Compute survey bootstrap weights

Bootstrap weights for infinite populations ('with replacement' sampling) are created by sampling with replacement from the PSUs in each stratum. subbootweights() samples n-1 PSUs from the n available (Rao and Wu), bootweights samples 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"))

Arguments

strata

Identifier for sampling strata (top level only)

stratas

data frame of strata for all stages of sampling

psu

Identifier for primary sampling units

clusters

data frame of identifiers for sampling units at each stage

replicates

Number of bootstrap replicates

fpc

Finite population correction (top level only)

fpctype

Is fpc the population size, sampling fraction, or 1-sampling fraction?

fpcs

survey_fpc object with population and sample size at each stage

compress

Should the replicate weights be compressed?

multicore

Use the multicore package to generate the replicates in parallel

Value

A set of replicate weights

warning

With multicore=TRUE the resampling procedure does not use the current random seed, so the results cannot be exactly reproduced even by using set.seed()

Note

These bootstraps are strictly appropriate only when the first stage of sampling is a simple or stratified random sample of PSUs with or without replacement, and not (eg) for PPS sampling. The functions will not enforce simple random sampling, so they can be used (approximately) for data that have had non-response corrections and other weight adjustments. It is preferable to apply these adjustments after creating the bootstrap replicate weights, but that may not be possible with public-use data.

References

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)

See also

as.svrepdesign