Bootstrap weights for infinite populations ('with replacement' sampling) are created by sampling with
replacement from the PSUs in each stratum.
n-1 PSUs from the
n available (Rao and Wu),
n (Canty and Davison).
For multistage designs or those with large sampling fractions,
mrbweights implements Preston's multistage rescaled
bootstrap. The multistage rescaled bootstrap is still useful for
single-stage designs with small sampling fractions, where it reduces
to a half-sample replicate method.
bootweights(strata, psu, replicates = 50, fpc = NULL, fpctype = c("population", "fraction", "correction"), compress = TRUE) subbootweights(strata, psu, replicates = 50, compress = TRUE) mrbweights(clusters, stratas, fpcs, replicates=50, multicore=getOption("survey.multicore"))
Identifier for sampling strata (top level only)
data frame of strata for all stages of sampling
Identifier for primary sampling units
data frame of identifiers for sampling units at each stage
Number of bootstrap replicates
Finite population correction (top level only)
fpc the population size, sampling fraction,
or 1-sampling fraction?
survey_fpc object with population and sample size at each stage
Should the replicate weights be compressed?
multicore package to generate the replicates in parallel
A set of replicate weights
multicore=TRUE the resampling procedure does not
use the current random seed, so the results cannot be exactly
reproduced even by using
Canty AJ, Davison AC. (1999) Resampling-based variance estimation for labour force surveys. The Statistician 48:379-391
Judkins, D. (1990), "Fay's Method for Variance Estimation" Journal of Official Statistics, 6, 223-239.
Preston J. (2009) Rescaled bootstrap for stratified multistage sampling. Survey Methodology 35(2) 227-234
Rao JNK, Wu CFJ. Bootstrap inference for sample surveys. Proc Section on Survey Research Methodology. 1993 (866--871)