How to implement the bootstrap in R

Question

So I posted a thread about this problem, but it got on hold. So I rephrased so it can be it a programming question. This is my code below. I am trying to find the stimulated confidence level of a sample using the bootstrap.

# Step One: Generating the data from lognormal distribution

MC <-1000; # Number of samples to simulate
xbar = c(1:MC);
mu = 1;
sigma= 1.5;
the_mean <- exp(mu+sigma^2/2);
n= 10;

for(i in 1:MC)
 {
mySample <- rlnorm(n=n meanlog=mu, sdlog=sigma);
xbar [i] <- the_mean(mySample);
 }

# Step Two: Compute 95% Bootstrap CI with B=1000

B = 1000
xbar_star = c(1:B)
for(b in 1:B)
{ 
 x_star = sample(n,n, replace=TRUE)
 xbar_star[b] = mean(x_star)
 }

quantile(xbar, p=c(0.025, 0.975))

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If you implement this code you can see that the output is 975.025 when it should actually be 0. 90. I don't understand why my output is wrong.

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We arent trying to find the Confidence Interval, but the stimulated Confidence Level. How does the actual coverage percentage (obtained through simulation) compare with the nominal confidence level (which is 95%)? This is my code when my samples were given in a practice problem...

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library(boot)
x = c(0.22, 0.23, 0.26, 0.27, 0.28, 0.28, 0.29,
  0.33, 0.34, 0.35, 0.38, 0.39, 0.39, 0.42, 0.42,
  0.43, 0.45, 0.46, 0.48, 0.5, 0.5, 0.51, 0.52,
  0.54, 0.56, 0.56, 0.57, 0.57, 0.6, 0.62, 0.63,
  0.67, 0.69, 0.72, 0.74, 0.76, 0.79, 0.81, 0.82,
  0.84, 0.89, 1.11, 1.13, 1.14, 1.14, 1.2, 1.33)

B = 10000
xbar = mean(x)
n = length(x)
xbar_star = c(1:B)
for(b in 1:B)
{
x_star = sample(x=x, size=n, replace=TRUE)
xbar_star[b] = mean(x_star)
}

# empirical percentile method

 quantile(xbar_star, p=c(0.025, 0.975))

> quantile(xbar_star, p=c(0.025, 0.975))
  2.5%     97.5% 
0.5221277 0.6797926