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Blocked Weighted Bootstrap

Source: R/bootBW.r
bootBW.Rd

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
)
#> ✔ x has the appropriate/expected data structure
#>        anc1
#> 1 0.2150171
#> 2 0.2171083
#> 3 0.1870469
#> 4 0.2743321
#> 5 0.2274025
#> 6 0.2379344
#> 7 0.1991383
#> 8 0.2138425
#> 9 0.2233429

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