def analyze_simu(simu):
simu_l = simu[0]
simu_y = simu[1]
# Fit the simulated observation
input_data = dict(N=N, y=simu_y)
fit = fit_model.sampling(data=input_data, seed=4938483, n_jobs=1)
# Compute diagnostics
warning_code = stan_utility.check_all_diagnostics(fit, quiet=True)
# Compute rank of prior draw with respect to thinned posterior draws
thinned_l = fit.extract()['lambda'][numpy.arange(0, 4000 - 7, 8)]
sbc_rank = len(list(filter(lambda x: x > simu_l, thinned_l)))
# Compute posterior sensitivities
summary = fit.summary(probs=[0.5])
post_mean_l = [x[0] for x in summary['summary']][0]
post_sd_l = [x[2] for x in summary['summary']][0]
prior_sd_l = 6.44787
z_score = (post_mean_l - simu_l) / post_sd_l
shrinkage = 1 - (post_sd_l / prior_sd_l)**2
return [warning_code, sbc_rank, z_score, shrinkage]
x1_mean[m] = mcmc_stats[0]
x1_se[m] = mcmc_stats[1]
return iters, x1_mean, x1_se
############################################################
# Normal Model
############################################################
# Compile Stan program and fit with dynamic Hamiltonian Monte Carlo
model = stan_utility.compile_model('normal.stan')
fit = model.sampling(seed=4838282)
# Check diagnostics
stan_utility.check_all_diagnostics(fit)
# Check MCMC estimators
print(fit)
############################################################
# Student-t Model
############################################################
model = stan_utility.compile_model('student_t.stan')
# 100 degrees of freedom
data = dict(nu=100)
fit100 = model.sampling(data=data,
seed=4838282,
control=dict(metric="unit_e",
x_obs = fit.extract()['x_obs'][0,:])
pystan.stan_rdump(data, 'selection.data.R')
############################################################
# Fit model
############################################################
data = pystan.read_rdump('selection.data.R')
model = stan_utility.compile_model('selection.stan')
fit = model.sampling(data=data, chains=4, seed=4938483,
control=dict(adapt_delta=0.9, max_treedepth=12))
# Check diagnostics
stan_utility.check_all_diagnostics(fit)
# Default visual summaries
params = fit.extract()
# Plot marginal posteriors
f, axarr = plot.subplots(2, 2)
for a in axarr[0,:]:
a.xaxis.set_ticks_position('bottom')
a.yaxis.set_ticks_position('none')
for a in axarr[1,:]:
a.xaxis.set_ticks_position('bottom')
a.yaxis.set_ticks_position('none')
axarr[0, 0].set_title("mu")
axarr[0, 0].hist(params['mu'], bins = 25, color = dark, ec = dark_highlight)
# gen.model = stan_model("gen.stan")
# gen.data = sampling(data=d, algorithm='Fixed_param', warmup=0, iter=200)
# gendf = as.data.frame(gen.data)
# dataset.1 = gendf[1,]
#############
## Fit the model
#############
model = util.compile_model("../pooled.stan")
fit = model.sampling(dict(log_radon=df.log_radon,
basement = df.basement,
N=df.shape[0]))
# #############
# ## Check diagnostics
# #############
util.check_all_diagnostics(fit)
# #############
# ## Check & graph fit
# #############
print(fit)
sample = fit.extract()
# # This line just plots our data by basement indicator
g = ggplot(df, aes(x='basement', y='log_radon')) + geom_point()
# This next snippet will select every 10th posterior sample and plot the
# fit lines corresponding to that sample. It's a good way to get a sense of
# the uncertainty / distribution of the posterior
for i in range(0, len(sample['alpha']), 100):
g += geom_abline(intercept = sample['alpha'][i],