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, replicates = 400)

Arguments

x	A data frame with primary sampling unit (PSU) in column named psu
w	A data frame with primary sampling unit (PSU) in column named psu and survey weight (i.e. PSU population) in column named pop
statistic	A function operating on data in x (see example)
params	Parameters (named columns in x) passed to the function specified in statistic
outputColumns	Names of columns in output data frame
replicates	Number of bootstrap replicates

Value

A data frame with:

• ncol = length(outputColumns)

• nrow = replicates

• names = outputColumns

Examples

# Example function - estimate a proportion for a binary (0/1) variable):

oneP <- function(x, params) {
  v1 <- params[1]
  v1Data <- x[[v1]]
  oneP <- mean(v1Data, na.rm = TRUE)
  return(oneP)
}

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

bootP <- bootBW(x = indicatorsHH,
                w = villageData,
                statistic = oneP,
                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 May 30, 2022, 9:10 a.m.