bootBW: Blocked Weighted Bootstrap

In bbw: Blocked Weighted Bootstrap

Blocked Weighted Bootstrap

Description

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population proportional sampling or PPS as used in SMART surveys) or posterior weighting (e.g. as used in RAM and S3M surveys).

Usage

bootBW(x, w, statistic, params, outputColumns = params, replicates = 400)

Arguments

x	A data.frame() with primary sampling unit (PSU) in variable named psu and at least one other variable containing data for estimation.
w	A data.frame() with primary sampling unit (PSU) in variable named psu and survey weights (i.e. PSU population) in variable named pop.
statistic	Am estimator function operating on variables in x containing data for estimation. The functions bootClassic() and bootPROBIT() are examples.
params	Parameters specified as names of columns in x that are to be passed to the function specified in statistic.
outputColumns	Names to be used for columns in output data.frame(). Default to names specified in params.
replicates	Number of bootstrap replicates to be performed. Default is 400.

Value

A data.frame() with:

• number of columns equal to length of outputColumns;

• number of rows equal to number of replicates; and,'

• names equal to outputColumns.'

Examples

# Example call to bootBW function using RAM-OP test data:

bootBW(
  x = indicatorsHH, w = villageData, statistic = bootClassic,
  params = "anc1", outputColumns = "anc1", replicates = 9
)

# Example estimate with 95% CI:
#quantile(bootP, probs = c(0.500, 0.025, 0.975), na.rm = TRUE)

bbw documentation built on April 12, 2025, 9:14 a.m.