'Creates a DataFrame with all available influence results.
Returns
frame : DataFrame
A DataFrame with all results.
Notes
The resultant DataFrame contains six variables in addition to the
DFBETAS. These are:
* cooks_d : Cook\'s Distance defined in `Influence.cooks_distance`
* standard_resid : Standardized residuals defined in
`Influence.resid_studentized_internal`
* hat_diag : The diagonal of the projection, or hat, matrix defined in
`Influence.hat_matrix_diag`
* dffits_internal : DFFITS statistics using internally Studentized
residuals defined in `Influence.dffits_internal`
* dffits : DFFITS statistics using externally Studentized residuals
defined in `Influence.dffits`
* student_resid : Externally Studentized residuals defined in
`Influence.resid_studentized_external`'

def summary_frame(self):

    from pandas import DataFrame
    data = self.results.model.data
    row_labels = data.row_labels
    beta_labels = [('dfb_' + i) for i in data.xnames]
    summary_data = DataFrame(dict(cooks_d=self.cooks_distance[0], standard_resid=self.resid_studentized_internal, hat_diag=self.hat_matrix_diag, dffits_internal=self.dffits_internal[0], student_resid=self.resid_studentized_external, dffits=self.dffits[0]), index=row_labels)
    dfbeta = DataFrame(self.dfbetas, columns=beta_labels, index=row_labels)
    return dfbeta.join(summary_data)
