Lab’s 7 reg logistique
E.ing NEKUI TIEFANG BIEDIANT
2023-03-25
conditions Reg logistiqueNombre d’observation doit être egale aumoins a 10*nbre de paramètres
tester l’hypothèse de linearite sur chaque variable explicative (a ne pas confondre avec le raintest de la rglm)
analyse de surdispersion qui risque de conduire a sous-estimer des erreurs standart des paramètres en evaluant le ratio deviance resi/ddl. si le ratio est trop sup a 1(2), il faudra plustot appliquer la valeur quasibinomial au paramètre family de la fonction glm
il est aussi a noter qu’en reg logi on interprete pas les
coefficients des param mais plutot leurs valeurs exponentielle ils sont
appeles odds ratio ou rapport de cote si odds ratio = 1 absence d’effet
si odds ratio sup a 1 augmentation du phenomène si odds ratio inf a 1
diminution du phénomène étudié la library(forstmodel) nous permet aussi
de deceler la significativité la variables graphiquement
utiliser la fonction dropl(model, test=“chisq”) la library MASS et le
summary de la fonction stepAIC(model) est aussi une alternative
tester la multicolinearité du modèle
significativité du model
inspecter l’influense des var independante
evaluer la qualité du modèle
diagnostic des residus et des valeurs a berrantes
la qualité du modèle se vois à la valeur du passage du Null deviance (nbre de residu non expliqué sans intervention de variable explicative) et du Residual deviance (nbre de residu non expliqué avec intervention de quelques variable explicative)
NB en reg logi les coefficient ne sont pas interpreté
nous pouvons finir avec la matrice de confusion et out les elements qui vont avec
1)linearité entre y et chaque x 2)absence de multicol 3)normalité des residus 4)homocedas 5)indepen des resi 6)significativité du model (auto)
passons a un exemple pratique avecle titanic
- importer la BD
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorstitanic1=read.csv("/home/nekui-tiefang/dataset/data3/titanic1.csv")
titanic2=read.csv("/home/nekui-tiefang/dataset/data3/titanic2.csv")description de la bd sibsp: nbre frere et soeur + nbre de conjoint parch: nbre enfant ou nbre de parent à bord du navire pour chaque passager. fare: prix payer cabin: numero de la cabine embarked: porte d’embarquement
- passons au data cleaning
titanic=bind_rows(titanic1,titanic2)
colnames(titanic)## [1] "PassengerId" "Survived" "Pclass" "Name" "Sex"
## [6] "Age" "SibSp" "Parch" "Ticket" "Fare"
## [11] "Cabin" "Embarked"remove(titanic1,titanic2)titanic= titanic[c( "Pclass", "Sex", "Age", "SibSp", "Fare", "Embarked", "Parch", "Survived")]
names(titanic)## [1] "Pclass" "Sex" "Age" "SibSp" "Fare" "Embarked" "Parch"
## [8] "Survived"summary(titanic)## Pclass Sex Age SibSp
## Min. :1.000 Length:1309 Min. : 0.17 Min. :0.0000
## 1st Qu.:2.000 Class :character 1st Qu.:21.00 1st Qu.:0.0000
## Median :3.000 Mode :character Median :28.00 Median :0.0000
## Mean :2.295 Mean :29.88 Mean :0.4989
## 3rd Qu.:3.000 3rd Qu.:39.00 3rd Qu.:1.0000
## Max. :3.000 Max. :80.00 Max. :8.0000
## NA's :263
## Fare Embarked Parch Survived
## Min. : 0.000 Length:1309 Min. :0.000 Min. :0.0000
## 1st Qu.: 7.896 Class :character 1st Qu.:0.000 1st Qu.:0.0000
## Median : 14.454 Mode :character Median :0.000 Median :0.0000
## Mean : 33.295 Mean :0.385 Mean :0.3838
## 3rd Qu.: 31.275 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :512.329 Max. :9.000 Max. :1.0000
## NA's :1 NA's :418sum(is.na(titanic)) #somme le nbre de valeur manquantes## [1] 682colMeans(is.na(titanic)) #pourcentage de valeurs manquante dans chaque colonne## Pclass Sex Age SibSp Fare Embarked
## 0.0000000000 0.0000000000 0.2009167303 0.0000000000 0.0007639419 0.0000000000
## Parch Survived
## 0.0000000000 0.3193277311boxplot(titanic$Age)
mean(titanic$Age,na.rm = T)## [1] 29.88114median(titanic$Age,na.rm = T)## [1] 28titanic[is.na(titanic$Age),]$Age=median(titanic$Age,na.rm = T)
summary(titanic)## Pclass Sex Age SibSp
## Min. :1.000 Length:1309 Min. : 0.17 Min. :0.0000
## 1st Qu.:2.000 Class :character 1st Qu.:22.00 1st Qu.:0.0000
## Median :3.000 Mode :character Median :28.00 Median :0.0000
## Mean :2.295 Mean :29.50 Mean :0.4989
## 3rd Qu.:3.000 3rd Qu.:35.00 3rd Qu.:1.0000
## Max. :3.000 Max. :80.00 Max. :8.0000
##
## Fare Embarked Parch Survived
## Min. : 0.000 Length:1309 Min. :0.000 Min. :0.0000
## 1st Qu.: 7.896 Class :character 1st Qu.:0.000 1st Qu.:0.0000
## Median : 14.454 Mode :character Median :0.000 Median :0.0000
## Mean : 33.295 Mean :0.385 Mean :0.3838
## 3rd Qu.: 31.275 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :512.329 Max. :9.000 Max. :1.0000
## NA's :1 NA's :418nous travaillerons avec la mediane parceque……
titanic=titanic[!is.na(titanic$Survived),]#delete all rows has survived don't have a value
titanic=titanic[!is.na(titanic$Embarked),]#replace value missed by the titanic=titanic[-c(62,830),]
str(titanic)## 'data.frame': 891 obs. of 8 variables:
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: chr "S" "C" "S" "S" ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: int 0 1 1 1 0 0 0 0 1 1 ...sum(is.na(titanic)) #somme le nbre de valeur manquantes## [1] 0colMeans(is.na(titanic)) #pourcentage de valeurs manquante dans chaque colonne## Pclass Sex Age SibSp Fare Embarked Parch Survived
## 0 0 0 0 0 0 0 0library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectstr(titanic)## 'data.frame': 891 obs. of 8 variables:
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: chr "S" "C" "S" "S" ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: int 0 1 1 1 0 0 0 0 1 1 ...titanic$Survived=factor(titanic$Survived)
titanic$Pclass=factor(titanic$Pclass)
titanic$Sex=factor(titanic$Sex)
titanic$Embarked=factor(titanic$Embarked)
str(titanic)## 'data.frame': 891 obs. of 8 variables:
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...Garder uniquement les variables qui nous sont utiles pour l’analyse. Notons que seul les variables ayant uniquement un porcentage vraimnent significatif seront traiter. La var Fare n’ayant pas un pourcentage de valeurs manquante ne sera pas traité a dessin
library(dplyr)
str(titanic)## 'data.frame': 891 obs. of 8 variables:
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...sum(is.na(titanic)) #somme le nbre de valeur manquantes## [1] 0colMeans(is.na(titanic)) #pourcentage de valeurs manquante dans chaque colonne## Pclass Sex Age SibSp Fare Embarked Parch Survived
## 0 0 0 0 0 0 0 0summary(titanic)## Pclass Sex Age SibSp Fare Embarked
## 1:216 female:314 Min. : 0.42 Min. :0.000 Min. : 0.00 : 2
## 2:184 male :577 1st Qu.:22.00 1st Qu.:0.000 1st Qu.: 7.91 C:168
## 3:491 Median :28.00 Median :0.000 Median : 14.45 Q: 77
## Mean :29.36 Mean :0.523 Mean : 32.20 S:644
## 3rd Qu.:35.00 3rd Qu.:1.000 3rd Qu.: 31.00
## Max. :80.00 Max. :8.000 Max. :512.33
## Parch Survived
## Min. :0.0000 0:549
## 1st Qu.:0.0000 1:342
## Median :0.0000
## Mean :0.3816
## 3rd Qu.:0.0000
## Max. :6.0000Gerons la variable Embarked
#titanic$Embarked= fct_recode(titanic$Embarked, "NA's"="")
str(titanic)## 'data.frame': 891 obs. of 8 variables:
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...filter(titanic, Embarked == "")## Pclass Sex Age SibSp Fare Embarked Parch Survived
## 1 1 female 38 0 80 0 1
## 2 1 female 62 0 80 0 1which(titanic$Embarked == "")## [1] 62 830titanic= titanic[-c(62,830), ]
summary(titanic)## Pclass Sex Age SibSp Fare
## 1:214 female:312 Min. : 0.42 Min. :0.0000 Min. : 0.000
## 2:184 male :577 1st Qu.:22.00 1st Qu.:0.0000 1st Qu.: 7.896
## 3:491 Median :28.00 Median :0.0000 Median : 14.454
## Mean :29.32 Mean :0.5242 Mean : 32.097
## 3rd Qu.:35.00 3rd Qu.:1.0000 3rd Qu.: 31.000
## Max. :80.00 Max. :8.0000 Max. :512.329
## Embarked Parch Survived
## : 0 Min. :0.0000 0:549
## C:168 1st Qu.:0.0000 1:340
## Q: 77 Median :0.0000
## S:644 Mean :0.3825
## 3rd Qu.:0.0000
## Max. :6.0000str(titanic)## 'data.frame': 889 obs. of 8 variables:
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num 22 38 26 35 35 28 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Embarked: Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Survived: Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...Voilà que le cleaning data est finish
3)Nous pouvons a present si l’on veux passer à la data visualisation
hist(titanic$Age, main = "âge passagers",
xlab = "âge", ylab = "Effectif",col = "red")
hist(titanic$SibSp, main = "nombre de frere et soeurs",
xlab = "frere et soeurs", ylab = "Effectif")
hist(titanic$Fare, main = "prix payé par passgers",
xlab = "prix payé en euro", ylab = "Effectif",col = "red")
hist(titanic$Parch, main = "nombre enfants",
xlab = "nombre enfants", ylab = "Effectif",col = "blue")
#var quali
table(titanic$Sex)##
## female male
## 312 577plot(titanic$Sex, main = "genre", ylab = "Effectif") #plus facile a interpreter en quali
table(titanic$Pclass)##
## 1 2 3
## 214 184 491plot(titanic$Pclass, main = "pclass", ylab = "Effectif") #plus facile a interpreter en quali
table(titanic$Embarked)##
## C Q S
## 0 168 77 644plot(titanic$Embarked, main = "embarked", ylab = "Effectif") #plus facile a interpreter en quali
table(titanic$Survived)##
## 0 1
## 549 340plot(titanic$Survived, main = "survived", ylab = "Effectif") #plus facile a interpreter en quali
#bivarié
plot(titanic$Age, titanic$Fare)
plot(titanic$Sex, titanic$Survived)
plot(titanic$Survived,titanic$Age )
Ainsi de suite……………………………………………………..
4)modelisation des données syndage des données
set.seed(1000)
tirage=sample(2,nrow(titanic),replace = TRUE,prob = c(0.8,0.2))
titTrain=titanic[tirage==1,]
titTest=titanic[tirage==2,]nous entrainerons le modèle avec la bd titTrain
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lift#titrain=trainControl(method = "cv",number = 10,savePredictions = T)
model1=glm(Survived~.,family = binomial(),data = titTrain)
#model11=train(Survived~., family=binomial(), methods="glm",data = titant1, trainControl=titrain)
summary(model1)##
## Call:
## glm(formula = Survived ~ ., family = binomial(), data = titTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5822 -0.5996 -0.4118 0.6227 2.4629
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.147532 0.538073 7.708 1.28e-14 ***
## Pclass2 -0.745244 0.337133 -2.211 0.0271 *
## Pclass3 -2.174654 0.340626 -6.384 1.72e-10 ***
## Sexmale -2.711133 0.225905 -12.001 < 2e-16 ***
## Age -0.036623 0.008623 -4.247 2.16e-05 ***
## SibSp -0.276330 0.126532 -2.184 0.0290 *
## Fare 0.001849 0.003227 0.573 0.5665
## EmbarkedQ -0.218349 0.415826 -0.525 0.5995
## EmbarkedS -0.648612 0.267885 -2.421 0.0155 *
## Parch -0.066040 0.129603 -0.510 0.6104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 956.65 on 718 degrees of freedom
## Residual deviance: 628.25 on 709 degrees of freedom
## AIC: 648.25
##
## Number of Fisher Scoring iterations: 5linearité des var signif
SurvivedPredic=predict.glm(model1,type = "response")
par(mfrow=c(3,3))
ggplot(titTrain, aes(x=titTrain$Pclass,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'
ggplot(titTrain, aes(x=titTrain$Sex,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'
ggplot(titTrain, aes(x=titTrain$Age,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
ggplot(titTrain, aes(x=titTrain$SibSp,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
ggplot(titTrain, aes(x=titTrain$Fare,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
ggplot(titTrain, aes(x=titTrain$Embarked,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'
ggplot(titTrain, aes(x=titTrain$Parch,y=SurvivedPredic))+geom_point()+geom_smooth(method = "glm", method.args=list(family ="binomial" ))## `geom_smooth()` using formula = 'y ~ x'## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
““”
a=predict(model1, type = “response”) b=colnames(titTrain[1:7]) logit=log(a/(1-a)) c=titTrain[1:7]%>%mutate(logit)%>%gather(key=“b”,value=“predictor.value”,-logit) ggplot(c,aes(logit ,predictor.value))+geom_point(size=0.5,alpha=0.5)+geom_smooth(method = “glm”)+facet_wrap(~b,scales = “free_y”)
significativité des varibles (odds ratio)
library(questionr)
options(scipen = 999)
o=round(odds.ratio(model1),3)## Waiting for profiling to be done...o## OR 2.5 % 97.5 % p
## (Intercept) 63.278 22.400 185.813 <0.0000000000000002 ***
## Pclass2 0.475 0.245 0.921 0.027 *
## Pclass3 0.114 0.058 0.222 <0.0000000000000002 ***
## Sexmale 0.066 0.042 0.102 <0.0000000000000002 ***
## Age 0.964 0.948 0.980 <0.0000000000000002 ***
## SibSp 0.759 0.585 0.961 0.029 *
## Fare 1.002 0.996 1.009 0.567
## EmbarkedQ 0.804 0.354 1.810 0.600
## EmbarkedS 0.523 0.309 0.885 0.015 *
## Parch 0.936 0.721 1.204 0.610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#z=round(exp(coef(model1)),3)library(forestmodel)
forest_model(model1)
#coollibrary(MASS)
summary(stepAIC(model1))## Start: AIC=648.25
## Survived ~ Pclass + Sex + Age + SibSp + Fare + Embarked + Parch
##
## Df Deviance AIC
## - Parch 1 628.51 646.51
## - Fare 1 628.60 646.60
## <none> 628.25 648.25
## - Embarked 2 634.73 650.73
## - SibSp 1 633.60 651.60
## - Age 1 647.62 665.62
## - Pclass 2 677.52 693.52
## - Sex 1 808.55 826.55
##
## Step: AIC=646.51
## Survived ~ Pclass + Sex + Age + SibSp + Fare + Embarked
##
## Df Deviance AIC
## - Fare 1 628.77 644.77
## <none> 628.51 646.51
## - Embarked 2 635.16 649.16
## - SibSp 1 634.95 650.95
## - Age 1 647.79 663.79
## - Pclass 2 680.24 694.24
## - Sex 1 814.49 830.49
##
## Step: AIC=644.77
## Survived ~ Pclass + Sex + Age + SibSp + Embarked
##
## Df Deviance AIC
## <none> 628.77 644.77
## - Embarked 2 636.05 648.05
## - SibSp 1 634.96 648.96
## - Age 1 648.52 662.52
## - Pclass 2 710.37 722.37
## - Sex 1 819.29 833.29##
## Call:
## glm(formula = Survived ~ Pclass + Sex + Age + SibSp + Embarked,
## family = binomial(), data = titTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5612 -0.5999 -0.4122 0.6184 2.4739
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.26732 0.46613 9.155 < 0.0000000000000002 ***
## Pclass2 -0.82890 0.30559 -2.712 0.00668 **
## Pclass3 -2.28816 0.28437 -8.046 0.000000000000000853 ***
## Sexmale -2.69674 0.21935 -12.294 < 0.0000000000000002 ***
## Age -0.03682 0.00859 -4.286 0.000018196416578272 ***
## SibSp -0.27780 0.11942 -2.326 0.02001 *
## EmbarkedQ -0.21762 0.41211 -0.528 0.59746
## EmbarkedS -0.67439 0.26369 -2.558 0.01054 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 956.65 on 718 degrees of freedom
## Residual deviance: 628.77 on 711 degrees of freedom
## AIC: 644.77
##
## Number of Fisher Scoring iterations: 5# Plus AIC est sup a l'AIC standard mieux c'est
# Il nous renvoi directement nous renvoi les var signifd’ou le modèle final
model2=glm(formula = Survived ~ Pclass + Sex + Age + SibSp, family = binomial(),
data = titTrain)
summary(model2)##
## Call:
## glm(formula = Survived ~ Pclass + Sex + Age + SibSp, family = binomial(),
## data = titTrain)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6803 -0.5591 -0.4102 0.6118 2.4193
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.954135 0.439095 9.005 < 0.0000000000000002 ***
## Pclass2 -1.057830 0.293021 -3.610 0.000306 ***
## Pclass3 -2.384327 0.272057 -8.764 < 0.0000000000000002 ***
## Sexmale -2.751328 0.217245 -12.665 < 0.0000000000000002 ***
## Age -0.037920 0.008555 -4.432 0.00000932 ***
## SibSp -0.314122 0.119482 -2.629 0.008563 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 956.65 on 718 degrees of freedom
## Residual deviance: 636.05 on 713 degrees of freedom
## AIC: 648.05
##
## Number of Fisher Scoring iterations: 5modèle retenu avec un AIC min qui vaut 628.46 il ya bien evlution dans le model plus AIC diminue mieux c’est
analyse de surdispersion
ratio=616.54/715
ratio## [1] 0.8622937nous pouvons dire qu’il n’y a pas surdispertion dans notre model puisque ratio inf a 1
multicolinarite
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somevif(model2)## GVIF Df GVIF^(1/(2*Df))
## Pclass 1.325180 2 1.072923
## Sex 1.149060 1 1.071942
## Age 1.296665 1 1.138712
## SibSp 1.127112 1 1.061655super pas de multicolinéarite
forestmodel::forest_model(model2)## Resized limits to included dashed line in forest panel
comme le montre l’image généré, Pclass 1 et sex female sont des sous variables pas significatives et sont present du fait de la significativité d’autres variable
essayons d’estimer la qualité du modèle avec la bd titTrain
#predictionTestModel11=predict(model11, titantTest)
#titantTest$predicTestmodel11=predictionTestModel11
#predictionTestModel11
#a=fitted.values(model1)
PredictionModel=predict.glm(model2,type = "response",newdata = titTrain)
PredictionModel## 1 2 3 4 5 6
## 0.088678199 0.900164558 0.641961571 0.909932762 0.075249134 0.095930678
## 7 8 9 10 11 12
## 0.300509692 0.099775111 0.633199667 0.886079260 0.751009916 0.852554259
## 13 15 16 17 18 19
## 0.125656592 0.738643053 0.692260024 0.074893108 0.285613859 0.520030951
## 20 21 23 24 25 26
## 0.624348812 0.234652796 0.731256666 0.535203451 0.580318929 0.453813778
## 27 29 30 31 32 33
## 0.095930678 0.624348812 0.095930678 0.422140537 0.929448863 0.624348812
## 35 36 37 38 39 40
## 0.456839085 0.330936176 0.095930678 0.121549235 0.564391605 0.673664479
## 42 43 44 45 46 47
## 0.826115586 0.095930678 0.921898300 0.700426130 0.095930678 0.071930798
## 48 49 50 51 52 53
## 0.624348812 0.053579352 0.639484231 0.062770248 0.121549235 0.855935599
## 54 55 56 57 58 60
## 0.814950494 0.220631248 0.535203451 0.890901748 0.094298868 0.040339490
## 61 63 64 66 68 70
## 0.117558085 0.306249168 0.093166912 0.071930798 0.129882223 0.057557881
## 71 72 73 74 75 77
## 0.255695539 0.352625099 0.342686933 0.077160913 0.083557824 0.095930678
## 78 79 80 81 82 83
## 0.095930678 0.528345224 0.606400856 0.117558085 0.092691969 0.624348812
## 85 86 87 88 89 90
## 0.904794070 0.348891257 0.108867440 0.095930678 0.894693934 0.109915908
## 91 94 95 96 97 98
## 0.092691969 0.077160913 0.031713083 0.095930678 0.183995083 0.581916794
## 99 100 101 102 103 104
## 0.832998038 0.188708451 0.624348812 0.095930678 0.600245213 0.080699547
## 105 106 108 109 110 111
## 0.038686754 0.095930678 0.095930678 0.067705593 0.548330281 0.359063261
## 113 114 115 116 117 119
## 0.117558085 0.621821398 0.716093911 0.121549235 0.020737040 0.572663625
## 120 121 122 123 124 125
## 0.559092782 0.217620657 0.095930678 0.197571169 0.840761597 0.300509692
## 129 130 131 132 133 134
## 0.548330281 0.052753980 0.080699547 0.125656592 0.371321265 0.814950494
## 135 137 138 140 141 142
## 0.309379246 0.962081325 0.374173380 0.572663625 0.624348812 0.676025936
## 143 144 145 146 147 149
## 0.585551755 0.129882223 0.368749966 0.291187269 0.099270169 0.224592233
## 150 152 153 154 155 157
## 0.190361587 0.942987032 0.036051852 0.061961383 0.095930678 0.723739603
## 158 159 160 163 164 166
## 0.089551770 0.095930678 0.008524475 0.102712705 0.138696292 0.179049448
## 170 171 172 173 174 176
## 0.095930678 0.247812784 0.069805081 0.771670111 0.121549235 0.101725094
## 177 178 183 184 185 186
## 0.039709517 0.886767171 0.058453775 0.372574724 0.805044678 0.535203451
## 187 188 189 190 191 192
## 0.548330281 0.376696851 0.046866968 0.072652542 0.843283619 0.359967966
## 193 194 195 196 198 199
## 0.630696577 0.429741644 0.907682097 0.852554259 0.058736614 0.624348812
## 201 202 203 204 205 206
## 0.095930678 0.008524475 0.077930729 0.051814525 0.134228139 0.816672995
## 207 209 210 211 212 213
## 0.062439591 0.723739603 0.422140537 0.109915908 0.827656090 0.117558085
## 214 215 216 217 219 222
## 0.270394336 0.071930798 0.921614936 0.633199667 0.939385723 0.293413468
## 223 224 225 226 227 228
## 0.042474868 0.095930678 0.365336971 0.117558085 0.359967966 0.123588258
## 231 232 233 234 235 236
## 0.909932762 0.092691969 0.109847299 0.530890876 0.317539374 0.624348812
## 237 239 240 242 244 245
## 0.137332552 0.359967966 0.248545879 0.548330281 0.117558085 0.089551770
## 246 249 250 251 252 254
## 0.250881694 0.374173380 0.098249868 0.095930678 0.538922750 0.067029440
## 255 256 257 258 259 260
## 0.503772087 0.615414104 0.947468245 0.943562866 0.932575132 0.731118792
## 261 262 263 264 265 267
## 0.095930678 0.072307840 0.252909695 0.422140537 0.624348812 0.045445673
## 268 270 271 272 273 275
## 0.079904750 0.932575132 0.535203451 0.106260539 0.792750632 0.624348812
## 276 277 278 279 280 281
## 0.777482467 0.465905186 0.285613859 0.062770248 0.482129327 0.025423751
## 282 283 284 286 287 289
## 0.095930678 0.143288564 0.129882223 0.080699547 0.089551770 0.190361587
## 290 291 292 293 295 297
## 0.676025936 0.951117377 0.948803652 0.227911357 0.109915908 0.111784611
## 298 300 301 302 303 304
## 0.972459562 0.886767171 0.624348812 0.053579352 0.129882223 0.862302338
## 305 306 307 310 311 312
## 0.095930678 0.701363308 0.947468245 0.943562866 0.954525188 0.933598825
## 313 314 315 316 317 318
## 0.831495562 0.095930678 0.141887108 0.641961571 0.841856111 0.129802969
## 319 320 321 322 325 328
## 0.941509257 0.893139105 0.117558085 0.099270169 0.008524475 0.822179744
## 329 330 332 333 334 336
## 0.520030951 0.966020090 0.372255632 0.440741693 0.081924331 0.095930678
## 337 338 340 341 342 343
## 0.447446231 0.916782761 0.376696851 0.439058158 0.891067397 0.285613859
## 344 345 346 347 348 349
## 0.309379246 0.227911357 0.879343229 0.798911639 0.548330281 0.166671564
## 350 351 352 353 355 357
## 0.058736614 0.113681027 0.535203451 0.112601188 0.095930678 0.957705990
## 358 360 361 363 365 367
## 0.810819805 0.624348812 0.046866968 0.465905186 0.071930798 0.796541162
## 369 370 371 372 373 374
## 0.624348812 0.954525188 0.485175719 0.101725094 0.129882223 0.591112662
## 375 376 377 379 380 382
## 0.625669362 0.929448863 0.676025936 0.125656592 0.129882223 0.822282275
## 383 384 385 386 387 388
## 0.083557824 0.909932762 0.095930678 0.368749966 0.057864282 0.822179744
## 389 390 391 394 396 398
## 0.095930678 0.904794070 0.383094506 0.940913782 0.117558085 0.168073564
## 400 401 403 404 405 406
## 0.862302338 0.065350907 0.612863850 0.071930798 0.692409441 0.188708451
## 407 408 409 410 411 412
## 0.042474868 0.429741644 0.121549235 0.393096051 0.095930678 0.095930678
## 413 414 415 416 417 420
## 0.915958025 0.285613859 0.054681335 0.624348812 0.784638341 0.766849710
## 421 422 423 424 425 426
## 0.095930678 0.121549235 0.092691969 0.548330281 0.101725094 0.095930678
## 427 428 429 430 432 433
## 0.820600900 0.898057504 0.095930678 0.083557824 0.548330281 0.728997928
## 434 435 436 437 438 440
## 0.138696292 0.267505903 0.957267925 0.536247496 0.795431561 0.262978890
## 441 442 444 445 446 447
## 0.705947911 0.125656592 0.862302338 0.095930678 0.740991131 0.917082022
## 448 449 451 452 453 454
## 0.478395306 0.679606546 0.177370642 0.071930798 0.516296037 0.275001371
## 455 456 457 459 460 461
## 0.095930678 0.092691969 0.220631248 0.731118792 0.095930678 0.350383906
## 462 463 464 465 467 468
## 0.077930729 0.359063261 0.157734481 0.095930678 0.285613859 0.284812870
## 469 470 471 472 474 475
## 0.095930678 0.713639023 0.095930678 0.067705593 0.883308967 0.676025936
## 476 477 479 480 482 483
## 0.535203451 0.188708451 0.117558085 0.816672995 0.285613859 0.044044139
## 484 485 486 487 488 490
## 0.305943991 0.485175719 0.393096051 0.909932762 0.269619920 0.137415677
## 491 492 493 494 495 496
## 0.071930798 0.121549235 0.292599563 0.183995083 0.121549235 0.095930678
## 497 498 499 500 501 502
## 0.830944352 0.095930678 0.936553754 0.109915908 0.138696292 0.684274923
## 504 505 506 507 509 510
## 0.541595764 0.966020090 0.551349631 0.838206776 0.095930678 0.102712705
## 512 513 514 515 516 518
## 0.095930678 0.459510715 0.830944352 0.109915908 0.359063261 0.095930678
## 519 521 522 524 525 526
## 0.771546494 0.943562866 0.117558085 0.907682097 0.095930678 0.061961383
## 527 529 531 532 533 534
## 0.731118792 0.065350907 0.924585297 0.095930678 0.105242816 0.624348812
## 535 536 537 540 542 544
## 0.606400856 0.932821763 0.376696851 0.957705990 0.493010759 0.200594268
## 545 547 548 549 550 551
## 0.267505903 0.865495397 0.285613859 0.060256211 0.384023482 0.636028266
## 552 553 554 555 556 557
## 0.293413468 0.095930678 0.117558085 0.676025936 0.240812226 0.860548689
## 558 559 561 562 563 565
## 0.535203451 0.896704667 0.095930678 0.063072574 0.285613859 0.624348812
## 566 567 568 569 571 573
## 0.061812541 0.129882223 0.615414104 0.095930678 0.099207465 0.459510715
## 575 576 577 579 580 581
## 0.143288564 0.129882223 0.832998038 0.548330281 0.083557824 0.836741977
## 582 583 584 586 588 589
## 0.896704667 0.129802969 0.459510715 0.963440696 0.199964971 0.117558085
## 590 591 592 593 594 595
## 0.095930678 0.075249134 0.841332325 0.049090211 0.624348812 0.171905248
## 596 598 599 600 601 602
## 0.054127699 0.045668623 0.095930678 0.275001371 0.795431561 0.095930678
## 603 604 605 606 607 608
## 0.535203451 0.054681335 0.468941892 0.054127699 0.089551770 0.544622746
## 609 611 612 613 614 616
## 0.851693259 0.444432231 0.095930678 0.548330281 0.095930678 0.841856111
## 618 619 620 621 622 623
## 0.567035590 0.892485212 0.301336226 0.074503667 0.330936176 0.095001375
## 625 626 628 630 631 632
## 0.121549235 0.247812784 0.959215566 0.095930678 0.138144065 0.042474868
## 633 634 635 636 637 638
## 0.497341853 0.535203451 0.571056463 0.862302338 0.083557824 0.206744054
## 639 641 642 643 644 645
## 0.503772087 0.125656592 0.954525188 0.634507316 0.095930678 0.713639023
## 648 649 650 651 654 655
## 0.284812870 0.095930678 0.667666101 0.095930678 0.624348812 0.708321898
## 656 657 659 660 661 662
## 0.198874097 0.095930678 0.325813193 0.269619920 0.210579608 0.063072574
## 665 666 667 668 669 671
## 0.095001375 0.154895984 0.309379246 0.095930678 0.056674892 0.743718157
## 672 673 674 675 676 677
## 0.428779059 0.075200336 0.262978890 0.285613859 0.134228139 0.108074642
## 679 681 682 683 684 685
## 0.407364089 0.624348812 0.544622746 0.125656592 0.036158717 0.079853194
## 687 688 689 691 692 694
## 0.048851412 0.129882223 0.134228139 0.428779059 0.805044678 0.106260539
## 696 697 698 699 700 701
## 0.138612517 0.054681335 0.624348812 0.275001371 0.058736614 0.950614589
## 702 703 704 706 707 708
## 0.468941892 0.708321898 0.106260539 0.208514579 0.766724271 0.403757661
## 709 710 711 712 715 716
## 0.957705990 0.071930798 0.954525188 0.535203451 0.138612517 0.129882223
## 718 719 720 722 723 724
## 0.866743297 0.095930678 0.080699547 0.105242816 0.241531262 0.147918325
## 725 726 728 730 732 734
## 0.466262730 0.125656592 0.624348812 0.576320463 0.168171668 0.325813193
## 735 736 737 738 740 741
## 0.325813193 0.094298868 0.362513195 0.468941892 0.095930678 0.535203451
## 742 743 745 746 749 751
## 0.383094506 0.926189111 0.086507812 0.146076275 0.541952455 0.919123940
## 753 754 755 757 758 759
## 0.080699547 0.113681027 0.681793141 0.095930678 0.368749966 0.077930729
## 760 761 762 763 765 767
## 0.937190236 0.095930678 0.060868497 0.125656592 0.143288564 0.535203451
## 768 769 770 774 775 776
## 0.601866497 0.071930798 0.083557824 0.095930678 0.630533179 0.134228139
## 777 778 779 781 783 784
## 0.095930678 0.799024307 0.095930678 0.745896971 0.525758977 0.071930798
## 785 786 787 788 789 790
## 0.106260539 0.106260539 0.708321898 0.060576053 0.177473016 0.367835875
## 791 792 794 796 797 798
## 0.095930678 0.386572349 0.535203451 0.208514579 0.890519280 0.597314590
## 799 801 802 803 804 806
## 0.089551770 0.241531262 0.803239243 0.615754107 0.231929593 0.086507812
## 807 808 809 810 811 812
## 0.431416930 0.708321898 0.208514579 0.915958025 0.102712705 0.065350907
## 813 814 815 816 818 819
## 0.234652796 0.521437128 0.088017903 0.535203451 0.206744054 0.056674892
## 820 821 822 823 824 825
## 0.075642153 0.841332325 0.099270169 0.440741693 0.633199667 0.074893108
## 826 828 829 831 832 833
## 0.095930678 0.526738517 0.095930678 0.665273880 0.450012798 0.095930678
## 834 835 836 837 838 839
## 0.113681027 0.134228139 0.896704667 0.121549235 0.095930678 0.083557824
## 840 841 842 843 844 845
## 0.535203451 0.125656592 0.386572349 0.943562866 0.076579168 0.138696292
## 846 847 848 849 850 851
## 0.058736614 0.008524475 0.075249134 0.285613859 0.929448863 0.069805081
## 852 853 854 855 856 857
## 0.018206566 0.713900910 0.966020090 0.713757618 0.708321898 0.873648959
## 858 859 860 861 863 864
## 0.324950746 0.659199296 0.095930678 0.033424091 0.894161897 0.118686855
## 866 867 868 869 870 871
## 0.786451319 0.826115586 0.506821396 0.095930678 0.161471134 0.102712705
## 873 874 875 876 878 879
## 0.487864221 0.049090211 0.820600900 0.731256666 0.129882223 0.095930678
## 880 881 882 883 885 886
## 0.861835157 0.875261747 0.080699547 0.676025936 0.106260539 0.522716492
## 887 888 889 890 891
## 0.293413468 0.962081325 0.548330281 0.554010222 0.083557824matrice de confusion
PredictionModelBin=ifelse(PredictionModel>0.5,1,0)
PredictionModelBin## 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21
## 0 1 1 1 0 0 0 0 1 1 1 1 0 1 1 0 0 1 1 0
## 23 24 25 26 27 29 30 31 32 33 35 36 37 38 39 40 42 43 44 45
## 1 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1
## 46 47 48 49 50 51 52 53 54 55 56 57 58 60 61 63 64 66 68 70
## 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0
## 71 72 73 74 75 77 78 79 80 81 82 83 85 86 87 88 89 90 91 94
## 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0
## 95 96 97 98 99 100 101 102 103 104 105 106 108 109 110 111 113 114 115 116
## 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0
## 117 119 120 121 122 123 124 125 129 130 131 132 133 134 135 137 138 140 141 142
## 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 1
## 143 144 145 146 147 149 150 152 153 154 155 157 158 159 160 163 164 166 170 171
## 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 172 173 174 176 177 178 183 184 185 186 187 188 189 190 191 192 193 194 195 196
## 0 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 1 1
## 198 199 201 202 203 204 205 206 207 209 210 211 212 213 214 215 216 217 219 222
## 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0
## 223 224 225 226 227 228 231 232 233 234 235 236 237 239 240 242 244 245 246 249
## 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0
## 250 251 252 254 255 256 257 258 259 260 261 262 263 264 265 267 268 270 271 272
## 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0
## 273 275 276 277 278 279 280 281 282 283 284 286 287 289 290 291 292 293 295 297
## 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
## 298 300 301 302 303 304 305 306 307 310 311 312 313 314 315 316 317 318 319 320
## 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1
## 321 322 325 328 329 330 332 333 334 336 337 338 340 341 342 343 344 345 346 347
## 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1
## 348 349 350 351 352 353 355 357 358 360 361 363 365 367 369 370 371 372 373 374
## 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1
## 375 376 377 379 380 382 383 384 385 386 387 388 389 390 391 394 396 398 400 401
## 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 1 0
## 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 420 421 422 423 424
## 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1
## 425 426 427 428 429 430 432 433 434 435 436 437 438 440 441 442 444 445 446 447
## 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 1 0 1 1
## 448 449 451 452 453 454 455 456 457 459 460 461 462 463 464 465 467 468 469 470
## 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## 471 472 474 475 476 477 479 480 482 483 484 485 486 487 488 490 491 492 493 494
## 0 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
## 495 496 497 498 499 500 501 502 504 505 506 507 509 510 512 513 514 515 516 518
## 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0
## 519 521 522 524 525 526 527 529 531 532 533 534 535 536 537 540 542 544 545 547
## 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1
## 548 549 550 551 552 553 554 555 556 557 558 559 561 562 563 565 566 567 568 569
## 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 0
## 571 573 575 576 577 579 580 581 582 583 584 586 588 589 590 591 592 593 594 595
## 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 1 0
## 596 598 599 600 601 602 603 604 605 606 607 608 609 611 612 613 614 616 618 619
## 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 1
## 620 621 622 623 625 626 628 630 631 632 633 634 635 636 637 638 639 641 642 643
## 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 1 1
## 644 645 648 649 650 651 654 655 656 657 659 660 661 662 665 666 667 668 669 671
## 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1
## 672 673 674 675 676 677 679 681 682 683 684 685 687 688 689 691 692 694 696 697
## 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0
## 698 699 700 701 702 703 704 706 707 708 709 710 711 712 715 716 718 719 720 722
## 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0
## 723 724 725 726 728 730 732 734 735 736 737 738 740 741 742 743 745 746 749 751
## 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 1 1
## 753 754 755 757 758 759 760 761 762 763 765 767 768 769 770 774 775 776 777 778
## 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1
## 779 781 783 784 785 786 787 788 789 790 791 792 794 796 797 798 799 801 802 803
## 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 1
## 804 806 807 808 809 810 811 812 813 814 815 816 818 819 820 821 822 823 824 825
## 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0
## 826 828 829 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847
## 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0
## 848 849 850 851 852 853 854 855 856 857 858 859 860 861 863 864 866 867 868 869
## 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 0
## 870 871 873 874 875 876 878 879 880 881 882 883 885 886 887 888 889 890 891
## 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 0moment=data.frame(titTrain,PredictionModelBin)table=table(predicted=PredictionModelBin,Actual=titTrain$Survived)
table ## Actual
## predicted 0 1
## 0 372 79
## 1 72 196sum(diag(table))/sum(table)## [1] 0.7899861on en conclu que le model c’est trompé de 20.9%
essayons d’estimer la qualité du modèle par la matrice de confusion titTrain
#predictionTestModel11=predict(model11, titantTest)
#titantTest$predicTestmodel11=predictionTestModel11
#predictionTestModel11
#a=fitted.values(model1)
PredictionModel21=predict.glm(model2, type = "response", newdata = titTrain)
PredictionModel21## 1 2 3 4 5 6
## 0.088678199 0.900164558 0.641961571 0.909932762 0.075249134 0.095930678
## 7 8 9 10 11 12
## 0.300509692 0.099775111 0.633199667 0.886079260 0.751009916 0.852554259
## 13 15 16 17 18 19
## 0.125656592 0.738643053 0.692260024 0.074893108 0.285613859 0.520030951
## 20 21 23 24 25 26
## 0.624348812 0.234652796 0.731256666 0.535203451 0.580318929 0.453813778
## 27 29 30 31 32 33
## 0.095930678 0.624348812 0.095930678 0.422140537 0.929448863 0.624348812
## 35 36 37 38 39 40
## 0.456839085 0.330936176 0.095930678 0.121549235 0.564391605 0.673664479
## 42 43 44 45 46 47
## 0.826115586 0.095930678 0.921898300 0.700426130 0.095930678 0.071930798
## 48 49 50 51 52 53
## 0.624348812 0.053579352 0.639484231 0.062770248 0.121549235 0.855935599
## 54 55 56 57 58 60
## 0.814950494 0.220631248 0.535203451 0.890901748 0.094298868 0.040339490
## 61 63 64 66 68 70
## 0.117558085 0.306249168 0.093166912 0.071930798 0.129882223 0.057557881
## 71 72 73 74 75 77
## 0.255695539 0.352625099 0.342686933 0.077160913 0.083557824 0.095930678
## 78 79 80 81 82 83
## 0.095930678 0.528345224 0.606400856 0.117558085 0.092691969 0.624348812
## 85 86 87 88 89 90
## 0.904794070 0.348891257 0.108867440 0.095930678 0.894693934 0.109915908
## 91 94 95 96 97 98
## 0.092691969 0.077160913 0.031713083 0.095930678 0.183995083 0.581916794
## 99 100 101 102 103 104
## 0.832998038 0.188708451 0.624348812 0.095930678 0.600245213 0.080699547
## 105 106 108 109 110 111
## 0.038686754 0.095930678 0.095930678 0.067705593 0.548330281 0.359063261
## 113 114 115 116 117 119
## 0.117558085 0.621821398 0.716093911 0.121549235 0.020737040 0.572663625
## 120 121 122 123 124 125
## 0.559092782 0.217620657 0.095930678 0.197571169 0.840761597 0.300509692
## 129 130 131 132 133 134
## 0.548330281 0.052753980 0.080699547 0.125656592 0.371321265 0.814950494
## 135 137 138 140 141 142
## 0.309379246 0.962081325 0.374173380 0.572663625 0.624348812 0.676025936
## 143 144 145 146 147 149
## 0.585551755 0.129882223 0.368749966 0.291187269 0.099270169 0.224592233
## 150 152 153 154 155 157
## 0.190361587 0.942987032 0.036051852 0.061961383 0.095930678 0.723739603
## 158 159 160 163 164 166
## 0.089551770 0.095930678 0.008524475 0.102712705 0.138696292 0.179049448
## 170 171 172 173 174 176
## 0.095930678 0.247812784 0.069805081 0.771670111 0.121549235 0.101725094
## 177 178 183 184 185 186
## 0.039709517 0.886767171 0.058453775 0.372574724 0.805044678 0.535203451
## 187 188 189 190 191 192
## 0.548330281 0.376696851 0.046866968 0.072652542 0.843283619 0.359967966
## 193 194 195 196 198 199
## 0.630696577 0.429741644 0.907682097 0.852554259 0.058736614 0.624348812
## 201 202 203 204 205 206
## 0.095930678 0.008524475 0.077930729 0.051814525 0.134228139 0.816672995
## 207 209 210 211 212 213
## 0.062439591 0.723739603 0.422140537 0.109915908 0.827656090 0.117558085
## 214 215 216 217 219 222
## 0.270394336 0.071930798 0.921614936 0.633199667 0.939385723 0.293413468
## 223 224 225 226 227 228
## 0.042474868 0.095930678 0.365336971 0.117558085 0.359967966 0.123588258
## 231 232 233 234 235 236
## 0.909932762 0.092691969 0.109847299 0.530890876 0.317539374 0.624348812
## 237 239 240 242 244 245
## 0.137332552 0.359967966 0.248545879 0.548330281 0.117558085 0.089551770
## 246 249 250 251 252 254
## 0.250881694 0.374173380 0.098249868 0.095930678 0.538922750 0.067029440
## 255 256 257 258 259 260
## 0.503772087 0.615414104 0.947468245 0.943562866 0.932575132 0.731118792
## 261 262 263 264 265 267
## 0.095930678 0.072307840 0.252909695 0.422140537 0.624348812 0.045445673
## 268 270 271 272 273 275
## 0.079904750 0.932575132 0.535203451 0.106260539 0.792750632 0.624348812
## 276 277 278 279 280 281
## 0.777482467 0.465905186 0.285613859 0.062770248 0.482129327 0.025423751
## 282 283 284 286 287 289
## 0.095930678 0.143288564 0.129882223 0.080699547 0.089551770 0.190361587
## 290 291 292 293 295 297
## 0.676025936 0.951117377 0.948803652 0.227911357 0.109915908 0.111784611
## 298 300 301 302 303 304
## 0.972459562 0.886767171 0.624348812 0.053579352 0.129882223 0.862302338
## 305 306 307 310 311 312
## 0.095930678 0.701363308 0.947468245 0.943562866 0.954525188 0.933598825
## 313 314 315 316 317 318
## 0.831495562 0.095930678 0.141887108 0.641961571 0.841856111 0.129802969
## 319 320 321 322 325 328
## 0.941509257 0.893139105 0.117558085 0.099270169 0.008524475 0.822179744
## 329 330 332 333 334 336
## 0.520030951 0.966020090 0.372255632 0.440741693 0.081924331 0.095930678
## 337 338 340 341 342 343
## 0.447446231 0.916782761 0.376696851 0.439058158 0.891067397 0.285613859
## 344 345 346 347 348 349
## 0.309379246 0.227911357 0.879343229 0.798911639 0.548330281 0.166671564
## 350 351 352 353 355 357
## 0.058736614 0.113681027 0.535203451 0.112601188 0.095930678 0.957705990
## 358 360 361 363 365 367
## 0.810819805 0.624348812 0.046866968 0.465905186 0.071930798 0.796541162
## 369 370 371 372 373 374
## 0.624348812 0.954525188 0.485175719 0.101725094 0.129882223 0.591112662
## 375 376 377 379 380 382
## 0.625669362 0.929448863 0.676025936 0.125656592 0.129882223 0.822282275
## 383 384 385 386 387 388
## 0.083557824 0.909932762 0.095930678 0.368749966 0.057864282 0.822179744
## 389 390 391 394 396 398
## 0.095930678 0.904794070 0.383094506 0.940913782 0.117558085 0.168073564
## 400 401 403 404 405 406
## 0.862302338 0.065350907 0.612863850 0.071930798 0.692409441 0.188708451
## 407 408 409 410 411 412
## 0.042474868 0.429741644 0.121549235 0.393096051 0.095930678 0.095930678
## 413 414 415 416 417 420
## 0.915958025 0.285613859 0.054681335 0.624348812 0.784638341 0.766849710
## 421 422 423 424 425 426
## 0.095930678 0.121549235 0.092691969 0.548330281 0.101725094 0.095930678
## 427 428 429 430 432 433
## 0.820600900 0.898057504 0.095930678 0.083557824 0.548330281 0.728997928
## 434 435 436 437 438 440
## 0.138696292 0.267505903 0.957267925 0.536247496 0.795431561 0.262978890
## 441 442 444 445 446 447
## 0.705947911 0.125656592 0.862302338 0.095930678 0.740991131 0.917082022
## 448 449 451 452 453 454
## 0.478395306 0.679606546 0.177370642 0.071930798 0.516296037 0.275001371
## 455 456 457 459 460 461
## 0.095930678 0.092691969 0.220631248 0.731118792 0.095930678 0.350383906
## 462 463 464 465 467 468
## 0.077930729 0.359063261 0.157734481 0.095930678 0.285613859 0.284812870
## 469 470 471 472 474 475
## 0.095930678 0.713639023 0.095930678 0.067705593 0.883308967 0.676025936
## 476 477 479 480 482 483
## 0.535203451 0.188708451 0.117558085 0.816672995 0.285613859 0.044044139
## 484 485 486 487 488 490
## 0.305943991 0.485175719 0.393096051 0.909932762 0.269619920 0.137415677
## 491 492 493 494 495 496
## 0.071930798 0.121549235 0.292599563 0.183995083 0.121549235 0.095930678
## 497 498 499 500 501 502
## 0.830944352 0.095930678 0.936553754 0.109915908 0.138696292 0.684274923
## 504 505 506 507 509 510
## 0.541595764 0.966020090 0.551349631 0.838206776 0.095930678 0.102712705
## 512 513 514 515 516 518
## 0.095930678 0.459510715 0.830944352 0.109915908 0.359063261 0.095930678
## 519 521 522 524 525 526
## 0.771546494 0.943562866 0.117558085 0.907682097 0.095930678 0.061961383
## 527 529 531 532 533 534
## 0.731118792 0.065350907 0.924585297 0.095930678 0.105242816 0.624348812
## 535 536 537 540 542 544
## 0.606400856 0.932821763 0.376696851 0.957705990 0.493010759 0.200594268
## 545 547 548 549 550 551
## 0.267505903 0.865495397 0.285613859 0.060256211 0.384023482 0.636028266
## 552 553 554 555 556 557
## 0.293413468 0.095930678 0.117558085 0.676025936 0.240812226 0.860548689
## 558 559 561 562 563 565
## 0.535203451 0.896704667 0.095930678 0.063072574 0.285613859 0.624348812
## 566 567 568 569 571 573
## 0.061812541 0.129882223 0.615414104 0.095930678 0.099207465 0.459510715
## 575 576 577 579 580 581
## 0.143288564 0.129882223 0.832998038 0.548330281 0.083557824 0.836741977
## 582 583 584 586 588 589
## 0.896704667 0.129802969 0.459510715 0.963440696 0.199964971 0.117558085
## 590 591 592 593 594 595
## 0.095930678 0.075249134 0.841332325 0.049090211 0.624348812 0.171905248
## 596 598 599 600 601 602
## 0.054127699 0.045668623 0.095930678 0.275001371 0.795431561 0.095930678
## 603 604 605 606 607 608
## 0.535203451 0.054681335 0.468941892 0.054127699 0.089551770 0.544622746
## 609 611 612 613 614 616
## 0.851693259 0.444432231 0.095930678 0.548330281 0.095930678 0.841856111
## 618 619 620 621 622 623
## 0.567035590 0.892485212 0.301336226 0.074503667 0.330936176 0.095001375
## 625 626 628 630 631 632
## 0.121549235 0.247812784 0.959215566 0.095930678 0.138144065 0.042474868
## 633 634 635 636 637 638
## 0.497341853 0.535203451 0.571056463 0.862302338 0.083557824 0.206744054
## 639 641 642 643 644 645
## 0.503772087 0.125656592 0.954525188 0.634507316 0.095930678 0.713639023
## 648 649 650 651 654 655
## 0.284812870 0.095930678 0.667666101 0.095930678 0.624348812 0.708321898
## 656 657 659 660 661 662
## 0.198874097 0.095930678 0.325813193 0.269619920 0.210579608 0.063072574
## 665 666 667 668 669 671
## 0.095001375 0.154895984 0.309379246 0.095930678 0.056674892 0.743718157
## 672 673 674 675 676 677
## 0.428779059 0.075200336 0.262978890 0.285613859 0.134228139 0.108074642
## 679 681 682 683 684 685
## 0.407364089 0.624348812 0.544622746 0.125656592 0.036158717 0.079853194
## 687 688 689 691 692 694
## 0.048851412 0.129882223 0.134228139 0.428779059 0.805044678 0.106260539
## 696 697 698 699 700 701
## 0.138612517 0.054681335 0.624348812 0.275001371 0.058736614 0.950614589
## 702 703 704 706 707 708
## 0.468941892 0.708321898 0.106260539 0.208514579 0.766724271 0.403757661
## 709 710 711 712 715 716
## 0.957705990 0.071930798 0.954525188 0.535203451 0.138612517 0.129882223
## 718 719 720 722 723 724
## 0.866743297 0.095930678 0.080699547 0.105242816 0.241531262 0.147918325
## 725 726 728 730 732 734
## 0.466262730 0.125656592 0.624348812 0.576320463 0.168171668 0.325813193
## 735 736 737 738 740 741
## 0.325813193 0.094298868 0.362513195 0.468941892 0.095930678 0.535203451
## 742 743 745 746 749 751
## 0.383094506 0.926189111 0.086507812 0.146076275 0.541952455 0.919123940
## 753 754 755 757 758 759
## 0.080699547 0.113681027 0.681793141 0.095930678 0.368749966 0.077930729
## 760 761 762 763 765 767
## 0.937190236 0.095930678 0.060868497 0.125656592 0.143288564 0.535203451
## 768 769 770 774 775 776
## 0.601866497 0.071930798 0.083557824 0.095930678 0.630533179 0.134228139
## 777 778 779 781 783 784
## 0.095930678 0.799024307 0.095930678 0.745896971 0.525758977 0.071930798
## 785 786 787 788 789 790
## 0.106260539 0.106260539 0.708321898 0.060576053 0.177473016 0.367835875
## 791 792 794 796 797 798
## 0.095930678 0.386572349 0.535203451 0.208514579 0.890519280 0.597314590
## 799 801 802 803 804 806
## 0.089551770 0.241531262 0.803239243 0.615754107 0.231929593 0.086507812
## 807 808 809 810 811 812
## 0.431416930 0.708321898 0.208514579 0.915958025 0.102712705 0.065350907
## 813 814 815 816 818 819
## 0.234652796 0.521437128 0.088017903 0.535203451 0.206744054 0.056674892
## 820 821 822 823 824 825
## 0.075642153 0.841332325 0.099270169 0.440741693 0.633199667 0.074893108
## 826 828 829 831 832 833
## 0.095930678 0.526738517 0.095930678 0.665273880 0.450012798 0.095930678
## 834 835 836 837 838 839
## 0.113681027 0.134228139 0.896704667 0.121549235 0.095930678 0.083557824
## 840 841 842 843 844 845
## 0.535203451 0.125656592 0.386572349 0.943562866 0.076579168 0.138696292
## 846 847 848 849 850 851
## 0.058736614 0.008524475 0.075249134 0.285613859 0.929448863 0.069805081
## 852 853 854 855 856 857
## 0.018206566 0.713900910 0.966020090 0.713757618 0.708321898 0.873648959
## 858 859 860 861 863 864
## 0.324950746 0.659199296 0.095930678 0.033424091 0.894161897 0.118686855
## 866 867 868 869 870 871
## 0.786451319 0.826115586 0.506821396 0.095930678 0.161471134 0.102712705
## 873 874 875 876 878 879
## 0.487864221 0.049090211 0.820600900 0.731256666 0.129882223 0.095930678
## 880 881 882 883 885 886
## 0.861835157 0.875261747 0.080699547 0.676025936 0.106260539 0.522716492
## 887 888 889 890 891
## 0.293413468 0.962081325 0.548330281 0.554010222 0.083557824PredictionModelBin=ifelse(PredictionModel21>0.5,1,0)
PredictionModelBin## 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21
## 0 1 1 1 0 0 0 0 1 1 1 1 0 1 1 0 0 1 1 0
## 23 24 25 26 27 29 30 31 32 33 35 36 37 38 39 40 42 43 44 45
## 1 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1
## 46 47 48 49 50 51 52 53 54 55 56 57 58 60 61 63 64 66 68 70
## 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0
## 71 72 73 74 75 77 78 79 80 81 82 83 85 86 87 88 89 90 91 94
## 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0
## 95 96 97 98 99 100 101 102 103 104 105 106 108 109 110 111 113 114 115 116
## 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0
## 117 119 120 121 122 123 124 125 129 130 131 132 133 134 135 137 138 140 141 142
## 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 1
## 143 144 145 146 147 149 150 152 153 154 155 157 158 159 160 163 164 166 170 171
## 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 172 173 174 176 177 178 183 184 185 186 187 188 189 190 191 192 193 194 195 196
## 0 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 1 1
## 198 199 201 202 203 204 205 206 207 209 210 211 212 213 214 215 216 217 219 222
## 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0
## 223 224 225 226 227 228 231 232 233 234 235 236 237 239 240 242 244 245 246 249
## 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0
## 250 251 252 254 255 256 257 258 259 260 261 262 263 264 265 267 268 270 271 272
## 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0
## 273 275 276 277 278 279 280 281 282 283 284 286 287 289 290 291 292 293 295 297
## 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
## 298 300 301 302 303 304 305 306 307 310 311 312 313 314 315 316 317 318 319 320
## 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1
## 321 322 325 328 329 330 332 333 334 336 337 338 340 341 342 343 344 345 346 347
## 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1
## 348 349 350 351 352 353 355 357 358 360 361 363 365 367 369 370 371 372 373 374
## 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1
## 375 376 377 379 380 382 383 384 385 386 387 388 389 390 391 394 396 398 400 401
## 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 1 0
## 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 420 421 422 423 424
## 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1
## 425 426 427 428 429 430 432 433 434 435 436 437 438 440 441 442 444 445 446 447
## 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 1 0 1 1
## 448 449 451 452 453 454 455 456 457 459 460 461 462 463 464 465 467 468 469 470
## 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## 471 472 474 475 476 477 479 480 482 483 484 485 486 487 488 490 491 492 493 494
## 0 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
## 495 496 497 498 499 500 501 502 504 505 506 507 509 510 512 513 514 515 516 518
## 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0
## 519 521 522 524 525 526 527 529 531 532 533 534 535 536 537 540 542 544 545 547
## 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 1
## 548 549 550 551 552 553 554 555 556 557 558 559 561 562 563 565 566 567 568 569
## 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 0
## 571 573 575 576 577 579 580 581 582 583 584 586 588 589 590 591 592 593 594 595
## 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 1 0
## 596 598 599 600 601 602 603 604 605 606 607 608 609 611 612 613 614 616 618 619
## 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 1
## 620 621 622 623 625 626 628 630 631 632 633 634 635 636 637 638 639 641 642 643
## 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 1 1
## 644 645 648 649 650 651 654 655 656 657 659 660 661 662 665 666 667 668 669 671
## 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1
## 672 673 674 675 676 677 679 681 682 683 684 685 687 688 689 691 692 694 696 697
## 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0
## 698 699 700 701 702 703 704 706 707 708 709 710 711 712 715 716 718 719 720 722
## 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0
## 723 724 725 726 728 730 732 734 735 736 737 738 740 741 742 743 745 746 749 751
## 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 1 1
## 753 754 755 757 758 759 760 761 762 763 765 767 768 769 770 774 775 776 777 778
## 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1
## 779 781 783 784 785 786 787 788 789 790 791 792 794 796 797 798 799 801 802 803
## 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 1
## 804 806 807 808 809 810 811 812 813 814 815 816 818 819 820 821 822 823 824 825
## 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0
## 826 828 829 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847
## 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0
## 848 849 850 851 852 853 854 855 856 857 858 859 860 861 863 864 866 867 868 869
## 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 0
## 870 871 873 874 875 876 878 879 880 881 882 883 885 886 887 888 889 890 891
## 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 0table=table(predicted=PredictionModelBin,Actual=titTrain$Survived)
confusionMatrix( table) ## Confusion Matrix and Statistics
##
## Actual
## predicted 0 1
## 0 372 79
## 1 72 196
##
## Accuracy : 0.79
## 95% CI : (0.7584, 0.8192)
## No Information Rate : 0.6175
## P-Value [Acc > NIR] : <0.0000000000000002
##
## Kappa : 0.5532
##
## Mcnemar's Test P-Value : 0.6254
##
## Sensitivity : 0.8378
## Specificity : 0.7127
## Pos Pred Value : 0.8248
## Neg Pred Value : 0.7313
## Prevalence : 0.6175
## Detection Rate : 0.5174
## Detection Prevalence : 0.6273
## Balanced Accuracy : 0.7753
##
## 'Positive' Class : 0
## on en conclu que le model est bon a 79.06%
Matrice de confusion titTest
#predictionTestModel11=predict(model11, titantTest)
#titantTest$predicTestmodel11=predictionTestModel11
#predictionTestModel11
#a=fitted.values(model1)
PredictionModel22=predict.glm(model2,type = "response",newdata = titTest)
PredictionModel22## 14 22 28 34 41 59 65
## 0.04859027 0.24153126 0.38697684 0.08645240 0.43509013 0.91625998 0.53520345
## 67 69 76 84 92 93 107
## 0.85773767 0.41791984 0.10626054 0.53520345 0.12565659 0.29825238 0.68427492
## 112 118 126 127 128 136 139
## 0.66948266 0.21945856 0.12447898 0.09593068 0.10991591 0.32581319 0.14328856
## 148 151 156 161 162 165 167
## 0.64572136 0.14320248 0.32495075 0.05468133 0.79891164 0.07756308 0.94746824
## 168 169 175 179 180 181 182
## 0.38919130 0.53520345 0.28481287 0.27039434 0.07265254 0.11868686 0.28561386
## 197 200 208 218 220 221 229
## 0.09593068 0.87934323 0.10271270 0.14656705 0.27039434 0.14328856 0.36874997
## 230 238 241 243 247 248 253
## 0.39309605 0.93040614 0.54833028 0.27794003 0.65062966 0.87934323 0.24081223
## 266 269 274 285 288 294 296
## 0.22791136 0.85255426 0.45010845 0.53520345 0.11755808 0.65919930 0.53520345
## 299 308 309 323 324 326 327
## 0.53520345 0.95236468 0.21303214 0.85304747 0.85169326 0.93015130 0.02946515
## 331 335 339 354 356 359 362
## 0.46998738 0.92944886 0.05275398 0.07990475 0.09593068 0.62434881 0.21945856
## 364 366 368 378 381 392 393
## 0.07524913 0.08955177 0.62434881 0.54462275 0.91384367 0.12154924 0.05357935
## 395 397 399 402 418 419 431
## 0.65919930 0.59731459 0.32581319 0.10271270 0.90147706 0.27039434 0.53520345
## 439 443 450 458 466 473 478
## 0.17679809 0.07990475 0.31668854 0.92944886 0.06770559 0.79097692 0.06944011
## 481 489 503 508 511 517 520
## 0.04337996 0.08955177 0.62434881 0.53520345 0.09269197 0.83299804 0.08355782
## 523 528 530 538 539 541 543
## 0.09593068 0.53520345 0.20498467 0.94356287 0.09593068 0.93015130 0.47407359
## 546 560 564 570 572 574 578
## 0.22722072 0.47266899 0.09593068 0.08355782 0.78853766 0.62434881 0.89670467
## 585 587 597 610 615 617 624
## 0.09593068 0.16283794 0.86230234 0.91963041 0.07524913 0.05814444 0.12154924
## 627 629 640 646 647 652 653
## 0.11748534 0.10271270 0.07193080 0.28262582 0.12988222 0.90147706 0.12154924
## 658 663 664 670 678 680 686
## 0.51056012 0.35906326 0.07265254 0.92944886 0.70832190 0.45951072 0.24654132
## 690 693 695 705 713 714 717
## 0.96724306 0.09593068 0.25494851 0.07716091 0.28262582 0.09269197 0.92506035
## 721 727 729 731 733 739 744
## 0.93515938 0.69345905 0.24654132 0.94554854 0.28561386 0.09593068 0.08273741
## 747 748 750 752 756 764 766
## 0.10886744 0.85304747 0.08650781 0.19638537 0.45151489 0.90677634 0.84632907
## 771 772 773 780 782 793 795
## 0.10991591 0.04735006 0.67587229 0.91081087 0.95236468 0.11868686 0.10626054
## 800 805 817 827 862 865 872
## 0.52948741 0.09927017 0.66766610 0.09593068 0.27578541 0.31753937 0.86503735
## 877 884
## 0.12565659 0.28561386PredictionModelBin2=ifelse(PredictionModel22>0.5,1,0)
PredictionModelBin2## 14 22 28 34 41 59 65 67 69 76 84 92 93 107 112 118 126 127 128 136
## 0 0 0 0 0 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0
## 139 148 151 156 161 162 165 167 168 169 175 179 180 181 182 197 200 208 218 220
## 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0
## 221 229 230 238 241 243 247 248 253 266 269 274 285 288 294 296 299 308 309 323
## 0 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 1 0 1
## 324 326 327 331 335 339 354 356 359 362 364 366 368 378 381 392 393 395 397 399
## 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 0
## 402 418 419 431 439 443 450 458 466 473 478 481 489 503 508 511 517 520 523 528
## 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1
## 530 538 539 541 543 546 560 564 570 572 574 578 585 587 597 610 615 617 624 627
## 0 1 0 1 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0
## 629 640 646 647 652 653 658 663 664 670 678 680 686 690 693 695 705 713 714 717
## 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1
## 721 727 729 731 733 739 744 747 748 750 752 756 764 766 771 772 773 780 782 793
## 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 1 1 0
## 795 800 805 817 827 862 865 872 877 884
## 0 1 0 1 0 0 0 1 0 0library(caret)
table=table(predicted=PredictionModelBin2,Actual=titTest$Survived)
confusionMatrix( table) ## Confusion Matrix and Statistics
##
## Actual
## predicted 0 1
## 0 86 20
## 1 19 45
##
## Accuracy : 0.7706
## 95% CI : (0.7, 0.8315)
## No Information Rate : 0.6176
## P-Value [Acc > NIR] : 0.00001576
##
## Kappa : 0.5129
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.8190
## Specificity : 0.6923
## Pos Pred Value : 0.8113
## Neg Pred Value : 0.7031
## Prevalence : 0.6176
## Detection Rate : 0.5059
## Detection Prevalence : 0.6235
## Balanced Accuracy : 0.7557
##
## 'Positive' Class : 0
## on en conclu que le model est bon a 79.41%
3)normalité des residus
shapiro.test(residuals.glm(model2))##
## Shapiro-Wilk normality test
##
## data: residuals.glm(model2)
## W = 0.94297, p-value = 0.0000000000000005377#plot(residuals.glm(model2))hist(residuals.glm(model2),col="yellow",freq=F)
densite <- density(residuals.lm(model2)) # estimer la densité que line représente ces différentes l'histogramme
lines(densite)
construction d’un individus pour prediction
ff<- data.frame(Sex="male", Age=28 ,Pclass="1",SibSp=0) ## on construit un personnage
predict(model2,ff,type = "response") ## 1
## 0.5352035