statsmodels.regression.linear_model.OLSResults.outlier_test

OLSResults.outlier_test(method='bonf', alpha=0.05, labels=None, order=False, cutoff=None)[source]

Test observations for outliers according to method

Parameters:
  • method (str) –
    • bonferroni : one-step correction
    • sidak : one-step correction
    • holm-sidak :
    • holm :
    • simes-hochberg :
    • hommel :
    • fdr_bh : Benjamini/Hochberg
    • fdr_by : Benjamini/Yekutieli

    See statsmodels.stats.multitest.multipletests for details.

  • alpha (float) – familywise error rate
  • labels (None or array_like) – If labels is not None, then it will be used as index to the returned pandas DataFrame. See also Returns below
  • order (bool) – Whether or not to order the results by the absolute value of the studentized residuals. If labels are provided they will also be sorted.
  • cutoff (None or float in [0, 1]) – If cutoff is not None, then the return only includes observations with multiple testing corrected p-values strictly below the cutoff. The returned array or dataframe can be empty if t
Returns:

table – Returns either an ndarray or a DataFrame if labels is not None. Will attempt to get labels from model_results if available. The columns are the Studentized residuals, the unadjusted p-value, and the corrected p-value according to method.
Return type:

ndarray or DataFrame

Notes

The unadjusted p-value is stats.t.sf(abs(resid), df) where df = df_resid - 1.