def test_sample_individual_estimates(testdata):
model = Model(testdata / 'nonmem' / 'pheno_real.mod')
np.random.seed(86)
samples = sample_individual_estimates(model)
assert len(samples) == 59 * 100
assert list(samples.columns) == ['ETA(1)', 'ETA(2)']
assert pytest.approx(samples.iloc[0]['ETA(1)'], 1e-5) == 0.0418399
assert pytest.approx(samples.iloc[0]['ETA(2)'], 1e-5) == -0.0587623
restricted = sample_individual_estimates(model,
parameters=['ETA(2)'],
samples_per_id=1)
assert len(restricted) == 59
assert restricted.columns == ['ETA(2)']
assert pytest.approx(restricted.iloc[0]['ETA(2)'], 1e-5) == -0.0167246
def test_sample_individual_estimates(testdata):
model = Model(testdata / 'nonmem' / 'pheno_real.mod')
rng = np.random.default_rng(86)
samples = sample_individual_estimates(model, seed=rng)
assert len(samples) == 59 * 100
assert list(samples.columns) == ['ETA(1)', 'ETA(2)']
assert pytest.approx(samples.iloc[0]['ETA(1)'],
1e-5) == 0.21179186940672637
assert pytest.approx(samples.iloc[0]['ETA(2)'],
1e-5) == -0.05771736555248238
restricted = sample_individual_estimates(model,
parameters=['ETA(2)'],
samples_per_id=1,
seed=rng)
assert len(restricted) == 59
assert restricted.columns == ['ETA(2)']
assert pytest.approx(restricted.iloc[0]['ETA(2)'],
1e-5) == 0.06399039578129821
def calculate_results_using_bipp(frem_model,
continuous,
categorical,
rescale=True,
samples=2000,
seed=None):
"""Estimate a covariance matrix for the frem model using the BIPP method
Bootstrap on the individual parameter posteriors
Only the individual estimates, individual unvertainties and the parameter estimates
are needed.
"""
if seed is None or isinstance(seed, int):
seed = np.random.default_rng(seed)
rvs, dist = frem_model.random_variables.iiv.distributions()[-1]
etas = [rv.name for rv in rvs]
pool = sample_individual_estimates(frem_model, parameters=etas, seed=seed)
ninds = len(pool.index.unique())
ishr = frem_model.modelfit_results.individual_shrinkage
lower_indices = np.tril_indices(len(etas))
pop_params = np.array(dist.sigma).astype(str)[lower_indices]
parameter_samples = np.empty((samples, len(pop_params)))
remaining_samples = samples
k = 0
while k < remaining_samples:
bootstrap = pool.sample(n=ninds,
replace=True,
random_state=seed.bit_generator)
ishk = ishr.loc[bootstrap.index]
cf = (1 / (1 - ishk.mean()))**(1 / 2)
corrected_bootstrap = bootstrap * cf
bootstrap_cov = corrected_bootstrap.cov()
if not is_posdef(bootstrap_cov.to_numpy()):
continue
parameter_samples[k, :] = bootstrap_cov.values[lower_indices]
k += 1
frame = pd.DataFrame(parameter_samples, columns=pop_params)
res = calculate_results_from_samples(frem_model,
continuous,
categorical,
frame,
rescale=rescale)
return res