set.seed(1234)
n <-100
u <-runif(n)
x1 <-round(60*(-log(1-u/12)))
x1
## [1] 1 3 3 3 4 3 0 1 3 3 4 3 1 5 1 4 1 1 1 1 2 2 1 0 1 4 3 5 4 0 2 1 2 3 1 4 1
## [38] 1 5 4 3 3 2 3 2 3 3 2 1 4 0 2 4 3 1 3 3 4 1 4 4 0 2 0 1 4 2 3 0 3 1 5 0 4
## [75] 0 3 2 0 2 3 5 2 1 3 1 5 2 2 1 5 1 5 1 1 1 3 2 0 2 4
set.seed(12345)
x2 <-round(runif(n))
x2
## [1] 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1
## [38] 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0 0
## [75] 0 1 1 1 1 0 1 1 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 0
set.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 1
set.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 3.03
set.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 2.73
summary(x44)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.310 2.410 2.795 2.711 3.040 3.800
b0 <- -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] 1.228 9.300 8.118 8.882 12.498 5.836 -3.042 4.016 9.762 5.280
## [11] 13.466 4.670 1.530 16.460 -1.120 11.002 -3.100 -0.900 -0.328 1.486
## [21] 6.092 6.092 1.574 0.450 0.716 9.924 9.740 15.860 9.270 -2.926
## [31] 4.898 2.548 5.058 9.168 -1.120 10.040 3.026 -0.290 13.402 10.634
## [41] 6.820 6.650 2.484 7.832 5.020 6.452 5.066 3.502 -1.318 13.174
## [51] -3.240 3.672 11.244 4.758 2.080 7.266 7.018 13.664 2.152 8.456
## [61] 11.546 -3.036 3.958 -3.086 2.366 10.524 6.020 10.406 -1.706 3.542
## [71] 2.366 16.234 -2.998 6.828 -3.498 6.886 3.034 -0.452 3.122 6.694
## [81] 14.348 4.480 -2.264 9.042 -2.506 12.968 6.394 6.592 0.562 14.310
## [91] -0.774 16.982 -0.350 4.148 0.018 6.864 6.262 -5.198 1.896 12.366
p <- exp(datapendukung)/(1+exp(datapendukung))
p
## [1] 0.773468336 0.999908584 0.999701965 0.999861153 0.999996266 0.997088003
## [7] 0.045564116 0.982294225 0.999942404 0.994933371 0.999998582 0.990714754
## [13] 0.822006314 0.999999929 0.246011284 0.999983332 0.043107255 0.289050497
## [19] 0.418727333 0.815477135 0.997744217 0.997744217 0.828353092 0.610639234
## [25] 0.671725582 0.999951018 0.999941123 0.999999871 0.999905800 0.050883154
## [31] 0.992593770 0.927439038 0.993681909 0.999895686 0.246011284 0.999956382
## [37] 0.953734995 0.428003867 0.999998488 0.999975917 0.998909470 0.998707650
## [43] 0.923012521 0.999603326 0.993438807 0.998425120 0.993731936 0.970744622
## [49] 0.211151234 0.999998101 0.037687891 0.975204863 0.999986915 0.991490279
## [55] 0.888944033 0.999301586 0.999105186 0.999998836 0.895855521 0.999787424
## [61] 0.999990325 0.045825756 0.981256742 0.043688449 0.914197619 0.999973117
## [67] 0.997576219 0.999969750 0.153683254 0.971859461 0.914197619 0.999999911
## [73] 0.047516308 0.998918150 0.029369190 0.998979050 0.954086713 0.388885353
## [79] 0.957791157 0.998763212 0.999999413 0.988793594 0.094148676 0.999881680
## [85] 0.075438627 0.999997666 0.998331233 0.998630583 0.636915175 0.999999390
## [91] 0.315614462 0.999999958 0.413382421 0.984449656 0.504499879 0.998956364
## [97] 0.998096204 0.005497222 0.869438134 0.999995739
set.seed(2)
y <- rbinom(n,1,p)
y
## [1] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1
## [38] 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1
## [75] 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1
set.seed(2)
y <- rbinom(n,1,p)
y
## [1] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1
## [38] 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1
## [75] 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1
datagab <-data.frame(y,x1,x2,x3,x4)
datagab
## y x1 x2 x3 x4
## 1 1 1 1 0 3.74
## 2 1 3 1 1 3.00
## 3 1 3 1 0 3.69
## 4 1 3 1 1 2.81
## 5 1 4 0 1 3.09
## 6 1 3 0 0 2.88
## 7 0 0 0 1 2.39
## 8 1 1 1 1 3.78
## 9 1 3 1 1 3.21
## 10 1 3 1 0 2.40
## 11 1 4 0 1 3.53
## 12 1 3 0 0 2.35
## 13 1 1 1 1 2.65
## 14 1 5 0 1 3.30
## 15 0 1 0 0 2.90
## 16 1 4 0 1 2.41
## 17 1 1 0 0 2.00
## 18 0 1 0 0 3.00
## 19 0 1 0 0 3.26
## 20 1 1 1 1 2.63
## 21 1 2 0 1 3.36
## 22 1 2 0 1 3.36
## 23 0 1 1 1 2.67
## 24 1 0 1 1 3.75
## 25 1 1 1 1 2.28
## 26 1 4 0 1 1.92
## 27 1 3 1 1 3.20
## 28 1 5 1 1 2.80
## 29 1 4 0 0 2.85
## 30 0 0 0 0 3.67
## 31 1 2 1 1 2.59
## 32 1 1 0 1 3.34
## 33 1 2 0 1 2.89
## 34 1 3 1 1 2.94
## 35 0 1 0 0 2.90
## 36 1 4 0 0 3.20
## 37 1 1 1 1 3.33
## 38 0 1 1 0 3.05
## 39 1 5 1 0 2.91
## 40 1 4 0 0 3.47
## 41 1 3 1 0 3.10
## 42 1 3 0 0 3.25
## 43 1 2 1 0 2.72
## 44 1 3 1 0 3.56
## 45 1 2 0 0 4.10
## 46 1 3 0 0 3.16
## 47 1 3 0 0 2.53
## 48 1 2 0 0 3.41
## 49 0 1 0 0 2.81
## 50 1 4 1 1 3.17
## 51 0 0 1 0 3.30
## 52 1 2 1 0 3.26
## 53 1 4 0 1 2.52
## 54 1 3 0 0 2.39
## 55 1 1 1 1 2.90
## 56 1 3 0 0 3.53
## 57 1 3 1 0 3.19
## 58 1 4 0 1 3.62
## 59 1 1 0 1 3.16
## 60 1 4 0 0 2.48
## 61 1 4 1 1 2.43
## 62 0 0 0 0 3.62
## 63 1 2 1 0 3.39
## 64 0 0 1 0 3.37
## 65 1 1 1 1 3.03
## 66 1 4 0 0 3.42
## 67 1 2 1 1 3.10
## 68 1 3 0 1 3.73
## 69 0 0 1 1 2.77
## 70 1 3 1 0 1.61
## 71 1 1 1 1 3.03
## 72 1 5 1 1 2.97
## 73 0 0 0 1 2.41
## 74 1 4 0 0 1.74
## 75 0 0 0 0 3.41
## 76 1 3 1 0 3.13
## 77 1 2 1 0 2.97
## 78 1 0 1 1 3.34
## 79 1 2 1 0 3.01
## 80 1 3 0 0 3.27
## 81 1 5 1 0 3.34
## 82 1 2 1 1 2.40
## 83 0 1 0 0 2.38
## 84 1 3 0 1 3.11
## 85 0 1 0 0 2.27
## 86 1 5 0 0 2.94
## 87 1 2 1 1 3.27
## 88 1 2 1 1 3.36
## 89 0 1 1 1 2.21
## 90 1 5 0 0 3.55
## 91 1 1 1 0 2.83
## 92 1 5 1 1 3.31
## 93 0 1 0 0 3.25
## 94 1 1 1 1 3.84
## 95 1 1 1 0 3.19
## 96 1 3 1 0 3.12
## 97 1 2 1 1 3.21
## 98 0 0 1 0 2.41
## 99 1 2 0 0 2.68
## 100 1 4 0 1 3.03
modelreglog <- glm(y~x1+x2+x3+x4, family = binomial(link = "logit"),data=datagab)
summary(modelreglog)
##
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4, family = binomial(link = "logit"),
## data = datagab)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -12.431 4.872 -2.552 0.01072 *
## x1 3.801 1.109 3.427 0.00061 ***
## x2 1.701 1.119 1.520 0.12854
## x3 2.537 1.169 2.170 0.02999 *
## x4 2.422 1.345 1.801 0.07170 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 100.080 on 99 degrees of freedom
## Residual deviance: 28.068 on 95 degrees of freedom
## AIC: 38.068
##
## Number of Fisher Scoring iterations: 8