scikit-posthocs 0.4.0

Statistical post-hoc analysis and outlier detection algorithms

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

Contents:

• Features

• Dependencies

• Compatibility

• Install

• Examples

  • List or NumPy array

  • Pandas DataFrame

  • Significance plots

• Credits

Features

• Parametric and nonparametric pairwise multiple comparisons tests:

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

  • Conover, Nemenyi, Siegel, and Miller tests for use with Friedman test.

  • Quade, van Waerden, and Durbin tests.

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

  • Anderson-Darling test.

  • Mack-Wolfe test.

  • Nashimoto and Wright’s test (NPM test).

  • Scheffe test.

  • Tamhane T2 test.

• Plotting (significance plots).

• Outliers detection algorithms:

  • Simple interquartile range (IQR) test.

  • Grubbs test.

  • Tietjen-Moore test.

  • Generalized Extreme Studentized Deviate test (ESD test).

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

Dependencies

• NumPy and SciPy packages

• Statsmodels

• Pandas

• Matplotlib

• Seaborn

Compatibility

Package is compatible with Python 2 and 3 versions.

Install

Package can be installed from PyPi:

pip install scikit-posthocs

You can also install the development version from GitHub:

pip install git+https://github.com/maximtrp/scikit-posthocs.git

Examples

List or NumPy array

import scikit_posthocs as sp
x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]
sp.posthoc_conover(x, p_adjust = 'holm')

array([[-1.        ,  0.00119517,  0.00278329],
       [ 0.00119517, -1.        ,  0.18672227],
       [ 0.00278329,  0.18672227, -1.        ]])

Pandas DataFrame

Columns specified with val_col and group_col arguments must be melted prior to making comparisons.

import scikit_posthocs as sp
import pandas as pd
x = pd.DataFrame({"a": [1,2,3,5,1], "b": [12,31,54,62,12], "c": [10,12,6,74,11]})
x = x.melt(var_name='groups', value_name='values')

   groups  values
0       a       1
1       a       2
2       a       3
3       a       5
4       a       1
5       b      12
6       b      31
7       b      54
8       b      62
9       b      12
10      c      10
11      c      12
12      c       6
13      c      74
14      c      11

sp.posthoc_conover(x, val_col='values', group_col='groups', p_adjust = 'fdr_bh')

          a         b         c
a -1.000000  0.000328  0.002780
b  0.000328 -1.000000  0.121659
c  0.002780  0.121659 -1.000000

Significance plots

P values can be plotted using a heatmap:

pc = sp.posthoc_conover(x, val_col='values', group_col='groups')
heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
sp.sign_plot(pc, **heatmap_args)

Custom colormap applied to a plot:

pc = sp.posthoc_conover(x, val_col='values', group_col='groups')
# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
cmap = ['1', '#fb6a4a',  '#08306b',  '#4292c6', '#c6dbef']
heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
sp.sign_plot(pc, **heatmap_args)

Credits

Thorsten Pohlert, PMCMR author and maintainer