pertama kita menginstall package ISLR
library(ISLR)## Warning: package 'ISLR' was built under R version 4.3.2data <- ISLR::Portfolio
attach(data)
data## X Y
## 1 -0.89525089 -0.23492353
## 2 -1.56245433 -0.88517599
## 3 -0.41708988 0.27188802
## 4 1.04435573 -0.73419750
## 5 -0.31556841 0.84198343
## 6 -1.73712385 -2.03719104
## 7 1.96641316 1.45295666
## 8 2.15286790 -0.43413863
## 9 -0.08120803 1.45080850
## 10 -0.89178179 0.82101623
## 11 -0.29320170 -1.04239112
## 12 0.50577917 0.60847783
## 13 0.52675125 -0.22249334
## 14 1.06646932 1.23135668
## 15 0.29401590 0.62858948
## 16 0.04254930 -1.26757362
## 17 1.83096958 -0.57275161
## 18 -0.32693750 -0.48747247
## 19 0.52148042 2.56598529
## 20 1.39986835 -0.35783613
## 21 -0.64544760 -1.41243139
## 22 -0.90435188 -0.56830479
## 23 -1.76458607 -0.74627256
## 24 -1.81048464 0.49374736
## 25 -1.16989891 -2.72528149
## 26 -0.68537574 -0.45761573
## 27 1.09091803 0.01449451
## 28 -0.43234011 -0.39983102
## 29 0.26881478 -0.20160835
## 30 -0.85184075 -1.74182929
## 31 -1.49708417 -0.82603333
## 32 0.08877475 -0.88736071
## 33 -1.60172431 -0.69529905
## 34 -1.24685724 -1.52958488
## 35 -1.06298913 -0.11063745
## 36 -0.26628306 0.04516347
## 37 1.67658383 2.52005288
## 38 0.11957257 0.53554278
## 39 -0.08600799 1.36359583
## 40 0.36808029 1.72937251
## 41 -0.27149421 1.37926733
## 42 -0.08592646 -0.12766257
## 43 -0.19075015 -0.46133336
## 44 -0.78167977 1.02239788
## 45 0.79243635 -0.81429809
## 46 -0.28286989 -1.03846881
## 47 -0.23662553 0.92845055
## 48 1.17183009 1.72983145
## 49 0.49694277 -0.92513983
## 50 -0.88737098 -2.28349796
## 51 -1.30695316 -2.38160058
## 52 -2.43276412 -2.02554559
## 53 -0.40718896 -0.33509864
## 54 -0.28566530 -1.30878131
## 55 1.52221488 1.20100315
## 56 -0.99810691 -0.94626890
## 57 -0.28997373 0.20625658
## 58 -1.23683924 -0.67544751
## 59 -0.35950696 -2.70015447
## 60 0.54355915 0.42254755
## 61 -0.40364728 -0.05438992
## 62 1.30330893 1.32896747
## 63 -0.71711724 1.33137980
## 64 -1.01270788 -0.92476923
## 65 0.83199290 2.24774587
## 66 1.33764360 0.86825646
## 67 0.60169351 -0.19821756
## 68 1.30285098 1.10466638
## 69 -0.88170058 -1.54068479
## 70 -0.82452907 -1.33700788
## 71 -0.98435652 -1.13916027
## 72 -1.38499151 0.70269993
## 73 -0.35884256 -1.69451277
## 74 -0.22661823 0.80193855
## 75 -0.94107744 -0.73318871
## 76 2.46033595 -0.04837282
## 77 0.71679728 0.60233676
## 78 -0.24808702 -1.01849037
## 79 1.01077289 0.05297802
## 80 2.31304863 1.75235888
## 81 0.83517980 0.98571488
## 82 -1.07190334 -1.24729787
## 83 -1.65052614 0.21546453
## 84 -0.60048569 -0.42094053
## 85 -0.05852938 0.12762087
## 86 0.07572674 -0.52214922
## 87 -1.15783156 0.59089374
## 88 1.67360609 0.11462332
## 89 -1.04398824 -0.41894428
## 90 0.01468748 -0.55874662
## 91 0.67532197 1.48262979
## 92 1.77834231 0.94277411
## 93 -1.29576364 -1.08520381
## 94 0.07960202 -0.53910081
## 95 2.26085771 0.67322484
## 96 0.47909092 1.45477446
## 97 -0.53502000 -0.39917481
## 98 -0.77312933 -0.95717485
## 99 0.40363434 1.39603817
## 100 -0.58849644 -0.49728509mencari estimasi, varians, dan standar error
Membuat dataframe
Miftah <- LETTERS[1:100]
item1 <- X
item2 <- Y
DF <- data.frame(Miftah,item1,item2)
DF## Miftah item1 item2
## 1 A -0.89525089 -0.23492353
## 2 B -1.56245433 -0.88517599
## 3 C -0.41708988 0.27188802
## 4 D 1.04435573 -0.73419750
## 5 E -0.31556841 0.84198343
## 6 F -1.73712385 -2.03719104
## 7 G 1.96641316 1.45295666
## 8 H 2.15286790 -0.43413863
## 9 I -0.08120803 1.45080850
## 10 J -0.89178179 0.82101623
## 11 K -0.29320170 -1.04239112
## 12 L 0.50577917 0.60847783
## 13 M 0.52675125 -0.22249334
## 14 N 1.06646932 1.23135668
## 15 O 0.29401590 0.62858948
## 16 P 0.04254930 -1.26757362
## 17 Q 1.83096958 -0.57275161
## 18 R -0.32693750 -0.48747247
## 19 S 0.52148042 2.56598529
## 20 T 1.39986835 -0.35783613
## 21 U -0.64544760 -1.41243139
## 22 V -0.90435188 -0.56830479
## 23 W -1.76458607 -0.74627256
## 24 X -1.81048464 0.49374736
## 25 Y -1.16989891 -2.72528149
## 26 Z -0.68537574 -0.45761573
## 27 <NA> 1.09091803 0.01449451
## 28 <NA> -0.43234011 -0.39983102
## 29 <NA> 0.26881478 -0.20160835
## 30 <NA> -0.85184075 -1.74182929
## 31 <NA> -1.49708417 -0.82603333
## 32 <NA> 0.08877475 -0.88736071
## 33 <NA> -1.60172431 -0.69529905
## 34 <NA> -1.24685724 -1.52958488
## 35 <NA> -1.06298913 -0.11063745
## 36 <NA> -0.26628306 0.04516347
## 37 <NA> 1.67658383 2.52005288
## 38 <NA> 0.11957257 0.53554278
## 39 <NA> -0.08600799 1.36359583
## 40 <NA> 0.36808029 1.72937251
## 41 <NA> -0.27149421 1.37926733
## 42 <NA> -0.08592646 -0.12766257
## 43 <NA> -0.19075015 -0.46133336
## 44 <NA> -0.78167977 1.02239788
## 45 <NA> 0.79243635 -0.81429809
## 46 <NA> -0.28286989 -1.03846881
## 47 <NA> -0.23662553 0.92845055
## 48 <NA> 1.17183009 1.72983145
## 49 <NA> 0.49694277 -0.92513983
## 50 <NA> -0.88737098 -2.28349796
## 51 <NA> -1.30695316 -2.38160058
## 52 <NA> -2.43276412 -2.02554559
## 53 <NA> -0.40718896 -0.33509864
## 54 <NA> -0.28566530 -1.30878131
## 55 <NA> 1.52221488 1.20100315
## 56 <NA> -0.99810691 -0.94626890
## 57 <NA> -0.28997373 0.20625658
## 58 <NA> -1.23683924 -0.67544751
## 59 <NA> -0.35950696 -2.70015447
## 60 <NA> 0.54355915 0.42254755
## 61 <NA> -0.40364728 -0.05438992
## 62 <NA> 1.30330893 1.32896747
## 63 <NA> -0.71711724 1.33137980
## 64 <NA> -1.01270788 -0.92476923
## 65 <NA> 0.83199290 2.24774587
## 66 <NA> 1.33764360 0.86825646
## 67 <NA> 0.60169351 -0.19821756
## 68 <NA> 1.30285098 1.10466638
## 69 <NA> -0.88170058 -1.54068479
## 70 <NA> -0.82452907 -1.33700788
## 71 <NA> -0.98435652 -1.13916027
## 72 <NA> -1.38499151 0.70269993
## 73 <NA> -0.35884256 -1.69451277
## 74 <NA> -0.22661823 0.80193855
## 75 <NA> -0.94107744 -0.73318871
## 76 <NA> 2.46033595 -0.04837282
## 77 <NA> 0.71679728 0.60233676
## 78 <NA> -0.24808702 -1.01849037
## 79 <NA> 1.01077289 0.05297802
## 80 <NA> 2.31304863 1.75235888
## 81 <NA> 0.83517980 0.98571488
## 82 <NA> -1.07190334 -1.24729787
## 83 <NA> -1.65052614 0.21546453
## 84 <NA> -0.60048569 -0.42094053
## 85 <NA> -0.05852938 0.12762087
## 86 <NA> 0.07572674 -0.52214922
## 87 <NA> -1.15783156 0.59089374
## 88 <NA> 1.67360609 0.11462332
## 89 <NA> -1.04398824 -0.41894428
## 90 <NA> 0.01468748 -0.55874662
## 91 <NA> 0.67532197 1.48262979
## 92 <NA> 1.77834231 0.94277411
## 93 <NA> -1.29576364 -1.08520381
## 94 <NA> 0.07960202 -0.53910081
## 95 <NA> 2.26085771 0.67322484
## 96 <NA> 0.47909092 1.45477446
## 97 <NA> -0.53502000 -0.39917481
## 98 <NA> -0.77312933 -0.95717485
## 99 <NA> 0.40363434 1.39603817
## 100 <NA> -0.58849644 -0.49728509Algoritma Rstudio
set.seed(1)
n <- nrow(DF)
b <- 10000
y <- matrix(sample(DF$item1,n*b,replace = TRUE),b)
x <- matrix(sample(DF$item2,n*b,replace = TRUE),b)
ybar <- apply(y,1,mean)
xbar <- apply(x,1,mean)
teta.i <- ybar/(xbar+ybar)
teta.b <- mean(teta.i)
var.b <- (sum(teta.i^2)-(b*teta.b^2))/(b-1)
se.b <- sqrt(var.b)
BOOTS.TRAP <- matrix(c(teta.b,var.b,se.b))
row.names(BOOTS.TRAP) <- c("ESTIMASI","VARIANS","STANDAR ERROR")
colnames(BOOTS.TRAP) <- c("HASIL BOOTSTRAP")
BOOTS.TRAP## HASIL BOOTSTRAP
## ESTIMASI 0.9272088
## VARIANS 552.3971070
## STANDAR ERROR 23.5031297mencari Estimasi, Varians, dan Standar Error
n <- nrow(DF)
teta.topi <- mean(DF$item1)/(mean(DF$item1)+mean(DF$item2))
y <- matrix(NA,n,n-1)
x <- matrix(NA,n,n-1)
for (i in 1:n) {
y[i,] <- DF$item1[-i]
x[i,] <- DF$item2[-i]
}
ybar <- apply(y,1,mean)
xbar <- apply(x,1,mean)
teta.i <- ybar/(xbar + ybar)
teta.jk <- n*teta.topi - (n-1)*mean(teta.i)
var.jk <- (n-1)/n*(sum(teta.i^2)-(n*mean(teta.i)^2))
se.jk <- sqrt(var.jk)
JACK.KNIFE <- matrix(c(teta.jk,var.jk,se.jk))
row.names(JACK.KNIFE) <- c("ESTIMASI","VARIANS","STANDAR ERROR")
colnames(JACK.KNIFE) <- c("HASIL JACKKNIFE")
JACK.KNIFE## HASIL JACKKNIFE
## ESTIMASI 0.47715352
## VARIANS 0.09622771
## STANDAR ERROR 0.31020591