Generate indices for resampling
Generate indices for resampling.
samp.bootstrap(n, R, size = n - reduceSize, reduceSize = 0) samp.permute(n, R, size = n - reduceSize, reduceSize = 0, groupSizes = NULL, returnGroup = NULL)
- sample size. For two-sample permutation tests, this is the sum of the two sample sizes.
- number of vectors of indices to produce.
- size of samples to produce. For example, to do "what-if" analyses, to estimate the variability of a statistic had the data been a different size, you may specify the size.
integer; if specified, then
size = n - reduceSize(for each sample or stratum). This is an alternate way to specify size. Typically bootstrap standard errors are too small; they correspond to using
nin the divisor of the sample variance, rather than
n-1. By specifying
reduceSize = 1, you can correct for that bias. This is particularly convenient in two-sample problems where the sample sizes differ.
NULL, or vector of positive integers that add to
NULL, or integer from 1 to
returnGroupmust be supplied together; then full permutations are created, but only subsets of size
To obtain disjoint samples without replacement,
call this function multiple times, after setting the same random
number seed, with the same
groupSizes but different values of
returnGroup. This is used for two-sample permutation tests.
groupSizes is supplied then
size is ignored.
groupSizes(returnGroup)rows). Each column contains indices for one bootstrap sample, or one permutation.
The value passed as
R to this function is typically the
block.size argument to
bootstrap and other
This discusses reduced sample size: Hesterberg, Tim C. (2004), Unbiasing the Bootstrap-Bootknife Sampling vs. Smoothing, Proceedings of the Section on Statistics and the Environment, American Statistical Association, 2924-2930, http://www.timhesterberg.net/articles/JSM04-bootknife.pdf.
samp.bootstrap(7, 8) samp.bootstrap(7, 8, size = 6) samp.bootstrap(7, 8, reduceSize = 1) # Full permutations set.seed(0) samp.permute(7, 8) # Disjoint samples without replacement = subsets of permutations set.seed(0) samp.permute(7, 8, groupSizes = c(2, 5), returnGroup = 1) set.seed(0) samp.permute(7, 8, groupSizes = c(2, 5), returnGroup = 2)