Compute period life tables from age-specific mortality rates

All available years and ages are included in the tables. $qx = mx/(1 + ((1-ax) * mx))$ as per Chiang (1984). Ages can be single years, abridged (0, 1, 5, 10, ...), or 5-year groups starting at age 5 or above. Missing mortality rates are interpolated (on the log scale) from neighbouring ages, with a warning.

Usage

life_table(.data, mortality)

Arguments

.data: A vital including an age variable and a variable containing mortality rates.
mortality: Variable in .data containing Mortality rates (mx). If omitted, the variable with name mx, Mortality or Rate will be used (not case sensitive).

Value

A vital object containing the index, keys, and the new life table variables mx, qx, lx, dx, Lx, Tx, ex, rx (survivorship ratios), nx (widths of the age groups) and ax (average years lived in each age group by those dying in it).

References

Chiang CL. (1984) The life table and its applications. Robert E Krieger Publishing Company: Malabar.

Keyfitz, N, and Caswell, H. (2005) Applied mathematical demography, Springer-Verlag: New York.

Preston, S.H., Heuveline, P., and Guillot, M. (2001) Demography: measuring and modeling population processes. Blackwell

Author

Rob J Hyndman

Examples

# Compute Norwegian life table for females in 2003
norway_mortality |>
  dplyr::filter(Sex == "Female", Year == 2003) |>
  life_table()
#> # A vital: 111 x 13 [?]
#> # Key:     Age x Sex [111 x 1]
#>     Year   Age Sex          mx        qx    lx        dx    Lx    Tx    ex    rx
#>    <int> <int> <chr>     <dbl>     <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  2003     0 Female 0.00309  0.00308   1     0.00308   0.997  81.9  81.9 0.997
#>  2  2003     1 Female 0.000434 0.000434  0.997 0.000433  0.997  80.9  81.2 1.000
#>  3  2003     2 Female 0.000278 0.000278  0.996 0.000277  0.996  79.9  80.2 1.000
#>  4  2003     3 Female 0.000136 0.000136  0.996 0.000135  0.996  78.9  79.2 1.000
#>  5  2003     4 Female 0.00024  0.000240  0.996 0.000239  0.996  77.9  78.2 1.000
#>  6  2003     5 Female 0.000034 0.0000340 0.996 0.0000339 0.996  76.9  77.3 1.000
#>  7  2003     6 Female 0.000099 0.0000990 0.996 0.0000986 0.996  76.0  76.3 1.000
#>  8  2003     7 Female 0.0001   0.0001000 0.996 0.0000996 0.996  75.0  75.3 1.000
#>  9  2003     8 Female 0.000166 0.000166  0.996 0.000165  0.996  74.0  74.3 1.000
#> 10  2003     9 Female 0.000033 0.0000330 0.995 0.0000328 0.995  73.0  73.3 1.000
#> # ℹ 101 more rows
#> # ℹ 2 more variables: nx <dbl>, ax <dbl>