This index is a composite consisting in the sum of thrice the sex
ratio index plus the age ratio index for males and females. This function is therefore
a wrapper to `ageRatioScore()`

and `sexRatioScore()`

.

ageSexAccuracy( Males, Females, Age, ageMin = 0, ageMax = max(Age), method = "UN", adjust = TRUE, OAG = TRUE )

Males | numeric. A vector of demographic counts in 5-year age groups for males. |
---|---|

Females | numeric. A vector of demographic counts in 5-year age groups for females. |

Age | numeric. A vector of ages corresponding to the lower integer bound of the counts. |

ageMin | integer. The lowest age included in calculations. Default 0. |

ageMax | integer. The upper age bound used for calculations. Default |

method | character. Either |

adjust | logical. Whether or not to adjust the measure when population size is under one million. Default |

OAG | logical. Whether or not the top age group is open. Default |

The value of the index.

Age groups must be of equal intervals. Five year age groups are assumed.
If the final element of `Males`

and `Females`

is the open age group,
then either make sure `ageMax`

is lower than it, or leave `OAG`

as `TRUE`

so that it is properly removed for calculations.
The method argument is passed to `ageRatioScore()`

, where it determines weightings of numerators and denominators,
except in the case of Das Gupta, where it's a different method entirely (see `ageSexAccuracyDasGupta()`

.

United Nations (1952).
“Accuracy tests for census age distributions tabulated in five-year and ten-year groups.”
*Population Bulletin*, 59--79.
Gupta AD (1955).
“Accuracy index of census age distributions.”
In *United Nations proceedings of the World Population Conference 1954 (Rome)*, volume IV, 63--74.
United States Census Bureau (2017).
“Population Analysis System (PAS) Software.”
https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

Males <- c(4677000,4135000,3825000,3647000,3247000,2802000,2409000,2212000, 1786000,1505000,1390000,984000,745000,537000,346000,335000) Females <- c(4544000,4042000,3735000,3647000,3309000,2793000,2353000,2112000, 1691000,1409000,1241000,887000,697000,525000,348000,366000) Age <- seq(0, 75, by = 5) ageSexAccuracy(Males, Females, Age) # 14.3, matches PAS#> [1] 14.30763ageSexAccuracy(Males, Females, Age, ageMax = 75)#> [1] 14.30763ageSexAccuracy(Males, Females, Age, ageMax = 75, adjust = FALSE)#> [1] 14.30763ageSexAccuracy(Males, Females, Age, method = "Zelnick")#> [1] 11.78753ageSexAccuracy(Males, Females, Age, method = "Ramachandran")#> [1] 10.52855# Das Gupta not a comparable magnitude, FYI. ageSexAccuracy(Males, Females, Age, method = "Das Gupta")#> [1] 3.271301