samp.bootstrap
Generate indices for resampling
Generate indices for resampling.
 Keywords
 htest, nonparametric
Usage
samp.bootstrap(n, R, size = n  reduceSize, reduceSize = 0)
samp.permute(n, R, size = n  reduceSize, reduceSize = 0, groupSizes = NULL, returnGroup = NULL)
Arguments
 n
 sample size. For twosample permutation tests, this is the sum of the two sample sizes.
 R
 number of vectors of indices to produce.
 size
 size of samples to produce. For example, to do "whatif" analyses, to estimate the variability of a statistic had the data been a different size, you may specify the size.
 reduceSize

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 usingn
in the divisor of the sample variance, rather thann1
. By specifyingreduceSize = 1
, you can correct for that bias. This is particularly convenient in twosample problems where the sample sizes differ.  groupSizes

NULL
, or vector of positive integers that add ton
.  returnGroup

NULL
, or integer from 1 tolength(groupSizes)
.groupSizes
andreturnGroup
must be supplied together; then full permutations are created, but only subsets of sizegroupSizes[returnGroup]
is returned.
Details
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 twosample permutation tests.
If groupSizes
is supplied then size
is ignored.
Value

matrix with
size
rows and R
columns
(or groupSizes(returnGroup)
rows).
Each column contains indices for one bootstrap sample, or one permutation.
Note
The value passed as R
to this function is typically the
block.size
argument to bootstrap
and other
resampling functions.
References
This discusses reduced sample size: Hesterberg, Tim C. (2004), Unbiasing the BootstrapBootknife Sampling vs. Smoothing, Proceedings of the Section on Statistics and the Environment, American Statistical Association, 29242930, http://www.timhesterberg.net/articles/JSM04bootknife.pdf.
See Also
Examples
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)