Make a new vital from means and differences of a measured variable by a key variable. The most common use case of this function is for computing migration numbers by sex, from the sex differences and mean of the numbers.
Arguments
- .data
A vital object
- .var
A bare variable name of the measured variable to use.
- key
A bare variable name specifying the key variable to use. This key variable must include the value
geometric_mean.- times
When the variable is a distribution, the product must be computed by simulation. This argument specifies the number of simulations to use.
References
Hyndman, R.J., Booth, H., & Yasmeen, F. (2013). Coherent mortality forecasting: the product-ratio method with functional time series models. Demography, 50(1), 261-283.
Examples
# Make sums and differences
mig <- net_migration(norway_mortality, norway_births) |>
dplyr::filter(Sex != "Total")
sd <- mig |>
make_sd(NetMigration)
# Undo products and ratios
sd |> undo_sd(NetMigration)
#> # A vital: 26,644 x 6 [1Y]
#> # Key: Age x Sex [111 x 2]
#> Year Age Sex Population Deaths NetMigration
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1900 -1 Female 32150 1745. 248.
#> 2 1900 0 Female 30070 1035. -86.2
#> 3 1900 1 Female 28960 594. 222.
#> 4 1900 2 Female 28043 281. 57.3
#> 5 1900 3 Female 27019 190. 26.8
#> 6 1900 4 Female 26854 155. 3.50
#> 7 1900 5 Female 25569 122. 5.37
#> 8 1900 6 Female 25534 102. 4.64
#> 9 1900 7 Female 24314 91.7 -5.27
#> 10 1900 8 Female 24979 92.9 -11.1
#> # ℹ 26,634 more rows