Given a survey dataset and a description of the survey design (ie, which combination of variables determines primary sampling units, and which combination of variables determines strata), take a bunch of bootstrap samples for the rescaled bootstrap estimator (see Details).
rescaled.bootstrap.sample(
survey.data,
survey.design,
parallel = FALSE,
paropts = NULL,
num.reps = 1
)A list with num.reps entries. Each entry is a dataset which
has at least the variables index (the row index of the original
dataset that was resampled) and weight.scale
(the factor by which to multiply the sampling weights in the original dataset).
The dataset to use
A formula describing the design of the survey (see Details)
If TRUE, use parallelization (via plyr)
An optional list of arguments passed on to plyr to control
details of parallelization
The number of bootstrap replication samples to draw
survey.design is a formula of the form
weight ~ psu_vars + strata(strata_vars)
where:
weight is the variable with the survey weights
psu_vars has the form psu_v1 + psu_v2 + ..., where primary
sampling units (PSUs) are determined by psu_v1, etc
strata_vars has the form strata_v1 + strata_v2 + ..., which
determine strata
Note that we assume that the formula uniquely specifies PSUs. This will always be true if the PSUs were selected without replacement. If they were selected with replacement, then it will be necessary to make each realization of a given PSU in the sample a unique id. The code below assumes that all observations within each PSU (as identified by the design formula) are from the same draw of the PSU.
The rescaled bootstrap technique works by adjusting the estimation weights based on the number of times each row is included in the resamples. If a row is never selected, it is still included in the returned results, but its weight will be set to 0. It is therefore important to use estimators that make use of the estimation weights on the resampled datasets.
We always take m_i = n_i - 1, according to the advice presented in Rao and Wu (1988) and Rust and Rao (1996).
(This is a C++ version; a previous version, written in pure R,
is called rescaled.bootstrap.sample.pureR() )
References:
Rust, Keith F., and J. N. K. Rao. "Variance estimation for complex surveys using replication techniques." Statistical methods in medical research 5.3 (1996): 283-310.
Rao, Jon NK, and C. F. J. Wu. "Resampling inference with complex survey data." Journal of the American Statistical Association 83.401 (1988): 231-241.
survey <- MU284.complex.surveys[[1]]
boot_surveys <- rescaled.bootstrap.sample(survey.data = survey,
survey.design = ~ CL,
num.reps = 2)
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