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Calculates population size from a mortAAR life table or numeric values

Source: R/population_size.R
lt.population_size.Rd

The estimation of the population size for a given cemetery is only possible if a stationary population is assumed. In this case, the number of deaths is simply multiplied with the life expectancy at birth and divided be the time span in years the cemetery was in use. Additionally, it is assumed that an unknown number of individuals is not represented in the cemetery and, therefore, the resulting number is multiplied by an arbitrary value k (Herrmann et al. 1990, 311f.).

Usage

lt.population_size(x, e0, k = 1.1, t)

Arguments

x

either an object of class mortaar_life_table or mortaar_life_table_list or an arbitrary numeric value representing the number of deaths.

e0

numeric. life expectancy at birth in years (if x is of class mortaar_life_table then e0 can be derived directly from the life table's ex column).

k

numeric. Arbitrary number to cater for individuals not represented in the number of deaths. Default: 1.1.

t

numeric. Time span of usage of cemetery in years.

Value

A data.frame with the following items:

  • D: Number of deaths.

  • e0: Life expectancy at birth in years.

  • k: Correction factor.

  • t: Time span of usage of cemetery in years.

  • P: Population size calculated by the formula \(P = D * e0 * k / t\)

References

herrmann_prahistorische_1990mortAAR

Examples


schleswig <- life.table(schleswig_ma[c("a", "Dx")])
lt.population_size(schleswig, t = 100)
#>   method value       description
#> 1      D 247.0  Number of deaths
#> 2     e0  30.7   Life expectancy
#> 3      k   1.1 Correction factor
#> 4      t 100.0         Time span
#> 5      P  83.3   Population size

odagsen <- life.table(list(
"corpus mandibulae" = odagsen_cm[c("a", "Dx")],
 "margo orbitalis" = odagsen_mo[c("a", "Dx")]
 ))
lt.population_size(odagsen, e0 = 30, t = 100)
#> $`corpus mandibulae`
#>   method value       description
#> 1      D  67.2  Number of deaths
#> 2     e0  30.0   Life expectancy
#> 3      k   1.1 Correction factor
#> 4      t 100.0         Time span
#> 5      P  22.2   Population size
#> 
#> $`margo orbitalis`
#>   method value       description
#> 1      D  74.8  Number of deaths
#> 2     e0  30.0   Life expectancy
#> 3      k   1.1 Correction factor
#> 4      t 100.0         Time span
#> 5      P  24.7   Population size
#> 

lt.population_size(x = 111, e0 = 32.2, k = 1.2, t = 100)
#>   method value       description
#> 1      D 111.0  Number of deaths
#> 2     e0  32.2   Life expectancy
#> 3      k   1.2 Correction factor
#> 4      t 100.0         Time span
#> 5      P  42.9   Population size