# File "Rboot3".

B = 1000

n = 30

g = 5

truncmean = function(x) {
    len = length(x)
    ord = order(x)
    s = 0
    for (ii in (g+1):(len-g))
        s = s + x[ord[ii]]
    return(s/(len-2*g))
}

# Generate some data.
x = runif(n)

thetahatlist = NULL

# Run the bootstrap, with B resamplings of n resamples each.

for (b in 1:B) {

    # Do n bootstrap resamples.
    resampled = rep(0,n)
    for (i in 1:n) {
	j = floor( runif(1,1,n+1) )  # uniform on {1,2,...,n}
	resampled[i] = x[j]
    }

    # Compute thetahat for this resample.
    thetahat = truncmean(resampled)

    thetahatlist = c(thetahatlist, thetahat)

}

# Compute the bootstrap estimate of mean, thetastar.
thetastar = sum(thetahatlist) / B

# Compute the bootstrap estimate of variance.
sqsum = 0
for (i in 1:B) {
    sqsum = sqsum + (thetahatlist[i] - thetastar)^2
}
varest = sqsum / (B-1)

print(varest)