RTI1
MEMBRE DU GROUPE 05
2024-12-07
chargement des données
setwd("C:/Users/ROSI/Desktop/MASTER 1 S7 2IE 2024/R_studio_GEAAH/")
sortie = read.csv(file="variable_rti.csv", header = TRUE, sep = ";", quote = "\"",
dec = ",", row.names = 1)ANALYSE DE DONNÉES
Matrice de corrélation linéaire:
sortie[1:10]## POP2019 PROSPECIFIQUE GENERE RECYCLE DECHARGE INCINERE MAL_GERE
## CIV 28193014 0.103 1059916 63595 317975 10599 678346
## TOGO 8463068 0.057 176074 10564 52822 1761 112687
## GUINEE 13034349 0.030 142726 8564 42818 1427 91345
## BENIN 12726761 0.043 199747 11985 59924 1997 127838
## BURKINA FASO 20961954 0.043 328998 19740 98699 3290 210559
## MALI 21068406 0.043 330669 19840 99201 3307 211628
## NIGER 22947765 0.043 360165 21610 108050 3602 230506
## CAMEROUN 25506096 0.046 428247 25695 128474 4282 274078
## NEGERIA 209485636 0.103 7875612 472537 2362684 78756 5040392
## GHANA 31258942 0.040 456381 27383 136914 4564 292084
## EMIT_OCEAN EMIT_SURFACE MORTALITE_ANIMAL
## CIV 4784 639645 0.40
## TOGO 436 106617 0.48
## GUINEE 2347 84430 0.63
## BENIN 1639 119807 0.55
## BURKINA FASO 2096 197935 0.72
## MALI 2107 198940 0.74
## NIGER 2295 216686 0.79
## CAMEROUN 10671 249703 0.45
## NEGERIA 18640 4769732 0.52
## GHANA 4185 273294 0.39cor(sortie[1:10])## POP2019 PROSPECIFIQUE GENERE RECYCLE DECHARGE
## POP2019 1.0000000 0.6673830 0.9960622 0.9960623 0.9960622
## PROSPECIFIQUE 0.6673830 1.0000000 0.7141018 0.7141013 0.7141018
## GENERE 0.9960622 0.7141018 1.0000000 1.0000000 1.0000000
## RECYCLE 0.9960623 0.7141013 1.0000000 1.0000000 1.0000000
## DECHARGE 0.9960622 0.7141018 1.0000000 1.0000000 1.0000000
## INCINERE 0.9960625 0.7141014 1.0000000 1.0000000 1.0000000
## MAL_GERE 0.9960622 0.7141017 1.0000000 1.0000000 1.0000000
## EMIT_OCEAN 0.8906212 0.6127992 0.8781629 0.8781632 0.8781629
## EMIT_SURFACE 0.9959988 0.7141560 0.9999983 0.9999983 0.9999983
## MORTALITE_ANIMAL -0.1426739 -0.4310263 -0.1604675 -0.1604674 -0.1604674
## INCINERE MAL_GERE EMIT_OCEAN EMIT_SURFACE MORTALITE_ANIMAL
## POP2019 0.9960625 0.9960622 0.8906212 0.9959988 -0.1426739
## PROSPECIFIQUE 0.7141014 0.7141017 0.6127992 0.7141560 -0.4310263
## GENERE 1.0000000 1.0000000 0.8781629 0.9999983 -0.1604675
## RECYCLE 1.0000000 1.0000000 0.8781632 0.9999983 -0.1604674
## DECHARGE 1.0000000 1.0000000 0.8781629 0.9999983 -0.1604674
## INCINERE 1.0000000 1.0000000 0.8781597 0.9999983 -0.1604627
## MAL_GERE 1.0000000 1.0000000 0.8781629 0.9999983 -0.1604675
## EMIT_OCEAN 0.8781597 0.8781629 1.0000000 0.8772738 -0.3257655
## EMIT_SURFACE 0.9999983 0.9999983 0.8772738 1.0000000 -0.1597501
## MORTALITE_ANIMAL -0.1604627 -0.1604675 -0.3257655 -0.1597501 1.0000000matcor=cor(sortie[1:10])Visualisation graphique de la corrélation
pairs(sortie[,1:10])
r=matcor
psych::principal(r)## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Principal Components Analysis
## Call: psych::principal(r = r)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 h2 u2 com
## POP2019 0.99 0.979 0.0207 1
## PROSPECIFIQUE 0.76 0.573 0.4266 1
## GENERE 1.00 0.991 0.0093 1
## RECYCLE 1.00 0.991 0.0093 1
## DECHARGE 1.00 0.991 0.0093 1
## INCINERE 1.00 0.991 0.0093 1
## MAL_GERE 1.00 0.991 0.0093 1
## EMIT_OCEAN 0.90 0.818 0.1820 1
## EMIT_SURFACE 1.00 0.990 0.0095 1
## MORTALITE_ANIMAL -0.23 0.054 0.9455 1
##
## PC1
## SS loadings 8.37
## Proportion Var 0.84
##
## Mean item complexity = 1
## Test of the hypothesis that 1 component is sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
##
## Fit based upon off diagonal values = 1