Y : Keputusan menolak/menerima murid baru di SMA x1 : Lama menempuh pendidikan SMP (bulan) x2 : Status murid saat ini (0: Murid baru, 1: Murid pindahan) x3 : Tingkat Pendidikan (0: Lulusan Sekolah Menengah Negeri, 1: Lulusan Sekolah Menengah Swasta) x4 : Nilai (skala 10)
## Membangkitkan data x1 x1 : Lama menempuh pendidikan SMP (bulan) Membangkitkan variabel x1 dengan lama pekerjaan 0-36 bulan dengan nilai tengah 15 dan banyak pendaftar adalah 1000
set.seed(1000)
n <- 1000
u <- runif(n)
x1 <- round(36*(-(log(1-u)/15)))
x1## [1] 1 3 0 3 2 0 3 2 1 1 1 3 1 5 3 0 2 2 0 2 0 4 1 0
## [25] 2 3 1 4 0 0 2 0 2 8 2 3 0 4 2 1 13 2 0 2 4 3 3 2
## [49] 1 2 3 3 0 2 2 3 3 2 5 2 1 0 3 8 1 6 1 6 0 4 2 3
## [73] 1 1 5 0 1 1 0 0 1 0 6 0 2 0 2 1 1 1 6 4 2 0 2 2
## [97] 0 3 0 1 2 1 0 3 3 4 0 3 2 1 4 3 0 2 2 0 13 1 1 0
## [121] 3 1 1 2 5 12 5 3 1 0 1 2 1 0 7 1 0 4 2 0 0 2 3 2
## [145] 2 1 6 2 1 5 1 2 2 2 6 0 3 2 2 5 5 1 2 5 1 7 6 6
## [169] 3 1 1 3 2 5 0 2 0 8 9 6 9 1 0 1 3 1 1 2 1 4 3 1
## [193] 3 2 4 5 1 0 9 3 0 1 2 2 1 1 4 2 2 3 2 2 3 2 0 1
## [217] 5 0 5 4 4 1 1 1 1 2 1 4 8 2 1 1 2 2 0 1 7 1 2 4
## [241] 3 7 1 0 3 5 5 3 2 4 1 4 1 2 0 2 2 1 1 3 1 1 4 1
## [265] 5 0 2 4 2 2 1 0 5 1 0 2 0 1 2 3 2 4 3 5 1 2 4 1
## [289] 0 3 1 1 5 1 7 0 2 7 3 0 0 1 4 0 2 3 6 4 2 3 0 3
## [313] 0 1 2 0 2 1 0 0 2 5 4 0 9 10 1 2 1 7 1 1 3 5 3 1
## [337] 1 4 2 2 2 4 1 2 1 0 1 0 0 3 0 1 9 3 5 4 2 5 1 3
## [361] 7 1 5 3 6 1 5 4 3 2 2 3 1 2 4 0 5 3 1 9 1 2 0 1
## [385] 1 1 2 0 3 0 7 8 3 7 2 4 2 6 0 2 4 1 4 3 0 3 1 1
## [409] 1 1 2 0 2 0 1 4 4 5 1 1 1 3 4 0 1 0 0 3 1 4 0 1
## [433] 2 2 1 2 1 8 1 0 0 9 1 4 0 0 3 3 6 4 2 0 0 1 3 0
## [457] 4 2 1 0 1 3 1 1 9 4 0 2 1 3 2 5 3 1 0 1 10 2 0 4
## [481] 1 0 3 4 2 0 3 9 2 0 2 2 3 1 3 0 2 0 0 1 3 8 1 3
## [505] 3 1 4 0 1 4 3 4 1 0 2 7 0 4 5 1 1 11 0 1 1 2 10 0
## [529] 8 2 2 3 2 3 4 3 7 4 2 9 0 8 3 3 4 0 1 0 1 1 1 0
## [553] 1 0 1 0 1 1 4 4 3 3 5 2 1 0 1 2 5 1 8 1 7 1 0 0
## [577] 9 4 1 0 0 0 1 5 1 11 0 1 0 3 1 0 1 1 2 13 1 2 1 4
## [601] 3 1 0 1 2 0 1 1 5 1 1 2 0 7 0 4 1 1 0 1 2 3 6 3
## [625] 0 5 2 8 2 1 1 1 1 1 1 3 0 2 6 0 1 3 3 1 6 4 0 1
## [649] 3 1 4 5 3 2 2 4 7 0 1 1 0 4 10 1 3 2 1 1 4 1 1 4
## [673] 3 1 2 1 4 3 4 1 1 0 1 0 4 0 1 2 4 1 3 4 3 5 3 1
## [697] 1 0 1 0 1 3 0 5 0 1 3 0 1 0 2 8 7 0 1 13 1 0 3 10
## [721] 1 1 1 1 1 9 1 5 0 5 1 4 1 0 3 0 1 7 4 2 0 1 6 2
## [745] 0 9 6 2 9 1 4 1 4 2 6 2 1 1 0 1 3 3 4 0 6 1 2 0
## [769] 0 6 8 4 1 2 1 0 1 1 5 1 7 1 0 3 2 3 0 1 4 2 1 10
## [793] 1 7 2 1 1 2 5 1 1 0 1 6 2 4 2 0 3 2 3 0 2 2 1 6
## [817] 2 4 2 0 2 2 2 3 0 4 1 2 1 2 1 1 3 2 1 2 1 2 1 0
## [841] 0 0 0 1 2 1 0 2 1 1 2 3 3 1 1 0 0 2 1 7 0 2 4 1
## [865] 3 0 2 2 2 6 3 0 2 3 5 0 3 1 1 1 1 10 2 0 0 1 3 2
## [889] 1 0 1 0 6 0 2 1 7 3 1 2 7 0 3 2 2 1 2 1 2 0 3 6
## [913] 9 2 1 0 3 4 0 1 2 3 5 6 3 0 1 4 1 4 3 3 0 3 6 1
## [937] 0 3 2 1 1 4 2 12 2 2 2 0 2 2 1 5 0 3 1 2 2 1 1 0
## [961] 2 1 1 0 0 0 5 3 2 5 3 0 2 0 8 2 8 1 3 1 1 2 1 0
## [985] 1 2 2 2 1 4 0 3 2 2 3 2 1 7 1 2x2: Status murid saat ini Keterangan yang digantikan (0=Murid baru) dan (1=Murid pindahan)
set.seed(100)
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 1 0 0 0 0 1 1 1 1 0 1 1
## [112] 1 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0
## [149] 0 0 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0
## [186] 0 0 0 0 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 1 0 0 1 0
## [223] 1 1 0 1 0 0 0 0 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1
## [260] 1 0 1 0 1 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0
## [297] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 0
## [334] 1 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 1
## [371] 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 1
## [408] 1 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1
## [445] 1 1 1 0 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0
## [482] 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0
## [519] 0 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1
## [556] 1 1 0 0 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 0
## [593] 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0
## [630] 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0
## [667] 1 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1
## [704] 0 0 0 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1
## [741] 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 0 1 1
## [778] 0 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1
## [815] 0 0 1 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1
## [852] 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1
## [889] 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 0 0 1 0 0 1 0
## [926] 1 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 0
## [963] 1 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 0 0
## [1000] 1x3: Tingkat pendidikan Keterangan yang digunakan (0=Lulusan Sekolah Menengah Negeri) dan (1=Lulusan Sekolah Menengah Swasta)
set.seed(111)
x3 <- round(runif(n))
x3## [1] 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0
## [38] 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0
## [75] 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 1 0
## [112] 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 0 1 0
## [149] 0 0 1 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 0 1 1 1 0 0
## [186] 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 0 1 1 1
## [223] 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1
## [260] 0 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 0 0 1
## [297] 0 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 1 1 1
## [334] 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0
## [371] 0 0 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0
## [408] 0 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 1
## [445] 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1
## [482] 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1
## [519] 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0
## [556] 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0
## [593] 0 0 0 1 0 0 1 0 1 1 1 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 0 1 0
## [630] 0 1 0 0 0 1 1 1 0 0 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 0 0 0
## [667] 0 1 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 1 1 0 0 1 0 0 0 1 0 0 1 1
## [704] 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1
## [741] 1 0 1 1 0 0 1 0 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1
## [778] 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 1
## [815] 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0
## [852] 1 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 1
## [889] 0 1 1 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 1 0 1 0
## [926] 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0
## [963] 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 0
## [1000] 1x4 adalah nilai pendaftar dengan skala 10
set.seed(125)
x4 <- sample(1:10,1000,replace = TRUE)
x4## [1] 10 8 8 3 9 9 3 4 3 10 7 5 10 7 6 1 9 10 3 4 2 10 10 7
## [25] 3 8 4 2 6 6 9 10 9 4 9 5 6 1 5 1 10 7 4 1 7 6 3 8
## [49] 9 1 3 3 6 6 7 7 7 7 5 1 3 1 4 3 1 9 1 10 2 8 2 3
## [73] 7 6 6 7 5 6 10 8 8 2 9 6 6 2 6 3 9 3 5 4 8 7 2 4
## [97] 10 2 8 10 10 9 7 2 10 2 7 5 6 1 6 10 9 3 7 10 6 8 10 9
## [121] 4 1 8 10 1 7 8 3 5 3 4 1 2 6 9 9 2 4 1 3 9 3 2 3
## [145] 2 7 5 3 7 10 6 5 9 8 7 8 1 5 8 8 1 7 5 7 1 5 4 4
## [169] 7 9 4 7 7 3 5 5 8 5 9 1 5 5 8 5 9 6 9 3 10 7 2 5
## [193] 2 6 8 7 8 2 10 3 6 3 1 7 8 5 9 2 2 7 1 6 6 10 10 2
## [217] 5 2 7 9 8 10 4 9 10 7 6 4 1 7 6 6 1 9 7 1 2 9 2 9
## [241] 3 6 4 5 5 6 1 3 2 10 4 2 5 8 6 2 4 8 2 1 3 3 8 2
## [265] 4 2 4 7 6 1 9 6 1 4 5 8 2 10 1 9 3 7 6 7 10 4 8 7
## [289] 1 8 7 6 2 1 5 9 5 9 8 2 8 7 10 2 8 3 1 8 9 1 9 4
## [313] 2 1 4 2 10 4 8 10 9 10 5 9 7 7 1 8 8 10 10 6 6 10 6 10
## [337] 3 5 6 2 9 6 10 1 2 9 2 3 1 8 5 1 6 10 9 5 5 2 3 7
## [361] 1 5 1 1 1 5 8 4 4 7 7 6 1 1 6 9 2 5 2 7 9 2 2 10
## [385] 10 4 3 4 3 10 1 2 2 7 7 2 1 7 7 10 8 8 2 9 10 6 10 2
## [409] 5 9 7 8 4 5 8 5 9 5 3 4 2 5 8 1 5 8 9 10 10 3 9 6
## [433] 9 8 5 8 10 4 3 4 2 8 8 7 3 6 1 3 7 6 2 8 1 1 4 5
## [457] 6 1 2 4 1 4 2 5 9 4 4 8 9 4 8 3 7 8 7 10 1 4 6 8
## [481] 4 9 3 1 4 5 1 6 10 2 10 8 7 8 5 5 7 6 2 9 4 10 9 8
## [505] 3 6 9 7 7 7 10 10 9 3 4 3 7 4 9 5 9 9 2 1 8 6 5 5
## [529] 3 8 7 6 9 5 5 1 4 6 8 2 3 8 1 3 2 8 9 6 10 5 9 3
## [553] 7 2 8 6 6 1 9 8 9 6 9 4 10 5 1 5 10 7 8 10 2 4 7 10
## [577] 7 7 10 10 9 6 7 10 7 8 6 8 3 9 9 5 1 9 3 4 3 8 6 9
## [601] 2 8 5 10 4 6 5 9 2 7 4 6 6 5 5 1 8 1 7 10 4 10 4 8
## [625] 8 8 2 5 7 1 8 4 6 10 10 6 1 6 1 2 1 4 6 1 8 6 3 7
## [649] 4 3 7 8 9 10 9 9 7 9 6 3 9 7 6 9 10 8 1 5 6 10 10 7
## [673] 3 5 5 5 10 5 9 6 1 10 5 7 10 1 7 6 7 10 6 1 7 6 2 7
## [697] 7 1 5 1 10 2 5 3 1 3 3 5 1 6 4 7 8 6 10 2 6 10 5 8
## [721] 8 6 5 4 1 2 4 10 7 6 10 9 4 3 2 10 3 2 2 5 6 5 5 7
## [745] 10 6 8 2 9 3 10 9 7 3 6 5 1 9 3 7 3 1 1 4 8 2 3 10
## [769] 5 6 8 10 7 5 1 6 3 9 9 7 8 10 6 8 5 3 2 1 4 8 1 5
## [793] 1 3 3 5 4 9 8 9 6 1 8 9 6 6 5 9 2 6 10 9 7 1 8 6
## [817] 1 8 5 10 6 4 6 6 6 1 3 5 2 7 5 7 8 7 7 9 8 10 1 8
## [841] 1 8 1 5 2 1 1 4 3 8 4 2 6 3 9 9 1 1 6 9 10 9 4 9
## [865] 2 2 3 1 3 2 1 4 7 2 2 6 6 9 2 8 10 9 9 7 9 4 9 4
## [889] 1 8 7 9 9 6 10 9 6 3 5 5 10 7 2 5 8 8 8 2 3 3 6 6
## [913] 6 7 2 1 3 7 3 9 3 1 3 9 9 2 8 6 6 5 6 5 9 3 3 1
## [937] 8 8 4 7 2 3 5 6 5 4 5 4 10 5 9 3 5 2 9 6 2 1 6 4
## [961] 8 3 6 8 8 7 1 4 9 1 5 2 6 8 10 2 3 4 2 6 4 4 2 5
## [985] 8 10 3 5 4 8 5 7 10 5 10 3 5 3 1 2menentukan koef
b0 <- -11
b1 <- 3.1
b2 <- 0.2
b3 <- 2.7
b4 <- 2.5set.seed(104)
datapendukung <- b0+(b1*x1)+(b2*x2)+(b3*x3)+(b4*x4)
datapendukung## [1] 19.8 21.0 9.2 8.5 17.7 11.5 6.0 7.9 -0.2 17.1 12.5 13.7 17.1 22.0
## [15] 13.5 -8.3 17.7 22.9 -3.5 8.1 -5.8 26.6 17.3 6.7 5.4 18.3 5.0 6.6
## [29] 6.9 6.7 17.7 16.9 17.7 24.0 17.9 13.7 4.0 6.8 10.6 -2.7 57.0 12.9
## [43] 1.9 0.6 21.8 13.3 6.0 18.1 17.3 0.4 8.5 8.5 4.0 10.2 15.6 18.5
## [57] 18.5 12.7 17.2 0.4 -0.4 -8.3 8.5 21.5 -2.7 30.1 -5.4 32.6 -6.0 21.6
## [71] 2.9 8.5 9.8 7.3 22.4 6.7 4.8 10.0 14.2 11.7 12.1 -3.1 30.3 4.2
## [85] 10.2 -5.8 13.1 2.3 17.3 2.5 23.0 14.1 17.9 9.2 3.1 5.2 16.9 3.3
## [99] 11.7 20.0 22.9 14.6 9.2 3.3 26.2 6.6 9.4 11.0 12.9 -2.5 16.6 26.2
## [113] 14.2 5.4 15.6 16.7 47.2 15.0 17.1 14.4 8.3 -2.7 14.8 23.1 9.7 43.7
## [127] 27.4 8.7 7.5 -3.5 2.1 0.6 0.0 6.7 33.4 14.6 -3.3 14.1 0.4 -3.3
## [141] 14.4 5.4 6.0 5.6 0.2 9.8 23.0 2.7 9.6 29.5 10.0 7.9 17.7 15.2
## [155] 25.3 11.9 3.7 7.9 17.9 27.4 7.2 9.8 7.7 22.0 -2.7 23.2 17.6 20.5
## [169] 16.0 17.5 4.8 18.5 15.6 12.2 1.7 10.4 9.2 29.2 42.1 10.3 32.1 7.5
## [183] 11.9 4.8 20.8 7.1 17.3 5.4 19.8 21.8 3.5 7.3 6.0 10.4 21.6 22.0
## [197] 12.1 -3.1 41.9 8.7 6.7 2.5 -2.1 15.6 14.8 4.8 26.8 0.4 0.2 18.5
## [211] -2.3 13.1 13.3 20.2 16.7 0.0 19.9 -5.8 22.0 26.6 24.3 19.8 5.0 17.5
## [225] 17.1 12.9 9.8 14.1 16.3 15.4 7.3 9.8 -2.1 20.6 6.5 -2.7 18.4 17.5
## [239] 3.1 26.8 8.5 25.9 5.0 1.7 10.8 19.7 9.9 5.8 2.9 26.4 2.1 6.6
## [253] 7.3 17.9 6.9 3.1 7.9 14.8 0.0 1.0 2.3 2.5 24.1 0.0 14.7 -5.8
## [267] 5.2 18.9 13.1 -2.1 14.8 6.9 7.0 2.1 1.7 18.1 -3.3 17.1 -2.1 20.8
## [281] 5.4 21.6 16.0 24.7 17.3 8.1 24.3 9.8 -5.8 18.3 12.3 10.0 12.2 -5.2
## [295] 23.2 14.2 7.9 33.4 21.2 -3.1 9.2 9.8 26.6 -3.1 15.2 8.7 12.8 24.3
## [309] 17.9 3.7 11.5 11.2 -6.0 -2.7 7.9 -3.1 20.4 2.3 11.9 16.9 20.4 29.7
## [323] 14.1 11.7 37.1 37.7 -2.5 18.1 15.0 35.9 19.8 9.8 16.0 32.4 16.0 17.1
## [337] 2.5 16.6 10.4 3.1 20.6 16.4 20.0 -2.3 -2.9 11.5 0.0 -0.6 -8.5 18.5
## [351] 1.5 -5.4 34.8 26.0 29.7 16.6 10.6 12.2 -0.2 15.8 13.2 4.6 7.2 0.8
## [365] 10.3 4.8 24.5 14.1 8.5 12.9 12.9 13.5 -2.7 -2.3 16.4 14.2 9.5 13.5
## [379] -0.2 34.6 17.3 2.9 -3.3 17.1 17.3 2.3 2.7 1.9 6.0 16.9 13.2 21.5
## [393] 3.5 28.2 12.9 9.3 -2.3 25.1 6.7 22.9 21.6 12.1 6.4 20.8 16.9 13.5
## [407] 17.3 -2.7 4.8 17.3 15.4 11.9 7.9 4.4 12.1 14.1 26.6 17.2 2.5 5.0
## [421] -2.7 11.0 24.3 -5.6 7.3 11.7 14.2 26.2 17.1 8.9 14.4 7.1 20.4 15.2
## [435] 7.3 18.1 17.3 26.7 2.3 -0.8 -3.3 39.6 12.1 21.8 -3.3 4.2 1.0 8.5
## [449] 25.3 19.1 2.9 9.2 -8.3 -5.2 8.3 4.4 16.6 -2.1 -2.9 -1.0 -2.5 11.2
## [463] 0.0 7.5 42.1 14.3 1.7 17.9 17.3 11.0 15.2 12.0 18.5 12.1 9.4 17.3
## [477] 25.4 5.4 6.9 21.6 4.8 11.5 8.7 6.6 7.9 4.2 3.5 32.1 23.1 -3.3
## [491] 20.4 17.9 18.5 14.8 10.8 4.4 12.7 6.7 -3.1 14.6 8.3 41.7 17.3 18.3
## [505] 8.5 9.8 24.1 9.2 9.8 19.1 23.3 26.4 17.3 -0.8 8.1 18.2 6.5 14.1
## [519] 27.0 7.5 17.5 45.8 -5.8 -5.2 12.1 10.2 32.7 1.7 24.2 17.9 12.9 13.5
## [533] 17.9 13.7 16.6 0.8 23.4 16.6 15.4 24.8 -0.6 36.7 3.7 6.0 9.3 11.9
## [547] 17.3 6.9 17.3 4.8 17.3 -3.3 12.3 -3.1 12.3 6.9 10.0 -5.4 26.6 21.4
## [561] 23.7 16.0 27.2 5.4 20.0 4.2 -5.2 7.9 32.4 9.8 34.0 17.1 18.6 4.8
## [575] 9.2 14.0 34.6 21.6 17.3 14.0 11.7 6.9 12.3 29.7 9.6 46.0 4.2 12.3
## [589] -0.8 21.0 14.6 1.5 -5.2 14.8 2.9 42.2 -0.2 15.2 9.8 23.9 6.0 15.0
## [603] 4.2 17.1 5.2 6.9 4.6 17.5 9.7 9.6 5.0 12.9 6.9 23.4 1.7 6.6
## [617] 12.1 -2.7 9.4 19.8 8.1 26.2 17.6 21.0 11.7 24.7 0.2 29.2 12.7 -5.4
## [631] 15.0 2.3 7.1 17.1 19.8 16.0 -5.8 10.4 10.3 -6.0 -2.7 11.2 13.5 -2.7
## [645] 27.8 19.1 -3.5 12.5 11.2 2.3 19.1 24.5 21.0 20.4 20.6 23.9 31.1 14.4
## [659] 7.1 -0.4 14.2 21.6 37.9 14.8 23.5 15.2 -5.2 7.3 16.6 17.3 19.8 18.9
## [673] 8.5 7.3 10.4 7.3 29.1 11.0 23.9 7.1 -5.2 16.7 4.6 9.4 29.3 -8.3
## [687] 9.8 13.1 21.6 20.0 16.2 6.8 15.8 19.5 6.2 9.8 9.8 -8.5 7.3 -8.3
## [701] 17.1 6.2 4.4 12.0 -8.5 2.3 5.8 4.4 -5.2 6.7 7.9 34.2 30.9 6.9
## [715] 19.8 37.0 7.3 14.0 13.5 42.9 12.1 9.8 7.5 4.8 -5.2 21.9 2.3 29.7
## [729] 9.4 22.4 19.8 23.9 2.1 -0.6 3.5 16.9 2.3 18.6 9.3 10.6 6.7 4.8
## [743] 23.0 15.6 14.2 32.1 30.5 0.2 42.1 -0.2 26.4 17.5 19.1 5.4 25.5 10.4
## [757] -5.4 14.6 -0.8 12.5 8.5 1.0 6.8 1.7 30.3 -2.7 5.4 16.9 1.5 25.5
## [771] 36.7 29.1 12.3 7.9 -2.7 4.2 2.5 14.6 29.7 12.3 33.4 19.8 6.9 18.3
## [785] 7.9 6.0 -6.0 -2.7 11.4 15.4 -2.5 32.5 -5.4 18.2 2.7 4.6 4.8 20.6
## [799] 27.2 14.8 7.1 -5.8 12.1 33.0 13.1 16.4 7.7 14.4 3.3 10.4 23.5 11.7
## [813] 15.4 0.6 12.1 22.6 -2.1 21.4 10.6 14.0 10.4 7.9 10.4 13.5 6.9 4.1
## [827] -0.4 7.7 -2.9 15.6 7.3 12.5 21.2 15.6 12.3 17.7 12.1 23.1 -5.4 9.2
## [841] -8.3 9.2 -5.6 4.6 2.9 -5.2 -5.6 5.2 -0.2 14.8 5.4 6.0 13.3 2.3
## [855] 14.6 11.7 -8.3 0.4 9.8 33.4 16.9 20.4 14.1 14.8 3.5 -6.0 5.4 0.6
## [869] 5.6 12.6 3.7 1.9 12.7 3.5 9.7 4.2 13.5 17.3 -0.2 12.3 17.1 45.4
## [883] 20.6 9.4 14.4 2.1 23.5 8.1 -5.2 11.9 12.5 11.7 30.1 4.0 20.2 14.6
## [897] 28.4 5.8 4.8 10.4 35.7 6.5 6.0 7.9 15.4 15.0 15.4 0.0 5.4 -3.5
## [911] 16.2 22.6 32.1 15.6 0.0 -5.6 5.8 21.6 -0.8 14.6 2.9 3.5 12.0 33.0
## [925] 20.8 -5.8 12.1 16.6 7.3 14.1 16.0 13.7 11.5 8.7 18.0 -5.2 9.0 18.5
## [939] 5.2 12.5 0.0 9.1 7.7 43.9 7.9 8.1 7.7 1.9 22.9 10.4 17.5 12.2
## [953] 4.4 6.2 14.8 10.4 3.1 -2.7 7.1 -0.8 18.1 -0.4 7.3 9.2 9.2 6.7
## [967] 9.7 11.2 20.4 7.2 11.0 -3.3 10.2 9.2 41.5 0.4 21.5 2.3 3.3 7.3
## [981] 5.0 5.4 -2.9 1.5 12.3 20.4 2.7 7.9 4.8 24.3 4.2 15.8 23.1 10.6
## [995] 26.0 5.6 4.6 20.9 -5.4 3.1p <- exp(datapendukung)/(1+exp(datapendukung))
p## [1] 0.9999999975 0.9999999992 0.9998989708 0.9997965730 0.9999999794
## [6] 0.9999898700 0.9975273768 0.9996293939 0.4501660027 0.9999999625
## [11] 0.9999962734 0.9999988776 0.9999999625 0.9999999997 0.9999986290
## [16] 0.0002484551 0.9999999794 0.9999999999 0.0293122308 0.9996965530
## [21] 0.0030184163 1.0000000000 0.9999999693 0.9987706014 0.9955037268
## [26] 0.9999999887 0.9933071491 0.9986414800 0.9989932292 0.9987706014
## [31] 0.9999999794 0.9999999542 0.9999999794 1.0000000000 0.9999999832
## [36] 0.9999988776 0.9820137900 0.9988874640 0.9999750846 0.0629733561
## [41] 1.0000000000 0.9999975020 0.8698915256 0.6456563062 0.9999999997
## [46] 0.9999983255 0.9975273768 0.9999999862 0.9999999693 0.5986876601
## [51] 0.9997965730 0.9997965730 0.9820137900 0.9999628311 0.9999998321
## [56] 0.9999999908 0.9999999908 0.9999969489 0.9999999661 0.5986876601
## [61] 0.4013123399 0.0002484551 0.9997965730 0.9999999995 0.0629733561
## [66] 1.0000000000 0.0044962732 1.0000000000 0.0024726232 0.9999999996
## [71] 0.9478464369 0.9997965730 0.9999445515 0.9993249173 0.9999999998
## [76] 0.9987706014 0.9918374288 0.9999546021 0.9999993192 0.9999917062
## [81] 0.9999944405 0.0431072549 1.0000000000 0.9852259683 0.9999628311
## [86] 0.0030184163 0.9999979548 0.9088770390 0.9999999693 0.9241418200
## [91] 0.9999999999 0.9999992476 0.9999999832 0.9998989708 0.9568927451
## [96] 0.9945137011 0.9999999542 0.9644288107 0.9999917062 0.9999999979
## [101] 0.9999999999 0.9999995436 0.9998989708 0.9644288107 1.0000000000
## [106] 0.9986414800 0.9999172828 0.9999832986 0.9999975020 0.0758581800
## [111] 0.9999999382 1.0000000000 0.9999993192 0.9955037268 0.9999998321
## [116] 0.9999999441 1.0000000000 0.9999996941 0.9999999625 0.9999994426
## [121] 0.9997515449 0.0629733561 0.9999996264 0.9999999999 0.9999387203
## [126] 1.0000000000 1.0000000000 0.9998334419 0.9994472214 0.0293122308
## [131] 0.8909031788 0.6456563062 0.5000000000 0.9987706014 1.0000000000
## [136] 0.9999995436 0.0355711893 0.9999992476 0.5986876601 0.0355711893
## [141] 0.9999994426 0.9955037268 0.9975273768 0.9963157601 0.5498339973
## [146] 0.9999445515 0.9999999999 0.9370266439 0.9999322759 1.0000000000
## [151] 0.9999546021 0.9996293939 0.9999999794 0.9999997495 1.0000000000
## [156] 0.9999932096 0.9758729786 0.9996293939 0.9999999832 1.0000000000
## [161] 0.9992539712 0.9999445515 0.9995473778 0.9999999997 0.0629733561
## [166] 0.9999999999 0.9999999773 0.9999999987 0.9999998875 0.9999999749
## [171] 0.9918374288 0.9999999908 0.9999998321 0.9999949696 0.8455347349
## [176] 0.9999695684 0.9998989708 1.0000000000 1.0000000000 0.9999663680
## [181] 1.0000000000 0.9994472214 0.9999932096 0.9918374288 0.9999999991
## [186] 0.9991755753 0.9999999693 0.9955037268 0.9999999975 0.9999999997
## [191] 0.9706877692 0.9993249173 0.9975273768 0.9999695684 0.9999999996
## [196] 0.9999999997 0.9999944405 0.0431072549 1.0000000000 0.9998334419
## [201] 0.9987706014 0.9241418200 0.1090968212 0.9999998321 0.9999996264
## [206] 0.9918374288 1.0000000000 0.5986876601 0.5498339973 0.9999999908
## [211] 0.0911229610 0.9999979548 0.9999983255 0.9999999983 0.9999999441
## [216] 0.5000000000 0.9999999977 0.0030184163 0.9999999997 1.0000000000
## [221] 1.0000000000 0.9999999975 0.9933071491 0.9999999749 0.9999999625
## [226] 0.9999975020 0.9999445515 0.9999992476 0.9999999166 0.9999997949
## [231] 0.9993249173 0.9999445515 0.1090968212 0.9999999989 0.9984988177
## [236] 0.0629733561 0.9999999898 0.9999999749 0.9568927451 1.0000000000
## [241] 0.9997965730 1.0000000000 0.9933071491 0.8455347349 0.9999796009
## [246] 0.9999999972 0.9999498278 0.9969815837 0.9478464369 1.0000000000
## [251] 0.8909031788 0.9986414800 0.9993249173 0.9999999832 0.9989932292
## [256] 0.9568927451 0.9996293939 0.9999996264 0.5000000000 0.7310585786
## [261] 0.9088770390 0.9241418200 1.0000000000 0.5000000000 0.9999995871
## [266] 0.0030184163 0.9945137011 0.9999999938 0.9999979548 0.1090968212
## [271] 0.9999996264 0.9989932292 0.9990889488 0.8909031788 0.8455347349
## [276] 0.9999999862 0.0355711893 0.9999999625 0.1090968212 0.9999999991
## [281] 0.9955037268 0.9999999996 0.9999998875 1.0000000000 0.9999999693
## [286] 0.9996965530 1.0000000000 0.9999445515 0.0030184163 0.9999999887
## [291] 0.9999954483 0.9999546021 0.9999949696 0.0054862989 0.9999999999
## [296] 0.9999993192 0.9996293939 1.0000000000 0.9999999994 0.0431072549
## [301] 0.9998989708 0.9999445515 1.0000000000 0.0431072549 0.9999997495
## [306] 0.9998334419 0.9999972392 1.0000000000 0.9999999832 0.9758729786
## [311] 0.9999898700 0.9999863260 0.0024726232 0.0629733561 0.9996293939
## [316] 0.0431072549 0.9999999986 0.9088770390 0.9999932096 0.9999999542
## [321] 0.9999999986 1.0000000000 0.9999992476 0.9999917062 1.0000000000
## [326] 1.0000000000 0.0758581800 0.9999999862 0.9999996941 1.0000000000
## [331] 0.9999999975 0.9999445515 0.9999998875 1.0000000000 0.9999998875
## [336] 0.9999999625 0.9241418200 0.9999999382 0.9999695684 0.9568927451
## [341] 0.9999999989 0.9999999246 0.9999999979 0.0911229610 0.0521535631
## [346] 0.9999898700 0.5000000000 0.3543436938 0.0002034270 0.9999999908
## [351] 0.8175744762 0.0044962732 1.0000000000 1.0000000000 1.0000000000
## [356] 0.9999999382 0.9999750846 0.9999949696 0.4501660027 0.9999998625
## [361] 0.9999981494 0.9900481981 0.9992539712 0.6899744811 0.9999663680
## [366] 0.9918374288 1.0000000000 0.9999992476 0.9997965730 0.9999975020
## [371] 0.9999975020 0.9999986290 0.0629733561 0.0911229610 0.9999999246
## [376] 0.9999993192 0.9999251538 0.9999986290 0.4501660027 1.0000000000
## [381] 0.9999999693 0.9478464369 0.0355711893 0.9999999625 0.9999999693
## [386] 0.9088770390 0.9370266439 0.8698915256 0.9975273768 0.9999999542
## [391] 0.9999981494 0.9999999995 0.9706877692 1.0000000000 0.9999975020
## [396] 0.9999085841 0.0911229610 1.0000000000 0.9987706014 0.9999999999
## [401] 0.9999999996 0.9999944405 0.9983411989 0.9999999991 0.9999999542
## [406] 0.9999986290 0.9999999693 0.0629733561 0.9918374288 0.9999999693
## [411] 0.9999997949 0.9999932096 0.9996293939 0.9878715650 0.9999944405
## [416] 0.9999992476 1.0000000000 0.9999999661 0.9241418200 0.9933071491
## [421] 0.0629733561 0.9999832986 1.0000000000 0.0036842399 0.9993249173
## [426] 0.9999917062 0.9999993192 1.0000000000 0.9999999625 0.9998636297
## [431] 0.9999994426 0.9991755753 0.9999999986 0.9999997495 0.9993249173
## [436] 0.9999999862 0.9999999693 1.0000000000 0.9088770390 0.3100255189
## [441] 0.0355711893 1.0000000000 0.9999944405 0.9999999997 0.0355711893
## [446] 0.9852259683 0.7310585786 0.9997965730 1.0000000000 0.9999999949
## [451] 0.9478464369 0.9998989708 0.0002484551 0.0054862989 0.9997515449
## [456] 0.9878715650 0.9999999382 0.1090968212 0.0521535631 0.2689414214
## [461] 0.0758581800 0.9999863260 0.5000000000 0.9994472214 1.0000000000
## [466] 0.9999993840 0.8455347349 0.9999999832 0.9999999693 0.9999832986
## [471] 0.9999997495 0.9999938558 0.9999999908 0.9999944405 0.9999172828
## [476] 0.9999999693 1.0000000000 0.9955037268 0.9989932292 0.9999999996
## [481] 0.9918374288 0.9999898700 0.9998334419 0.9986414800 0.9996293939
## [486] 0.9852259683 0.9706877692 1.0000000000 0.9999999999 0.0355711893
## [491] 0.9999999986 0.9999999832 0.9999999908 0.9999996264 0.9999796009
## [496] 0.9878715650 0.9999969489 0.9987706014 0.0431072549 0.9999995436
## [501] 0.9997515449 1.0000000000 0.9999999693 0.9999999887 0.9997965730
## [506] 0.9999445515 1.0000000000 0.9998989708 0.9999445515 0.9999999949
## [511] 0.9999999999 1.0000000000 0.9999999693 0.3100255189 0.9996965530
## [516] 0.9999999875 0.9984988177 0.9999992476 1.0000000000 0.9994472214
## [521] 0.9999999749 1.0000000000 0.0030184163 0.0054862989 0.9999944405
## [526] 0.9999628311 1.0000000000 0.8455347349 1.0000000000 0.9999999832
## [531] 0.9999975020 0.9999986290 0.9999999832 0.9999988776 0.9999999382
## [536] 0.6899744811 0.9999999999 0.9999999382 0.9999997949 1.0000000000
## [541] 0.3543436938 1.0000000000 0.9758729786 0.9975273768 0.9999085841
## [546] 0.9999932096 0.9999999693 0.9989932292 0.9999999693 0.9918374288
## [551] 0.9999999693 0.0355711893 0.9999954483 0.0431072549 0.9999954483
## [556] 0.9989932292 0.9999546021 0.0044962732 1.0000000000 0.9999999995
## [561] 0.9999999999 0.9999998875 1.0000000000 0.9955037268 0.9999999979
## [566] 0.9852259683 0.0054862989 0.9996293939 1.0000000000 0.9999445515
## [571] 1.0000000000 0.9999999625 0.9999999916 0.9918374288 0.9998989708
## [576] 0.9999991685 1.0000000000 0.9999999996 0.9999999693 0.9999991685
## [581] 0.9999917062 0.9989932292 0.9999954483 1.0000000000 0.9999322759
## [586] 1.0000000000 0.9852259683 0.9999954483 0.3100255189 0.9999999992
## [591] 0.9999995436 0.8175744762 0.0054862989 0.9999996264 0.9478464369
## [596] 1.0000000000 0.4501660027 0.9999997495 0.9999445515 1.0000000000
## [601] 0.9975273768 0.9999996941 0.9852259683 0.9999999625 0.9945137011
## [606] 0.9989932292 0.9900481981 0.9999999749 0.9999387203 0.9999322759
## [611] 0.9933071491 0.9999975020 0.9989932292 0.9999999999 0.8455347349
## [616] 0.9986414800 0.9999944405 0.0629733561 0.9999172828 0.9999999975
## [621] 0.9996965530 1.0000000000 0.9999999773 0.9999999992 0.9999917062
## [626] 1.0000000000 0.5498339973 1.0000000000 0.9999969489 0.0044962732
## [631] 0.9999996941 0.9088770390 0.9991755753 0.9999999625 0.9999999975
## [636] 0.9999998875 0.0030184163 0.9999695684 0.9999663680 0.0024726232
## [641] 0.0629733561 0.9999863260 0.9999986290 0.0629733561 1.0000000000
## [646] 0.9999999949 0.0293122308 0.9999962734 0.9999863260 0.9088770390
## [651] 0.9999999949 1.0000000000 0.9999999992 0.9999999986 0.9999999989
## [656] 1.0000000000 1.0000000000 0.9999994426 0.9991755753 0.4013123399
## [661] 0.9999993192 0.9999999996 1.0000000000 0.9999996264 0.9999999999
## [666] 0.9999997495 0.0054862989 0.9993249173 0.9999999382 0.9999999693
## [671] 0.9999999975 0.9999999938 0.9997965730 0.9993249173 0.9999695684
## [676] 0.9993249173 1.0000000000 0.9999832986 1.0000000000 0.9991755753
## [681] 0.0054862989 0.9999999441 0.9900481981 0.9999172828 1.0000000000
## [686] 0.0002484551 0.9999445515 0.9999979548 0.9999999996 0.9999999979
## [691] 0.9999999079 0.9988874640 0.9999998625 0.9999999966 0.9979746796
## [696] 0.9999445515 0.9999445515 0.0002034270 0.9993249173 0.0002484551
## [701] 0.9999999625 0.9979746796 0.9878715650 0.9999938558 0.0002034270
## [706] 0.9088770390 0.9969815837 0.9878715650 0.0054862989 0.9987706014
## [711] 0.9996293939 1.0000000000 1.0000000000 0.9989932292 0.9999999975
## [716] 1.0000000000 0.9993249173 0.9999991685 0.9999986290 1.0000000000
## [721] 0.9999944405 0.9999445515 0.9994472214 0.9918374288 0.0054862989
## [726] 0.9999999997 0.9088770390 1.0000000000 0.9999172828 0.9999999998
## [731] 0.9999999975 1.0000000000 0.8909031788 0.3543436938 0.9706877692
## [736] 0.9999999542 0.9088770390 0.9999999916 0.9999085841 0.9999750846
## [741] 0.9987706014 0.9918374288 0.9999999999 0.9999998321 0.9999993192
## [746] 1.0000000000 1.0000000000 0.5498339973 1.0000000000 0.4501660027
## [751] 1.0000000000 0.9999999749 0.9999999949 0.9955037268 1.0000000000
## [756] 0.9999695684 0.0044962732 0.9999995436 0.3100255189 0.9999962734
## [761] 0.9997965730 0.7310585786 0.9988874640 0.8455347349 1.0000000000
## [766] 0.0629733561 0.9955037268 0.9999999542 0.8175744762 1.0000000000
## [771] 1.0000000000 1.0000000000 0.9999954483 0.9996293939 0.0629733561
## [776] 0.9852259683 0.9241418200 0.9999995436 1.0000000000 0.9999954483
## [781] 1.0000000000 0.9999999975 0.9989932292 0.9999999887 0.9996293939
## [786] 0.9975273768 0.0024726232 0.0629733561 0.9999888046 0.9999997949
## [791] 0.0758581800 1.0000000000 0.0044962732 0.9999999875 0.9370266439
## [796] 0.9900481981 0.9918374288 0.9999999989 1.0000000000 0.9999996264
## [801] 0.9991755753 0.0030184163 0.9999944405 1.0000000000 0.9999979548
## [806] 0.9999999246 0.9995473778 0.9999994426 0.9644288107 0.9999695684
## [811] 0.9999999999 0.9999917062 0.9999997949 0.6456563062 0.9999944405
## [816] 0.9999999998 0.1090968212 0.9999999995 0.9999750846 0.9999991685
## [821] 0.9999695684 0.9996293939 0.9999695684 0.9999986290 0.9989932292
## [826] 0.9836975006 0.4013123399 0.9995473778 0.0521535631 0.9999998321
## [831] 0.9993249173 0.9999962734 0.9999999994 0.9999998321 0.9999954483
## [836] 0.9999999794 0.9999944405 0.9999999999 0.0044962732 0.9998989708
## [841] 0.0002484551 0.9998989708 0.0036842399 0.9900481981 0.9478464369
## [846] 0.0054862989 0.0036842399 0.9945137011 0.4501660027 0.9999996264
## [851] 0.9955037268 0.9975273768 0.9999983255 0.9088770390 0.9999995436
## [856] 0.9999917062 0.0002484551 0.5986876601 0.9999445515 1.0000000000
## [861] 0.9999999542 0.9999999986 0.9999992476 0.9999996264 0.9706877692
## [866] 0.0024726232 0.9955037268 0.6456563062 0.9963157601 0.9999966280
## [871] 0.9758729786 0.8698915256 0.9999969489 0.9706877692 0.9999387203
## [876] 0.9852259683 0.9999986290 0.9999999693 0.4501660027 0.9999954483
## [881] 0.9999999625 1.0000000000 0.9999999989 0.9999172828 0.9999994426
## [886] 0.8909031788 0.9999999999 0.9996965530 0.0054862989 0.9999932096
## [891] 0.9999962734 0.9999917062 1.0000000000 0.9820137900 0.9999999983
## [896] 0.9999995436 1.0000000000 0.9969815837 0.9918374288 0.9999695684
## [901] 1.0000000000 0.9984988177 0.9975273768 0.9996293939 0.9999997949
## [906] 0.9999996941 0.9999997949 0.5000000000 0.9955037268 0.0293122308
## [911] 0.9999999079 0.9999999998 1.0000000000 0.9999998321 0.5000000000
## [916] 0.0036842399 0.9969815837 0.9999999996 0.3100255189 0.9999995436
## [921] 0.9478464369 0.9706877692 0.9999938558 1.0000000000 0.9999999991
## [926] 0.0030184163 0.9999944405 0.9999999382 0.9993249173 0.9999992476
## [931] 0.9999998875 0.9999988776 0.9999898700 0.9998334419 0.9999999848
## [936] 0.0054862989 0.9998766054 0.9999999908 0.9945137011 0.9999962734
## [941] 0.5000000000 0.9998883467 0.9995473778 1.0000000000 0.9996293939
## [946] 0.9996965530 0.9995473778 0.8698915256 0.9999999999 0.9999695684
## [951] 0.9999999749 0.9999949696 0.9878715650 0.9979746796 0.9999996264
## [956] 0.9999695684 0.9568927451 0.0629733561 0.9991755753 0.3100255189
## [961] 0.9999999862 0.4013123399 0.9993249173 0.9998989708 0.9998989708
## [966] 0.9987706014 0.9999387203 0.9999863260 0.9999999986 0.9992539712
## [971] 0.9999832986 0.0355711893 0.9999628311 0.9998989708 1.0000000000
## [976] 0.5986876601 0.9999999995 0.9088770390 0.9644288107 0.9993249173
## [981] 0.9933071491 0.9955037268 0.0521535631 0.8175744762 0.9999954483
## [986] 0.9999999986 0.9370266439 0.9996293939 0.9918374288 1.0000000000
## [991] 0.9852259683 0.9999998625 0.9999999999 0.9999750846 1.0000000000
## [996] 0.9963157601 0.9900481981 0.9999999992 0.0044962732 0.9568927451set.seed(3)
y <- rbinom(n,1,p)
y## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 0 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 0 1
## [112] 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1
## [149] 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
## [186] 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 0 1 1 1 1 1 1 0 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 0 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
## [260] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1
## [297] 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1
## [408] 0 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 0 0 1 1 1
## [445] 0 1 1 1 1 1 0 1 0 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
## [519] 1 1 1 1 0 0 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 0 1 0 1
## [556] 1 1 0 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 1 1 0
## [593] 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 0 1 1 1 1 1 1 1 1 0 1 1
## [630] 0 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
## [667] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1
## [704] 1 0 1 1 1 0 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
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 1 1 1
## [852] 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
## [889] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1
## [926] 0 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 0 1 0 1 0
## [963] 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
## [1000] 1datagab <- data.frame(y,x1,x2,x3,x4)
datagab## y x1 x2 x3 x4
## 1 1 1 0 1 10
## 2 1 3 0 1 8
## 3 1 0 1 0 8
## 4 1 3 0 1 3
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## 809 1 3 0 0 2
## 810 1 2 1 0 6
## 811 1 3 1 0 10
## 812 1 0 1 0 9
## 813 1 2 0 1 7
## 814 0 2 1 1 1
## 815 1 1 0 0 8
## 816 1 6 0 0 6
## 817 1 2 1 0 1
## 818 1 4 0 0 8
## 819 1 2 1 1 5
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## 821 1 2 1 0 6
## 822 1 2 0 1 4
## 823 1 2 1 0 6
## 824 1 3 1 0 6
## 825 1 0 1 1 6
## 826 1 4 1 0 1
## 827 1 1 0 0 3
## 828 1 2 0 0 5
## 829 1 1 0 0 2
## 830 1 2 1 1 7
## 831 1 1 0 1 5
## 832 1 1 1 1 7
## 833 1 3 1 1 8
## 834 1 2 1 1 7
## 835 1 1 0 1 7
## 836 1 2 0 0 9
## 837 1 1 0 0 8
## 838 1 2 1 1 10
## 839 0 1 0 0 1
## 840 1 0 1 0 8
## 841 0 0 1 0 1
## 842 1 0 1 0 8
## 843 0 0 1 1 1
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## 845 1 2 0 1 2
## 846 0 1 1 0 1
## 847 0 0 1 1 1
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## 849 1 1 1 0 3
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## 851 1 2 1 0 4
## 852 1 3 0 1 2
## 853 1 3 0 0 6
## 854 1 1 0 1 3
## 855 1 1 0 0 9
## 856 1 0 1 0 9
## 857 0 0 1 0 1
## 858 0 2 0 1 1
## 859 1 1 0 1 6
## 860 1 7 1 0 9
## 861 1 0 1 1 10
## 862 1 2 0 1 9
## 863 1 4 0 1 4
## 864 1 1 1 0 9
## 865 1 3 1 0 2
## 866 0 0 0 0 2
## 867 1 2 0 1 3
## 868 1 2 1 1 1
## 869 1 2 1 1 3
## 870 1 6 0 0 2
## 871 1 3 1 1 1
## 872 1 0 1 1 4
## 873 1 2 0 0 7
## 874 1 3 1 0 2
## 875 1 5 1 0 2
## 876 1 0 1 0 6
## 877 1 3 1 0 6
## 878 1 1 0 1 9
## 879 0 1 0 1 2
## 880 1 1 1 0 8
## 881 1 1 0 0 10
## 882 1 10 1 1 9
## 883 1 2 1 1 9
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## 885 1 0 1 1 9
## 886 1 1 0 0 4
## 887 1 3 0 1 9
## 888 1 2 1 1 4
## 889 0 1 1 0 1
## 890 1 0 1 1 8
## 891 1 1 1 1 7
## 892 1 0 1 0 9
## 893 1 6 0 0 9
## 894 1 0 0 0 6
## 895 1 2 0 0 10
## 896 1 1 0 0 9
## 897 1 7 0 1 6
## 898 1 3 0 0 3
## 899 1 1 1 0 5
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## 901 1 7 0 0 10
## 902 1 0 0 0 7
## 903 1 3 0 1 2
## 904 1 2 1 0 5
## 905 1 2 1 0 8
## 906 1 1 1 1 8
## 907 1 2 1 0 8
## 908 0 1 1 1 2
## 909 1 2 0 1 3
## 910 0 0 0 0 3
## 911 1 3 1 1 6
## 912 1 6 0 0 6
## 913 1 9 1 0 6
## 914 1 2 1 1 7
## 915 1 1 1 1 2
## 916 0 0 1 1 1
## 917 1 3 0 0 3
## 918 1 4 0 1 7
## 919 0 0 0 1 3
## 920 1 1 0 0 9
## 921 1 2 1 0 3
## 922 1 3 0 1 1
## 923 1 5 0 0 3
## 924 1 6 1 1 9
## 925 1 3 0 0 9
## 926 0 0 1 0 2
## 927 1 1 0 0 8
## 928 1 4 1 0 6
## 929 1 1 1 0 6
## 930 1 4 1 0 5
## 931 1 3 0 1 6
## 932 1 3 1 1 5
## 933 1 0 0 0 9
## 934 1 3 1 1 3
## 935 1 6 1 1 3
## 936 0 1 1 0 1
## 937 1 0 0 0 8
## 938 1 3 1 0 8
## 939 1 2 0 0 4
## 940 1 1 1 1 7
## 941 1 1 1 1 2
## 942 1 4 1 0 3
## 943 1 2 0 0 5
## 944 1 12 0 1 6
## 945 1 2 1 0 5
## 946 1 2 1 1 4
## 947 1 2 0 0 5
## 948 1 0 1 1 4
## 949 1 2 0 1 10
## 950 1 2 0 1 5
## 951 1 1 1 1 9
## 952 1 5 1 0 3
## 953 1 0 1 1 5
## 954 1 3 1 1 2
## 955 1 1 1 0 9
## 956 1 2 1 0 6
## 957 1 2 1 1 2
## 958 0 1 0 1 1
## 959 1 1 0 0 6
## 960 0 0 1 0 4
## 961 1 2 1 1 8
## 962 0 1 0 0 3
## 963 1 1 1 0 6
## 964 1 0 1 0 8
## 965 1 0 1 0 8
## 966 1 0 1 0 7
## 967 1 5 0 1 1
## 968 1 3 1 1 4
## 969 1 2 0 1 9
## 970 1 5 1 0 1
## 971 1 3 1 0 5
## 972 0 0 0 1 2
## 973 1 2 0 0 6
## 974 1 0 1 0 8
## 975 1 8 0 1 10
## 976 1 2 1 0 2
## 977 1 8 1 0 3
## 978 1 1 1 0 4
## 979 1 3 0 0 2
## 980 1 1 1 0 6
## 981 1 1 1 1 4
## 982 1 2 1 0 4
## 983 0 1 0 0 2
## 984 0 0 0 0 5
## 985 1 1 1 0 8
## 986 1 2 1 0 10
## 987 1 2 0 0 3
## 988 1 2 1 0 5
## 989 1 1 0 1 4
## 990 1 4 1 1 8
## 991 1 0 0 1 5
## 992 1 3 0 0 7
## 993 1 2 1 1 10
## 994 1 2 1 1 5
## 995 1 3 0 1 10
## 996 1 2 1 1 3
## 997 1 1 0 0 5
## 998 1 7 0 1 3
## 999 0 1 0 0 1
## 1000 1 2 1 1 2modelreglog <- 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) -12.0184 1.6235 -7.403 1.33e-13 ***
## x1 3.9907 0.5605 7.120 1.08e-12 ***
## x2 1.0811 0.5067 2.134 0.032874 *
## x3 1.9862 0.5435 3.654 0.000258 ***
## x4 2.7064 0.3490 7.754 8.88e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 729.86 on 999 degrees of freedom
## Residual deviance: 113.25 on 995 degrees of freedom
## AIC: 123.25
##
## Number of Fisher Scoring iterations: 10Dalam contoh di atas, variabel x1, x3, dan x4 memiliki nilai p masing-masing 1.08e-12, 0.000258, dan 8.88e-15, yang lebih kecil dari 0.05, sehingga kita dapat menyimpulkan bahwa keduanya signifikan terhadap model.