Y : Keputusan menolak/menerima pelamar Kerja pada PT A posisi B X1 : Lama Pengalaman Kerja sebelumnya (bulan) X2 : Status pekerjaan saat ini (0: Pekerja 1: Tidak Bekerja) X3 : Tingkat pendidikan (0: Lulus Sekolah Menengah, 1: Lulusan Perguruan Tinggi) X4 : IPK (Skala 4)
X1 : Lama Pengalaman Kerja sebelumnya (bulan) Membangkitkan Variabel XL 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(24)
x2 <- round(runif(n))
x2## [1] 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1
## [38] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1
## [75] 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0X3 : Tingkat Pendidikan Keterangan yang digunakan (0: Lulus Sekolah Menengah, 1: Lulusan Perguruan Tinggi)
set.seed(124)
x3 <- round(runif(n))
x3## [1] 0 0 1 0 0 0 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0
## [38] 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 1 1 1 1
## [75] 1 0 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1X4 adalah data ipk pelamar dengan skala 4
set.seed(436)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.66 2.67 2.32 4.09 4.14 1.85 2.81 3.34 2.52 3.09 3.15 3.32 3.64 4.14 2.65
## [16] 2.91 3.06 3.13 3.07 2.51 2.29 3.84 2.90 3.78 2.81 2.55 2.73 3.20 2.39 3.25
## [31] 2.71 2.45 3.02 2.36 2.57 3.64 2.88 1.95 2.51 3.25 3.16 2.71 3.68 2.35 2.79
## [46] 3.10 2.54 3.00 3.00 3.28 3.12 2.74 2.75 3.87 2.74 3.54 3.84 2.98 3.71 3.05
## [61] 2.87 3.23 3.56 2.62 2.74 2.81 2.11 3.68 3.64 3.61 3.03 2.46 3.08 2.91 2.37
## [76] 3.03 3.02 3.49 2.62 2.03 3.01 3.27 3.25 2.89 2.02 3.46 2.92 2.86 4.08 2.43
## [91] 2.81 2.23 2.34 2.78 3.51 2.03 2.54 2.87 2.90 2.91##Membangkitkan data Y
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] 7.712 1.044 13.624 2.588 13.248 5.920 28.692 7.188 14.264 2.388
## [11] 19.780 40.824 10.848 15.948 21.980 19.812 4.992 8.716 9.024 18.032
## [21] 13.028 25.488 12.780 25.596 8.492 1.160 24.936 38.240 13.848 6.400
## [31] 10.872 42.340 5.664 51.752 18.724 41.848 2.216 15.940 78.032 3.400
## [41] 6.112 35.372 31.476 28.020 15.428 9.420 27.828 37.100 2.100 6.496
## [51] 5.984 4.468 4.000 8.884 12.268 4.328 4.788 4.736 18.372 4.960
## [61] 8.684 16.836 56.392 18.384 11.468 4.992 6.752 14.476 4.648 21.552
## [71] 11.896 6.572 15.556 61.012 17.284 16.196 34.164 27.868 28.884 -4.004
## [81] 9.632 16.964 48.100 70.948 -1.836 16.772 25.544 2.152 9.056 21.776
## [91] 39.992 0.136 3.988 8.396 42.232 2.996 11.628 1.684 0.980 25.512p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.9995528 0.7396211 0.9999988 0.9300853 0.9999982 0.9973220 1.0000000
## [8] 0.9992450 0.9999994 0.9159077 1.0000000 1.0000000 0.9999806 0.9999999
## [15] 1.0000000 1.0000000 0.9932538 0.9998361 0.9998795 1.0000000 0.9999978
## [22] 1.0000000 0.9999972 1.0000000 0.9997949 0.7613327 1.0000000 1.0000000
## [29] 0.9999990 0.9983412 0.9999810 1.0000000 0.9965434 1.0000000 1.0000000
## [36] 1.0000000 0.9016771 0.9999999 1.0000000 0.9677045 0.9977888 1.0000000
## [43] 1.0000000 1.0000000 0.9999998 0.9999189 1.0000000 1.0000000 0.8909032
## [50] 0.9984928 0.9974876 0.9886598 0.9820138 0.9998614 0.9999953 0.9869779
## [57] 0.9917397 0.9913026 1.0000000 0.9930359 0.9998308 1.0000000 1.0000000
## [64] 1.0000000 0.9999895 0.9932538 0.9988328 0.9999995 0.9905102 1.0000000
## [71] 0.9999932 0.9986030 0.9999998 1.0000000 1.0000000 0.9999999 1.0000000
## [78] 1.0000000 1.0000000 0.0179157 0.9999344 1.0000000 1.0000000 1.0000000
## [85] 0.1375251 0.9999999 1.0000000 0.8958555 0.9998833 1.0000000 1.0000000
## [92] 0.5339477 0.9818006 0.9997743 1.0000000 0.9523931 0.9999911 0.8434335
## [99] 0.7271082 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 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 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 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 0 0 3.66
## 2 1 1 0 0 2.67
## 3 1 4 1 1 2.32
## 4 1 0 1 0 4.09
## 5 1 3 1 0 4.14
## 6 1 3 1 0 1.85
## 7 1 8 0 1 2.81
## 8 1 2 1 0 3.34
## 9 1 4 1 1 2.52
## 10 1 1 0 0 3.09
## 11 1 5 1 1 3.15
## 12 1 11 0 1 3.32
## 13 1 2 1 1 3.64
## 14 1 3 1 1 4.14
## 15 1 7 0 0 2.65
## 16 1 6 1 0 2.91
## 17 1 1 0 1 3.06
## 18 1 2 0 1 3.13
## 19 1 2 1 1 3.07
## 20 1 6 0 0 2.51
## 21 1 4 0 1 2.29
## 22 1 6 1 1 3.84
## 23 1 4 1 0 2.90
## 24 1 7 0 0 3.78
## 25 1 3 0 0 2.81
## 26 1 1 1 0 2.55
## 27 1 7 0 1 2.73
## 28 1 11 1 0 3.20
## 29 1 4 1 1 2.39
## 30 1 2 0 0 3.25
## 31 1 3 0 1 2.71
## 32 1 13 0 0 2.45
## 33 1 2 0 0 3.02
## 34 1 15 0 1 2.36
## 35 1 6 1 0 2.57
## 36 1 11 0 1 3.64
## 37 1 1 1 0 2.88
## 38 1 5 1 1 1.95
## 39 1 23 1 0 2.51
## 40 1 1 1 0 3.25
## 41 1 2 0 0 3.16
## 42 1 10 0 1 2.71
## 43 1 8 0 1 3.68
## 44 1 9 0 0 2.35
## 45 1 5 0 0 2.79
## 46 1 3 0 0 3.10
## 47 1 8 0 1 2.54
## 48 1 11 0 0 3.00
## 49 1 1 0 0 3.00
## 50 1 2 0 0 3.28
## 51 1 2 0 0 3.12
## 52 1 1 1 1 2.74
## 53 1 1 0 1 2.75
## 54 1 2 1 0 3.87
## 55 1 4 1 0 2.74
## 56 1 1 1 0 3.54
## 57 1 1 0 0 3.84
## 58 1 1 0 1 2.98
## 59 1 5 0 0 3.71
## 60 1 1 0 1 3.05
## 61 1 3 0 0 2.87
## 62 1 5 0 0 3.23
## 63 1 16 0 0 3.56
## 64 1 6 0 0 2.62
## 65 1 3 1 1 2.74
## 66 1 2 0 0 2.81
## 67 1 3 1 0 2.11
## 68 1 3 1 1 3.68
## 69 1 1 1 0 3.64
## 70 1 6 0 0 3.61
## 71 1 3 0 1 3.03
## 72 1 2 0 1 2.46
## 73 1 4 0 1 3.08
## 74 1 17 1 1 2.91
## 75 1 5 1 1 2.37
## 76 1 5 0 0 3.03
## 77 1 10 1 0 3.02
## 78 1 7 1 1 3.49
## 79 1 9 0 0 2.62
## 80 0 0 1 0 2.03
## 81 1 3 1 0 3.01
## 82 1 5 0 0 3.27
## 83 1 13 1 1 3.25
## 84 1 20 0 1 2.89
## 85 0 0 0 1 2.02
## 86 1 4 0 1 3.46
## 87 1 7 0 1 2.92
## 88 1 1 1 0 2.86
## 89 1 2 0 0 4.08
## 90 1 7 1 0 2.43
## 91 1 12 0 0 2.81
## 92 0 1 1 0 2.23
## 93 1 2 1 0 2.34
## 94 1 3 0 0 2.78
## 95 1 12 0 0 3.51
## 96 1 2 1 0 2.03
## 97 1 4 1 0 2.54
## 98 1 1 0 0 2.87
## 99 0 0 0 1 2.90
## 100 1 7 0 1 2.91modelreglog <- 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) -348.52 99574.21 -0.004 0.997
## x1 60.62 13275.69 0.005 0.996
## x2 10.28 46349.95 0.000 1.000
## x3 -11.97 46743.86 0.000 1.000
## x4 116.23 29443.15 0.004 0.997
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3.3589e+01 on 99 degrees of freedom
## Residual deviance: 5.1457e-08 on 95 degrees of freedom
## AIC: 10
##
## Number of Fisher Scoring iterations: 25