surveybootstrap (version 0.0.3)

bootstrap.estimates: bootstrap.estimates

Description

Use a given bootstrap method to estimate sampling uncertainty from a given estimator.

Usage

bootstrap.estimates(
  survey.data,
  survey.design,
  bootstrap.fn,
  estimator.fn,
  num.reps,
  weights = NULL,
  ...,
  summary.fn = NULL,
  verbose = TRUE,
  parallel = FALSE,
  paropts = NULL
)

Value

If summary.fn is not specified, then return the list of estimates produced by estimator.fn; if summary.fn is specified, then return its output

Arguments

survey.data

The dataset to use

survey.design

A formula describing the design of the survey (see Details below)

bootstrap.fn

Name of the method to be used to take bootstrap resamples

estimator.fn

The name of a function which, given a dataset like survey.data and arguments in ..., will produce an estimate of interest

num.reps

The number of bootstrap replication samples to draw

weights

Weights to use in estimation (or NULL, if none)

...

additional arguments which will be passed on to estimator.fn

summary.fn

(Optional) Name of a function which, given the set of estimates produced by estimator.fn, summarizes them. If not specified, all of the estimates are returned in a list.

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

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, ... identifies the strata

Examples

# example using a simple random sample

survey <- MU284.surveys[[1]]

estimator <- function(survey.data, weights) {
  plyr::summarise(survey.data,
                  T82.hat = sum(S82 * weights))
}

ex.mu284 <- bootstrap.estimates(
   survey.design = ~1,
   num.reps = 10,
   estimator.fn = estimator,
   weights='sample_weight',
   bootstrap.fn = 'srs.bootstrap.sample',
   survey.data=survey)

if (FALSE) {
idu.est <- bootstrap.estimates(
  ## this describes the sampling design of the
  ## survey; here, the PSUs are given by the
  ## variable cluster, and the strata are given
  ## by the variable region
  survey.design = ~ cluster + strata(region),
  ## the number of bootstrap resamples to obtain
  num.reps=1000,
  ## this is the name of the function
  ## we want to use to produce an estimate
  ## from each bootstrapped dataset
  estimator.fn="our.estimator",
  ## these are the sampling weights
  weights="indweight",
  ## this is the name of the type of bootstrap
  ## we wish to use
  bootstrap.fn="rescaled.bootstrap.sample",
  ## our dataset
  survey.data=example.survey,
  ## other parameters we need to pass
  ## to the estimator function
  d.hat.vals=d.hat,
  total.popn.size=tot.pop.size,
  y.vals="clients",
  missing="complete.obs")

}