{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# scikit-posthocs\n",
"\n",
"## Pairwise Multiple Comparisons Post-hoc Tests\n",
"\n",
"in Python 3 (Ubuntu Linux)\n",
"\n",
"https://github.com/maximtrp/scikit-posthocs"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1. , 0.00119517, 0.00278329],\n",
" [ 0.00119517, -1. , 0.18672227],\n",
" [ 0.00278329, 0.18672227, -1. ]])"
]
},
"execution_count": 1,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"import scikit_posthocs as sp\n",
"x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]\n",
"sp.posthoc_conover(x, p_adjust = 'holm')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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]
},
"execution_count": 2,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"import scikit_posthocs as sp\n",
"import pandas as pd\n",
"x = pd.DataFrame({\"a\": [1,2,3,5,1], \"b\": [12,31,54,62,12], \"c\": [10,12,6,74,11]})\n",
"x = x.melt(var_name='groups', value_name='values')\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" -1.000000 | \n",
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"
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"
"
]
},
"execution_count": 3,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"sp.posthoc_conover(x, val_col='values', group_col='groups')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(,\n",
" )"
]
},
"execution_count": 4,
"metadata": {
},
"output_type": "execute_result"
},
{
"data": {
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},
"execution_count": 4,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"pc = sp.posthoc_conover(x, val_col='values', group_col='groups')\n",
"heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}\n",
"sp.sign_plot(pc, **heatmap_args)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(,\n",
" )"
]
},
"execution_count": 5,
"metadata": {
},
"output_type": "execute_result"
},
{
"data": {
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},
"execution_count": 5,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"pc = sp.posthoc_conover(x, val_col='values', group_col='groups')\n",
"# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05\n",
"cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']\n",
"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]}\n",
"sp.sign_plot(pc, **heatmap_args)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (Ubuntu Linux)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}