def test_VNJ_gln(self):
model = apc.Model()
model.data_from_df(apc.loss_VNJ(), data_format='CL')
model.fit('gen_log_normal_response', 'APC')
model.simulate(repetitions=10)
model.simulate(repetitions=10, fitted_values=model.fitted_values * 10)
model.simulate(repetitions=10, sigma2=10)
def test_VNJ(self):
model = apc.Model()
model.data_from_df(apc.loss_VNJ(), data_format='CL')
model.fit('log_normal_response', 'AC')
sub_models = [model.sub_model(coh_from_to=(1,5)),
model.sub_model(coh_from_to=(6,10))]
f = apc.f_test(model, sub_models)
self.assertEqual(round(f['F_stat'], 3), 0.242)
self.assertEqual(round(f['p_value'], 3), 0.912)
def test_VNJ(self):
model = apc.Model()
model.data_from_df(apc.loss_VNJ(), data_format='CL')
model.fit('log_normal_response', 'AC')
sub_models = [
model.sub_model(coh_from_to=(1, 5)),
model.sub_model(coh_from_to=(6, 10))
]
bartlett = apc.bartlett_test(sub_models)
self.assertEqual(round(bartlett['B'], 3), 2.794)
self.assertEqual(round(bartlett['LR'], 3), 2.956)
self.assertEqual(round(bartlett['C'], 3), 1.058)
self.assertEqual(round(bartlett['m'], 3), 2)
self.assertEqual(round(bartlett['p_value'], 3), 0.095)
def test_VNJ(self):
r = apc.r_test(apc.loss_VNJ(), 'gen_log_normal_response', 'AC')
self.assertEqual(round(r['R_stat'], 3), 113.185)
self.assertEqual(round(r['p_value'], 5), 0.00114)
self.assertEqual(round(r['power_at_R'], 5), 0.82657)