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

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:

array_like

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.

Notes

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