04-effets_aleatoires.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effets fixes et aléatoires\n",
    "\n",
    "- *Auteurs :* Cristian Preda, Quentin Grimonprez\n",
    "- *Date :* 8 mars 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectif du TP\n",
    "\n",
    "L’objectif de ce TP est d'introduire les effets aléatoires dans un modèle d'analyse de la variance et plus généralement dans un modèle de régression. On fait appel à ce type d’effets (technique) dans le contexte des mesures répétées ou l'hypothèse d'indépendance des observations n'est plus valide. Nous allons illustrer cela de manière progressive à l'aide d’un exemple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Présentation du problème et des données.\n",
    "\n",
    "Il s'agit de voir si le passage du sucre dans le sang (absorption) est différent chez les patients obèses et chez les patients contrôle (non-obèses). Pour cela, on réalise le plan d'expérience suivant : on forme un échantillon aléatoire de 13 patients obèses et un échantillon aléatoire de 20 patients contrôle. À chaque patient, on administre une quantité fixée de sucre (10 mg) et on regarde ensuite la glycémie (unité de mesure non-précisée) à 8 instants de temps différents : à t 0 = 0 (avant la dose du sucre), à t_1 = 0.5 heures après la prise de sucre, puis à t_2 = 1h, t_3 = 1.5h, t_4 = 2h, t_5 = 3h, t_6 = 4h et t_7 = 5h.\n",
    "\n",
    "La base de données est disponible en format csv (séparateur \";\") à l’adresse : <http://math.univ-lille1.fr/~preda/GIS5/glycemie.csv>.\n",
    "Remarquez la présence d'un en-tête pour les noms de variables dont un identificateur pour chaque patient\n",
    "(id). Pour des raisons qui seront évidentes plus tard, n'utilisez pas cette colonne comme row.names lors de la lecture des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import statsmodels.api as sm  # API classique\n",
    "import statsmodels.formula.api as smf  # API avec des formules\n",
    "from scipy.stats import ranksums, shapiro, ttest_ind, norm\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = 12, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>id</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
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       "      <td>4.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.4</td>\n",
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       "      <td>3.9</td>\n",
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       "      <td>3.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.9</td>\n",
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       "      <td>3.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.8</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.6</td>\n",
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       "      <td>3.6</td>\n",
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       "      <td>6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>control</td>\n",
       "      <td>7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
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       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.3</td>\n",
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       "      <td>control</td>\n",
       "      <td>10</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>control</td>\n",
       "      <td>11</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.9</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>control</td>\n",
       "      <td>12</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.8</td>\n",
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       "      <td>13</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.7</td>\n",
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       "      <td>4.3</td>\n",
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       "      <td>2.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>3.4</td>\n",
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       "      <td>4.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.9</td>\n",
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       "      <th>15</th>\n",
       "      <td>control</td>\n",
       "      <td>16</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
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       "      <td>2.6</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.7</td>\n",
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       "      <td>19</td>\n",
       "      <td>4.8</td>\n",
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       "      <td>2.9</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>control</td>\n",
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       "      <td>3.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>3.5</td>\n",
       "      <td>3.7</td>\n",
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       "    <tr>\n",
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       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>obese</td>\n",
       "      <td>23</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>obese</td>\n",
       "      <td>24</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>obese</td>\n",
       "      <td>25</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>obese</td>\n",
       "      <td>26</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.3</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>obese</td>\n",
       "      <td>27</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>obese</td>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>obese</td>\n",
       "      <td>29</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>obese</td>\n",
       "      <td>30</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>obese</td>\n",
       "      <td>31</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>obese</td>\n",
       "      <td>32</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>obese</td>\n",
       "      <td>33</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     groupe  id   t0   t1   t2   t3   t4   t5   t6   t7\n",
       "0   control   1  4.3  3.3  3.0  2.6  2.2  2.5  3.4  4.4\n",
       "1   control   2  3.7  2.6  2.6  1.9  2.9  3.2  3.1  3.9\n",
       "2   control   3  4.0  4.1  3.1  2.3  2.9  3.1  3.9  4.0\n",
       "3   control   4  3.6  3.0  2.2  2.8  2.9  3.9  3.8  4.0\n",
       "4   control   5  4.1  3.8  2.1  3.0  3.6  3.4  3.6  3.7\n",
       "5   control   6  3.8  2.2  2.0  2.6  3.8  3.6  3.0  3.5\n",
       "6   control   7  3.8  3.0  2.4  2.5  3.1  3.4  3.5  3.7\n",
       "7   control   8  4.4  3.9  2.8  2.1  3.6  3.8  4.0  3.9\n",
       "8   control   9  5.0  4.0  3.4  3.4  3.3  3.6  4.0  4.3\n",
       "9   control  10  3.7  3.1  2.9  2.2  1.5  2.3  2.7  2.8\n",
       "10  control  11  3.7  2.6  2.6  2.3  2.9  2.2  3.1  3.9\n",
       "11  control  12  4.4  3.7  3.1  3.2  3.7  4.3  3.9  4.8\n",
       "12  control  13  4.7  3.1  3.2  3.3  3.2  4.2  3.7  4.3\n",
       "13  control  14  4.3  3.3  3.0  2.6  2.2  2.5  2.4  3.4\n",
       "14  control  15  5.0  4.9  4.1  3.7  3.7  4.1  4.7  4.9\n",
       "15  control  16  4.6  4.4  3.9  3.9  3.7  4.2  4.8  5.0\n",
       "16  control  17  4.3  3.9  3.1  3.1  3.1  3.1  3.6  4.0\n",
       "17  control  18  3.1  3.1  3.3  2.6  2.6  1.9  2.3  2.7\n",
       "18  control  19  4.8  5.0  2.9  2.8  2.2  3.1  3.5  3.6\n",
       "19  control  20  3.7  3.1  3.3  2.8  2.9  3.6  4.3  4.4\n",
       "20    obese  21  5.4  4.7  3.9  4.1  2.8  3.7  3.5  3.7\n",
       "21    obese  22  3.0  2.5  2.3  2.2  2.1  2.6  3.2  3.5\n",
       "22    obese  23  4.9  5.0  4.1  3.7  3.7  4.1  4.7  4.9\n",
       "23    obese  24  4.8  4.3  4.7  4.6  4.7  3.7  3.6  3.9\n",
       "24    obese  25  4.4  4.2  4.2  3.4  3.5  3.4  3.8  4.0\n",
       "25    obese  26  4.9  4.3  4.0  4.0  3.3  4.1  4.2  4.3\n",
       "26    obese  27  5.1  4.1  4.6  4.1  3.4  4.2  4.4  4.9\n",
       "27    obese  28  4.8  4.6  4.6  4.4  4.1  4.0  3.8  3.8\n",
       "28    obese  29  4.2  3.5  3.8  3.6  3.3  3.1  3.5  3.9\n",
       "29    obese  30  6.6  6.1  5.2  4.1  4.3  3.8  4.2  4.8\n",
       "30    obese  31  3.6  3.4  3.1  2.8  2.1  2.4  2.5  3.5\n",
       "31    obese  32  4.5  4.0  3.7  3.3  2.4  2.3  3.1  3.3\n",
       "32    obese  33  4.6  4.4  3.8  3.8  3.8  3.6  3.8  3.8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glycemie = pd.read_table(\"data/glycemie.csv\", sep=\";\", dtype={\"id\": str})\n",
    "glycemie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "Voici quelques tâches qui vous sont demandées :\n",
    "\n",
    "1. Statistiques descriptives pour chaque variable temps.\n",
    "Préciser notamment la moyenne et l’écart-type.\n",
    "1. Représentation graphique des données.\n",
    "1. Comparaison des deux groupes par l'évolution moyenne de la glycémie.\n",
    "1. Les premiers tests statistiques pour comparer les groupes.\n",
    "Pour chaque temps, comparer les deux groupes selon le niveau moyen de la glycémie.\n",
    "Note : Selon que l'hypothèse de normalité des données est vérifiée (à l'aide du test de Shapiro), on utilisera le test de Student, ou, dans le cas contraire, le test de Wilcoxon. Pour rappel, les tests de Student et Wilcoxon permettent de vérifier l'hypothèse nulle selon laquelle les deux groupes ont la même espérance de la glycémie. Le test de Wilcoxon est un test non-paramétrique - c'est-à-dire que son utilisation n'est pas conditionnée par la loi des données.\n",
    "Au vu des résultats numériques (et graphiques) il y a donc des différences significatives entre les deux groupes. Allons plus en détail.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistiques descriptives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>groupe</th>\n",
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       "      <td>33</td>\n",
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       "      <td>33.000000</td>\n",
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       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
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       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>freq</th>\n",
       "      <td>20</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3.793939</td>\n",
       "      <td>3.363636</td>\n",
       "      <td>3.145455</td>\n",
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       "      <td>3.363636</td>\n",
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       "      <td>0.859120</td>\n",
       "      <td>0.806930</td>\n",
       "      <td>0.731476</td>\n",
       "      <td>0.713187</td>\n",
       "      <td>0.689120</td>\n",
       "      <td>0.633951</td>\n",
       "      <td>0.575066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.700000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3.100000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.800000</td>\n",
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       "      <td>3.100000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.600000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3.900000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.300000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4.600000</td>\n",
       "      <td>4.700000</td>\n",
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       "      <td>4.800000</td>\n",
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       "         groupe   id         t0         t1         t2         t3         t4  \\\n",
       "count        33   33  33.000000  33.000000  33.000000  33.000000  33.000000   \n",
       "unique        2   33        NaN        NaN        NaN        NaN        NaN   \n",
       "top     control    1        NaN        NaN        NaN        NaN        NaN   \n",
       "freq         20    1        NaN        NaN        NaN        NaN        NaN   \n",
       "mean        NaN  NaN   4.357576   3.793939   3.363636   3.145455   3.136364   \n",
       "std         NaN  NaN   0.708886   0.859120   0.806930   0.731476   0.713187   \n",
       "min         NaN  NaN   3.000000   2.200000   2.000000   1.900000   1.500000   \n",
       "25%         NaN  NaN   3.800000   3.100000   2.900000   2.600000   2.800000   \n",
       "50%         NaN  NaN   4.400000   3.900000   3.200000   3.100000   3.200000   \n",
       "75%         NaN  NaN   4.800000   4.300000   3.900000   3.700000   3.700000   \n",
       "max         NaN  NaN   6.600000   6.100000   5.200000   4.600000   4.700000   \n",
       "\n",
       "               t5         t6         t7  \n",
       "count   33.000000  33.000000  33.000000  \n",
       "unique        NaN        NaN        NaN  \n",
       "top           NaN        NaN        NaN  \n",
       "freq          NaN        NaN        NaN  \n",
       "mean     3.363636   3.624242   3.984848  \n",
       "std      0.689120   0.633951   0.575066  \n",
       "min      1.900000   2.300000   2.700000  \n",
       "25%      3.100000   3.200000   3.700000  \n",
       "50%      3.600000   3.600000   3.900000  \n",
       "75%      3.900000   4.000000   4.300000  \n",
       "max      4.300000   4.800000   5.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glycemie.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>25%</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "<p>64 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "groupe      control      obese\n",
       "t0 count  20.000000  13.000000\n",
       "   mean    4.150000   4.676923\n",
       "   std     0.515548   0.858442\n",
       "   min     3.100000   3.000000\n",
       "   25%     3.700000   4.400000\n",
       "...             ...        ...\n",
       "t7 min     2.700000   3.300000\n",
       "   25%     3.675000   3.700000\n",
       "   50%     3.950000   3.900000\n",
       "   75%     4.325000   4.300000\n",
       "   max     5.000000   4.900000\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glycemie.groupby(\"groupe\").describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>mean</th>\n",
       "      <td>2.950000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.541441</td>\n",
       "      <td>0.740495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.225000</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.100000</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">t3</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.785000</td>\n",
       "      <td>3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.529424</td>\n",
       "      <td>0.658281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.450000</td>\n",
       "      <td>3.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.125000</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.900000</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">t4</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.346154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.622389</td>\n",
       "      <td>0.815082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.825000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">t5</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.715983</td>\n",
       "      <td>0.661486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.950000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.825000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">t6</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.565000</td>\n",
       "      <td>3.715385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.669859</td>\n",
       "      <td>0.588566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.100000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.925000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">t7</th>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.960000</td>\n",
       "      <td>4.023077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.608190</td>\n",
       "      <td>0.541839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>3.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.675000</td>\n",
       "      <td>3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.950000</td>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.325000</td>\n",
       "      <td>4.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "groupe      control      obese\n",
       "t0 count  20.000000  13.000000\n",
       "   mean    4.150000   4.676923\n",
       "   std     0.515548   0.858442\n",
       "   min     3.100000   3.000000\n",
       "   25%     3.700000   4.400000\n",
       "   50%     4.200000   4.800000\n",
       "   75%     4.450000   4.900000\n",
       "   max     5.000000   6.600000\n",
       "t1 count  20.000000  13.000000\n",
       "   mean    3.505000   4.238462\n",
       "   std     0.747258   0.854925\n",
       "   min     2.200000   2.500000\n",
       "   25%     3.075000   4.000000\n",
       "   50%     3.300000   4.300000\n",
       "   75%     3.925000   4.600000\n",
       "   max     5.000000   6.100000\n",
       "t2 count  20.000000  13.000000\n",
       "   mean    2.950000   4.000000\n",
       "   std     0.541441   0.740495\n",
       "   min     2.000000   2.300000\n",
       "   25%     2.600000   3.800000\n",
       "   50%     3.000000   4.000000\n",
       "   75%     3.225000   4.600000\n",
       "   max     4.100000   5.200000\n",
       "t3 count  20.000000  13.000000\n",
       "   mean    2.785000   3.700000\n",
       "   std     0.529424   0.658281\n",
       "   min     1.900000   2.200000\n",
       "   25%     2.450000   3.400000\n",
       "   50%     2.700000   3.800000\n",
       "   75%     3.125000   4.100000\n",
       "   max     3.900000   4.600000\n",
       "t4 count  20.000000  13.000000\n",
       "   mean    3.000000   3.346154\n",
       "   std     0.622389   0.815082\n",
       "   min     1.500000   2.100000\n",
       "   25%     2.825000   2.800000\n",
       "   50%     3.000000   3.400000\n",
       "   75%     3.600000   3.800000\n",
       "   max     3.800000   4.700000\n",
       "t5 count  20.000000  13.000000\n",
       "   mean    3.300000   3.461538\n",
       "   std     0.715983   0.661486\n",
       "   min     1.900000   2.300000\n",
       "   25%     2.950000   3.100000\n",
       "   50%     3.400000   3.700000\n",
       "   75%     3.825000   4.000000\n",
       "   max     4.300000   4.200000\n",
       "t6 count  20.000000  13.000000\n",
       "   mean    3.565000   3.715385\n",
       "   std     0.669859   0.588566\n",
       "   min     2.300000   2.500000\n",
       "   25%     3.100000   3.500000\n",
       "   50%     3.600000   3.800000\n",
       "   75%     3.925000   4.200000\n",
       "   max     4.800000   4.700000\n",
       "t7 count  20.000000  13.000000\n",
       "   mean    3.960000   4.023077\n",
       "   std     0.608190   0.541839\n",
       "   min     2.700000   3.300000\n",
       "   25%     3.675000   3.700000\n",
       "   50%     3.950000   3.900000\n",
       "   75%     4.325000   4.300000\n",
       "   max     5.000000   4.900000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", 64)\n",
    "glycemie.groupby(\"groupe\").describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Représentation graphique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons mettre en forme les données pour faire plus facilement nos analyses et graphes dans la suite. Pour cela, nous devons créer une variable contenant l'ensemble des temps et une contenant la valeur de glycémie. La méthode melt permet de transformer un DataFrame en un nouveau avec des colonnes d'identifiants choisies. Toutes les autres colonnes seront combinées en 2 colonnes : variable et value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>groupe</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>control</td>\n",
       "      <td>t0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>t0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>control</td>\n",
       "      <td>t0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>control</td>\n",
       "      <td>t0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>control</td>\n",
       "      <td>t0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>29</td>\n",
       "      <td>obese</td>\n",
       "      <td>t7</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>30</td>\n",
       "      <td>obese</td>\n",
       "      <td>t7</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>31</td>\n",
       "      <td>obese</td>\n",
       "      <td>t7</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>32</td>\n",
       "      <td>obese</td>\n",
       "      <td>t7</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>33</td>\n",
       "      <td>obese</td>\n",
       "      <td>t7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id   groupe variable  value\n",
       "0     1  control       t0    4.3\n",
       "1     2  control       t0    3.7\n",
       "2     3  control       t0    4.0\n",
       "3     4  control       t0    3.6\n",
       "4     5  control       t0    4.1\n",
       "..   ..      ...      ...    ...\n",
       "259  29    obese       t7    3.9\n",
       "260  30    obese       t7    4.8\n",
       "261  31    obese       t7    3.5\n",
       "262  32    obese       t7    3.3\n",
       "263  33    obese       t7    3.8\n",
       "\n",
       "[264 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melted = glycemie.melt(id_vars=[\"id\", \"groupe\"])\n",
    "df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>groupe</th>\n",
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       "  </thead>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>t1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>t2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
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       "      <td>2.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>t4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>9</td>\n",
       "      <td>control</td>\n",
       "      <td>t3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>9</td>\n",
       "      <td>control</td>\n",
       "      <td>t4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>9</td>\n",
       "      <td>control</td>\n",
       "      <td>t5</td>\n",
       "      <td>3.6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>9</td>\n",
       "      <td>control</td>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>9</td>\n",
       "      <td>control</td>\n",
       "      <td>t7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id   groupe variable  value  time\n",
       "0    1  control       t0    4.3   0.0\n",
       "33   1  control       t1    3.3   0.5\n",
       "66   1  control       t2    3.0   1.0\n",
       "99   1  control       t3    2.6   1.5\n",
       "132  1  control       t4    2.2   2.0\n",
       "..  ..      ...      ...    ...   ...\n",
       "107  9  control       t3    3.4   1.5\n",
       "140  9  control       t4    3.3   2.0\n",
       "173  9  control       t5    3.6   3.0\n",
       "206  9  control       t6    4.0   4.0\n",
       "239  9  control       t7    4.3   5.0\n",
       "\n",
       "[264 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_temps = {\"t0\": 0, \"t1\": 0.5, \"t2\": 1, \"t3\": 1.5, \"t4\": 2, \"t5\": 3, \"t6\": 4, \"t7\": 5}\n",
    "df_melted[\"time\"] = df_melted[\"variable\"]\n",
    "df_melted[\"time\"].replace(dict_temps, inplace=True)\n",
    "df_melted.sort_values([\"id\", \"time\"], inplace=True)\n",
    "df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_melted, x=\"time\", y=\"value\", units=\"id\", hue=\"groupe\", style=\"groupe\", estimator=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparaison des deux groupes par l'évolution moyenne de la glycémie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_melted, x=\"time\", y=\"value\", hue=\"groupe\", estimator=\"mean\", ci=95)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_melted, x=\"time\", y=\"value\", hue=\"groupe\", order=np.arange(0, 5.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Les premiers tests statistiques pour comparer les groupes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous comparons l'effet moyen de chaque groupe pour chaque instant de temps. On effectue d'abord un test de Shapiro (`shapiro`) pour tester la normalité des données puis un test de Student (`ttest_ind`) ou un test de Wilcoxon (`ranksums`) selon le résultat du test de Shapiro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pval = np.zeros(8)\n",
    "\n",
    "for i in range(len(pval)):\n",
    "    var = \"t\" + str(i)\n",
    "    shap_control = shapiro(glycemie[var][glycemie[\"groupe\"] == \"control\"])\n",
    "    shap_obese = shapiro(glycemie[var][glycemie[\"groupe\"] == \"obese\"])\n",
    "\n",
    "    if (shap_control.pvalue > 0.05) & (shap_obese.pvalue > 0.05):\n",
    "        pval[i] = ttest_ind(glycemie[var][glycemie[\"groupe\"] == \"control\"], glycemie[var][glycemie[\"groupe\"] == \"obese\"]).pvalue\n",
    "    else:\n",
    "        pval[i] = ranksums(glycemie[var][glycemie[\"groupe\"] == \"control\"], glycemie[var][glycemie[\"groupe\"] == \"obese\"]).pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.46711090e-02, 1.40254157e-02, 4.95542967e-05, 1.16304398e-04,\n",
       "       1.76977738e-01, 5.19180773e-01, 5.14157990e-01, 7.63550232e-01])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True, False, False, False, False])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pval < 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'hypothèse d'égalité des moyennes est rejetée pour les 4 premiers temps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au vu des résultats numériques (et graphiques) il y a donc des différences significatives entre les deux groupes. Allons plus en détail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un modèle de régression quadratique \n",
    "\n",
    "L'évolution de la glycémie en fonction du temps semble une fonction quadratique, c’est à dire une courbe (parabole) en \"U\" :\n",
    "\n",
    "y(t) = a + bt + ct^2 + ε\n",
    "\n",
    "avec a, b et c des coefficients et ε une erreur aléatoire."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimons un modèle de régression quadratique pour chaque groupe séparément. La variable explicative est donc le temps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans l'API de statsmodels avec des formules, les fonctions ont le même nom que dans l'API classique mais écrit en minuscule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>value</td>      <th>  R-squared:         </th> <td>   0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   30.14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>8.38e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:49:27</td>     <th>  Log-Likelihood:    </th> <td> -157.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   160</td>      <th>  AIC:               </th> <td>   320.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   157</td>      <th>  BIC:               </th> <td>   329.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    3.8811</td> <td>    0.114</td> <td>   34.114</td> <td> 0.000</td> <td>    3.656</td> <td>    4.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>              <td>   -0.8051</td> <td>    0.116</td> <td>   -6.926</td> <td> 0.000</td> <td>   -1.035</td> <td>   -0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th> <td>    0.1714</td> <td>    0.022</td> <td>    7.618</td> <td> 0.000</td> <td>    0.127</td> <td>    0.216</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.181</td> <th>  Durbin-Watson:     </th> <td>   0.870</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.336</td> <th>  Jarque-Bera (JB):  </th> <td>   1.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.027</td> <th>  Prob(JB):          </th> <td>   0.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.507</td> <th>  Cond. No.          </th> <td>    34.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  value   R-squared:                       0.277\n",
       "Model:                            OLS   Adj. R-squared:                  0.268\n",
       "Method:                 Least Squares   F-statistic:                     30.14\n",
       "Date:                Wed, 08 Mar 2023   Prob (F-statistic):           8.38e-12\n",
       "Time:                        17:49:27   Log-Likelihood:                -157.21\n",
       "No. Observations:                 160   AIC:                             320.4\n",
       "Df Residuals:                     157   BIC:                             329.6\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             3.8811      0.114     34.114      0.000       3.656       4.106\n",
       "time                 -0.8051      0.116     -6.926      0.000      -1.035      -0.576\n",
       "np.power(time, 2)     0.1714      0.022      7.618      0.000       0.127       0.216\n",
       "==============================================================================\n",
       "Omnibus:                        2.181   Durbin-Watson:                   0.870\n",
       "Prob(Omnibus):                  0.336   Jarque-Bera (JB):                1.640\n",
       "Skew:                           0.027   Prob(JB):                        0.440\n",
       "Kurtosis:                       2.507   Cond. No.                         34.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_control = smf.ols(\"value ~ time + np.power(time, 2)\", data=df_melted[df_melted[\"groupe\"] == \"control\"])\n",
    "res_control = mod_control.fit()\n",
    "res_control.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous testons la normalité des résidus avec le test de Shapiro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9921519160270691, pvalue=0.5321125984191895)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(res_control.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La p-valeur est > 0.05, on ne rejette donc pas l'hypothèse nulle de normalité des résidus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous testons l'homoscédasticité des résidus avec le test de Breusch-Pagan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5068825072007854, 0.7761253381551653, 0.2494795853310602, 0.779514499414012)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.het_breuschpagan(res_control.resid, res_control.model.exog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La fonction effectue 2 tests. Le 1er correspond à celui fait en R. Le 1er élément est la statistique de test, le 2ème la p-valeur. Ici on ne rejette pas l'hypothèse nulle d'homoscédasticité des résidus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous testons l'autocorrélation des résidus avec le test de Durbin-Watson. Dans statsmodels, il existe une fonction pour calculer la statistique de test mais pas la p-valeur, nous créons donc cette fonction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dw_test(X, residuals):\n",
    "    \"\"\" Test de Durbin-Watson\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array-like\n",
    "        Matrice de design de la régression\n",
    "    residuals : array-like\n",
    "        Vecteur des résidus de la régression\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tuple\n",
    "        Statistique de test, p-valeur\n",
    "    \"\"\"\n",
    "    dw = np.sum(np.square(np.diff(residuals)))/np.sum(np.square(residuals))\n",
    "    n, k = X.shape\n",
    "    AX = X[:-2, ] * -1 + X[1:-1, ] * 2 + X[2:, ] * -1\n",
    "    AX = np.vstack((X[0, :] - X[1, :], AX, X[-1, :] - X[-2, :]))\n",
    "    _, r = np.linalg.qr(X)\n",
    "    Q1 = np.linalg.inv(np.matmul(r.T, r))\n",
    "    XAXQ = np.matmul(np.matmul(X.T, AX), Q1)\n",
    "    P = 2 * (n - 1) - XAXQ.trace()\n",
    "    Q = 2 * (3 * n - 4) - 2 * np.matmul(np.matmul(AX.T, AX), Q1).trace() + np.matmul(XAXQ, XAXQ).trace()\n",
    "    dmean = P/(n - k)\n",
    "    dvar = 2/((n - k) * (n - k + 2)) * (Q - P * dmean)\n",
    "    pval = norm.cdf(dw, dmean, np.sqrt(dvar))\n",
    "\n",
    "    return dw, pval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On crée la matrice X qui contient une colonne de 1, le temps et le temps au carré (matrice de design de notre régression)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack((np.ones(len(df_melted[df_melted[\"groupe\"] == \"control\"])),\n",
    "              df_melted[df_melted[\"groupe\"] == \"control\"][\"time\"],\n",
    "              df_melted[df_melted[\"groupe\"] == \"control\"][\"time\"] ** 2), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8700764537276818, 2.258372712745064e-13)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dw_test(X, res_control.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La p-valeur est inférieure à 0.05, on rejette l'hypothèse nulle de non autocorrélation. On a donc un problème d’autocorrélation des résidus! Corrélation due au temps!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut faire le même travail avec le groupe obèse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>value</td>      <th>  R-squared:         </th> <td>   0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   16.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>7.89e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:49:27</td>     <th>  Log-Likelihood:    </th> <td> -110.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   104</td>      <th>  AIC:               </th> <td>   227.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   101</td>      <th>  BIC:               </th> <td>   235.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    4.6562</td> <td>    0.154</td> <td>   30.288</td> <td> 0.000</td> <td>    4.351</td> <td>    4.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>              <td>   -0.8712</td> <td>    0.157</td> <td>   -5.547</td> <td> 0.000</td> <td>   -1.183</td> <td>   -0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th> <td>    0.1517</td> <td>    0.030</td> <td>    4.991</td> <td> 0.000</td> <td>    0.091</td> <td>    0.212</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.125</td> <th>  Durbin-Watson:     </th> <td>   0.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.346</td> <th>  Jarque-Bera (JB):  </th> <td>   1.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.220</td> <th>  Prob(JB):          </th> <td>   0.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.402</td> <th>  Cond. No.          </th> <td>    34.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  value   R-squared:                       0.243\n",
       "Model:                            OLS   Adj. R-squared:                  0.228\n",
       "Method:                 Least Squares   F-statistic:                     16.20\n",
       "Date:                Wed, 08 Mar 2023   Prob (F-statistic):           7.89e-07\n",
       "Time:                        17:49:27   Log-Likelihood:                -110.55\n",
       "No. Observations:                 104   AIC:                             227.1\n",
       "Df Residuals:                     101   BIC:                             235.0\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             4.6562      0.154     30.288      0.000       4.351       4.961\n",
       "time                 -0.8712      0.157     -5.547      0.000      -1.183      -0.560\n",
       "np.power(time, 2)     0.1517      0.030      4.991      0.000       0.091       0.212\n",
       "==============================================================================\n",
       "Omnibus:                        2.125   Durbin-Watson:                   0.533\n",
       "Prob(Omnibus):                  0.346   Jarque-Bera (JB):                1.542\n",
       "Skew:                          -0.220   Prob(JB):                        0.463\n",
       "Kurtosis:                       3.402   Cond. No.                         34.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_obese = smf.ols(\"value ~ time + np.power(time, 2)\", data=df_melted[df_melted[\"groupe\"] == \"obese\"])\n",
    "res_obese = mod_obese.fit()\n",
    "res_obese.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9781584739685059, pvalue=0.08322436362504959)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(res_obese.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.9578583310329707,\n",
       " 0.22788158123141172,\n",
       " 1.4783123481936398,\n",
       " 0.23291356351270606)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.het_breuschpagan(res_obese.resid, res_obese.model.exog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5326766146928679, 1.4091209326309271e-14)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.stack((np.ones(len(df_melted[df_melted[\"groupe\"] == \"obese\"])),\n",
    "              df_melted[df_melted[\"groupe\"] == \"obese\"][\"time\"],\n",
    "              df_melted[df_melted[\"groupe\"] == \"obese\"][\"time\"] ** 2), axis=1)\n",
    "dw_test(X, res_obese.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traçons les deux fonctions de régression sur le même graphique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_control = res_control.predict(pd.DataFrame({\"time\": list(dict_temps.values())}))\n",
    "pred_obese = res_obese.predict(pd.DataFrame({\"time\": list(dict_temps.values())}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.881115\n",
       "1    3.521394\n",
       "2    3.247353\n",
       "3    3.058992\n",
       "4    2.956309\n",
       "5    3.007982\n",
       "6    3.402373\n",
       "7    4.139481\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x=list(dict_temps.values()), y=pred_control, label=\"control\")\n",
    "sns.lineplot(x=list(dict_temps.values()), y=pred_obese, label=\"obese\", ax=ax)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Fonction de régression\")\n",
    "ax.set_title(\"Fonction de régression pour les deux groupes\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparaison des fonctions de régression :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            3.881115\n",
       "time                -0.805120\n",
       "np.power(time, 2)    0.171359\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_control.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            4.656199\n",
       "time                -0.871185\n",
       "np.power(time, 2)    0.151689\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_obese.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On teste un autre modèle en y incorporant la variable groupe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>value</td>      <th>  R-squared:         </th> <td>   0.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   25.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>1.30e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:49:28</td>     <th>  Log-Likelihood:    </th> <td> -268.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   264</td>      <th>  AIC:               </th> <td>   548.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   258</td>      <th>  BIC:               </th> <td>   569.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                         <td>    3.8811</td> <td>    0.118</td> <td>   32.931</td> <td> 0.000</td> <td>    3.649</td> <td>    4.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]</th>                   <td>    0.7751</td> <td>    0.188</td> <td>    4.128</td> <td> 0.000</td> <td>    0.405</td> <td>    1.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                              <td>   -0.8051</td> <td>    0.120</td> <td>   -6.686</td> <td> 0.000</td> <td>   -1.042</td> <td>   -0.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:time</th>              <td>   -0.0661</td> <td>    0.192</td> <td>   -0.344</td> <td> 0.731</td> <td>   -0.444</td> <td>    0.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th>                 <td>    0.1714</td> <td>    0.023</td> <td>    7.354</td> <td> 0.000</td> <td>    0.125</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:np.power(time, 2)</th> <td>   -0.0197</td> <td>    0.037</td> <td>   -0.530</td> <td> 0.597</td> <td>   -0.093</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.337</td> <th>  Durbin-Watson:     </th> <td>   0.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.845</td> <th>  Jarque-Bera (JB):  </th> <td>   0.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.085</td> <th>  Prob(JB):          </th> <td>   0.840</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.949</td> <th>  Cond. No.          </th> <td>    84.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  value   R-squared:                       0.327\n",
       "Model:                            OLS   Adj. R-squared:                  0.314\n",
       "Method:                 Least Squares   F-statistic:                     25.11\n",
       "Date:                Wed, 08 Mar 2023   Prob (F-statistic):           1.30e-20\n",
       "Time:                        17:49:28   Log-Likelihood:                -268.17\n",
       "No. Observations:                 264   AIC:                             548.3\n",
       "Df Residuals:                     258   BIC:                             569.8\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================================\n",
       "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------\n",
       "Intercept                             3.8811      0.118     32.931      0.000       3.649       4.113\n",
       "groupe[T.obese]                       0.7751      0.188      4.128      0.000       0.405       1.145\n",
       "time                                 -0.8051      0.120     -6.686      0.000      -1.042      -0.568\n",
       "groupe[T.obese]:time                 -0.0661      0.192     -0.344      0.731      -0.444       0.312\n",
       "np.power(time, 2)                     0.1714      0.023      7.354      0.000       0.125       0.217\n",
       "groupe[T.obese]:np.power(time, 2)    -0.0197      0.037     -0.530      0.597      -0.093       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.337   Durbin-Watson:                   0.725\n",
       "Prob(Omnibus):                  0.845   Jarque-Bera (JB):                0.349\n",
       "Skew:                          -0.085   Prob(JB):                        0.840\n",
       "Kurtosis:                       2.949   Cond. No.                         84.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = smf.ols(\"value ~ groupe * (time + np.power(time, 2))\", data=df_melted)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`groupe[T.obese]` est une constante présente uniquement pour le groupe obèse. Donc, la constante pour une personne du groupe contrôle est 3.8811 et pour celle du groupe obèse 3.8811+0.7751=4.6562. Pareil pour les variables associées aux temps et aux carrés du temps. À peu de choses près, on obtient les 2 modèles précédents réunis en un seul."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9954620599746704, pvalue=0.6339918971061707)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(res.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.609310466619119, 0.346108405590119, 1.120165825634961, 0.3500090415400841)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.het_breuschpagan(res.resid, res.model.exog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7245727321282358, 7.142407484359622e-26)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(pd.DataFrame({\"time\": df_melted[\"time\"], \"residuals\": res.resid}))\n",
    "resid = smf.ols(\"residuals ~ time\", data=X).fit().resid\n",
    "dw_test(X.values, resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour résoudre le problème d'autocorrélation, nous allons faire un modèle mixte. On commence avec la variante la plus simple : intercept aléatoire."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos groupes sont les individus, l'effet aléatoire (`re_formula`) est 1 (intercept)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>value</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>264</td>         <td>Method:</td>         <td>REML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>33</td>          <td>Scale:</td>         <td>0.2047</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>8</td>      <td>Log-Likelihood:</td>   <td>-216.2237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>8</td>        <td>Converged:</td>         <td>Yes</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>8.0</td>            <td></td>               <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                   <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                          <td>3.881</td>   <td>0.139</td>  <td>27.908</td> <td>0.000</td>  <td>3.609</td>  <td>4.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]</th>                    <td>0.775</td>   <td>0.222</td>   <td>3.498</td> <td>0.000</td>  <td>0.341</td>  <td>1.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                              <td>-0.805</td>   <td>0.081</td>  <td>-9.989</td> <td>0.000</td> <td>-0.963</td> <td>-0.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:time</th>              <td>-0.066</td>   <td>0.128</td>  <td>-0.514</td> <td>0.607</td> <td>-0.318</td>  <td>0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th>                  <td>0.171</td>   <td>0.016</td>  <td>10.987</td> <td>0.000</td>  <td>0.141</td>  <td>0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:np.power(time, 2)</th> <td>-0.020</td>   <td>0.025</td>  <td>-0.792</td> <td>0.429</td> <td>-0.068</td>  <td>0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                          <td>0.262</td>   <td>0.172</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                   Mixed Linear Model Regression Results\n",
       "============================================================================\n",
       "Model:                   MixedLM        Dependent Variable:        value    \n",
       "No. Observations:        264            Method:                    REML     \n",
       "No. Groups:              33             Scale:                     0.2047   \n",
       "Min. group size:         8              Log-Likelihood:            -216.2237\n",
       "Max. group size:         8              Converged:                 Yes      \n",
       "Mean group size:         8.0                                                \n",
       "----------------------------------------------------------------------------\n",
       "                                  Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
       "----------------------------------------------------------------------------\n",
       "Intercept                          3.881    0.139 27.908 0.000  3.609  4.154\n",
       "groupe[T.obese]                    0.775    0.222  3.498 0.000  0.341  1.209\n",
       "time                              -0.805    0.081 -9.989 0.000 -0.963 -0.647\n",
       "groupe[T.obese]:time              -0.066    0.128 -0.514 0.607 -0.318  0.186\n",
       "np.power(time, 2)                  0.171    0.016 10.987 0.000  0.141  0.202\n",
       "groupe[T.obese]:np.power(time, 2) -0.020    0.025 -0.792 0.429 -0.068  0.029\n",
       "Group Var                          0.262    0.172                           \n",
       "============================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_mixte1 = smf.mixedlm(\"value ~ groupe * (time + np.power(time, 2))\", df_melted, re_formula=\"1\", groups=df_melted[\"id\"])\n",
    "res_mixte1 = mod_mixte1.fit()\n",
    "res_mixte1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les effets aléatoires :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': Group   -0.172543\n",
       " dtype: float64,\n",
       " '10': Group   -0.685047\n",
       " dtype: float64,\n",
       " '11': Group   -0.445878\n",
       " dtype: float64,\n",
       " '12': Group    0.442462\n",
       " dtype: float64,\n",
       " '13': Group    0.283016\n",
       " dtype: float64,\n",
       " '14': Group   -0.400322\n",
       " dtype: float64,\n",
       " '15': Group    0.898021\n",
       " dtype: float64,\n",
       " '16': Group    0.829687\n",
       " dtype: float64,\n",
       " '17': Group    0.112181\n",
       " dtype: float64,\n",
       " '18': Group   -0.639491\n",
       " dtype: float64,\n",
       " '19': Group    0.078014\n",
       " dtype: float64,\n",
       " '2': Group   -0.377544\n",
       " dtype: float64,\n",
       " '20': Group    0.100792\n",
       " dtype: float64,\n",
       " '21': Group    0.072714\n",
       " dtype: float64,\n",
       " '22': Group   -1.111739\n",
       " dtype: float64,\n",
       " '23': Group    0.44855\n",
       " dtype: float64,\n",
       " '24': Group    0.357439\n",
       " dtype: float64,\n",
       " '25': Group   -0.029787\n",
       " dtype: float64,\n",
       " '26': Group    0.220771\n",
       " dtype: float64,\n",
       " '27': Group    0.414383\n",
       " dtype: float64,\n",
       " '28': Group    0.334661\n",
       " dtype: float64,\n",
       " '29': Group   -0.257566\n",
       " dtype: float64,\n",
       " '3': Group    0.02107\n",
       " dtype: float64,\n",
       " '30': Group    0.904109\n",
       " dtype: float64,\n",
       " '31': Group   -0.88396\n",
       " dtype: float64,\n",
       " '32': Group   -0.519512\n",
       " dtype: float64,\n",
       " '33': Group    0.049936\n",
       " dtype: float64,\n",
       " '4': Group   -0.115598\n",
       " dtype: float64,\n",
       " '5': Group    0.009681\n",
       " dtype: float64,\n",
       " '6': Group   -0.309211\n",
       " dtype: float64,\n",
       " '7': Group   -0.20671\n",
       " dtype: float64,\n",
       " '8': Group    0.146348\n",
       " dtype: float64,\n",
       " '9': Group    0.431073\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mixte1.random_effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie que les effets aléatoires ont bien une moyenne nulle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0455018150669552e-15"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(list(res_mixte1.random_effects.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La matrice de covariance des effets aléatoires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>0.262334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Group\n",
       "Group  0.262334"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mixte1.cov_re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9968621730804443, pvalue=0.8855305314064026)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(res_mixte1.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4780358084308232, 9.060999243561599e-06)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(pd.DataFrame({\"time\": df_melted[\"time\"], \"residuals\": res_mixte1.resid}))\n",
    "resid = smf.ols(\"residuals ~ time\", data=X).fit().resid\n",
    "dw_test(X.values, resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=range(len(res_mixte1.resid)), y=res_mixte1.resid)\n",
    "ax.set_ylabel(\"Résidus (res_mixte1)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Réalisons un modèle mixte basé sur notre modèle quadratique avec intercept et pente aléatoires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/quentin/.local/lib/python3.10/site-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>value</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>264</td>         <td>Method:</td>         <td>REML</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>33</td>          <td>Scale:</td>         <td>0.1759</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>8</td>      <td>Log-Likelihood:</td>   <td>-211.0955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>8</td>        <td>Converged:</td>         <td>Yes</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>8.0</td>            <td></td>               <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                   <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                          <td>3.881</td>   <td>0.149</td>  <td>25.990</td>  <td>0.000</td>  <td>3.588</td>  <td>4.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]</th>                    <td>0.775</td>   <td>0.238</td>   <td>3.258</td>  <td>0.001</td>  <td>0.309</td>  <td>1.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                              <td>-0.805</td>   <td>0.078</td>  <td>-10.329</td> <td>0.000</td> <td>-0.958</td> <td>-0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:time</th>              <td>-0.066</td>   <td>0.124</td>  <td>-0.532</td>  <td>0.595</td> <td>-0.309</td>  <td>0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th>                  <td>0.171</td>   <td>0.014</td>  <td>11.852</td>  <td>0.000</td>  <td>0.143</td>  <td>0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>groupe[T.obese]:np.power(time, 2)</th> <td>-0.020</td>   <td>0.023</td>  <td>-0.854</td>  <td>0.393</td> <td>-0.065</td>  <td>0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                          <td>0.339</td>   <td>0.259</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group x time Cov</th>                  <td>-0.028</td>   <td>0.043</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time Var</th>                           <td>0.010</td>   <td>0.012</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                    Mixed Linear Model Regression Results\n",
       "=============================================================================\n",
       "Model:                    MixedLM        Dependent Variable:        value    \n",
       "No. Observations:         264            Method:                    REML     \n",
       "No. Groups:               33             Scale:                     0.1759   \n",
       "Min. group size:          8              Log-Likelihood:            -211.0955\n",
       "Max. group size:          8              Converged:                 Yes      \n",
       "Mean group size:          8.0                                                \n",
       "-----------------------------------------------------------------------------\n",
       "                                  Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "-----------------------------------------------------------------------------\n",
       "Intercept                          3.881    0.149  25.990 0.000  3.588  4.174\n",
       "groupe[T.obese]                    0.775    0.238   3.258 0.001  0.309  1.241\n",
       "time                              -0.805    0.078 -10.329 0.000 -0.958 -0.652\n",
       "groupe[T.obese]:time              -0.066    0.124  -0.532 0.595 -0.309  0.177\n",
       "np.power(time, 2)                  0.171    0.014  11.852 0.000  0.143  0.200\n",
       "groupe[T.obese]:np.power(time, 2) -0.020    0.023  -0.854 0.393 -0.065  0.025\n",
       "Group Var                          0.339    0.259                            \n",
       "Group x time Cov                  -0.028    0.043                            \n",
       "time Var                           0.010    0.012                            \n",
       "=============================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_mixte2 = smf.mixedlm(\"value ~ groupe * (time + np.power(time, 2))\", df_melted, re_formula=\"time\", groups=df_melted[\"id\"])\n",
    "res_mixte2 = mod_mixte2.fit()\n",
    "res_mixte2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': Group   -0.202766\n",
       " time     0.012898\n",
       " dtype: float64,\n",
       " '10': Group   -0.512104\n",
       " time    -0.083301\n",
       " dtype: float64,\n",
       " '11': Group   -0.525072\n",
       " time     0.033831\n",
       " dtype: float64,\n",
       " '12': Group    0.318413\n",
       " time     0.059472\n",
       " dtype: float64,\n",
       " '13': Group    0.245271\n",
       " time     0.018939\n",
       " dtype: float64,\n",
       " '14': Group   -0.177288\n",
       " time    -0.104684\n",
       " dtype: float64,\n",
       " '15': Group    0.959431\n",
       " time    -0.023098\n",
       " dtype: float64,\n",
       " '16': Group    0.760310\n",
       " time     0.036568\n",
       " dtype: float64,\n",
       " '17': Group    0.189944\n",
       " time    -0.035069\n",
       " dtype: float64,\n",
       " '18': Group   -0.412439\n",
       " time    -0.107887\n",
       " dtype: float64,\n",
       " '19': Group    0.356421\n",
       " time    -0.127393\n",
       " dtype: float64,\n",
       " '2': Group   -0.516174\n",
       " time     0.061510\n",
       " dtype: float64,\n",
       " '20': Group   -0.123065\n",
       " time     0.103361\n",
       " dtype: float64,\n",
       " '21': Group    0.267341\n",
       " time    -0.088954\n",
       " dtype: float64,\n",
       " '22': Group   -1.494142\n",
       " time     0.169273\n",
       " dtype: float64,\n",
       " '23': Group    0.296981\n",
       " time     0.072143\n",
       " dtype: float64,\n",
       " '24': Group    0.494640\n",
       " time    -0.060968\n",
       " dtype: float64,\n",
       " '25': Group   -0.065802\n",
       " time     0.016368\n",
       " dtype: float64,\n",
       " '26': Group    0.146020\n",
       " time     0.035577\n",
       " dtype: float64,\n",
       " '27': Group    0.273234\n",
       " time     0.067165\n",
       " dtype: float64,\n",
       " '28': Group    0.461348\n",
       " time    -0.056270\n",
       " dtype: float64,\n",
       " '29': Group   -0.346337\n",
       " time     0.039298\n",
       " dtype: float64,\n",
       " '3': Group    0.018182\n",
       " time     0.001446\n",
       " dtype: float64,\n",
       " '30': Group    1.235741\n",
       " time    -0.147140\n",
       " dtype: float64,\n",
       " '31': Group   -0.954462\n",
       " time     0.027352\n",
       " dtype: float64,\n",
       " '32': Group   -0.385959\n",
       " time    -0.064274\n",
       " dtype: float64,\n",
       " '33': Group    0.071395\n",
       " time    -0.009569\n",
       " dtype: float64,\n",
       " '4': Group   -0.335952\n",
       " time     0.100523\n",
       " dtype: float64,\n",
       " '5': Group    0.022213\n",
       " time    -0.005699\n",
       " dtype: float64,\n",
       " '6': Group   -0.421813\n",
       " time     0.049947\n",
       " dtype: float64,\n",
       " '7': Group   -0.300949\n",
       " time     0.042098\n",
       " dtype: float64,\n",
       " '8': Group    0.128228\n",
       " time     0.009151\n",
       " dtype: float64,\n",
       " '9': Group    0.529209\n",
       " time    -0.042613\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mixte2.random_effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie que les effets aléatoires ont bien une moyenne nulle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.40068755e-15, -1.39114309e-15])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(list(res_mixte2.random_effects.values()), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La matrice de covariance des effets aléatoires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Group</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>0.339028</td>\n",
       "      <td>-0.027678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>-0.027678</td>\n",
       "      <td>0.009864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Group      time\n",
       "Group  0.339028 -0.027678\n",
       "time  -0.027678  0.009864"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mixte2.cov_re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.7512764940070507, 0.02003068466054412)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(pd.DataFrame({\"time\": df_melted[\"time\"], \"residuals\": res_mixte2.resid}))\n",
    "resid = smf.ols(\"residuals ~ time\", data=X).fit().resid\n",
    "dw_test(X.values, resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=df_melted[\"time\"], y=res_mixte2.resid)\n",
    "ax.set_ylabel(\"Résidus (res_mixte1)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=res_mixte2.fittedvalues, y=res_mixte2.resid)\n",
    "ax.set_xlabel(\"Valeurs prédites\")\n",
    "ax.set_ylabel(\"Résidus (res_mixte2)\")\n",
    "plt.show()"
   ]
  }
 ],
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