library(readxl)
Data_CCA <- read_excel("C:/Users/HP/OneDrive/文件/Data_CCA.xlsx")
Data_CCA
## # A tibble: 34 × 8
## `Prevalensi balita gizi buruk` Prevalensi balita gizi kur…¹ `Balita Obesitas`
## <dbl> <dbl> <dbl>
## 1 5.9 18.9 3
## 2 5.3 13.1 5.9
## 3 3.3 14.2 3
## 4 4.2 14 5.5
## 5 3 10.5 5
## 6 2.1 10.2 4.2
## 7 2.3 11.9 4.4
## 8 3.5 15 4.2
## 9 3.7 13 7.8
## 10 3 13.4 4.4
## # ℹ 24 more rows
## # ℹ abbreviated name: ¹`Prevalensi balita gizi kurang`
## # ℹ 5 more variables: `Rata-rata lama sekolah ibu` <dbl>,
## # `Persentase Penduduk yang Memiliki Jaminan Kesehatan` <dbl>,
## # Kemiskinan <dbl>, `Imunisasi Dasar Lengkap` <dbl>, `Air Layak` <dbl>
Y1 <- Data_CCA$`Prevalensi balita gizi buruk`
Y2 <- Data_CCA$`Prevalensi balita gizi kurang`
Y3 <- Data_CCA$`Balita Obesitas`
X1 <- Data_CCA$`Rata-rata lama sekolah ibu`
X2 <- Data_CCA$`Persentase Penduduk yang Memiliki Jaminan Kesehatan`
X3 <- Data_CCA$Kemiskinan
X4 <- Data_CCA$`Imunisasi Dasar Lengkap`
X5 <- Data_CCA$`Air Layak`
data <- data.frame(Y1,Y2,Y3,X1,X2,X3,X4,X5)
data
## Y1 Y2 Y3 X1 X2 X3 X4 X5
## 1 5.9 18.9 3.0 8.62 62.85 15.92 23.19 64.85
## 2 5.3 13.1 5.9 8.96 25.12 9.28 27.10 70.07
## 3 3.3 14.2 3.0 8.60 28.76 6.75 35.59 68.83
## 4 4.2 14.0 5.5 8.49 17.63 7.41 31.73 75.12
## 5 3.0 10.5 5.0 7.70 18.22 7.90 46.99 65.73
## 6 2.1 10.2 4.2 7.67 15.90 13.10 47.40 64.02
## 7 2.3 11.9 4.4 8.16 25.79 15.59 43.48 43.83
## 8 3.5 15.0 4.2 7.49 26.47 13.04 50.92 53.79
## 9 3.7 13.0 7.8 7.48 19.75 5.30 58.33 68.14
## 10 3.0 13.4 4.4 9.57 19.40 6.13 57.40 83.95
## 11 3.0 11.0 6.8 10.61 36.50 3.78 52.43 88.93
## 12 2.9 12.2 3.8 7.69 23.92 7.83 43.01 70.50
## 13 3.0 14.0 4.0 6.78 32.95 12.23 63.64 76.09
## 14 2.4 10.2 5.5 8.73 40.71 12.36 71.28 77.19
## 15 2.9 12.6 5.0 6.78 24.70 11.20 55.51 75.54
## 16 4.0 15.7 4.7 7.98 21.35 5.59 25.46 66.11
## 17 2.0 6.6 8.1 7.75 23.72 4.14 67.60 90.85
## 18 4.3 18.3 3.5 6.27 33.42 15.05 59.73 70.48
## 19 7.4 20.9 3.8 6.87 42.25 21.38 57.96 65.20
## 20 6.5 19.4 5.2 6.49 19.49 7.86 41.02 68.77
## 21 6.0 17.6 5.8 7.91 14.76 5.26 43.16 63.90
## 22 4.6 16.4 6.2 7.52 15.51 4.70 61.89 60.62
## 23 4.4 14.9 4.7 8.93 17.52 6.08 44.84 82.75
## 24 4.5 15.3 5.2 8.44 29.75 6.96 56.13 83.78
## 25 3.3 12.0 9.9 9.19 31.38 7.90 54.07 73.29
## 26 6.2 19.9 3.1 8.00 34.62 14.22 50.21 67.10
## 27 4.9 17.9 3.1 7.63 38.61 9.48 47.68 76.34
## 28 6.5 17.3 4.8 7.95 32.76 11.97 59.10 79.83
## 29 6.0 17.5 4.5 7.56 58.25 17.14 51.32 75.00
## 30 4.9 19.9 2.4 7.08 43.81 11.18 47.65 60.66
## 31 5.8 17.9 3.6 9.17 28.03 18.29 32.66 68.34
## 32 4.1 13.4 2.1 8.17 19.51 6.44 32.09 65.73
## 33 6.6 17.4 5.4 6.90 48.71 23.12 31.06 73.12
## 34 6.8 12.8 5.7 5.44 18.97 27.76 19.72 59.09
#uji linearitas
pairs(Data_CCA,
panel = function(x, y) {
points(x, y, pch = 16, col = "blue")
abline(lm(y ~ x), col = "red")
})
library(lmtest)
## Warning: package 'lmtest' was built under R version 4.4.2
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
model1 <- lm(Y1 ~ X1+X2+X3+X4+X5, data = Data_CCA)
model2 <- lm(Y2 ~ X1+X2+X3+X4+X5, data = Data_CCA)
model3 <- lm(Y3 ~ X1+X2+X3+X4+X5, data = Data_CCA)
resettest(model1)
##
## RESET test
##
## data: model1
## RESET = 2.2419, df1 = 2, df2 = 26, p-value = 0.1264
resettest(model2)
##
## RESET test
##
## data: model2
## RESET = 0.87498, df1 = 2, df2 = 26, p-value = 0.4288
resettest(model3)
##
## RESET test
##
## data: model3
## RESET = 0.17078, df1 = 2, df2 = 26, p-value = 0.8439
#Uji Normalitas
library(MVN)
## Warning: package 'MVN' was built under R version 4.4.3
mvn(data,mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 136.696505090106 0.141401538264125 YES
## 2 Mardia Kurtosis -0.171812983918227 0.8635845576294 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Y1 0.5910 0.1160 YES
## 2 Anderson-Darling Y2 0.3092 0.5401 YES
## 3 Anderson-Darling Y3 0.5360 0.1578 YES
## 4 Anderson-Darling X1 0.2265 0.8013 YES
## 5 Anderson-Darling X2 0.9396 0.0153 NO
## 6 Anderson-Darling X3 0.9250 0.0166 NO
## 7 Anderson-Darling X4 0.3832 0.3777 YES
## 8 Anderson-Darling X5 0.3114 0.5351 YES
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Y1 34 4.391176 1.539061 4.250 2.00 7.40 3.0000 5.8750 0.2289809
## Y2 34 14.861765 3.377198 14.550 6.60 20.90 12.6500 17.5750 -0.1599033
## Y3 34 4.832353 1.640532 4.700 2.10 9.90 3.8000 5.5000 0.9312241
## X1 34 7.899412 1.027893 7.830 5.44 10.61 7.4825 8.5725 0.1361015
## X2 34 29.149706 11.950329 26.130 14.76 62.85 19.4950 34.3200 1.0528769
## X3 34 10.951176 5.787295 9.380 3.78 27.76 6.5175 13.9400 1.0006762
## X4 34 46.804412 13.207991 47.665 19.72 71.28 36.9475 57.0825 -0.2972104
## X5 34 70.515882 9.681519 69.450 43.83 90.85 65.3325 75.9525 -0.1719432
## Kurtosis
## Y1 -1.2349233
## Y2 -0.6792524
## Y3 1.1123709
## X1 0.2709132
## X2 0.5605395
## X3 0.4425544
## X4 -0.8571337
## X5 0.3936252
#Dengan QQPlot
matriks_data <- as.matrix(data,34,8)
x_bar <- colMeans(matriks_data)
cov_matriks <- cov(matriks_data)
Di<-mahalanobis(matriks_data,x_bar,cov_matriks)
Di
## [1] 13.503214 6.386890 3.892721 4.428709 4.095138 5.870978 13.386616
## [8] 4.539456 5.795129 8.062790 8.781778 2.972739 5.531405 8.688437
## [15] 4.763592 7.696798 12.192754 8.436687 8.888807 7.674797 6.950276
## [22] 8.378728 5.056804 2.350529 14.895927 4.240727 3.298606 7.305578
## [29] 7.348938 6.744215 11.597549 10.453701 9.485377 20.303607
hasil <- data.frame(Obs = 1:length(Di),
Mahalanobis_Distance = Di)
hasil<- hasil[order(hasil$Mahalanobis_Distance), ]
hasil$Rank <- c(1:34)
hasil$Probability<-((hasil$Rank-0.5)/34)
hasil
## Obs Mahalanobis_Distance Rank Probability
## 24 24 2.350529 1 0.01470588
## 12 12 2.972739 2 0.04411765
## 27 27 3.298606 3 0.07352941
## 3 3 3.892721 4 0.10294118
## 5 5 4.095138 5 0.13235294
## 26 26 4.240727 6 0.16176471
## 4 4 4.428709 7 0.19117647
## 8 8 4.539456 8 0.22058824
## 15 15 4.763592 9 0.25000000
## 23 23 5.056804 10 0.27941176
## 13 13 5.531405 11 0.30882353
## 9 9 5.795129 12 0.33823529
## 6 6 5.870978 13 0.36764706
## 2 2 6.386890 14 0.39705882
## 30 30 6.744215 15 0.42647059
## 21 21 6.950276 16 0.45588235
## 28 28 7.305578 17 0.48529412
## 29 29 7.348938 18 0.51470588
## 20 20 7.674797 19 0.54411765
## 16 16 7.696798 20 0.57352941
## 10 10 8.062790 21 0.60294118
## 22 22 8.378728 22 0.63235294
## 18 18 8.436687 23 0.66176471
## 14 14 8.688437 24 0.69117647
## 11 11 8.781778 25 0.72058824
## 19 19 8.888807 26 0.75000000
## 33 33 9.485377 27 0.77941176
## 32 32 10.453701 28 0.80882353
## 31 31 11.597549 29 0.83823529
## 17 17 12.192754 30 0.86764706
## 7 7 13.386616 31 0.89705882
## 1 1 13.503214 32 0.92647059
## 25 25 14.895927 33 0.95588235
## 34 34 20.303607 34 0.98529412
hasil$X2<-qchisq(hasil$Probability,8)
hasil
## Obs Mahalanobis_Distance Rank Probability X2
## 24 24 2.350529 1 0.01470588 1.849081
## 12 12 2.972739 2 0.04411765 2.620351
## 27 27 3.298606 3 0.07352941 3.121984
## 3 3 3.892721 4 0.10294118 3.527323
## 5 5 4.095138 5 0.13235294 3.881573
## 26 26 4.240727 6 0.16176471 4.204310
## 4 4 4.428709 7 0.19117647 4.506090
## 8 8 4.539456 8 0.22058824 4.793406
## 15 15 4.763592 9 0.25000000 5.070640
## 23 23 5.056804 10 0.27941176 5.340970
## 13 13 5.531405 11 0.30882353 5.606840
## 9 9 5.795129 12 0.33823529 5.870236
## 6 6 5.870978 13 0.36764706 6.132855
## 2 2 6.386890 14 0.39705882 6.396214
## 30 30 6.744215 15 0.42647059 6.661732
## 21 21 6.950276 16 0.45588235 6.930794
## 28 28 7.305578 17 0.48529412 7.204804
## 29 29 7.348938 18 0.51470588 7.485238
## 20 20 7.674797 19 0.54411765 7.773700
## 16 16 7.696798 20 0.57352941 8.071981
## 10 10 8.062790 21 0.60294118 8.382142
## 22 22 8.378728 22 0.63235294 8.706612
## 18 18 8.436687 23 0.66176471 9.048330
## 14 14 8.688437 24 0.69117647 9.410940
## 11 11 8.781778 25 0.72058824 9.799084
## 19 19 8.888807 26 0.75000000 10.218855
## 33 33 9.485377 27 0.77941176 10.678521
## 32 32 10.453701 28 0.80882353 11.189774
## 31 31 11.597549 29 0.83823529 11.770000
## 17 17 12.192754 30 0.86764706 12.446834
## 7 7 13.386616 31 0.89705882 13.268439
## 1 1 13.503214 32 0.92647059 14.331263
## 25 25 14.895927 33 0.95588235 15.880848
## 34 34 20.303607 34 0.98529412 19.028894
plot(hasil$Mahalanobis_Distance,hasil$X2,
xlab= "Mahalanobis Distance",ylab="Chi-Square",,col="red",
main = "QQ plot Normalitas",
pch=19, cex=0.8)
abline(a=0,b=1,col="blue",lwd=2)
#Menentukan multikolineritas
VIF <- function(x){
VIF <- diag(solve(cor(x)))
result <- ifelse(VIF > 10,"multicolinearity","non
multicolinearity")
data1 <- data.frame(VIF,result)
return(data1)
}
VIF(data)
## VIF result
## Y1 7.485918 non\n multicolinearity
## Y2 7.533326 non\n multicolinearity
## Y3 2.132599 non\n multicolinearity
## X1 1.751251 non\n multicolinearity
## X2 2.125571 non\n multicolinearity
## X3 3.262086 non\n multicolinearity
## X4 1.975972 non\n multicolinearity
## X5 2.016151 non\n multicolinearity
#menghitung covarian matriks
cov_matriks<-cov(matriks_data)
cov_matriks
## Y1 Y2 Y3 X1 X2 X3
## Y1 2.3687077 4.1641979 -0.5266756 -0.49555080 6.1662398 4.574374
## Y2 4.1641979 11.4054635 -2.7544831 -0.93529590 16.9989884 6.591440
## Y3 -0.5266756 -2.7544831 2.6913458 0.29583779 -4.9593841 -2.625676
## X1 -0.4955508 -0.9352959 0.2958378 1.05656328 0.1430604 -2.907599
## X2 6.1662398 16.9989884 -4.9593841 0.14306043 142.8103605 31.962291
## X3 4.5743743 6.5914403 -2.6256756 -2.90759929 31.9622913 33.492786
## X4 -7.6676569 -8.4119171 5.7865499 -0.01339127 4.6124922 -20.415578
## X5 -2.1967950 -7.9253440 4.8536524 3.77421569 11.4857594 -20.797619
## X4 X5
## Y1 -7.66765686 -2.196795
## Y2 -8.41191711 -7.925344
## Y3 5.78654991 4.853652
## X1 -0.01339127 3.774216
## X2 4.61249225 11.485759
## X3 -20.41557807 -20.797619
## X4 174.45103146 47.823994
## X5 47.82399447 93.731807
P11 <- cov_matriks [1:3,1:3]
P11
## Y1 Y2 Y3
## Y1 2.3687077 4.164198 -0.5266756
## Y2 4.1641979 11.405463 -2.7544831
## Y3 -0.5266756 -2.754483 2.6913458
P12 <- cov_matriks [1:3,4:8]
P12
## X1 X2 X3 X4 X5
## Y1 -0.4955508 6.166240 4.574374 -7.667657 -2.196795
## Y2 -0.9352959 16.998988 6.591440 -8.411917 -7.925344
## Y3 0.2958378 -4.959384 -2.625676 5.786550 4.853652
P21 <- cov_matriks [4:8,1:3]
P21
## Y1 Y2 Y3
## X1 -0.4955508 -0.9352959 0.2958378
## X2 6.1662398 16.9989884 -4.9593841
## X3 4.5743743 6.5914403 -2.6256756
## X4 -7.6676569 -8.4119171 5.7865499
## X5 -2.1967950 -7.9253440 4.8536524
P22<-cov_matriks[4:8,4:8]
P22
## X1 X2 X3 X4 X5
## X1 1.05656328 0.1430604 -2.907599 -0.01339127 3.774216
## X2 0.14306043 142.8103605 31.962291 4.61249225 11.485759
## X3 -2.90759929 31.9622913 33.492786 -20.41557807 -20.797619
## X4 -0.01339127 4.6124922 -20.415578 174.45103146 47.823994
## X5 3.77421569 11.4857594 -20.797619 47.82399447 93.731807
#mencari nilai sigma 11ˆ-1/2
eig.P11<-eigen(P11)
nilai.eigen.P11<-eig.P11$values
nilai.eigen.P11
## [1] 13.7222108 2.1738365 0.5694696
l1.11<-nilai.eigen.P11[1]
l2.11<-nilai.eigen.P11[2]
l3.11<-nilai.eigen.P11[3]
vektor.eigen.P11<-eig.P11$vectors
vektor.eigen.P11
## [,1] [,2] [,3]
## [1,] -0.3439442 0.38942118 0.8544317
## [2,] -0.9070259 0.09761324 -0.4096043
## [3,] 0.2429124 0.91587270 -0.3196416
v1.11<-matrix(vektor.eigen.P11[,1])
v1.11
## [,1]
## [1,] -0.3439442
## [2,] -0.9070259
## [3,] 0.2429124
v2.11<-matrix(vektor.eigen.P11[,2])
v2.11
## [,1]
## [1,] 0.38942118
## [2,] 0.09761324
## [3,] 0.91587270
v3.11<-matrix(vektor.eigen.P11[,3])
v3.11
## [,1]
## [1,] 0.8544317
## [2,] -0.4096043
## [3,] -0.3196416
sig11<-((v1.11%*%t(v1.11))/sqrt(l1.11))+
((v2.11%*%t(v2.11))/sqrt(l2.11))+
((v3.11%*%t(v3.11))/sqrt(l3.11))
sig11
## [,1] [,2] [,3]
## [1,] 1.1022196 -0.3537762 -0.1425651
## [2,] -0.3537762 0.4508795 0.1746551
## [3,] -0.1425651 0.1746551 0.7202477
eig.P22<-eigen(P22)
nilai.eigen.P22<-eig.P22$values
nilai.eigen.P22
## [1] 201.4367883 151.7919980 73.9844835 17.6844350 0.6448436
l1.22<-nilai.eigen.P22[1]
l2.22<-nilai.eigen.P22[2]
l3.22<-nilai.eigen.P22[3]
l4.22<-nilai.eigen.P22[4]
l5.22<-nilai.eigen.P22[5]
vektor.eigen.P22<-eig.P22$vectors
vektor.eigen.P22
## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.01024253 0.002951847 -0.053517664 -0.10257453 0.99322742
## [2,] -0.07389692 -0.962072702 0.009267111 -0.26129426 -0.02438835
## [3,] 0.14730465 -0.261974153 0.223860199 0.92064151 0.10943811
## [4,] -0.88728066 0.059533797 0.454616643 0.04599661 0.01991928
## [5,] -0.43066078 -0.047251170 -0.860383810 0.26740847 -0.02304408
v1.22<-matrix(vektor.eigen.P22[,1])
v1.22
## [,1]
## [1,] -0.01024253
## [2,] -0.07389692
## [3,] 0.14730465
## [4,] -0.88728066
## [5,] -0.43066078
v2.22<-matrix(vektor.eigen.P22[,2])
v2.22
## [,1]
## [1,] 0.002951847
## [2,] -0.962072702
## [3,] -0.261974153
## [4,] 0.059533797
## [5,] -0.047251170
v3.22<-matrix(vektor.eigen.P22[,3])
v3.22
## [,1]
## [1,] -0.053517664
## [2,] 0.009267111
## [3,] 0.223860199
## [4,] 0.454616643
## [5,] -0.860383810
v4.22<-matrix(vektor.eigen.P22[,4])
v4.22
## [,1]
## [1,] -0.10257453
## [2,] -0.26129426
## [3,] 0.92064151
## [4,] 0.04599661
## [5,] 0.26740847
v5.22<-matrix(vektor.eigen.P22[,5])
v5.22
## [,1]
## [1,] 0.99322742
## [2,] -0.02438835
## [3,] 0.10943811
## [4,] 0.01991928
## [5,] -0.02304408
sig22<-((v1.22%*%t(v1.22))/sqrt(l1.22))+
((v2.22%*%t(v2.22))/sqrt(l2.22))+
((v3.22%*%t(v3.22))/sqrt(l3.22))
sig22
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0003410834 -0.0002348339 -0.001561920 -0.0021740180 0.005652742
## [2,] -0.0002348339 0.0755209251 0.019931222 0.0004606789 0.005005063
## [3,] -0.0015619196 0.0199312219 0.012925497 0.0013570275 -0.025857322
## [4,] -0.0021740180 0.0004606789 0.001357028 0.0797851194 -0.018779535
## [5,] 0.0056527422 0.0050050633 -0.025857322 -0.0187795348 0.099311516
#mencari nilai eigen dan vektor eigen a untuk sigma 11
M.sig.11<-sig11%*%P12%*%solve(P22)%*%P21%*%sig11
M.sig.11
## [,1] [,2] [,3]
## [1,] 0.53394307 -0.03555622 -0.16331927
## [2,] -0.03555622 0.27625647 -0.09249571
## [3,] -0.16331927 -0.09249571 0.13233604
eigen11<-eigen(M.sig.11)
nilai.eigenM11<-eigen11$values
nilai.eigenM11
## [1] 0.59199558 0.31671524 0.03382475
l1.M11<-nilai.eigenM11[1]
l1.M11
## [1] 0.5919956
l2.M11<-nilai.eigenM11[2]
l2.M11
## [1] 0.3167152
l3.M11<-nilai.eigenM11[3]
l3.M11
## [1] 0.03382475
vektor.eigen.M11<-eigen11$vectors
vektor.eigen.M11
## [,1] [,2] [,3]
## [1,] 0.942795886 0.1186176 -0.3115538
## [2,] -0.008541964 -0.9256548 -0.3782727
## [3,] -0.333261086 0.3592952 -0.8716903
e1<-matrix(vektor.eigen.M11[,1])
e1
## [,1]
## [1,] 0.942795886
## [2,] -0.008541964
## [3,] -0.333261086
e2<-matrix(vektor.eigen.M11[,2])
e2
## [,1]
## [1,] 0.1186176
## [2,] -0.9256548
## [3,] 0.3592952
e3<-matrix(vektor.eigen.M11[,3])
e3
## [,1]
## [1,] -0.3115538
## [2,] -0.3782727
## [3,] -0.8716903
#mencari nilai eigen dan vektor eigen a untuk sigma 22
M.sig.22<-sig22%*%P21%*%solve(P11)%*%P12%*%sig22
M.sig.22
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000289100 -0.004659921 -0.002448337 -0.001715776 0.004281856
## [2,] -0.004659921 0.171876786 0.080025170 -0.102901427 -0.091653597
## [3,] -0.002448337 0.080025170 0.039673115 -0.055501737 -0.049784679
## [4,] -0.001715776 -0.102901427 -0.055501737 0.307317303 0.037014918
## [5,] 0.004281856 -0.091653597 -0.049784679 0.037014918 0.077117721
eigen22<-eigen(M.sig.22)
nilai.eigenM22<-eigen22$values
nilai.eigenM22
## [1] 4.098485e-01 1.670233e-01 1.940216e-02 3.469447e-17 2.452929e-19
l1.M22<-nilai.eigenM22[1]
l1.M22
## [1] 0.4098485
l2.M22<-nilai.eigenM22[2]
l2.M22
## [1] 0.1670233
l3.M22<-nilai.eigenM22[3]
l3.M22
## [1] 0.01940216
l4.M22<-nilai.eigenM22[4]
l4.M22
## [1] 3.469447e-17
l5.M22<-nilai.eigenM22[5]
l5.M22
## [1] 2.452929e-19
vektor.eigen.M22<-eigen22$vectors
vektor.eigen.M22
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.007133915 0.0378472 -0.03865825 0.00000000 0.998510008
## [2,] -0.522983183 -0.5643698 -0.58232793 0.26241725 0.002582803
## [3,] -0.263881751 -0.2571294 0.06850850 -0.92701320 0.014283837
## [4,] 0.764596600 -0.6359662 -0.09179616 -0.04779952 0.015088774
## [5,] 0.268693427 0.4576961 -0.80391528 -0.26362649 -0.050392392
v1.M22<-matrix(vektor.eigen.M22[,1])
v1.M22
## [,1]
## [1,] 0.007133915
## [2,] -0.522983183
## [3,] -0.263881751
## [4,] 0.764596600
## [5,] 0.268693427
v2.M22<-matrix(vektor.eigen.M22[,2])
v2.M22
## [,1]
## [1,] 0.0378472
## [2,] -0.5643698
## [3,] -0.2571294
## [4,] -0.6359662
## [5,] 0.4576961
v3.M22<-matrix(vektor.eigen.M22[,3])
v3.M22
## [,1]
## [1,] -0.03865825
## [2,] -0.58232793
## [3,] 0.06850850
## [4,] -0.09179616
## [5,] -0.80391528
v4.M22<-matrix(vektor.eigen.M22[,4])
v4.M22
## [,1]
## [1,] 0.00000000
## [2,] 0.26241725
## [3,] -0.92701320
## [4,] -0.04779952
## [5,] -0.26362649
v5.M22<-matrix(vektor.eigen.M22[,5])
v5.M22
## [,1]
## [1,] 0.998510008
## [2,] 0.002582803
## [3,] 0.014283837
## [4,] 0.015088774
## [5,] -0.050392392
#mencari nilai koefisien a dan b
a1<-sig11%*%e1
a1
## [,1]
## [1,] 1.0897015
## [2,] -0.3955958
## [3,] -0.3759322
a2<-sig11%*%e2
a2
## [,1]
## [1,] 0.40699431
## [2,] -0.39657015
## [3,] 0.08020051
a3<-sig11%*%e3
a3
## [,1]
## [1,] -0.08530426
## [2,] -0.21258020
## [3,] -0.64948352
b1<-sig22%*%v1.M22
b1
## [,1]
## [1,] 0.0003940174
## [2,] -0.0430602737
## [3,] -0.0197557532
## [4,] 0.0553429620
## [5,] 0.0165716208
b2<-sig22%*%v2.M22
b2
## [,1]
## [1,] 0.004516898
## [2,] -0.045757699
## [3,] -0.027329039
## [4,] -0.060027167
## [5,] 0.061435557
b3<-sig22%*%v3.M22
b3
## [,1]
## [1,] -0.004328199
## [2,] -0.046669343
## [3,] 0.010001907
## [4,] 0.007681933
## [5,] -0.083018715
b4<-sig22%*%v4.M22
b4
## [,1]
## [1,] -1.192622e-17
## [2,] -1.687019e-16
## [3,] 2.428613e-17
## [4,] -3.469447e-18
## [5,] -2.428613e-16
b5<-sig22%*%v5.M22
b5
## [,1]
## [1,] -1.084202e-19
## [2,] -4.228388e-18
## [3,] -6.505213e-19
## [4,] 3.252607e-19
## [5,] -3.469447e-18
U1V1<-(t(a1)%*%P12%*%b1)/((sqrt(t(a1)%*%P11%*%a1))*(sqrt(t(b1)%*%P22%*%b1)))
U1V1
## [,1]
## [1,] -0.5632154
U2V2<-(t(a2)%*%P12%*%b2)/((sqrt(t(a2)%*%P11%*%a2))*(sqrt(t(b2)%*%P22%*%b2)))
U2V2
## [,1]
## [1,] 0.3603139
U3V3<-(t(a3)%*%P12%*%b3)/((sqrt(t(a3)%*%P11%*%a3))*(sqrt(t(b3)%*%P22%*%b3)))
U3V3
## [,1]
## [1,] 0.1379696
an<-(t(a1)%*%P11%*%a1)
an
## [,1]
## [1,] 1
X<-data.frame(X1,X2,X3,X4,X5)
Y<-data.frame(Y1,Y2,Y3)
#MENGHITUNG ANALISIS KORELASI KANONIK LANGSUNG
cor_XY<-cancor(X,Y)
cor_XY
## $cor
## [1] 0.7694125 0.5627746 0.1839151
##
## $xcoef
## [,1] [,2] [,3] [,4] [,5]
## X1 0.028625700 -0.071063542 -0.0955717491 0.087053752 0.1542592310
## X2 0.002380808 0.015504923 0.0047578227 -0.005471698 0.0056507616
## X3 -0.022223610 -0.019821630 0.0006222929 0.032988038 0.0087915855
## X4 0.008965178 -0.000710117 0.0030260343 0.011637966 -0.0007646028
## X5 -0.008334934 -0.011780295 0.0147869761 -0.006926294 0.0020390523
##
## $ycoef
## [,1] [,2] [,3]
## Y1 -0.1896927 -0.07084862 0.01484957
## Y2 0.0688644 0.06903400 0.03700546
## Y3 0.0654414 -0.01396112 0.11306057
##
## $xcenter
## X1 X2 X3 X4 X5
## 7.899412 29.149706 10.951176 46.804412 70.515882
##
## $ycenter
## Y1 Y2 Y3
## 4.391176 14.861765 4.832353
#uji korelasi kanonik secara simultan
simultan.cor<-cancor(data[,c("Y1","Y2")],
data[,c("X1","X2","X3","X4")])
simultan.cor$cor
## [1] 0.6190132 0.4368614
#Uji korelasi kanonik secara parsial(antara Y1 dengan X)
parsial.cor_Y1<-cancor(data$Y1, data[,c("X1","X2","X3","X4")])
parsial.cor_Y1$cor
## [1] 0.6179249
parsial.cor_Y2<-cancor(data$Y2, data[,c("X1","X2","X3","X4")])
parsial.cor_Y2$cor
## [1] 0.5463871