def compute(
self,
observation,
prediction,
observation_name='observation',
prediction_name='prediction',
mcmc_iter=110000,
mcmc_burn=10000,
effect_size_type='mode', # 'mean'
assume_normal=False,
**kwargs):
if not pymc:
raise ImportError('Module best or pymc could not be loaded!')
data_dict = {
observation_name: observation,
prediction_name: prediction
}
best_model = self.make_model(data_dict, assume_normal)
M = MCMC(best_model)
M.sample(iter=mcmc_iter, burn=mcmc_burn)
group1_data = M.get_node(observation_name).value
group2_data = M.get_node(prediction_name).value
N1 = len(group1_data)
N2 = len(group2_data)
posterior_mean1 = M.trace('group1_mean')[:]
posterior_mean2 = M.trace('group2_mean')[:]
diff_means = posterior_mean1 - posterior_mean2
posterior_std1 = M.trace('group1_std')[:]
posterior_std2 = M.trace('group2_std')[:]
pooled_var = ((N1 - 1) * posterior_std1**2 +
(N2 - 1) * posterior_std2**2) / (N1 + N2 - 2)
self.effect_size = diff_means / np.sqrt(pooled_var)
stats = best.calculate_sample_statistics(self.effect_size)
self.score = best_effect_size(stats[effect_size_type])
self.score.mcmc_iter = mcmc_iter
self.score.mcmc_burn = mcmc_burn
self.score.data_size = [N1, N2]
self.score.HDI = (stats['hdi_min'], stats['hdi_max'])
self.HDI = self.score.HDI
return self.score
def compute(
self,
observation,
prediction,
observation_name='observation',
prediction_name='prediction',
mcmc_iter=110000,
mcmc_burn=10000,
effect_size_type='mode', # 'mean'
**kwargs):
self.mcmc_iter = mcmc_iter
self.mcmc_burn = mcmc_burn
data_dict = {
observation_name: observation,
prediction_name: prediction
}
best_model = self.make_model(data_dict)
M = MCMC(best_model)
M.sample(iter=mcmc_iter, burn=mcmc_burn)
group1_data = M.get_node(observation_name).value
group2_data = M.get_node(prediction_name).value
N1 = len(group1_data)
N2 = len(group2_data)
self.data_size = [N1, N2]
posterior_mean1 = M.trace('group1_mean')[:]
posterior_mean2 = M.trace('group2_mean')[:]
diff_means = posterior_mean1 - posterior_mean2
posterior_std1 = M.trace('group1_std')[:]
posterior_std2 = M.trace('group2_std')[:]
pooled_var = ((N1 - 1) * posterior_std1**2 +
(N2 - 1) * posterior_std2**2) / (N1 + N2 - 2)
self.effect_size = diff_means / np.sqrt(pooled_var)
stats = best.calculate_sample_statistics(self.effect_size)
self.HDI = (stats['hdi_min'], stats['hdi_max'])
self.score = best_effect_size(stats[effect_size_type])
return self.score
