Make a new vital from products and ratios of a measured variable by a key variable. The most common use case of this function is for computing mortality rates by sex, from the sex ratios and geometric mean of the rates.
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.
Details
Note that when a measured variable takes value 0, the geometric mean is set to 10^-6 to avoid infinite values in the ratio. Therefore, when the transformation is undone, the results will not be identical to the original in the case that the original data was 0.
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 products and ratios
orig_data <- norway_mortality |>
dplyr::filter(Year > 2015, Sex != "Total")
pr <- orig_data |>
make_pr(Mortality)
# Compare original data with product/ratio version
orig_data
#> # A vital: 1,776 x 7 [1Y]
#> # Key: Age x Sex [111 x 2]
#> Year Age OpenInterval Sex Population Deaths Mortality
#> <int> <int> <lgl> <chr> <dbl> <dbl> <dbl>
#> 1 2016 0 FALSE Female 28851 57 0.00197
#> 2 2016 1 FALSE Female 29195 7 0.00024
#> 3 2016 2 FALSE Female 29631 6 0.000203
#> 4 2016 3 FALSE Female 30416 2 0.000066
#> 5 2016 4 FALSE Female 30582 0 0
#> 6 2016 5 FALSE Female 31567 1 0.000032
#> 7 2016 6 FALSE Female 31963 2 0.000063
#> 8 2016 7 FALSE Female 31318 2 0.000063
#> 9 2016 8 FALSE Female 30710 0 0
#> 10 2016 9 FALSE Female 30882 1 0.000032
#> # ℹ 1,766 more rows
pr
#> # A vital: 2,664 x 7 [1Y]
#> # Key: Age x Sex [111 x 3]
#> Year Age OpenInterval Sex Population Deaths Mortality
#> <int> <int> <lgl> <chr> <dbl> <dbl> <dbl>
#> 1 2016 0 FALSE Female 28851 57 0.924
#> 2 2016 1 FALSE Female 29195 7 1.36
#> 3 2016 2 FALSE Female 29631 6 1.78
#> 4 2016 3 FALSE Female 30416 2 1.44
#> 5 2016 4 FALSE Female 30582 0 0
#> 6 2016 5 FALSE Female 31567 1 0.724
#> 7 2016 6 FALSE Female 31963 2 1.45
#> 8 2016 7 FALSE Female 31318 2 0.841
#> 9 2016 8 FALSE Female 30710 0 0
#> 10 2016 9 FALSE Female 30882 1 1.79
#> # ℹ 2,654 more rows
# Undo products and ratios
pr |> undo_pr(Mortality)
#> # A vital: 1,776 x 7 [1Y]
#> # Key: Age x Sex [111 x 2]
#> Year Age OpenInterval Sex Population Deaths Mortality
#> <int> <int> <lgl> <chr> <dbl> <dbl> <dbl>
#> 1 2016 0 FALSE Female 28851 57 0.00197
#> 2 2016 1 FALSE Female 29195 7 0.00024
#> 3 2016 2 FALSE Female 29631 6 0.000203
#> 4 2016 3 FALSE Female 30416 2 0.000066
#> 5 2016 4 FALSE Female 30582 0 0
#> 6 2016 5 FALSE Female 31567 1 0.000032
#> 7 2016 6 FALSE Female 31963 2 0.000063
#> 8 2016 7 FALSE Female 31318 2 0.000063
#> 9 2016 8 FALSE Female 30710 0 0
#> 10 2016 9 FALSE Female 30882 1 0.000032
#> # ℹ 1,766 more rows