def lincombo_hierregress_taubymu(yy, stderrs, XX, maxtau=None, guess_range=False, draws=100):
yy, stdvars, XX = helpers.check_arguments(yy, stderrs, XX)
nummus = XX.shape[1]
print "Sampling tau..."
if maxtau is None:
maxtau = pooling.estimated_maxtau(yy, stderrs, XX)
print "Using maximum tau =", maxtau
if maxtau > 0:
probability_prior_tau = uniform(0, maxtau)
# Prepare to sample from from p(tau | yy)
# Create pdf for p(tau | yy)
def pdf(tau):
# Requires mus, but is invarient to them
return probability_tau([1] * nummus, tau, yy, stdvars, XX, probability_prior_tau)
dist = ContinuousSampled(pdf)
if guess_range:
mini, maxi = dist.guess_ranges(0, maxtau, draws * 10)
else:
mini, maxi = 0, maxtau
dist.prepare_draws(mini, maxi, count=draws)
else:
# maxtau == 0
dist = MultivariateDelta(np.zeros(nummus))
print "Sampling mus..."
return sample_posterior(yy, stderrs, XX, dist, draws)
def lincombo_hiernorm_taubyalpha(betas, stderrs, portions, maxtau=None, guess_range=False, draws=100):
betas, stdvars, portions = helpers.check_arguments(betas, stderrs, portions)
numalphas = portions.shape[1]
print "Sampling taus..."
observed_tau = 2 * np.sqrt(np.var(betas) + max(stderrs)**2)
if observed_tau == 0:
return None, None, None
if maxtau is None:
maxtau = pooling.estimated_maxtau(betas, stderrs, portions)
print "Using maximum tau =", maxtau, "vs.", observed_tau
if maxtau > 0:
probability_prior_taus = MultivariateUniform([0] * numalphas, [maxtau] * numalphas)
# Prepare to sample from from p(taus | betas)
# Create pdf for p(taus | betas)
def pdf(*taus): # taus is [tau1s, tau2s, ...]
transtaus = np.transpose(taus) # [[tau1, tau2, ...], ...]
values = []
for ii in range(len(taus[0])):
## Observation taus
obstaus = np.array([sum(portions[ii, :] * transtaus[ii]) for ii in range(portions.shape[0])])
# Requires alphas, but is invarient to them
values.append(probability_tau([np.mean(betas)] * numalphas, transtaus[ii], obstaus, betas, stdvars, portions, probability_prior_taus))
return values
dist = MultivariateSampled(pdf, numalphas)
if guess_range:
mins, maxs = dist.guess_ranges([0] * numalphas, [maxtau] * numalphas, draws * 10)
else:
mins = [0] * numalphas
maxs = [maxtau] * numalphas
dist.prepare_draws(mins, maxs, count=draws)
else:
# maxtau == 0
dist = MultivariateDelta(np.zeros(numalphas))
print "Sampling alphas..."
taus2obstaus = lambda taus: np.array([sum(portions[ii, :] * taus) for ii in range(portions.shape[0])])
return sample_posterior(betas, stderrs, portions, dist, taus2obstaus, draws)
