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, 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)