def mean_method(df, target_col):
df_t0 = df.loc[(df['treatment'] == 0) & (df['in_delta'] == 1)]
df_t1 = df.loc[(df['treatment'] == 1) & (df['in_delta'] == 1)]
df_t0 = df_t0[target_col]
df_t1 = df_t1[target_col]
mean_0 = df_t0.mean()
mean_1 = df_t1.mean()
print(f"{'=' * 10}Mean Method Results{'=' * 10}")
effect = mean_1 - mean_0
print(f"Treatment effect on {target_col} is {mean_1 - mean_0}")
cm = CompareMeans.from_data(df_t1.values, df_t0.values)
result = cm.ttest_ind(alternative='two-sided')
print(f"Two-sided T test: CI={np.round(cm.tconfint_diff(), 3)} pvalue={round(result[1], 3)}")
return effect
def test_comparemeans_convenient_interface(self):
x1_2d, x2_2d = self.x1_2d, self.x2_2d
d1 = DescrStatsW(x1_2d)
d2 = DescrStatsW(x2_2d)
cm1 = CompareMeans(d1, d2)
# smoke test for summary
from statsmodels.iolib.table import SimpleTable
for use_t in [True, False]:
for usevar in ['pooled', 'unequal']:
smry = cm1.summary(use_t=use_t, usevar=usevar)
assert_(isinstance(smry, SimpleTable))
# test for from_data method
cm2 = CompareMeans.from_data(x1_2d, x2_2d)
assert_(str(cm1.summary()) == str(cm2.summary()))
def test_comparemeans_convenient_interface(self):
x1_2d, x2_2d = self.x1_2d, self.x2_2d
d1 = DescrStatsW(x1_2d)
d2 = DescrStatsW(x2_2d)
cm1 = CompareMeans(d1, d2)
# smoke test for summary
from statsmodels.iolib.table import SimpleTable
for use_t in [True, False]:
for usevar in ['pooled', 'unequal']:
smry = cm1.summary(use_t=use_t, usevar=usevar)
assert_(isinstance(smry, SimpleTable))
# test for from_data method
cm2 = CompareMeans.from_data(x1_2d, x2_2d)
assert_(str(cm1.summary()) == str(cm2.summary()))
