scikit-posthocs 0.3.4

Statistical post-hoc analysis and outlier detection algorithms

This Python package provides statistical post-hoc tests for pairwise multiple comparisons and outlier detection algorithms.

Features

• Multiple comparisons parametric and nonparametric tests (some are ported from R’s PMCMR package):

  • Conover, Dunn, and Nemenyi tests for use with Kruskal-Wallis test.

  • Quade, van Waerden, and Durbin tests.

  • Conover and Nemenyi tests for use with Friedman test.

  • Student, Mann-Whitney, Wilcoxon, and TukeyHSD tests.

  All tests are capable of p adjustments for multiple pairwise comparisons.

• Plotting functionality (e.g. significance plots).

• Outlier detection algorithms:

  • Simple test based on interquartile range (IQR).

  • Grubbs test.

  • Tietjen-Moore test.

  • Generalized Extreme Studentized Deviate test (ESD test).

Dependencies

• NumPy and SciPy packages

• Statsmodels

• Pandas

• Matplotlib

• Seaborn

Compatibility

Package is compatible with Python 2 and Python 3.

Install

You can install the package with: pip install scikit-posthocs

Example

>>> import scikit_posthocs as sp
>>> x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]
>>> # This will return a symmetric array of p values
>>> sp.posthoc_conover(x, p_adjust = 'holm')
array([[-1.        ,  0.00119517,  0.00278329],
       [ 0.00119517, -1.        ,  0.18672227],
       [ 0.00278329,  0.18672227, -1.        ]])

Credits

Thorsten Pohlert, PMCMR author and maintainer