def __init__(self, y, x, weights=None, intercept=True, nw_lags=None,
entity_effects=False, time_effects=False, x_effects=None,
cluster=None, dropped_dummies=None, verbose=False,
nw_overlap=False):
import warnings
warnings.warn("The pandas.stats.plm module is deprecated and will be "
"removed in a future version. We refer to external packages "
"like statsmodels, see some examples here: "
"http://www.statsmodels.org/stable/mixed_linear.html",
FutureWarning, stacklevel=4)
self._x_orig = x
self._y_orig = y
self._weights = weights
self._intercept = intercept
self._nw_lags = nw_lags
self._nw_overlap = nw_overlap
self._entity_effects = entity_effects
self._time_effects = time_effects
self._x_effects = x_effects
self._dropped_dummies = dropped_dummies or {}
self._cluster = com._get_cluster_type(cluster)
self._verbose = verbose
(self._x, self._x_trans,
self._x_filtered, self._y,
self._y_trans) = self._prepare_data()
self._index = self._x.index.levels[0]
self._T = len(self._index)
def __init__(self, y, x, weights=None, intercept=True, nw_lags=None,
entity_effects=False, time_effects=False, x_effects=None,
cluster=None, dropped_dummies=None, verbose=False,
nw_overlap=False):
self._x_orig = x
self._y_orig = y
self._weights = weights
self._intercept = intercept
self._nw_lags = nw_lags
self._nw_overlap = nw_overlap
self._entity_effects = entity_effects
self._time_effects = time_effects
self._x_effects = x_effects
self._dropped_dummies = dropped_dummies or {}
self._cluster = com._get_cluster_type(cluster)
self._verbose = verbose
(self._x, self._x_trans,
self._x_filtered, self._y,
self._y_trans) = self._prepare_data()
self._index = self._x.index.levels[0]
self._T = len(self._index)
def __init__(self, y, x, weights=None, intercept=True, nw_lags=None,
entity_effects=False, time_effects=False, x_effects=None,
cluster=None, dropped_dummies=None, verbose=False,
nw_overlap=False):
self._x_orig = x
self._y_orig = y
self._weights = weights
self._intercept = intercept
self._nw_lags = nw_lags
self._nw_overlap = nw_overlap
self._entity_effects = entity_effects
self._time_effects = time_effects
self._x_effects = x_effects
self._dropped_dummies = dropped_dummies or {}
self._cluster = com._get_cluster_type(cluster)
self._verbose = verbose
(self._x, self._x_trans,
self._x_filtered, self._y,
self._y_trans) = self._prepare_data()
self._index = self._x.index.levels[0]
self._T = len(self._index)
def __init__(self, y, x, intercept=True, nw_lags=None, entity_effects=False,
time_effects=False, x_effects=None, cluster=None,
dropped_dummies=None, verbose=False, nw_overlap=False):
self._x_orig = x
self._y_orig = y
self._intercept = intercept
self._nw_lags = nw_lags
self._nw_overlap = nw_overlap
self._entity_effects = entity_effects
self._time_effects = time_effects
self._x_effects = x_effects
self._dropped_dummies = dropped_dummies or {}
self._cluster = common._get_cluster_type(cluster)
self._verbose = verbose
(self._x, self._x_trans,
self._x_filtered, self._y,
self._y_trans) = self._prepare_data()
self._x_trans_raw = self._x_trans.values
self._y_trans_raw = self._y_trans.values.squeeze()
self._index = self._x.major_axis
self._T = len(self._index)
def __init__(self, y, x, weights=None, intercept=True, nw_lags=None,
entity_effects=False, time_effects=False, x_effects=None,
cluster=None, dropped_dummies=None, verbose=False,
nw_overlap=False):
import warnings
warnings.warn("The pandas.stats.plm module is deprecated and will be "
"removed in a future version. We refer to external packages "
"like statsmodels, see some examples here: "
"http://www.statsmodels.org/stable/mixed_linear.html",
FutureWarning, stacklevel=4)
self._x_orig = x
self._y_orig = y
self._weights = weights
self._intercept = intercept
self._nw_lags = nw_lags
self._nw_overlap = nw_overlap
self._entity_effects = entity_effects
self._time_effects = time_effects
self._x_effects = x_effects
self._dropped_dummies = dropped_dummies or {}
self._cluster = com._get_cluster_type(cluster)
self._verbose = verbose
(self._x, self._x_trans,
self._x_filtered, self._y,
self._y_trans) = self._prepare_data()
self._index = self._x.index.levels[0]
self._T = len(self._index)
