Interpolate between two population age distributions.

Source: R/AGEINT.R

The interpolation is done by age (not cohort) using a linear or exponential function. This comes from the PAS spreadsheet called ADJINT.

interpolatePop(
  Pop1,
  Pop2,
  Date1,
  Date2,
  DesiredDate,
  method = "linear",
  roundoutput = FALSE
)

Arguments

Pop1

numeric. A vector of demographic counts by age at time 1 (earlier date).
Pop2

numeric. A vector of demographic counts by age at time 2 (later date).
Date1

date. The date corresponding to the population age distribution at time 1. See details for ways to express it.
Date2

date. The date corresponding to the population age distribution at time 2. See details for ways to express it.
DesiredDate

date. The desired date of the output population age distribution. See details for ways to express it.
method

string. The method to use for the interpolation, either "linear" or "exponential". Default "linear".
roundoutput

logical. Whether or not to return integers. Default FALSE.

Value

A vector of the interpolated population for the requested date.

Details

The age group structure of the output is the same as that of the input. Ideally, DesiredDate should be between the Date1 and Date2. Dates can be given in three ways 1) a Date class object, 2) an unambiguous character string in the format "YYYY-MM-DD", or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date.

References

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.

Author

Sean Fennel

Examples

# YYYY-MM-DD dates as character
EarlyDate       <- "1980-04-07"
LaterDate       <- "1990-10-06"
DesiredDate     <- "1985-07-01"

interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate)

#>  [1] 151269.21 698637.56 935163.70 838408.52 669361.53 555363.48 452291.31
#>  [8] 373698.99 370798.20 334150.36 258103.71 223758.80 190651.53 158396.80
#> [15] 119288.68  80400.82  46479.94  51836.50
interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate, method = "exponential")

#>  [1] 142611.79 658653.23 881642.53 790424.84 631052.72 523578.99 426405.83
#>  [8] 352311.49 349576.73 315026.30 243331.95 210952.67 179740.18 149331.46
#> [15] 112461.57  75799.33  43819.81  48869.80
if (FALSE) {
plot(popA_earlier, t ='l', col='red',
    ylim = c(400,1220000),
    ylab = 'The counts', xlab = 'Age groups')

lines(interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate),
     t = 'l', col='black')

lines(popA_later,
     t = 'l', col='blue')

legend(12,1000000,
      legend = c('Pop in 1980-04-07',
                 'Pop in 1985-07-01',
                 'Pop in 1990-10-06'),
      col=c('red','black','blue'),
      lty = 1)
}