Dataframe berdasarkan Data Numerik

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),
  YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
  PriorDefault = c(1, 1, 1, 1, 1),
  Employed = c(1, 1, 0, 1, 0),
  CreditScore = c(1, 6, 0, 5, 0),
  ZipCode = c(202, 43, 280, 100, 120),
  Income = c(0, 560, 824, 3, 0),
  Approved = c(1, 1, 1, 1, 1)
)

print(data)
##     Age  Debt YearsEmployed PriorDefault Employed CreditScore ZipCode Income
## 1 30.83 0.000          1.25            1        1           1     202      0
## 2 58.67 4.460          3.04            1        1           6      43    560
## 3 24.50 0.500          1.50            1        0           0     280    824
## 4 27.83 1.540          3.75            1        1           5     100      3
## 5 20.17 5.625          1.71            1        0           0     120      0
##   Approved
## 1        1
## 2        1
## 3        1
## 4        1
## 5        1

a) Variance-Covariance Matrix

cov_matrix <- cov(data)
print(cov_matrix)
##                       Age        Debt YearsEmployed PriorDefault  Employed
## Age            231.361400    9.345663      6.999375            0   5.03250
## Debt             9.345663    6.187675      0.605225            0  -0.31875
## YearsEmployed    6.999375    0.605225      1.182050            0   0.32250
## PriorDefault     0.000000    0.000000      0.000000            0   0.00000
## Employed         5.032500   -0.318750      0.322500            0   0.30000
## CreditScore     33.300000    1.340000      2.810000            0   1.20000
## ZipCode       -831.032500 -161.461250    -73.207500            0 -25.50000
## Income        2046.972500 -112.313750    -42.775000            0 -67.30000
## Approved         0.000000    0.000000      0.000000            0   0.00000
##               CreditScore    ZipCode      Income Approved
## Age                 33.30  -831.0325   2046.9725        0
## Debt                 1.34  -161.4613   -112.3137        0
## YearsEmployed        2.81   -73.2075    -42.7750        0
## PriorDefault         0.00     0.0000      0.0000        0
## Employed             1.20   -25.5000    -67.3000        0
## CreditScore          8.30  -207.0000     11.5500        0
## ZipCode           -207.00  8612.0000  12109.2500        0
## Income              11.55 12109.2500 151957.8000        0
## Approved             0.00     0.0000      0.0000        0

b) Eigen Value dan Eigen Vector

eig <- eigen(cov_matrix)

# menampilkan hasil Eigen Value dan eigen vector
print(eig$values)  
## [1]  1.529991e+05  7.738312e+03  7.510055e+01  4.614250e+00  1.746734e-11
## [6]  9.587267e-12  4.312483e-16  0.000000e+00 -2.185975e-13
print(eig$vectors)
##                [,1]          [,2]        [,3]          [,4]          [,5]
##  [1,] -1.289706e-02 -1.315608e-01  0.97836973  1.389102e-01  0.0000000000
##  [2,]  8.188138e-04 -1.960524e-02 -0.12802815  6.382658e-01 -0.6193192401
##  [3,]  3.179447e-04 -9.024809e-03 -0.01834222 -3.327994e-01  0.2363524236
##  [4,]  5.421011e-20  1.734723e-18  0.00000000  1.110223e-16 -0.0000247429
##  [5,]  4.517906e-04 -2.638208e-03  0.04508399 -1.161630e-01 -0.3862406868
##  [6,]  3.495339e-05 -2.713566e-02  0.08401060 -6.700607e-01 -0.6407623109
##  [7,] -8.349366e-02  9.873768e-01  0.12815197  9.231879e-03 -0.0286334831
##  [8,] -9.964244e-01 -8.105384e-02 -0.02348931 -2.229401e-03  0.0017681799
##  [9,]  0.000000e+00  0.000000e+00  0.00000000  0.000000e+00  0.0000000000
##                [,6]          [,7] [,8]          [,9]
##  [1,]  7.760180e-02  0.000000e+00    0  0.0000000000
##  [2,]  4.385016e-01  1.446780e-05    0 -0.0007738974
##  [3,]  8.117272e-01 -4.672998e-04    0 -0.4172083384
##  [4,] -3.330669e-16 -9.999994e-01    0  0.0011060457
##  [5,] -3.648606e-01 -9.172550e-04    0 -0.8379501476
##  [6,]  9.426717e-02  4.049592e-04    0  0.3517979487
##  [7,]  2.784542e-02  4.618528e-06    0  0.0035351609
##  [8,] -2.880460e-03 -9.259101e-07    0 -0.0007975797
##  [9,]  0.000000e+00  0.000000e+00    1  0.0000000000

c) Correlation Matrix

cor_matrix <- cor(data)  
## Warning in cor(data): the standard deviation is zero
print(cor_matrix)
##                      Age       Debt YearsEmployed PriorDefault   Employed
## Age            1.0000000  0.2470022     0.4232489           NA  0.6040567
## Debt           0.2470022  1.0000000     0.2237872           NA -0.2339515
## YearsEmployed  0.4232489  0.2237872     1.0000000           NA  0.5415657
## PriorDefault          NA         NA            NA            1         NA
## Employed       0.6040567 -0.2339515     0.5415657           NA  1.0000000
## CreditScore    0.7599058  0.1869829     0.8971175           NA  0.7604691
## ZipCode       -0.5887359 -0.6994434    -0.7255806           NA -0.5016809
## Income         0.3452272 -0.1158264    -0.1009278           NA -0.3152049
## Approved              NA         NA            NA           NA         NA
##               CreditScore    ZipCode      Income Approved
## Age            0.75990576 -0.5887359  0.34522724       NA
## Debt           0.18698295 -0.6994434 -0.11582643       NA
## YearsEmployed  0.89711754 -0.7255806 -0.10092775       NA
## PriorDefault           NA         NA          NA       NA
## Employed       0.76046910 -0.5016809 -0.31520488       NA
## CreditScore    1.00000000 -0.7742466  0.01028446       NA
## ZipCode       -0.77424657  1.0000000  0.33473701       NA
## Income         0.01028446  0.3347370  1.00000000       NA
## Approved               NA         NA          NA        1

Cara Publikasi dengan RPubs

  1. Ubah dokumen ke format HTML dengan klik knit
  2. Klik Publish di pojok kanan
  3. Pilih RPubs untuk publikasi
  4. Masukkan Judul dan Deskripsi untuk laporan
  5. Klik Publish untuk menerbitkan laporan e RPubs