How Do I Set Seed to Archive Reproducibility of Simulation Result with Replicate Function in R

Question

I wrote an R function and will like to run it kth times and will want the same result anytime same is run in the same environment. I think of setting seed but can not achieve the same result as I ran the same function with the same seed two consecutive times.

## Load packages and prepare multicore process
library(forecast)
library(future.apply)
plan(multisession)
library(parallel)
library(foreach)
library(doParallel)
n_cores <- detectCores()
cl <- makeCluster(n_cores)
registerDoParallel(cores = n_cores)
bootstrap1 <- function(n, phi){
  ts <- arima.sim(n, model = list(ar=phi, order = c(1, 1, 0)), sd = 1)
  #ts <- numeric(n)
  #ts[1] <- rnorm(1)
  #for(i in 2:length(ts))
  #  ts[i] <- 2 * ts[i - 1] + rnorm(1)
  ########################################################
  ## create a vector of block sizes
  t <- length(ts)    # the length of the time series
  lb <- seq(n-2)+1   # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)
  ########################################################
  ## This section create matrix to store block means
  BOOTSTRAP <- matrix(nrow = 1, ncol = length(lb))
  colnames(BOOTSTRAP) <-lb
  #BOOTSTRAP <- list(length(lb))
  ########################################################
  ## This section use foreach function to do detail in the brace
  BOOTSTRAP <- foreach(b = 1:length(lb), .combine = 'cbind') %dopar%{
    l <- lb[b]# block size at each instance 
    m <- ceiling(t / l)                                 # number of blocks
    blk <- split(ts, rep(1:m, each=l, length.out = t))  # divides the series into blocks
    ######################################################
    res<-sample(blk, replace=T, 1000)        # resamples the blocks
    res.unlist <- unlist(res, use.names = FALSE)   # unlist the bootstrap series
    train <- head(res.unlist, round(length(res.unlist) - 10)) # Train set
    test <- tail(res.unlist, length(res.unlist) - length(train)) # Test set
    nfuture <- forecast::forecast(train, model = forecast::auto.arima(train), lambda=0, biasadj=TRUE, h = length(test))$mean        # makes the `forecast of test set
    RMSE <- Metrics::rmse(test, nfuture)      # RETURN RMSE
    BOOTSTRAP[b] <- RMSE
  }
  BOOTSTRAPS <- matrix(BOOTSTRAP, nrow = 1, ncol = length(lb))
  colnames(BOOTSTRAPS) <- lb
  BOOTSTRAPS
  return(list("BOOTSTRAPS" = BOOTSTRAPS))
}

First Trial

set.seed(123, kind = "L'Ecuyer-CMRG")
t(replicate(3, bootstrap1(10, 0.5)$BOOTSTRAPS[1,]))
#            2        3        4        5        6        7        8        9
#[1,] 3.353364 4.097191 3.759332 3.713234 4.541143 4.151920 4.603380 5.237056
#[2,] 4.490765 5.037171 4.289265 3.964172 3.225878 5.345506 4.646740 2.593153
#[3,] 4.514881 4.838114 3.701961 5.069747 4.165742 4.130256 3.951216 4.133241

Second Trial

set.seed(123, kind = "L'Ecuyer-CMRG")
t(replicate(3, bootstrap1(10, 0.5)$BOOTSTRAPS[1,]))
#            2        3        4        5        6        7        8        9
#[1,] 3.271285 3.701031 2.725770 3.867532 3.283368 3.713057 3.274201 4.141896
#[2,] 3.987040 3.767720 5.440987 3.850190 3.306520 5.399880 5.337676 3.288834
#[3,] 5.157924 3.895024 3.996077 4.855608 4.443317 5.224098 5.335144 2.918870

How do I set seed or what will I do to get the same result?

Edit

I am operating R on Windows.

Answer 1

You should only be setting the seed once. It seems like you're setting the seed twice (once with set.seed(1) inside your bootstrap function, and again with set.seed(123, kind = "L'Ecuyer-CMRG") outside your bootstrap function.

Try only using only one set.seed() function (and using the same value both times), and see if that fixes it.