statsmodels.regression.linear_model.OLSResults.outlier_test

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

Test observations for outliers according to method.

Parameters:

• method (str) –

  The method to use in the outlier test. Must be one of:

  • 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) – The familywise error rate (FWER).

• 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:

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:

array_like

Notes

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