def _drop_missing(self) -> NDArray:
data = (self.portfolios, self.factors)
missing = np.any(np.c_[[dh.isnull for dh in data]], 0)
if any(missing):
if all(missing):
raise ValueError("All observations contain missing data. "
"Model cannot be estimated.")
self.portfolios.drop(missing)
self.factors.drop(missing)
missing_warning(missing)
return missing
def _drop_missing(self) -> ndarray:
data = (self.dependent, self.exog, self.endog, self.instruments, self.weights)
missing = any(c_[[dh.isnull for dh in data]], 0) # type: ndarray
if any(missing):
if npall(missing):
raise ValueError('All observations contain missing data. '
'Model cannot be estimated.')
self.dependent.drop(missing)
self.exog.drop(missing)
self.endog.drop(missing)
self.instruments.drop(missing)
self.weights.drop(missing)
missing_warning(missing)
return missing
def _drop_missing(self) -> ndarray:
missing = to_numpy(self.dependent.isnull)
missing |= to_numpy(self.exog.isnull)
missing |= to_numpy(self._absorb_inter.cat.isnull().any(1))
missing |= to_numpy(self._absorb_inter.cont.isnull().any(1))
for interact in self._interaction_list:
missing |= to_numpy(interact.isnull)
if npany(missing):
self.dependent.drop(missing)
self.exog.drop(missing)
self._absorb_inter.drop(missing)
for interact in self._interaction_list:
interact.drop(missing)
missing_warning(missing)
return missing
def _drop_missing(self):
k = len(self._dependent)
nobs = self._dependent[0].shape[0]
missing = np.zeros(nobs, dtype=np.bool)
for i in range(k):
missing |= self._dependent[i].isnull
missing |= self._exog[i].isnull
missing |= self._weights[i].isnull
missing_warning(missing)
if np.any(missing):
for i in range(k):
self._dependent[i].drop(missing)
self._exog[i].drop(missing)
self._weights[i].drop(missing)
def test_missing_warning():
missing = np.zeros(500, dtype=np.bool)
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 0
missing[0] = True
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 1
original = linearmodels.WARN_ON_MISSING
linearmodels.WARN_ON_MISSING = False
with warnings.catch_warnings(record=True) as w:
missing_warning(missing)
assert len(w) == 0
linearmodels.WARN_ON_MISSING = original
import numpy as np import linearmodels as lm lm.WARN_ON_MISSING = False from linearmodels import utility utility.missing_warning(np.array([True, True, False])) from linearmodels.panel import PanelOLS, RandomEffects, PooledOLS from linearmodels.datasets import wage_panel import statsmodels.api as sm data = wage_panel.load() data = data.set_index(['nr','year']) dependent = data.lwage exog = sm.add_constant(data[['expersq','married','union']]) mod = PanelOLS(dependent, exog, entity_effects=True, time_effects=True) res = mod.fit(cov_type='unadjusted') res2 = mod.fit(cov_type='robust') exog = sm.add_constant(data[['exper', 'expersq','married','union']]) mod = PanelOLS(dependent, exog, entity_effects=True) res3 = mod.fit(cov_type='clustered',cluster_entity=True) mod = RandomEffects(dependent, exog) res4 = mod.fit(cov_type='robust') from linearmodels.panel.results import compare exog = sm.add_constant(data[['exper', 'expersq','married','union']].copy()) import pandas as pd exog['year'] = pd.Categorical(data.reset_index()['year']) mod = PooledOLS(dependent, exog) res5 = mod.fit(cov_type='robust') print(compare([res,res2, res3, res4, res5])) print(data.columns)
