OPAG: Redistribute population over a specified age based on a...

In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data

Redistribute population over a specified age based on a stable standard fit to the data

Description

This can be used as an external check of population counts in older ages, assuming the stable population standard is representative enough, or it can be used to redistribute population in ages above a specified ages Redistribute_from. This is handy, for instance, for ensuring all censuses extend to a specified maximum age (e.g. 100+) prior to intercensal interpolations. The assumption is that, at least in ages including Age_fit and higher ages, the population should follow a stable pattern proportional to a given survival curve subject to constant growth, r.

Usage

OPAG(
  Pop,
  Age_Pop,
  nLx,
  Age_nLx,
  Age_fit = NULL,
  AgeInt_fit = NULL,
  Redistribute_from = max(Age_Pop),
  OAnew = max(Age_nLx),
  method = "mono"
)

Arguments

Pop	numeric vector of population counts
Age_Pop	integer vector of the lower bounds of the population age groups
nLx	numeric vector of stationary population age structure in arbitrary integer age groups
Age_nLx	integer vector of lower bounds of age groups of nLx
Age_fit	integer vector of lower bounds for age groups of Pop_fit
AgeInt_fit	integer vector of widths of age groups of Pop_fit
Redistribute_from	integer lower age bound that forms the cutoff, above which we redistribute counts using the stable standard.
OAnew	integer. Desired open age group in the output (must being element of Age_nLx)
method	character, graduation method used for intermediate graduation. Default "mono". Other reasonable choices include "pclm" or "uniform".

Details

It may be helpful to try more than one fitting possibility, and more than one Redistribute_from cut point, as results may vary.

Redistribute_from can be lower than your current open age group, and OAnew can be higher, as long as it is within the range of Age_nLx. If Age_nLx doesn't go high enough for your needs, you can extrapolate it ahead of time. For this, you'd want the nMx the underlie it, and you can use lt_abridged(), specifying a higher open age, and then extracting nLx again from it.

Examples

# India Males, 1971
Pop            <- smooth_age_5(pop1m_ind,
                         Age = 0:100,
                         method = "Arriaga")
Age_Pop        <- names2age(Pop)
AgeInt_Pop     <- age2int(Age_Pop, OAvalue = 1)

nLx            <- downloadnLx(NULL, "India","male",1971)
Age_nLx        <- names2age(nLx)
AgeInt_nLx     <- age2int(Age_nLx, OAvalue = 1)

Pop_fit <- OPAG(Pop,
    Age_Pop = Age_Pop,
    nLx = nLx,
    Age_nLx = Age_nLx,
    Age_fit =  c(60,70),
    AgeInt_fit = c(10,10),
    Redistribute_from = 80)

## Not run: 
# look at 75+
ind <- Age_Pop >= 75
plot(Age_Pop[ind], Pop[ind])
lines(Age_Pop[ind], Pop_fit$Pop_out[ind], col = "blue")

# relative differences in ages 80+
ind <- Age_Pop >= 80
plot(Age_Pop[ind],  (Pop_fit$Pop_out[ind] - Pop[ind]) / Pop[ind])

## End(Not run)

timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.