'create a summary table with all influence and outlier measures
This does currently not distinguish between statistics that can be
calculated from the original regression results and for which a
leave-one-observation-out loop is needed
Returns
res : SimpleTable instance
SimpleTable instance with the results, can be printed
Notes
This also attaches table_data to the instance.'

def summary_table(self, float_fmt='%6.3f'):

    table_raw = [('obs', np.arange(self.nobs)), ('endog', self.endog), ('fitted\nvalue', self.results.fittedvalues), ("Cook's\nd", self.cooks_distance[0]), ('student.\nresidual', self.resid_studentized_internal), ('hat    diag', self.hat_matrix_diag), ('dffits    \ninternal', self.dffits_internal[0]), ('ext.stud.\nresidual', self.resid_studentized_external), ('dffits', self.dffits[0])]
    (colnames, data) = lzip(*table_raw)
    data = np.column_stack(data)
    self.table_data = data
    from statsmodels.iolib.table import SimpleTable, default_html_fmt
    from statsmodels.iolib.tableformatting import fmt_base
    from copy import deepcopy
    fmt = deepcopy(fmt_base)
    fmt_html = deepcopy(default_html_fmt)
    fmt['data_fmts'] = (['%4d'] + ([float_fmt] * (data.shape[1] - 1)))
    return SimpleTable(data, headers=colnames, txt_fmt=fmt, html_fmt=fmt_html)
