Solved – Monte Carlo study to estimate bootstrap CI

You may get better answers on stackoverflow, as this question relates more to programming than statistics.

It's not completely clear what you would like the code to produce, since a vector is 1 dimensional, so you could hold 1 item in the vector for each bootstrap confidence interval – but the information you want includes more than 1 thing – the type, the confidence level, the upper bound and the lower bound etc. So one simple way to do this is to use a list and enclose your code in a for loop, and add the object (which is in fact another list) returned by boot.ci like this:

require(boot)                   # for boot(), boot.ci()  N <-100 B    <- 100                 # number of replicates mu   <- 1                   # for generating data: true mean sd   <- 1                   # for generating data: true sd  getM <- function(orgDV, idx)      {bsM  <- mean(orgDV[idx])                       # M*     bsS2M <- (((N-1) / N) * var(orgDV[idx])) / N    # S^2*(M)     c(bsM, bsS2M) }  N.CI <- 10             # number of botstrap confidence intervals list.CI <- list(N.CI)  # a list to hold the objects returned by boot.ci  for (i in 1:N.CI ) {      Data <- rnorm(N, mu, sd)    # simulated data: original sample     bOot = boot(Data, statistic=getM, R=50)     list.CI[[i]] <- boot.ci(bOot, conf=0.95, type=c("basic", "perc")) } 

list.CI now contains 10 boostrap confidence intervals. You can access each one by list.CI[[1]], list.CI[[2]] etc, and then you can access the components of each CI like this:

> list.CI[[i]]$perc          conf                                   [1,] 0.95 1.28 49.73 0.7794841 1.318904 > list.CI[[i]]$basic     conf                               [1,] 0.95 49.73 1.28 0.7980327 1.337453 

Perhaps a better way, which will help your subsequent analysis is to store the bootstrap CIs in a matrix or a dataframe instead of a list, but I'll leave that for you to do.