{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Nous travaillons avec la version 6.0.3 du Notebook Jupyter en langage R version 3.4.1 (2017-06-30).\n", "\n", "# Sujet 6 : Autour du Paradoxe de Simpson\n", "## Importation des données\n", "Le jeux de données est mis à disposition sur Github, néanmoins, pour nous protéger contre une éventuelle disparition ou modification des données d'origine, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAge
Yes Alive21.0
Yes Alive19.3
No Dead 57.5
No Alive47.1
Yes Alive81.4
No Alive36.8
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " Smoker & Status & Age\\\\\n", "\\hline\n", "\t Yes & Alive & 21.0 \\\\\n", "\t Yes & Alive & 19.3 \\\\\n", "\t No & Dead & 57.5 \\\\\n", "\t No & Alive & 47.1 \\\\\n", "\t Yes & Alive & 81.4 \\\\\n", "\t No & Alive & 36.8 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Smoker | Status | Age | \n", "|---|---|---|---|---|---|\n", "| Yes | Alive | 21.0 | \n", "| Yes | Alive | 19.3 | \n", "| No | Dead | 57.5 | \n", "| No | Alive | 47.1 | \n", "| Yes | Alive | 81.4 | \n", "| No | Alive | 36.8 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age \n", "1 Yes Alive 21.0\n", "2 Yes Alive 19.3\n", "3 No Dead 57.5\n", "4 No Alive 47.1\n", "5 Yes Alive 81.4\n", "6 No Alive 36.8" ] }, "metadata": {}, "output_type": "display_data" } ], "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\"\n", "\n", "if (!file.exists(data_file)) {\n", " download.file(data_url, data_file, method=\"auto\")\n", "}\n", "\n", "data <- read.csv(data_file)\n", "head(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données s'affichent correctement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Première analyse générale des données\n", "Après une observation brève du fichier csv, nous regardons les caractéristiques du jeux de données." ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t1314 obs. of 3 variables:\n", " $ Smoker: Factor w/ 2 levels \"No\",\"Yes\": 2 2 1 1 2 1 1 2 2 2 ...\n", " $ Status: Factor w/ 2 levels \"Alive\",\"Dead\": 1 1 2 1 1 1 1 2 1 1 ...\n", " $ Age : num 21 19.3 57.5 47.1 81.4 36.8 23.8 57.5 24.8 49.5 ...\n" ] }, { "data": { "text/plain": [ " Smoker Status Age \n", " No :732 Alive:945 Min. :18.00 \n", " Yes:582 Dead :369 1st Qu.:31.30 \n", " Median :44.80 \n", " Mean :47.36 \n", " 3rd Qu.:60.60 \n", " Max. :89.90 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "str(data)\n", "summary(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le jeux de données comprend 1314 observations et regroupe 3 variables : état de tabagisme (oui/non), statut de santé (vivante/morte) et l'âge.\n", "\n", "Avec cette première analyse générale, on remarque qu'il y a presque autant de fumeuses que de non fumeuses, environ 1/4 des femmes sont décédées, l'âge minimal est de 18 ans, l'âge maximal de 89 ans et l'âge moyen de 47 ans." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Analyse de l'effectif et de la mortalité\n", "Nous cherchons dans un premier temps à étudier l'effectif de la population selon l'état de tabagisme et le statut de santé." ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " \n", " Alive Dead\n", " No 502 230\n", " Yes 443 139" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Var1 Var2 Freq\n", "1 No Alive 502\n", "2 Yes Alive 443\n", "3 No Dead 230\n", "4 Yes Dead 139\n" ] } ], "source": [ "#création d'un tableau de fréquence\n", "analyse <- table(data$Smoker,data$Status)\n", "analyse\n", "\n", "#transformation du tableau en dataset\n", "analyse_data <- as.data.frame(analyse)\n", "print(analyse_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant calculer la mortalité définit comme le nombre de femmes mortes rapporté au nombre de femmes total selon l'état de tabagisme.\n", "\n", "Comme je ne maitrise pas R, j'utilise une méthode peu optimisée et reproductible mais c'est le mieux que je puisse faire." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Freqmortality
No Alive 502 0.6857923
Yes Alive 443 0.7611684
No Dead 230 0.3142077
Yes Dead 139 0.2388316
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " Var1 & Var2 & Freq & mortality\\\\\n", "\\hline\n", "\t No & Alive & 502 & 0.6857923\\\\\n", "\t Yes & Alive & 443 & 0.7611684\\\\\n", "\t No & Dead & 230 & 0.3142077\\\\\n", "\t Yes & Dead & 139 & 0.2388316\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Freq | mortality | \n", "|---|---|---|---|\n", "| No | Alive | 502 | 0.6857923 | \n", "| Yes | Alive | 443 | 0.7611684 | \n", "| No | Dead | 230 | 0.3142077 | \n", "| Yes | Dead | 139 | 0.2388316 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Freq mortality\n", "1 No Alive 502 0.6857923\n", "2 Yes Alive 443 0.7611684\n", "3 No Dead 230 0.3142077\n", "4 Yes Dead 139 0.2388316" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Freqmortality
3No Dead 230 0.3142077
4Yes Dead 139 0.2388316
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & Var1 & Var2 & Freq & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 230 & 0.3142077\\\\\n", "\t4 & Yes & Dead & 139 & 0.2388316\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Freq | mortality | \n", "|---|---|\n", "| 3 | No | Dead | 230 | 0.3142077 | \n", "| 4 | Yes | Dead | 139 | 0.2388316 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Freq mortality\n", "3 No Dead 230 0.3142077\n", "4 Yes Dead 139 0.2388316" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#création de la variable \"mortality\"\n", "analyse_data$mortality <- ifelse(analyse_data$Var1=='No', analyse_data$Freq/(analyse_data$Freq[1]+analyse_data$Freq[3]), \n", " analyse_data$Freq/(analyse_data$Freq[2]+analyse_data$Freq[4]))\n", "analyse_data\n", "\n", "#sélection des données d'intérêt\n", "analyse_data_2 <- analyse_data[analyse_data$Var2==\"Dead\",] # (les valeurs des lignes \"vivantes\" ne sont pas pertinentes)\n", "analyse_data_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous remarquons que la mortalité est plus élevée chez les non fumeuses. Ce résultat est surprenant car le tabagisme est associé à de nombreuses maladies. Représentons ces données avec un graphique." ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title “Mortalité selon l'état de tabagisme”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x=analyse_data_2$Var1, y=analyse_data_2$mortality, ylim=c(0,1), main=\"Mortalité selon l'état de tabagisme\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Analyse de l'effectif et de la mortalité par groupe d'âge\n", "Etudions maintenant l'effectif de la population selon l'état de tabagisme, le statut de santé et l'âge des individus.\n", "\n", "Tout d'abord nous créons une nouvelle variable pour répartir les femmes en 4 groupes selon leur âge : 18-34 ans, 34-54 ans, 54-64 ans, 65 ans et plus." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ ", , = 18-34 ans\n", "\n", " \n", " Alive Dead\n", " No 213 6\n", " Yes 174 5\n", "\n", ", , = 34-54 ans\n", "\n", " \n", " Alive Dead\n", " No 180 19\n", " Yes 198 41\n", "\n", ", , = 54-64 ans\n", "\n", " \n", " Alive Dead\n", " No 80 39\n", " Yes 64 51\n", "\n", ", , = plus de 65 ans\n", "\n", " \n", " Alive Dead\n", " No 29 166\n", " Yes 7 42\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Var1 Var2 Var3 Freq\n", "1 No Alive 18-34 ans 213\n", "2 Yes Alive 18-34 ans 174\n", "3 No Dead 18-34 ans 6\n", "4 Yes Dead 18-34 ans 5\n", "5 No Alive 34-54 ans 180\n", "6 Yes Alive 34-54 ans 198\n", "7 No Dead 34-54 ans 19\n", "8 Yes Dead 34-54 ans 41\n", "9 No Alive 54-64 ans 80\n", "10 Yes Alive 54-64 ans 64\n", "11 No Dead 54-64 ans 39\n", "12 Yes Dead 54-64 ans 51\n", "13 No Alive plus de 65 ans 29\n", "14 Yes Alive plus de 65 ans 7\n", "15 No Dead plus de 65 ans 166\n", "16 Yes Dead plus de 65 ans 42\n" ] } ], "source": [ "#création de la variable \"AgeGroup\"\n", "data$AgeGroup <- ifelse(data$Age<34, '18-34 ans', ifelse(data$Age<54, '34-54 ans', ifelse(data$Age<64, '54-64 ans', 'plus de 65 ans'))) \n", "\n", "#création d'un tableau de fréquence\n", "age <- table(data$Smoker,data$Status,data$AgeGroup)\n", "age\n", "\n", "#transformation du tableau en dataset\n", "age_data <- as.data.frame(age)\n", "print(age_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons voir que peu de femmes entre 18 et 34 ans au moment de la première étude sont décedées, contrairement aux femmes de plus de 65 ans (ce qui est assez logique).\n", "\n", "Etudions la mortalité dans chacun de ces groupes. Encore une fois, la méthode utilisée n'est clairement pas la plus optimable et reproductible car je connais mal R mais elle me permet d'avoir des données pour la suite." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
No Alive 18-34 ans 213 398 0.97260274
Yes Alive 18-34 ans 174 398 0.97206704
No Dead 18-34 ans 6 398 0.02739726
Yes Dead 18-34 ans 5 398 0.02793296
No Alive 34-54 ans 180 438 0.90452261
Yes Alive 34-54 ans 198 438 0.82845188
No Dead 34-54 ans 19 438 0.09547739
Yes Dead 34-54 ans 41 438 0.17154812
No Alive 54-64 ans 80 234 0.67226891
Yes Alive 54-64 ans 64 234 0.55652174
No Dead 54-64 ans 39 234 0.32773109
Yes Dead 54-64 ans 51 234 0.44347826
No Alive plus de 65 ans 29 244 0.14871795
Yes Alive plus de 65 ans 7 244 0.14285714
No Dead plus de 65 ans166 244 0.85128205
Yes Dead plus de 65 ans 42 244 0.85714286
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t No & Alive & 18-34 ans & 213 & 398 & 0.97260274 \\\\\n", "\t Yes & Alive & 18-34 ans & 174 & 398 & 0.97206704 \\\\\n", "\t No & Dead & 18-34 ans & 6 & 398 & 0.02739726 \\\\\n", "\t Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296 \\\\\n", "\t No & Alive & 34-54 ans & 180 & 438 & 0.90452261 \\\\\n", "\t Yes & Alive & 34-54 ans & 198 & 438 & 0.82845188 \\\\\n", "\t No & Dead & 34-54 ans & 19 & 438 & 0.09547739 \\\\\n", "\t Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812 \\\\\n", "\t No & Alive & 54-64 ans & 80 & 234 & 0.67226891 \\\\\n", "\t Yes & Alive & 54-64 ans & 64 & 234 & 0.55652174 \\\\\n", "\t No & Dead & 54-64 ans & 39 & 234 & 0.32773109 \\\\\n", "\t Yes & Dead & 54-64 ans & 51 & 234 & 0.44347826 \\\\\n", "\t No & Alive & plus de 65 ans & 29 & 244 & 0.14871795 \\\\\n", "\t Yes & Alive & plus de 65 ans & 7 & 244 & 0.14285714 \\\\\n", "\t No & Dead & plus de 65 ans & 166 & 244 & 0.85128205 \\\\\n", "\t Yes & Dead & plus de 65 ans & 42 & 244 & 0.85714286 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| No | Alive | 18-34 ans | 213 | 398 | 0.97260274 | \n", "| Yes | Alive | 18-34 ans | 174 | 398 | 0.97206704 | \n", "| No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "| No | Alive | 34-54 ans | 180 | 438 | 0.90452261 | \n", "| Yes | Alive | 34-54 ans | 198 | 438 | 0.82845188 | \n", "| No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "| No | Alive | 54-64 ans | 80 | 234 | 0.67226891 | \n", "| Yes | Alive | 54-64 ans | 64 | 234 | 0.55652174 | \n", "| No | Dead | 54-64 ans | 39 | 234 | 0.32773109 | \n", "| Yes | Dead | 54-64 ans | 51 | 234 | 0.44347826 | \n", "| No | Alive | plus de 65 ans | 29 | 244 | 0.14871795 | \n", "| Yes | Alive | plus de 65 ans | 7 | 244 | 0.14285714 | \n", "| No | Dead | plus de 65 ans | 166 | 244 | 0.85128205 | \n", "| Yes | Dead | plus de 65 ans | 42 | 244 | 0.85714286 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "1 No Alive 18-34 ans 213 398 0.97260274\n", "2 Yes Alive 18-34 ans 174 398 0.97206704\n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "4 Yes Dead 18-34 ans 5 398 0.02793296\n", "5 No Alive 34-54 ans 180 438 0.90452261\n", "6 Yes Alive 34-54 ans 198 438 0.82845188\n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "8 Yes Dead 34-54 ans 41 438 0.17154812\n", "9 No Alive 54-64 ans 80 234 0.67226891\n", "10 Yes Alive 54-64 ans 64 234 0.55652174\n", "11 No Dead 54-64 ans 39 234 0.32773109\n", "12 Yes Dead 54-64 ans 51 234 0.44347826\n", "13 No Alive plus de 65 ans 29 244 0.14871795\n", "14 Yes Alive plus de 65 ans 7 244 0.14285714\n", "15 No Dead plus de 65 ans 166 244 0.85128205\n", "16 Yes Dead plus de 65 ans 42 244 0.85714286" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
3No Dead 18-34 ans 6 398 0.02739726
4Yes Dead 18-34 ans 5 398 0.02793296
7No Dead 34-54 ans 19 438 0.09547739
8Yes Dead 34-54 ans 41 438 0.17154812
11No Dead 54-64 ans 39 234 0.32773109
12Yes Dead 54-64 ans 51 234 0.44347826
15No Dead plus de 65 ans166 244 0.85128205
16Yes Dead plus de 65 ans 42 244 0.85714286
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 18-34 ans & 6 & 398 & 0.02739726 \\\\\n", "\t4 & Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296 \\\\\n", "\t7 & No & Dead & 34-54 ans & 19 & 438 & 0.09547739 \\\\\n", "\t8 & Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812 \\\\\n", "\t11 & No & Dead & 54-64 ans & 39 & 234 & 0.32773109 \\\\\n", "\t12 & Yes & Dead & 54-64 ans & 51 & 234 & 0.44347826 \\\\\n", "\t15 & No & Dead & plus de 65 ans & 166 & 244 & 0.85128205 \\\\\n", "\t16 & Yes & Dead & plus de 65 ans & 42 & 244 & 0.85714286 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|---|---|---|---|\n", "| 3 | No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| 4 | Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "| 7 | No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| 8 | Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "| 11 | No | Dead | 54-64 ans | 39 | 234 | 0.32773109 | \n", "| 12 | Yes | Dead | 54-64 ans | 51 | 234 | 0.44347826 | \n", "| 15 | No | Dead | plus de 65 ans | 166 | 244 | 0.85128205 | \n", "| 16 | Yes | Dead | plus de 65 ans | 42 | 244 | 0.85714286 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "4 Yes Dead 18-34 ans 5 398 0.02793296\n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "8 Yes Dead 34-54 ans 41 438 0.17154812\n", "11 No Dead 54-64 ans 39 234 0.32773109\n", "12 Yes Dead 54-64 ans 51 234 0.44347826\n", "15 No Dead plus de 65 ans 166 244 0.85128205\n", "16 Yes Dead plus de 65 ans 42 244 0.85714286" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#création de la variable \"mortality\" calculée à partir des effectifs de population par état de tabagisme et groupe d'âge\n", "age_data$mortality <- ifelse(age_data$Var1=='No' & age_data$Var3=='18-34 ans', age_data$Freq/(age_data$Freq[1]+age_data$Freq[3]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='18-34 ans', age_data$Freq/(age_data$Freq[2]+age_data$Freq[4]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='34-54 ans', age_data$Freq/(age_data$Freq[5]+age_data$Freq[7]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='34-54 ans', age_data$Freq/(age_data$Freq[6]+age_data$Freq[8]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='54-64 ans', age_data$Freq/(age_data$Freq[9]+age_data$Freq[11]),\n", " ifelse(age_data$Var1=='Yes' & age_data$Var3=='54-64 ans', age_data$Freq/(age_data$Freq[10]+age_data$Freq[12]),\n", " ifelse(age_data$Var1=='No' & age_data$Var3=='plus de 65 ans', age_data$Freq/(age_data$Freq[13]+age_data$Freq[15]),\n", " age_data$Freq/(age_data$Freq[14]+age_data$Freq[16])))))))) \n", "age_data\n", "\n", "#sélection des données d'intérêt\n", "age_data_2 <- age_data[age_data$Var2==\"Dead\",] # (les valeurs des lignes \"vivantes\" ne sont pas pertinentes)\n", "age_data_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous remarquons que pour les femmes qui étaient jeunes au moment de la première étude comme pour les femmes de plus de 65 ans, la mortalité est semblable entre les fumeuses et les non fumeuses. En revanche pour les femmes ayant entre 34 et 54 ans, les fumeuses ont une mortalité plus élevée.\n", "\n", "Représentons ces données avec un graphique. Comme je ne maitrise pas les graphiques non plus, je créé de nouvelles tables selon le groupe d'âge et produit un graphique pour chaque groupe d'âge." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
3No Dead 18-34 ans 6 398 0.02739726
4Yes Dead 18-34 ans 5 398 0.02793296
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 18-34 ans & 6 & 398 & 0.02739726\\\\\n", "\t4 & Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|\n", "| 3 | No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| 4 | Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "4 Yes Dead 18-34 ans 5 398 0.02793296" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
7No Dead 34-54 ans 19 438 0.09547739
8Yes Dead 34-54 ans 41 438 0.17154812
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t7 & No & Dead & 34-54 ans & 19 & 438 & 0.09547739\\\\\n", "\t8 & Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|\n", "| 7 | No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| 8 | Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "8 Yes Dead 34-54 ans 41 438 0.17154812" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
11No Dead 54-64 ans39 234 0.3277311
12Yes Dead 54-64 ans51 234 0.4434783
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t11 & No & Dead & 54-64 ans & 39 & 234 & 0.3277311\\\\\n", "\t12 & Yes & Dead & 54-64 ans & 51 & 234 & 0.4434783\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|\n", "| 11 | No | Dead | 54-64 ans | 39 | 234 | 0.3277311 | \n", "| 12 | Yes | Dead | 54-64 ans | 51 | 234 | 0.4434783 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality\n", "11 No Dead 54-64 ans 39 234 0.3277311\n", "12 Yes Dead 54-64 ans 51 234 0.4434783" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
15No Dead plus de 65 ans166 244 0.8512821
16Yes Dead plus de 65 ans 42 244 0.8571429
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t15 & No & Dead & plus de 65 ans & 166 & 244 & 0.8512821 \\\\\n", "\t16 & Yes & Dead & plus de 65 ans & 42 & 244 & 0.8571429 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|\n", "| 15 | No | Dead | plus de 65 ans | 166 | 244 | 0.8512821 | \n", "| 16 | Yes | Dead | plus de 65 ans | 42 | 244 | 0.8571429 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality\n", "15 No Dead plus de 65 ans 166 244 0.8512821\n", "16 Yes Dead plus de 65 ans 42 244 0.8571429" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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x8xkJDgmFhD2j7equrq58yePrHSJkSlQkhwTLzvI7UsrS0N3kYqP+W21qhxhATH\nxP4RoV3Prlu3saWTQYQEx/BZO0CAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQI\nCRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQ\nICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAk\nQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECA\nkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAA\nAUICBAgJECAkQICQAAFCQsgTPdyh7rduF9KgHu6t7nrr4Q51v3W3kFYlfUckbdVb2/PXJz3/\npF1/iDuuu4XUmPQdkbTGt7bnX0x6/kl78RB3XHcLyTuhh3uru35+D3eo+y3ukNo2N65evWZr\nJ6N4sQGOiTek5nnV6QfQUYt2Ro0jJDgm1pC2jbGx9QsaGq6dNszGNUcMJCQ4JtaQZpavzCy1\nLiuZGzGQkOCYWEMaOiO7PHVkxEBCgmNiDal8cXZ5Ye+IgYQEx8QaUs2F2eUpoyMGEhIcE2tI\nc0uW7E4v7bjOol6xJyQ4JtaQto+3qrr6ObOnT6y0CVGpEBIcE+/7SC1La0uDt5HKT7mtNWoc\nIcExsX9EaNez69ZtbOlkECHBMd3us3ZAEggJEEgqpE11dXmXbBmS/eu0Stsh2AYQm6RCWr/f\n3yLue6ixw9ess7Mo4LCSVEi7mpoirn2EkOCWw/MciZDgmMPzD/sICY45PP+wj5DgmMPzD/sI\nCY45PP+wj5DgmMPzD/sICY45PP+wj5DgmMPzD/sICY45PP+wj5DgmMPzD/sICY45PP+wj5Dg\nmMPzD/sICY7hs3aAACEBAoQECBASIEBIgAAhAQKEBAgQEiBweIa0Npn/ozVw6NYe9GHe9SF5\nTz7hqLNOX4G34PSzkr4HD9WTB3+UxxCSs+rrk56B23rU/iOk4nrUgdAFetT+I6TietSB0AV6\n1P4jpOJ61IHQBXrU/iOk4nrUgdAFetT+I6TietSB0AV61P4jpOJ61IHQBXrU/iOk4nrUgdAF\netT+I6TietSB0AV61P4jpOJ61IHQBXrU/iOk4i6/POkZuK1H7T9CKq456n+ygU71qP1HSIAA\nIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEj5VljF\nH9JLx4xLdibOebjkpH3ppZtsdrJTiR0h5VthVpdeIqSD9Rn7eurrlr4j/5zwVOJGSPlW2ARb\nkVoipIP1xvCq54OvH7f7k55K3Agp3wr7SU116r82kArpufph5W+b/NuEJ+WKe+1c/98f2iWe\n99KsUeVHTnnc/3Z3w/sG9H9vw76kJ9elCCnfCvvpvZb6798EIW2t7v/55YuHV/wq6Wk5Yqqt\n9l4/eshr3is1R8xfceOIioc971K7+NZvf7KbnzURUr4Vdp83peRRLx3SdP/A8LwNpackPS1H\nvDx4+Buz7Aeed0VZ8L+P3Fp1oudVnhpcc9X5rQnPrUsRUr4gpK393rs3FVLbEUe1BReeZq8l\nPS9H3Gln9prseW1Hjn8x8DF70zti2MtJz6rrEVK+ICRviTWkQtpmZ6QunGmPJjwtZ3zMBrzg\nnyF1/H+Nn/FusQF/+90Xkp5XFyOkfKmQ9r6v8rkgpI02OXXhHGtMeFrOeMAu8//daLX/mbbd\n89ac289Kzn4u6Zl1KULKlwrJe7Rksjd2nPdi5hHpUnss2Vm5o9E+4wWPSLW5F+5unF5ybEtC\nM4oFIeVLh+R92la/e5znDT46dY50csn2ZGfljnRI3pF9UnvslY7Lr7Bu/R4CIeXLhNQ8ZMS7\n/JAusx/736wvqUt4Vu7IhHSFXeP/+8rQT3i/GXZncMFs++9E59XFCClfJiTvTjM/pD8N7X/N\nnddXVz2V8KzckQnp5VF26fIbR5X/zNv7nt6fXvatGb1Oa0t6al2JkPK1h+R9OAjJ23rp0WXV\nF21IdEpOyYTkvXjFyLKB5wRP5/7vs8dUHjHuxjeTnVcXIyRAgJAAAUICBAgJECAkQICQAAFC\nAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIE\nCAkQICRAgJAAAUICBAgJEB1SR18AAACsSURBVCAkQICQAAFCAgQICRAgJECAkAABQgIECAkQ\nICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAk\nQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECA\nkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgT+H9HwONjCcJe4AAAAAElFTkSuQmCC\n", "text/plain": [ "Plot with title “Mortalité selon l'état de tabagisme des 18-34 ans”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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y/7g9d0fMWouwtCygzxvM1zxlVVv/uz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", "text/plain": [ "Plot with title “Mortalité selon l'état de tabagisme des 34-54 ans”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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91TEFJ2iOdtnDmqsuodn2rI29ZLnz3l\nmIqqt135VHgqb0Duu3Zx200KKbKZuOnlJpRx4Jbjewy66HfPWOZt3rhpxq3m38ze5n1zbM/+\n5z0ZDIlu4sCy4ysHXfT0g9E9kX9r40OK23jS/KL7L2P/1ycO6N7/jNt3t55z0KdEr11/Qq+q\nkxbtLDIi7t5s3edxm0yVMyFB5m7/93qp59DpEFLXsfHm2RcHv+DPMzu/1HPpdAip69hU5hf0\nyK8+7j8b+lmp59LpEFIXsij7itw+XeqZdD6E1JWsvWhYRWXNtEdKPY9OiJAAAUICBAgJECAk\nQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECA\nkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIEUgjpyScApzx5\n+Ed5+4e0zgDHrDvsw7z9Q3rU9rb7NgChvfboYV+HkIAChAQIEBIgQEiAACEBAoQECBASIJB2\nSM1b1qxevXZbG6MICY5JN6SG+YMynwKPWLw7aRwhwTGphrR9lI2uW7h06Q3Th9iYhoSBhATH\npBrSrIpV2aWm5WXzEgYSEhyTakiDZ+aWpw1PGEhIcEyqIVUsyS0v6pEwkJDgmFRDqpmaW54y\nMmEgIcExqYY0r2zZnszSrhttQcJAQoJjUg1pxzirrq2bO2fGxCqb8HrCQEKCY9L9HGnvrWPL\ng4+RKsbf0ZQ0jpDgmNS/ItT43Pr1m9rKhJDgGL5rBwgQEiBQqpA219YWnHPg4TWtbiMkuKVU\nIW2wwrVsHdi/VZXtEmwDSE2pQmqsr0+49GuW9OY40OF0zNdIhATHlCykVzclXEhIcEzJQlqQ\ntBZCgmMICRAgJEAg1ZBOzjOYkNCJpBpSt26VrcoJCZ1IqiEtqM69VcdTO3QmqYa0712n7GtZ\nJiR0Jum+2bCx1ydaFgkJnUnK79rt/L+WpUduThhGSHAMXxECBAgJECAkQICQAAFCAgQICRAg\nJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRA\ngJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQ\nAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAAB\nQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUIC\nBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAIGShLTv\nd0/sSRxASHBMuiGtnTjyg7/xHhpi1nd50jhCgmNSDemx7ta3W+/H+g7/yNT+9pOEgYQEx6Qa\n0uTBT3mvvG/EmN2e1zDynISBhATHpBrSmz7r/7POVgTL/zIgYSAhwTGphtT9W/4/2+2BYPmu\n7gkDCQmOSTWkYxb6/zxitwfL1x+TMJCQ4JhUQ7p4wH/tffqdJ4x4wfM29r8oYSAhwTGphvRs\ntZkN2FhT9b7Tu5f/NmEgIcEx6X6OVD/9tLr/8epPLbM3/yBpHCHBMaX5itDrryRfTkhwDN+1\nAwQICRAoVUiba2sLzmmYfWWrCYQEt5QqpA1WuBZCgsNKFVJjfX3CpTy1g2N4jQQIpB1S85Y1\nq1ev3dbGKEKCY9INqWH+IAuNWLw7aRwhwTGphrR9lI2uW7h06Q3Th9iYhoSBhATHpBrSrIpV\n2aWm5WXzEgYSEhyTakiDZ+aWpw1PGEhIcEyqIVUsyS0v6pEwkJDgmFRDqpmaW54yMmEgIcEx\nqYY0r2xZ9r9nt+tGW5AwkJDgmFRD2jHOqmvr5s6ZMbHKJiSlQkhwTLqfI+29dWx58DFSxfg7\nmpLGERIck/pXhBqfW79+0942BhESHMN37QABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFC\nAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIE\nCAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJ\nECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAg\nJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRA\ngJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQKEVITU8/ui15BCHBMemG9Ogc\n/5+Vx5jZmJ8njSMkOCbVkB7u0afZu9f6fHj233arfCJhICHBMamGNHHQJs8bVbPdX/xNr8kJ\nAwkJjkk1pL6f8LzX7PZw+Yp+CQMJCY5JNaTen/a8PWX3hcuf6ZkwkJDgmFRDes/oNzzvbz4R\nLO4ZMyZhICHBMamG9CMb99P964+9+419v3m/fT1hICHBMem+/X1nb+t1Yo2Vl1vZPzYnjCMk\nOCblD2RfWnZ2TXXlm06+Zn3iMEKCY/iKECBASIAAIQECpQppc21twTlbB/ZvVWV/EWwDSE2p\nQtpghWs58PCaVvN4RIJbShVSY319wqU8tYNjeI0ECKQdUvOWNatXr23j7/oICa5JN6SG+YMs\nNGLx7qRxhATHpBrS9lE2um7h0qU3TB9iYxoSBhISHJNqSLMqVmWXmpaXzUsYSEhwTKohDZ6Z\nW542PGEgIcExqYZUsSS3vKhHwkBCgmNSDalmam55ysiEgYQEx6Qa0ryyZXsyS7tutAUJAwkJ\njkk1pB3jrLq2bu6cGROrbEJSKoQEx6T7OdLeW8eWBx8jVYy/oylpHCHBMal/RajxufXrN+1t\nYxAhwTF81w4QICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAA\nAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFC\nAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIE\nCAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJ\nECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICREfK6LO9L9RkjIt8O6uB1HuOMICfmuL/WB\nXGrXH+GOIyRElPpALrUj3W+EhIg1XdyR7jdCAgQICRAgJECAkAABQgIECAkQICRAgJAAgdKE\ntHPBs4mXExIcU5qQnrcfJV5OSHBMqiHNajHdzpo1K2EgIcExqYZ0yF8PJCQ4JtWQri0f+9CO\nwDN2z46kP/wgJDgm3ddI68aWXfWax2skdDopv9mw/3O9hnyPkNDppP6u3eZam7yNkNDJlODt\n728O6LOQkNC5lOJzpJcvNkJC51KaD2QfnL8x8XJCgmP4rh0gQEiAQKlC2lxbW3DOgYdz/ymX\neYQEt5QqpA0HfUVo68D+rarsL4JtAKkpVUiN9fUJl/LUDo7hNRIgkHZIzVvWrF69dlsbowgJ\njkk3pIb5gzJ/QjFi8e6kcYQEx6Qa0vZRNrpu4dKlN0wfYmMaEgYSEhyT7l/IVqzKLjUtL5uX\nMJCQ4JhUQxo8M7c8bXjCQEKCY1INqWJJbnlRj4SBhATHpBpSzdTc8pSRCQMJCY5JNaR5Zcv2\nZJZ23WgLEgYSEhyTakg7xll1bd3cOTMmVtmEpFQICY5J93OkvbeOLQ8+RqoYf0dT0jhCgmNS\n/4pQ43Pr12/a28YgQoJj+K4dIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQE\nCBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASINDpQtrSxSnvBhy6\nTheSdXHKuwGHrrOFdFupD+RSu016R+BQdbaQni31gVxqz0rvCByqzhaS9+EuTnk34NB1upCA\nUiAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAg\nJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRA\ngJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQ\nAAFCAgQICRAgJECAkAABQgIECAkQKFlIDX9IuJCQ4Jh0Q3rq3JozljeFiwuS1kJIcEyqIf2q\n0qoq7L0NwTIhoTNJNaRJFd9v3nNrxbt3eYSEziXVkIZfGvy7tse5TYSEziXVkCpuDH98y64h\nJHQuqYY07LzMz+tsKSGhU0k1pGvKvrQv+Nk8wz5+NSGhE0k1pFdH2AfCheZrzAgJnUi6nyP9\nefbHs0v3HUdI6ET4ihAgQEiAACEBAqUKaXNtbcE5DbOvbDWBkOCWUoW04aB37QgJDitVSI31\n9QmX8tQOjuE1EiCQdkjNW9asXr12WxujCAmOSTekhvmDLDRi8e6kcYQEx6Qa0vZRNrpu4dKl\nN0wfYmMaEgYSEhyTakizKlZll5qWl81LGEhIcEyqIQ2emVueNjxhICHBMen+Yd+S3PKiHgkD\nCQmOSTWkmqm55SkjEwYSEhyTakjzypbtySztutEWJAwkJDgm1ZB2jLPq2rq5c2ZMrLIJSakQ\nEhyT7udIe28dWx58jFQx/o6mpHGEBMek/hWhxufWr9+0t41BhATH8F07QICQAAFCAgQICRAg\nJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRA\ngJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQ\nAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAAB\nQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUIC\nBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgTS\nDql5y5rVq9dua2MUIcEx6YbUMH+QhUYs3p00jpDgmFRD2j7KRtctXLr0hulDbExDwkBCgmNS\nDWlWxarsUtPysnkJAwkJjkk1pMEzc8vThicMJCQ4JtWQKpbklhf1SBhISHBMqiHVTM0tTxmZ\nMJCQ4JhUQ5pXtmxPZmnXjbYgYSAhwTGphrRjnFXX1s2dM2NilU1ISoWQ4Jh0P0fae+vY8uBj\npIrxdzQljSMkOCb1rwg1Prd+/aa9bQwiJDiG79oBAoQECJQqpM21tQXnbB3Yv1WV7RJsA0hN\nqULaYIVrOfDwmla3WVuvooAOpVQhNdbXJ1z6KCHBLR3zNRIhwTEd8w/7CAmO6Zh/2EdIcEzH\n/MM+QoJjOuYf9hESHNMx/7CPkOCYjvmHfYQEx3TMP+wjJDimY/5hHyHBMR3zD/sICY7pmH/Y\nR0hwTMf8wz5CgmP4rh0gQEiAACEBAoQECBASIEBIgAAhAQKEBAh0zJDWGeCYdYd9mLd/SN6T\nTzjqnDNX4q9w5jmlvgeP1JOHf5SnEJKz6upKPQO3dan9R0jFdakDoR10qf1HSMV1qQOhHXSp\n/UdIxXWpA6EddKn9R0jFdakDoR10qf1HSMV1qQOhHXSp/UdIxXWpA6EddKn9R0jFdakDoR10\nqf1HSMV1qQOhHXSp/UdIxXWpA6EddKn9R0jFXXllqWfgti61/wipuIak/8kG2tSl9h8hAQKE\nBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIRVaaZW/\nzywdN6a0M3HOI2WnHsgs3WxzSjuV1BFSoZVmtZklQjpcH7Uvhj+39hr+lxJPJW2EVGilTbCV\n4RIhHa6dQ6ufD35+0B4s9VTSRkiFVtoPagaF/7WBMKQ/1g2peNPk35Z4Uq643873//2uXep5\nL80eUXH0lMf9k3uWntS3zzuXHij15NoVIRVaaQ/cb+F//yYIadugPp9csWRo5S9LPS1HTLPV\n3mvHDnzVe6XmqAUrbxpW+YjnXWaXfPVrf9fJXzURUqGV9iNvStljXiakGf6B4Xkby8eXelqO\neHnA0J2z7Tued1X34H8fua36FM+rOj245NoLm0o8t3ZFSIWCkLb1fuf+MKTmo45pDs48w14t\n9bwccbed1W2y5zUfPe7FwNn2unfUkJdLPav2R0iFgpC8ZbY0DGm7vT88c5Y9VuJpOeNs6/uC\n/wqp9f9r/Ix3u/X9h2+8UOp5tTNCKhSGtP+kqj8GIW2yyeGZc21NiafljIfscv/fTTb2Jxk7\nPG/t+b2t7Nw/lnpm7YqQCoUheY+VTfZGj/FezD4iXWa/Ke2s3LHGPuoFj0hj88/cs2ZG2Vv2\nlmhGqSCkQpmQvCts9dvHeN6AY8PXSKeV7SjtrNyRCck7ume4x15pPf8q69SfIRBSoWxIDQOH\nneiHdLl93z+xoay2xLNyRzakq+x6/99XBn/I+/WQu4Mz5th/l3Re7YyQCmVD8u4280P60+A+\n19/9mUHVT5V4Vu7IhvTyCLtsxU0jKn7m7X9HjyuWf2VmtzOaSz219kRIhVpC8t4XhORtu+zY\n7oMu3ljSKTklG5L34lXDu/c7L3g6938fP67qqDE3vV7aebUzQgIECAkQICRAgJAAAUICBAgJ\nECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAg\nJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRA\ngJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQ\nAAFCAgQICRAgJECAkAABQnYn5f0AAABDSURBVAIECAkQICRAgJAAAUICBAgJECAkQICQAAFC\nAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQOD/AeMyFW+gcDKCAAAAAElFTkSu\nQmCC", "text/plain": [ "Plot with title “Mortalité selon l'état de tabagisme des 54-64 ans”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JzLUzs4htdIgEDSIbVuWb1q1Zpt7YwiJDgm2ZCa5g2y0IhFu+LGERIck2hI20fZ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\nTkSuQmCC", "text/plain": [ "Plot with title “Mortalité selon l'état de tabagisme des plus de 65 ans”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sélection des données d'intérêt\n", "age_data_18 <- age_data_2[age_data_2$Var3==\"18-34 ans\",]\n", "age_data_18 \n", "\n", "age_data_34 <- age_data_2[age_data_2$Var3==\"34-54 ans\",]\n", "age_data_34\n", "\n", "age_data_54 <- age_data_2[age_data_2$Var3==\"54-64 ans\",]\n", "age_data_54\n", "\n", "age_data_65 <- age_data_2[age_data_2$Var3==\"plus de 65 ans\",]\n", "age_data_65\n", "\n", "#graphiques\n", "plot(x=age_data_18$Var1, y=age_data_18$mortality, ylim=c(0,1), , main=\"Mortalité selon l'état de tabagisme des 18-34 ans\")\n", "plot(x=age_data_34$Var1, y=age_data_34$mortality, ylim=c(0,1), , main=\"Mortalité selon l'état de tabagisme des 34-54 ans\")\n", "plot(x=age_data_54$Var1, y=age_data_54$mortality, ylim=c(0,1), , main=\"Mortalité selon l'état de tabagisme des 54-64 ans\")\n", "plot(x=age_data_65$Var1, y=age_data_65$mortality, ylim=c(0,1), , main=\"Mortalité selon l'état de tabagisme des plus de 65 ans\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons produire des graphiques par état de tabagisme pour comparer la mortalité selon l'âge mais ça ne nous apprend pas grand chose." ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
3No Dead 18-34 ans 6 398 0.02739726
7No Dead 34-54 ans 19 438 0.09547739
11No Dead 54-64 ans 39 234 0.32773109
15No Dead plus de 65 ans166 244 0.85128205
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t3 & No & Dead & 18-34 ans & 6 & 398 & 0.02739726 \\\\\n", "\t7 & No & Dead & 34-54 ans & 19 & 438 & 0.09547739 \\\\\n", "\t11 & No & Dead & 54-64 ans & 39 & 234 & 0.32773109 \\\\\n", "\t15 & No & Dead & plus de 65 ans & 166 & 244 & 0.85128205 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|\n", "| 3 | No | Dead | 18-34 ans | 6 | 398 | 0.02739726 | \n", "| 7 | No | Dead | 34-54 ans | 19 | 438 | 0.09547739 | \n", "| 11 | No | Dead | 54-64 ans | 39 | 234 | 0.32773109 | \n", "| 15 | No | Dead | plus de 65 ans | 166 | 244 | 0.85128205 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "3 No Dead 18-34 ans 6 398 0.02739726\n", "7 No Dead 34-54 ans 19 438 0.09547739\n", "11 No Dead 54-64 ans 39 234 0.32773109\n", "15 No Dead plus de 65 ans 166 244 0.85128205" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroupmortality
4Yes Dead 18-34 ans 5 398 0.02793296
8Yes Dead 34-54 ans 41 438 0.17154812
12Yes Dead 54-64 ans 51 234 0.44347826
16Yes Dead plus de 65 ans42 244 0.85714286
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Var1 & Var2 & Var3 & Freq & PopGroup & mortality\\\\\n", "\\hline\n", "\t4 & Yes & Dead & 18-34 ans & 5 & 398 & 0.02793296 \\\\\n", "\t8 & Yes & Dead & 34-54 ans & 41 & 438 & 0.17154812 \\\\\n", "\t12 & Yes & Dead & 54-64 ans & 51 & 234 & 0.44347826 \\\\\n", "\t16 & Yes & Dead & plus de 65 ans & 42 & 244 & 0.85714286 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Var1 | Var2 | Var3 | Freq | PopGroup | mortality | \n", "|---|---|---|---|\n", "| 4 | Yes | Dead | 18-34 ans | 5 | 398 | 0.02793296 | \n", "| 8 | Yes | Dead | 34-54 ans | 41 | 438 | 0.17154812 | \n", "| 12 | Yes | Dead | 54-64 ans | 51 | 234 | 0.44347826 | \n", "| 16 | Yes | Dead | plus de 65 ans | 42 | 244 | 0.85714286 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup mortality \n", "4 Yes Dead 18-34 ans 5 398 0.02793296\n", "8 Yes Dead 34-54 ans 41 438 0.17154812\n", "12 Yes Dead 54-64 ans 51 234 0.44347826\n", "16 Yes Dead plus de 65 ans 42 244 0.85714286" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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2bN+tYyISRYhs/aAQKEBAhEFdKGykrf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\nB1shy7c6sd6qAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “Mortalité non fumeuses selon leur âge”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "Plot with title “Mortalité des fumeuses selon leur âge”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sélection des variables d'intérêt\n", "age_data_no <- age_data_2[age_data_2$Var1==\"No\",]\n", "age_data_no \n", "\n", "age_data_yes <- age_data_2[age_data_2$Var1==\"Yes\",]\n", "age_data_yes\n", "\n", "plot(x=age_data_no$Var3, y=age_data_no$mortality, ylim=c(0,1), , main=\"Mortalité non fumeuses selon leur âge\")\n", "plot(x=age_data_yes$Var3, y=age_data_yes$mortality, ylim=c(0,1), , main=\"Mortalité des fumeuses selon leur âge\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Régression logistique\n", "Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, il est envisageable d'essayer de réaliser une régression logistique. Nous allons étudier la probabilité de décès en fonction de l'âge selon que l'on considère le groupe des fumeuses ou des non fumeuses." ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAgeMortDeath
Yes Alive21.0 0 0
Yes Alive19.3 0 0
No Dead 57.5 1 1
No Alive47.1 0 0
Yes Alive81.4 0 0
No Alive36.8 0 0
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " Smoker & Status & Age & Mort & Death\\\\\n", "\\hline\n", "\t Yes & Alive & 21.0 & 0 & 0 \\\\\n", "\t Yes & Alive & 19.3 & 0 & 0 \\\\\n", "\t No & Dead & 57.5 & 1 & 1 \\\\\n", "\t No & Alive & 47.1 & 0 & 0 \\\\\n", "\t Yes & Alive & 81.4 & 0 & 0 \\\\\n", "\t No & Alive & 36.8 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Smoker | Status | Age | Mort | Death | \n", "|---|---|---|---|---|---|\n", "| Yes | Alive | 21.0 | 0 | 0 | \n", "| Yes | Alive | 19.3 | 0 | 0 | \n", "| No | Dead | 57.5 | 1 | 1 | \n", "| No | Alive | 47.1 | 0 | 0 | \n", "| Yes | Alive | 81.4 | 0 | 0 | \n", "| No | Alive | 36.8 | 0 | 0 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age Mort Death\n", "1 Yes Alive 21.0 0 0 \n", "2 Yes Alive 19.3 0 0 \n", "3 No Dead 57.5 1 1 \n", "4 No Alive 47.1 0 0 \n", "5 Yes Alive 81.4 0 0 \n", "6 No Alive 36.8 0 0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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HK2U9maWjiYBQS4OzT3DIgcmBgybP6pOwbDe/OfWTR0Wnr7Fs06Bmn6d2+n6T0t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\n2rA/AAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data$Death <- ifelse(data$Status=='Dead', 1, 0)\n", "head(data)\n", "\n", "#plot(x=data$Age, y=data$Mort, main=\"Mort selon l'âge\")\n", "ggplot(data,aes(x=Age,y=Death)) + geom_point(alpha=.3,size=3) + theme_bw()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hW3YepC+SiOTCwwVgGdMyo3SOXevPH0v+j7WHIUWmMVcCbEEkoLh64o3ruRuWAP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TpfJGOgXIf+RLADAKSm8G7Cvr01v7FdI7WnDLVNquyI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k9CLVAamKYAcACUdmqtt7yrTvtElqD8x3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\nhWL9+vWVlZULFiyABuhEItHT0+P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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data,aes(x=Age,y=Death)) + geom_point(alpha=.3,size=3) + theme_bw() + geom_smooth(method = \"glm\",\n", "method.args = list(family = \"binomial\"))" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAgeMortDeath
Yes Alive21.0 0 0
Yes Alive19.3 0 0
No Dead 57.5 1 1
No Alive47.1 0 0
Yes Alive81.4 0 0
No Alive36.8 0 0
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " Smoker & Status & Age & Mort & Death\\\\\n", "\\hline\n", "\t Yes & Alive & 21.0 & 0 & 0 \\\\\n", "\t Yes & Alive & 19.3 & 0 & 0 \\\\\n", "\t No & Dead & 57.5 & 1 & 1 \\\\\n", "\t No & Alive & 47.1 & 0 & 0 \\\\\n", "\t Yes & Alive & 81.4 & 0 & 0 \\\\\n", "\t No & Alive & 36.8 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Smoker | Status | Age | Mort | Death | \n", "|---|---|---|---|---|---|\n", "| Yes | Alive | 21.0 | 0 | 0 | \n", "| Yes | Alive | 19.3 | 0 | 0 | \n", "| No | Dead | 57.5 | 1 | 1 | \n", "| No | Alive | 47.1 | 0 | 0 | \n", "| Yes | Alive | 81.4 | 0 | 0 | \n", "| No | Alive | 36.8 | 0 | 0 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age Mort Death\n", "1 Yes Alive 21.0 0 0 \n", "2 Yes Alive 19.3 0 0 \n", "3 No Dead 57.5 1 1 \n", "4 No Alive 47.1 0 0 \n", "5 Yes Alive 81.4 0 0 \n", "6 No Alive 36.8 0 0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAgeMortDeath
1Yes Alive21.0 0 0
2Yes Alive19.3 0 0
5Yes Alive81.4 0 0
8Yes Dead 57.5 1 1
9Yes Alive24.8 0 0
10Yes Alive49.5 0 0
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Smoker & Status & Age & Mort & Death\\\\\n", "\\hline\n", "\t1 & Yes & Alive & 21.0 & 0 & 0 \\\\\n", "\t2 & Yes & Alive & 19.3 & 0 & 0 \\\\\n", "\t5 & Yes & Alive & 81.4 & 0 & 0 \\\\\n", "\t8 & Yes & Dead & 57.5 & 1 & 1 \\\\\n", "\t9 & Yes & Alive & 24.8 & 0 & 0 \\\\\n", "\t10 & Yes & Alive & 49.5 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Smoker | Status | Age | Mort | Death | \n", "|---|---|---|---|---|---|\n", "| 1 | Yes | Alive | 21.0 | 0 | 0 | \n", "| 2 | Yes | Alive | 19.3 | 0 | 0 | \n", "| 5 | Yes | Alive | 81.4 | 0 | 0 | \n", "| 8 | Yes | Dead | 57.5 | 1 | 1 | \n", "| 9 | Yes | Alive | 24.8 | 0 | 0 | \n", "| 10 | Yes | Alive | 49.5 | 0 | 0 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age Mort Death\n", "1 Yes Alive 21.0 0 0 \n", "2 Yes Alive 19.3 0 0 \n", "5 Yes Alive 81.4 0 0 \n", "8 Yes Dead 57.5 1 1 \n", "9 Yes Alive 24.8 0 0 \n", "10 Yes Alive 49.5 0 0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
SmokerStatusAgeMortDeath
3No Dead 57.5 1 1
4No Alive47.1 0 0
6No Alive36.8 0 0
7No Alive23.8 0 0
12No Dead 66.0 1 1
14No Alive58.4 0 0
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Smoker & Status & Age & Mort & Death\\\\\n", "\\hline\n", "\t3 & No & Dead & 57.5 & 1 & 1 \\\\\n", "\t4 & No & Alive & 47.1 & 0 & 0 \\\\\n", "\t6 & No & Alive & 36.8 & 0 & 0 \\\\\n", "\t7 & No & Alive & 23.8 & 0 & 0 \\\\\n", "\t12 & No & Dead & 66.0 & 1 & 1 \\\\\n", "\t14 & No & Alive & 58.4 & 0 & 0 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Smoker | Status | Age | Mort | Death | \n", "|---|---|---|---|---|---|\n", "| 3 | No | Dead | 57.5 | 1 | 1 | \n", "| 4 | No | Alive | 47.1 | 0 | 0 | \n", "| 6 | No | Alive | 36.8 | 0 | 0 | \n", "| 7 | No | Alive | 23.8 | 0 | 0 | \n", "| 12 | No | Dead | 66.0 | 1 | 1 | \n", "| 14 | No | Alive | 58.4 | 0 | 0 | \n", "\n", "\n" ], "text/plain": [ " Smoker Status Age Mort Death\n", "3 No Dead 57.5 1 1 \n", "4 No Alive 47.1 0 0 \n", "6 No Alive 36.8 0 0 \n", "7 No Alive 23.8 0 0 \n", "12 No Dead 66.0 1 1 \n", "14 No Alive 58.4 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data)\n", "data_fumeuses <- data[data$Smoker=='Yes',]\n", "head(data_fumeuses)\n", "data_non_fumeuses <- data[data$Smoker=='No',]\n", "head(data_non_fumeuses)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "glm(formula = Death ~ Age, family = binomial(link = \"logit\"), \n", " data = data_fumeuses)\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.0745 -0.6464 -0.3756 -0.2013 2.6560 \n", "\n", "Coefficients:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -5.508106 0.466221 -11.81 <2e-16 ***\n", "Age 0.088977 0.008721 10.20 <2e-16 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 639.89 on 581 degrees of freedom\n", "Residual deviance: 480.41 on 580 degrees of freedom\n", "AIC: 484.41\n", "\n", "Number of Fisher Scoring iterations: 5\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logistic_reg_fum = glm(data=data_fumeuses, Death ~ Age, family=binomial(link='logit'))\n", "summary(logistic_reg_fum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'estimateur le plus probable du paramètre d'âge est 0.088977 et l'erreur standard de cet estimateur est de 0.008721 et la p-value est <2e-16, autrement dit on peut distinguer un impact particulier de l'âge." ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "glm(formula = Death ~ Age, family = binomial(link = \"logit\"), \n", " data = data_non_fumeuses)\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.4019 -0.5179 -0.2003 0.4728 3.0457 \n", "\n", "Coefficients:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -6.795507 0.479430 -14.17 <2e-16 ***\n", "Age 0.107275 0.007806 13.74 <2e-16 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 911.23 on 731 degrees of freedom\n", "Residual deviance: 519.08 on 730 degrees of freedom\n", "AIC: 523.08\n", "\n", "Number of Fisher Scoring iterations: 6\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logistic_reg_non_fum = glm(data=data_non_fumeuses, Death ~ Age, family=binomial(link='logit'))\n", "summary(logistic_reg_non_fum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'estimateur le plus probable du paramètre d'âge est 0.107275 et l'erreur standard de cet estimateur est de 0.007806 et la p-value est <2e-16, autrement dit on peut distinguer un impact particulier de l'âge." ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "glm(formula = Death ~ Age * Smoker, family = binomial(link = \"logit\"), \n", " data = data)\n", "\n", "Deviance Residuals: \n", " Min 1Q Median 3Q Max \n", "-2.4019 -0.6010 -0.2854 0.4339 3.0457 \n", "\n", "Coefficients:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -6.795507 0.479341 -14.177 <2e-16 ***\n", "Age 0.107275 0.007805 13.745 <2e-16 ***\n", "SmokerYes 1.287401 0.668678 1.925 0.0542 . \n", "Age:SmokerYes -0.018299 0.011703 -1.564 0.1179 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "(Dispersion parameter for binomial family taken to be 1)\n", "\n", " Null deviance: 1560.32 on 1313 degrees of freedom\n", "Residual deviance: 999.49 on 1310 degrees of freedom\n", "AIC: 1007.5\n", "\n", "Number of Fisher Scoring iterations: 5\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logistic_reg = glm(data=data, Death ~ Age*Smoker, family=binomial(link='logit'))\n", "summary(logistic_reg)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TQ6OurxeOhHr9d77ty5tboGhUIhGlREG5lujegemEo/umWKRCI0dkoul2ezWZlM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s33VjGtT3CSC2EOwAIMo0SXUdPstrq4uU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\n8xHOPWmilWJcaaUdlNBG0ykDaJW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ZP2xpmZGbrIlI7iNE2aGh0F6WBJj6PR6JYtW+jINzs7azKZpNBATU7UFCsVeBiG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\nvBA3OFbliYUe4hMxCBD9EwoF2gL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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(data_fumeuses,aes(x=Age,y=Death)) + geom_point(alpha=.3,size=3) + theme_bw() + geom_smooth(method = \"glm\",\n", "method.args = list(family = \"binomial\"))\n", "\n", "ggplot(data_non_fumeuses,aes(x=Age,y=Death)) + geom_point(alpha=.3,size=3) + theme_bw() + geom_smooth(method = \"glm\",\n", "method.args = list(family = \"binomial\"))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Var1Var2Var3FreqPopGroup
No Alive 18-34 ans 213 398
Yes Alive 18-34 ans 174 398
No Dead 18-34 ans 6 398
Yes Dead 18-34 ans 5 398
No Alive 34-54 ans 180 438
Yes Alive 34-54 ans 198 438
No Dead 34-54 ans 19 438
Yes Dead 34-54 ans 41 438
No Alive 54-64 ans 80 234
Yes Alive 54-64 ans 64 234
No Dead 54-64 ans 39 234
Yes Dead 54-64 ans 51 234
No Alive plus de 65 ans 29 244
Yes Alive plus de 65 ans 7 244
No Dead plus de 65 ans166 244
Yes Dead plus de 65 ans 42 244
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " Var1 & Var2 & Var3 & Freq & PopGroup\\\\\n", "\\hline\n", "\t No & Alive & 18-34 ans & 213 & 398 \\\\\n", "\t Yes & Alive & 18-34 ans & 174 & 398 \\\\\n", "\t No & Dead & 18-34 ans & 6 & 398 \\\\\n", "\t Yes & Dead & 18-34 ans & 5 & 398 \\\\\n", "\t No & Alive & 34-54 ans & 180 & 438 \\\\\n", "\t Yes & Alive & 34-54 ans & 198 & 438 \\\\\n", "\t No & Dead & 34-54 ans & 19 & 438 \\\\\n", "\t Yes & Dead & 34-54 ans & 41 & 438 \\\\\n", "\t No & Alive & 54-64 ans & 80 & 234 \\\\\n", "\t Yes & Alive & 54-64 ans & 64 & 234 \\\\\n", "\t No & Dead & 54-64 ans & 39 & 234 \\\\\n", "\t Yes & Dead & 54-64 ans & 51 & 234 \\\\\n", "\t No & Alive & plus de 65 ans & 29 & 244 \\\\\n", "\t Yes & Alive & plus de 65 ans & 7 & 244 \\\\\n", "\t No & Dead & plus de 65 ans & 166 & 244 \\\\\n", "\t Yes & Dead & plus de 65 ans & 42 & 244 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Var1 | Var2 | Var3 | Freq | PopGroup | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| No | Alive | 18-34 ans | 213 | 398 | \n", "| Yes | Alive | 18-34 ans | 174 | 398 | \n", "| No | Dead | 18-34 ans | 6 | 398 | \n", "| Yes | Dead | 18-34 ans | 5 | 398 | \n", "| No | Alive | 34-54 ans | 180 | 438 | \n", "| Yes | Alive | 34-54 ans | 198 | 438 | \n", "| No | Dead | 34-54 ans | 19 | 438 | \n", "| Yes | Dead | 34-54 ans | 41 | 438 | \n", "| No | Alive | 54-64 ans | 80 | 234 | \n", "| Yes | Alive | 54-64 ans | 64 | 234 | \n", "| No | Dead | 54-64 ans | 39 | 234 | \n", "| Yes | Dead | 54-64 ans | 51 | 234 | \n", "| No | Alive | plus de 65 ans | 29 | 244 | \n", "| Yes | Alive | plus de 65 ans | 7 | 244 | \n", "| No | Dead | plus de 65 ans | 166 | 244 | \n", "| Yes | Dead | plus de 65 ans | 42 | 244 | \n", "\n", "\n" ], "text/plain": [ " Var1 Var2 Var3 Freq PopGroup\n", "1 No Alive 18-34 ans 213 398 \n", "2 Yes Alive 18-34 ans 174 398 \n", "3 No Dead 18-34 ans 6 398 \n", "4 Yes Dead 18-34 ans 5 398 \n", "5 No Alive 34-54 ans 180 438 \n", "6 Yes Alive 34-54 ans 198 438 \n", "7 No Dead 34-54 ans 19 438 \n", "8 Yes Dead 34-54 ans 41 438 \n", "9 No Alive 54-64 ans 80 234 \n", "10 Yes Alive 54-64 ans 64 234 \n", "11 No Dead 54-64 ans 39 234 \n", "12 Yes Dead 54-64 ans 51 234 \n", "13 No Alive plus de 65 ans 29 244 \n", "14 Yes Alive plus de 65 ans 7 244 \n", "15 No Dead plus de 65 ans 166 244 \n", "16 Yes Dead plus de 65 ans 42 244 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "age_data$PopGroup <- ifelse(age_data$Var3=='18-34 ans', age_data$Freq[1]+age_data$Freq[2]+age_data$Freq[3]+age_data$Freq[4], \n", " ifelse(age_data$Var3=='34-54 ans', age_data$Freq[5]+age_data$Freq[6]+age_data$Freq[7]+age_data$Freq[8], \n", " ifelse(age_data$Var3=='54-64 ans', age_data$Freq[9]+age_data$Freq[10]+age_data$Freq[11]+age_data$Freq[12], \n", " age_data$Freq[13]+age_data$Freq[14]+age_data$Freq[15]+age_data$Freq[16])))\n", "age_data$PopGroupFum <- ifelse(age_data$Var3=='18-34 ans', age_data$Freq[1]+age_data$Freq[2]+age_data$Freq[3]+age_data$Freq[4], \n", " ifelse(age_data$Var3=='34-54 ans', age_data$Freq[5]+age_data$Freq[6]+age_data$Freq[7]+age_data$Freq[8], \n", " ifelse(age_data$Var3=='54-64 ans', age_data$Freq[9]+age_data$Freq[10]+age_data$Freq[11]+age_data$Freq[12], \n", " age_data$Freq[13]+age_data$Freq[14]+age_data$Freq[15]+age_data$Freq[16])))\n", "\n", "age_data" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 4 }