Simple small area estimation methods.
devtools::install_github("bayesiandemography/smoothscale",
build_vignettes = TRUE)library(smoothscale)
library(dplyr, warn.conflicts = FALSE)
syn_census |>
inner_join(syn_survey, by = c("age", "sex")) |>
group_by(age, sex) |>
mutate(smoothed = smooth_prob(x = child_labour,
size = all_children),
scaled = scale_prob(unscaled = smoothed,
benchmark = prob_child_labour))
#> # A tibble: 100 × 8
#> # Groups: age, sex [4]
#> area age sex child_labour all_children prob_child_labour smoothed scaled
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 Area… 5-9 Fema… 134 372 0.297 0.351 0.414
#> 2 Area… 5-9 Fema… 14 35 0.297 0.325 0.390
#> 3 Area… 5-9 Fema… 92 388 0.297 0.236 0.310
#> 4 Area… 5-9 Fema… 46 345 0.297 0.140 0.223
#> 5 Area… 5-9 Fema… 25 102 0.297 0.241 0.314
#> 6 Area… 5-9 Fema… 2 5 0.297 0.252 0.324
#> 7 Area… 5-9 Fema… 4 13 0.297 0.252 0.324
#> 8 Area… 5-9 Fema… 10 34 0.297 0.264 0.335
#> 9 Area… 5-9 Fema… 2 52 0.297 0.0995 0.186
#> 10 Area… 5-9 Fema… 578 2087 0.297 0.276 0.346
#> # ℹ 90 more rows