def test_phreg(self):
np.random.seed(8742)
n = 300
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
event_time = np.random.exponential(size=n) * np.exp(x1)
obs_time = np.random.exponential(size=n)
time = np.where(event_time < obs_time, event_time, obs_time)
status = np.where(time == event_time, 1, 0)
df = pd.DataFrame({"time": time, "status": status, "x1": x1, "x2": x2})
df.loc[10:40, 'time'] = np.nan
df.loc[10:40, 'status'] = np.nan
df.loc[30:50, 'x1'] = np.nan
df.loc[40:60, 'x2'] = np.nan
from statsmodels.duration.hazard_regression import PHReg
idata = mice.MICEData(df)
idata.set_imputer("time",
"0 + x1 + x2",
model_class=PHReg,
init_kwds={"status": mice.PatsyFormula("status")},
predict_kwds={"pred_type": "hr"})
x = idata.next_sample()
assert (isinstance(x, pd.DataFrame))
def test_phreg(self):
np.random.seed(8742)
n = 300
x1 = np.random.normal(size=n)
x2 = np.random.normal(size=n)
event_time = np.random.exponential(size=n) * np.exp(x1)
obs_time = np.random.exponential(size=n)
time = np.where(event_time < obs_time, event_time, obs_time)
status = np.where(time == event_time, 1, 0)
df = pd.DataFrame({"time": time, "status": status, "x1": x1, "x2": x2})
df.loc[10:40, 'time'] = np.nan
df.loc[10:40, 'status'] = np.nan
df.loc[30:50, 'x1'] = np.nan
df.loc[40:60, 'x2'] = np.nan
from statsmodels.duration.hazard_regression import PHReg
# Save the dataset size at each iteration.
hist = []
def cb(imp):
hist.append(imp.data.shape)
for pm in "gaussian", "boot":
idata = mice.MICEData(df, perturbation_method=pm, history_callback=cb)
idata.set_imputer("time", "0 + x1 + x2", model_class=PHReg,
init_kwds={"status": mice.PatsyFormula("status")},
predict_kwds={"pred_type": "hr"},
perturbation_method=pm)
x = idata.next_sample()
assert(isinstance(x, pd.DataFrame))
assert(all([val == (299, 4) for val in hist]))
