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Add TP3
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Analyse Multidimensionnelle/TP1/.RData
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Analyse Multidimensionnelle/TP1/.RData
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Analyse Multidimensionnelle/TP1/.Rhistory
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Analyse Multidimensionnelle/TP1/.Rhistory
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knitr::opts_chunk$set(echo = TRUE)
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autos <- read.table("autos.csv", sep=";",header=TRUE)
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rownames(autos)<-autos$Modele
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autos$Modele<-NULL
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autos<-autos[,c(1:6,8)]
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library(FactoMineR)
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help(PCA)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX") )
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summary(res.autos, nb.dec=2, nb.elements =Inf, nbind = Inf, ncp=3) #les résultats avec deux décimales, pour tous les individus, toutes les variables, sur les 3 premières CP
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eigenvalues <- res.autos$eig # pour faire l'eboulis des valeurs propres
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bplt <- barplot(eigenvalues[, 2], names.arg=1:nrow(eigenvalues),
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main = "Eboulis des valeurs propres",
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xlab = "Principal Components",
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ylab = "Percentage of variances",
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col ="steelblue",
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)
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lines(x = bplt, eigenvalues[, 2], type="b", pch=19, col = "red")
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alim <- read.table('alimentation.csv', sep=';', header=TRUE)
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rownames(alim)<-alim$ROW_LABEL
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alim$ROW_LABEL<-NULL
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corr <- cor(alim)
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corr <- cor(alim)
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corr
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res.alim<-PCA(alim, scale.unit=TRUE, quanti.sup = c())
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summary(res.alim, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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help(cor)
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corr <- cor(alim)
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corr
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data(iris)
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head(iris)
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View(iris)
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corr.iris <- cor(iris)
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), quali.sup = c("OUVR"))
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), quali.sup = c("OUVR", "PRIN"))
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library(FactoMineR)
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help(PCA)
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind = c("OUVR", "PRIN"))
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c("OUVR", "PRIN"))
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(3, 7))
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summary(res.alim2, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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res.iris <- PCA(iris, scale.unit = TRUE)
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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res.iris <- PCA(iris, scale.unit = TRUE, quanti.sup = c('Species'))
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res.iris <- PCA(iris, scale.unit = TRUE, ind.sup = c('Species'))
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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summary(res.iris, nbelements = Inf, nbind = Inf, ncp = 3)
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knitr::opts_chunk$set(echo = TRUE)
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(8))
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knitr::opts_chunk$set(echo = TRUE)
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knitr::opts_chunk$set(echo = TRUE)
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knitr::opts_chunk$set(echo = TRUE)
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autos <- read.table("autos.csv", sep=";",header=TRUE)
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rownames(autos)<-autos$Modele
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autos$Modele<-NULL
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autos<-autos[,c(1:6,8)]
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library(FactoMineR)
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help(PCA)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX") )
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summary(res.autos, nb.dec=2, nb.elements =Inf, nbind = Inf, ncp=3) #les résultats avec deux décimales, pour tous les individus, toutes les variables, sur les 3 premières CP
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eigenvalues <- res.autos$eig # pour faire l'eboulis des valeurs propres
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bplt <- barplot(eigenvalues[, 2], names.arg=1:nrow(eigenvalues),
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main = "Eboulis des valeurs propres",
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xlab = "Principal Components",
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ylab = "Percentage of variances",
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col ="steelblue",
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)
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lines(x = bplt, eigenvalues[, 2], type="b", pch=19, col = "red")
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alim <- read.table('alimentation.csv', sep=';', header=TRUE)
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rownames(alim)<-alim$ROW_LABEL
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alim$ROW_LABEL<-NULL
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help(cor)
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corr <- cor(alim)
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corr
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res.alim<-PCA(alim, scale.unit=TRUE, quanti.sup = c())
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summary(res.alim, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(8))
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summary(res.alim2, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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data(iris)
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head(iris)
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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summary(res.iris, nbelements = Inf, nbind = Inf, ncp = 3)
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knitr::opts_chunk$set(echo = TRUE)
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autos <- read.table("autos.csv", sep=";",header=TRUE)
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rownames(autos)<-autos$Modele
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autos$Modele<-NULL
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autos<-autos[,c(1:6,8)]
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library(FactoMineR)
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help(PCA)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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plot.CPA(res.iris)
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = none)
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = None)
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = NONE)
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = NULL)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = NULL)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "None")
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = NA)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5)
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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dimdesc(res.iris)
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knitr::opts_chunk$set(echo = TRUE)
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autos <- read.table("autos.csv", sep=";",header=TRUE)
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rownames(autos)<-autos$Modele
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autos$Modele<-NULL
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autos<-autos[,c(1:6,8)]
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library(FactoMineR)
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help(PCA)
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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summary(res.autos, nb.dec=2, nb.elements =Inf, nbind = Inf, ncp=3) #les résultats avec deux décimales, pour tous les individus, toutes les variables, sur les 3 premières CP
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eigenvalues <- res.autos$eig # pour faire l'eboulis des valeurs propres
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bplt <- barplot(eigenvalues[, 2], names.arg=1:nrow(eigenvalues),
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main = "Eboulis des valeurs propres",
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xlab = "Principal Components",
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ylab = "Percentage of variances",
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col ="steelblue",
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)
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lines(x = bplt, eigenvalues[, 2], type="b", pch=19, col = "red")
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alim <- read.table('alimentation.csv', sep=';', header=TRUE)
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rownames(alim)<-alim$ROW_LABEL
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alim$ROW_LABEL<-NULL
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help(cor)
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corr <- cor(alim)
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corr
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res.alim<-PCA(alim, scale.unit=TRUE, quanti.sup = c())
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summary(res.alim, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(8))
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summary(res.alim2, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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data(iris)
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head(iris)
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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dimdesc(res.iris)
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summary(res.iris, nbelements = Inf, nbind = Inf, ncp = 3)
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@@ -59,7 +59,7 @@ help(PCA)
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```{r,echo=FALSE}
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX") )
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res.autos<-PCA(autos, scale.unit=TRUE, quanti.sup = c("PRIX"))
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```
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```{r}
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summary(res.autos, nb.dec=2, nb.elements =Inf, nbind = Inf, ncp=3) #les résultats avec deux décimales, pour tous les individus, toutes les variables, sur les 3 premières CP
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@@ -134,7 +134,7 @@ summary(res.alim, nb.dec = 2, nbelements = Inf, nbind = Inf, ncp = 3)
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* Relancez l'ACP en prenant en compte cette modification
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```{r}
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(3, 7))
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res.alim2 <- PCA(alim, scale.unit=TRUE, quanti.sup = c(), ind.sup = c(8))
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```
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```{r}
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@@ -151,6 +151,8 @@ head(iris)
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```
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```{r}
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res.iris <- PCA(iris, scale.unit = TRUE, quali.sup = c('Species'))
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plot.PCA(res.iris, choix = "ind", habillage = 5, label = "none")
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dimdesc(res.iris)
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```
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```{r}
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summary(res.iris, nbelements = Inf, nbind = Inf, ncp = 3)
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243
Analyse Multidimensionnelle/TP3/TP3-Enonce.Rmd
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243
Analyse Multidimensionnelle/TP3/TP3-Enonce.Rmd
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---
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title: "TP3 : Suite ACP"
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output:
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html_document: default
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pdf_document: default
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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```
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Exercice 1
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----------------------------------------------------------------------------------------
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```{r}
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Notes<- matrix(c(6,6,5,5.5,8,8,8,8,6,7,11,9.5,14.5,14.5,15.5,15,14,14,12,12.5,11,
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10,5.5,7,5.5,7,14,11.5,13,12.5,8.5,9.5,9,9.5,12.5,12,
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12,11.5,14,12,6,8,8,7,15,16,14,12),nrow=12,byrow=T)
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rownames(Notes) <- c("Rémi","Thomas","Gaëtan","Ahmed","Louise","Kylian",
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"Antoine","Raphaël","Jean","Rayan","Matthieu","Sophie")
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colnames(Notes) <- c("Math","Phys","Fr","Ang")
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```
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* Effectuer l'analyse ACP
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```{r}
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library(FactoMineR)
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res.acp <- PCA(Notes, scale.unit=TRUE)
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```
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```{r}
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summary(res.acp, nbind = Inf, nbelements = Inf)
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```
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# Individus : Contribution moyenne, Axes 1 et 2, Qualité de représentation
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```{r}
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mean(res.acp$ind$contrib)
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indiv_contrib_axe_1 <- sort(res.acp$ind$contrib[,1], decreasing = TRUE)
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head(indiv_contrib_axe_1, 3)
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indiv_contrib_axe_2 <- sort(res.acp$ind$contrib[,2], decreasing = TRUE)
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head(indiv_contrib_axe_2, 3)
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mal_representes <- rownames(res.acp$ind$cos2)[rowSums(res.acp$ind$cos2[,1:2]) <= mean(res.acp$ind$cos2[,1:2])]
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mal_representes
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```
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# Variables : Contribution moyenne, Axes 1 et 2, Qualité de représentation
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```{r}
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mean(res.acp$var$contrib)
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var_contrib_axe_1 <- sort(res.acp$var$contrib[,1], decreasing = TRUE)
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head(var_contrib_axe_1, 3)
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var_contrib_axe_2 <- sort(res.acp$var$contrib[,2], decreasing = TRUE)
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head(var_contrib_axe_2, 3)
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mal_representes <- rownames(res.acp$var$cos2[,1:2])[rowSums(res.acp$var$cos2[,1:2]) <= mean(res.acp$var$cos2[,1:2])]
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mal_representes
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```
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Le premier axe va donc classer les individus selon leur moyenne alors que le second axe va classer les individus selon leur profil : scientifique ou littéraire.
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----------------------------------------------------------------------------------------
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Exercice 2
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Six marques de jus d’orange 100% pur jus présentes dans les supermarchés français ont été évaluées par un panel d’experts selon sept variables sensorielles (intensité de l’odeur, typicité de l’odeur, teneur en pulpe, intensité du goût, acidité, amertume, douceur). Ces 6 marques sont Pampryl amb. (conservation à température ambiante), Tropicana amb., Fruvita amb., Joker amb., Tropicana fr. (conservation au frais), Pampryl fr.
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1) Importer le jeu de données "jusdorange.csv" et appeler le "jus".
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```{r}
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jus <- read.table("jusdorange.csv", header = TRUE, sep = ";", row.names = 1)
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```
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2) Créer le tableau individus-variables "jus" associé et afficher le. (Deja inclus dans question 1.)
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```{r}
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# jus_table <- jus[-1]
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# rownames(jus_table) <- jus[,1]
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```
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3) Afficher le descriptif des variables.
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```{r}
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summary(jus)
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```
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4) Afficher les 6 premières lignes de "jus".
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```{r}
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jus[1:6,]
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```
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5) Afficher la matrice de corrélation associée à ce jeu données "jus" Commenter brièvement les corrélations .
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```{r}
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cor(jus)
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```
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6) Lancer FactoMineR sur ce jeu de données afin de faire l'ACP . On prendra soin d'afficher les résultats de l'ACP avec une décimale seulement, pour les 4 premières composantes principales, toutes les variables et tous les individus .
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```{r}
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res.jus <- PCA(jus, scale.unit=TRUE)
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```
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```{r}
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summary(res.jus, nbelements = Inf, nbind = Inf, ncp = 4, nb.dec = 1)
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```
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7) Faîtes l'analyse statistique complète de l'ACP associée . On prendra soin de justifier le nombre d'axes factoriels à retenir, de faire l'analyse des individus, des variables et la synthèse.
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# Eboulis valeurs propres
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```{r}
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eigen_values <- res.jus$eig
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bplot <- barplot(
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eigen_values[, 1],
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names.arg = 1:nrow(eigen_values),
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main = "Eboulis des valeurs propres",
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xlab = "Principal Components",
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ylab = "Eigenvalues",
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col = "lightblue"
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)
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lines(x = bplot, eigen_values[, 1], type = "b", col = "red")
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abline(h=1, col = "darkgray", lty = 5)
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```
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Par le critère de Kaiser, on garde les deux premières valeurs propres, donc on garde deux axes principaux
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# Individus : Contribution moyenne, Axes, Qualité de représentation
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```{r}
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mean(res.jus$ind$contrib)
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indiv_contrib_axe_1 <- sort(res.jus$ind$contrib[,1], decreasing = TRUE)
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head(indiv_contrib_axe_1, 3)
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indiv_contrib_axe_2 <- sort(res.jus$ind$contrib[,2], decreasing = TRUE)
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head(indiv_contrib_axe_2, 3)
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mal_representes <- rownames(res.acp$ind$cos2)[rowSums(res.jus$ind$cos2[,1:2]) <= mean(res.jus$ind$cos2[,1:2])]
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mal_representes
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```
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# Variables : Contribution moyenne, Axes, Qualité de représentation
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```{r}
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mean(res.jus$var$contrib)
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var_contrib_axe_1 <- sort(res.jus$var$contrib[,1], decreasing = TRUE)
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head(var_contrib_axe_1, 3)
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var_contrib_axe_2 <- sort(res.jus$var$contrib[,2], decreasing = TRUE)
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head(var_contrib_axe_2, 3)
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|
||||
mal_representes <- rownames(res.jus$var$cos2[,1:2])[rowSums(res.jus$var$cos2[,1:2]) <= 0.7]
|
||||
mal_representes
|
||||
```
|
||||
Le premier axe décrit l'amertume ou la douceur du jus d'orange.
|
||||
|
||||
----------------------------------------------------------------------------------------
|
||||
|
||||
Exercice 3
|
||||
|
||||
* Importation des données (compiler ce qui est ci-dessous sans le modifier)
|
||||
|
||||
```{r}
|
||||
|
||||
library(FactoMineR)
|
||||
|
||||
data("decathlon")
|
||||
decathlon<-decathlon[1:13, 1:10]
|
||||
|
||||
res.decathlon <- PCA(decathlon, scale.unit = TRUE)
|
||||
```
|
||||
|
||||
```{r}
|
||||
summary(res.decathlon, nbelements = Inf, nbind = Inf, ncp = 4, nb.dec = 1)
|
||||
```
|
||||
|
||||
* Effectuer l'analyse ACP de ce jeu de données
|
||||
|
||||
# Eboulis valeurs propres
|
||||
|
||||
```{r}
|
||||
eigen_values <- res.decathlon$eig
|
||||
|
||||
bplot <- barplot(
|
||||
eigen_values[, 1],
|
||||
names.arg = 1:nrow(eigen_values),
|
||||
main = "Eboulis des valeurs propres",
|
||||
xlab = "Principal Components",
|
||||
ylab = "Eigenvalues",
|
||||
col = "lightblue"
|
||||
)
|
||||
lines(x = bplot, eigen_values[, 1], type = "b", col = "red")
|
||||
abline(h=1, col = "darkgray", lty = 5)
|
||||
```
|
||||
|
||||
Par le critère de Kaiser, on garde les quatre premières valeurs propres, donc on garde quatre axes principaux
|
||||
|
||||
# Individus : Contribution moyenne, Axes, Qualité de représentation
|
||||
|
||||
```{r}
|
||||
mean(res.decathlon$ind$contrib)
|
||||
|
||||
indiv_contrib_axe_1 <- sort(res.decathlon$ind$contrib[,1], decreasing = TRUE)
|
||||
head(indiv_contrib_axe_1, 3)
|
||||
indiv_contrib_axe_2 <- sort(res.decathlon$ind$contrib[,2], decreasing = TRUE)
|
||||
head(indiv_contrib_axe_2, 3)
|
||||
indiv_contrib_axe_3 <- sort(res.decathlon$ind$contrib[,3], decreasing = TRUE)
|
||||
head(indiv_contrib_axe_3, 3)
|
||||
indiv_contrib_axe_4 <- sort(res.decathlon$ind$contrib[,4], decreasing = TRUE)
|
||||
head(indiv_contrib_axe_4, 3)
|
||||
|
||||
mal_representes <- rownames(res.decathlon$ind$cos2)[rowSums(res.decathlon$ind$cos2[,1:4]) <= 0.8] # mean(res.decathlon$ind$cos2[,1:4]
|
||||
mal_representes
|
||||
```
|
||||
|
||||
# Variables : Contribution moyenne, Axes, Qualité de représentation
|
||||
|
||||
```{r}
|
||||
mean(res.decathlon$var$contrib)
|
||||
|
||||
var_contrib_axe_1 <- sort(res.decathlon$var$contrib[,1], decreasing = TRUE)
|
||||
head(var_contrib_axe_1, 3)
|
||||
var_contrib_axe_2 <- sort(res.decathlon$var$contrib[,2], decreasing = TRUE)
|
||||
head(var_contrib_axe_2, 3)
|
||||
var_contrib_axe_3 <- sort(res.decathlon$var$contrib[,3], decreasing = TRUE)
|
||||
head(var_contrib_axe_3, 3)
|
||||
var_contrib_axe_4 <- sort(res.decathlon$var$contrib[,4], decreasing = TRUE)
|
||||
head(var_contrib_axe_4, 3)
|
||||
|
||||
mal_representes <- rownames(res.decathlon$var$cos2[,1:4])[rowSums(res.decathlon$var$cos2[,1:4]) <= 0.8]
|
||||
mal_representes
|
||||
```
|
||||
13
Analyse Multidimensionnelle/TP3/TP3.Rproj
Normal file
13
Analyse Multidimensionnelle/TP3/TP3.Rproj
Normal file
@@ -0,0 +1,13 @@
|
||||
Version: 1.0
|
||||
|
||||
RestoreWorkspace: Default
|
||||
SaveWorkspace: Default
|
||||
AlwaysSaveHistory: Default
|
||||
|
||||
EnableCodeIndexing: Yes
|
||||
UseSpacesForTab: Yes
|
||||
NumSpacesForTab: 2
|
||||
Encoding: UTF-8
|
||||
|
||||
RnwWeave: Sweave
|
||||
LaTeX: pdfLaTeX
|
||||
7
Analyse Multidimensionnelle/TP3/jusdorange.csv
Normal file
7
Analyse Multidimensionnelle/TP3/jusdorange.csv
Normal file
@@ -0,0 +1,7 @@
|
||||
;intensite-odeur;typicite-odeur;pulpe;intensite-gout;acidite;amertume;douceur
|
||||
Pampryl amb.;2.82;2.53;1.66;3.46;3.15;2.97;2.6
|
||||
Tropicana amb.;2.76;2.82;1.91;3.23;2.55;2.08;3.32
|
||||
Fruvita amb.;2.83;2.88;4;3.45;2.42;1.76;3.38
|
||||
Joker amb.;2.76;2.59;1.66;3.37;3.05;2.56;2.8
|
||||
Tropicana fr.;3.2;3.02;3.69;3.12;2.33;1.97;3.34
|
||||
Pampryl fr. ;3.07;2.73;3.34;3.54;3.31;2.63;2.9
|
||||
|
Reference in New Issue
Block a user