{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du Paradoxe de Simpson" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtention et pré-traitement des données\n", "\n", "Les données sont présentes sur le Gitlab du MOOC. Par sécurité elles sont téléchargées localement. Il n'est néanmoins pas nécessaire (et contre-productif) de re-télécharger le fichier à chaque exécution, le téléchargement n'a lieux que si le fichier de données n'est pas présent sur la machine.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url=\"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv?inline=false\"\n", "data_file=\"Subject6_smoking.csv.csv\"\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On affiche un aperçu des données :" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
\n", "

1314 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "0 Yes Alive 21.0\n", "1 Yes Alive 19.3\n", "2 No Dead 57.5\n", "3 No Alive 47.1\n", "4 Yes Alive 81.4\n", "5 No Alive 36.8\n", "6 No Alive 23.8\n", "7 Yes Dead 57.5\n", "8 Yes Alive 24.8\n", "9 Yes Alive 49.5\n", "10 Yes Alive 30.0\n", "11 No Dead 66.0\n", "12 Yes Alive 49.2\n", "13 No Alive 58.4\n", "14 No Dead 60.6\n", "15 No Alive 25.1\n", "16 No Alive 43.5\n", "17 No Alive 27.1\n", "18 No Alive 58.3\n", "19 Yes Alive 65.7\n", "20 No Dead 73.2\n", "21 Yes Alive 38.3\n", "22 No Alive 33.4\n", "23 Yes Dead 62.3\n", "24 No Alive 18.0\n", "25 No Alive 56.2\n", "26 Yes Alive 59.2\n", "27 No Alive 25.8\n", "28 No Dead 36.9\n", "29 No Alive 20.2\n", "... ... ... ...\n", "1284 Yes Dead 36.0\n", "1285 Yes Alive 48.3\n", "1286 No Alive 63.1\n", "1287 No Alive 60.8\n", "1288 Yes Dead 39.3\n", "1289 No Alive 36.7\n", "1290 No Alive 63.8\n", "1291 No Dead 71.3\n", "1292 No Alive 57.7\n", "1293 No Alive 63.2\n", "1294 No Alive 46.6\n", "1295 Yes Dead 82.4\n", "1296 Yes Alive 38.3\n", "1297 Yes Alive 32.7\n", "1298 No Alive 39.7\n", "1299 Yes Dead 60.0\n", "1300 No Dead 71.0\n", "1301 No Alive 20.5\n", "1302 No Alive 44.4\n", "1303 Yes Alive 31.2\n", "1304 Yes Alive 47.8\n", "1305 Yes Alive 60.9\n", "1306 No Dead 61.4\n", "1307 Yes Alive 43.0\n", "1308 No Alive 42.1\n", "1309 Yes Alive 35.9\n", "1310 No Alive 22.3\n", "1311 Yes Dead 62.1\n", "1312 No Dead 88.6\n", "1313 No Alive 39.1\n", "\n", "[1314 rows x 3 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie qu'aucune ligne ne soit vide de valeur." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerStatusAge
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Smoker, Status, Age]\n", "Index: []" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aucun soucis n'a été repéré sur les données, elles semblent être exploitables en l'état." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data=raw_data #we rename for coherence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Première exploitation des données\n", "\n", "On effectue une analyse simple (simpliste?) sur les données. On commence par compter le nombre de fumeurs et non-fumeur" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nombre de fumeurs = 582\n", "Nombre de non fumeurs = 732\n", "Taille de l'échantillon = 1314\n" ] } ], "source": [ "smokers=pd.DataFrame.sum(data['Smoker']=='Yes')\n", "print('Nombre de fumeurs =',smokers)\n", "non_smokers=pd.DataFrame.sum(data['Smoker']=='No')\n", "print('Nombre de non fumeurs =',non_smokers)\n", "total=smokers+non_smokers\n", "print('Taille de l\\'échantillon =',total)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On calcule maintenant le taux de mortalité pour ces deux groupes :" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mortalité fumeur = 0.239\n", "Mortalité non fumeur = 0.314\n", "Mortalité de l'échantillon = 0.281\n" ] } ], "source": [ "deaths_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')&(data['Status']=='Dead'))\n", "death_rate_smokers=deaths_smokers/smokers\n", "deaths_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')&(data['Status']=='Dead'))\n", "death_rate_non_smokers=deaths_non_smokers/non_smokers\n", "death_rate_total=(deaths_smokers+deaths_non_smokers)/total\n", "print('Mortalité fumeur =',round(death_rate_smokers,3))\n", "print('Mortalité non fumeur =', round(death_rate_non_smokers,3))\n", "print('Mortalité de l\\'échantillon =',round(death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On arrange ces informations sous forme d'un tableau" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fumeurs Non-fumeurs Total\n", " ------------------------------------\n", "Taille du groupe 582 732 1314\n", "Vivant 443 502 945\n", "Mort 139 230 369\n", "Mortalité 0.239 0.314 0.281\n" ] } ], "source": [ "print(' Fumeurs Non-fumeurs Total')\n", "print(' ------------------------------------')\n", "print('Taille du groupe ',smokers,' ',non_smokers,' ',total)\n", "print('Vivant ',smokers-deaths_smokers,' ',non_smokers-deaths_non_smokers,' ',total-deaths_smokers-deaths_non_smokers)\n", "print('Mort ',deaths_smokers,' ',deaths_non_smokers,' ',deaths_smokers+deaths_non_smokers)\n", "print('Mortalité ',round(death_rate_smokers,3),' ',round(death_rate_non_smokers,3),' ',round(death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut également les représenter sous forme de graphique circulaire" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = 'Non-fumeurs', 'Fumeurs'\n", "sizes = [non_smokers/total,(smokers/total)]\n", "\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, labels=labels,shadow=True,startangle=90,autopct='%1.1f%%')\n", "ax1.axis('equal') \n", "plt.title('Répartition de l\\'échantillon')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = 'Vivants', 'Morts'\n", "sizes = [1-death_rate_smokers,death_rate_smokers]\n", "explode = (0, 0.1)\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, labels=labels,explode=explode,startangle=90,shadow=True,autopct='%1.1f%%',colors=('green','red'))\n", "ax1.axis('equal') \n", "plt.title('Mortalité échantillon de fumeurs')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = 'Vivants', 'Morts'\n", "sizes = [1-death_rate_non_smokers,death_rate_non_smokers]\n", "explode = (0, 0.1)\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, labels=labels,explode=explode,startangle=90,shadow=True,autopct='%1.1f%%',colors=('green','red'))\n", "ax1.axis('equal') \n", "plt.title('Mortalité échantillon de non-fumeurs')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intervalle de confiance ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il apparait alors que la mortalité est plus importante au sein de l'échantillon 'non-fumeur', une conclusion hâtive peut donc nous amener à mettre en doute la plus connues des inscription figurant sur les paquets de cigarettes actuels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prise en compte de l'âge\n", "\n", "Notre analyse précédante nous mêne à une contradiction avec le célèbre _Fumer Tue_. On se penche donc sur la répartition d'âge au sein des groupes afin de voir si cela peut mener à une explication.\n", "On commence par regrouper par tranche d'âge (18-34,34-54,55-64,65+)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "data.loc[data['Age']<35,'Categorie d\\'age'] = 'A'\n", "data.loc[(data['Age']<55) & (data['Age']>=35),'Categorie d\\'age'] = 'B'\n", "data.loc[(data['Age']<65) & (data['Age']>=55),'Categorie d\\'age'] = 'C'\n", "data.loc[data['Age']>=65,'Categorie d\\'age'] = 'D'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie que la somme des sous-groupe soit bien égale au nombre total des donnés. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Categorie d'âge 18-34 35-54 55-64 65+ total\n", "-------------------------------------------------------------------------------------------\n", "Taille de l'échantillon 416 420 236 242 1314\n" ] } ], "source": [ "A_total=pd.DataFrame.sum((data['Categorie d\\'age']=='A'))\n", "B_total=pd.DataFrame.sum((data['Categorie d\\'age']=='B'))\n", "C_total=pd.DataFrame.sum((data['Categorie d\\'age']=='C'))\n", "D_total=pd.DataFrame.sum((data['Categorie d\\'age']=='D'))\n", "print('Categorie d\\'âge 18-34 35-54 55-64 65+ total')\n", "print('-------------------------------------------------------------------------------------------')\n", "print('Taille de l\\'échantillon ',A_total,' ',B_total,' ',C_total,' ',D_total,' ',A_total+B_total+C_total+D_total)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il ne semble pas y avoir d'erreur sur le découapage en sous-échantillons, on procède donc aux même analyses que précédement appliquées cette fois-ci par tranches d'âges." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Catégorie d'âge 18-34" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fumeurs Non-fumeurs Total\n", " ------------------------------------\n", "Taille du groupe 189 227 416\n", "Vivant 182 221 403\n", "Mort 7 6 13\n", "Mortalité 0.037 0.026 0.031\n" ] } ], "source": [ "A_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')& (data['Categorie d\\'age']=='A'))\n", "A_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')& (data['Categorie d\\'age']=='A'))\n", "A_total=A_smokers+A_non_smokers\n", "\n", "A_deaths_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='A'))\n", "A_death_rate_smokers=A_deaths_smokers/A_smokers\n", "A_deaths_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='A'))\n", "A_death_rate_non_smokers=A_deaths_non_smokers/A_non_smokers\n", "A_death_rate_total=(A_deaths_smokers+A_deaths_non_smokers)/A_total\n", "\n", "\n", "print(' Fumeurs Non-fumeurs Total')\n", "print(' ------------------------------------')\n", "print('Taille du groupe ',A_smokers,' ',A_non_smokers,' ',A_total)\n", "print('Vivant ',A_smokers-A_deaths_smokers,' ',A_non_smokers-A_deaths_non_smokers,' ',A_total-A_deaths_smokers-A_deaths_non_smokers)\n", "print('Mort ',A_deaths_smokers,' ',A_deaths_non_smokers,' ',A_deaths_smokers+A_deaths_non_smokers)\n", "print('Mortalité ',round(A_death_rate_smokers,3),' ',round(A_death_rate_non_smokers,3),' ',round(A_death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Catégorie d'age 35-54" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fumeurs Non-fumeurs Total\n", " ------------------------------------\n", "Taille du groupe 229 191 420\n", "Vivant 190 172 362\n", "Mort 39 19 58\n", "Mortalité 0.17 0.099 0.138\n" ] } ], "source": [ "B_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')& (data['Categorie d\\'age']=='B'))\n", "B_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')& (data['Categorie d\\'age']=='B'))\n", "B_total=B_smokers+B_non_smokers\n", "\n", "B_deaths_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='B'))\n", "B_death_rate_smokers=B_deaths_smokers/B_smokers\n", "B_deaths_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='B'))\n", "B_death_rate_non_smokers=B_deaths_non_smokers/B_non_smokers\n", "B_death_rate_total=(B_deaths_smokers+B_deaths_non_smokers)/B_total\n", "\n", "\n", "print(' Fumeurs Non-fumeurs Total')\n", "print(' ------------------------------------')\n", "print('Taille du groupe ',B_smokers,' ',B_non_smokers,' ',B_total)\n", "print('Vivant ',B_smokers-B_deaths_smokers,' ',B_non_smokers-B_deaths_non_smokers,' ',B_total-B_deaths_smokers-B_deaths_non_smokers)\n", "print('Mort ',B_deaths_smokers,' ',B_deaths_non_smokers,' ',B_deaths_smokers+B_deaths_non_smokers)\n", "print('Mortalité ',round(B_death_rate_smokers,3),' ',round(B_death_rate_non_smokers,3),' ',round(B_death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Catégorie d'age 55-64" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fumeurs Non-fumeurs Total\n", " ------------------------------------\n", "Taille du groupe 115 121 236\n", "Vivant 64 81 145\n", "Mort 51 40 91\n", "Mortalité 0.443 0.331 0.386\n" ] } ], "source": [ "C_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')& (data['Categorie d\\'age']=='C'))\n", "C_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')& (data['Categorie d\\'age']=='C'))\n", "C_total=C_smokers+C_non_smokers\n", "\n", "C_deaths_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='C'))\n", "C_death_rate_smokers=C_deaths_smokers/C_smokers\n", "C_deaths_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='C'))\n", "C_death_rate_non_smokers=C_deaths_non_smokers/C_non_smokers\n", "C_death_rate_total=(C_deaths_smokers+C_deaths_non_smokers)/C_total\n", "\n", "\n", "print(' Fumeurs Non-fumeurs Total')\n", "print(' ------------------------------------')\n", "print('Taille du groupe ',C_smokers,' ',C_non_smokers,' ',C_total)\n", "print('Vivant ',C_smokers-C_deaths_smokers,' ',C_non_smokers-C_deaths_non_smokers,' ',C_total-C_deaths_smokers-C_deaths_non_smokers)\n", "print('Mort ',C_deaths_smokers,' ',C_deaths_non_smokers,' ',C_deaths_smokers+C_deaths_non_smokers)\n", "print('Mortalité ',round(C_death_rate_smokers,3),' ',round(C_death_rate_non_smokers,3),' ',round(C_death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Catégorie d'âge 65+" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fumeurs Non-fumeurs Total\n", " ------------------------------------\n", "Taille du groupe 49 193 242\n", "Vivant 7 28 35\n", "Mort 42 165 207\n", "Mortalité 0.857 0.855 0.855\n" ] } ], "source": [ "D_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')& (data['Categorie d\\'age']=='D'))\n", "D_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')& (data['Categorie d\\'age']=='D'))\n", "D_total=D_smokers+D_non_smokers\n", "\n", "D_deaths_smokers=pd.DataFrame.sum((data['Smoker']=='Yes')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='D'))\n", "D_death_rate_smokers=D_deaths_smokers/D_smokers\n", "D_deaths_non_smokers=pd.DataFrame.sum((data['Smoker']=='No')&(data['Status']=='Dead')&(data['Categorie d\\'age']=='D'))\n", "D_death_rate_non_smokers=D_deaths_non_smokers/D_non_smokers\n", "D_death_rate_total=(D_deaths_smokers+D_deaths_non_smokers)/D_total\n", "\n", "\n", "print(' Fumeurs Non-fumeurs Total')\n", "print(' ------------------------------------')\n", "print('Taille du groupe ',D_smokers,' ',D_non_smokers,' ',D_total)\n", "print('Vivant ',D_smokers-D_deaths_smokers,' ',D_non_smokers-D_deaths_non_smokers,' ',D_total-D_deaths_smokers-D_deaths_non_smokers)\n", "print('Mort ',D_deaths_smokers,' ',D_deaths_non_smokers,' ',D_deaths_smokers+D_deaths_non_smokers)\n", "print('Mortalité ',round(D_death_rate_smokers,3),' ',round(D_death_rate_non_smokers,3),' ',round(D_death_rate_total,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyse" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = ['18-34', '35-54', '55-64', '65+']\n", "nn_smkers_dth_rt = [A_death_rate_non_smokers,B_death_rate_non_smokers,C_death_rate_non_smokers,D_death_rate_non_smokers]\n", "nn_smkers_dth_rt = [round(num, 2) for num in nn_smkers_dth_rt]\n", "smkers_dth_rt = [A_death_rate_smokers,B_death_rate_smokers,C_death_rate_smokers,D_death_rate_smokers]\n", "smkers_dth_rt = [round(num, 2) for num in smkers_dth_rt]\n", "\n", "x = np.arange(len(labels)) # the label locations\n", "width = 0.35 # the width of the bars\n", "\n", "fig, ax = plt.subplots()\n", "rects1 = ax.bar(x - width/2, nn_smkers_dth_rt, width, label='Non-fumeur')\n", "rects2 = ax.bar(x + width/2, smkers_dth_rt, width, label='Fumeur')\n", "\n", "# Add some text for labels, title and custom x-axis tick labels, etc.\n", "ax.set_ylabel('Taux de mortalité')\n", "ax.set_title('Taux de mortalité par tabagisme et catégorie d\\'âge')\n", "ax.set_xticks(x)\n", "ax.set_xticklabels(labels)\n", "ax.legend()\n", "\n", "\n", "def autolabel(rects):\n", " \n", " for rect in rects:\n", " height = rect.get_height()\n", " ax.annotate('{}'.format(height),\n", " xy=(rect.get_x() + rect.get_width() / 2, height),\n", " xytext=(0, 3), # 3 points vertical offset\n", " textcoords=\"offset points\",\n", " ha='center', va='bottom')\n", "\n", "\n", "autolabel(rects1)\n", "autolabel(rects2)\n", "\n", "fig.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = ['18-34', '35-54', '55-64', '65+']\n", "nn_smkrs = [A_non_smokers,B_non_smokers,C_non_smokers,D_non_smokers]\n", "smkrs = [A_smokers,B_smokers,C_smokers,D_smokers]\n", "\n", "x = np.arange(len(labels)) # the label locations\n", "width = 0.35 # the width of the bars\n", "\n", "fig, ax = plt.subplots()\n", "rects1 = ax.bar(x - width/2, nn_smkrs, width, label='Non-fumeur')\n", "rects2 = ax.bar(x + width/2, smkrs, width, label='Fumeur')\n", "\n", "# Add some text for labels, title and custom x-axis tick labels, etc.\n", "ax.set_ylabel('Taille du groupe')\n", "ax.set_title('Répartition du tabagisme en fonction de la catégorie d\\'âge')\n", "ax.set_xticks(x)\n", "ax.set_xticklabels(labels)\n", "ax.legend()\n", "\n", "\n", "def autolabel(rects):\n", " \n", " for rect in rects:\n", " height = rect.get_height()\n", " ax.annotate('{}'.format(height),\n", " xy=(rect.get_x() + rect.get_width() / 2, height),\n", " xytext=(0, 3), # 3 points vertical offset\n", " textcoords=\"offset points\",\n", " ha='center', va='bottom')\n", "\n", "\n", "autolabel(rects1)\n", "autolabel(rects2)\n", "\n", "fig.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ces deux graphique mettent en évidences plusieurs choses :\n", " - le taux de mortalité à 20 ans est très dépendant de l'âge (ce qui après réflexion semble évident),\n", " - la proportion de fumeur dépend de l'âge,\n", " - pour chaque catégorie d'âge la mortalité des fumeurs est plus importantes que celles des non-fumeurs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Régression logistique" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, on réalise une régression logistique. Pour cela on introduit la variable Death qui vaut 1 si l'individu est décédé dans la période de 20 ans, 0 sinon." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "data['Death']=0\n", "data.loc[data['Status']=='Dead','Death'] = 1\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "x=data['Age']\n", "x=sm.add_constant(x)\n", "y=data['Death']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.382339\n", " Iterations 7\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Logit Regression Results
Dep. Variable: Death No. Observations: 1314
Model: Logit Df Residuals: 1312
Method: MLE Df Model: 1
Date: Tue, 28 Apr 2020 Pseudo R-squ.: 0.3560
Time: 22:33:45 Log-Likelihood: -502.39
converged: True LL-Null: -780.16
LLR p-value: 7.883e-123
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
const -6.1045 0.321 -18.992 0.000 -6.735 -5.475
Age 0.0977 0.006 17.578 0.000 0.087 0.109
" ], "text/plain": [ "\n", "\"\"\"\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Death No. Observations: 1314\n", "Model: Logit Df Residuals: 1312\n", "Method: MLE Df Model: 1\n", "Date: Tue, 28 Apr 2020 Pseudo R-squ.: 0.3560\n", "Time: 22:33:45 Log-Likelihood: -502.39\n", "converged: True LL-Null: -780.16\n", " LLR p-value: 7.883e-123\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -6.1045 0.321 -18.992 0.000 -6.735 -5.475\n", "Age 0.0977 0.006 17.578 0.000 0.087 0.109\n", "==============================================================================\n", "\"\"\"" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = sm.Logit(y, x)\n", "result = model.fit(method='newton')\n", "result.summary()\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Frequency'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Frequency'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Constant'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Age\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"line\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m 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2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m 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'Frequency'" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_pred = pd.DataFrame({'Age': np.linspace(start=18, stop=100, num=100), 'Constant': 1})\n", "data_pred['Frequency'] = result.predict(data_pred[['Constant','Age']])\n", "data_pred.plot(x=\"Age\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", "plt.scatter(x=data[\"Age\"],y=data[\"Frequency\"])\n", "plt.grid(True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }