How to make my function acceptable as MonteCarlo simulation input in R - r

Question

I wrote the below R function to do the following task:
Simulate 10 time series data set from ARIMA model through arima.sim() function
Split the series into sub-series of possible 2s, 3s, 4s, 5s, 6s, 7s, 8s, and 9s.
For each size take a resample the blocks with replacement, for new series and obtain the best ARIMA model from the subseries from each block size through auto.arima() function.
Obtain for each subseries of each block sizes RMSE.
.
## 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 = detectCores())
## simulate ARIMA(1,0, 0)
#n=10; phi <- 0.6; order <- c(1, 0, 0)
bootstrap1 <- function(n, phi){
ts <- arima.sim(n, model = list(ar=phi, order = c(1, 0, 0)), sd = 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
########################################################
## This section use foreach function to do detail in the brace
BOOTSTRAP <- foreach(b = 1:length(lb), .combine = 'cbind') %do%{
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, 10) # 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))$mean # makes the `forecast of test set
RMSE <- Metrics::rmse(test, nfuture) # RETURN RMSE
BOOTSTRAP[b] <- RMSE
}
BOOTSTRAPS <- matrix(BOOTSTRAP, nrow = 1, ncol = length(lb))
colnames(BOOTSTRAPS) <- lb
BOOTSTRAPS
return(list(BOOTSTRAPS))
}
If the function is called as below:
bootstrap1(10, 0.6)
I get the following result:
##$BOOTSTRAPS
## 2 3 4 5 6 7 8 9
##[1,] 1.287224 2.264574 2.998069 2.349261 1.677791 1.183126 2.021157 1.357658
My attempt to use Monte Carlo function to make my function run three(3) different and distinct times.
param_list=list("n"=10, "phi"=0.6)
library(MonteCarlo)
MC_result<-MonteCarlo(func = bootstrap1, nrep=3, param_list = param_list)
I got the following error message:
Error in MonteCarlo(func = bootstrap1, nrep = 3, param_list = param_list) :
func has to return a list with named components. Each component has to be scalar.
Please help me to get right what I did wrong either on my function or the MonteCarlo() function.

Answer 1

Based on the error message, I would try replacing the end of your function with something like:
names(BOOTSTRAPS) <- letters[1:10]
return(as.list(BOOTSTRAPS))
Then the resulting output is a named list with names letters[1:10].