##Skenario Y: Keputusan menolak/menrima 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 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 7Keterangan yang digunakan (Tidak Bekerja) dan (Bekerja)
set.seed(322) ## mengunci data set seed
x2 <- round(runif(n))
x2## [1] 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1
## [38] 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 0 1
## [75] 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1x3: Tingkat pendidikan x3: Tingkat pendidikan (0: Lulus Sekolah Menengah, 1: Lulus Sekolah Kejuruan)
set.seed(100)
x3 <- round(runif(n))
x3## [1] 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0 1 1
## [75] 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 1X4: IPK (skala 4)
set.seed(11)
x4 <- round(runif(n))
x4## [1] 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0
## [38] 0 0 0 0 0 0 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0
## [75] 1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0b0 <- -10
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 1.2set.seed(2)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] -3.0 -6.5 7.9 -9.5 0.5 2.2 21.2 -3.0 7.9 -6.0 10.2 31.7 -1.8 2.2 18.9
## [16] 15.4 -6.5 -3.0 -3.0 14.2 7.2 15.4 7.2 17.7 0.5 -6.5 17.7 31.7 7.2 -3.0
## [31] 1.7 38.2 -2.5 45.7 14.9 32.4 -6.0 10.2 73.7 -6.5 -3.0 27.7 20.7 25.4 10.7
## [46] 1.7 22.4 31.7 -4.8 -1.8 -1.3 -5.3 -6.0 -2.5 6.7 -6.5 -6.0 -5.3 10.7 -5.3
## [61] 0.5 11.4 49.2 15.4 1.7 -2.5 1.0 0.5 -6.0 14.9 2.2 -3.0 6.7 52.7 11.9
## [76] 11.9 27.7 17.7 24.7 -9.5 1.0 10.2 39.4 63.2 -8.3 7.2 17.2 -4.8 -1.3 17.2
## [91] 34.7 -4.8 -2.5 0.5 35.9 -3.0 8.4 -6.0 -8.3 17.7p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 4.742587e-02 1.501182e-03 9.996294e-01 7.484623e-05 6.224593e-01
## [6] 9.002495e-01 1.000000e+00 4.742587e-02 9.996294e-01 2.472623e-03
## [11] 9.999628e-01 1.000000e+00 1.418511e-01 9.002495e-01 1.000000e+00
## [16] 9.999998e-01 1.501182e-03 4.742587e-02 4.742587e-02 9.999993e-01
## [21] 9.992540e-01 9.999998e-01 9.992540e-01 1.000000e+00 6.224593e-01
## [26] 1.501182e-03 1.000000e+00 1.000000e+00 9.992540e-01 4.742587e-02
## [31] 8.455347e-01 1.000000e+00 7.585818e-02 1.000000e+00 9.999997e-01
## [36] 1.000000e+00 2.472623e-03 9.999628e-01 1.000000e+00 1.501182e-03
## [41] 4.742587e-02 1.000000e+00 1.000000e+00 1.000000e+00 9.999775e-01
## [46] 8.455347e-01 1.000000e+00 1.000000e+00 8.162571e-03 1.418511e-01
## [51] 2.141650e-01 4.966802e-03 2.472623e-03 7.585818e-02 9.987706e-01
## [56] 1.501182e-03 2.472623e-03 4.966802e-03 9.999775e-01 4.966802e-03
## [61] 6.224593e-01 9.999888e-01 1.000000e+00 9.999998e-01 8.455347e-01
## [66] 7.585818e-02 7.310586e-01 6.224593e-01 2.472623e-03 9.999997e-01
## [71] 9.002495e-01 4.742587e-02 9.987706e-01 1.000000e+00 9.999932e-01
## [76] 9.999932e-01 1.000000e+00 1.000000e+00 1.000000e+00 7.484623e-05
## [81] 7.310586e-01 9.999628e-01 1.000000e+00 1.000000e+00 2.484551e-04
## [86] 9.992540e-01 1.000000e+00 8.162571e-03 2.141650e-01 1.000000e+00
## [91] 1.000000e+00 8.162571e-03 7.585818e-02 6.224593e-01 1.000000e+00
## [96] 4.742587e-02 9.997752e-01 2.472623e-03 2.484551e-04 1.000000e+00set.seed(3)
y <- rbinom(n,1,p)
y## [1] 0 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 0
## [38] 1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 0 2 0 0 0
## 2 0 1 0 0 0
## 3 1 4 0 1 1
## 4 0 0 1 0 0
## 5 1 3 0 0 0
## 6 1 3 1 0 1
## 7 1 8 1 1 0
## 8 0 2 0 0 0
## 9 1 4 0 1 1
## 10 0 1 1 0 0
## 11 1 5 0 1 0
## 12 1 11 1 1 0
## 13 0 2 0 0 1
## 14 1 3 1 0 1
## 15 1 7 1 1 1
## 16 1 6 1 1 1
## 17 0 1 0 0 0
## 18 0 2 0 0 0
## 19 0 2 0 0 0
## 20 1 6 1 1 0
## 21 1 4 1 1 0
## 22 1 6 1 1 1
## 23 1 4 1 1 0
## 24 1 7 1 1 0
## 25 1 3 0 0 0
## 26 0 1 0 0 0
## 27 1 7 1 1 0
## 28 1 11 1 1 0
## 29 1 4 1 1 0
## 30 0 2 0 0 0
## 31 1 3 0 0 1
## 32 1 13 0 1 0
## 33 0 2 1 0 0
## 34 1 15 1 1 0
## 35 1 6 0 1 1
## 36 1 11 0 1 1
## 37 0 1 1 0 0
## 38 1 5 0 1 0
## 39 1 23 1 1 0
## 40 0 1 0 0 0
## 41 0 2 0 0 0
## 42 1 10 0 1 0
## 43 1 8 0 1 0
## 44 1 9 0 1 1
## 45 1 5 1 1 0
## 46 1 3 0 0 1
## 47 1 8 1 1 1
## 48 1 11 1 1 0
## 49 0 1 1 0 1
## 50 0 2 0 0 1
## 51 0 2 1 0 1
## 52 0 1 0 0 1
## 53 0 1 1 0 0
## 54 0 2 1 0 0
## 55 1 4 0 1 0
## 56 0 1 0 0 0
## 57 0 1 1 0 0
## 58 0 1 0 0 1
## 59 1 5 1 1 0
## 60 0 1 0 0 1
## 61 1 3 0 0 0
## 62 1 5 0 1 1
## 63 1 16 1 1 0
## 64 1 6 1 1 1
## 65 1 3 0 0 1
## 66 0 2 1 0 0
## 67 0 3 1 0 0
## 68 1 3 0 0 0
## 69 0 1 1 0 0
## 70 1 6 0 1 1
## 71 1 3 1 0 1
## 72 0 2 0 0 0
## 73 1 4 0 1 0
## 74 1 17 1 1 0
## 75 1 5 1 1 1
## 76 1 5 1 1 1
## 77 1 10 0 1 0
## 78 1 7 1 1 0
## 79 1 9 1 1 0
## 80 0 0 1 0 0
## 81 1 3 1 0 0
## 82 1 5 0 1 0
## 83 1 13 0 1 1
## 84 1 20 1 1 0
## 85 0 0 1 0 1
## 86 1 4 1 1 0
## 87 1 7 0 1 0
## 88 0 1 1 0 1
## 89 0 2 1 0 1
## 90 1 7 0 1 0
## 91 1 12 0 1 0
## 92 0 1 1 0 1
## 93 0 2 1 0 0
## 94 1 3 0 0 0
## 95 1 12 0 1 1
## 96 0 2 0 0 0
## 97 1 4 1 1 1
## 98 0 1 1 0 0
## 99 0 0 1 0 1
## 100 1 7 1 1 0modelreglog <- 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) -162.29 42024.25 -0.004 0.997
## x1 61.10 15805.21 0.004 0.997
## x2 -20.99 9820.82 -0.002 0.998
## x3 -38.43 29861.73 -0.001 0.999
## x4 20.28 8859.52 0.002 0.998
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 132.8128 on 99 degrees of freedom
## Residual deviance: 2.7726 on 95 degrees of freedom
## AIC: 12.773
##
## Number of Fisher Scoring iterations: 25