Post-stratification analysis

Usage

estimate_total(est_df, pop_df, strata)

Arguments

est_df: A data.frame() of stratified indicator estimates to get overall estimates of. est_df should have a variable named est for the values of the indicator estimate, a variable named strata for information on the stratification or grouping of the estimates, and a variable named se for the standard errors for the values of the indicator estimate. This is usually produced via a call to boot_bw_estimate().
pop_df: A data.frame() with at least two variables: strata for the stratification/grouping information that matches strata in est_df and pop for information on population for the given strata.
strata: A character value of the variable name in est_df that corresponds to the strata values to match with values in pop_df

Value

A vector of values for the overall estimate, overall 95% lower confidence limit, and overall 95% upper confidence limit for each of the strata in est_df.

Examples

est_df <- boot_bw(
  x = indicatorsHH, w = villageData, statistic = bootClassic, 
  params = "anc1", strata = "region", replicates = 9, parallel = TRUE
) |>
  boot_bw_estimate()
#> 
#> ── Resampling in parallel ──
#> 
#> ℹ Setting up 3 parallel operations
#> ✔ Setting up 3 parallel operations [292ms]
#> 
#> ℹ Resampling by region - 9 replicates in parallel
#> ✔ Resampling by region - 9 replicates in parallel [855ms]
#> 
#> ℹ Tidying up resampling outputs
#> ✔ Tidying up resampling outputs [19ms]
#> 
#> ℹ Closing 3 parallel operations
#> ✔ Closing 3 parallel operations [15ms]
#> 

## Add population ----
pop_df <- somalia_population |>
  subset(select = c(region, total))

names(pop_df) <- c("strata", "pop")

estimate_total(est_df, pop_df, strata = "region")
#> ✔ est_df has the appropriate/expected variables
#> ✔ pop_df has the appropriate/expected variables
#>    strata indicator       est       lcl       ucl          se
#> 1 Overall      anc1 0.4659361 0.3959141 0.5359582 0.001276314