R 1000 次的置信区间

Question

我已经获得了代码中列出的区间估计和公式。 但在这种情况下，我需要获得 1000 个置信区间，即jack_lambda和jack_beta 。 我想知道我是否应该将for(i in 1:N){}放在编码的哪一部分。

# rm(list=ls())  it's ok to remind us but not ok to wipe our workspace
library(bootstrap)
library(maxLik)
est<-NULL
set.seed(20)
lambda <- 0.02
beta <- 0.5
alpha <-  0.10
n <- 40
N <- 1000
lambda_hat <- NULL
beta_hat <- NULL
lambda_jk<-NULL
beta_jk<-NULL
cp <- NULL
jack_lambda <- matrix(NA, nrow = N, ncol = 2)
jack_beta <- matrix(NA, nrow = N, ncol = 2)

### group all data frame generated from for loop into a list of data frame
data_full <- list()

u <- runif(n)
c_i <- rexp(n, 0.0001)
t_i <- (log(1 - (1 / lambda) * log(1 - u))) ^ (1 / beta)
s_i <- 1 * (t_i < c_i)
t <- pmin(t_i, c_i)
data<- data.frame(t,s_i)


LLF <- function(para,y) {
  lambda <- para[1]
  beta <- para[2]
  e <- y[,2]*log(lambda*y[,1]^(beta-1)*beta*exp(y[,1]^beta)*exp(lambda*(1-exp(y[,1]^beta))))
  r <- (1-y[,2])*log(exp(lambda*(1-exp(y[,1]^beta))))
  f <- sum(e + r)
  return(f)
}
mle <- maxLik(LLF, y=data,start = c(para = c(0.02, 0.5)))
lambda_hat[i] <- mle$estimate[1]
beta_hat[i] <- mle$estimate[2]
est<-rbind(est,mle$estimate)

### statistic function for jackknife()
jack<-matrix(0, nrow = n, ncol = 2)

for(i in 1:n){
  fit.jack<-maxLik(logLik=LLF,y=data[-i,],method="NR",start=c(0.02, 0.5))
  jack[i,]<-coef(fit.jack)
}
estjack<-rbind(jack)

meanlambda = mean(estjack[,1])
meanbeta = mean(estjack[,2])
lambda_jk[i] =lambda_hat[i]-(n-1)*(meanlambda-lambda_hat[i])  #jackknife estimate
beta_jk[i] = beta_hat[i]-(n-1)*(meanbeta-beta_hat[i])
SElambda<-sqrt(var(estjack[,1])/n-1)  #std error
SEbeta<-sqrt(var(estjack[,2])/n-1)
jack_lambda[i,] <- lambda_jk[i]+c(-1,1)*qt((1-alpha)/2,n-1)*SElambda   #confidence interval
jack_beta[i,] <- beta_jk[i]+c(-1,1)*qt((1-alpha)/2,n-1)*SEbeta

（我非常感谢任何想法）