R Markdown
# Membuat data frame dengan kolom kategori
data <- data.frame(
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, 1, 1, 1),
YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
PriorDefault = c(1, 1, 1, 1, 1),
Employed = c(1, 0, 0, 1, 0),
CreditScore = c(1, 6, 0, 5, 0),
DriversLicense = 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),
Industry = c("Industrials", "Materials", "Materials", "Industrials", "Industrials")
)
print(data)
## Age Debt Married BankCustomer YearsEmployed PriorDefault Employed
## 1 30.83 0.000 1 1 1.25 1 1
## 2 58.67 4.460 1 1 3.04 1 0
## 3 24.50 0.500 1 1 1.50 1 0
## 4 27.83 1.540 1 1 3.75 1 1
## 5 20.17 5.625 1 1 1.71 1 0
## CreditScore DriversLicense Citizen ZipCode Income Approved Industry
## 1 1 0 ByBirth 202 0 1 Industrials
## 2 6 0 ByBirth 43 560 1 Materials
## 3 0 0 ByBirth 280 824 1 Materials
## 4 5 1 ByBirth 100 3 1 Industrials
## 5 0 0 ByOtherMeans 120 0 1 Industrials
# Memilih hanya kolom numerik
data_numeric <- data[, sapply(data, is.numeric)]
print(data_numeric)
## Age Debt Married BankCustomer YearsEmployed PriorDefault Employed
## 1 30.83 0.000 1 1 1.25 1 1
## 2 58.67 4.460 1 1 3.04 1 0
## 3 24.50 0.500 1 1 1.50 1 0
## 4 27.83 1.540 1 1 3.75 1 1
## 5 20.17 5.625 1 1 1.71 1 0
## CreditScore DriversLicense ZipCode Income Approved
## 1 1 0 202 0 1
## 2 6 0 43 560 1
## 3 0 0 280 824 1
## 4 5 1 100 3 1
## 5 0 0 120 0 1
# Menghitung matriks kovarians
cov_matrix <- cov(data_numeric)
# Menghitung eigenvalue dan eigenvector
eigen_result <- eigen(cov_matrix)
print(eigen_result$values) # Eigenvalues
## [1] 1.529992e+05 7.738283e+03 7.501468e+01 4.805269e+00 3.749373e-12
## [6] 1.124666e-12 6.384180e-16 3.953463e-16 4.549920e-17 0.000000e+00
## [11] -2.703328e-28 -1.242917e-12
print(eigen_result$vectors) # Eigenvectors
## [,1] [,2] [,3] [,4] [,5]
## [1,] -1.289707e-02 -0.1315593985 9.789852e-01 1.323413e-01 0.000000e+00
## [2,] 8.188081e-04 -0.0196056439 -1.279454e-01 6.259308e-01 -4.531161e-01
## [3,] 0.000000e+00 0.0000000000 -5.537237e-15 6.902881e-13 -6.439283e-01
## [4,] -5.421011e-20 0.0000000000 0.000000e+00 -3.330669e-16 -1.674482e-04
## [5,] 3.179452e-04 -0.0090247788 -1.843066e-02 -3.260510e-01 2.061046e-01
## [6,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000e+00 -6.938894e-17
## [7,] 8.979960e-04 0.0015995422 2.667112e-02 -1.467340e-01 -1.649762e-01
## [8,] 3.495333e-05 -0.0271354051 8.390325e-02 -6.569377e-01 -5.369565e-01
## [9,] 4.535474e-04 -0.0008272575 -1.330900e-02 -1.766469e-01 1.468506e-01
## [10,] -8.349351e-02 0.9873790313 1.280551e-01 8.929661e-03 -2.136631e-02
## [11,] -9.964240e-01 -0.0810516636 -2.349159e-02 -2.286556e-03 1.383096e-03
## [12,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000e+00 0.000000e+00
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.0000000000 0.000000e+00 0.000000e+00 0.000000e+00 0
## [2,] -0.3813129286 3.336325e-03 7.452735e-03 -5.477737e-03 0
## [3,] 0.7650669619 -2.148602e-03 -4.921552e-03 9.702402e-05 0
## [4,] -0.0003991872 -9.304003e-01 3.654914e-01 -2.777035e-02 0
## [5,] 0.1750280249 7.222275e-02 2.198498e-01 4.700251e-01 0
## [6,] 0.0000000000 -1.892153e-12 -2.077227e-13 -7.386453e-13 0
## [7,] -0.1324045124 3.419945e-01 8.473592e-01 -3.027973e-01 0
## [8,] -0.4537118949 -9.328227e-02 -2.342834e-01 5.157773e-02 0
## [9,] 0.1221729050 -5.878479e-02 -2.122775e-01 -8.269822e-01 0
## [10,] -0.0180275113 -2.399060e-03 -5.731991e-03 5.324620e-03 0
## [11,] 0.0011734574 5.049939e-04 1.215391e-03 -9.481885e-04 0
## [12,] 0.0000000000 0.000000e+00 0.000000e+00 0.000000e+00 1
## [,11] [,12]
## [1,] 0.000000e+00 -8.123723e-02
## [2,] 3.808437e-15 -4.905559e-01
## [3,] -5.140519e-15 1.057359e-12
## [4,] -1.705074e-12 -4.440892e-16
## [5,] 5.294686e-13 -7.387029e-01
## [6,] 1.000000e+00 0.000000e+00
## [7,] 5.992492e-13 7.963880e-02
## [8,] -1.870205e-13 -1.514710e-02
## [9,] -7.661419e-13 -4.468889e-01
## [10,] -1.795740e-15 -2.802053e-02
## [11,] 5.073119e-16 2.628416e-03
## [12,] 0.000000e+00 0.000000e+00
# Menampilkan matriks kovarians
print(cov_matrix)
## Age Debt Married BankCustomer YearsEmployed
## Age 231.361400 9.345663 0 0 6.999375
## Debt 9.345663 6.187675 0 0 0.605225
## Married 0.000000 0.000000 0 0 0.000000
## BankCustomer 0.000000 0.000000 0 0 0.000000
## YearsEmployed 6.999375 0.605225 0 0 1.182050
## PriorDefault 0.000000 0.000000 0 0 0.000000
## Employed -1.535000 -0.827500 0 0 0.125000
## CreditScore 33.300000 1.340000 0 0 2.810000
## DriversLicense -1.142500 -0.221250 0 0 0.375000
## ZipCode -831.032500 -161.461250 0 0 -73.207500
## Income 2046.972500 -112.313750 0 0 -42.775000
## Approved 0.000000 0.000000 0 0 0.000000
## PriorDefault Employed CreditScore DriversLicense ZipCode
## Age 0 -1.5350 33.30 -1.14250 -831.0325
## Debt 0 -0.8275 1.34 -0.22125 -161.4613
## Married 0 0.0000 0.00 0.00000 0.0000
## BankCustomer 0 0.0000 0.00 0.00000 0.0000
## YearsEmployed 0 0.1250 2.81 0.37500 -73.2075
## PriorDefault 0 0.0000 0.00 0.00000 0.0000
## Employed 0 0.3000 0.30 0.15000 1.0000
## CreditScore 0 0.3000 8.30 0.65000 -207.0000
## DriversLicense 0 0.1500 0.65 0.20000 -12.2500
## ZipCode 0 1.0000 -207.00 -12.25000 8612.0000
## Income 0 -137.9500 11.55 -68.60000 12109.2500
## Approved 0 0.0000 0.00 0.00000 0.0000
## Income Approved
## Age 2046.9725 0
## Debt -112.3137 0
## Married 0.0000 0
## BankCustomer 0.0000 0
## YearsEmployed -42.7750 0
## PriorDefault 0.0000 0
## Employed -137.9500 0
## CreditScore 11.5500 0
## DriversLicense -68.6000 0
## ZipCode 12109.2500 0
## Income 151957.8000 0
## Approved 0.0000 0
# Menampilkan matriks korelasi
cor_matrix <- cor(data_numeric)
## Warning in cor(data_numeric): the standard deviation is zero
print(cor_matrix)
## Age Debt Married BankCustomer YearsEmployed
## Age 1.0000000 0.2470022 NA NA 0.4232489
## Debt 0.2470022 1.0000000 NA NA 0.2237872
## Married NA NA 1 NA NA
## BankCustomer NA NA NA 1 NA
## YearsEmployed 0.4232489 0.2237872 NA NA 1.0000000
## PriorDefault NA NA NA NA NA
## Employed -0.1842478 -0.6073564 NA NA 0.2099092
## CreditScore 0.7599058 0.1869829 NA NA 0.8971175
## DriversLicense -0.1679561 -0.1988861 NA NA 0.7712556
## ZipCode -0.5887359 -0.6994434 NA NA -0.7255806
## Income 0.3452272 -0.1158264 NA NA -0.1009278
## Approved NA NA NA NA NA
## PriorDefault Employed CreditScore DriversLicense ZipCode
## Age NA -0.18424780 0.75990576 -0.1679561 -0.58873594
## Debt NA -0.60735642 0.18698295 -0.1988861 -0.69944336
## Married NA NA NA NA NA
## BankCustomer NA NA NA NA NA
## YearsEmployed NA 0.20990919 0.89711754 0.7712556 -0.72558057
## PriorDefault 1 NA NA NA NA
## Employed NA 1.00000000 0.19011728 0.6123724 0.01967376
## CreditScore NA 0.19011728 1.00000000 0.5044978 -0.77424657
## DriversLicense NA 0.61237244 0.50449784 1.0000000 -0.29516787
## ZipCode NA 0.01967376 -0.77424657 -0.2951679 1.00000000
## Income NA -0.64609976 0.01028446 -0.3935026 0.33473701
## Approved NA NA NA NA NA
## Income Approved
## Age 0.34522724 NA
## Debt -0.11582643 NA
## Married NA NA
## BankCustomer NA NA
## YearsEmployed -0.10092775 NA
## PriorDefault NA NA
## Employed -0.64609976 NA
## CreditScore 0.01028446 NA
## DriversLicense -0.39350261 NA
## ZipCode 0.33473701 NA
## Income 1.00000000 NA
## Approved NA 1