From b6b0a4af5f4b61fbb550df135b94e207226628d3 Mon Sep 17 00:00:00 2001
From: Hugues de Courson <>
Date: Sun, 5 Apr 2020 18:30:56 +0200
Subject: [PATCH] V2 du commit de l'exo

---
 module3/exo3/exercice_fr.Rmd |  69 ++++---
 module3/exo3/exercice_fr.log | 342 ++++++++++++++++++++++++++---------
 module3/exo3/exercice_fr.out |   0
 module3/exo3/exercice_fr.pdf | Bin 245114 -> 248764 bytes
 4 files changed, 300 insertions(+), 111 deletions(-)
 create mode 100644 module3/exo3/exercice_fr.out

diff --git a/module3/exo3/exercice_fr.Rmd b/module3/exo3/exercice_fr.Rmd
index 0d509f6..90c6382 100644
--- a/module3/exo3/exercice_fr.Rmd
+++ b/module3/exo3/exercice_fr.Rmd
@@ -54,7 +54,8 @@ vivantes_nonfum <- sum(data$Status[data$Smoker=="No"]=="Alive")
 
 decedees_fum <- sum(data$Status[data$Smoker=="Yes"]=="Dead")
 decedees_nonfum  <- sum(data$Status[data$Smoker=="No"]=="Dead")
-tab_1 <- data.frame(Vivantes = c(vivantes_fum,vivantes_nonfum), Decedees = c(decedees_fum,decedees_nonfum), row.names = c("Fumeuses","Non fumeuses"))
+tab_1 <- data.frame(Vivantes = c(vivantes_fum,vivantes_nonfum), 
+  Decedees = c(decedees_fum,decedees_nonfum), row.names = c("Fumeuses","Non fumeuses"))
 kable(tab_1,align = 'l')
 ```
 
@@ -73,7 +74,10 @@ nb_viv <- sum(data$Status=="Alive")
 nb_deces <-sum(data$Status=="Dead")
 tot <- sum(data$Status=="Alive"|data$Status=="Dead")
 
-tab_2 <- data.frame(cbind(rbind(mort_fum, vivant_fum, nb_fum)), rbind(mort_nonfum,vivant_nonfum, nb_nonfum),rbind(nb_deces,nb_viv,tot))
+tab_2 <- data.frame(cbind(rbind(mort_fum, vivant_fum, nb_fum)), 
+                    rbind(mort_nonfum,vivant_nonfum, nb_nonfum),
+                    rbind(nb_deces,nb_viv,tot))
+
 row.names(tab_2) <- c("Decedees", "Vivantes","Total")
 names(tab_2) <- c("Fumeuses", "Non Fumeuses", "Total")
 
@@ -83,8 +87,10 @@ kable(tab_2,align = "c")
 # Representation graphique
 Ceci passe par la création d'un tableau avec les pourcentages de mortalité
 ```{r representation graphique taux de mortalite}
-tab_3 <- data.frame(cbind(rbind((sum(data$Status[data$Smoker=="Yes"]=="Dead")/sum(data$Smoker=="Yes"))*100,(sum(data$Status[data$Smoker=="Yes"]=="Alive")/sum(data$Smoker=="Yes"))*100)), 
-                    rbind((sum(data$Status[data$Smoker=="No"]=="Dead")/sum(data$Smoker=="No"))*100,(sum(data$Status[data$Smoker=="No"]=="Alive")/sum(data$Smoker=="No"))*100))
+tab_3 <- data.frame(cbind(rbind((sum(data$Status[data$Smoker=="Yes"]=="Dead")/sum(data$Smoker=="Yes"))*100,
+                                (sum(data$Status[data$Smoker=="Yes"]=="Alive")/sum(data$Smoker=="Yes"))*100)), 
+                    rbind((sum(data$Status[data$Smoker=="No"]=="Dead")/sum(data$Smoker=="No"))*100,
+                          (sum(data$Status[data$Smoker=="No"]=="Alive")/sum(data$Smoker=="No"))*100))
                                 
 names(tab_3) <- c("Fumeuses", "Non fumeuses")
 row.names(tab_3) <- c("Decedees", "Vivantes")
@@ -156,10 +162,12 @@ tab_5 <- ftable(data[,c(1,2,4)])
 tab_5 <- round(prop.table(tab_5,2)*100,1)
 tab_5 <- rbind(tab_5, tab1(data$cl_age)$output.table[,1])
 tab_5 <- as.table(tab_5)
-row.names(tab_5) <- c("Non fumeuses vivantes ", "Non fumeuses decedees", "Fumeuses vivantes", "Fumeuses decedees" ,"Effectifs")
+row.names(tab_5) <- c("Non fumeuses vivantes ", "Non fumeuses decedees", 
+                      "Fumeuses vivantes", "Fumeuses decedees" ,"Effectifs")
 colnames(tab_5) <- c("[18;34]", "[35;54]", "[55;64]", "[65;[")
 
-kable(tab_5,"latex", align = "c") %>% kable_styling(latex_options = "striped", stripe_index = c(1,2))
+kable(tab_5,"latex", align = "c") %>% kable_styling(latex_options = "striped", 
+                                                    stripe_index = c(1,2))
 ```
 
 ## Et maintenant on trace les barplots
@@ -170,14 +178,18 @@ Et autre [lien](https://sites.google.com/site/rgraphiques/realiser-des-graphique
 ```{r deuxieme barplot}
 
 par(mar = c(5,5,5,13))
-barplot(tab_5[c(1:4),], col = c("gray","black","gray","black"), density = c(30,30,40,100),angle = c(70,70,0,0))
+barplot(tab_5[c(1:4),], col = c("gray","black","gray","black"), density = c(30,30,40,100),
+        angle = c(70,70,0,0))
 xmin <- par("usr")[1]
 xmax <- par("usr")[2]
 ymin <- par("usr")[3]
 ymax <- par("usr")[4]
 par(xpd=TRUE)
 lambda <- 0.025
-legend(((1 + lambda) * par("usr")[2] - lambda * par("usr")[1]),50, legend = c("Non fumeuses vivantes", "Non fumeuses decedees", "Fumeuses vivantes", "Fumeuses decedees"), fill = c("gray","black","gray","black"),density = c(30,30,40,100),angle = (c(70,70,0,0)))
+legend(((1 + lambda) * par("usr")[2] - lambda * par("usr")[1]),50, 
+       legend = c("Non fumeuses vivantes", "Non fumeuses decedees", 
+                  "Fumeuses vivantes", "Fumeuses decedees"), 
+       fill = c("gray","black","gray","black"),density = c(30,30,40,100),angle = (c(70,70,0,0)))
 
 
 ```
@@ -199,8 +211,8 @@ On récupère les valeurs min et max du plot précédent
 
 
 ## Et maintenant on peut obtenir les intervalles de confiance
-<!--# Pour le premier groupe d'age non fumeuses decedees-->
-```{r IC premier groupe dage non fum, include=FALSE}
+#Pour le premier groupe d'age non fumeuses decedees
+```{r IC premier groupe dage non fum}
 
 x <- tab_5[2,1]
 n <- tab_5[5,1]
@@ -209,8 +221,8 @@ IC1<- round(IC(x),1)
 IC1 <- paste(c(IC1[1],"[",IC1[2],"-",IC1[3],"]"),collapse = "")
 
 ```
-<!--# Pour le premier groupe d'age fumeuses decedees-->
-```{r IC premier groupe dage fum, include=FALSE}
+# Pour le premier groupe d'age fumeuses decedees
+```{r IC premier groupe dage fum}
 
 x <- tab_5[4,1]
 n <- tab_5[5,1]
@@ -219,8 +231,8 @@ IC2 <- round(IC(x),1)
 IC2 <- paste(c(IC2[1],"[",IC2[2],"-",IC2[3],"]"), collapse = "")
 ```
 
-<!--# Pour le deuxième groupe d'age non fum-->
-```{r IC deuxieme groupe dage non fum, include=FALSE}
+# Pour le deuxième groupe d'age non fum
+```{r IC deuxieme groupe dage non fum}
 
 x <- tab_5[2,2]
 n <- tab_5[5,2]
@@ -229,8 +241,8 @@ IC3 <- round(IC(x),1)
 IC3 <- paste(c(IC3[1],"[",IC3[2],"-",IC3[3],"]"),collapse = "")
 
 ```
-<!--# Pour le deuxième groupe d'age fum-->
-```{r IC deuxieme groupe dage fum, include=FALSE}
+# Pour le deuxième groupe d'age fum
+```{r IC deuxieme groupe dage fum}
 
 x <- tab_5[4,2]
 n <- tab_5[5,2]
@@ -239,8 +251,8 @@ IC4 <- round(IC(x),1)
 IC4 <- paste(c(IC4[1],"[",IC4[2],"-",IC4[3],"]"),collapse = "")
 
 ```
-<!--# Pour le troisieme groupe d'age non fum-->
-```{r IC troisieme groupe dage non fum, include=FALSE}
+# Pour le troisieme groupe d'age non fum
+```{r IC troisieme groupe dage non fum}
 
 x <- tab_5[2,3]
 n <- tab_5[5,3]
@@ -249,8 +261,8 @@ IC5 <- round(IC(x),1)
 IC5 <- paste(c(IC5[1],"[",IC5[2],"-",IC5[3],"]"),collapse = "")
 
 ```
-<!--# Pour le troisieme groupe d'age  fum-->
-```{r IC troisieme groupe dage  fum, include=FALSE}
+# Pour le troisieme groupe d'age  fum
+```{r IC troisieme groupe dage  fum}
 
 x <- tab_5[4,3]
 n <- tab_5[5,3]
@@ -258,8 +270,8 @@ n <- tab_5[5,3]
 IC6 <- round(IC(x),1)
 IC6 <- paste(c(IC6[1],"[",IC6[2],"-",IC6[3],"]"),collapse = "")
 ```
-<!--# Pour le quatrieme groupe d'age non fum-->
-```{r IC quatrieme groupe dage non fum, include=FALSE}
+# Pour le quatrieme groupe d'age non fum
+```{r IC quatrieme groupe dage non fum}
 
 x <- tab_5[2,4]
 n <- tab_5[5,4]
@@ -269,8 +281,8 @@ IC7 <- paste(c(IC7[1],"[",IC7[2],"-",IC7[3],"]"),collapse = "")
 
 ```
 
-<!--# Pour le quatrieme groupe d'age fum-->
-```{r IC quatrieme groupe dage fum, include=FALSE}
+Pour le quatrieme groupe d'age fum
+```{r IC quatrieme groupe dage fum}
 
 x <- tab_5[4,4]
 n <- tab_5[5,4]
@@ -334,7 +346,8 @@ Pour cela on introduit un polynome fractionnaire dans le modèle de régression.
 
 library(mfp)
 
-reguniage2 <- mfp(deces~fp(Age, df =4, select = 1, scale = T), family = binomial, data = data)
+reguniage2 <- mfp(deces~fp(Age, df =4, select = 1, scale = T), 
+family = binomial, data = data)
 
 summary(reguniage2)
 ```
@@ -351,4 +364,8 @@ logistic.display(regtot)
 ```
 
 
-Et là on constate une inversion de l'odds ratio associé au tabac lorsque l'on ajuste sur l'âge...
\ No newline at end of file
+Et là on constate une inversion de l'odds ratio associé au tabac lorsque l'on ajuste sur l'âge...
+Soit l'âge est un facteur confondant sur la relation entre le fait d'être fumeur et le risque de décés, soit il s'agit d'un modificateur de l'effet. 
+
+Dans tous les cas l'interprétation brute du de la relation entre le fait de fumer et l'âge n'est pas juste.
+
diff --git a/module3/exo3/exercice_fr.log b/module3/exo3/exercice_fr.log
index 2f3524e..bbc0e2d 100644
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+++ b/module3/exo3/exercice_fr.log
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 (./exercice_fr.aux)
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 Here is how much of TeX's memory you used:
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- 214645 string characters out of 6129481
- 340591 words of memory out of 5000000
- 18375 multiletter control sequences out of 15000+600000
- 52237 words of font info for 72 fonts, out of 8000000 for 9000
+ 14775 strings out of 492616
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+ 342038 words of memory out of 5000000
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+ 52320 words of font info for 73 fonts, out of 8000000 for 9000
  1141 hyphenation exceptions out of 8191
- 32i,9n,38p,1190b,455s stack positions out of 5000i,500n,10000p,200000b,80000s
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 {/usr/local/texlive/2019/texmf-dist/fonts/enc/dvips/lm/lm-ec.enc}{/usr/local/
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-Output written on exercice_fr.pdf (8 pages, 245114 bytes).
+Output written on exercice_fr.pdf (10 pages, 248764 bytes).
 PDF statistics:
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- 168 compressed objects within 2 object streams
- 28 named destinations out of 1000 (max. 500000)
- 10413 words of extra memory for PDF output out of 12000 (max. 10000000)
+ 241 PDF objects out of 1000 (max. 8388607)
+ 206 compressed objects within 3 object streams
+ 37 named destinations out of 1000 (max. 500000)
+ 10469 words of extra memory for PDF output out of 12000 (max. 10000000)
 
diff --git a/module3/exo3/exercice_fr.out b/module3/exo3/exercice_fr.out
new file mode 100644
index 0000000..e69de29
diff --git a/module3/exo3/exercice_fr.pdf b/module3/exo3/exercice_fr.pdf
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DiuvUD

-- 
2.18.1