def __init__(self, endog, exog, window=None, weights=None, min_nobs=None,
missing='drop'):
# Call Model.__init__ twice to use const detection in first pass
# But to not drop in the second pass
missing = string_like(missing, 'missing',
options=('drop', 'raise', 'skip'))
temp_msng = 'drop' if missing != 'raise' else 'raise'
Model.__init__(self, endog, exog, missing=temp_msng, hasconst=None)
k_const = self.k_constant
const_idx = self.data.const_idx
Model.__init__(self, endog, exog, missing='none', hasconst=False)
self.k_constant = k_const
self.data.const_idx = const_idx
self._y = array_like(endog, 'endog')
nobs = self._y.shape[0]
self._x = array_like(exog, 'endog', ndim=2, shape=(nobs, None))
window = int_like(window, 'window', optional=True)
weights = array_like(weights, 'weights', optional=True, shape=(nobs,))
self._window = window if window is not None else self._y.shape[0]
self._weighted = weights is not None
self._weights = np.ones(nobs) if weights is None else weights
w12 = np.sqrt(self._weights)
self._wy = w12 * self._y
self._wx = w12[:, None] * self._x
self._is_nan = np.zeros_like(self._y, dtype=np.bool)
self._has_nan = self._find_nans()
self.const_idx = self.data.const_idx
self._skip_missing = missing == 'skip'
min_nobs = int_like(min_nobs, 'min_nobs', optional=True)
self._min_nobs = min_nobs if min_nobs is not None else self._x.shape[1]
if self._min_nobs < self._x.shape[1] or self._min_nobs > self._window:
raise ValueError('min_nobs must be larger than the number of '
'regressors in the model and less than window')
def __init__(
self,
endog,
exog,
window=None,
*,
weights=None,
min_nobs=None,
missing="drop",
expanding=False
):
# Call Model.__init__ twice to use const detection in first pass
# But to not drop in the second pass
missing = string_like(
missing, "missing", options=("drop", "raise", "skip")
)
temp_msng = "drop" if missing != "raise" else "raise"
Model.__init__(self, endog, exog, missing=temp_msng, hasconst=None)
k_const = self.k_constant
const_idx = self.data.const_idx
Model.__init__(self, endog, exog, missing="none", hasconst=False)
self.k_constant = k_const
self.data.const_idx = const_idx
self._y = array_like(endog, "endog")
nobs = self._y.shape[0]
self._x = array_like(exog, "endog", ndim=2, shape=(nobs, None))
window = int_like(window, "window", optional=True)
weights = array_like(weights, "weights", optional=True, shape=(nobs,))
self._window = window if window is not None else self._y.shape[0]
self._weighted = weights is not None
self._weights = np.ones(nobs) if weights is None else weights
w12 = np.sqrt(self._weights)
self._wy = w12 * self._y
self._wx = w12[:, None] * self._x
min_nobs = int_like(min_nobs, "min_nobs", optional=True)
self._min_nobs = min_nobs if min_nobs is not None else self._x.shape[1]
if self._min_nobs < self._x.shape[1] or self._min_nobs > self._window:
raise ValueError(
"min_nobs must be larger than the number of "
"regressors in the model and less than window"
)
self._expanding = expanding
self._is_nan = np.zeros_like(self._y, dtype=bool)
self._has_nan = self._find_nans()
self.const_idx = self.data.const_idx
self._skip_missing = missing == "skip"
def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
return Model._handle_data(self, endog, exog, missing, hasconst,
**kwargs)
