# Mebuat data frame dengan kolom kategori
data <- data.frame(
Gender = c(0, 1, 0, 1, 1),
Age = c(30.83, 58.67, 24.50, 27.83, 20.17),
Debt = c(0.000, 4.460, 0.500, 1.540, 5.625),
Married = c(1, 1, 1, 1, 1),
BankCustomer = c(1, 1, 0, 1, 1),
Industry = c("Industrials", "Materials", "Materials", "Industrials", "Industrials"),
YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
PriorDefault = c(1, 1, 0, 1, 0),
Employed = c(1, 1, 0, 1, 0),
CreditScore = c(1, 6, 0, 5, 0),
DriverLicense = c(0, 0, 0, 1, 0),
Citizen = c("ByBirth", "ByBirth", "ByBirth", "ByBirth", "ByOtherMeans"),
ZipCode = c(202, 43, 280, 100, 120),
Income = c(0, 560, 824, 3, 0),
Approved = c(1, 1, 1, 1, 1)
)
# Memilih hanya kolom numerik
data_numeric <- data[, sapply(data, is.numeric)]
# Menghitung matriks kovarians
cov_matrix <- cov(data_numeric)
# Menghitung eigen value dan eigen vector
eigen_result <- eigen(cov_matrix)
eigen_values <- eigen_result$values
eigen_vectors <- eigen_result$vectors
# Hitung standar deviasi untuk setiap kolom
sd_values <- apply(data_numeric, 2, sd)
# Hapus kolom dengan standar deviasi nol
data_numeric <- data_numeric[, sd_values != 0]
# Menghitung matriks korelasi
cor_matrix <- cor(data_numeric)
# Menampilkan hasil
print("Eigen Values:")
## [1] "Eigen Values:"
print(eigen_values)
## [1] 1.529993e+05 7.738641e+03 7.533480e+01 4.829109e+00 9.346965e-15
## [6] 5.570814e-15 3.535152e-15 1.089908e-15 5.045945e-16 0.000000e+00
## [11] -5.147909e-17 -8.281943e-14 -1.231356e-12
print("Eigen Vectors:")
## [1] "Eigen Vectors:"
print(eigen_vectors)
## [,1] [,2] [,3] [,4] [,5]
## [1,] -4.632107e-04 -0.0052112392 -2.742659e-02 8.455471e-03 9.995744e-01
## [2,] 1.289699e-02 -0.1315580552 9.768263e-01 -1.382182e-01 2.729173e-02
## [3,] -8.188169e-04 -0.0196046486 -1.279263e-01 -6.236024e-01 1.662434e-03
## [4,] 2.168404e-19 0.0000000000 -3.330669e-16 -1.558476e-14 1.249001e-16
## [5,] -9.076584e-04 -0.0027845437 1.245692e-02 -2.163038e-02 5.098315e-04
## [6,] -3.179487e-04 -0.0090247020 -1.826291e-02 3.253522e-01 -3.300478e-03
## [7,] -4.517919e-04 -0.0026381795 4.503155e-02 1.134386e-01 2.620389e-04
## [8,] -4.517919e-04 -0.0026381795 4.503155e-02 1.134386e-01 2.620389e-04
## [9,] -3.496570e-05 -0.0271352527 8.398211e-02 6.547687e-01 -3.375897e-03
## [10,] -4.535473e-04 -0.0008273017 -1.321034e-02 1.762923e-01 -1.858262e-03
## [11,] 8.349394e-02 0.9873552051 1.279519e-01 -8.902261e-03 8.772309e-03
## [12,] 9.964236e-01 -0.0810589450 -2.343948e-02 2.316635e-03 -6.235820e-04
## [13,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000e+00 0.000000e+00
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.0000000000 0.000000000 0.0000000000 0.0000000000 0
## [2,] 0.0013425514 0.009230988 0.0018796298 0.0007604077 0
## [3,] 0.0065649090 0.021391182 0.0061059603 0.0025583714 0
## [4,] -0.8870260473 0.332624245 -0.3022215863 -0.0259518181 0
## [5,] -0.3770320865 -0.432719409 0.5747989721 0.5596586869 0
## [6,] -0.1830339861 -0.475449644 0.0204837436 -0.3027446459 0
## [7,] 0.0660983476 -0.451182039 -0.4739878288 0.1382331933 0
## [8,] -0.0929828276 -0.359417605 -0.3370655532 -0.1579370105 0
## [9,] 0.0497434746 0.340881986 0.2661949241 -0.0247224674 0
## [10,] 0.1483068171 0.162974088 -0.4106361025 0.7414726248 0
## [11,] -0.0010297392 0.003328462 0.0069340263 -0.0010798946 0
## [12,] -0.0002704843 -0.001208080 -0.0006155211 0.0008236463 0
## [13,] 0.0000000000 0.000000000 0.0000000000 0.0000000000 1
## [,11] [,12] [,13]
## [1,] 0.0000000000 0.000000000 0.000000000
## [2,] 0.0007339675 0.087311045 -0.027793855
## [3,] -0.0005291892 0.172497166 -0.751043992
## [4,] 0.1010726239 -0.017186661 -0.004843796
## [5,] -0.0495889305 -0.152031116 -0.028010842
## [6,] 0.0594907296 0.724565629 -0.116437754
## [7,] 0.6158792365 -0.340101621 -0.195992739
## [8,] -0.7478553332 -0.335172734 -0.192572410
## [9,] 0.0471443672 -0.171655731 -0.584502375
## [10,] -0.2072830381 0.395620146 -0.047973693
## [11,] 0.0012365449 0.014875501 -0.036693095
## [12,] -0.0002922733 -0.002274225 0.002536019
## [13,] 0.0000000000 0.000000000 0.000000000
print("Variance-Covariance Matrix:")
## [1] "Variance-Covariance Matrix:"
print(cov_matrix)
## Gender Age Debt Married BankCustomer
## Gender 0.3000 2.367500 1.087500 0 0.15000
## Age 2.3675 231.361400 9.345663 0 1.97500
## Debt 1.0875 9.345663 6.187675 0 0.48125
## Married 0.0000 0.000000 0.000000 0 0.00000
## BankCustomer 0.1500 1.975000 0.481250 0 0.20000
## YearsEmployed 0.4375 6.999375 0.605225 0 0.18750
## PriorDefault 0.0500 5.032500 -0.318750 0 0.15000
## Employed 0.0500 5.032500 -0.318750 0 0.15000
## CreditScore 0.9500 33.300000 1.340000 0 0.60000
## DriverLicense 0.1000 -1.142500 -0.221250 0 0.05000
## ZipCode -46.0000 -831.032500 -161.461250 0 -32.75000
## Income -67.3000 2046.972500 -112.313750 0 -136.65000
## Approved 0.0000 0.000000 0.000000 0 0.00000
## YearsEmployed PriorDefault Employed CreditScore DriverLicense
## Gender 0.437500 0.05000 0.05000 0.95 0.10000
## Age 6.999375 5.03250 5.03250 33.30 -1.14250
## Debt 0.605225 -0.31875 -0.31875 1.34 -0.22125
## Married 0.000000 0.00000 0.00000 0.00 0.00000
## BankCustomer 0.187500 0.15000 0.15000 0.60 0.05000
## YearsEmployed 1.182050 0.32250 0.32250 2.81 0.37500
## PriorDefault 0.322500 0.30000 0.30000 1.20 0.10000
## Employed 0.322500 0.30000 0.30000 1.20 0.10000
## CreditScore 2.810000 1.20000 1.20000 8.30 0.65000
## DriverLicense 0.375000 0.10000 0.10000 0.65 0.20000
## ZipCode -73.207500 -25.50000 -25.50000 -207.00 -12.25000
## Income -42.775000 -67.30000 -67.30000 11.55 -68.60000
## Approved 0.000000 0.00000 0.00000 0.00 0.00000
## ZipCode Income Approved
## Gender -46.0000 -67.3000 0
## Age -831.0325 2046.9725 0
## Debt -161.4613 -112.3137 0
## Married 0.0000 0.0000 0
## BankCustomer -32.7500 -136.6500 0
## YearsEmployed -73.2075 -42.7750 0
## PriorDefault -25.5000 -67.3000 0
## Employed -25.5000 -67.3000 0
## CreditScore -207.0000 11.5500 0
## DriverLicense -12.2500 -68.6000 0
## ZipCode 8612.0000 12109.2500 0
## Income 12109.2500 151957.8000 0
## Approved 0.0000 0.0000 0
print("Correlation Matrix:")
## [1] "Correlation Matrix:"
print(cor_matrix)
## Gender Age Debt BankCustomer YearsEmployed
## Gender 1.0000000 0.2841737 0.7981874 0.6123724 0.7346822
## Age 0.2841737 1.0000000 0.2470022 0.2903399 0.4232489
## Debt 0.7981874 0.2470022 1.0000000 0.4326055 0.2237872
## BankCustomer 0.6123724 0.2903399 0.4326055 1.0000000 0.3856278
## YearsEmployed 0.7346822 0.4232489 0.2237872 0.3856278 1.0000000
## PriorDefault 0.1666667 0.6040567 -0.2339515 0.6123724 0.5415657
## Employed 0.1666667 0.6040567 -0.2339515 0.6123724 0.5415657
## CreditScore 0.6020380 0.7599058 0.1869829 0.4656903 0.8971175
## DriverLicense 0.4082483 -0.1679561 -0.1988861 0.2500000 0.7712556
## ZipCode -0.9049929 -0.5887359 -0.6994434 -0.7891223 -0.7255806
## Income -0.3152049 0.3452272 -0.1158264 -0.7838503 -0.1009278
## PriorDefault Employed CreditScore DriverLicense ZipCode
## Gender 0.1666667 0.1666667 0.60203804 0.4082483 -0.9049929
## Age 0.6040567 0.6040567 0.75990576 -0.1679561 -0.5887359
## Debt -0.2339515 -0.2339515 0.18698295 -0.1988861 -0.6994434
## BankCustomer 0.6123724 0.6123724 0.46569032 0.2500000 -0.7891223
## YearsEmployed 0.5415657 0.5415657 0.89711754 0.7712556 -0.7255806
## PriorDefault 1.0000000 1.0000000 0.76046910 0.4082483 -0.5016809
## Employed 1.0000000 1.0000000 0.76046910 0.4082483 -0.5016809
## CreditScore 0.7604691 0.7604691 1.00000000 0.5044978 -0.7742466
## DriverLicense 0.4082483 0.4082483 0.50449784 1.0000000 -0.2951679
## ZipCode -0.5016809 -0.5016809 -0.77424657 -0.2951679 1.0000000
## Income -0.3152049 -0.3152049 0.01028446 -0.3935026 0.3347370
## Income
## Gender -0.31520488
## Age 0.34522724
## Debt -0.11582643
## BankCustomer -0.78385032
## YearsEmployed -0.10092775
## PriorDefault -0.31520488
## Employed -0.31520488
## CreditScore 0.01028446
## DriverLicense -0.39350261
## ZipCode 0.33473701
## Income 1.00000000