exposureBirths: Calculate exposure to use for modelling births.

In StatisticsNZ/dembase: Analysing Cross-Classified Data about Populations

Description

In models of births, the relevant exposure term is usually the person-years lived by women of reproductive age. exposureBirths makes it easy to calculate this term in typical cases.

Usage

exposureBirths(
  object,
  triangles = FALSE,
  births = NULL,
  dominant = c("Female", "Male")
)

## S4 method for signature 'Counts'
exposureBirths(
  object,
  triangles = FALSE,
  births = NULL,
  dominant = c("Female", "Male")
)

Arguments

object	An object of class Counts.
triangles	Logical. If TRUE, the return value includes a dimension with dimtype code"triangle". Defaults to FALSE.
births	A DemographicArray object giving birth counts or rates.
dominant	"Female" (the default) or "Male". Whether to use the number of potential mothers or potential fathers when calculating exposures.

Details

The births argument is a tabulation of births or birth rates, typically including the age of mothers. Minimum and maxium ages for exposure are taken from this tabulation.

Fertility models are almost always "female dominant", that is, they only take account of the number of reproductive-age females, and ignore the number of reproductive age-males. By default, if object has two sexes, then exposureBirths only includes females. However, if dominant is set to "Male", it will only include males.

births can contain a sex dimension. However, this dimension describes the sex of the child, not the parent. The sex of the parent is still determined by dominant argument, and the same exposure is used for female and male births.

If births contains origin-destination or parent-child dimensions exposures are repeated once for each value of the destination or child dimension(s).

Value

A Counts object, with the same dimensions as births.

See Also

To calculate exposure for quantities other than births, see exposure (and, for origin-destination or parent-child arrays, possibly also addPair). To tidy fertility data, see reallocateToEndAges.

Examples

## prepare data
library(demdata)
popn <- demdata::nz.popn.reg
births <- demdata::nz.births.reg
popn <- Counts(popn, dimscales = c(year = "Points"))
births <- Counts(births, dimscales = c(year = "Intervals"))
popn <- subarray(popn, year %in% c(1996, 2001, 2006, 2011, 2016))
births <- collapseDimension(births, dimension = "region")
births <- collapseIntervals(births, dimension = "year", width = 5)
births.nosex <- collapseDimension(births, dimension = "sex")

## no triangles
exposureBirths(popn, births = births.nosex)

## with triangles
exposureBirths(popn, triangles = TRUE, births = births.nosex)

## dominant
exposureBirths(popn, births = births.nosex, dominant = "Male")

## distinguish sex of births
exposureBirths(popn, births = births)

## origin-destination - same exposure for each destination
popn <- Counts(array(11:14,
                     dim = c(2, 2),
                     dimnames = list(time = c(2010, 2020),
                                     region = c("A", "B"))))
births <- Values(array(c(0.3, 0.2, 0.5, 0.4),
                       dim = c(2, 2, 1),
                       dimnames = list(region_orig = c("A", "B"),
                                       region_dest = c("A", "B"),
                                       time = "2011-2020")))
exposureBirths(popn, births = births)

StatisticsNZ/dembase documentation built on Dec. 25, 2021, 4:49 p.m.