upper=1., value=0.5)
p0p = pymc.Uniform('p0p', lower=0., upper=1., value=0.5)
# Second Level
theta_j = pymc.Normal('theta_j', mu = [theta]*22, tau=[1/tau_sq]*22, value=[0]*22)
@deterministic
def p0(th=theta_j,p1=p1p):
c1 = np.exp(-th) + p1/(1+p1)
return c1/(1+c1)
@deterministic
def p1(th=theta_j,p0=p0p):
c0 = np.exp(-th) + p0/(1+p0)
return c0/(1+c0)
# Likelihood function
y_j_obs_1 = pymc.Binomial('y_j_obs_1', value=y1, p=p1, n=n1, observed=True)
y_j_obs_0 = pymc.Binomial('y_j_obs_0', value=y0, p=p0, n=n0, observed=True)
return vars()
def mod_1():
# Data
y0, n0, y1, n1 = data.full()
y_j = np.log(y0 / (n0 - y0)) - np.log(y1 / (n1 - y1))
V_j = 1.0 / y1 + 1.0 / (n1 - y1) + 1.0 / y0 + 1.0 / (n0 - y1)
sig2_c = sum(V_j)
# Priors
# tau_sq = pymc.Uniform('tau_sq', lower=0., upper=1., value=0.5)
tau_sq = pymc.Beta('tau_sq', alpha=1, beta=6, value=0.01)
theta = pymc.Normal('theta', mu=0, tau=10**-6, value=0)
# Second Level
theta_j = pymc.Normal('theta_j',
mu=[theta] * 22,
tau=[1 / tau_sq] * 22,
value=[0] * 22)
# Likelihood function
y_j_obs = pymc.Normal('y_j_obs',
value=y_j,
mu=theta_j,
tau=1 / V_j,
observed=True)
return vars()
def mod_2():
# Data
y0, n0, y1, n1 = data.full()
# y_j = np.log(y0/(n0 - y0)) - np.log(y1/(n1 - y1))
# V_j = 1.0/y1 + 1.0/(n1 - y1) + 1.0/y0 + 1.0/(n0 - y1)
# Priors
# tau_sq = pymc.Uniform('tau_sq', lower=0., upper=1., value=0.5)
tau_sq = pymc.Beta('tau_sq', alpha=1, beta=6, value=0.01)
theta = pymc.Normal('theta', mu=0, tau=10**-6, value=0)
p1p = pymc.Uniform('p1p', lower=0., upper=1., value=0.5)
p0p = pymc.Uniform('p0p', lower=0., upper=1., value=0.5)
# Second Level
theta_j = pymc.Normal('theta_j',
mu=[theta] * 22,
tau=[1 / tau_sq] * 22,
value=[0] * 22)
@deterministic
def p0(th=theta_j, p1=p1p):
c1 = np.exp(-th) + p1 / (1 + p1)
return c1 / (1 + c1)
@deterministic
def p1(th=theta_j, p0=p0p):
c0 = np.exp(-th) + p0 / (1 + p0)
return c0 / (1 + c0)
# Likelihood function
y_j_obs_1 = pymc.Binomial('y_j_obs_1', value=y1, p=p1, n=n1, observed=True)
y_j_obs_0 = pymc.Binomial('y_j_obs_0', value=y0, p=p0, n=n0, observed=True)
return vars()
def mod_1():
data_x, data_y = data.full()
# Covariates:
# Intercept, Age, Weight, Race, Visit
beta = pymc.Normal('beta', mu = [0]*5, tau=[10**-6]*5 , value=[0]*5)
@deterministic
def y_mean(beta=beta, data=data_x):
return 1.0/(1.0 + np.exp(-np.dot(data_x,beta)))
y_obs = pymc.Bernoulli('y_obs', value=data_y, p=y_mean, observed=True)
return vars()
def mod_1():
data_x, data_y = data.full()
# Covariates:
# Intercept, Age, Weight, Race, Visit
beta = pymc.Normal('beta', mu=[0] * 5, tau=[10**-6] * 5, value=[0] * 5)
@deterministic
def y_mean(beta=beta, data=data_x):
return 1.0 / (1.0 + np.exp(-np.dot(data_x, beta)))
y_obs = pymc.Bernoulli('y_obs', value=data_y, p=y_mean, observed=True)
return vars()
def mod_4():
data_x, data_y = data.full()
data_x = data_x[:,1:3]
data_x[:,0] = 1
# Covariates:
# Intercept, Weight
beta = pymc.Normal('beta', mu = [0]*2, tau=[10**-6]*2 , value=[0]*2)
@deterministic
def y_mean(beta=beta, data=data_x):
return 1.0/(1.0 + np.exp(-np.dot(data_x,beta)))
y_obs = pymc.Bernoulli('y_obs', value=data_y, p=y_mean, observed=True)
return vars()
def mod_4():
data_x, data_y = data.full()
data_x = data_x[:, 1:3]
data_x[:, 0] = 1
# Covariates:
# Intercept, Weight
beta = pymc.Normal('beta', mu=[0] * 2, tau=[10**-6] * 2, value=[0] * 2)
@deterministic
def y_mean(beta=beta, data=data_x):
return 1.0 / (1.0 + np.exp(-np.dot(data_x, beta)))
y_obs = pymc.Bernoulli('y_obs', value=data_y, p=y_mean, observed=True)
return vars()
def mod_1():
# Data
y0, n0, y1, n1 = data.full()
y_j = np.log(y0/(n0 - y0)) - np.log(y1/(n1 - y1))
V_j = 1.0/y1 + 1.0/(n1 - y1) + 1.0/y0 + 1.0/(n0 - y1)
sig2_c = sum(V_j)
# Priors
# tau_sq = pymc.Uniform('tau_sq', lower=0., upper=1., value=0.5)
tau_sq = pymc.Beta('tau_sq', alpha=1, beta=6, value=0.01)
theta = pymc.Normal('theta', mu = 0, tau=10**-6, value=0)
# Second Level
theta_j = pymc.Normal('theta_j', mu = [theta]*22, tau=[1/tau_sq]*22, value=[0]*22)
# Likelihood function
y_j_obs = pymc.Normal('y_j_obs', value=y_j, mu=theta_j, tau=1/V_j, observed=True)
return vars()
