This function creates a dataset with rescaled bootstrap weights;
it can be a helpful alternative to bootstrap.estimates in some situations
Usage
rescaled.bootstrap.weights(
survey.data,
survey.design,
num.reps,
weights = NULL,
idvar,
verbose = TRUE,
parallel = FALSE,
paropts = NULL
)Arguments
- survey.data
The dataset to use
- survey.design
A formula describing the design of the survey (see Details in
bootstrap.estimates()help page)- num.reps
the number of bootstrap replication samples to draw
- weights
weights to use in estimation (or NULL, if none)
- idvar
the name of the column in
survey.datathat has the respondent id- verbose
if TRUE, produce lots of feedback about what is going on
- parallel
if TRUE, use the plyr library's .parallel argument to produce bootstrap resamples and estimates in parallel
- paropts
if not NULL, additional arguments to pass along to the parallelization routine
Value
if no summary.fn is specified, then return the list of estimates produced by estimator.fn; if summary.fn is specified, then return its output
Details
The formula describing the survey design should have the form
~ psu_v1 + psu_v2 + ... + strata(strata_v1 + strata_v2 + ...),
where psu_v1, ... are the variables identifying primary sampling units (PSUs)
and strata_v1, ... identify the strata
Examples
survey <- MU284.complex.surveys[[1]]
rescaled.bootstrap.weights(survey.data = survey,
survey.design = ~ CL,
weights='sample_weight',
idvar='LABEL',
num.reps = 2)
#> LABEL boot_weight_1 boot_weight_2
#> 1 96 0 33.33333
#> 2 99 0 33.33333
#> 3 101 0 33.33333
#> 4 16 0 20.83333
#> 5 18 0 20.83333
#> 6 19 0 20.83333
#> 7 221 0 0.00000
#> 8 223 0 0.00000
#> 9 224 0 0.00000
#> 10 188 50 0.00000
#> 11 190 50 0.00000
#> 12 191 50 0.00000
#> 13 199 50 50.00000
#> 14 200 50 50.00000
#> 15 203 50 50.00000
if (FALSE) { # \dontrun{
bootweights <- rescaled.bootstrap.weights(
# formula describing survey design:
# psu and strata
survey.design = ~ psu +
stratum(stratum_analysis),
num.reps=10000,
# column with respondent ids
idvar='caseid',
# column with sampling weight
weights='wwgt',
# survey dataset
survey.data=mw.ego)
} # }