bootweights: Compute survey bootstrap weights

Description

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.

Usage

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()

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