Y : Keputusan 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 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(123)
X2 <- round(runif(n))
X2## [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 1X3 : Tingkat pendidikan Keterangan yang digunakan (0=lulus SMA/Tidak kuliah) dan (1=lulus kulaih)
set.seed(12)
X3 <- round(runif(n))
X3## [1] 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1
## [38] 1 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 0
## [75] 0 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0X4 : data IPK pelamar dengan skala 4
set.seed(1)
X4 <- round(rnorm(n,3,0.5),2)
X4## [1] 2.69 3.09 2.58 3.80 3.16 2.59 3.24 3.37 3.29 2.85 3.76 3.19 2.69 1.89 3.56
## [16] 2.98 2.99 3.47 3.41 3.30 3.46 3.39 3.04 2.01 3.31 2.97 2.92 2.26 2.76 3.21
## [31] 3.68 2.95 3.19 2.97 2.31 2.79 2.80 2.97 3.55 3.38 2.92 2.87 3.35 3.28 2.66
## [46] 2.65 3.18 3.38 2.94 3.44 3.20 2.69 3.17 2.44 3.72 3.99 2.82 2.48 3.28 2.93
## [61] 4.20 2.98 3.34 3.01 2.63 3.09 2.10 3.73 3.08 4.09 3.24 2.65 3.31 2.53 2.37
## [76] 3.15 2.78 3.00 3.04 2.71 2.72 2.93 3.59 2.24 3.30 3.17 3.53 2.85 3.19 3.13
## [91] 2.73 3.60 3.58 3.35 3.79 3.28 2.36 2.71 2.39 2.76b0 <- -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] 4.608 5.588 13.956 1.660 10.112 7.788 27.868 9.984 14.028 1.620
## [11] 19.032 40.408 5.108 6.048 24.892 20.036 2.068 9.804 9.612 21.060
## [21] 14.572 24.048 13.228 23.132 10.592 2.504 26.044 35.232 11.832 8.972
## [31] 11.776 47.140 9.408 54.204 20.092 39.128 4.660 18.704 80.860 3.316
## [41] 8.044 35.884 30.420 30.996 17.712 7.980 27.176 38.316 1.908 10.208
## [51] 6.240 1.108 3.144 3.808 18.104 7.968 4.224 3.636 20.196 4.576
## [61] 16.140 16.036 58.388 22.332 8.416 5.888 6.720 14.636 2.856 25.788
## [71] 10.368 7.680 16.792 56.596 14.084 19.280 35.596 26.300 30.228 0.372
## [81] 10.904 19.076 45.988 66.668 2.260 13.144 25.296 4.820 6.708 23.516
## [91] 42.436 4.520 7.456 10.720 45.828 6.496 13.752 1.172 -3.352 22.832p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.99012671 0.99627145 0.99999913 0.84023800 0.99995941 0.99958549
## [7] 1.00000000 0.99995387 0.99999919 0.83479513 0.99999999 1.00000000
## [13] 0.99398819 0.99764299 1.00000000 1.00000000 0.88775382 0.99994477
## [19] 0.99993308 1.00000000 0.99999953 1.00000000 0.99999820 1.00000000
## [25] 0.99997488 0.92442176 1.00000000 1.00000000 0.99999273 0.99987310
## [31] 0.99999231 1.00000000 0.99991794 1.00000000 1.00000000 1.00000000
## [37] 0.99062231 0.99999999 1.00000000 0.96497364 0.99967908 1.00000000
## [43] 1.00000000 1.00000000 0.99999998 0.99965788 1.00000000 1.00000000
## [49] 0.87079429 0.99996313 0.99805394 0.75175606 0.95867165 0.97828930
## [55] 0.99999999 0.99965375 0.98557127 0.97431932 1.00000000 0.98980893
## [61] 0.99999990 0.99999989 1.00000000 1.00000000 0.99977875 0.99723515
## [67] 0.99879492 0.99999956 0.94562800 1.00000000 0.99996858 0.99953824
## [73] 0.99999995 1.00000000 0.99999924 1.00000000 1.00000000 1.00000000
## [79] 1.00000000 0.59194216 0.99998162 0.99999999 1.00000000 1.00000000
## [85] 0.90550963 0.99999804 1.00000000 0.99199777 0.99878039 1.00000000
## [91] 1.00000000 0.98922827 0.99942237 0.99997790 1.00000000 0.99849281
## [97] 0.99999893 0.76350633 0.03382973 1.00000000set.seed(1)
y <- rbinom(n,1,p)
y## [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 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 0 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 1 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 0 0 2.69
## 2 1 1 1 1 3.09
## 3 1 4 0 1 2.58
## 4 0 0 1 0 3.80
## 5 1 3 1 0 3.16
## 6 1 3 0 0 2.59
## 7 1 8 1 0 3.24
## 8 1 2 1 1 3.37
## 9 1 4 1 0 3.29
## 10 1 1 0 0 2.85
## 11 1 5 1 0 3.76
## 12 1 11 0 1 3.19
## 13 1 2 1 0 2.69
## 14 1 3 1 0 1.89
## 15 1 7 0 0 3.56
## 16 1 6 1 0 2.98
## 17 1 1 0 0 2.99
## 18 1 2 0 1 3.47
## 19 1 2 0 1 3.41
## 20 1 6 1 0 3.30
## 21 1 4 1 0 3.46
## 22 1 6 1 1 3.39
## 23 1 4 1 0 3.04
## 24 1 7 1 1 2.01
## 25 1 3 1 0 3.31
## 26 1 1 1 0 2.97
## 27 1 7 1 1 2.92
## 28 1 11 1 0 2.26
## 29 1 4 0 0 2.76
## 30 1 2 0 1 3.21
## 31 1 3 1 0 3.68
## 32 1 13 1 1 2.95
## 33 1 2 1 1 3.19
## 34 1 15 1 1 2.97
## 35 1 6 0 1 2.31
## 36 1 11 0 1 2.79
## 37 1 1 1 1 2.80
## 38 1 5 0 1 2.97
## 39 1 23 0 0 3.55
## 40 1 1 0 0 3.38
## 41 1 2 0 1 2.92
## 42 1 10 0 1 2.87
## 43 1 8 0 1 3.35
## 44 1 9 0 0 3.28
## 45 1 5 0 1 2.66
## 46 1 3 0 0 2.65
## 47 1 8 0 0 3.18
## 48 1 11 0 0 3.38
## 49 1 1 0 0 2.94
## 50 1 2 1 1 3.44
## 51 1 2 0 0 3.20
## 52 0 1 0 0 2.69
## 53 1 1 1 0 3.17
## 54 1 2 0 0 2.44
## 55 1 4 1 1 3.72
## 56 1 1 0 1 3.99
## 57 1 1 0 1 2.82
## 58 1 1 1 1 2.48
## 59 1 5 1 1 3.28
## 60 1 1 0 1 2.93
## 61 1 3 1 1 4.20
## 62 1 5 0 0 2.98
## 63 1 16 0 1 3.34
## 64 1 6 0 1 3.01
## 65 1 3 1 0 2.63
## 66 1 2 0 0 3.09
## 67 1 3 1 0 2.10
## 68 1 3 1 1 3.73
## 69 1 1 1 0 3.08
## 70 1 6 0 1 4.09
## 71 1 3 1 0 3.24
## 72 1 2 1 1 2.65
## 73 1 4 1 1 3.31
## 74 1 17 0 0 2.53
## 75 1 5 0 0 2.37
## 76 1 5 0 1 3.15
## 77 1 10 0 1 2.78
## 78 1 7 1 1 3.00
## 79 1 9 0 0 3.04
## 80 0 0 0 1 2.71
## 81 1 3 0 1 2.72
## 82 1 5 1 1 2.93
## 83 1 13 0 0 3.59
## 84 1 20 1 0 2.24
## 85 1 0 0 1 3.30
## 86 1 4 0 0 3.17
## 87 1 7 1 0 3.53
## 88 1 1 1 1 2.85
## 89 1 2 1 0 3.19
## 90 1 7 0 0 3.13
## 91 1 12 0 1 2.73
## 92 1 1 1 0 3.60
## 93 1 2 0 0 3.58
## 94 1 3 1 0 3.35
## 95 1 12 0 1 3.79
## 96 1 2 0 0 3.28
## 97 1 4 1 1 2.36
## 98 1 1 0 0 2.71
## 99 0 0 0 0 2.39
## 100 1 7 1 0 2.76modelreglog <- 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)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.844e-03 2.000e-08 2.000e-08 2.000e-08 1.919e-03
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4717.8 119964.4 -0.039 0.969
## X1 1141.7 46339.9 0.025 0.980
## X2 -337.0 20317.1 -0.017 0.987
## X3 643.2 24248.5 0.027 0.979
## X4 1324.5 32254.0 0.041 0.967
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3.3589e+01 on 99 degrees of freedom
## Residual deviance: 7.7936e-06 on 95 degrees of freedom
## AIC: 10
##
## Number of Fisher Scoring iterations: 25