def _store_inferential_results(self,
                                   value_array,
                                   index_names,
                                   attribute_name,
                                   series_name=None,
                                   column_names=None):
        """
        Store the estimation results that relate to statistical inference, such
        as parameter estimates, standard errors, p-values, etc.

        Parameters
        ----------
        value_array : 1D or 2D ndarray.
            Contains the values that are to be stored on the model instance.
        index_names : list of strings.
            Contains the names that are to be displayed on the 'rows' for each
            value being stored. There should be one element for each value of
            `value_array.`
        series_name : string or None, optional.
            The name of the pandas series being created for `value_array.` This
            kwarg should be None when `value_array` is a 1D ndarray.
        attribute_name : string.
            The attribute name that will be exposed on the model instance and
            related to the passed `value_array.`
        column_names : list of strings, or None, optional.
            Same as `index_names` except that it pertains to the columns of a
            2D ndarray. When `value_array` is a 2D ndarray, There should be one
            element for each column of `value_array.` This kwarg should be None
            otherwise.

        Returns
        -------
        None. Stores a pandas series or dataframe on the model instance.
        """
        if len(value_array.shape) == 1:
            assert series_name is not None
            new_attribute_value = pd.Series(value_array,
                                            index=index_names,
                                            name=series_name)
        elif len(value_array.shape) == 2:
            assert column_names is not None
            new_attribute_value = pd.DataFrame(value_array,
                                               index=index_names,
                                               columns=column_names)

        setattr(self, attribute_name, new_attribute_value)

        return None
def _store_inferential_results ( self , value_array , index_names , attribute_name , series_name = None , column_names = None ) : if len ( value_array . shape ) == 1 : assert series_name is not None new_attribute_value = pd . Series ( value_array , index = index_names , name = series_name ) elif len ( value_array . shape ) == 2 : assert column_names is not None new_attribute_value = pd . DataFrame ( value_array , index = index_names , columns = column_names ) setattr ( self , attribute_name , new_attribute_value ) return None
Store the estimation results that relate to statistical inference, such
        as parameter estimates, standard errors, p-values, etc.

        Parameters
        ----------
        value_array : 1D or 2D ndarray.
            Contains the values that are to be stored on the model instance.
        index_names : list of strings.
            Contains the names that are to be displayed on the 'rows' for each
            value being stored. There should be one element for each value of
            `value_array.`
        series_name : string or None, optional.
            The name of the pandas series being created for `value_array.` This
            kwarg should be None when `value_array` is a 1D ndarray.
        attribute_name : string.
            The attribute name that will be exposed on the model instance and
            related to the passed `value_array.`
        column_names : list of strings, or None, optional.
            Same as `index_names` except that it pertains to the columns of a
            2D ndarray. When `value_array` is a 2D ndarray, There should be one
            element for each column of `value_array.` This kwarg should be None
            otherwise.

        Returns
        -------
        None. Stores a pandas series or dataframe on the model instance.