library(readxl)
kanonik11 <- read_excel("D:/DATA C/SEMESTER 4/STATISTIKA MULTIVARIAT/Data Analisis Kanonik.xlsx")
head(kanonik11)
X1<-kanonik11$`Makanan Manis`
X2<-kanonik11$`Makanan Asin`
X3<-kanonik11$`Makanan Berlemak`
X4<-kanonik11$`Makanan Yang Dibakar`
X5<-kanonik11$`Hewani Berpengawet`
X6<-kanonik11$`Makanan berpenyedap`
X7<-kanonik11$Kopi
X8<-kanonik11$`Kafein selain kopi`
Y1<-kanonik11$Kanker
Y2<-kanonik11$Asma
Y3<-kanonik11$PPOK
Y4<-kanonik11$Diabetes
Y5<-kanonik11$Hipertiroid
Y6<-kanonik11$Hipertensi
Y7<-kanonik11$`Gagal Jantung`
Y8<-kanonik11$Stroke
Y9<-kanonik11$`Penyakit Jantung`
Y10<-kanonik11$`Ginjal kronis`
Y11<-kanonik11$`Batu ginjal`
Y12<-kanonik11$`Penyakit sendi`
kanonik1<-data.frame(X1,X2,X3,X4,X5,X6,X7,X8,Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10,Y11,Y12)
kanonik1
kanonik.s=scale(kanonik1)
#kanonik1
matriks_dataA<-as.matrix(kanonik.s,33,20)
matriks_dataA
## X1 X2 X3 X4 X5 X6
## [1,] -0.03055092 -0.5284928 -0.79356003 -0.4428114 -0.18446378 -2.87767801
## [2,] 0.89588519 -0.1796875 -0.77767439 -0.5487864 -0.36986392 -2.33414938
## [3,] -0.41202462 -1.0613896 0.24694953 -0.5605614 -0.18446378 -2.01776705
## [4,] 0.20559946 -0.2087546 -0.47584719 -0.3957114 0.17088649 0.38349374
## [5,] -0.03055092 0.4888558 -1.07155877 -0.4428114 -0.24626383 0.09144851
## [6,] 0.96854684 1.9422109 -0.34081923 -0.5252364 -0.32351389 0.50517925
## [7,] -0.91157350 0.3628984 -0.79356003 -0.5370114 -0.13811374 0.87023579
## [8,] 0.60523856 1.4190031 -0.77767439 -0.6665364 -0.46256399 0.75666264
## [9,] -0.92065621 -0.9548103 -0.69824618 -0.1484364 -0.38531393 1.13794392
## [10,] 0.71423105 -0.8772980 0.23900671 -0.3486114 -0.19991379 0.20502165
## [11,] 0.79597541 0.2466300 1.31923038 -0.3721614 0.26358657 0.35915664
## [12,] -0.23037048 2.6688885 1.50191526 -0.4899114 0.03183639 1.11360681
## [13,] 0.85047165 1.2252224 2.31208301 -0.5841114 -0.32351389 0.78911211
## [14,] 1.50442655 -0.5188037 1.54957219 -0.6076614 5.37754050 0.35915664
## [15,] -0.43927274 0.6341913 1.45425833 -0.5605614 -0.27716385 0.57819056
## [16,] -0.50285169 1.5352715 1.39865859 -0.4781364 -0.19991379 0.77288738
## [17,] -2.74628031 -1.1001458 -1.01595903 -0.6312114 -0.07631370 -0.07079884
## [18,] -1.85617502 -0.6641392 -0.40436180 -0.2073114 -0.12266373 0.92702236
## [19,] -2.05599458 -0.9257432 -1.84995524 -0.3839364 -0.43166397 -0.30605750
## [20,] 0.55982503 1.1864663 -0.42024744 -0.4192614 0.20178652 0.10767324
## [21,] 1.35910324 0.5469900 0.84266111 -0.2190864 0.03183639 0.67553897
## [22,] 1.61341904 -0.1118643 0.36609184 -0.3839364 0.09363644 0.74855028
## [23,] 0.72331375 -0.2087546 -0.57910387 -0.3957114 0.04728640 -0.34661934
## [24,] 0.10568968 -1.1582800 0.91414650 0.4874137 -0.44711398 0.13201034
## [25,] -0.25761860 -0.8191638 -0.04693485 1.1468138 -0.52436404 0.25369586
## [26,] -0.16679153 0.1594287 -0.49173283 0.3578887 -0.18446378 0.30237006
## [27,] -0.74808477 -0.6060050 -1.05567313 0.5580637 -0.33896390 -0.38718118
## [28,] -0.07596446 -0.9063651 1.04917446 0.8995387 -0.55526406 0.10767324
## [29,] -0.04871634 1.1961553 -1.07950159 0.4520887 -0.60161410 -0.76035008
## [30,] 0.86863707 -0.8288528 0.14369285 0.6287137 0.24813655 0.74855028
## [31,] -0.18495694 -0.4509805 0.54083391 0.4991887 -0.32351389 -0.58187800
## [32,] 0.75964458 -0.8482309 -0.33287641 0.5227387 -0.06086368 -0.21682146
## [33,] -0.91157350 -0.6544502 -0.87298825 4.7970642 0.49533674 -2.02587942
## X7 X8 Y1 Y2 Y3 Y4
## [1,] 0.5605195 1.43989442 0.13161384 -0.37094912 0.12733325 0.74122371
## [2,] -1.6984238 -1.29550389 -0.44748707 -1.35025479 -0.25466650 0.74122371
## [3,] -0.8363150 -0.58959465 0.56593953 -1.16663498 -0.58209486 -0.19956023
## [4,] -1.3164768 -0.50135600 -0.88181275 -1.59508121 -1.07323740 -0.76403060
## [5,] -0.8363150 -0.01604339 0.27638907 -1.35025479 -1.07323740 -0.57587381
## [6,] 1.0952452 -0.19252070 -0.88181275 -1.28904818 -0.69123765 -0.95218738
## [7,] 0.8006005 -0.19252070 0.85548998 -1.59508121 -0.96409462 -0.95218738
## [8,] 0.6478217 -1.07490726 -0.88181275 -1.83990763 -1.45523716 -1.32850096
## [9,] 0.6914728 -0.76607196 -0.01316138 -0.18732930 -0.25466650 1.30569408
## [10,] -0.6726234 1.08693980 0.42116430 -0.55456893 -1.07323740 -0.19956023
## [11,] -0.1051594 0.64574652 0.85548998 0.36353014 -0.74580904 2.05832123
## [12,] 0.2440492 0.38103056 -0.44748707 0.24111693 -0.03638093 -0.19956023
## [13,] -0.9781810 -0.72195263 1.14504044 0.24111693 -0.36380929 0.36491014
## [14,] -1.5565578 -0.32487869 4.04054500 1.40404241 -0.52752347 2.24647802
## [15,] 0.7460366 -0.63371398 0.42116430 0.30232353 -0.25466650 1.30569408
## [16,] 0.2986131 -0.94254927 -0.44748707 -0.49336233 -0.74580904 -0.19956023
## [17,] 2.1646967 0.99870114 1.00026521 0.97559618 -0.30923790 -0.19956023
## [18,] 0.9097282 -0.76607196 -1.02658798 0.30232353 0.72761858 -0.95218738
## [19,] 2.0119179 -0.85431062 -0.44748707 1.64886883 3.23790267 -0.38771702
## [20,] 1.8154880 1.57225240 -0.73703752 -0.86060195 -0.30923790 -1.14034417
## [21,] 0.2767875 -0.89842995 -0.88181275 0.66956316 0.12733325 -0.38771702
## [22,] -0.9017916 3.73409946 0.42116430 1.09800939 0.50933301 -0.01140344
## [23,] -0.6835362 -0.06016272 0.56593953 -0.30974251 -0.69123765 1.68200766
## [24,] 0.4732174 0.60162720 0.56593953 0.05749711 -0.03638093 1.87016444
## [25,] 0.1021832 -0.67783331 -0.59226230 1.95490185 2.14647481 0.36491014
## [26,] -0.1924616 -0.14840137 0.56593953 1.28162920 1.43704669 0.36491014
## [27,] -1.1636981 0.33691123 -0.30271184 0.42473674 0.45476161 -0.57587381
## [28,] -0.3125020 0.16043392 -1.60568889 0.48594334 0.61847579 0.17675335
## [29,] 0.9097282 -0.94254927 -0.30271184 0.73076976 1.43704669 -1.14034417
## [30,] -0.7926639 0.02807594 -0.44748707 0.42473674 0.12733325 -0.76403060
## [31,] -0.5744085 0.33691123 -0.15793661 0.24111693 0.61847579 -0.38771702
## [32,] -0.6726234 -0.41311734 -1.02658798 -0.61577554 -0.85495183 -0.76403060
## [33,] -0.4543680 0.68986585 -0.30271184 0.73076976 0.72761858 -1.14034417
## Y5 Y6 Y7 Y8 Y9 Y10
## [1,] -0.1313035 0.35082565 -0.245564074 0.08153834 1.5614401 1.6718280
## [2,] -0.1313035 -0.92185038 -0.064139870 -0.19681669 0.3903600 -0.2985407
## [3,] -0.1313035 -0.42920159 -0.064139870 0.45267839 0.9759001 -0.2985407
## [4,] -1.3693084 -1.16817477 -0.124614605 -1.03188179 -1.3662601 -1.2837251
## [5,] -0.7503060 -0.59341786 -0.608412481 -1.31023682 -1.3662601 -0.2985407
## [6,] -1.3693084 -0.75763412 -0.426988277 -0.56795673 -0.1951800 -1.2837251
## [7,] -0.7503060 -0.42920159 -0.366513543 0.26710836 -0.7807201 -0.2985407
## [8,] -0.7503060 -0.59341786 -0.366513543 -1.26384432 -1.3662601 0.6866436
## [9,] 0.4876989 0.43293378 -0.547937746 1.51970601 0.9759001 -1.2837251
## [10,] -0.7503060 -0.01866094 0.177759068 0.54546340 -0.1951800 -1.2837251
## [11,] 2.3447062 0.47398785 0.056809599 1.51970601 1.5614401 -1.2837251
## [12,] 1.1067013 0.67925818 -0.003665135 0.08153834 0.3903600 0.6866436
## [13,] 1.1067013 0.26871752 0.238233803 0.59185590 0.3903600 0.6866436
## [14,] 2.3447062 1.62350168 0.661556945 1.79806105 0.9759001 0.6866436
## [15,] 1.7257037 0.76136631 0.298708537 1.24135098 0.3903600 0.6866436
## [16,] 0.4876989 -0.10076907 -0.306038808 -0.61434924 0.3903600 -0.2985407
## [17,] 0.4876989 -0.05971500 -0.064139870 -0.52156423 -0.1951800 -0.2985407
## [18,] -0.7503060 -0.88079632 -0.608412481 -0.89270427 -1.3662601 -1.2837251
## [19,] 0.4876989 -0.67552599 -0.245564074 -1.03188179 -0.7807201 0.6866436
## [20,] -1.3693084 -0.34709346 -0.366513543 -0.28960170 -0.7807201 -0.2985407
## [21,] -0.7503060 0.72031224 -0.426988277 -0.10403168 -0.7807201 -0.2985407
## [22,] -0.7503060 1.74666388 -0.487463012 1.28774349 0.3903600 -0.2985407
## [23,] -0.1313035 0.59715004 -0.366513543 0.59185590 0.3903600 -1.2837251
## [24,] 1.1067013 2.52669113 -0.003665135 2.03002357 1.5614401 1.6718280
## [25,] 0.4876989 1.13085290 -0.124614605 0.45267839 2.1469802 2.6570124
## [26,] 1.1067013 0.59715004 -0.426988277 0.31350087 0.9759001 0.6866436
## [27,] -0.1313035 -0.51130973 -0.608412481 -0.75352676 -0.1951800 -0.2985407
## [28,] -0.1313035 0.92558257 -0.487463012 0.87021094 -0.1951800 1.6718280
## [29,] -0.1313035 0.26871752 -0.426988277 -0.24320920 -0.7807201 -0.2985407
## [30,] -0.7503060 -0.92185038 -0.306038808 -1.03188179 0.3903600 -0.2985407
## [31,] -0.7503060 -0.79868818 -0.729361950 -0.84631177 -1.3662601 -0.2985407
## [32,] -0.7503060 -1.57871543 3.987667345 -1.03188179 -0.7807201 0.6866436
## [33,] -0.7503060 -2.31768861 3.382920000 -1.91333939 -1.3662601 -0.2985407
## Y11 Y12
## [1,] 1.78156706 2.19799917
## [2,] -0.83139796 -0.54802886
## [3,] -0.39590379 0.64469039
## [4,] -1.26689213 -0.99183136
## [5,] -0.39590379 -0.49255354
## [6,] -0.83139796 -0.54802886
## [7,] -0.39590379 -0.04875103
## [8,] 0.03959038 0.31183851
## [9,] -1.70238630 -1.26920793
## [10,] -0.83139796 -1.24147028
## [11,] 0.03959038 -0.40934057
## [12,] 1.34607289 1.97609791
## [13,] 1.34607289 0.22862553
## [14,] 3.08804957 -1.32468325
## [15,] 0.91057872 0.20088788
## [16,] -0.39590379 -0.24291463
## [17,] 0.91057872 2.47537574
## [18,] -0.83139796 -0.15970166
## [19,] 0.91057872 0.61695273
## [20,] -0.39590379 0.81111633
## [21,] -0.39590379 0.61695273
## [22,] -0.39590379 -0.24291463
## [23,] -0.39590379 -0.60350417
## [24,] 0.03959038 -0.02101338
## [25,] 1.34607289 0.28410085
## [26,] 0.03959038 0.06219959
## [27,] 0.03959038 0.45052679
## [28,] 0.47508455 0.00672428
## [29,] -1.26689213 -0.65897948
## [30,] 0.03959038 -0.40934057
## [31,] -0.39590379 -1.24147028
## [32,] -0.83139796 -1.82396107
## [33,] -0.39590379 1.39360712
## attr(,"scaled:center")
## X1 X2 X3 X4 X5 X6 X7
## 52.6363636 17.7545455 31.1909091 7.3606061 5.1939394 73.3727273 29.1636364
## X8 Y1 Y2 Y3 Y4 Y5 Y6
## 6.0363636 1.3090909 4.6060606 4.0666667 1.4060606 0.3212121 8.8454545
## Y7 Y8 Y9 Y10 Y11 Y12
## 0.1406061 6.4242424 0.4333333 0.2303030 0.4909091 10.3757576
## attr(,"scaled:scale")
## X1 X2 X3 X4 X5 X6 X7
## 11.0099335 10.3209463 12.5899852 8.4925681 6.4724869 12.3268577 9.1635766
## X8 Y1 Y2 Y3 Y4 Y5 Y6
## 2.2665803 0.6907259 1.6338106 1.8324619 0.5314717 0.1615503 2.4358124
## Y7 Y8 Y9 Y10 Y11 Y12
## 0.1653583 2.1555206 0.1707825 0.1015038 0.2296242 3.6052072
#uji linearitas
pairs(kanonik.s[, 1:7],
upper.panel = function(x, y) { # Panel atas untuk regresi linear
points(x, y, pch = 16, col = "blue")
abline(lm(y ~ x), col = "red", lwd = 2) # Garis regresi
},
lower.panel = function(x, y) { # Panel bawah untuk scatter plot
points(x, y, pch = 16, col = "blue")
},
main="Scatter Plot Matrix (Part 1)")
pairs(kanonik.s[, 8:14],
upper.panel = function(x, y) { # Panel atas untuk regresi linear
points(x, y, pch = 16, col = "blue")
abline(lm(y ~ x), col = "red", lwd = 2) # Garis regresi
},
lower.panel = function(x, y) { # Panel bawah untuk scatter plot
points(x, y, pch = 16, col = "blue")
},
main="Scatter Plot Matrix (Part 2)")
pairs(kanonik.s[, 15:20],
upper.panel = function(x, y) { # Panel atas untuk regresi linear
points(x, y, pch = 16, col = "blue")
abline(lm(y ~ x), col = "red", lwd = 2) # Garis regresi
},
lower.panel = function(x, y) { # Panel bawah untuk scatter plot
points(x, y, pch = 16, col = "blue")
},
main="Scatter Plot Matrix (Part 3)")
#uji multikolinearitas
VIF <- function(x){
VIF<-diag(solve(cor(x)))
result<-ifelse(VIF>10,"multicolinearity","non multicolinearity")
data1 <- data.frame(VIF,result)
return(data1)
}
VIF(kanonik.s)
#Karena terjadi pelanggaran asumsi non multikolinearitas maka variabel yang memiliki
#pengaruh tertinggi akan di hapus, yaitu Y2,Y5,Y6 dan Y11
kanonik2<-data.frame(X1,X2,X3,X4,X5,X6,X7,X8,Y1,Y3,Y4,Y7,Y8,Y9,Y10,Y12)
kanonik<-scale(kanonik2)
kanonik
## X1 X2 X3 X4 X5 X6
## [1,] -0.03055092 -0.5284928 -0.79356003 -0.4428114 -0.18446378 -2.87767801
## [2,] 0.89588519 -0.1796875 -0.77767439 -0.5487864 -0.36986392 -2.33414938
## [3,] -0.41202462 -1.0613896 0.24694953 -0.5605614 -0.18446378 -2.01776705
## [4,] 0.20559946 -0.2087546 -0.47584719 -0.3957114 0.17088649 0.38349374
## [5,] -0.03055092 0.4888558 -1.07155877 -0.4428114 -0.24626383 0.09144851
## [6,] 0.96854684 1.9422109 -0.34081923 -0.5252364 -0.32351389 0.50517925
## [7,] -0.91157350 0.3628984 -0.79356003 -0.5370114 -0.13811374 0.87023579
## [8,] 0.60523856 1.4190031 -0.77767439 -0.6665364 -0.46256399 0.75666264
## [9,] -0.92065621 -0.9548103 -0.69824618 -0.1484364 -0.38531393 1.13794392
## [10,] 0.71423105 -0.8772980 0.23900671 -0.3486114 -0.19991379 0.20502165
## [11,] 0.79597541 0.2466300 1.31923038 -0.3721614 0.26358657 0.35915664
## [12,] -0.23037048 2.6688885 1.50191526 -0.4899114 0.03183639 1.11360681
## [13,] 0.85047165 1.2252224 2.31208301 -0.5841114 -0.32351389 0.78911211
## [14,] 1.50442655 -0.5188037 1.54957219 -0.6076614 5.37754050 0.35915664
## [15,] -0.43927274 0.6341913 1.45425833 -0.5605614 -0.27716385 0.57819056
## [16,] -0.50285169 1.5352715 1.39865859 -0.4781364 -0.19991379 0.77288738
## [17,] -2.74628031 -1.1001458 -1.01595903 -0.6312114 -0.07631370 -0.07079884
## [18,] -1.85617502 -0.6641392 -0.40436180 -0.2073114 -0.12266373 0.92702236
## [19,] -2.05599458 -0.9257432 -1.84995524 -0.3839364 -0.43166397 -0.30605750
## [20,] 0.55982503 1.1864663 -0.42024744 -0.4192614 0.20178652 0.10767324
## [21,] 1.35910324 0.5469900 0.84266111 -0.2190864 0.03183639 0.67553897
## [22,] 1.61341904 -0.1118643 0.36609184 -0.3839364 0.09363644 0.74855028
## [23,] 0.72331375 -0.2087546 -0.57910387 -0.3957114 0.04728640 -0.34661934
## [24,] 0.10568968 -1.1582800 0.91414650 0.4874137 -0.44711398 0.13201034
## [25,] -0.25761860 -0.8191638 -0.04693485 1.1468138 -0.52436404 0.25369586
## [26,] -0.16679153 0.1594287 -0.49173283 0.3578887 -0.18446378 0.30237006
## [27,] -0.74808477 -0.6060050 -1.05567313 0.5580637 -0.33896390 -0.38718118
## [28,] -0.07596446 -0.9063651 1.04917446 0.8995387 -0.55526406 0.10767324
## [29,] -0.04871634 1.1961553 -1.07950159 0.4520887 -0.60161410 -0.76035008
## [30,] 0.86863707 -0.8288528 0.14369285 0.6287137 0.24813655 0.74855028
## [31,] -0.18495694 -0.4509805 0.54083391 0.4991887 -0.32351389 -0.58187800
## [32,] 0.75964458 -0.8482309 -0.33287641 0.5227387 -0.06086368 -0.21682146
## [33,] -0.91157350 -0.6544502 -0.87298825 4.7970642 0.49533674 -2.02587942
## X7 X8 Y1 Y3 Y4 Y7
## [1,] 0.5605195 1.43989442 0.13161384 0.12733325 0.74122371 -0.245564074
## [2,] -1.6984238 -1.29550389 -0.44748707 -0.25466650 0.74122371 -0.064139870
## [3,] -0.8363150 -0.58959465 0.56593953 -0.58209486 -0.19956023 -0.064139870
## [4,] -1.3164768 -0.50135600 -0.88181275 -1.07323740 -0.76403060 -0.124614605
## [5,] -0.8363150 -0.01604339 0.27638907 -1.07323740 -0.57587381 -0.608412481
## [6,] 1.0952452 -0.19252070 -0.88181275 -0.69123765 -0.95218738 -0.426988277
## [7,] 0.8006005 -0.19252070 0.85548998 -0.96409462 -0.95218738 -0.366513543
## [8,] 0.6478217 -1.07490726 -0.88181275 -1.45523716 -1.32850096 -0.366513543
## [9,] 0.6914728 -0.76607196 -0.01316138 -0.25466650 1.30569408 -0.547937746
## [10,] -0.6726234 1.08693980 0.42116430 -1.07323740 -0.19956023 0.177759068
## [11,] -0.1051594 0.64574652 0.85548998 -0.74580904 2.05832123 0.056809599
## [12,] 0.2440492 0.38103056 -0.44748707 -0.03638093 -0.19956023 -0.003665135
## [13,] -0.9781810 -0.72195263 1.14504044 -0.36380929 0.36491014 0.238233803
## [14,] -1.5565578 -0.32487869 4.04054500 -0.52752347 2.24647802 0.661556945
## [15,] 0.7460366 -0.63371398 0.42116430 -0.25466650 1.30569408 0.298708537
## [16,] 0.2986131 -0.94254927 -0.44748707 -0.74580904 -0.19956023 -0.306038808
## [17,] 2.1646967 0.99870114 1.00026521 -0.30923790 -0.19956023 -0.064139870
## [18,] 0.9097282 -0.76607196 -1.02658798 0.72761858 -0.95218738 -0.608412481
## [19,] 2.0119179 -0.85431062 -0.44748707 3.23790267 -0.38771702 -0.245564074
## [20,] 1.8154880 1.57225240 -0.73703752 -0.30923790 -1.14034417 -0.366513543
## [21,] 0.2767875 -0.89842995 -0.88181275 0.12733325 -0.38771702 -0.426988277
## [22,] -0.9017916 3.73409946 0.42116430 0.50933301 -0.01140344 -0.487463012
## [23,] -0.6835362 -0.06016272 0.56593953 -0.69123765 1.68200766 -0.366513543
## [24,] 0.4732174 0.60162720 0.56593953 -0.03638093 1.87016444 -0.003665135
## [25,] 0.1021832 -0.67783331 -0.59226230 2.14647481 0.36491014 -0.124614605
## [26,] -0.1924616 -0.14840137 0.56593953 1.43704669 0.36491014 -0.426988277
## [27,] -1.1636981 0.33691123 -0.30271184 0.45476161 -0.57587381 -0.608412481
## [28,] -0.3125020 0.16043392 -1.60568889 0.61847579 0.17675335 -0.487463012
## [29,] 0.9097282 -0.94254927 -0.30271184 1.43704669 -1.14034417 -0.426988277
## [30,] -0.7926639 0.02807594 -0.44748707 0.12733325 -0.76403060 -0.306038808
## [31,] -0.5744085 0.33691123 -0.15793661 0.61847579 -0.38771702 -0.729361950
## [32,] -0.6726234 -0.41311734 -1.02658798 -0.85495183 -0.76403060 3.987667345
## [33,] -0.4543680 0.68986585 -0.30271184 0.72761858 -1.14034417 3.382920000
## Y8 Y9 Y10 Y12
## [1,] 0.08153834 1.5614401 1.6718280 2.19799917
## [2,] -0.19681669 0.3903600 -0.2985407 -0.54802886
## [3,] 0.45267839 0.9759001 -0.2985407 0.64469039
## [4,] -1.03188179 -1.3662601 -1.2837251 -0.99183136
## [5,] -1.31023682 -1.3662601 -0.2985407 -0.49255354
## [6,] -0.56795673 -0.1951800 -1.2837251 -0.54802886
## [7,] 0.26710836 -0.7807201 -0.2985407 -0.04875103
## [8,] -1.26384432 -1.3662601 0.6866436 0.31183851
## [9,] 1.51970601 0.9759001 -1.2837251 -1.26920793
## [10,] 0.54546340 -0.1951800 -1.2837251 -1.24147028
## [11,] 1.51970601 1.5614401 -1.2837251 -0.40934057
## [12,] 0.08153834 0.3903600 0.6866436 1.97609791
## [13,] 0.59185590 0.3903600 0.6866436 0.22862553
## [14,] 1.79806105 0.9759001 0.6866436 -1.32468325
## [15,] 1.24135098 0.3903600 0.6866436 0.20088788
## [16,] -0.61434924 0.3903600 -0.2985407 -0.24291463
## [17,] -0.52156423 -0.1951800 -0.2985407 2.47537574
## [18,] -0.89270427 -1.3662601 -1.2837251 -0.15970166
## [19,] -1.03188179 -0.7807201 0.6866436 0.61695273
## [20,] -0.28960170 -0.7807201 -0.2985407 0.81111633
## [21,] -0.10403168 -0.7807201 -0.2985407 0.61695273
## [22,] 1.28774349 0.3903600 -0.2985407 -0.24291463
## [23,] 0.59185590 0.3903600 -1.2837251 -0.60350417
## [24,] 2.03002357 1.5614401 1.6718280 -0.02101338
## [25,] 0.45267839 2.1469802 2.6570124 0.28410085
## [26,] 0.31350087 0.9759001 0.6866436 0.06219959
## [27,] -0.75352676 -0.1951800 -0.2985407 0.45052679
## [28,] 0.87021094 -0.1951800 1.6718280 0.00672428
## [29,] -0.24320920 -0.7807201 -0.2985407 -0.65897948
## [30,] -1.03188179 0.3903600 -0.2985407 -0.40934057
## [31,] -0.84631177 -1.3662601 -0.2985407 -1.24147028
## [32,] -1.03188179 -0.7807201 0.6866436 -1.82396107
## [33,] -1.91333939 -1.3662601 -0.2985407 1.39360712
## attr(,"scaled:center")
## X1 X2 X3 X4 X5 X6 X7
## 52.6363636 17.7545455 31.1909091 7.3606061 5.1939394 73.3727273 29.1636364
## X8 Y1 Y3 Y4 Y7 Y8 Y9
## 6.0363636 1.3090909 4.0666667 1.4060606 0.1406061 6.4242424 0.4333333
## Y10 Y12
## 0.2303030 10.3757576
## attr(,"scaled:scale")
## X1 X2 X3 X4 X5 X6 X7
## 11.0099335 10.3209463 12.5899852 8.4925681 6.4724869 12.3268577 9.1635766
## X8 Y1 Y3 Y4 Y7 Y8 Y9
## 2.2665803 0.6907259 1.8324619 0.5314717 0.1653583 2.1555206 0.1707825
## Y10 Y12
## 0.1015038 3.6052072
matriks_data<-as.matrix(kanonik,33,16)
matriks_data
## X1 X2 X3 X4 X5 X6
## [1,] -0.03055092 -0.5284928 -0.79356003 -0.4428114 -0.18446378 -2.87767801
## [2,] 0.89588519 -0.1796875 -0.77767439 -0.5487864 -0.36986392 -2.33414938
## [3,] -0.41202462 -1.0613896 0.24694953 -0.5605614 -0.18446378 -2.01776705
## [4,] 0.20559946 -0.2087546 -0.47584719 -0.3957114 0.17088649 0.38349374
## [5,] -0.03055092 0.4888558 -1.07155877 -0.4428114 -0.24626383 0.09144851
## [6,] 0.96854684 1.9422109 -0.34081923 -0.5252364 -0.32351389 0.50517925
## [7,] -0.91157350 0.3628984 -0.79356003 -0.5370114 -0.13811374 0.87023579
## [8,] 0.60523856 1.4190031 -0.77767439 -0.6665364 -0.46256399 0.75666264
## [9,] -0.92065621 -0.9548103 -0.69824618 -0.1484364 -0.38531393 1.13794392
## [10,] 0.71423105 -0.8772980 0.23900671 -0.3486114 -0.19991379 0.20502165
## [11,] 0.79597541 0.2466300 1.31923038 -0.3721614 0.26358657 0.35915664
## [12,] -0.23037048 2.6688885 1.50191526 -0.4899114 0.03183639 1.11360681
## [13,] 0.85047165 1.2252224 2.31208301 -0.5841114 -0.32351389 0.78911211
## [14,] 1.50442655 -0.5188037 1.54957219 -0.6076614 5.37754050 0.35915664
## [15,] -0.43927274 0.6341913 1.45425833 -0.5605614 -0.27716385 0.57819056
## [16,] -0.50285169 1.5352715 1.39865859 -0.4781364 -0.19991379 0.77288738
## [17,] -2.74628031 -1.1001458 -1.01595903 -0.6312114 -0.07631370 -0.07079884
## [18,] -1.85617502 -0.6641392 -0.40436180 -0.2073114 -0.12266373 0.92702236
## [19,] -2.05599458 -0.9257432 -1.84995524 -0.3839364 -0.43166397 -0.30605750
## [20,] 0.55982503 1.1864663 -0.42024744 -0.4192614 0.20178652 0.10767324
## [21,] 1.35910324 0.5469900 0.84266111 -0.2190864 0.03183639 0.67553897
## [22,] 1.61341904 -0.1118643 0.36609184 -0.3839364 0.09363644 0.74855028
## [23,] 0.72331375 -0.2087546 -0.57910387 -0.3957114 0.04728640 -0.34661934
## [24,] 0.10568968 -1.1582800 0.91414650 0.4874137 -0.44711398 0.13201034
## [25,] -0.25761860 -0.8191638 -0.04693485 1.1468138 -0.52436404 0.25369586
## [26,] -0.16679153 0.1594287 -0.49173283 0.3578887 -0.18446378 0.30237006
## [27,] -0.74808477 -0.6060050 -1.05567313 0.5580637 -0.33896390 -0.38718118
## [28,] -0.07596446 -0.9063651 1.04917446 0.8995387 -0.55526406 0.10767324
## [29,] -0.04871634 1.1961553 -1.07950159 0.4520887 -0.60161410 -0.76035008
## [30,] 0.86863707 -0.8288528 0.14369285 0.6287137 0.24813655 0.74855028
## [31,] -0.18495694 -0.4509805 0.54083391 0.4991887 -0.32351389 -0.58187800
## [32,] 0.75964458 -0.8482309 -0.33287641 0.5227387 -0.06086368 -0.21682146
## [33,] -0.91157350 -0.6544502 -0.87298825 4.7970642 0.49533674 -2.02587942
## X7 X8 Y1 Y3 Y4 Y7
## [1,] 0.5605195 1.43989442 0.13161384 0.12733325 0.74122371 -0.245564074
## [2,] -1.6984238 -1.29550389 -0.44748707 -0.25466650 0.74122371 -0.064139870
## [3,] -0.8363150 -0.58959465 0.56593953 -0.58209486 -0.19956023 -0.064139870
## [4,] -1.3164768 -0.50135600 -0.88181275 -1.07323740 -0.76403060 -0.124614605
## [5,] -0.8363150 -0.01604339 0.27638907 -1.07323740 -0.57587381 -0.608412481
## [6,] 1.0952452 -0.19252070 -0.88181275 -0.69123765 -0.95218738 -0.426988277
## [7,] 0.8006005 -0.19252070 0.85548998 -0.96409462 -0.95218738 -0.366513543
## [8,] 0.6478217 -1.07490726 -0.88181275 -1.45523716 -1.32850096 -0.366513543
## [9,] 0.6914728 -0.76607196 -0.01316138 -0.25466650 1.30569408 -0.547937746
## [10,] -0.6726234 1.08693980 0.42116430 -1.07323740 -0.19956023 0.177759068
## [11,] -0.1051594 0.64574652 0.85548998 -0.74580904 2.05832123 0.056809599
## [12,] 0.2440492 0.38103056 -0.44748707 -0.03638093 -0.19956023 -0.003665135
## [13,] -0.9781810 -0.72195263 1.14504044 -0.36380929 0.36491014 0.238233803
## [14,] -1.5565578 -0.32487869 4.04054500 -0.52752347 2.24647802 0.661556945
## [15,] 0.7460366 -0.63371398 0.42116430 -0.25466650 1.30569408 0.298708537
## [16,] 0.2986131 -0.94254927 -0.44748707 -0.74580904 -0.19956023 -0.306038808
## [17,] 2.1646967 0.99870114 1.00026521 -0.30923790 -0.19956023 -0.064139870
## [18,] 0.9097282 -0.76607196 -1.02658798 0.72761858 -0.95218738 -0.608412481
## [19,] 2.0119179 -0.85431062 -0.44748707 3.23790267 -0.38771702 -0.245564074
## [20,] 1.8154880 1.57225240 -0.73703752 -0.30923790 -1.14034417 -0.366513543
## [21,] 0.2767875 -0.89842995 -0.88181275 0.12733325 -0.38771702 -0.426988277
## [22,] -0.9017916 3.73409946 0.42116430 0.50933301 -0.01140344 -0.487463012
## [23,] -0.6835362 -0.06016272 0.56593953 -0.69123765 1.68200766 -0.366513543
## [24,] 0.4732174 0.60162720 0.56593953 -0.03638093 1.87016444 -0.003665135
## [25,] 0.1021832 -0.67783331 -0.59226230 2.14647481 0.36491014 -0.124614605
## [26,] -0.1924616 -0.14840137 0.56593953 1.43704669 0.36491014 -0.426988277
## [27,] -1.1636981 0.33691123 -0.30271184 0.45476161 -0.57587381 -0.608412481
## [28,] -0.3125020 0.16043392 -1.60568889 0.61847579 0.17675335 -0.487463012
## [29,] 0.9097282 -0.94254927 -0.30271184 1.43704669 -1.14034417 -0.426988277
## [30,] -0.7926639 0.02807594 -0.44748707 0.12733325 -0.76403060 -0.306038808
## [31,] -0.5744085 0.33691123 -0.15793661 0.61847579 -0.38771702 -0.729361950
## [32,] -0.6726234 -0.41311734 -1.02658798 -0.85495183 -0.76403060 3.987667345
## [33,] -0.4543680 0.68986585 -0.30271184 0.72761858 -1.14034417 3.382920000
## Y8 Y9 Y10 Y12
## [1,] 0.08153834 1.5614401 1.6718280 2.19799917
## [2,] -0.19681669 0.3903600 -0.2985407 -0.54802886
## [3,] 0.45267839 0.9759001 -0.2985407 0.64469039
## [4,] -1.03188179 -1.3662601 -1.2837251 -0.99183136
## [5,] -1.31023682 -1.3662601 -0.2985407 -0.49255354
## [6,] -0.56795673 -0.1951800 -1.2837251 -0.54802886
## [7,] 0.26710836 -0.7807201 -0.2985407 -0.04875103
## [8,] -1.26384432 -1.3662601 0.6866436 0.31183851
## [9,] 1.51970601 0.9759001 -1.2837251 -1.26920793
## [10,] 0.54546340 -0.1951800 -1.2837251 -1.24147028
## [11,] 1.51970601 1.5614401 -1.2837251 -0.40934057
## [12,] 0.08153834 0.3903600 0.6866436 1.97609791
## [13,] 0.59185590 0.3903600 0.6866436 0.22862553
## [14,] 1.79806105 0.9759001 0.6866436 -1.32468325
## [15,] 1.24135098 0.3903600 0.6866436 0.20088788
## [16,] -0.61434924 0.3903600 -0.2985407 -0.24291463
## [17,] -0.52156423 -0.1951800 -0.2985407 2.47537574
## [18,] -0.89270427 -1.3662601 -1.2837251 -0.15970166
## [19,] -1.03188179 -0.7807201 0.6866436 0.61695273
## [20,] -0.28960170 -0.7807201 -0.2985407 0.81111633
## [21,] -0.10403168 -0.7807201 -0.2985407 0.61695273
## [22,] 1.28774349 0.3903600 -0.2985407 -0.24291463
## [23,] 0.59185590 0.3903600 -1.2837251 -0.60350417
## [24,] 2.03002357 1.5614401 1.6718280 -0.02101338
## [25,] 0.45267839 2.1469802 2.6570124 0.28410085
## [26,] 0.31350087 0.9759001 0.6866436 0.06219959
## [27,] -0.75352676 -0.1951800 -0.2985407 0.45052679
## [28,] 0.87021094 -0.1951800 1.6718280 0.00672428
## [29,] -0.24320920 -0.7807201 -0.2985407 -0.65897948
## [30,] -1.03188179 0.3903600 -0.2985407 -0.40934057
## [31,] -0.84631177 -1.3662601 -0.2985407 -1.24147028
## [32,] -1.03188179 -0.7807201 0.6866436 -1.82396107
## [33,] -1.91333939 -1.3662601 -0.2985407 1.39360712
## attr(,"scaled:center")
## X1 X2 X3 X4 X5 X6 X7
## 52.6363636 17.7545455 31.1909091 7.3606061 5.1939394 73.3727273 29.1636364
## X8 Y1 Y3 Y4 Y7 Y8 Y9
## 6.0363636 1.3090909 4.0666667 1.4060606 0.1406061 6.4242424 0.4333333
## Y10 Y12
## 0.2303030 10.3757576
## attr(,"scaled:scale")
## X1 X2 X3 X4 X5 X6 X7
## 11.0099335 10.3209463 12.5899852 8.4925681 6.4724869 12.3268577 9.1635766
## X8 Y1 Y3 Y4 Y7 Y8 Y9
## 2.2665803 0.6907259 1.8324619 0.5314717 0.1653583 2.1555206 0.1707825
## Y10 Y12
## 0.1015038 3.6052072
#uji multikolinearitas
VIF <- function(x){
VIF<-diag(solve(cor(x)))
result<-ifelse(VIF>10,"multicolinearity","non multicolinearity")
data1 <- data.frame(VIF,result)
return(data1)
}
VIF(kanonik)
#menghitung rata-rata
x_bar<-colMeans(matriks_data)
x_bar
## X1 X2 X3 X4 X5
## 2.554775e-16 4.457714e-17 -6.917867e-17 4.962360e-17 -6.497328e-17
## X6 X7 X8 Y1 Y3
## -2.683039e-16 -6.854786e-17 1.022961e-16 9.724965e-18 1.816729e-16
## Y4 Y7 Y8 Y9 Y10
## 1.414062e-16 6.718111e-17 1.614870e-16 -1.084991e-16 -6.728624e-17
## Y12
## 1.964443e-16
#menghitung covarian matriks
cov_matriks<-cov(matriks_data)
cov_matriks
## X1 X2 X3 X4 X5 X6
## X1 1.00000000 0.27386827 0.42198090 -0.15067184 0.302023978 0.07656340
## X2 0.27386827 1.00000000 0.26996631 -0.29692897 -0.085330797 0.33885854
## X3 0.42198090 0.26996631 1.00000000 -0.15500063 0.285127674 0.36783610
## X4 -0.15067184 -0.29692897 -0.15500063 1.00000000 -0.038958936 -0.31436529
## X5 0.30202398 -0.08533080 0.28512767 -0.03895894 1.000000000 0.06166326
## X6 0.07656340 0.33885854 0.36783610 -0.31436529 0.061663261 1.00000000
## X7 -0.55389837 0.19044891 -0.28197519 -0.13982869 -0.308367119 0.18728660
## X8 0.15536324 -0.12919784 0.02817791 0.09048687 0.053183735 -0.06668047
## Y1 0.13650461 -0.13113951 0.29938526 -0.22288357 0.717809067 0.01239866
## Y3 -0.37474690 -0.25396240 -0.24405990 0.36660427 -0.159210798 -0.15053292
## Y4 0.21283511 -0.24663068 0.44462096 -0.20509120 0.363934156 -0.01566725
## Y7 0.06155773 -0.18799742 0.01303195 0.56029891 0.210141403 -0.26140120
## Y8 0.28894219 -0.12158042 0.54307815 -0.30148824 0.260846036 0.20840764
## Y9 0.18397955 -0.16452621 0.38282225 -0.14105478 0.133060418 -0.07288459
## Y10 -0.01779460 -0.09559044 0.20025253 0.16347324 0.008374493 -0.13268831
## Y12 -0.43915931 0.13463056 -0.09507792 0.10643280 -0.186116475 -0.26389825
## X7 X8 Y1 Y3 Y4 Y7
## X1 -0.553898372 0.155363238 0.13650461 -0.37474690 0.21283511 0.061557732
## X2 0.190448915 -0.129197844 -0.13113951 -0.25396240 -0.24663068 -0.187997417
## X3 -0.281975189 0.028177913 0.29938526 -0.24405990 0.44462096 0.013031952
## X4 -0.139828687 0.090486874 -0.22288357 0.36660427 -0.20509120 0.560298908
## X5 -0.308367119 0.053183735 0.71780907 -0.15921080 0.36393416 0.210141403
## X6 0.187286603 -0.066680467 0.01239866 -0.15053292 -0.01566725 -0.261401197
## X7 1.000000000 0.001660504 -0.22967329 0.26567861 -0.22273765 -0.194854930
## X8 0.001660504 1.000000000 0.14888778 -0.02776325 0.03742683 0.002023818
## Y1 -0.229673285 0.148887784 1.00000000 -0.18689824 0.59828368 0.018555147
## Y3 0.265678609 -0.027763251 -0.18689824 1.00000000 -0.06235658 -0.095327379
## Y4 -0.222737651 0.037426830 0.59828368 -0.06235658 1.00000000 -0.072582517
## Y7 -0.194854930 0.002023818 0.01855515 -0.09532738 -0.07258252 1.000000000
## Y8 -0.087570452 0.203535101 0.53737609 -0.07257551 0.81412582 -0.213792212
## Y9 -0.114418495 0.120287854 0.40001643 0.09952257 0.74826056 -0.125780616
## Y10 0.064048397 -0.026672117 0.02269118 0.39538121 0.18765113 0.127338322
## Y12 0.454506347 0.250639255 -0.07244278 0.22489224 -0.14116088 -0.070269278
## Y8 Y9 Y10 Y12
## X1 0.28894219 0.18397955 -0.017794598 -0.43915931
## X2 -0.12158042 -0.16452621 -0.095590438 0.13463056
## X3 0.54307815 0.38282225 0.200252527 -0.09507792
## X4 -0.30148824 -0.14105478 0.163473241 0.10643280
## X5 0.26084604 0.13306042 0.008374493 -0.18611648
## X6 0.20840764 -0.07288459 -0.132688313 -0.26389825
## X7 -0.08757045 -0.11441849 0.064048397 0.45450635
## X8 0.20353510 0.12028785 -0.026672117 0.25063925
## Y1 0.53737609 0.40001643 0.022691183 -0.07244278
## Y3 -0.07257551 0.09952257 0.395381207 0.22489224
## Y4 0.81412582 0.74826056 0.187651133 -0.14116088
## Y7 -0.21379221 -0.12578062 0.127338322 -0.07026928
## Y8 1.00000000 0.72778678 0.195069324 -0.15301331
## Y9 0.72778678 1.00000000 0.390585607 0.10743081
## Y10 0.19506932 0.39058561 1.000000000 0.32913672
## Y12 -0.15301331 0.10743081 0.329136716 1.00000000
det(cov_matriks)
## [1] 0.000102377
#Uji Normalitas
Di<-mahalanobis(matriks_data,x_bar,cov_matriks)
Di
## [1] 17.897470 14.019971 19.053485 8.331252 12.201472 11.098447 12.554215
## [8] 14.221467 14.815528 7.872990 10.972433 18.800661 16.619917 30.579539
## [15] 8.308387 14.530908 17.818607 10.983302 20.358209 11.761759 18.737023
## [22] 21.315676 10.921750 12.072259 16.170338 7.253168 8.236702 17.879457
## [29] 16.046516 15.934483 18.405185 27.324930 28.902492
hasil <- data.frame(Obs = 1:length(Di), Mahalanobis_Distance = Di)
hasil<- hasil[order(hasil$Mahalanobis_Distance), ]
hasil$Rank <- c(1:33)
hasil$Probability<-((hasil$Rank-0.5)/33)
hasil
hasil$X2<-qchisq(hasil$Probability,16)
hasil
plot(hasil$Mahalanobis_Distance,hasil$X2,
xlab= "Mahalanobis Distance",ylab="Chi-Square",,col="red",main = "QQ plot Normalitas",
pch=19,
cex=0.8)
P11<-cov_matriks[1:8, 1:8]
P11
## X1 X2 X3 X4 X5 X6
## X1 1.0000000 0.2738683 0.42198090 -0.15067184 0.30202398 0.07656340
## X2 0.2738683 1.0000000 0.26996631 -0.29692897 -0.08533080 0.33885854
## X3 0.4219809 0.2699663 1.00000000 -0.15500063 0.28512767 0.36783610
## X4 -0.1506718 -0.2969290 -0.15500063 1.00000000 -0.03895894 -0.31436529
## X5 0.3020240 -0.0853308 0.28512767 -0.03895894 1.00000000 0.06166326
## X6 0.0765634 0.3388585 0.36783610 -0.31436529 0.06166326 1.00000000
## X7 -0.5538984 0.1904489 -0.28197519 -0.13982869 -0.30836712 0.18728660
## X8 0.1553632 -0.1291978 0.02817791 0.09048687 0.05318373 -0.06668047
## X7 X8
## X1 -0.553898372 0.155363238
## X2 0.190448915 -0.129197844
## X3 -0.281975189 0.028177913
## X4 -0.139828687 0.090486874
## X5 -0.308367119 0.053183735
## X6 0.187286603 -0.066680467
## X7 1.000000000 0.001660504
## X8 0.001660504 1.000000000
P12<-cov_matriks[1:8,9:16]
P12
## Y1 Y3 Y4 Y7 Y8 Y9
## X1 0.13650461 -0.37474690 0.21283511 0.061557732 0.28894219 0.18397955
## X2 -0.13113951 -0.25396240 -0.24663068 -0.187997417 -0.12158042 -0.16452621
## X3 0.29938526 -0.24405990 0.44462096 0.013031952 0.54307815 0.38282225
## X4 -0.22288357 0.36660427 -0.20509120 0.560298908 -0.30148824 -0.14105478
## X5 0.71780907 -0.15921080 0.36393416 0.210141403 0.26084604 0.13306042
## X6 0.01239866 -0.15053292 -0.01566725 -0.261401197 0.20840764 -0.07288459
## X7 -0.22967329 0.26567861 -0.22273765 -0.194854930 -0.08757045 -0.11441849
## X8 0.14888778 -0.02776325 0.03742683 0.002023818 0.20353510 0.12028785
## Y10 Y12
## X1 -0.017794598 -0.43915931
## X2 -0.095590438 0.13463056
## X3 0.200252527 -0.09507792
## X4 0.163473241 0.10643280
## X5 0.008374493 -0.18611648
## X6 -0.132688313 -0.26389825
## X7 0.064048397 0.45450635
## X8 -0.026672117 0.25063925
P21<-cov_matriks[9:16,1:8]
P21
## X1 X2 X3 X4 X5 X6
## Y1 0.13650461 -0.13113951 0.29938526 -0.2228836 0.717809067 0.01239866
## Y3 -0.37474690 -0.25396240 -0.24405990 0.3666043 -0.159210798 -0.15053292
## Y4 0.21283511 -0.24663068 0.44462096 -0.2050912 0.363934156 -0.01566725
## Y7 0.06155773 -0.18799742 0.01303195 0.5602989 0.210141403 -0.26140120
## Y8 0.28894219 -0.12158042 0.54307815 -0.3014882 0.260846036 0.20840764
## Y9 0.18397955 -0.16452621 0.38282225 -0.1410548 0.133060418 -0.07288459
## Y10 -0.01779460 -0.09559044 0.20025253 0.1634732 0.008374493 -0.13268831
## Y12 -0.43915931 0.13463056 -0.09507792 0.1064328 -0.186116475 -0.26389825
## X7 X8
## Y1 -0.22967329 0.148887784
## Y3 0.26567861 -0.027763251
## Y4 -0.22273765 0.037426830
## Y7 -0.19485493 0.002023818
## Y8 -0.08757045 0.203535101
## Y9 -0.11441849 0.120287854
## Y10 0.06404840 -0.026672117
## Y12 0.45450635 0.250639255
P22<-cov_matriks[9:16,9:16]
P22
## Y1 Y3 Y4 Y7 Y8 Y9
## Y1 1.00000000 -0.18689824 0.59828368 0.01855515 0.53737609 0.40001643
## Y3 -0.18689824 1.00000000 -0.06235658 -0.09532738 -0.07257551 0.09952257
## Y4 0.59828368 -0.06235658 1.00000000 -0.07258252 0.81412582 0.74826056
## Y7 0.01855515 -0.09532738 -0.07258252 1.00000000 -0.21379221 -0.12578062
## Y8 0.53737609 -0.07257551 0.81412582 -0.21379221 1.00000000 0.72778678
## Y9 0.40001643 0.09952257 0.74826056 -0.12578062 0.72778678 1.00000000
## Y10 0.02269118 0.39538121 0.18765113 0.12733832 0.19506932 0.39058561
## Y12 -0.07244278 0.22489224 -0.14116088 -0.07026928 -0.15301331 0.10743081
## Y10 Y12
## Y1 0.02269118 -0.07244278
## Y3 0.39538121 0.22489224
## Y4 0.18765113 -0.14116088
## Y7 0.12733832 -0.07026928
## Y8 0.19506932 -0.15301331
## Y9 0.39058561 0.10743081
## Y10 1.00000000 0.32913672
## Y12 0.32913672 1.00000000
#mencari nilai sigma 11^-1/2
eig.P11<-eigen(P11)
nilai.eigen.P11<-eig.P11$values
nilai.eigen.P11
## [1] 2.2278226 1.8153405 0.9997164 0.8782457 0.7728930 0.6050261 0.4550098
## [8] 0.2459459
l1.11<-nilai.eigen.P11[1]
l2.11<-nilai.eigen.P11[2]
l3.11<-nilai.eigen.P11[3]
l4.11<-nilai.eigen.P11[4]
l5.11<-nilai.eigen.P11[5]
l6.11<-nilai.eigen.P11[6]
l7.11<-nilai.eigen.P11[7]
l8.11<-nilai.eigen.P11[8]
vektor.eigen.P11<-eig.P11$vectors
vektor.eigen.P11
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.52317942 -0.22213326 -0.056753170 0.39925223 0.14688351 -0.04473560
## [2,] 0.30034268 0.44670054 0.005300608 0.47246084 -0.11874841 -0.56221085
## [3,] 0.51890426 0.02271799 -0.032694623 -0.11997201 -0.41285711 0.22953103
## [4,] -0.25234587 -0.39678322 0.002052974 0.07488693 -0.80274021 -0.29351985
## [5,] 0.33227111 -0.28000676 0.056144736 -0.64922203 0.18861327 -0.58842574
## [6,] 0.30250943 0.44413209 -0.119647215 -0.37440242 -0.33118024 0.29499622
## [7,] -0.31713416 0.52292875 -0.275256779 -0.18352725 -0.02026508 -0.33103922
## [8,] 0.02544359 -0.20988290 -0.949970445 0.04503783 0.06176739 0.01499006
## [,7] [,8]
## [1,] -0.30017055 0.63296007
## [2,] 0.01128945 -0.39584831
## [3,] 0.69491211 0.09339013
## [4,] -0.17935084 0.10276134
## [5,] -0.03394620 -0.05982035
## [6,] -0.59755931 -0.05442289
## [7,] 0.19061451 0.60843353
## [8,] 0.01171599 -0.21599158
v1.11<-matrix(vektor.eigen.P11[,1])
v1.11
## [,1]
## [1,] 0.52317942
## [2,] 0.30034268
## [3,] 0.51890426
## [4,] -0.25234587
## [5,] 0.33227111
## [6,] 0.30250943
## [7,] -0.31713416
## [8,] 0.02544359
v2.11<-matrix(vektor.eigen.P11[,2])
v2.11
## [,1]
## [1,] -0.22213326
## [2,] 0.44670054
## [3,] 0.02271799
## [4,] -0.39678322
## [5,] -0.28000676
## [6,] 0.44413209
## [7,] 0.52292875
## [8,] -0.20988290
v3.11<-matrix(vektor.eigen.P11[,3])
v3.11
## [,1]
## [1,] -0.056753170
## [2,] 0.005300608
## [3,] -0.032694623
## [4,] 0.002052974
## [5,] 0.056144736
## [6,] -0.119647215
## [7,] -0.275256779
## [8,] -0.949970445
v4.11<-matrix(vektor.eigen.P11[,4])
v4.11
## [,1]
## [1,] 0.39925223
## [2,] 0.47246084
## [3,] -0.11997201
## [4,] 0.07488693
## [5,] -0.64922203
## [6,] -0.37440242
## [7,] -0.18352725
## [8,] 0.04503783
v5.11<-matrix(vektor.eigen.P11[,5])
v5.11
## [,1]
## [1,] 0.14688351
## [2,] -0.11874841
## [3,] -0.41285711
## [4,] -0.80274021
## [5,] 0.18861327
## [6,] -0.33118024
## [7,] -0.02026508
## [8,] 0.06176739
v6.11<-matrix(vektor.eigen.P11[,6])
v6.11
## [,1]
## [1,] -0.04473560
## [2,] -0.56221085
## [3,] 0.22953103
## [4,] -0.29351985
## [5,] -0.58842574
## [6,] 0.29499622
## [7,] -0.33103922
## [8,] 0.01499006
v7.11<-matrix(vektor.eigen.P11[,7])
v7.11
## [,1]
## [1,] -0.30017055
## [2,] 0.01128945
## [3,] 0.69491211
## [4,] -0.17935084
## [5,] -0.03394620
## [6,] -0.59755931
## [7,] 0.19061451
## [8,] 0.01171599
v8.11<-matrix(vektor.eigen.P11[,8])
v8.11
## [,1]
## [1,] 0.63296007
## [2,] -0.39584831
## [3,] 0.09339013
## [4,] 0.10276134
## [5,] -0.05982035
## [6,] -0.05442289
## [7,] 0.60843353
## [8,] -0.21599158
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))+((v4.11%*%t(v4.11))/sqrt(l4.11))+((v5.11%*%t(v5.11))/sqrt(l5.11))+((v6.11%*%t(v6.11))/sqrt(l6.11))+((v7.11%*%t(v7.11))/sqrt(l7.11))+((v8.11%*%t(v8.11))/sqrt(l8.11))
sig11
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.361863509 -0.26514447 -0.14333477 0.10248125 -0.11303196 0.004250748
## [2,] -0.265144468 1.18530671 -0.12175988 0.09099367 0.09403243 -0.116316414
## [3,] -0.143334769 -0.12175988 1.19230682 0.02087415 -0.11637950 -0.218774415
## [4,] 0.102481250 0.09099367 0.02087415 1.07821917 0.02097490 0.126583486
## [5,] -0.113031955 0.09403243 -0.11637950 0.02097490 1.07959888 -0.029881143
## [6,] 0.004250748 -0.11631641 -0.21877442 0.12658349 -0.02988114 1.143577807
## [7,] 0.447441594 -0.22487556 0.15383665 0.10320239 0.09550802 -0.139182213
## [8,] -0.154798167 0.10662633 -0.02258337 -0.05077093 -0.00788608 0.027396947
## [,7] [,8]
## [1,] 0.44744159 -0.15479817
## [2,] -0.22487556 0.10662633
## [3,] 0.15383665 -0.02258337
## [4,] 0.10320239 -0.05077093
## [5,] 0.09550802 -0.00788608
## [6,] -0.13918221 0.02739695
## [7,] 1.32373725 -0.10364561
## [8,] -0.10364561 1.03676718
eig.P22<-eigen(P22)
nilai.eigen.P22<-eig.P22$values
nilai.eigen.P22
## [1] 3.0384951 1.7325660 1.0872274 0.8159287 0.5616005 0.3996495 0.2015177
## [8] 0.1630151
l1.22<-nilai.eigen.P22[1]
l2.22<-nilai.eigen.P22[2]
l3.22<-nilai.eigen.P22[3]
l4.22<-nilai.eigen.P22[4]
l5.22<-nilai.eigen.P22[5]
l6.22<-nilai.eigen.P22[6]
l7.22<-nilai.eigen.P22[7]
l8.22<-nilai.eigen.P22[8]
vektor.eigen.P22<-eig.P22$vectors
vektor.eigen.P22
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.39420033 0.21371849 0.16838649 0.3031091642 0.71381446 0.34173720
## [2,] -0.01003019 -0.56366946 -0.14498286 -0.5188521826 0.56358753 -0.26051245
## [3,] 0.52976370 0.08640709 0.04239515 -0.0712919005 -0.01468276 -0.22253258
## [4,] -0.08760795 0.02566068 0.92109707 -0.0655121938 0.02346207 -0.33306588
## [5,] 0.52276588 0.08286662 -0.10810109 -0.0934301832 -0.14605692 -0.01546238
## [6,] 0.49731380 -0.17421082 -0.01840334 0.0006141094 -0.27856926 -0.35209222
## [7,] 0.18591606 -0.57247528 0.29223422 -0.1037708750 -0.26661568 0.67843187
## [8,] -0.03179304 -0.51335392 -0.05462088 0.7810537074 0.04532750 -0.26536298
## [,7] [,8]
## [1,] -0.200263192 0.11081783
## [2,] 0.063210590 0.04955088
## [3,] 0.196075940 -0.78538032
## [4,] 0.112755657 0.12148060
## [5,] 0.630887528 0.52900201
## [6,] -0.676181249 0.25139544
## [7,] -0.004723311 -0.10080679
## [8,] 0.222434259 -0.02189805
v1.22<-matrix(vektor.eigen.P22[,1])
v1.22
## [,1]
## [1,] 0.39420033
## [2,] -0.01003019
## [3,] 0.52976370
## [4,] -0.08760795
## [5,] 0.52276588
## [6,] 0.49731380
## [7,] 0.18591606
## [8,] -0.03179304
v2.22<-matrix(vektor.eigen.P22[,2])
v2.22
## [,1]
## [1,] 0.21371849
## [2,] -0.56366946
## [3,] 0.08640709
## [4,] 0.02566068
## [5,] 0.08286662
## [6,] -0.17421082
## [7,] -0.57247528
## [8,] -0.51335392
v3.22<-matrix(vektor.eigen.P22[,3])
v3.22
## [,1]
## [1,] 0.16838649
## [2,] -0.14498286
## [3,] 0.04239515
## [4,] 0.92109707
## [5,] -0.10810109
## [6,] -0.01840334
## [7,] 0.29223422
## [8,] -0.05462088
v4.22<-matrix(vektor.eigen.P22[,4])
v4.22
## [,1]
## [1,] 0.3031091642
## [2,] -0.5188521826
## [3,] -0.0712919005
## [4,] -0.0655121938
## [5,] -0.0934301832
## [6,] 0.0006141094
## [7,] -0.1037708750
## [8,] 0.7810537074
v5.22<-matrix(vektor.eigen.P22[,5])
v5.22
## [,1]
## [1,] 0.71381446
## [2,] 0.56358753
## [3,] -0.01468276
## [4,] 0.02346207
## [5,] -0.14605692
## [6,] -0.27856926
## [7,] -0.26661568
## [8,] 0.04532750
v6.22<-matrix(vektor.eigen.P22[,6])
v6.22
## [,1]
## [1,] 0.34173720
## [2,] -0.26051245
## [3,] -0.22253258
## [4,] -0.33306588
## [5,] -0.01546238
## [6,] -0.35209222
## [7,] 0.67843187
## [8,] -0.26536298
v7.22<-matrix(vektor.eigen.P22[,7])
v7.22
## [,1]
## [1,] -0.200263192
## [2,] 0.063210590
## [3,] 0.196075940
## [4,] 0.112755657
## [5,] 0.630887528
## [6,] -0.676181249
## [7,] -0.004723311
## [8,] 0.222434259
v8.22<-matrix(vektor.eigen.P22[,8])
v8.22
## [,1]
## [1,] 0.11081783
## [2,] 0.04955088
## [3,] -0.78538032
## [4,] 0.12148060
## [5,] 0.52900201
## [6,] 0.25139544
## [7,] -0.10080679
## [8,] -0.02189805
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))+((v4.22%*%t(v4.22))/sqrt(l4.22))+((v5.22%*%t(v5.22))/sqrt(l5.22))+((v6.22%*%t(v6.22))/sqrt(l6.22))+((v7.22%*%t(v7.22))/sqrt(l7.22))+((v8.22%*%t(v8.22))/sqrt(l8.22))
sig22
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.237160098 0.090091063 -0.3205590469 -0.06353729 -0.20086437
## [2,] 0.090091063 1.105811851 0.0068881136 0.08475434 0.08049054
## [3,] -0.320559047 0.006888114 1.8660184077 -0.05259205 -0.57785700
## [4,] -0.063537292 0.084754338 -0.0525920512 1.06441357 0.20782892
## [5,] -0.200864368 0.080490540 -0.5778569994 0.20782892 1.79145808
## [6,] -0.003605126 -0.054818776 -0.5160561239 0.03788545 -0.41798610
## [7,] 0.048688407 -0.230063059 -0.0005689794 -0.15212478 -0.10319543
## [8,] -0.042783683 -0.048953180 0.1250471688 0.07720098 0.16459320
## [,6] [,7] [,8]
## [1,] -0.003605126 0.0486884074 -0.04278368
## [2,] -0.054818776 -0.2300630592 -0.04895318
## [3,] -0.516056124 -0.0005689794 0.12504717
## [4,] 0.037885446 -0.1521247840 0.07720098
## [5,] -0.417986098 -0.1031954295 0.16459320
## [6,] 1.639965859 -0.2108169916 -0.15737099
## [7,] -0.210816992 1.2107789146 -0.18293625
## [8,] -0.157370993 -0.1829362507 1.10454763
#mencari nilai eigen dan vektor eigen a untuk sigma 11
M<-sig11%*%P12%*%solve(P22)%*%P21%*%sig11
M
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.32982417 -0.10854632 0.11959266 -0.05355461 -0.09869535 0.15126420
## [2,] -0.10854632 0.23947095 -0.12073194 -0.07974456 -0.06215040 -0.01441861
## [3,] 0.11959266 -0.12073194 0.37631720 -0.08010337 0.04098624 -0.03644638
## [4,] -0.05355461 -0.07974456 -0.08010337 0.49126657 -0.03539288 -0.11896152
## [5,] -0.09869535 -0.06215040 0.04098624 -0.03539288 0.57169267 -0.06331168
## [6,] 0.15126420 -0.01441861 -0.03644638 -0.11896152 -0.06331168 0.25190907
## [7,] -0.13719695 0.03165748 0.08538655 0.08833198 -0.13186890 -0.04075613
## [8,] -0.10405391 0.08353560 0.10184998 -0.03975935 0.04637706 -0.01589684
## [,7] [,8]
## [1,] -0.13719695 -0.10405391
## [2,] 0.03165748 0.08353560
## [3,] 0.08538655 0.10184998
## [4,] 0.08833198 -0.03975935
## [5,] -0.13186890 0.04637706
## [6,] -0.04075613 -0.01589684
## [7,] 0.25883354 0.15711619
## [8,] 0.15711619 0.24803406
eigen11<-eigen(M)
nilai.eigenM11<-eigen11$values
nilai.eigenM11
## [1] 0.687112283 0.656236969 0.548778229 0.507637845 0.203968751 0.125869174
## [7] 0.035947512 0.001797469
l1.M11<-nilai.eigenM11[1]
l1.M11
## [1] 0.6871123
l2.M11<-nilai.eigenM11[2]
l2.M11
## [1] 0.656237
l3.M11<-nilai.eigenM11[3]
l3.M11
## [1] 0.5487782
l4.M11<-nilai.eigenM11[4]
l4.M11
## [1] 0.5076378
l5.M11<-nilai.eigenM11[5]
l5.M11
## [1] 0.2039688
l6.M11<-nilai.eigenM11[6]
l6.M11
## [1] 0.1258692
l7.M11<-nilai.eigenM11[7]
l7.M11
## [1] 0.03594751
l8.M11<-nilai.eigenM11[8]
l8.M11
## [1] 0.001797469
vektor.eigen.M11<-eigen11$vectors
vektor.eigen.M11
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.58129754 -0.16493963 0.11514049 0.28198191 -0.13856215 0.4681821
## [2,] -0.17584317 -0.08416986 -0.20011281 -0.50297349 0.02349432 0.6595346
## [3,] 0.19176450 0.15662761 -0.47457335 0.62155960 0.21118557 0.1348476
## [4,] -0.50508586 -0.21694477 0.51144077 0.47155329 -0.29752664 0.2856736
## [5,] -0.06229396 0.90585338 0.17286994 0.02714244 -0.24167879 0.0473162
## [6,] 0.38188955 -0.13453767 -0.03143661 -0.15071404 -0.76410853 -0.2212865
## [7,] -0.36779603 -0.20610424 -0.41089551 0.18652227 -0.20996770 -0.3246172
## [8,] -0.23300839 0.11385701 -0.51009666 0.02372847 -0.40096157 0.2990712
## [,7] [,8]
## [1,] -0.05407611 -0.54845130
## [2,] 0.48075512 0.04799823
## [3,] 0.31437515 0.40684520
## [4,] 0.08715386 0.19016253
## [5,] 0.20371337 -0.20683759
## [6,] 0.25229509 0.34040361
## [7,] 0.36684209 -0.57843373
## [8,] -0.64791797 0.04439091
e1<-matrix(vektor.eigen.M11[,1])
e1
## [,1]
## [1,] 0.58129754
## [2,] -0.17584317
## [3,] 0.19176450
## [4,] -0.50508586
## [5,] -0.06229396
## [6,] 0.38188955
## [7,] -0.36779603
## [8,] -0.23300839
e2<-matrix(vektor.eigen.M11[,2])
e2
## [,1]
## [1,] -0.16493963
## [2,] -0.08416986
## [3,] 0.15662761
## [4,] -0.21694477
## [5,] 0.90585338
## [6,] -0.13453767
## [7,] -0.20610424
## [8,] 0.11385701
e3<-matrix(vektor.eigen.M11[,3])
e3
## [,1]
## [1,] 0.11514049
## [2,] -0.20011281
## [3,] -0.47457335
## [4,] 0.51144077
## [5,] 0.17286994
## [6,] -0.03143661
## [7,] -0.41089551
## [8,] -0.51009666
e4<-matrix(vektor.eigen.M11[,4])
e4
## [,1]
## [1,] 0.28198191
## [2,] -0.50297349
## [3,] 0.62155960
## [4,] 0.47155329
## [5,] 0.02714244
## [6,] -0.15071404
## [7,] 0.18652227
## [8,] 0.02372847
e5<-matrix(vektor.eigen.M11[,5])
e5
## [,1]
## [1,] -0.13856215
## [2,] 0.02349432
## [3,] 0.21118557
## [4,] -0.29752664
## [5,] -0.24167879
## [6,] -0.76410853
## [7,] -0.20996770
## [8,] -0.40096157
e6<-matrix(vektor.eigen.M11[,6])
e6
## [,1]
## [1,] 0.4681821
## [2,] 0.6595346
## [3,] 0.1348476
## [4,] 0.2856736
## [5,] 0.0473162
## [6,] -0.2212865
## [7,] -0.3246172
## [8,] 0.2990712
e7<-matrix(vektor.eigen.M11[,7])
e7
## [,1]
## [1,] -0.05407611
## [2,] 0.48075512
## [3,] 0.31437515
## [4,] 0.08715386
## [5,] 0.20371337
## [6,] 0.25229509
## [7,] 0.36684209
## [8,] -0.64791797
e8<-matrix(vektor.eigen.M11[,8])
e8
## [,1]
## [1,] -0.54845130
## [2,] 0.04799823
## [3,] 0.40684520
## [4,] 0.19016253
## [5,] -0.20683759
## [6,] 0.34040361
## [7,] -0.57843373
## [8,] 0.04439091
#mencari nilai eigen dan vektor eigen a untuk sigma 22
N<-sig22%*%P21%*%solve(P11)%*%P12%*%sig22
N
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.60024705 -0.058180998 0.13484690 0.03538066 -0.01846382 -0.05255143
## [2,] -0.05818100 0.284248923 -0.01948198 0.18714322 -0.07890857 -0.06769337
## [3,] 0.13484690 -0.019481976 0.22948751 0.04436044 0.06354395 0.09771889
## [4,] 0.03538066 0.187143220 0.04436044 0.39877516 -0.04339612 0.01342657
## [5,] -0.01846382 -0.078908565 0.06354395 -0.04339612 0.44624258 0.09669591
## [6,] -0.05255143 -0.067693372 0.09771889 0.01342657 0.09669591 0.11600730
## [7,] -0.04360893 0.005849641 0.09183361 0.08539985 0.07381091 0.05893563
## [8,] 0.03260265 0.113323783 -0.06674782 0.01554170 0.02089074 -0.03449103
## [,7] [,8]
## [1,] -0.043608932 0.03260265
## [2,] 0.005849641 0.11332378
## [3,] 0.091833609 -0.06674782
## [4,] 0.085399853 0.01554170
## [5,] 0.073810912 0.02089074
## [6,] 0.058935634 -0.03449103
## [7,] 0.098166736 -0.01562571
## [8,] -0.015625714 0.59417297
eigen22<-eigen(N)
nilai.eigenM22<-eigen22$values
nilai.eigenM22
## [1] 0.687112283 0.656236969 0.548778229 0.507637845 0.203968751 0.125869174
## [7] 0.035947512 0.001797469
l1.M22<-nilai.eigenM22[1]
l1.M22
## [1] 0.6871123
l2.M22<-nilai.eigenM22[2]
l2.M22
## [1] 0.656237
l3.M22<-nilai.eigenM22[3]
l3.M22
## [1] 0.5487782
l4.M22<-nilai.eigenM22[4]
l4.M22
## [1] 0.5076378
l5.M22<-nilai.eigenM22[5]
l5.M22
## [1] 0.2039688
l6.M22<-nilai.eigenM22[6]
l6.M22
## [1] 0.1258692
l7.M22<-nilai.eigenM22[7]
l7.M22
## [1] 0.03594751
l8.M22<-nilai.eigenM22[8]
l8.M22
## [1] 0.001797469
vektor.eigen.M22<-eigen22$vectors
vektor.eigen.M22
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.03903714 0.94124601 -0.004515051 0.14681945 0.1816007 -0.01145476
## [2,] 0.44634638 -0.09984005 -0.260177111 -0.23411257 0.2459819 0.70426072
## [3,] -0.16947902 0.30234168 -0.008781023 -0.34327840 -0.5654992 0.43871650
## [4,] 0.32712434 0.10386533 -0.428792830 -0.59540262 0.1372608 -0.51351157
## [5,] -0.27305731 0.01403605 0.616152141 -0.50465696 0.5257273 0.08241281
## [6,] -0.17412544 -0.02249782 0.117468204 -0.26677018 -0.3961575 -0.16729496
## [7,] -0.04888343 -0.01039039 0.009640019 -0.35124901 -0.2564779 -0.03774302
## [8,] 0.74582035 0.03283723 0.595660908 0.04549901 -0.2622466 -0.10730945
## [,7] [,8]
## [1,] -1.692158e-02 -0.23991529
## [2,] -1.600743e-01 -0.29358475
## [3,] -8.865836e-05 0.49968756
## [4,] -6.920816e-02 0.23771759
## [5,] -2.014118e-02 0.08563786
## [6,] -6.482816e-01 -0.52821549
## [7,] 7.405013e-01 -0.50841518
## [8,] 1.682713e-02 0.07224884
f1<-matrix(vektor.eigen.M22[,1])
f1
## [,1]
## [1,] 0.03903714
## [2,] 0.44634638
## [3,] -0.16947902
## [4,] 0.32712434
## [5,] -0.27305731
## [6,] -0.17412544
## [7,] -0.04888343
## [8,] 0.74582035
f2<-matrix(vektor.eigen.M22[,2])
f2
## [,1]
## [1,] 0.94124601
## [2,] -0.09984005
## [3,] 0.30234168
## [4,] 0.10386533
## [5,] 0.01403605
## [6,] -0.02249782
## [7,] -0.01039039
## [8,] 0.03283723
f3<-matrix(vektor.eigen.M22[,3])
f3
## [,1]
## [1,] -0.004515051
## [2,] -0.260177111
## [3,] -0.008781023
## [4,] -0.428792830
## [5,] 0.616152141
## [6,] 0.117468204
## [7,] 0.009640019
## [8,] 0.595660908
f4<-matrix(vektor.eigen.M22[,4])
f4
## [,1]
## [1,] 0.14681945
## [2,] -0.23411257
## [3,] -0.34327840
## [4,] -0.59540262
## [5,] -0.50465696
## [6,] -0.26677018
## [7,] -0.35124901
## [8,] 0.04549901
f5<-matrix(vektor.eigen.M22[,5])
f5
## [,1]
## [1,] 0.1816007
## [2,] 0.2459819
## [3,] -0.5654992
## [4,] 0.1372608
## [5,] 0.5257273
## [6,] -0.3961575
## [7,] -0.2564779
## [8,] -0.2622466
f6<-matrix(vektor.eigen.M22[,6])
f6
## [,1]
## [1,] -0.01145476
## [2,] 0.70426072
## [3,] 0.43871650
## [4,] -0.51351157
## [5,] 0.08241281
## [6,] -0.16729496
## [7,] -0.03774302
## [8,] -0.10730945
f7<-matrix(vektor.eigen.M22[,7])
f7
## [,1]
## [1,] -1.692158e-02
## [2,] -1.600743e-01
## [3,] -8.865836e-05
## [4,] -6.920816e-02
## [5,] -2.014118e-02
## [6,] -6.482816e-01
## [7,] 7.405013e-01
## [8,] 1.682713e-02
f8<-matrix(vektor.eigen.M22[,8])
f8
## [,1]
## [1,] -0.23991529
## [2,] -0.29358475
## [3,] 0.49968756
## [4,] 0.23771759
## [5,] 0.08563786
## [6,] -0.52821549
## [7,] -0.50841518
## [8,] 0.07224884
#mencari nilai koefisien a dan b
a1<-sig11%*%e1
a1
## [,1]
## [1,] 0.63918996
## [2,] -0.42427892
## [3,] 0.02857305
## [4,] -0.47611193
## [5,] -0.22710547
## [6,] 0.40042448
## [7,] -0.24480294
## [8,] -0.27992146
a2<-sig11%*%e2
a2
## [,1]
## [1,] -0.45979778
## [2,] 0.06447068
## [3,] 0.10584271
## [4,] -0.28028783
## [5,] 0.94934606
## [6,] -0.20175537
## [7,] -0.23255439
## [8,] 0.15261039
a3<-sig11%*%e3
a3
## [,1]
## [1,] 0.2057372
## [2,] -0.1054794
## [3,] -0.6122312
## [4,] 0.5182689
## [5,] 0.1864748
## [6,] 0.1944288
## [7,] -0.3938683
## [8,] -0.5428982
a4<-sig11%*%e4
a4
## [,1]
## [1,] 0.55269187
## [2,] -0.72304788
## [3,] 0.82972885
## [4,] 0.50407901
## [5,] -0.09018109
## [6,] -0.21506255
## [7,] 0.65157711
## [8,] -0.13433308
a5<-sig11%*%e5
a5
## [,1]
## [1,] -0.26350403
## [2,] 0.08241625
## [3,] 0.43463568
## [4,] -0.43155748
## [5,] -0.26792238
## [6,] -0.93554300
## [7,] -0.21861525
## [8,] -0.37867921
a6<-sig11%*%e6
a6
## [,1]
## [1,] 0.274844164
## [2,] 0.802266189
## [3,] 0.005544367
## [4,] 0.343122741
## [5,] 0.023729357
## [6,] -0.269161834
## [7,] -0.313989443
## [8,] 0.317577476
a7<-sig11%*%e7
a7
## [,1]
## [1,] 0.005240147
## [2,] 0.392063082
## [3,] 0.318027352
## [4,] 0.245700801
## [5,] 0.269095882
## [6,] 0.099728435
## [7,] 0.462148595
## [8,] -0.656348539
a8<-sig11%*%e8
a8
## [,1]
## [1,] -1.0393300
## [2,] 0.2458425
## [3,] 0.4214349
## [4,] 0.1384926
## [5,] -0.2659223
## [6,] 0.4043322
## [7,] -1.0114086
## [8,] 0.1881068
b1<-sig22%*%f1
b1
## [,1]
## [1,] 0.143236682
## [2,] 0.485952633
## [3,] -0.001957234
## [4,] 0.394126738
## [5,] -0.094581178
## [6,] -0.203245596
## [7,] -0.281192186
## [8,] 0.795736788
b2<-sig22%*%f2
b2
## [,1]
## [1,] 1.04731061
## [2,] -0.01157483
## [3,] 0.26391144
## [4,] 0.03256926
## [5,] -0.30919720
## [6,] -0.19575018
## [7,] 0.03753165
## [8,] 0.05446480
b3<-sig22%*%f3
b3
## [,1]
## [1,] -0.14816801
## [2,] -0.31273859
## [3,] -0.33636703
## [4,] -0.30069167
## [5,] 1.04768132
## [6,] -0.15810585
## [7,] -0.06077209
## [8,] 0.71782910
b4<-sig22%*%f4
b4
## [,1]
## [1,] 0.391699952
## [2,] -0.245898828
## [3,] -0.222749949
## [4,] -0.702914258
## [5,] -0.722540144
## [6,] 0.007232926
## [7,] -0.173510457
## [8,] -0.010281792
b5<-sig22%*%f5
b5
## [,1]
## [1,] 0.3139453
## [2,] 0.4319854
## [3,] -1.2509731
## [4,] 0.2981763
## [5,] 1.4293367
## [6,] -0.4912012
## [7,] -0.3016081
## [8,] -0.1737985
b6<-sig22%*%f6
b6
## [,1]
## [1,] -0.07192842
## [2,] 0.76698824
## [3,] 0.87949615
## [4,] -0.50099776
## [5,] -0.09745222
## [6,] -0.56838353
## [7,] -0.08401754
## [8,] -0.09050094
b7<-sig22%*%f7
b7
## [,1]
## [1,] 0.01078644
## [2,] -0.32167172
## [3,] 0.35566741
## [4,] -0.22624917
## [5,] 0.13742617
## [6,] -1.20723954
## [7,] 1.07878397
## [8,] -0.01496646
b8<-sig22%*%f8
b8
## [,1]
## [1,] -0.5416886
## [2,] -0.1733943
## [3,] 1.2272351
## [4,] 0.2978178
## [5,] 0.2237774
## [6,] -1.0381400
## [7,] -0.5068611
## [8,] 0.3755040
U1V1<-(t(a1)%*%P12%*%b1)/((sqrt(t(a1)%*%P11%*%a1))*(sqrt(t(b1)%*%P22%*%b1)))
U1V1
## [,1]
## [1,] -0.8289224
U2V2<-(t(a2)%*%P12%*%b2)/((sqrt(t(a2)%*%P11%*%a2))*(sqrt(t(b2)%*%P22%*%b2)))
U2V2
## [,1]
## [1,] 0.8100845
U3V3<-(t(a3)%*%P12%*%b3)/((sqrt(t(a3)%*%P11%*%a3))*(sqrt(t(b3)%*%P22%*%b3)))
U3V3
## [,1]
## [1,] -0.7407957
U4V4<-(t(a4)%*%P12%*%b4)/((sqrt(t(a4)%*%P11%*%a4))*(sqrt(t(b4)%*%P22%*%b4)))
U4V4
## [,1]
## [1,] -0.7124871
U5V5<-(t(a5)%*%P12%*%b5)/((sqrt(t(a5)%*%P11%*%a5))*(sqrt(t(b5)%*%P22%*%b5)))
U5V5
## [,1]
## [1,] -0.451629
U6V6<-(t(a6)%*%P12%*%b6)/((sqrt(t(a6)%*%P11%*%a6))*(sqrt(t(b6)%*%P22%*%b6)))
U6V6
## [,1]
## [1,] -0.3547805
U7V7<-(t(a7)%*%P12%*%b7)/((sqrt(t(a7)%*%P11%*%a7))*(sqrt(t(b7)%*%P22%*%b7)))
U7V7
## [,1]
## [1,] 0.1895983
U8V8<-(t(a8)%*%P12%*%b8)/((sqrt(t(a8)%*%P11%*%a8))*(sqrt(t(b8)%*%P22%*%b8)))
U8V8
## [,1]
## [1,] 0.04239657
#uji korelasi kanonik secara simultan
lambda<-((1-(U1V1)^2)*(1-(U2V2)^2)*(1-(U3V3)^2)*(1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B<-(-((33-1)-(0.5*(8+8+1)))*log(lambda))
B
## [,1]
## [1,] 97.17488
#uji korelasi kanonik secara individu
lambda1<-((1-(U1V1)^2)*(1-(U2V2)^2)*(1-(U3V3)^2)*(1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B1<-(-((33-1)-(0.5*(8+8+1)))*log(lambda1))
B1
## [,1]
## [1,] 97.17488
lambda1<-((1-(U1V1)^2)*(1-(U2V2)^2)*(1-(U3V3)^2)*(1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B1<-(-((33-1)-(0.5*(8+8+1)))*log(lambda1))
B1
## [,1]
## [1,] 97.17488
lambda2<-((1-(U2V2)^2)*(1-(U3V3)^2)*(1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B2<-(-((33-1)-(0.5*(8+8+1)))*log(lambda2))
B2
## [,1]
## [1,] 69.86998
lambda3<-((1-(U3V3)^2)*(1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B3<-(-((33-1)-(0.5*(8+8+1)))*log(lambda3))
B3
## [,1]
## [1,] 44.77661
lambda4<-((1-(U4V4)^2)*(1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B4<-(-((33-1)-(0.5*(8+8+1)))*log(lambda4))
B4
## [,1]
## [1,] 26.0754
lambda5<-((1-(U5V5)^2)*(1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B5<-(-((33-1)-(0.5*(8+8+1)))*log(lambda5))
B5
## [,1]
## [1,] 9.424691
lambda6<-((1-(U6V6)^2)*(1-(U7V7)^2)*(1-(U8V8)^2))
B6<-(-((33-1)-(0.5*(8+8+1)))*log(lambda6))
B6
## [,1]
## [1,] 4.063946
lambda7<-((1-(U7V7)^2)*(1-(U8V8)^2))
B7<-(-((33-1)-(0.5*(8+8+1)))*log(lambda7))
B7
## [,1]
## [1,] 0.9026027
lambda8<-((1-(U8V8)^2))
B8<-(-((33-1)-(0.5*(8+8+1)))*log(lambda8))
B8
## [,1]
## [1,] 0.04227853
#BOBOT KANONIK SET PERTAMA
BOBOT_KANONIK_11<- a1
BOBOT_KANONIK_11
## [,1]
## [1,] 0.63918996
## [2,] -0.42427892
## [3,] 0.02857305
## [4,] -0.47611193
## [5,] -0.22710547
## [6,] 0.40042448
## [7,] -0.24480294
## [8,] -0.27992146
BOBOT_KANONIK_12<- b1
BOBOT_KANONIK_12
## [,1]
## [1,] 0.143236682
## [2,] 0.485952633
## [3,] -0.001957234
## [4,] 0.394126738
## [5,] -0.094581178
## [6,] -0.203245596
## [7,] -0.281192186
## [8,] 0.795736788
#BOBOT KANONIK SET KEDUA
BOBOT_KANONIK_21<- a2
BOBOT_KANONIK_21
## [,1]
## [1,] -0.45979778
## [2,] 0.06447068
## [3,] 0.10584271
## [4,] -0.28028783
## [5,] 0.94934606
## [6,] -0.20175537
## [7,] -0.23255439
## [8,] 0.15261039
BOBOT_KANONIK_22<- b2
BOBOT_KANONIK_22
## [,1]
## [1,] 1.04731061
## [2,] -0.01157483
## [3,] 0.26391144
## [4,] 0.03256926
## [5,] -0.30919720
## [6,] -0.19575018
## [7,] 0.03753165
## [8,] 0.05446480
#LOADING KANONIK SET PERTAMA
loadingX11 = P11 %*% a1
loadingX11
## [,1]
## X1 0.66096038
## X2 0.04446925
## X3 0.40123292
## X4 -0.55899851
## X5 0.11413845
## X6 0.42458869
## X7 -0.47657428
## X8 -0.20726097
loadingX12 = P22 %*% b1
loadingX12
## [,1]
## Y1 -0.1375981
## Y3 0.4761466
## Y4 -0.3693447
## Y7 0.3046647
## Y8 -0.4632619
## Y9 -0.2418012
## Y10 0.1280862
## Y12 0.7673152
#LOADING KANONIK SET KEDUA
loadingX21 = P11 %*% a1
loadingX21
## [,1]
## X1 0.66096038
## X2 0.04446925
## X3 0.40123292
## X4 -0.55899851
## X5 0.11413845
## X6 0.42458869
## X7 -0.47657428
## X8 -0.20726097
loadingX22 = P22 %*% b1
loadingX22
## [,1]
## Y1 -0.1375981
## Y3 0.4761466
## Y4 -0.3693447
## Y7 0.3046647
## Y8 -0.4632619
## Y9 -0.2418012
## Y10 0.1280862
## Y12 0.7673152