Analyse Factorielle Multiple (AFM) avec FactoMineR sur la description sensorielle de vins François Husson

Transcription

1 Analyse Factorielle Multiple (AFM) avec FactoMineR sur la description sensorielle de vins François Husson Chargement de FactoMineR library(factominer) data(wine) L AFM res <- MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)), ncp=5, name.group=c("orig","olf","vis","olfag","gust","ens"), num.group.sup=c(1,6)) 1

2 olf vis olfag gust Env4 Env2 Saumur Chinon Bourgueuil Env1 Reference

3 Partial axes orig olf vis olfag gust ens Dim2.olf Dim2.olfag Dim2.gust Dim1.orig Dim4.orig Dim5.orig Dim5.olfag Dim3.olf Dim1.vis Dim1.olf Dim3.orig Dim2.ens Dim3.vis Dim4.gust Dim4.olf Dim5.olf Dim3.olfag Dim1.gust Dim4.olfag Dim5.gust Dim3.gust Dim1.olfag Dim2.vis Dim1.ens Dim2.orig

4 olf vis olfag gust 2ING 1VAU T2 T1 3EL 4EL PER1 1TUR DOM1 2BOU 1BOI 1DAM 1FON 1ING 1ROC 1POY 1BEN 2EL 2DAM 2BEA 1CHA

5 Correlation circle olf vis olfag gust ens Odor. Visual.intensity Nuance.before.shaking.before.shaking

6 ING 1VAU T2 T1 3EL PER1 4EL 1DAM 1TUR DOM1 2BOU 1BOI 1FON 1ING 1ROC 1POY 1BEN 2EL 2DAM 2BEA 1CHA

7 Groups representation orig ens olf vis olfag gust Résumé des résultats On peut obtenir un résumé des principaux résultats en utilisant la fonction summary. summary(res) Nous demandons ici à avoir les résultats sur les 2 premières dimensions pour éviter d avoir des tableaux trop grands (par défaut, la fonction retourne les résultats des 3 premières dimensions). summary(res, ncp=2) ## ## Call: ## MFA(base = wine, group = c(2, 5, 3, 10, 9, 2), type = c("n", 7

8 ## rep("s", 5)), ncp = 5, name.group = c("orig", "olf", "vis", ## "olfag", "gust", "ens"), num.group.sup = c(1, 6)) ## ## ## Eigenvalues ## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 ## Variance ## % of var ## Cumulative % of var ## Dim.7 Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 ## Variance ## % of var ## Cumulative % of var ## Dim.13 Dim.14 Dim.15 Dim.16 Dim.17 Dim.18 ## Variance ## % of var ## Cumulative % of var ## Dim.19 Dim.20 ## Variance ## % of var ## Cumulative % of var ## ## Groups ## Dim.1 ctr cos2 Dim.2 ctr ## olf ## vis ## olfag ## gust ## cos2 ## olf ## vis ## olfag ## gust ## ## Supplementary groups ## Dim.1 cos2 Dim.2 cos2 ## orig ## ens ## ## Individuals (the 10 first) ## Dim.1 ctr cos2 Dim.2 ctr ## 2EL ## 1CHA ## 1FON ## 1VAU ## 1DAM ## 2BOU ## 1BOI ## 3EL ## DOM ## 1TUR ## cos2 ## 2EL ## 1CHA

9 ## 1FON ## 1VAU ## 1DAM ## 2BOU ## 1BOI ## 3EL ## DOM ## 1TUR ## ## Continuous variables (the 10 first) ## Dim.1 ctr cos2 Dim.2 ctr ## ## ##.before.shaking ##.before.shaking ## ## Visual.intensity ## Nuance ## ## Odor ## ## cos2 ## ## ##.before.shaking ##.before.shaking ## ## Visual.intensity ## Nuance ## ## Odor ## ## ## Supplementary continuous variables ## Dim.1 cos2 Dim.2 cos2 ## ## ## ## Supplementary categories ## Dim.1 cos2 v.test Dim.2 cos2 ## Saumur ## Bourgueuil ## Chinon ## Reference ## Env ## Env ## Env ## v.test ## Saumur ## Bourgueuil ## Chinon ## Reference ## Env ## Env

10 ## Env Graphe des individus et des modalités plot(res) Individual factor map ING 1VAU T2 Env4 T1 3EL PER1 Saumur Env2 4EL Chinon DOM1 1DAM 1FON 2BOU 1BOI 1TUR1ROC 1ING Reference Env1 Bourgueuil 1POY 1BEN 2EL 2DAM 2BEA 1CHA plot(res, invisible="quali") 10

11 ING 1VAU 1CHA 1TUR 1FON 3EL T2 T1 4EL PER1 DOM1 2BOU 1DAM 1ROC 1BOI 1ING 1POY 1BEN 2EL 2DAM 2BEA plot(res, invisible="quali", cex=0.8) 11

12 ING 1VAU 1CHA 3EL T2 T1 1FON DOM1 1TUR 1ROC 2EL 1BEN 4EL PER1 2BOU 1DAM 1BOI 1ING 2DAM 1POY 2BEA plot(res, invisible="quali", cex=0.8, partial=c("1vau","per1")) 12

13 ING olf vis olfag gust 1VAU 1CHA T2 T1 3EL 4EL PER1 1DAM DOM1 1TUR 2BOU 1BOI 1FON 1ROC 1ING 1POY 1BEN 2EL 2DAM 2BEA Graphe des individus avec sélection plot(res, invisible="quali", cex=0.8, partial=c("1vau","per1"), select="cos2 0.4") 13

14 ING olf vis olfag gust 1VAU T2 T1 1DAM 2DAM plot(res,,invisible="quali", partial="all", palette=palette(c("black","transparent","transparent","blue","transparent"))) 14

15 olfag 1VAU 2ING T2 T1 3EL 4EL PER1 DOM1 2BOU 1BOI 1DAM 1TUR 1FON 1ROC 1ING 1POY 1BEN 2EL 2BEA 2DAM 1CHA plot(res, invisible="quali", habillage=1, cex=0.8, select="cos2 0.4") 15

16 Saumur Bourgueuil Chinon 2ING 1VAU T2 T1 1DAM 2DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="cos2 0.4") 16

17 Reference Env1 Env2 Env4 2ING 1VAU T2 T1 1DAM 2DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="contrib 5") 17

18 Reference Env1 Env2 Env4 2ING 1VAU T2 T1 1DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="contrib 5", unselect=0) 18

19 Reference Env1 Env2 Env4 2ING 1VAU T2 T1 1DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="contrib 5", unselect=1) 19

20 Reference Env1 Env2 Env4 2ING 1VAU T2 T1 1DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="contrib 5", unselect="grey70") 20

21 Reference Env1 Env2 Env4 2ING 1VAU T2 T1 1DAM plot(res, invisible="quali", habillage="soil", cex=0.8, select="contrib 5", unselect="grey70", axes=3:4) 21

22 Dim 4 (5.31%) T1 Reference Env1 Env2 Env4 2BEA 2BOU Dim 3 (8.78%) 1CHA 3EL plot(res, invisible="quali", habillage="soil", cex=0.8, select=c("1vau","per1"), unselect="grey70", axes=3:4) 22

23 Dim 4 (5.31%) Reference Env1 Env2 Env4 PER1 1VAU Dim 3 (8.78%) Graphe des variables plot(res, choix="var") 23

24 Correlation circle olf vis olfag gust ens Odor. Visual.intensity Nuance.before.shaking.before.shaking plot(res, choix="var", shadow=true) 24

25 Correlation circle olf vis olfag gust ens.before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor. plot(res, choix="var", shadow=true, select="contrib 5") 25

26 Correlation circle olf vis olfag gust ens Visual.intensity plot(res, choix="var", shadow=true, select="contrib 5", axes=3:4) 26

27 Correlation circle Dim 4 (5.31%) olf vis olfag gust ens.before.shaking.before.shaking Dim 3 (8.78%) Graphe des axes partiels res <- MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)), ncp=3, name.group=c("orig","olf","vis","olfag","gust","ens"), num.group.sup=c(1,6)) 27

28 olf vis olfag gust Env4 Env2 Saumur Chinon Env1 Bourgueuil Reference

29 Partial axes Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens Dim1.orig Dim2.orig Dim3.orig Dim1.olf Dim2.olf Dim3.olf Dim1.vis Dim2.vis Dim3.vis Dim1.olfag Dim2.olfag Dim3.olfag Dim1.gust Dim2.gust Dim3.gust Dim1.ens Dim2.ens orig olf vis olfag gust ens 29

30 olf vis olfag gust 2ING 1VAU T2 T1 3EL 4EL PER1 1TUR DOM1 2BOU 1BOI 1DAM 1FON 1ING 1ROC 1POY 1BEN 2EL 2DAM 2BEA 1CHA

31 Correlation circle olf vis olfag gust ens.before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor..before.shaking.before.shaking Visual.intensity Nuance Odor. 31

32 ING 1VAU 1CHA 1FON T2 T1 3EL PER1 4EL DOM1 1DAM 2BOU 1TUR 1BOI 1ROC 1ING 1POY 1BEN 2EL 2DAM 2BEA

33 Groups representation orig ens olf vis olfag gust plot(res, choix="axes") 33

34 Partial axes orig olf vis olfag gust ens Dim2.olf Dim2.olfag Dim2.gust Dim1.orig Dim3.olf Dim3.orig Dim3.vis Dim3.olfag Dim2.ens Dim3.gust Dim2.vis Dim2.orig Dim1.vis Dim1.olf Dim1.gust Dim1.olfag Dim1.ens Description des dimensions dimdesc(res) ## $Dim.1 ## $Dim.1$quanti ## correlation p.value ## e-11 ## e-09 ## e-09 ## e-09 ## e-08 ## Visual.intensity e-07 ## Nuance e-07 34

35 ## e-06 ## e-06 ## e-06 ## e-05 ## e-05 ## e-05 ## e-05 ## e-05 ## e-05 ## e-05 ## e-04 ##.before.shaking e-04 ## Odor e-03 ## e-03 ##.before.shaking e-02 ## e-02 ## ## $Dim.1$category ## Estimate p.value ## Reference ## ## ## $Dim.2 ## $Dim.2$quanti ## correlation p.value ## e-07 ## e-04 ## e-04 ## e-03 ## e-03 ## Odor e-03 ## e-02 ## e-02 ## e-02 ## e-02 ## e-02 ## e-03 ## e-03 ## ## $Dim.2$quali ## R2 p.value ## Soil e-06 ## ## $Dim.2$category ## Estimate p.value ## Env e-07 ## ## ## $Dim.3 ## $Dim.3$quanti ## correlation p.value ##.before.shaking ## ##

36 ##.before.shaking