scikit_posthocs.outliers_tietjen¶
- scikit_posthocs.outliers_tietjen(x: Union[List, ndarray], k: int, hypo: bool = False, alpha: float = 0.05) Union[ndarray, bool] ¶
Tietjen-Moore test 1 to detect multiple outliers in a univariate data set that follows an approximately normal distribution. The Tietjen-Moore test 2 is a generalization of the Grubbs’ test to the case of multiple outliers. If testing for a single outlier, the Tietjen-Moore test is equivalent to the Grubbs’ test.
The null hypothesis implies that there are no outliers in the data set.
- Parameters
x (Union[List, np.ndarray]) – An array, any object exposing the array interface, containing data to test for an outlier in.
k (int) – Number of potential outliers to test for. Function tests for outliers in both tails.
hypo (bool = False) –
Specifies whether to return a bool value of a hypothesis test result. Returns
True
when we can reject the null hypothesis. Otherwise,False
. Available options are:True
: return a hypothesis test resultFalse
: return a filtered array without outliers (default).
alpha (float = 0.05) – Significance level for a hypothesis test.
- Returns
Returns a filtered array if alternative hypothesis is true, otherwise an unfiltered array. Returns null hypothesis test result instead of an array if
hypo
argument is set to True.- Return type
Union[numpy.ndarray, bool]
Notes
- 1
Tietjen and Moore (August 1972), Some Grubbs-Type Statistics for the Detection of Outliers, Technometrics, 14(3), pp. 583-597.
- 2
http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h2.htm
Examples
>>> x = np.array([-1.40, -0.44, -0.30, -0.24, -0.22, -0.13, -0.05, 0.06, 0.10, 0.18, 0.20, 0.39, 0.48, 0.63, 1.01]) >>> outliers_tietjen(x, 2) array([-0.44, -0.3 , -0.24, -0.22, -0.13, -0.05, 0.06, 0.1 , 0.18, 0.2 , 0.39, 0.48, 0.63])