Add ax onto a data.table for ages <1 and 1-4 years, with algorithm based on under-1 mx.
gen_u5_ax_from_mx(dt, id_cols)[data.table()]
Columns:
'age_start' with values 0 (for under-1) and 1 (for 1-4). Other ages
permitted but will result in NA output for ax or unchanged ax if ax in
input dt.
'age_end' with values 1 (for under-1) and 5 (for 1-4).
'sex' where values must be 'male', 'female', or 'both'
'mx' for mortality rate
All additional columns from 'id_cols'
[character()]
Columns that uniquely identify
observations in dt. Must include 'age_start', 'age_end', and 'sex'.
Modifies dt in place with 'ax' column added on.
Takes a data.table with 'age_start', 'age_end', 'sex', and
infant mortality rate ('mx') and adds a column 'ax'.
The following table shows the conversions from 1m0 to 1a0 and 4a1. Note that when sex is "both" the relationship is a mean of the male and female relationships.
| Males | Females | |||
| 1a0: | ||||
| If 1m0 >= 0.107 | 0.330 | 0.350 | ||
| If 1m0 < 0.107 | 0.045 + 2.684 * 1m0 | 0.053 + 2.800 * 1m0 | ||
| 4a1: | ||||
| If 1m0 >= 0.107 | 1.352 | 1.361 | ||
| If 1m0 < 0.107 | 1.651 - 2.816 * 1m0 | 1.522 - 1.518 * 1m0 |
Preston Samuel H, Patrick H, Michel G. Demography: measuring and modeling population processes. MA: Blackwell Publishing. 2001.
dt <- data.table::data.table(
age_start = c(0, 1),
age_end = c(1, 5),
mx = c(0.09, 0.12),
sex = c("male", "male")
)
gen_u5_ax_from_mx(dt, id_cols = c("age_start", "age_end", "sex"))
#> Indices: <age_end__age_start>, <age_end__age_start__sex>
#> age_start age_end mx sex ax
#> <num> <num> <num> <char> <num>
#> 1: 0 1 0.09 male 0.28656
#> 2: 1 5 0.12 male 1.39756