Menjalankan Data
Rosita D
2024-03-27
Menjalankan Data
skenario
Y : Keputusan menolak/menerima pelamar kerja pada PT A posisi B X1 : Lama pengalaman kerja sebelum (bulan) X2 : Status pekerjaan saat ini (0: Bekerja, 1: Tidak bekerja) X3 : Tingkat pendidikan (0: Lulusan Sekolah Menengah, 1: Tidak bekerja) x4 : IPK (skala 4)
Menjalankan 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 7?runif## starting httpd help server ... doneMenjalankan data X2
X2 : Status pekerjaan keterangan yang digunakan (0=Tidak Bekerja) dan (1=Bekerja)
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 0Menjalankan Data
X3 : Tingkat pendidikan keterangan yang digunakan (0=lulus SMA/Tidak Kuliah) dan (1=lulus kuliah)
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 1Menjalankan Data X4
X4 adalah data IPK Pelamar dengan skala 4
set.seed## function (seed, kind = NULL, normal.kind = NULL, sample.kind = NULL)
## {
## kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper",
## "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002",
## "L'Ecuyer-CMRG", "default")
## n.kinds <- c("Buggy Kinderman-Ramage", "Ahrens-Dieter", "Box-Muller",
## "user-supplied", "Inversion", "Kinderman-Ramage", "default")
## s.kinds <- c("Rounding", "Rejection", "default")
## if (length(kind)) {
## if (!is.character(kind) || length(kind) > 1L)
## stop("'kind' must be a character string of length 1 (RNG to be used).")
## if (is.na(i.knd <- pmatch(kind, kinds) - 1L))
## stop(gettextf("'%s' is not a valid abbreviation of an RNG",
## kind), domain = NA)
## if (i.knd == length(kinds) - 1L)
## i.knd <- -1L
## }
## else i.knd <- NULL
## if (!is.null(normal.kind)) {
## if (!is.character(normal.kind) || length(normal.kind) !=
## 1L)
## stop("'normal.kind' must be a character string of length 1")
## normal.kind <- pmatch(normal.kind, n.kinds) - 1L
## if (is.na(normal.kind))
## stop(gettextf("'%s' is not a valid choice", normal.kind),
## domain = NA)
## if (normal.kind == 0L)
## stop("buggy version of Kinderman-Ramage generator is not allowed",
## domain = NA)
## if (normal.kind == length(n.kinds) - 1L)
## normal.kind <- -1L
## }
## if (!is.null(sample.kind)) {
## if (!is.character(sample.kind) || length(sample.kind) !=
## 1L)
## stop("'sample.kind' must be a character string of length 1")
## sample.kind <- pmatch(sample.kind, s.kinds) - 1L
## if (is.na(sample.kind))
## stop(gettextf("'%s' is not a valid choice", sample.kind),
## domain = NA)
## if (sample.kind == 0L)
## warning("non-uniform 'Rounding' sampler used", domain = NA)
## if (sample.kind == length(s.kinds) - 1L)
## sample.kind <- -1L
## }
## .Internal(set.seed(seed, i.knd, normal.kind, sample.kind))
## }
## <bytecode: 0x00000297b9cde978>
## <environment: namespace:base>x4 <- round(rnorm(n,3,0.5),2)
x4## [1] 3.13 2.99 2.98 3.68 2.89 3.76 2.23 3.29 3.06 3.11 3.19 2.75 2.83 2.49 2.46
## [16] 3.15 3.22 3.03 3.46 4.03 2.75 1.85 3.50 2.65 2.66 3.51 2.86 2.39 3.09 2.93
## [31] 3.00 3.19 2.81 3.32 2.89 3.17 3.55 3.22 2.84 3.57 3.50 3.27 3.12 2.69 3.68
## [46] 2.70 4.09 3.77 2.88 2.49 2.64 3.13 2.88 2.83 2.52 2.98 2.61 2.17 2.81 3.46
## [61] 2.71 3.30 2.19 2.97 3.26 3.15 3.05 2.68 2.58 2.49 3.06 2.53 2.75 2.87 3.92
## [76] 2.67 3.12 3.04 2.52 2.96 3.72 3.23 3.02 2.79 1.97 3.57 2.27 3.37 3.95 2.28
## [91] 3.35 2.87 2.21 2.24 2.20 2.73 2.27 3.34 4.05 2.36Membangkitkan data Y
Melakukan koefisien
b0 <- -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] 6.516 5.268 13.036 3.976 11.448 11.532 26.836 9.728 15.992 2.952
## [11] 19.408 36.300 8.256 10.168 21.372 22.780 2.804 5.696 7.072 26.096
## [21] 14.500 18.620 17.400 25.180 11.212 6.432 25.852 38.348 12.888 5.376
## [31] 12.300 47.408 7.692 55.324 19.248 37.644 7.060 17.304 79.088 3.924
## [41] 7.700 34.464 27.484 29.608 18.276 8.140 30.088 39.564 1.716 7.168
## [51] 4.948 3.016 4.416 5.056 14.264 2.036 1.352 2.144 18.192 3.572
## [61] 11.372 17.060 52.508 20.004 13.132 6.080 12.460 10.776 3.956 18.468
## [71] 12.492 7.296 14.500 57.684 19.044 15.544 34.484 26.428 29.064 -1.528
## [81] 11.904 20.036 44.164 70.628 -4.696 14.424 23.964 6.484 11.840 20.796
## [91] 42.220 4.884 3.072 9.868 38.540 5.236 13.464 3.688 1.960 23.752p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.998522610 0.994872518 0.999997820 0.981584945 0.999989329 0.999990189
## [7] 1.000000000 0.999940412 0.999999887 0.950357929 0.999999996 1.000000000
## [13] 0.999740372 0.999961622 0.999999999 1.000000000 0.942891594 0.996651872
## [19] 0.999152185 1.000000000 0.999999496 0.999999992 0.999999972 1.000000000
## [25] 0.999986489 0.998393356 1.000000000 1.000000000 0.999997472 0.995395014
## [31] 0.999995448 1.000000000 0.999543744 1.000000000 0.999999996 1.000000000
## [37] 0.999141959 0.999999969 1.000000000 0.980621075 0.999547378 1.000000000
## [43] 1.000000000 1.000000000 0.999999988 0.999708448 1.000000000 1.000000000
## [49] 0.847612893 0.999229731 0.992952431 0.953291742 0.988061778 0.993669340
## [55] 0.999999361 0.884525335 0.794456411 0.895106767 0.999999987 0.972668408
## [61] 0.999988487 0.999999961 1.000000000 0.999999998 0.999998019 0.997717047
## [67] 0.999996121 0.999979105 0.981219922 0.999999990 0.999996243 0.999322213
## [73] 0.999999496 1.000000000 0.999999995 0.999999822 1.000000000 1.000000000
## [79] 1.000000000 0.178286498 0.999993237 0.999999998 1.000000000 1.000000000
## [85] 0.009049097 0.999999456 1.000000000 0.998474643 0.999992790 0.999999999
## [91] 1.000000000 0.992490138 0.955722883 0.999948196 1.000000000 0.994706668
## [97] 0.999998579 0.975588820 0.876532952 1.000000000set.seed(2)
y <- rbinom(n,1,p)
y## [1] 1 1 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
## [38] 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 1 1 1 1
## [75] 1 1 1 1 1 0 1 1 1 1 0 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 1 0 3.13
## 2 1 1 1 1 2.99
## 3 1 4 1 0 2.98
## 4 1 0 1 1 3.68
## 5 1 3 0 1 2.89
## 6 1 3 0 0 3.76
## 7 1 8 0 1 2.23
## 8 1 2 1 1 3.29
## 9 1 4 1 1 3.06
## 10 1 1 1 0 3.11
## 11 1 5 0 1 3.19
## 12 1 11 0 0 2.75
## 13 1 2 1 1 2.83
## 14 1 3 0 1 2.49
## 15 1 7 0 0 2.46
## 16 1 6 0 1 3.15
## 17 0 1 0 0 3.22
## 18 1 2 0 0 3.03
## 19 1 2 0 0 3.46
## 20 1 6 1 1 4.03
## 21 1 4 0 1 2.75
## 22 1 6 0 1 1.85
## 23 1 4 1 1 3.50
## 24 1 7 1 1 2.65
## 25 1 3 1 1 2.66
## 26 1 1 0 1 3.51
## 27 1 7 1 1 2.86
## 28 1 11 1 1 2.39
## 29 1 4 0 0 3.09
## 30 1 2 0 0 2.93
## 31 1 3 1 1 3.00
## 32 1 13 0 1 3.19
## 33 1 2 0 1 2.81
## 34 1 15 1 1 3.32
## 35 1 6 0 0 2.89
## 36 1 11 0 0 3.17
## 37 1 1 1 1 3.55
## 38 1 5 1 0 3.22
## 39 1 23 1 0 2.84
## 40 1 1 0 0 3.57
## 41 1 2 1 0 3.50
## 42 1 10 0 0 3.27
## 43 1 8 1 0 3.12
## 44 1 9 1 0 2.69
## 45 1 5 0 0 3.68
## 46 1 3 0 0 2.70
## 47 1 8 0 0 4.09
## 48 1 11 0 0 3.77
## 49 1 1 0 0 2.88
## 50 1 2 1 1 2.49
## 51 1 2 1 0 2.64
## 52 1 1 1 0 3.13
## 53 1 1 0 1 2.88
## 54 1 2 0 0 2.83
## 55 1 4 1 1 2.52
## 56 1 1 0 0 2.98
## 57 1 1 1 0 2.61
## 58 1 1 0 1 2.17
## 59 1 5 0 1 2.81
## 60 1 1 0 0 3.46
## 61 1 3 1 1 2.71
## 62 1 5 0 0 3.30
## 63 1 16 1 0 2.19
## 64 1 6 1 0 2.97
## 65 1 3 1 1 3.26
## 66 1 2 0 0 3.15
## 67 1 3 1 1 3.05
## 68 1 3 0 1 2.68
## 69 1 1 1 1 2.58
## 70 1 6 1 0 2.49
## 71 1 3 1 1 3.06
## 72 1 2 1 1 2.53
## 73 1 4 0 1 2.75
## 74 1 17 0 0 2.87
## 75 1 5 0 0 3.92
## 76 1 5 1 0 2.67
## 77 1 10 1 0 3.12
## 78 1 7 1 1 3.04
## 79 1 9 1 0 2.52
## 80 0 0 0 0 2.96
## 81 1 3 1 0 3.72
## 82 1 5 1 1 3.23
## 83 1 13 0 0 3.02
## 84 1 20 0 1 2.79
## 85 0 0 0 0 1.97
## 86 1 4 0 0 3.57
## 87 1 7 1 1 2.27
## 88 1 1 1 1 3.37
## 89 1 2 1 1 3.95
## 90 1 7 0 0 2.28
## 91 1 12 1 0 3.35
## 92 1 1 1 1 2.87
## 93 1 2 0 0 2.21
## 94 1 3 1 1 2.24
## 95 1 12 1 0 2.20
## 96 1 2 1 0 2.73
## 97 1 4 1 1 2.27
## 98 1 1 1 0 3.34
## 99 0 0 0 0 4.05
## 100 1 7 0 1 2.36modelreglog <- 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) -1.965e+01 5.757e+03 -0.003 0.997
## x1 2.092e+01 5.757e+03 0.004 0.997
## x2 2.104e+01 2.038e+04 0.001 0.999
## X3 2.082e+01 2.084e+04 0.001 0.999
## x4 3.538e-02 4.206e+00 0.008 0.993
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 33.589 on 99 degrees of freedom
## Residual deviance: 5.004 on 95 degrees of freedom
## AIC: 15.004
##
## Number of Fisher Scoring iterations: 24R Markdown
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