R - Block resampling and saving samples in a list - r

Question

I have a vector on which I want to do block resampling to get, say, 1000 samples of the same size of the vector, and then save all this samples in a list.
This is the code that performs normal resampling, i.e. randomly draws one observation per time, and saves the result in a list:
myvector <- c(1:200)
mylist <- list()
for(i in 1:1000){
mylist[[i]] <- sample(myvector, length(myvector), replace=TRUE)
}
I need a code that does exactly the same thing, except that instead of drawing single observations it draws blocks of observations (let's use blocks of dimension equal to 5).
I know there are packages that perform bootstrap operations, but I don't need statistics or confidence intervals or anything, just all the samples in a list. Both overlapping and non-overlapping blocks are ok, so the code for just one of the two procedures is enough. Of course, if you are so kind to give me the code for both it's appreciated. Thanks to anybody who can help me with this.

Answer 1

Not sure how you're wanting to store the final structure.
The following takes a block dimension, samples your vector by that block size (e.g. 200 element vector with block size 5 gives 40 observations of randomly sampled elements) and adds those blocks to an index of the final list. Using your example, the final result is a list with 1000 entries; each entry containing 40 randomly sampled observations.
myvector <- c(1:200)
rm(.Random.seed, envir=globalenv())
block_dimension <- 5
res = list()
for(i in 1:1000) {
name <- paste('sample_', i, sep='')
rep_num <- length(myvector) / block_dimension
all_blocks <- replicate(rep_num, sample(myvector, block_dimension))
tmp <- split(all_blocks, ceiling(seq_along(all_blocks)/block_dimension))
res[[name]] <- tmp
}
Here are the first 6 sampled observations for the first entry:

Answer 2

How about the following? Note that you can use lapply, which should be slightly faster than filling the list in a for loop in this case.
As reference, here is the case where you sample individual observations.
# Sample individual observations
set.seed(2017);
mylist <- lapply(1:1000, function(x) sample(myvector, length(myvector), replace = TRUE));
Next we sample blocks of 5 observations.
# Sample blocks of n observations
n <- 5;
set.seed(2017);
mylist <- lapply(1:1000, function(x) {
idx <- sample(1:(length(myvector) - n), length(myvector) / n, replace = TRUE);
idx <- c(t(sapply(0:(n - 1), function(i) idx + i)));
myvector[idx];
})

Answer 3

One solution, assuming blocks consist of contiguous elements of myvector, is to pre-define the blocks in rows of a data frame with start/end columns (e.g. blocks <- data.frame(start=seq(1,96,5),end=seq(5,100,5))). Create a set of sample indexes (with replacement) from [1:number of blocks] and concatenate values indexing from myvector using the start/end values from the defined blocks. You can add randomization within blocks as well, if you need to. This gives you control over the block contents, overlap, size, etc.

Answer 4

I found a way to perform the task with non-overlapping blocks:
myvector <- c(1:200)
n <- 5
mymatrix <- matrix(myvector, nrow = length(myvector)/n, byrow = TRUE)
mylist <- list()
for(i in 1:1000){
mylist[[i]] <- as.vector(t(mymatrix[sample(nrow(mymatrix), size = length(myvector)/n, replace = TRUE),]))
}