Y : Keputusan menolak/menerima pelamar kerja pada PT A posisi B X1 : Lama pengalaman kerja sebelum (bulan) X2 : Status pekerjaan saat ini (0: Bekerja, 1: Tidak bekerja) X3 : Tingkat pendidikan (0: Lulusan Sekolah Menengah, 1: Lulusan Peruruan Tinggi) X4 : IPK (skala 4)
X1 : Lama pengalaman kerja sebelumnya (bulan) Membangkitkan variabel 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 7X2 : Status pekerjaan Keterangan yang digunakan (0=Tidak bekerja) dan (1=Bekerja)
set.seed(157)
X2 <- round(runif(n))
X2## [1] 1 0 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 0 1
## [38] 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0
## [75] 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0X3 : Tingkat pendidikan keterangan yang digunakan (0=lulus SMA/Tidak kuliah) dan (1=lulus kuliah)
set.seed(111)
X3 <- round(runif(n))
X3## [1] 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0
## [38] 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0
## [75] 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1X4 adalah data IPK Pelamar dengan skala 4
set.seed(123)
X4 <- round(rnorm(n,3,0.5),2)
X4## [1] 2.72 2.88 3.78 3.04 3.06 3.86 3.23 2.37 2.66 2.78 3.61 3.18 3.20 3.06 2.72
## [16] 3.89 3.25 2.02 3.35 2.76 2.47 2.89 2.49 2.64 2.69 2.16 3.42 3.08 2.43 3.63
## [31] 3.21 2.85 3.45 3.44 3.41 3.34 3.28 2.97 2.85 2.81 2.65 2.90 2.37 4.08 3.60
## [46] 2.44 2.80 2.77 3.39 2.96 3.13 2.99 2.98 3.68 2.89 3.76 2.23 3.29 3.06 3.11
## [61] 3.19 2.75 2.83 2.49 2.46 3.15 3.22 3.03 3.46 4.03 2.75 1.85 3.50 2.65 2.66
## [76] 3.51 2.86 2.39 3.09 2.93 3.00 3.19 2.81 3.32 2.89 3.17 3.55 3.22 2.84 3.57
## [91] 3.50 3.27 3.12 2.69 3.68 2.70 4.09 3.77 2.88 2.49Menentukan koef
b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 3.2set.seed(1)
datapendukung <- b0+(b1*X1)+(b2*X2)+(b3*X3)+(b4*X4)
datapendukung## [1] 7.904 4.416 15.596 1.928 9.792 12.352 27.836 6.284 12.012 1.896
## [11] 20.752 40.376 6.240 9.792 22.204 22.448 3.400 5.164 7.220 22.032
## [21] 10.904 19.748 11.468 21.948 11.308 -0.088 27.644 37.356 13.476 10.816
## [31] 10.272 46.820 7.040 53.008 20.912 40.888 3.496 18.704 81.820 4.192
## [41] 7.680 33.280 27.784 36.756 21.220 7.308 26.460 39.064 6.548 8.672
## [51] 8.716 4.768 2.036 7.776 15.448 7.232 2.336 3.528 16.292 5.652
## [61] 10.208 15.800 54.056 18.468 10.072 6.080 10.304 9.196 3.572 23.396
## [71] 11.000 4.620 14.200 56.980 18.212 18.232 33.652 24.348 30.388 1.076
## [81] 9.600 19.908 43.492 70.124 -1.252 13.644 28.060 6.004 8.288 27.624
## [91] 44.900 5.664 8.684 10.808 45.476 4.640 19.288 5.064 0.916 24.168p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.9996309 0.9880618 0.9999998 0.8730279 0.9999441 0.9999957 1.0000000
## [8] 0.9981376 0.9999939 0.8694381 1.0000000 1.0000000 0.9980539 0.9999441
## [15] 1.0000000 1.0000000 0.9677045 0.9943137 0.9992687 1.0000000 0.9999816
## [22] 1.0000000 0.9999895 1.0000000 0.9999877 0.4780142 1.0000000 1.0000000
## [29] 0.9999986 0.9999799 0.9999654 1.0000000 0.9991246 1.0000000 1.0000000
## [36] 1.0000000 0.9705737 1.0000000 1.0000000 0.9851091 0.9995382 1.0000000
## [43] 1.0000000 1.0000000 1.0000000 0.9993303 1.0000000 1.0000000 0.9985691
## [50] 0.9998287 0.9998361 0.9915742 0.8845253 0.9995805 0.9999998 0.9992774
## [57] 0.9118150 0.9714740 0.9999999 0.9965018 0.9999631 0.9999999 1.0000000
## [64] 1.0000000 0.9999578 0.9977170 0.9999665 0.9998986 0.9726684 1.0000000
## [71] 0.9999833 0.9902433 0.9999993 1.0000000 1.0000000 1.0000000 1.0000000
## [78] 1.0000000 1.0000000 0.7457363 0.9999323 1.0000000 1.0000000 1.0000000
## [85] 0.2223541 0.9999988 1.0000000 0.9975372 0.9997485 1.0000000 1.0000000
## [92] 0.9965434 0.9998308 0.9999798 1.0000000 0.9904347 1.0000000 0.9937195
## [99] 0.7142264 1.0000000set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 1 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
## [38] 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 1
## [75] 1 1 1 1 1 1 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 1 2.72
## 2 1 1 0 1 2.88
## 3 1 4 1 0 3.78
## 4 1 0 1 1 3.04
## 5 1 3 1 0 3.06
## 6 1 3 1 0 3.86
## 7 1 8 1 0 3.23
## 8 1 2 0 1 2.37
## 9 1 4 1 0 2.66
## 10 1 1 1 0 2.78
## 11 1 5 0 1 3.61
## 12 1 11 0 1 3.18
## 13 1 2 0 0 3.20
## 14 1 3 1 0 3.06
## 15 1 7 0 0 2.72
## 16 1 6 0 0 3.89
## 17 1 1 1 0 3.25
## 18 1 2 0 1 2.02
## 19 1 2 1 0 3.35
## 20 1 6 1 1 2.76
## 21 1 4 0 0 2.47
## 22 1 6 1 0 2.89
## 23 1 4 1 0 2.49
## 24 1 7 0 0 2.64
## 25 1 3 1 1 2.69
## 26 0 1 1 0 2.16
## 27 1 7 1 1 3.42
## 28 1 11 0 0 3.08
## 29 1 4 0 1 2.43
## 30 1 2 1 1 3.63
## 31 1 3 1 0 3.21
## 32 1 13 1 1 2.85
## 33 1 2 0 0 3.45
## 34 1 15 1 0 3.44
## 35 1 6 0 0 3.41
## 36 1 11 0 1 3.34
## 37 1 1 1 0 3.28
## 38 1 5 0 1 2.97
## 39 1 23 1 1 2.85
## 40 1 1 0 1 2.81
## 41 1 2 1 1 2.65
## 42 1 10 0 0 2.90
## 43 1 8 1 1 2.37
## 44 1 9 1 1 4.08
## 45 1 5 1 1 3.60
## 46 1 3 0 0 2.44
## 47 1 8 1 0 2.80
## 48 1 11 0 1 2.77
## 49 1 1 1 1 3.39
## 50 1 2 1 1 2.96
## 51 1 2 0 1 3.13
## 52 1 1 0 1 2.99
## 53 0 1 0 0 2.98
## 54 1 2 0 0 3.68
## 55 1 4 1 1 2.89
## 56 1 1 0 1 3.76
## 57 1 1 0 1 2.23
## 58 1 1 1 0 3.29
## 59 1 5 0 0 3.06
## 60 1 1 1 1 3.11
## 61 1 3 1 0 3.19
## 62 1 5 1 0 2.75
## 63 1 16 0 0 2.83
## 64 1 6 1 0 2.49
## 65 1 3 0 1 2.46
## 66 1 2 0 0 3.15
## 67 1 3 1 0 3.22
## 68 1 3 0 0 3.03
## 69 1 1 0 0 3.46
## 70 1 6 1 0 4.03
## 71 1 3 0 1 2.75
## 72 1 2 0 1 1.85
## 73 1 4 0 0 3.50
## 74 1 17 0 0 2.65
## 75 1 5 1 1 2.66
## 76 1 5 1 0 3.51
## 77 1 10 1 0 2.86
## 78 1 7 1 1 2.39
## 79 1 9 0 0 3.09
## 80 1 0 0 1 2.93
## 81 1 3 1 0 3.00
## 82 1 5 1 1 3.19
## 83 1 13 0 0 2.81
## 84 1 20 1 0 3.32
## 85 0 0 1 0 2.89
## 86 1 4 1 0 3.17
## 87 1 7 1 1 3.55
## 88 1 1 1 1 3.22
## 89 1 2 1 1 2.84
## 90 1 7 0 1 3.57
## 91 1 12 0 1 3.50
## 92 1 1 0 1 3.27
## 93 1 2 0 1 3.12
## 94 1 3 0 1 2.69
## 95 1 12 0 1 3.68
## 96 1 2 0 0 2.70
## 97 1 4 1 1 4.09
## 98 1 1 1 0 3.77
## 99 0 0 0 1 2.88
## 100 1 7 0 1 2.49modelreglog <- glm(y ~ X1 + X2 + X3 + X4, family = binomial(link="logit"),data=datagab)## Warning: glm.fit: algorithm did not converge## 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) -2437.7 158954.0 -0.015 0.988
## X1 422.5 21440.9 0.020 0.984
## X2 371.6 103370.5 0.004 0.997
## X3 698.3 105741.9 0.007 0.995
## X4 598.8 30550.1 0.020 0.984
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3.3589e+01 on 99 degrees of freedom
## Residual deviance: 1.2846e-06 on 95 degrees of freedom
## AIC: 10
##
## Number of Fisher Scoring iterations: 25