Y : Keputuan menolak/menerima pelamar kerja pada PT A posisi B X1 : Lama pengalaman kerja sebelumnya (bulan) X2 : Status pekerjaan saat ini (0:Bekerja, 1:Tidak bekerja) X3 : Tingkat pendidikan (0:Lulusan Sekolah Menengah, 1:Lulusan Perguruan Tiggi) X4 : IPK (skala 4)
X1 : Lama pengalaman Kerja sebelumnya (bulan) Membangkitkan vriabel X1 dengan lama pekerjaan 0-60 bulan dengan nilai tengah 12 dan banyak pelamar adalah 100
set.seed(100)
n <- 100
u <- runif(n)
X1 <- round(60*(-(log(1-u)/12)))
X1## [1] 2 1 4 0 3 3 8 2 4 1 5 11 2 3 7 6 1 2 2 6 4 6 4 7 3
## [26] 1 7 11 4 2 3 13 2 15 6 11 1 5 23 1 2 10 8 9 5 3 8 11 1 2
## [51] 2 1 1 2 4 1 1 1 5 1 3 5 16 6 3 2 3 3 1 6 3 2 4 17 5
## [76] 5 10 7 9 0 3 5 13 20 0 4 7 1 2 7 12 1 2 3 12 2 4 1 0 7##Membangkitkan data X2 X2 : Status pekerjaan Keterangan yang digunakan (0=Tidak Bekerja) dan (1=Bekerja)
set.seed(12345)
X2 <- round(runif(n))
X2## [1] 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1
## [38] 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0 0
## [75] 0 1 1 1 1 0 1 1 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 0X3 : Tingkat pendidikan Keterangan yang digunakan (0=Lulus SMA/Tidak Kuiah) dan (1=Lulus Kuliah)
set.seed(123)
X3 <- round(runif(n))
X3## [1] 0 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0
## [75] 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1X4 : Data IPK Peamar dengan skala 4
set.seed(3456)
X4 <- round(rnorm(n,3,0.5),2)
X4## [1] 3.38 3.55 3.01 3.55 2.37 2.97 2.47 3.83 3.28 2.38 2.88 2.18 2.98 3.11 3.10
## [16] 3.62 2.28 2.01 3.23 3.14 2.47 3.04 2.68 3.32 2.57 3.27 3.26 3.47 2.57 3.43
## [31] 3.38 4.26 3.15 3.61 3.01 3.00 3.86 2.28 2.58 3.10 2.67 2.26 3.49 2.67 2.93
## [46] 3.51 3.14 3.00 2.46 3.82 2.34 2.37 3.21 3.43 3.30 2.48 2.70 2.87 2.98 2.92
## [61] 2.66 2.88 2.83 2.59 2.41 3.28 2.60 3.31 2.79 2.99 2.54 2.37 4.00 2.12 2.93
## [76] 3.45 2.93 2.69 2.24 2.95 3.03 3.73 2.75 2.87 3.35 2.88 2.99 3.03 3.14 3.00
## [91] 2.98 2.93 3.67 4.48 2.88 2.89 3.08 3.26 2.52 2.57Menentukan koefisien
b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 3.2set.seed(123456)
datapendukung <- b0 + (b1*X1) + (b2*X2) + (b3*X3) + (b4*X4)
datapendukung## [1] 7.316 7.060 13.132 3.560 9.784 9.004 27.604 11.456 16.696 0.616
## [11] 18.416 34.476 8.736 12.152 23.420 24.284 -0.204 2.432 6.336 23.248
## [21] 13.604 22.428 14.776 27.324 10.924 5.664 27.132 41.804 11.224 6.976
## [31] 13.516 50.832 8.780 56.252 19.632 37.100 8.052 14.296 78.256 2.420
## [41] 5.044 31.232 28.668 29.544 15.876 10.732 27.048 37.100 0.372 11.424
## [51] 3.988 0.584 5.472 6.976 16.760 0.436 1.640 4.384 18.736 1.844
## [61] 11.212 15.716 54.556 18.788 10.412 6.496 11.020 12.792 4.628 20.068
## [71] 10.828 6.784 18.500 55.284 15.876 18.040 33.876 25.308 28.168 -1.560
## [81] 9.696 21.636 43.300 70.884 -0.280 12.216 26.268 5.396 9.248 23.100
## [91] 41.036 5.076 7.744 17.036 40.716 5.748 16.056 3.432 -2.936 24.424P <- exp(datapendukung)/(1+exp(datapendukung))
P## [1] 0.99933563 0.99914196 0.99999802 0.97234758 0.99994366 0.99987710
## [7] 1.00000000 0.99998941 0.99999994 0.64930827 0.99999999 1.00000000
## [13] 0.99983933 0.99999472 1.00000000 1.00000000 0.44917614 0.91923514
## [19] 0.99823176 1.00000000 0.99999876 1.00000000 0.99999962 1.00000000
## [25] 0.99998198 0.99654338 1.00000000 1.00000000 0.99998665 0.99906684
## [31] 0.99999865 1.00000000 0.99984625 1.00000000 1.00000000 1.00000000
## [37] 0.99968164 0.99999938 1.00000000 0.91833974 0.99359340 1.00000000
## [43] 1.00000000 1.00000000 0.99999987 0.99997817 1.00000000 1.00000000
## [49] 0.59194216 0.99998907 0.98180061 0.64198729 0.99581477 0.99906684
## [55] 0.99999995 0.60730550 0.83753494 0.98767836 0.99999999 0.86342109
## [61] 0.99998649 0.99999985 1.00000000 0.99999999 0.99996993 0.99849281
## [67] 0.99998363 0.99999722 0.99032032 1.00000000 0.99998016 0.99886954
## [73] 0.99999999 1.00000000 0.99999987 0.99999999 1.00000000 1.00000000
## [79] 1.00000000 0.17364665 0.99993847 1.00000000 1.00000000 1.00000000
## [85] 0.43045378 0.99999505 1.00000000 0.99548579 0.99990371 1.00000000
## [91] 1.00000000 0.99379392 0.99956685 0.99999996 1.00000000 0.99682099
## [97] 0.99999989 0.96868978 0.05040238 1.00000000set.seed(3)
y <- rbinom(n,1,P)
y## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1datagab <- data.frame(y, X1, X2, X3, X4)
datagab## y X1 X2 X3 X4
## 1 1 2 1 0 3.38
## 2 1 1 1 1 3.55
## 3 1 4 1 0 3.01
## 4 1 0 1 1 3.55
## 5 1 3 0 1 2.37
## 6 1 3 0 0 2.97
## 7 1 8 0 1 2.47
## 8 1 2 1 1 3.83
## 9 1 4 1 1 3.28
## 10 1 1 1 0 2.38
## 11 1 5 0 1 2.88
## 12 1 11 0 0 2.18
## 13 1 2 1 1 2.98
## 14 1 3 0 1 3.11
## 15 1 7 0 0 3.10
## 16 1 6 0 1 3.62
## 17 0 1 0 0 2.28
## 18 1 2 0 0 2.01
## 19 1 2 0 0 3.23
## 20 1 6 1 1 3.14
## 21 1 4 0 1 2.47
## 22 1 6 0 1 3.04
## 23 1 4 1 1 2.68
## 24 1 7 1 1 3.32
## 25 1 3 1 1 2.57
## 26 1 1 0 1 3.27
## 27 1 7 1 1 3.26
## 28 1 11 1 1 3.47
## 29 1 4 0 0 2.57
## 30 1 2 0 0 3.43
## 31 1 3 1 1 3.38
## 32 1 13 0 1 4.26
## 33 1 2 0 1 3.15
## 34 1 15 1 1 3.61
## 35 1 6 0 0 3.01
## 36 1 11 0 0 3.00
## 37 1 1 1 1 3.86
## 38 1 5 1 0 2.28
## 39 1 23 1 0 2.58
## 40 1 1 0 0 3.10
## 41 1 2 1 0 2.67
## 42 1 10 0 0 2.26
## 43 1 8 1 0 3.49
## 44 1 9 1 0 2.67
## 45 1 5 0 0 2.93
## 46 1 3 0 0 3.51
## 47 1 8 0 0 3.14
## 48 1 11 0 0 3.00
## 49 1 1 0 0 2.46
## 50 1 2 1 1 3.82
## 51 1 2 1 0 2.34
## 52 1 1 1 0 2.37
## 53 1 1 0 1 3.21
## 54 1 2 0 0 3.43
## 55 1 4 1 1 3.30
## 56 0 1 0 0 2.48
## 57 1 1 1 0 2.70
## 58 1 1 0 1 2.87
## 59 1 5 0 1 2.98
## 60 0 1 0 0 2.92
## 61 1 3 1 1 2.66
## 62 1 5 0 0 2.88
## 63 1 16 1 0 2.83
## 64 1 6 1 0 2.59
## 65 1 3 1 1 2.41
## 66 1 2 0 0 3.28
## 67 1 3 1 1 2.60
## 68 1 3 0 1 3.31
## 69 1 1 1 1 2.79
## 70 1 6 1 0 2.99
## 71 1 3 1 1 2.54
## 72 1 2 1 1 2.37
## 73 1 4 0 1 4.00
## 74 1 17 0 0 2.12
## 75 1 5 0 0 2.93
## 76 1 5 1 0 3.45
## 77 1 10 1 0 2.93
## 78 1 7 1 1 2.69
## 79 1 9 1 0 2.24
## 80 0 0 0 0 2.95
## 81 1 3 1 0 3.03
## 82 1 5 1 1 3.73
## 83 1 13 0 0 2.75
## 84 1 20 0 1 2.87
## 85 0 0 0 0 3.35
## 86 1 4 0 0 2.88
## 87 1 7 1 1 2.99
## 88 1 1 1 1 3.03
## 89 1 2 1 1 3.14
## 90 1 7 0 0 3.00
## 91 1 12 1 0 2.98
## 92 1 1 1 1 2.93
## 93 1 2 0 0 3.67
## 94 1 3 1 1 4.48
## 95 1 12 1 0 2.88
## 96 1 2 1 0 2.89
## 97 1 4 1 1 3.08
## 98 1 1 1 0 3.26
## 99 0 0 0 0 2.52
## 100 1 7 0 1 2.57modelreglog <- glm(y~X1+X2+X3+X4, family = binomial(link = logit), data = datagab)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelreglog)##
## Call:
## glm(formula = y ~ X1 + X2 + X3 + X4, family = binomial(link = logit),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -27.672 5527.734 -0.005 0.996
## X1 20.863 5527.725 0.004 0.997
## X2 22.875 20015.984 0.001 0.999
## X3 21.341 20017.177 0.001 0.999
## X4 2.401 3.180 0.755 0.450
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 45.3935 on 99 degrees of freedom
## Residual deviance: 6.1118 on 95 degrees of freedom
## AIC: 16.112
##
## Number of Fisher Scoring iterations: 24