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(123)
n <- 100
u <- runif(n)
x1 <- round(60*(-(log(1-u)/12)))
x1## [1] 2 8 3 11 14 0 4 11 4 3 16 3 6 4 1 12 1 0 2 15 11 6 5 26 5
## [26] 6 4 5 2 1 16 12 6 8 0 3 7 1 2 1 1 3 3 2 1 1 1 3 2 10
## [51] 0 3 8 1 4 1 1 7 11 2 5 0 2 2 8 3 8 8 8 3 7 5 6 0 3
## [76] 1 2 5 2 1 1 6 3 8 1 3 21 11 11 1 1 5 2 5 2 1 8 0 3 4set.seed(1234)
x2 <- round(runif(n))
x2## [1] 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0
## [38] 0 1 1 1 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1
## [75] 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1set.seed(123)
x3 <- round(runif(n))
x3## [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 1set.seed(222)
x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.74 3.00 3.69 2.81 3.09 2.88 2.39 3.78 3.21 2.40 3.53 2.35 2.65 3.30 2.90
## [16] 2.41 2.00 3.00 3.26 2.63 3.36 3.36 2.67 3.75 2.28 1.92 3.20 2.80 2.85 3.67
## [31] 2.59 3.34 2.89 2.94 2.90 3.20 3.33 3.05 2.91 3.47 3.10 3.25 2.72 3.56 4.10
## [46] 3.16 2.53 3.41 2.81 3.17 3.30 3.26 2.52 2.39 2.90 3.53 3.19 3.62 3.16 2.48
## [61] 2.43 3.62 3.39 3.37 3.03 3.42 3.10 3.73 2.77 1.61 3.03 2.97 2.41 1.74 3.41
## [76] 3.13 2.97 3.34 3.01 3.27 3.34 2.40 2.38 3.11 2.27 2.94 3.27 3.36 2.21 3.55
## [91] 2.83 3.31 3.25 3.84 3.19 3.12 3.21 2.41 2.68 3.03set.seed(222)
x44 <- round(rnorm(n,2.7,0.5),2)
x44## [1] 3.44 2.70 3.39 2.51 2.79 2.58 2.09 3.48 2.91 2.10 3.23 2.05 2.35 3.00 2.60
## [16] 2.11 1.70 2.70 2.96 2.33 3.06 3.06 2.37 3.45 1.98 1.62 2.90 2.50 2.55 3.37
## [31] 2.29 3.04 2.59 2.64 2.60 2.90 3.03 2.75 2.61 3.17 2.80 2.95 2.42 3.26 3.80
## [46] 2.86 2.23 3.11 2.51 2.87 3.00 2.96 2.22 2.09 2.60 3.23 2.89 3.32 2.86 2.18
## [61] 2.13 3.32 3.09 3.07 2.73 3.12 2.80 3.43 2.47 1.31 2.73 2.67 2.11 1.44 3.11
## [76] 2.83 2.67 3.04 2.71 2.97 3.04 2.10 2.08 2.81 1.97 2.64 2.97 3.06 1.91 3.25
## [91] 2.53 3.01 2.95 3.54 2.89 2.82 2.91 2.11 2.38 2.73summary(x44)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.310 2.410 2.795 2.711 3.040 3.800b0 <- -11
b1 <- 3.5
b2 <- 0.5
b3 <- 2.7
b4 <- 2.2
set.seed(1)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 4.228 26.800 8.118 36.882 47.998 -4.164 10.958 38.516 13.262 5.280
## [11] 55.966 5.170 18.530 13.460 -1.120 39.502 -3.100 -4.400 3.172 49.986
## [21] 37.592 20.092 15.074 90.950 14.216 17.424 13.240 15.860 2.770 0.574
## [31] 53.398 41.048 19.058 26.668 -4.620 7.040 23.526 -0.790 2.902 0.634
## [41] -0.180 7.150 5.484 4.332 1.520 -0.048 -1.434 7.002 2.182 34.174
## [51] -3.740 6.672 25.744 -1.742 12.080 0.766 -0.482 24.664 37.152 1.956
## [61] 15.046 -3.036 3.458 3.414 26.366 7.524 26.520 28.406 25.794 3.542
## [71] 22.866 16.234 18.002 -6.672 7.002 -0.114 2.534 16.548 2.622 0.194
## [81] 0.348 17.980 4.736 27.042 -2.506 6.468 72.394 37.592 35.062 0.810
## [91] -1.274 16.982 3.150 17.648 3.018 -0.136 26.762 -5.698 5.396 12.866p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.985628041 1.000000000 0.999701965 1.000000000 1.000000000 0.015307297
## [7] 0.999982582 1.000000000 0.999998261 0.994933371 1.000000000 0.994347563
## [13] 0.999999991 0.999998573 0.246011284 1.000000000 0.043107255 0.012128435
## [19] 0.959766884 1.000000000 1.000000000 0.999999998 0.999999716 1.000000000
## [25] 0.999999330 0.999999973 0.999998222 0.999999871 0.941032987 0.639685641
## [31] 1.000000000 1.000000000 0.999999995 1.000000000 0.009756666 0.999124640
## [37] 1.000000000 0.312168669 0.947945216 0.653395896 0.455121108 0.999215751
## [43] 0.995864488 0.987029213 0.820538481 0.488002303 0.192476203 0.999090767
## [49] 0.898621419 1.000000000 0.023202938 0.998735736 1.000000000 0.149059076
## [55] 0.999994328 0.682654979 0.381779966 1.000000000 1.000000000 0.876099408
## [61] 0.999999708 0.045825756 0.969468824 0.968139217 1.000000000 0.999460323
## [67] 1.000000000 1.000000000 1.000000000 0.971859461 1.000000000 0.999999911
## [73] 0.999999985 0.001264264 0.999090767 0.471530825 0.926491239 0.999999935
## [79] 0.932264111 0.548348458 0.586132500 0.999999984 0.991302637 1.000000000
## [85] 0.075438627 0.998450079 1.000000000 1.000000000 1.000000000 0.692109504
## [91] 0.218573287 0.999999958 0.958908722 0.999999978 0.953380714 0.466052309
## [97] 1.000000000 0.003341461 0.995485787 0.999997416set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## [38] 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0
## [75] 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 2 0 0 3.74
## 2 1 8 1 1 3.00
## 3 1 3 1 0 3.69
## 4 1 11 1 1 2.81
## 5 1 14 1 1 3.09
## 6 0 0 1 0 2.88
## 7 1 4 0 1 2.39
## 8 1 11 0 1 3.78
## 9 1 4 1 1 3.21
## 10 1 3 1 0 2.40
## 11 1 16 1 1 3.53
## 12 1 3 1 0 2.35
## 13 1 6 0 1 2.65
## 14 1 4 1 1 3.30
## 15 0 1 0 0 2.90
## 16 1 12 1 1 2.41
## 17 0 1 0 0 2.00
## 18 0 0 0 0 3.00
## 19 1 2 0 0 3.26
## 20 1 15 0 1 2.63
## 21 1 11 0 1 3.36
## 22 1 6 0 1 3.36
## 23 1 5 0 1 2.67
## 24 1 26 0 1 3.75
## 25 1 5 0 1 2.28
## 26 1 6 1 1 1.92
## 27 1 4 1 1 3.20
## 28 1 5 1 1 2.80
## 29 1 2 1 0 2.85
## 30 1 1 0 0 3.67
## 31 1 16 0 1 2.59
## 32 1 12 0 1 3.34
## 33 1 6 0 1 2.89
## 34 1 8 1 1 2.94
## 35 0 0 0 0 2.90
## 36 1 3 1 0 3.20
## 37 1 7 0 1 3.33
## 38 0 1 0 0 3.05
## 39 0 2 1 0 2.91
## 40 1 1 1 0 3.47
## 41 0 1 1 0 3.10
## 42 1 3 1 0 3.25
## 43 1 3 0 0 2.72
## 44 1 2 1 0 3.56
## 45 1 1 0 0 4.10
## 46 1 1 1 0 3.16
## 47 1 1 1 0 2.53
## 48 1 3 0 0 3.41
## 49 1 2 0 0 2.81
## 50 1 10 1 1 3.17
## 51 1 0 0 0 3.30
## 52 1 3 0 0 3.26
## 53 1 8 1 1 2.52
## 54 0 1 1 0 2.39
## 55 1 4 0 1 2.90
## 56 0 1 1 0 3.53
## 57 1 1 0 0 3.19
## 58 1 7 1 1 3.62
## 59 1 11 0 1 3.16
## 60 1 2 1 0 2.48
## 61 1 5 1 1 2.43
## 62 0 0 0 0 3.62
## 63 1 2 0 0 3.39
## 64 1 2 0 0 3.37
## 65 1 8 0 1 3.03
## 66 1 3 1 0 3.42
## 67 1 8 0 1 3.10
## 68 1 8 1 1 3.73
## 69 1 8 0 1 2.77
## 70 1 3 1 0 1.61
## 71 1 7 0 1 3.03
## 72 1 5 1 1 2.97
## 73 1 6 0 1 2.41
## 74 0 0 1 0 1.74
## 75 1 3 0 0 3.41
## 76 1 1 1 0 3.13
## 77 1 2 0 0 2.97
## 78 1 5 0 1 3.34
## 79 1 2 0 0 3.01
## 80 1 1 1 0 3.27
## 81 1 1 1 0 3.34
## 82 1 6 0 1 2.40
## 83 1 3 0 0 2.38
## 84 1 8 1 1 3.11
## 85 0 1 0 0 2.27
## 86 1 3 1 0 2.94
## 87 1 21 0 1 3.27
## 88 1 11 0 1 3.36
## 89 1 11 0 1 2.21
## 90 1 1 1 0 3.55
## 91 1 1 0 0 2.83
## 92 1 5 1 1 3.31
## 93 1 2 0 0 3.25
## 94 1 5 0 1 3.84
## 95 1 2 0 0 3.19
## 96 0 1 1 0 3.12
## 97 1 8 0 1 3.21
## 98 0 0 0 0 2.41
## 99 1 3 0 0 2.68
## 100 1 4 1 1 3.03modelreglog <- 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) -9.2468 3.6984 -2.500 0.0124 *
## x1 2.5559 0.7891 3.239 0.0012 **
## x2 -0.3774 0.9252 -0.408 0.6833
## x3 11.0730 2665.5093 0.004 0.9967
## x4 2.3755 1.0839 2.191 0.0284 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 84.542 on 99 degrees of freedom
## Residual deviance: 32.492 on 95 degrees of freedom
## AIC: 42.492
##
## Number of Fisher Scoring iterations: 20