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)
set.seed(1)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/12)))
x1## [1] 2 2 4 12 1 11 14 5 5 0 1 1 6 2 7 3 6 24 2 8 14 1 5 1 2
## [26] 2 0 2 10 2 3 5 3 1 9 6 8 1 6 3 9 5 8 4 4 8 0 3 7 6
## [51] 3 10 3 1 0 1 2 4 5 3 12 2 3 2 5 1 3 7 0 10 2 9 2 2 3
## [76] 11 10 2 8 16 3 6 3 2 7 1 6 1 1 1 1 0 5 10 8 8 3 3 8 5X2 : Status pekerjaan saat ini (0: Bekerja, 1: tidak bekerja)
set.seed(100) ## mengunci data set seed
x2 <- round(runif(n))
x2## [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 1x3 : Tingkat Pendidikan X3 : Tingkat pendidikan (0: Lulusan Sekolah Menengah, 1: Lulusan Perguruan Tinggi)
set.seed(100) ## mengunci data set seed
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(rnorm(n,3,0.5),2)
x4## [1] 2.70 3.01 2.24 2.32 3.59 2.53 3.66 3.31 2.98 2.50 2.59 2.83 2.23 2.87 2.43
## [16] 3.01 2.89 3.44 2.70 2.67 2.66 2.99 2.78 3.18 3.04 3.00 2.91 2.62 2.89 2.51
## [31] 2.45 2.53 3.34 2.21 2.57 3.24 2.91 3.77 2.69 2.83 2.18 3.01 3.45 2.56 3.45
## [46] 2.83 1.91 3.44 3.36 3.11 3.39 2.89 2.59 3.25 3.08 3.27 2.92 3.22 3.74 3.03
## [61] 2.58 4.17 2.94 2.02 3.27 3.85 2.60 2.46 2.70 3.38 3.23 2.94 2.62 3.11 3.56
## [76] 3.08 2.66 3.23 2.47 3.20 2.97 3.16 2.70 2.55 4.13 2.70 2.35 3.25 2.57 2.25
## [91] 3.60 2.49 3.47 2.73 3.26 2.82 3.66 2.43 3.71 2.70menentukan Koefesien
b0 <- -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] 0.240 0.612 9.888 34.784 -2.192 31.536 46.592 11.472 14.276 -7.000
## [11] -0.192 0.096 13.676 0.444 20.616 7.312 14.468 78.128 0.240 24.404
## [21] 45.392 0.288 14.036 0.516 0.648 0.600 -3.308 3.344 31.668 0.012
## [31] 3.440 13.736 4.508 -0.648 27.784 18.088 21.492 1.224 17.428 3.896
## [41] 24.116 14.312 25.340 10.272 11.340 21.396 -4.508 7.828 18.532 14.732
## [51] 4.568 28.468 3.608 -2.600 -3.104 -2.576 0.504 7.864 15.188 4.136
## [61] 35.096 5.204 7.228 2.624 11.424 -1.880 3.620 17.452 -6.760 32.256
## [71] 0.876 25.028 3.344 3.932 7.972 35.396 31.392 4.076 24.164 49.840
## [81] 4.064 17.992 6.940 3.260 19.456 -0.060 17.020 -2.600 -3.416 -0.600
## [91] 1.020 -7.012 11.664 28.276 25.112 21.384 8.092 3.416 22.452 13.940p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.5597136493 0.6483968940 0.9999492222 1.0000000000 0.1004711956
## [6] 1.0000000000 1.0000000000 0.9999895824 0.9999993690 0.0009110512
## [11] 0.4521469144 0.5239815850 0.9999988503 0.6092117368 0.9999999989
## [16] 0.9993329645 0.9999994793 1.0000000000 0.5597136493 1.0000000000
## [21] 1.0000000000 0.5715064295 0.9999991979 0.6262119578 0.6565596258
## [26] 0.6456563062 0.0352977596 0.9659078077 1.0000000000 0.5029999640
## [31] 0.9689315158 0.9999989172 0.9890996480 0.3434403742 1.0000000000
## [36] 0.9999999861 0.9999999995 0.7727667090 0.9999999730 0.9800817580
## [41] 1.0000000000 0.9999993913 1.0000000000 0.9999654131 0.9999881124
## [46] 0.9999999995 0.0109003520 0.9996017371 0.9999999911 0.9999996001
## [51] 0.9897279145 1.0000000000 0.9736093406 0.0691384203 0.0429425601
## [56] 0.0706990829 0.6233988846 0.9996158142 0.9999997465 0.9842648817
## [61] 1.0000000000 0.9945354828 0.9992745553 0.9323902975 0.9999890701
## [66] 0.1323888735 0.9739159249 0.9999999737 0.0011578869 1.0000000000
## [71] 0.7059926376 1.0000000000 0.9659078077 0.9807725192 0.9996551307
## [76] 1.0000000000 1.0000000000 0.9833081176 1.0000000000 1.0000000000
## [81] 0.9831100119 0.9999999846 0.9990326670 0.9630307907 0.9999999964
## [86] 0.4850044984 0.9999999594 0.0691384203 0.0317991499 0.3543436938
## [91] 0.7349725995 0.0009001937 0.9999914022 1.0000000000 1.0000000000
## [96] 0.9999999995 0.9996941164 0.9682008501 0.9999999998 0.9999991171set.seed(3)
y <- rbinom(n,1,p)
y## [1] 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 0 0 2.70
## 2 0 2 0 0 3.01
## 3 1 4 1 1 2.24
## 4 1 12 0 0 2.32
## 5 0 1 0 0 3.59
## 6 1 11 0 0 2.53
## 7 1 14 1 1 3.66
## 8 1 5 0 0 3.31
## 9 1 5 1 1 2.98
## 10 0 0 0 0 2.50
## 11 1 1 1 1 2.59
## 12 1 1 1 1 2.83
## 13 1 6 0 0 2.23
## 14 1 2 0 0 2.87
## 15 1 7 1 1 2.43
## 16 1 3 1 1 3.01
## 17 1 6 0 0 2.89
## 18 1 24 0 0 3.44
## 19 1 2 0 0 2.70
## 20 1 8 1 1 2.67
## 21 1 14 1 1 2.66
## 22 0 1 1 1 2.99
## 23 1 5 1 1 2.78
## 24 1 1 1 1 3.18
## 25 1 2 0 0 3.04
## 26 1 2 0 0 3.00
## 27 0 0 1 1 2.91
## 28 1 2 1 1 2.62
## 29 1 10 1 1 2.89
## 30 0 2 0 0 2.51
## 31 1 3 0 0 2.45
## 32 1 5 1 1 2.53
## 33 1 3 0 0 3.34
## 34 0 1 1 1 2.21
## 35 1 9 1 1 2.57
## 36 1 6 1 1 3.24
## 37 1 8 0 0 2.91
## 38 1 1 1 1 3.77
## 39 1 6 1 1 2.69
## 40 1 3 0 0 2.83
## 41 1 9 0 0 2.18
## 42 1 5 1 1 3.01
## 43 1 8 1 1 3.45
## 44 1 4 1 1 2.56
## 45 1 4 1 1 3.45
## 46 1 8 0 0 2.83
## 47 0 0 1 1 1.91
## 48 1 3 1 1 3.44
## 49 1 7 0 0 3.36
## 50 1 6 0 0 3.11
## 51 1 3 0 0 3.39
## 52 1 10 0 0 2.89
## 53 1 3 0 0 2.59
## 54 0 1 0 0 3.25
## 55 0 0 1 1 3.08
## 56 0 1 0 0 3.27
## 57 0 2 0 0 2.92
## 58 1 4 0 0 3.22
## 59 1 5 1 1 3.74
## 60 1 3 0 0 3.03
## 61 1 12 0 0 2.58
## 62 1 2 1 1 4.17
## 63 1 3 1 1 2.94
## 64 1 2 1 1 2.02
## 65 1 5 0 0 3.27
## 66 0 1 0 0 3.85
## 67 1 3 0 0 2.60
## 68 1 7 0 0 2.46
## 69 0 0 0 0 2.70
## 70 1 10 1 1 3.38
## 71 1 2 0 0 3.23
## 72 1 9 0 0 2.94
## 73 1 2 1 1 2.62
## 74 1 2 1 1 3.11
## 75 1 3 1 1 3.56
## 76 1 11 1 1 3.08
## 77 1 10 1 1 2.66
## 78 1 2 1 1 3.23
## 79 1 8 1 1 2.47
## 80 1 16 0 0 3.20
## 81 1 3 0 0 2.97
## 82 1 6 1 1 3.16
## 83 1 3 1 1 2.70
## 84 1 2 1 1 2.55
## 85 1 7 0 0 4.13
## 86 1 1 1 1 2.70
## 87 1 6 1 1 2.35
## 88 0 1 0 0 3.25
## 89 0 1 0 0 2.57
## 90 1 1 1 1 2.25
## 91 1 1 1 1 3.60
## 92 0 0 0 0 2.49
## 93 1 5 0 0 3.47
## 94 1 10 0 0 2.73
## 95 1 8 1 1 3.26
## 96 1 8 0 0 2.82
## 97 1 3 1 1 3.66
## 98 1 3 0 0 2.43
## 99 1 8 0 0 3.71
## 100 1 5 1 1 2.70modelreglog <- 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: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -48.079 7492.092 -0.006 0.995
## x1 21.503 3746.044 0.006 0.995
## x2 22.277 3746.045 0.006 0.995
## x3 NA NA NA NA
## x4 2.007 1.811 1.108 0.268
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 91.177 on 99 degrees of freedom
## Residual deviance: 19.473 on 96 degrees of freedom
## AIC: 27.473
##
## Number of Fisher Scoring iterations: 23