##import data
data <- read.csv("hcvdat0.csv", header = TRUE, sep = ",")
data_num <- data[, sapply(data, is.numeric)]
head(data_num)## X Age ALB ALP ALT AST BIL CHE CHOL CREA GGT PROT
## 1 1 32 38.5 52.5 7.7 22.1 7.5 6.93 3.23 106 12.1 69.0
## 2 2 32 38.5 70.3 18.0 24.7 3.9 11.17 4.80 74 15.6 76.5
## 3 3 32 46.9 74.7 36.2 52.6 6.1 8.84 5.20 86 33.2 79.3
## 4 4 32 43.2 52.0 30.6 22.6 18.9 7.33 4.74 80 33.8 75.7
## 5 5 32 39.2 74.1 32.6 24.8 9.6 9.15 4.32 76 29.9 68.7
## 6 6 32 41.6 43.3 18.5 19.7 12.3 9.92 6.05 111 91.0 74.0##correlatrion matrix
cor_matrix <- cor(data_num, use = "complete.obs")
cor_matrix## X Age ALB ALP ALT AST
## X 1.00000000 0.44305790 -0.315204550 0.01794376 -0.20023304 0.30360292
## Age 0.44305790 1.00000000 -0.191093637 0.17771977 -0.04057647 0.07273886
## ALB -0.31520455 -0.19109364 1.000000000 -0.14611991 0.03949714 -0.17760895
## ALP 0.01794376 0.17771977 -0.146119911 1.00000000 0.22160301 0.06702428
## ALT -0.20023304 -0.04057647 0.039497139 0.22160301 1.00000000 0.19865775
## AST 0.30360292 0.07273886 -0.177608947 0.06702428 0.19865775 1.00000000
## BIL 0.17651109 0.03965486 -0.169597498 0.05837241 -0.10679662 0.30957974
## CHE -0.27853454 -0.07586328 0.360919403 0.02948169 0.22434447 -0.19727042
## CHOL -0.05794709 0.12474161 0.210419878 0.12590008 0.14999727 -0.20121300
## CREA -0.02016270 -0.02514225 0.001433247 0.15390895 -0.03610554 -0.01794810
## GGT 0.22146275 0.14337927 -0.147598318 0.46130000 0.21970686 0.47777362
## PROT -0.16648242 -0.15975998 0.570725680 -0.06308514 0.01678633 0.01740394
## BIL CHE CHOL CREA GGT PROT
## X 0.17651109 -0.27853454 -0.057947087 -0.020162704 0.221462754 -0.16648242
## Age 0.03965486 -0.07586328 0.124741615 -0.025142253 0.143379268 -0.15975998
## ALB -0.16959750 0.36091940 0.210419878 0.001433247 -0.147598318 0.57072568
## ALP 0.05837241 0.02948169 0.125900079 0.153908950 0.461299996 -0.06308514
## ALT -0.10679662 0.22434447 0.149997271 -0.036105541 0.219706857 0.01678633
## AST 0.30957974 -0.19727042 -0.201213004 -0.017948098 0.477773617 0.01740394
## BIL 1.00000000 -0.32071323 -0.181569556 0.019909617 0.210566559 -0.05257491
## CHE -0.32071323 1.00000000 0.428018276 -0.012119999 -0.095716131 0.30628754
## CHOL -0.18156956 0.42801828 1.000000000 -0.051464078 0.008822692 0.24504950
## CREA 0.01990962 -0.01212000 -0.051464078 1.000000000 0.125353469 -0.03011070
## GGT 0.21056656 -0.09571613 0.008822692 0.125353469 1.000000000 -0.03712701
## PROT -0.05257491 0.30628754 0.245049503 -0.030110695 -0.037127008 1.00000000##Variance–Covariance Matrix
cov_matrix <- cov(data_num, use = "complete.obs")
cov_matrix## X Age ALB ALP ALT AST
## X 30325.61267 766.254175 -316.2678135 80.997414 -727.477884 1737.677172
## Age 766.25418 98.631388 -10.9348172 45.750544 -8.407388 23.742827
## ALB -316.26781 -10.934817 33.1982701 -21.823283 4.747912 -33.634186
## ALP 80.99741 45.750544 -21.8232826 671.901949 119.841675 57.100968
## ALT -727.47788 -8.407388 4.7479116 119.841675 435.269784 136.220708
## AST 1737.67717 23.742827 -33.6341863 57.100968 136.220708 1080.231200
## BIL 535.04466 6.855155 -17.0094554 26.337454 -38.783770 177.110426
## CHE -106.27733 -1.650806 4.5564303 1.674411 10.255372 -14.206173
## CHOL -11.39233 1.398606 1.3687395 3.684302 3.532962 -7.466047
## CREA -178.00646 -12.658841 0.4186596 202.254881 -38.188738 -29.906045
## GGT 2094.23092 77.323769 -46.1804560 649.315069 248.909775 852.706557
## PROT -155.07302 -8.486697 17.5892871 -8.746677 1.873261 3.059630
## BIL CHE CHOL CREA GGT PROT
## X 535.044661 -106.277326 -11.3923339 -178.0064562 2094.2309247 -155.073024
## Age 6.855155 -1.650806 1.3986055 -12.6588412 77.3237685 -8.486697
## ALB -17.009455 4.556430 1.3687395 0.4186596 -46.1804560 17.589287
## ALP 26.337454 1.674411 3.6843023 202.2548814 649.3150694 -8.746677
## ALT -38.783770 10.255372 3.5329616 -38.1887382 248.9097752 1.873261
## AST 177.110426 -14.206173 -7.4660468 -29.9060449 852.7065571 3.059630
## BIL 302.988734 -12.231702 -3.5680635 17.5694569 199.0314564 -4.895025
## CHE -12.231702 4.800799 1.0587548 -1.3462991 -11.3883550 3.589626
## CHOL -3.568064 1.058755 1.2745375 -2.9455248 0.5408745 1.479767
## CREA 17.569457 -1.346299 -2.9455248 2570.1849279 345.0941704 -8.165186
## GGT 199.031456 -11.388355 0.5408745 345.0941704 2948.7514092 -10.783808
## PROT -4.895025 3.589626 1.4797666 -8.1651857 -10.7838076 28.610549##Eigen Value & Eigen Vector
eigen_result <- eigen(cov_matrix)
eigen_result$values## [1] 3.064615e+04 3.411952e+03 2.431355e+03 8.106778e+02 4.759857e+02
## [6] 3.597636e+02 2.295242e+02 7.550312e+01 4.386454e+01 1.225674e+01
## [11] 3.543169e+00 8.832592e-01eigen_result$vectors## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9943168408 0.0781794505 -0.0428895929 -0.0328121735 0.020212889
## [2,] 0.0252084504 -0.0048342063 0.0049027524 -0.0592379614 0.060040370
## [3,] -0.0104849795 0.0082864828 -0.0043684634 -0.0004021013 -0.025903024
## [4,] 0.0044080667 -0.2356681437 0.0356393108 -0.4374522941 0.710618877
## [5,] -0.0230437543 -0.1041992516 0.0916985555 0.0673718565 0.547112440
## [6,] 0.0607143554 -0.2580042619 0.1999667225 0.8489173365 0.256978864
## [7,] 0.0184370838 -0.0576707031 0.0297876343 0.1542686413 -0.079875667
## [8,] -0.0035236442 0.0013755262 -0.0001678846 -0.0100541902 0.011976556
## [9,] -0.0003865148 0.0002186455 0.0009124350 -0.0103444081 0.005339638
## [10,] -0.0053562951 -0.4023281847 -0.9087666493 0.1021482314 0.004426586
## [11,] 0.0771038647 -0.8340483849 0.3488376959 -0.2101014484 -0.343945278
## [12,] -0.0050747796 0.0005044839 0.0043774244 0.0163142811 -0.009811142
## [,6] [,7] [,8] [,9] [,10]
## [1,] -0.029899000 0.0117861445 0.028276257 -0.0075378056 0.002522380
## [2,] 0.013200918 -0.0142637105 -0.987882489 -0.1224848387 -0.020137828
## [3,] -0.041230519 -0.0196324002 0.073071176 -0.7117277819 0.693190933
## [4,] 0.462008490 -0.1601735463 0.082208949 -0.0250189827 0.017575507
## [5,] -0.680285668 0.4612712030 0.014509161 -0.0004604439 -0.009521025
## [6,] 0.171499060 -0.2691408215 -0.025811818 -0.0013787705 0.020143047
## [7,] 0.524197933 0.8290819581 -0.013118146 -0.0459489418 0.009776830
## [8,] -0.030163171 -0.0150483056 0.002265340 -0.1084487331 -0.017859713
## [9,] -0.009869622 -0.0007455209 -0.011252758 -0.0491322591 -0.030609508
## [10,] -0.039663866 0.0110805318 -0.008986684 -0.0015590011 -0.003436025
## [11,] -0.119064616 0.0091672413 -0.002651554 0.0038121325 -0.002258407
## [12,] -0.006258120 -0.0244543592 0.099611920 -0.6793031525 -0.718958094
## [,11] [,12]
## [1,] 1.166495e-03 -0.0005701834
## [2,] -1.638485e-02 -0.0153975609
## [3,] -6.807153e-02 0.0016019737
## [4,] -4.373501e-03 -0.0027458982
## [5,] -2.126558e-02 -0.0043095508
## [6,] 9.352837e-03 0.0072123962
## [7,] 2.699176e-02 -0.0001634271
## [8,] 9.680597e-01 -0.2222163944
## [9,] 2.170998e-01 0.9742461763
## [10,] 4.016040e-04 0.0012264068
## [11,] 6.161861e-05 -0.0015769054
## [12,] -9.765782e-02 -0.0337955255##Matriks korelasi menunjukkan hubungan antar variabel numerik. Matriks kovarians menunjukkan variasi dan hubungan antar variabel. Eigen value terbesar menunjukkan komponen utama yang paling dominan.