Y : Keputusan menolak/menerima pelamar kerja PT A posisi B x1 : Lama pengalaman kerja sebelumnya (sebulan) 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)
## Membangkitkan data x1 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 digantikan (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
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.10 3.78 3.46 3.18 3.09 2.58 3.49 3.90 3.06 2.94 2.89 3.72 3.20 3.46 3.72
## [16] 2.81 3.10 2.60 3.15 3.70 3.51 3.24 2.66 3.08 2.81 3.47 2.68 2.95 3.52 3.19
## [31] 2.37 2.61 3.21 2.81 2.39 3.51 3.22 2.38 2.70 3.33 4.03 3.25 2.13 3.36 3.01
## [46] 2.30 3.63 2.94 2.64 2.39 3.30 2.42 3.22 3.10 2.65 2.54 2.49 3.30 3.87 2.83
## [61] 3.60 3.51 2.98 3.69 1.90 2.94 2.34 2.29 3.44 3.20 3.34 4.18 2.49 3.15 3.78
## [76] 2.58 3.66 3.24 3.09 3.13 4.30 3.54 2.20 2.96 3.18 2.56 1.34 2.77 3.22 2.70
## [91] 3.34 3.32 2.69 3.21 3.44 3.01 3.91 2.77 2.94 3.38menentukan koef
b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 1.2set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 2.920 -0.264 7.652 -3.984 3.708 3.096 21.688 3.380 7.172 -3.472
## [11] 12.668 34.664 -0.160 4.152 17.964 13.372 -3.280 1.820 0.280 17.640
## [21] 7.212 14.388 6.692 17.196 6.072 -2.836 19.916 31.040 9.924 3.028
## [31] 2.844 40.832 -0.148 45.372 12.868 34.412 -3.136 12.056 75.940 -0.804
## [41] 4.036 27.900 22.756 27.732 13.312 2.260 21.856 33.728 -1.132 2.068
## [51] 2.660 -1.896 -3.636 -0.280 9.380 -1.752 -1.812 -3.040 11.144 -0.904
## [61] 4.320 11.212 48.576 14.928 4.480 -0.472 2.808 2.248 -3.372 14.340
## [71] 6.208 3.716 5.988 52.280 14.236 10.096 28.892 20.588 24.208 -4.544
## [81] 5.160 13.948 37.140 63.052 -6.684 6.572 18.308 -0.976 3.064 19.440
## [91] 37.708 -0.816 1.928 6.052 37.828 -0.388 10.892 -3.676 -4.772 20.256p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.948826299 0.434380675 0.999525133 0.018271004 0.976060622 0.956727447
## [7] 1.000000000 0.967073605 0.999232803 0.030119502 0.999996850 1.000000000
## [13] 0.460085115 0.984510772 0.999999984 0.999998442 0.036263716 0.860566127
## [19] 0.569546224 0.999999978 0.999262863 0.999999436 0.998760739 0.999999966
## [25] 0.997698752 0.055409523 0.999999998 1.000000000 0.999951018 0.953823164
## [31] 0.945007705 1.000000000 0.463067390 1.000000000 0.999997421 1.000000000
## [37] 0.041646475 0.999994190 1.000000000 0.309170531 0.982638736 1.000000000
## [43] 1.000000000 1.000000000 0.999998345 0.905509631 1.000000000 1.000000000
## [49] 0.243792197 0.887753822 0.934624667 0.130561866 0.025680683 0.430453776
## [55] 0.999915612 0.147795117 0.140396581 0.045651171 0.999985538 0.288229190
## [61] 0.986874682 0.999986489 1.000000000 0.999999671 0.988793594 0.384142980
## [67] 0.943106601 0.904477880 0.033182087 0.999999408 0.997990785 0.976246843
## [73] 0.997497601 1.000000000 0.999999343 0.999958757 1.000000000 0.999999999
## [79] 1.000000000 0.010518973 0.994291079 0.999999124 1.000000000 1.000000000
## [85] 0.001249202 0.998602958 0.999999989 0.273686192 0.955383113 0.999999996
## [91] 1.000000000 0.306613408 0.873027885 0.997652373 1.000000000 0.404198853
## [97] 0.999981394 0.024698600 0.008392408 0.999999998set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 1 1 3.10
## 2 1 1 0 1 3.78
## 3 1 4 1 0 3.46
## 4 0 0 1 1 3.18
## 5 1 3 1 0 3.09
## 6 1 3 1 0 2.58
## 7 1 8 1 0 3.49
## 8 1 2 0 1 3.90
## 9 1 4 1 0 3.06
## 10 0 1 1 0 2.94
## 11 1 5 0 1 2.89
## 12 1 11 0 1 3.72
## 13 1 2 0 0 3.20
## 14 1 3 1 0 3.46
## 15 1 7 0 0 3.72
## 16 1 6 0 0 2.81
## 17 1 1 1 0 3.10
## 18 1 2 0 1 2.60
## 19 1 2 1 0 3.15
## 20 1 6 1 1 3.70
## 21 1 4 0 0 3.51
## 22 1 6 1 0 3.24
## 23 1 4 1 0 2.66
## 24 1 7 0 0 3.08
## 25 1 3 1 1 2.81
## 26 0 1 1 0 3.47
## 27 1 7 1 1 2.68
## 28 1 11 0 0 2.95
## 29 1 4 0 1 3.52
## 30 1 2 1 1 3.19
## 31 1 3 1 0 2.37
## 32 1 13 1 1 2.61
## 33 0 2 0 0 3.21
## 34 1 15 1 0 2.81
## 35 1 6 0 0 2.39
## 36 1 11 0 1 3.51
## 37 0 1 1 0 3.22
## 38 1 5 0 1 2.38
## 39 1 23 1 1 2.70
## 40 1 1 0 1 3.33
## 41 1 2 1 1 4.03
## 42 1 10 0 0 3.25
## 43 1 8 1 1 2.13
## 44 1 9 1 1 3.36
## 45 1 5 1 1 3.01
## 46 1 3 0 0 2.30
## 47 1 8 1 0 3.63
## 48 1 11 0 1 2.94
## 49 1 1 1 1 2.64
## 50 0 2 1 1 2.39
## 51 1 2 0 1 3.30
## 52 0 1 0 1 2.42
## 53 0 1 0 0 3.22
## 54 0 2 0 0 3.10
## 55 1 4 1 1 2.65
## 56 0 1 0 1 2.54
## 57 1 1 0 1 2.49
## 58 0 1 1 0 3.30
## 59 1 5 0 0 3.87
## 60 1 1 1 1 2.83
## 61 0 3 1 0 3.60
## 62 1 5 1 0 3.51
## 63 1 16 0 0 2.98
## 64 1 6 1 0 3.69
## 65 1 3 0 1 1.90
## 66 1 2 0 0 2.94
## 67 1 3 1 0 2.34
## 68 1 3 0 0 2.29
## 69 0 1 0 0 3.44
## 70 1 6 1 0 3.20
## 71 1 3 0 1 3.34
## 72 1 2 0 1 4.18
## 73 1 4 0 0 2.49
## 74 1 17 0 0 3.15
## 75 1 5 1 1 3.78
## 76 1 5 1 0 2.58
## 77 1 10 1 0 3.66
## 78 1 7 1 1 3.24
## 79 1 9 0 0 3.09
## 80 0 0 0 1 3.13
## 81 1 3 1 0 4.30
## 82 1 5 1 1 3.54
## 83 1 13 0 0 2.20
## 84 1 20 1 0 2.96
## 85 0 0 1 0 3.18
## 86 1 4 1 0 2.56
## 87 1 7 1 1 1.34
## 88 0 1 1 1 2.77
## 89 1 2 1 1 3.22
## 90 1 7 0 1 2.70
## 91 1 12 0 1 3.34
## 92 0 1 0 1 3.32
## 93 1 2 0 1 2.69
## 94 1 3 0 1 3.21
## 95 1 12 0 1 3.44
## 96 1 2 0 0 3.01
## 97 1 4 1 1 3.91
## 98 0 1 1 0 2.77
## 99 0 0 0 1 2.94
## 100 1 7 0 1 3.38modelreglog <- 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) -5.69002 3.46908 -1.640 0.100962
## x1 2.31039 0.60149 3.841 0.000122 ***
## x2 -0.04335 0.81467 -0.053 0.957566
## x3 2.01705 0.93225 2.164 0.030493 *
## x4 0.48016 0.98294 0.488 0.625202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 97.245 on 99 degrees of freedom
## Residual deviance: 39.855 on 95 degrees of freedom
## AIC: 49.855
##
## Number of Fisher Scoring iterations: 9