{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A new statistical method to analyze Morris Water Maze data using Dirichlet distribution\n",
"\n",
"This notebook shows how you can easily reproduce the results presented in our paper and apply the Dirichlet test to your own data using Python. You only have to modify the names of the files to load your own data.\n",
"\n",
"Follow the instructions alongside the code and run the code to obtain your results !\n",
"\n",
"*Remember*: you need to have a working installation of Python (2 or 3) and Jupyter."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import sys\n",
"sys.path.insert(0, '../')\n",
"from dirichlet import dirichlet\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load data\n",
"Data must be passed as a numpy 2D array where each row [TQ,AQ1,OQ,AQ2] represents one sample (note that each row will be normalized so that its sum is 1). If your data is in a `.csv` file, you can use `pandas` or `numpy`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### With pandas (if this doesn't work, just use numpy)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data_3tg = pd.read_csv('3Tg.csv')\n",
"data_wt = pd.read_csv('wt.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TQ</th>\n",
" <th>AQ1</th>\n",
" <th>OQ</th>\n",
" <th>AQ2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>45.3</td>\n",
" <td>15.7</td>\n",
" <td>27.6</td>\n",
" <td>11.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>21.2</td>\n",
" <td>21.9</td>\n",
" <td>24.4</td>\n",
" <td>32.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>26.2</td>\n",
" <td>31.3</td>\n",
" <td>21.5</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>37.0</td>\n",
" <td>24.5</td>\n",
" <td>12.3</td>\n",
" <td>26.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>30.2</td>\n",
" <td>32.7</td>\n",
" <td>16.8</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>28.9</td>\n",
" <td>20.9</td>\n",
" <td>18.8</td>\n",
" <td>31.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>22.8</td>\n",
" <td>30.0</td>\n",
" <td>29.1</td>\n",
" <td>18.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TQ AQ1 OQ AQ2\n",
"0 45.3 15.7 27.6 11.4\n",
"1 21.2 21.9 24.4 32.5\n",
"2 26.2 31.3 21.5 21.0\n",
"3 37.0 24.5 12.3 26.3\n",
"4 30.2 32.7 16.8 20.3\n",
"5 28.9 20.9 18.8 31.5\n",
"6 22.8 30.0 29.1 18.2"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_3tg"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Pandas data frames need to be converted to numpy arrays\n",
"data_3tg = data_3tg.to_numpy()\n",
"data_wt = data_wt.to_numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### With numpy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Load .csv files...\n",
"data_3tg = np.loadtxt('3Tg.csv', delimiter=',', skiprows=1)\n",
"data_wt = np.loadtxt('wt.csv', delimiter=',', skiprows=1)\n",
"\n",
"# or simple text files ...\n",
"data_3tg = np.loadtxt('3Tg.txt')\n",
"data_wt = np.loadtxt('wt.txt')\n",
"\n",
"# or create 2d arrays manually\n",
"data_3tg = np.array([[45.3, 15.7, 27.6, 11.4],\n",
" [21.2, 21.9, 24.4, 32.5],\n",
" [26.2, 31.3, 21.5, 21. ],\n",
" [37. , 24.5, 12.3, 26.3],\n",
" [30.2, 32.7, 16.8, 20.3],\n",
" [28.9, 20.9, 18.8, 31.5],\n",
" [22.8, 30. , 29.1, 18.2]])\n",
"data_wt = np.array([[44.3, 12.9, 26.2, 16.7],\n",
" [28.2, 26.3, 27.6, 18. ],\n",
" [41.1, 15.2, 13.9, 29.9],\n",
" [57.5, 13.3, 13.2, 16.1],\n",
" [52.9, 9.3, 10.3, 27.5],\n",
" [41.9, 35.3, 14.7, 8.1],\n",
" [30.9, 26.9, 17.4, 24.8]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`data_3tg` should be a numpy 2D array at this point."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[45.3, 15.7, 27.6, 11.4],\n",
" [21.2, 21.9, 24.4, 32.5],\n",
" [26.2, 31.3, 21.5, 21. ],\n",
" [37. , 24.5, 12.3, 26.3],\n",
" [30.2, 32.7, 16.8, 20.3],\n",
" [28.9, 20.9, 18.8, 31.5],\n",
" [22.8, 30. , 29.1, 18.2]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_3tg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Apply the test\n",
"\n",
"You can use the `test_uniform` function separetely. The first output is the $\\Lambda$ statistics, with our Bartlett correction if `do_MWM_correction` is set to `True`. The second output is the $p$-value. The third and fourth are the estimated $\\alpha_i$ parameters for the best fit (alternative) and the uniformity (null) hypotheses."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Result of the Dirichlet uniformity test for group 3Tg:\n",
"# likelihood-ratio statistic (with MWM correction) = 3.95865\n",
"# p-value = 0.265964\n",
"# MLE params under null hypothesis (uniformity) :[8.40402434 8.40402434 8.40402434 8.40402434]\n",
"# MLE params under alternative hypothesis :[12.55460935 10.6084696 9.02098363 9.49037119]\n",
"\n",
"Result of the Dirichlet uniformity test for group wt:\n",
"# likelihood-ratio statistic (with MWM correction) = 14.621\n",
"# p-value = 0.00217089\n",
"# MLE params under null hypothesis (uniformity) :[3.01649146 3.01649146 3.01649146 3.01649146]\n",
"# MLE params under alternative hypothesis :[11.42349023 5.25568502 4.89513099 5.44866848]\n"
]
}
],
"source": [
"stat, pval, alpha_best, alpha_uni = dirichlet.test_uniform(data_3tg, label='3Tg', do_MWM_correction=True, verbose=True)\n",
"stat, pval, alpha_best, alpha_uni = dirichlet.test_uniform(data_wt, label='wt', do_MWM_correction=True, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Or use the `plot` function to make the plot. If `do_test_uniform` is `True`, then the uniformity test is run. `save_figure` allows you to save to a file."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Result of the Dirichlet uniformity test for group 3Tg:\n",
"# likelihood-ratio statistic (with MWM correction) = 3.95865\n",
"# p-value = 0.265964\n",
"# MLE params under null hypothesis (uniformity) :[8.40402434 8.40402434 8.40402434 8.40402434]\n",
"# MLE params under alternative hypothesis :[12.55460935 10.6084696 9.02098363 9.49037119]\n",
"\n",
"Result of the Dirichlet uniformity test for group wt:\n",
"# likelihood-ratio statistic (with MWM correction) = 14.621\n",
"# p-value = 0.00217089\n",
"# MLE params under null hypothesis (uniformity) :[3.01649146 3.01649146 3.01649146 3.01649146]\n",
"# MLE params under alternative hypothesis :[11.42349023 5.25568502 4.89513099 5.44866848]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 144x216 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 239,
"width": 162
},
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 144x216 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 239,
"width": 162
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dirichlet.plot(data_3tg, label='3Tg', do_test_uniform=True, do_MWM_correction=True, verbose=True, save_figure='3Tg.png')\n",
"dirichlet.plot(data_wt, label='wt', do_test_uniform=True, do_MWM_correction=True, verbose=True, save_figure='wt.png')"
]
},
{
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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